research topics in financial derivatives

A Study on The Impact of Derivatives on Bank Risk and Profitability

28 Pages Posted: 9 Mar 2021

Independent

Date Written: March 1, 2021

This paper examines the impact of derivatives on bank risk and profitability, with a sample of 25 banks from developed markets during the period 2015 to 2019. The main findings suggest that banks’ use of financial derivatives has decreased bank risk. The major variables include Total Risk, Idiosyncratic Risk, and Systematic Risk. This decrease in risk can be linked to the use of derivatives, to reduce or hedge the risks involved in the bank’s operations. This study also shows that the use of financial derivatives has no significant relationship with a bank’s profitability. Overall, this study contributes to understanding the impact of derivatives use on bank risk and profitability and the consequences of a bank’s business model choice.

Keywords: Bank risk, Bank Profit, Financial derivatives, Risk management

JEL Classification: G00, G20, G21, G23, G24, G32

Suggested Citation: Suggested Citation

Rida Ahmed (Contact Author)

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Aims and scope

The proliferation of derivative assets during the past two decades is unprecedented. With this growth in derivatives comes the need for financial institutions, institutional investors, and corporations to use sophisticated quantitative techniques to take full advantage of the spectrum of these new financial instruments. Academic research has significantly contributed to our understanding of derivative assets and markets. The growth of derivative asset markets has been accompanied by a commensurate growth in the volume of scientific research. The Review of Derivatives Research provides an international forum for researchers involved in the general areas of derivative assets. The Review publishes high-quality articles dealing with the pricing and hedging of derivative assets on any underlying asset (commodity, interest rate, currency, equity, real estate, traded or non-traded, etc.).

Specific topics include but are not limited to:

econometric analyses of derivative markets (efficiency, anomalies, performance, etc.) analysis of swap markets market microstructure and volatility issues regulatory and taxation issues credit risk new areas of applications such as corporate finance (capital budgeting, debt innovations), international trade (tariffs and quotas), banking and insurance (embedded options, asset-liability management) risk-sharing issues and the design of optimal derivative securities risk management, management and control valuation and analysis of the options embedded in capital projects valuation and hedging of exotic options new areas for further development (i.e. natural resources, environmental economics.

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Introduction to Financial Derivatives: modeling, pricing and hedging

Research output : Book/Report › Book › Professional

• Derivatives
• stock exchanges

Access to Document

• 10.26116/openpresstiu-schumacher-04-2020 Licence: CC BY-NC-ND
• INTRODUCTION_TO_FINANCIAL_DERIVATIVES Final published version, 5.31 MB Licence: CC BY-NC-ND

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• Mathematics Mathematics 100%
• Probability Theory Mathematics 100%
• Continuous Time Model Mathematics 100%
• Linear Algebra Mathematics 100%
• Call Option Mathematics 100%
• Mathematical Finance Mathematics 100%
• Ten Year Mathematics 100%
• Calculus Mathematics 100%

T1 - Introduction to Financial Derivatives

T2 - modeling, pricing and hedging

AU - Schumacher, J.M.

N2 - Financial derivatives are widely used as instruments to modify exposures to various types of financial risk. Examples include call options on a stock index, interest rate derivatives such as swaptions, and credit derivatives. The theory of financial derivatives, as it has been developed in recent decades, is based on a mix of economic ideas and concepts from mathematics.The material in this Open Press textbook originates from notes for a course that the author has taught at Tilburg University for more than ten years, as part of the MSc program in Quantitative Finance and Actuarial Science. The text aims to provide students with an introduction to continuous-time models that are used to analyze derivative contracts in finance and insurance. Users are expected to have a solid background in standard calculus, linear algebra, and probability; prior experience with stochastic calculus, however, is not a prerequisite.

AB - Financial derivatives are widely used as instruments to modify exposures to various types of financial risk. Examples include call options on a stock index, interest rate derivatives such as swaptions, and credit derivatives. The theory of financial derivatives, as it has been developed in recent decades, is based on a mix of economic ideas and concepts from mathematics.The material in this Open Press textbook originates from notes for a course that the author has taught at Tilburg University for more than ten years, as part of the MSc program in Quantitative Finance and Actuarial Science. The text aims to provide students with an introduction to continuous-time models that are used to analyze derivative contracts in finance and insurance. Users are expected to have a solid background in standard calculus, linear algebra, and probability; prior experience with stochastic calculus, however, is not a prerequisite.

KW - Derivatives

KW - finance

KW - economy

KW - markets

KW - stock exchanges

U2 - 10.26116/openpresstiu-schumacher-04-2020

DO - 10.26116/openpresstiu-schumacher-04-2020

SN - 978-94-6240-612-4

BT - Introduction to Financial Derivatives

PB - Open Press TiU

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Handbook of Investment Analysis, Portfolio Management, and Financial Derivatives

• Edited by: 
• Cheng Few Lee ( Rutgers University, USA ) , 
• Alice C Lee ( Center for PBBEF Research, USA ) , and 
• John C Lee ( Center for PBBEF Research, USA )
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Introductory Price available till Dec 31, 2024

ISBN: 978-981-126-993-6 Hardcover (List Price) US$1980 / £1820 / S$2930

• Description
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This four-volume handbook covers important topics in the fields of investment analysis, portfolio management, and financial derivatives. Investment analysis papers cover technical analysis, fundamental analysis, contrarian analysis, and dynamic asset allocation. Portfolio analysis papers include optimization, minimization, and other methods which will be used to obtain the optimal weights of portfolio and their applications. Mutual fund and hedge fund papers are also included as one of the applications of portfolio analysis in this handbook.

The topic of financial derivatives, which includes futures, options, swaps, and risk management, is very important for both academicians and partitioners. Papers of financial derivatives in this handbook include (i) valuation of future contracts and hedge ratio determination, (ii) options valuation, hedging, and their application in investment analysis and portfolio management, and (iii) theories and applications of risk management.

Led by worldwide known Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues of investment analysis, portfolio management, and financial derivatives based on his years of academic and industry experience.

• Introduction to Investment Analysis, Portfolio Management, and Financial Derivatives (Cheng Few Lee)
• Analyst Characteristics-Based Consensus Forecasts (Yu-An Chen and Dan Palmon)
• Models of Option Pricing (Jia Shao, Nathan Lael Joseph, and Ahmed A El-Masry)
• Realized Diversification Benefits of Risk Portfolio Models (Wan-Jiun Paul Chiou, Wen-Yi Lee, and Jing-Rung Yu)
• VIX Implied Volatility as a Time-Invariant, Stationary Assessor of Market Nervousness/Uncertainty (Ehud I Ronn)
• Investment and Saving in the European Union: Another Look at Feldstein–Horioka (Anastassios A Drakos, Georgios P Kouretas, Stavros Stavroyiannis, and Leonidas Zarangas)
• A Three-Stage Procedure for Predicting Stock Returns (Bharat Sarath and Yixun Zhou)
• Temporal Aggregation and the Estimation of Reverse Regressions for Commodities Market Models (Phillip A Cartwright and Natalija Riabko)
• Correlation and Dependence between Oil Prices, Stock Returns, Policy Uncertainty, and Financial Stress During COVID-19 Pandemic: New Evidence from a Multicountry Analysis Using Cross-Quantilogram Method (Aviral Kumar Tiwari, Emmanuel Joel Aikins Abakah, Richard Adjei Dwumfour, and Luis Alberiko Gil-Alana)
• Predicting the Equity Premium with the Implied Volatility Spread (Charles Cao, Timothy Simin, and Han Xiao)
• Does Equity Market Timing have a Persistent Impact on Capital Structure? Evidence from China (Yang Zhao, Cheng Few Lee, and Min-Teh Yu)
• The Joint Determinants of Capital Structure and Stock Rate of Return: A LISREL Model Approach (Hong-Yi Chen, Cheng Few Lee, and Tzu Tai)
• Alternative Methods for Estimating Firm's Growth Rate: Update and Extension (Ivan E Brick, Hong-Yi Chen, Chia-Hsun Hsieh, and Cheng Few Lee)
• Technical, Fundamental, and Combined Information for Separating Winners from Losers (Hong-Yi Chen, Cheng Few Lee, and Wei-Kang Shih)
• Alternative Methods to Derive Option Pricing Models: Review and Comparison (Cheng Few Lee, Yibing Chen, and John Lee)
• An Assessment of Copula Functions Approach in Conjunction with Factor Model in Portfolio Credit Risk Management (Lie-Jane Kao, Po-Cheng Wu, and Cheng Few Lee)
• Forecast Performance of the Taiwan Weighted Stock Index: Update and Expansion (Deng-Yuan Ji, Hsiao-Yin Chen, and Cheng Few Lee)
• Does Revenue Momentum Drive or Ride Earnings or Price Momentum? (Hong-Yi Chen, Sheng-Syan Chen, Chin-Wen Hsin, and Cheng Few Lee)
• Do Investors Still Benefit from Culturally Home-biased Diversification? An Empirical Study of China, Hong Kong, and Taiwan (Paul W Chiou and Cheng Few Lee)
• Product Market Competition and Real Activities Manipulations: Theory, Implications, and Applications (Cheng Few Lee and Hao-Chang Sung)
• Gold in Portfolio: A Long-Term or Short-Term Diversifier? (Fu-Lai Lin, Sheng-Yung Yang, and Yu-Fen Chen)
• Fuzzy Multicriteria Decision-Making for Evaluating Mutual Fund Strategies (Shin-Yun Wang and Cheng Few Lee)
• Mutual Fund Herding and Its Impact on Stock Returns: Evidence from the Taiwan Stock Market (Weifeng Hung, Chia-Chi Lu, and Cheng Few Lee)
• Stock Return, Risk, and Legal Environment around the World (Paul W Chiou, Alice C Lee, and Cheng Few Lee)
• Further Analysis of Bitcoin, Fintech and P2P Lending: Perspectives and Recommendations from Industry 4.0 (Dinh Tran Ngoc Huy, Vu Quynh Nam, Hoang Thanh Hanh, and Nguyen Ngoc Thach)
• Earnings Quality and the Coinsurance Effect (Julia Nasev and Dominik von der Emde)
• Alternative Methods for Determining Option Bounds: A Review and Comparison (Cheng Few Lee, Zhaodong Zhong, Tzu Tai, and Hongwei Chuang)
• Economic Policy Uncertainty and Short-term Reversals (Andy C W Chui)
• Time Aggregation and the Estimation of the Market Model: Revision and Extension (Cheng Few Lee and Phillip Cartwright)
• Leases on Balance Sheets (Peter Chinloy, Matthew Imes, and Wendy Liu)
• Financial Econometrics, Mathematics, Statistics, and Financial Technology: An Overall View (Cheng Few Lee)
• Entropic Two-Asset Option (Tumellano Sebehela)
• Joint Normality Test for the Returns on the Futures and Spot (Sheng-Syan Chen, Cheng Few Lee, and Keshab Shrestha)
• Analysis of Theoretical and Empirical Relationships between the Treasury Bills and Eurodollar (Cheng Few Lee, Keshab Shrestha, and Robert L Welch)
• Volatility Risk Measures and Banks' Leverage (Giulio Anselmi)
• The Reactions to On-Air Stock Reports: Prices, Volume, and Order Submission Behavior (Chaoshin Chiao, Tung-Ying Lin, and Cheng Few Lee)
• Mutual Fund Competition for Ranking: When Risk-Taking Comes with Managerial Effort (Thi Thanh Huyen Nguyen, Duc De Ngo, and Mouloud Tensaout)
• Hedge Ratios: Theory and Application (Sheng-Syan Chen, Cheng Few Lee, Fu-Lai Lin, and Keshab Shrestha)
• A Note on Stock Market Seasonality: The Impact of Stock Price Volatility on the Application of Dummy Variable Regression Model (Chin-Chen Chien, Cheng Few Lee, and Andrew M L Wang)
• Time-Changed GARCH versus GARJI Model for Extreme Events: An Empirical Study (Lie-Jane Kao, Po-Cheng Wu, and Cheng Few Lee)
• Corporate Financial Hedging and the Cost of Equity Capital (Hany B Ahmed and Yilmaz Guney)
• Does Trading Volume Contain Information to Predict Stock Returns? Evidence from China's Stock Markets (Cheng Few Lee and Oliver M Rui)
• Financial Statement Analysis (Orla Lenihan)
• Expected Credit Losses under IFRS 9: Concept, Models, and Disclosures (Alessandra Allini, Bikki Jaggi, Annamaria Zampella, and Martina Prisco)
• Hedging with the International Equity Index Futures: The Conventional Model versus the Error Correction Model (Fu-Lai Lin, Cheng Few Lee, Win-Lin Chow, and Dennis Kin-Keung Fan)
• Technical Analysis in Investing (Cohen Gil)
• A Comparative Static Analysis Approach to Derive Greek Letters: Theory and Applications (Cheng Few Lee)
• A Correlation-Based Portfolio Choice Algorithm (Jonathan Ross, Joshua Madsen, and Gordon Alexander)
• Stock Returns and Volatility on China's Stock Markets (Cheng Few Lee and Oliver M Rui)
• Value Line Investment Survey Rank Changes and Beta Coefficients (Cheng Few Lee and Hun Y Park (Deceased))
• International Hedge Ratios for Index Futures Market: A Simultaneous Equations Approach (Cheng Few Lee, Fu-Lai Lin, and Mei-Ling Chen)
• Empirical Studies of Structural Credit Risk Models and the Application in Default Prediction: Review and New Evidence (Han-Hsing Lee, Ren-Raw Chen, and Cheng Few Lee)
• Predicting Stock Return Movement Directions with Sentiment Analysis of News Headlines: A Machine Learning Approach (Hanxin Hu and Ting Sun)
• Style Investing: Momentum, and Co-movement (Chinchi Wu and Xinyuan Tao)
• Mining for "Green Diamonds" — Value Relevance of Greenhouse Gas Emissions (Carsten Homburg, Laurens O J Lapp, and Roman Schick)
• Risk Estimation, Diversification, and Optimal Weights (Cheng Few Lee)
• The Role of Family Ownership and Founder Presence in Investment Analysis (Bin Srinidhi)
• Financial Statement Analyses and Firm Valuation: Johnson and Johnson as a Case Study (Cheng Few Lee and Wen-Chi Yeh)
• Technical Analysis in the Stock Market: A Review (Yufeng Han, Yang Liu, Guofu Zhou, and Yingzi Zhu)
• The Sovereign Rating Channel in the European Debt Crisis: Spillover Effects on Sovereign CDS and Other Systemic Risk Indicators (Dimitris Georgoutsos and George Moratis)
• Interest Rate Sensitivity and Investor Disagreement: How to Explain Bank Stocks Turnover (Mark Iarovyi, Sasson Bar-Yosef, and Itzhak Venezia)
• A Novel Semi-Static Method for the Index Tracking Problem (Chun-Chong Fu, Chuan-Hsiang Han, and Kun Wang)
• Fundamental Analysis: A Practical Approach (Andreas G Koutoupis and Leonidas G Davidopoulos)
• Lessons on Risk, Return, and Portfolio Construction from the Great Investors (John M Longo)
• Sources of Liquidity Premium: Risk or Mispricing? (Pin-Huang Chou, Kuan-Cheng Ko, and K C John Wei)
• Analysis of IBEX-35 Listed Companies: Recent CSR Reports and Behavior of the Main Indicators. Existence of a Proportional Relationship Between Greenwashing and Deficient CSR Reports (Cristina Chueca Vergara and Luis Ferruz Agudo)
• Return Volatility, Skewness, and Momentum Effects (Alex Yi Hou Huang and Ming-Che Hu)
• Predicting Implied Volatility with Historical Volatility (Xinjie Wang, Ge Wu, and Suyang Zhao)
• Estimating Binomial and Black & Scholes Option Pricing Models: Excel, R Language, and SAS Program Approach (LiJane Kao, John Lee, and Cheng Few Lee)
• Value Contributions (Peter Chinloy and Matthew Imes)
• Using Computational Science Methods in Accounting and Finance Research (David A Ziebart, Mark Cheng, Sohee Kim, Wenyin Li, Anh Pham, and Darren Woodward)
• Stock Buybacks and Financial Turmoil: Pros and Cons for Investors (Foued Hamouda)
• The Roles of Financial Analysts in the Stock Market (Guanming He and April Zhichao Li)
• Funding Liquidity and CDS-Bond Basis: Evidence from the CDS Big Bang (Xinjie Wang and Zhaodong (Ken) Zhong)
• Issues and Challenges of Weather and Freight Derivatives: Impact of Pandemic Situation (G V Satya Sekhar)
• On a Long-Term Investment Strategy in a Stock Market (Guanming He, April Zhichao Li, and Dongxiao Shen)
• European Option, American Option, and Option Bounds: Theory, Method, and Some Empirical Results (Cheng Few Lee)
• Improving the Stock Market Prediction with Social Media via Broad Learning (Xi Zhang and Philip S Yu)
• Bond Portfolio Management, Swap Strategy, Duration, and Convexity (Cheng Few Lee)
• Do CFA Charterholders Make Better Hedge Fund Managers? (Yao Zheng and Eric Osmer)
• Impact of Bank Activity and Funding Strategies on Liquidity Management: International Evidence (Yu-Li Huang and Kun-Li Lin)
• Accounting Information and Firm Valuation (Cathy Zishang Liu, Kai-Cheung Kenneth Chu, and Agnes Cheng, C S)
• Developments in CDS Markets: A Review on Recent CDS Studies (Xingyi Hu and Zhaodong (Ken) Zhong)
• Decision Tree and Microsoft Excel Approach for Option Pricing Model (Jow-Ran Chang and John Lee)
• Comparisons between the Markowitz Model and the Black–Litterman Model (Huei-Wen Teng)
• Empirical Performance of the Constant Elasticity Variance Option Pricing Model (Ren Raw Chen, Cheng Few Lee, and Han-Hsing Lee)
• Asset Allocation with Cryptocurrencies (Han-Hsing Lee and Ken-Kuan Su)
• Market-Based, Accounting-Based, and Composite-Based Beta Forecasting (Cheng Few Lee)
• Utility Theory, Capital Asset Allocation, and Markowitz Portfolio Selection Model (Cheng Few Lee)
• Single-Index Model, Multiple-Index Model, and Portfolio Selection (Cheng Few Lee)
• Sharpe Performance Measure and Treynor Performance Measure Approach to Portfolio Analysis (Cheng Few Lee and Paul W Chiou)
• Modeling Different REIT Cash Flows (Tamala Amelia Manda)
• Bayesian Portfolio Mean-Variance Efficiency Test with Sharpe Error of Sharpe Ratio (Lie-Jane Kao, Huei Ching Soo, and Cheng Few Lee)
• Fundamental Analysis, Technical Analysis, and Mutual Fund Performance (Cheng Few Lee)
• Synthetic Options, Portfolio Insurance, and Contingent Immunization (Cheng Few Lee)
• Global International ELM versus Momentum (Robert Snigaroff and David Wroblewski)
• Estimating the Probabilities of Default under the Assumption of Unobserved Heterogeneity (Jacob Oded and Itzhak Venezia)
• A Factor Model for Graph Data (Wei-Fang Niu and Henry Horng-Shing Lu)
• A Dynamic CAPM with Supply Effect: Theory and Empirical Results (Cheng Few Lee, Chiung-Min Tsai, and Alice C Lee)
• Indices Herding Behaviour and Its Impact on Listed Real Estate and Other Two Asset Classes: A Case of Developed versus Emerging Markets (Sibongile Zwane)
• Price Momentum, Earnings Forecasting, and Valuation: Implications for Inefficient Markets (Christopher C Geczy and John B Guerard, Jr.)
• Advancement of Optimal Portfolio Models with Short Sales and Transaction Costs: Methodology and Effectiveness (Paul W Chiou and Jing-Rung Yu)
• Implied Variance Estimates for Black–Scholes and CEV OPM: Review and Comparison (Cheng Few Lee, Yibing Chen, and John Lee)
• On the Treatment of the Momentum Factor in Accounting-Based Anomalies: A Discussion (Philip Keejae Hong, Kyonghee Kim, and Sukesh Patro)
• Constant Elasticity of Variance Option Pricing Model: Integration and Detailed Derivation (Y L Hsu, T L Lin, and Cheng Few Lee)
• Options, Put–Call Parities, and Option Strategies: Theory and Empirical Results (Cheng Few Lee and Wen-Chi Yeh)
• A Cross-sectional Asset Pricing Test with More Power: An Instrumental Variable Approach (Jungshik Hur)
• Current vs Permanent Earnings for Estimating Alternative Dividend Payment Behavioral Model: Theory, Methods, and Applications (Cheng Few Lee, Hong-Yi Chen, Alice Lee, and Yuhsin Tai)
• Differential Effect of Inside Debt, CEO Compensation Diversification, and Firm Investment (Cheng Few Lee, Chengru Hu, and Maggie Foley)
• Optimal Payout Ratio under Uncertainty and the Flexibility Hypothesis: Theory, Empirical Evidence, and Implications (Cheng Few Lee, Manak C Gupta, Hong-Yi Chen, and Alice C Lee)
• Sustainable Growth Rate, Optimal Growth Rate, and Optimal Payout Ratio: A Joint Optimization Approach (Hong-Yi Chen, Manak C Gupta, Alice C Lee, and Cheng Few Lee)
• Author Index
• Subject Index

FRONT MATTER

• Cheng Few Lee ,
• Alice C. Lee , and 
• John C. Lee
• Pages: i–xlii

https://doi.org/10.1142/9789811269943_fmatter

• Advisory Board
• About the Editors
• List of Contributors
• Contents of Volume 1
• Contents of Volume 2
• Contents of Volume 3
• Contents of Volume 4

Chapter 1: Introduction to Investment Analysis, Portfolio Management, and Financial Derivatives

• Cheng Few Lee
• Pages: 1–68

https://doi.org/10.1142/9789811269943_0001

The main purposes of this introduction chapter are (i) to give an overview of the following 109 papers, which discuss investment analysis, portfolio management, and financial derivatives; (ii) to classify these 109 chapters into nine topics; and (iii) to classify the keywords in terms of chapter numbers.

Chapter 2: Analyst Characteristics-Based Consensus Forecasts

• Yu-An Chen  and 
• Pages: 69–115

https://doi.org/10.1142/9789811269943_0002

Consensus earnings forecasts matter to investment practitioners in forming profitable investment portfolios and matter to researchers in studying how earnings inform the market. This study revisits the issue of considering analyst heterogeneity in forming better analyst consensus earnings forecasts (Clement, 1999; Clement and Tse, 2003; Brown and Mohd, 2003). Based on quarterly data for US firms from 1994 to 2017, the study finds that characteristics-based consensus forecasts outperform the simple mean consensus to predict abnormal returns in both the three-day window around and the two-month drift window after earnings announcements. They perform as well as the median consensus does to predict abnormal returns in the three-day window around earnings announcements but outperform the median consensus in the drift window. Investors may find these findings relevant to their investment decisions, and researchers may find them relevant to the studies of earnings informativeness.

Chapter 3: Models of Option Pricing

• Nathan Lael Joseph , and 
• Ahmed A. El-Masry
• Pages: 117–170

https://doi.org/10.1142/9789811269943_0003

Starting from humble beginnings, the use of financial options has substantially increased as an important financial tool for both speculation and hedging over the last 50 years. This chapter discusses both the theoretical and practical applications of financial options and related models. While the content is somewhat technical, we provide illustrations of their applications in simple settings. We address particular stylized features of option pricing models.

Chapter 4: Realized Diversification Benefits of Risk Portfolio Models

• Wan-Jiun Paul Chiou ,
• Wen-Yi Lee , and 
• Jing-Rung Yu
• Pages: 171–190

https://doi.org/10.1142/9789811269943_0004

In this chapter, we apply various portfolio models to rebalance portfolios and further analyze their realized performance, including mean-variance (MV), conditional value-at-risk (CVaR), and Omega models. To ensure feasibility, we consider the transaction costs and the optimization of short-selling weights. The empirical results using the daily returns of international funds across 21 countries over 20 years show that the risky portfolios realize higher performance than a naïve diversification, particularly the CVaR model with a confidence level of 95% despite their higher trading costs. The CVaR model, mainly focusing on controlling loss, yields higher performance than those that are based on trade-off between return and volatility, such as the mean−variance and Omega models. The Omega model, however, generates lower downside risk. The superiority of risky portfolios over the equally weighted diversification varies intertemporarily across various models. The excess returns of risky portfolios over the equally weighted diversification are larger when the market is volatile, such as the periods of the subprime mortgage financial crisis and the 2020 COVID-19 recession.

Chapter 5: VIX Implied Volatility as a Time-Invariant, Stationary Assessor of Market Nervousness/Uncertainty

• Ehud I. Ronn
• Pages: 191–197

https://doi.org/10.1142/9789811269943_0005

Financial markets serve numerous roles, amongst them of course is the uncoerced exchange of securities. In addition to that role, they serve a very useful function of conveying to market observers information about the future, the challenge being our ability to elicit and interpret that information.

This paper addresses that the latter function regarding the option markets which provide the value for the VIX 30-day implied volatility on the S&P 500 Market Index. It demonstrated that the peak value of VIX during Persian Gulf I, 1990/1991, and Persian Gulf II, 2003, was nearly identical.

Chapter 6: Investment and Saving in the European Union: Another Look at Feldstein–Horioka

• Anastassios A. Drakos ,
• Georgios P. Kouretas ,
• Stavros Stavroyiannis , and 
• Leonidas Zarangas
• Pages: 199–234

https://doi.org/10.1142/9789811269943_0006

In this chapter, we employ data from a panel consisting of 28 European Union (EU) member countries over the period 1990–2020 to examine the validity of the famous Feldstein– Horioka (F–H) puzzle. Despite many criticisms, the F–H saving–investment correlation hypothesis is still used in the literature to infer the degree of capital mobility among countries. To this end, we apply a battery of panel unit root and cointegration tests. The finding of the presence of cointegration of the savings and investment ratios and the observed magnitude of the estimated average saving-retention coefficient for the panel reveal that for this panel of EU member countries, the F–H puzzle is not valid and the long-run international solvency condition is maintained in most of these countries. The observed low saving-retention coefficients for these countries imply a moderate degree of capital mobility and the absence of the F–H puzzle. This finding of the prevalence of a moderate degree of capital mobility is consistent with the macroeconomic experience of these countries during the period under investigation.

Chapter 7: A Three-Stage Procedure for Predicting Stock Returns

• Bharat Sarath  and 
• Pages: 235–260

https://doi.org/10.1142/9789811269943_0007

In this chapter, we apply a three-stage approach using an intermediate classification period between the estimation and test periods. In the intermediate period, we stratify individual firms into deciles based on the predictive power of the Carhart 4-factor model, measured by the out-of-sample R -squared prediction in this period. Our motive for this stratification is that firms with poor out-of-sample predictive power of the estimated model are likely to suffer from coeffcient instability and that these instabilities will result in a mismeasurement of expected returns in the test period. The empirical results show that lower predictive power deciles have larger averaged absolute changes of estimated coeffcients, which is our proxy for coeffcient instability, and lower averaged out-of-sample R -squared in the test period.

Chapter 8: Temporal Aggregation and the Estimation of Reverse Regressions for Commodities Market Models

• Phillip A. Cartwright  and 
• Natalija Riabko
• Pages: 261–281

https://doi.org/10.1142/9789811269943_0008

The main purpose of this article is to empirically demonstrate the effects of temporal aggregation when applying reverse regression models hypothesizing that spot prices today help predict forward rates in the future. This paper essentially reviews results from earlier research indicating that time-series aggregation will most certainly influence standard errors on parameter estimates. Standard errors are likely to increase with aggregation. The relationships between futures prices and spot oil prices are analyzed along with the importance of the effects of temporal aggregation and alternative model specification for understanding empirical relationships between the markets. Model specification and time-series aggregation over daily, weekly, and monthly aggregations confirm evidence that estimated standard errors are likely to increase with aggregation and t -ratios change as well. While goodness-of-fit measures might increase with aggregation, forecast accuracy with macrolevel aggregation might deteriorate owing to information loss due to the averaging of observations associated with underlying microstructures.

Chapter 9: Correlation and Dependence between Oil Prices, Stock Returns, Policy Uncertainty, and Financial Stress During COVID-19 Pandemic: New Evidence from a Multicountry Analysis Using Cross-Quantilogram Method

• Aviral Kumar Tiwari ,
• Emmanuel Joel Aikins Abakah ,
• Richard Adjei Dwumfour , and 
• Luis Alberiko Gil-Alana
• Pages: 283–320

https://doi.org/10.1142/9789811269943_0009

In this chapter, we examine risk spillover between the returns series of oil and stock prices of worst-affected countries due to the COVID-19 outbreak in unconditional and conditional frameworks, where the relationship was conditioned upon the US economic policy uncertainty and financial stress indices. Specifically, we used three different measures of oil prices, namely, WTI, OPEC, and Dubai oil prices. We also examined the risk spillover from US and Chinese stock markets to stock markets of affected countries, such as the UK, France, Germany, Italy, Spain, Switzerland, and Turkey, for the time period from 31st December 2019 to 22nd April 2020. Our results provide evidence that during the COVID-19 outbreak, Dubai and OPEC oil prices have had a strong positive effect on stock price when both of them are at their lower quantiles, which suggests that during extreme markets conditions, oil price affects stock price. Furthermore, evidence of the directional predictability from stock returns of the US/China to all other stock returns shows positive predictability from the US to France, Germany, Italy, Spain, Switzerland, and the UK at lower quantiles. Last but not least, when the relationships were conditioned by the policy uncertainty and financial stress, evidence of directional predictability became stronger and spread to more quantiles, suggesting that the interrelationships between oil price and stock price returns and between stock price returns of the US/China to all other stock price returns were not driven by the systemic risk but rather uncertainties during the COVID-19 outbreak.

Chapter 10: Predicting the Equity Premium with the Implied Volatility Spread

• Charles Cao ,
• Timothy Simin , and 
• Pages: 321–361

https://doi.org/10.1142/9789811269943_0010

We show that the call-put implied volatility spread ( IVS ) outperforms many well-known predictors of the U.S. equity premium at return horizons up to six months over the period from 1996:1 to 2017:12. The predictive ability of the IVS is unrelated to the dividend yield and is useful in explaining the cross-section of returns. Decomposing the IVS , we find the longer run predictive ability of the IVS operates primarily through a cash flow channel. We also find the IVS is significantly related to indicators of aggregate market direction and expected market conditions. Our results are consistent with the IVS reflecting market sentiment as well as information about informed trading.

Chapter 11: Does Equity Market Timing have a Persistent Impact on Capital Structure? Evidence from China

• Yang Zhao ,
• Cheng Few Lee , and 
• Pages: 363–397

https://doi.org/10.1142/9789811269943_0011

This paper uses the change in individual securities accounts as a measure of equity funding supply to examine whether the persistent timing effect on capital structure exists for the Chinese equity market. This new equity timing measure avoids previous criticisms over a timing measure not being independent of a firm’s characteristics of capital structure. Our empirical results show that this new measure is an effective market timing variable for issuing equity in the Chinese equity market, and that a persistent effect of equity market timing on firm capital structure exists for more than 7 years. This paper offers evidence that the market conditions of equity funding supply play an important role in corporate financing decisions in China.

Chapter 12: The Joint Determinants of Capital Structure and Stock Rate of Return: A LISREL Model Approach

• Hong-Yi Chen ,
• Pages: 399–449

https://doi.org/10.1142/9789811269943_0012

We develop a simultaneous determination model of capital structure and stock returns. Specifically, we incorporate the managerial investment autonomy theory into the structural equation modeling with confirmatory factor analysis to jointly determine the capital structure and stock return. Besides attributes introduced in previous studies, we introduce indicators affecting a firm’s financing decision, such as managerial entrenchment, macroeconomic factors, government financial policy, and pricing factors. Empirical results show that stock returns, asset structure, growth, industry classification, uniqueness, volatility, financial rating, profitability, government financial policy, and managerial entrenchment are major factors of the capital structure.

Chapter 13: Alternative Methods for Estimating Firm’s Growth Rate: Update and Extension

• Ivan E. Brick ,
• Chia-Hsun Hsieh , and 
• Pages: 451–481

https://doi.org/10.1142/9789811269943_0013

The growth rate plays an important role in determining a firm’s asset and equity values. The premier model used to price equity is the basic dividend growth model of Gordon and Shapiro (1956). Nevertheless, the basic assumptions of the growth rate estimation model are less well understood. In this paper, we demonstrate that the model makes strong assumptions regarding the financing mix of the firm. In addition, we discuss various estimation methods of the firms’ growth rate. We demonstrate that the arithmetic average method is very sensitive to extreme observations, and the regression methods yield similar but somewhat smaller estimates of the growth rate compared to the compound-sum method. Interestingly, the ex post forecast shows that arithmetic average and compoundsum methods (continuous regression) yield the best (worst) performance with respect to estimating a firm’s future dividend growth rate. Firm characteristics, like size, book-to-market ratio, and systematic risk, have a significant influence on the forecast errors of dividend and sales growth rate estimation.

Chapter 14: Technical, Fundamental, and Combined Information for Separating Winners from Losers

• Wei K. Shih
• Pages: 483–526

https://doi.org/10.1142/9789811269943_0014

This study examines how fundamental accounting information can be used to supplement technical information to separate momentum winners from losers. We first introduce a ratio of liquidity buy volume to liquidity sell volume (BOS ratio) to proxy the level of information asymmetry for stocks and show that the BOS momentum strategy can enhance the profits of momentum strategy. We further propose a unified framework, produced by incorporating two fundamental indicators—the FSCORE (Piotroski, 2000) and the GSCORE (Mohanram, 2005)—into momentum strategy. The empirical results show that the combined investment strategy includes stocks with a larger information content that the market cannot reflect in time, and therefore, the combined investment strategy outperforms momentum strategy by generating significantly higher returns.

Chapter 15: Alternative Methods to Derive Option Pricing Models: Review and Comparison

• Yibing Chen , and 
• Pages: 527–571

https://doi.org/10.1142/9789811269943_0015

The main purposes of this paper are (i) to review three alternative methods for deriving option pricing models (OPM), (ii) to discuss the relationship between binomial OPM and Black–Scholes OPM, (iii) to compare the Cox et al. (1979) method and Rendleman and Bartter method for deriving Black–Scholes OPM, (iv) to discuss the lognormal distribution method to derive Black–Scholes OPM, and (v) to show how the Black–Scholes model can be derived by stochastic calculus.

This chapter shows that the main methodologies used to derive the Black–Scholes model are binomial distribution, lognormal distribution, and differential and integral calculus. If we assume risk neutrality, then we don’t need stochastic calculus to derive the Black– Scholes model. However, the stochastic calculus approach for deriving the Black–Scholes model is still presented in Section 15.6. In sum, this chapter can help statisticians and mathematicians understand how alternative methods can be used to derive the Black– Scholes option model.

Chapter 16: An Assessment of Copula Functions Approach in Conjunction with Factor Model in Portfolio Credit Risk Management

• Lie-Jane Kao ,
• Po-Cheng Wu , and 
• Pages: 573–591

https://doi.org/10.1142/9789811269943_0016

In credit risk modeling, factor models, either static or dynamic, are often used to account for correlated defaults among a set of financial assets. Within the realm of factor models, default dependence is due to a set of common systematic risk factors. By coupling with a copula function, e.g., the normal, t- , Clayton, Frank, and Gumbel copula functions, an analytic formulation of the joint distribution of assets’ default times can be derived. On the other hand, factor models fail to account for the contagion mechanism of defaults in which a firm’s default risk increases due to their commercial or financial counterparties’ defaults. This study considers the dynamic factor model of Duffee (1999) coupling with a Hawkes process, a class of counting processes allowing intensities to depend on the timing of previous events (Hawkes, 1971) for the contagious effect. Using the factor- contagious-effect model, Monte Carlo simulation is performed to generate default times of two hypothesized firms. It is demonstrated that as the contagious effect increases, the goodness of fit of the joint distribution of assets’ default times based on copula functions decreases, which highlights the deficiency of the copula function approach.

Chapter 17: Forecast Performance of the Taiwan Weighted Stock Index: Update and Expansion

• Deng-Yuan Ji ,
• Hsiao-Yin Chen , and 
• Pages: 593–612

https://doi.org/10.1142/9789811269943_0017

This research introduces the following to establish a TAIEX prediction model: intervention analysis integrated into the ARIMA–GARCH model, ECM, intervention analysis integrated into the transfer function model, the simple average combination forecasting model, and the minimum error combination forecasting model. The results show that intervention analysis integrated into the transfer function model yields a more accurate prediction model than ECM and intervention analysis integrated into the ARIMA–GARCH model. The minimum error combination forecasting model can improve prediction accuracy much better than non-combination models and also maintain robustness. Intervention analysis integrated into the transfer function model shows that the TAIEX is affected by external factors, the INDU, the exchange rate, and the consumer price index; therefore, facing the different issues of the TAIEX, the government could pursue some macroeconomic policies to reach the goals of policies.

Chapter 18: Does Revenue Momentum Drive or Ride Earnings or Price Momentum?

• Sheng-Syan Chen ,
• Chin-Wen Hsin , and 
• Pages: 613–666

https://doi.org/10.1142/9789811269943_0018

This paper examines the profits of revenue, earnings, and price momentum strategies in an attempt to understand investor reactions when facing multiple information of firm performance in various scenarios. We first offer evidence that there is no dominating momentum strategy among the revenue, earnings, and price momentums, suggesting that revenue surprises, earnings surprises, and prior returns each carry some exclusive unpriced information content. We next show that the profits of momentum driven by firm fundamental performance information (revenue or earnings) depend upon the accompanying firm market performance information (price), and vice versa. The robust monotonicity in multivariate momentum returns is consistent with the argument that the market does not only underestimate the individual information but also the joint implications of multiple information on firm performance, particularly when they point in the same direction. A three-way combined momentum strategy may offer monthly return as high as 1.44%. The information conveyed by revenue surprises and earnings surprises combined account for about 19% of price momentum effects, which finding adds to the large literature on tracing the sources of price momentum.

Chapter 19: Do Investors Still Benefit from Culturally Home-biased Diversification? An Empirical Study of China, Hong Kong, and Taiwan

• Paul W. Chiou  and 
• Pages: 667–716

https://doi.org/10.1142/9789811269943_0019

Due to the continual economic integration and the accumulation of wealth in China, Hong Kong, and Taiwan since early 1990’s, understanding portfolio strategies and the benefits of diversification for these countries is an indispensible element in managing global assets. Using weekly industry-level data, we analyze the culturally home-biased diversification and find that local investors still benefit from regional investments. The time-varying benefits of diversification exist even as the economies of this region have become increasingly integrated. Our analysis suggests that stricter weighting bounds reduce the economic values of diversification but enhance the feasibility of the optimal portfolio allocations. The larger benefits gained by Chinese investors suggest that international diversification is more advantageous to investors in emerging economies than those in developed markets. The robustness tests generate similar findings when we evaluate the out-of-sample effectiveness and the benefits of diversification under various parameter estimation windows.

Chapter 20: Product Market Competition and Real Activities Manipulations: Theory, Implications, and Applications

• Cheng Few Lee  and 
• Hao-Chang Sung
• Pages: 717–748

https://doi.org/10.1142/9789811269943_0020

We investigate how a firm manipulates its real activities in production to meet the earnings target in product market competition against its product-market rivals. We show that the equilibratory way to reach the earnings target is to set a higher first-period output level, reaching a higher short-term profit level. However, once the expected level of demand uncertainty is high, a firm will exploit this effect on its output choice by taking a mixed strategy and raising its short-term output level. This result suggests that one should consider longer-horizon paths of variables to detect opportunistic real activities manipulation. Based on our results, we further argue that competitive strategy is an omitted variable in real activities manipulation estimation models and recommend that capacity utilization, which is related to a firm’s output competitive decisions, should be included in the first-stage models of normal investment levels in Roychowdhury (2006) and Gunny (2010).

Chapter 21: Gold in Portfolio: A Long-Term or Short-Term Diversifier?

• Fu-Lai Lin ,
• Sheng-Yung Yang , and 
• Yu-Fen Chen
• Pages: 749–773

https://doi.org/10.1142/9789811269943_0021

The purpose of this chapter is to evaluate the role played by gold in a diversified portfolio comprised of bonds and stocks. The continuous wavelet transform analysis is applied to capture the correlation features between gold and other risky assets at a specific time horizon to determine whether gold should be included in a diversified portfolio. This chapter uses the U.S. stock, bond, and gold data from 1990 until 2020 to investigate the optimal weights of gold obtained from the minimum variance portfolio. Empirical findings suggest that little evidence supports that gold acts as an efficient diversifier in traditional stocks and bond portfolios. Gold typically has been a long-term diversifier in the traditional port-folio comprised of bonds and stocks only before the early 2000s and acts as a short-term diversifier in times of crisis periods. The significant drop in the long-term weight of gold indicates that gold losses much of its long-term role in the diversified portfolio. These findings are useful for portfolio managers to justify the gold’s diversification benefits over different investment horizons.

Chapter 22: Fuzzy Multicriteria Decision-Making for Evaluating Mutual Fund Strategies

• Shin-Yun Wang  and 
• Pages: 775–794

https://doi.org/10.1142/9789811269943_0022

Investors often need to evaluate the investment strategies according to their own subjective preferences in terms of numerical values based upon various criteria when making investment in mutual funds. This situation can be regarded as a fuzzy multiple criteria decision-making (MCDM) problem. The purpose of this study is to propose an alternative approach, fuzzy multiple criteria decision-making with fuzzy integral. This approach relaxes the independence assumption among criteria for the evaluation of the MCDM problems, which is oftentimes the basic assumption in applying a hierarchical system for evaluating the strategies of selecting the mutual funds investment style. We also employ triangular fuzzy numbers to represent the decision makers’ subjective preferences on the criteria, as well as for the criteria measurements to evaluate mutual funds investment style. To achieve this objective, first, we employ factor analysis to extract four independent common factors from those criteria. Second, we construct the evaluation frame using a hierarchical system composed of the above four common factors with 16 evaluation criteria and then derive the relative weights with respect to the considered criteria. Third, the synthetic utility value corresponding to each mutual fund’s investment style is aggregated by the fuzzy weights with fuzzy performance values. Finally, we compare with empirical data and find that the model of FMCDM predicts the rate of return very accurately in certain ranges of λ, hence the non-additive fuzzy integral technique is an effective method for evaluating mutual funds’ strategy.

Chapter 23: Mutual Fund Herding and Its Impact on Stock Returns: Evidence from the Taiwan Stock Market

• Weifeng Hung ,
• Chia-Chi Lu , and 
• Pages: 795–820

https://doi.org/10.1142/9789811269943_0023

Using quarterly ownership data which identify identity codes of mutual funds in Taiwan, we investigate mutual fund herding and its impact on stock price. We show that mutual funds tend to follow their own steps in trading rather than follow trades made by other funds. More importantly, evidence of price continuation following mutual fund herd buying suggests that such herding is based on value-relevant information and is consistent with the investigative herding hypothesis. Alternatively, evidence of return reversal following mutual fund herd selling suggests that such herding is non-informational and is consistent with the characteristic herding hypothesis.

Chapter 24: Stock Return, Risk, and Legal Environment around the World

• Paul W. Chiou ,
• Pages: 821–846

https://doi.org/10.1142/9789811269943_0024

This chapter shows how the legal environment in a country influences performance and risk of stock across countries at different developmental stages and of various rules of jurisdiction. Using data of 4,916 stocks from 37 countries, our empirical findings confirm that equities in countries with English common-law origin have higher risk premiums than those in civil-law countries, particularly for countries of the French/Spanish code. The indicators representing high efficiency in law system, low corruption, strong legal protection of investors’ rights, and reliable political environment are associated with low risk and high performance. The various elements of legal procedural formalism, however, have differing effects on volatility and return.

Chapter 25: Further Analysis of Bitcoin, Fintech, and P2P Lending: Perspectives and Recommendations from Industry 4.0

• Dinh Tran Ngoc Huy ,
• Vu Quynh Nam ,
• Hoang Thanh Hanh , and 
• Nguyen Ngoc Thach
• Pages: 847–859

https://doi.org/10.1142/9789811269943_0025

In several countries in the world, Bitcoin and P2P lending have been accepted and developed strongly. This study aims to evaluate the suitability, pros, and cons of Bitcoin, Fintech, P2P lending, and its platform in emerging markets such as Vietnam. The research used qualitative analysis combined with data collection method published, statistics, analysis, synthesis, comparison, to generate qualitative comments and discussion; evaluate results, the article analyzed and evaluated the impacts of Fintech, P2P lending, and Bitcoin and virtual currency on society of Vietnam, both positive and negative sides. It was found that we need to improve regulations on Fintech and shadow banking to overcome the weaknesses of commercial banks, to reduce risks as many nations in the world accept it. Experiences of other countries such as the United States, Japan, China, or the developed countries of the European Union and consequences for the economy became a lesson for well as developing countries. Hence, we need to implement risk management plans to reduce technological and IT risks. Proper solutions and development orientation as well as risk management for Bitcoin and cryptocurrencies are suggested. Last but not least, the research was limited to the case of Vietnam; hence, we can expand research to other Asian countries or other emerging markets.

Chapter 26: Earnings Quality and the Coinsurance Effect

• Julia Nasev  and 
• Dominik von der Emde
• Pages: 861–891

https://doi.org/10.1142/9789811269943_0026

We argue that coinsurance among a firm’s business units changes the properties of reported earnings through less volatile operations (financial synergies) and fewer estimation errors in the accrual process (accounting synergies). Consistent with a coinsurance effect, we find that diversified firms have on average higher earnings quality compared to industry-matched portfolios of focused firms. Specifically, diversification leads to more predictable earnings, superior mapping of accruals to cash flows, and lower absolute abnormal accruals. In addition, we find higher earnings quality for diversified firms with less correlated segment earnings and that the coinsurance effect is stronger for firms that operate in more volatile and uncertain environments. We contribute by identifying the coinsurance effect of diversification as a new determinant of earnings quality. Our findings complement prior literature on agency-related disadvantages of diversification for earnings quality by highlighting coinsurance related benefits of diversification for earnings quality.

Chapter 27: Alternative Methods for Determining Option Bounds: A Review and Comparison

• Zhaodong Zhong ,
• Tzu Tai , and 
• Hongwei Chuang
• Pages: 893–921

https://doi.org/10.1142/9789811269943_0027

This chapter first reviews alternative methods for determining option bounds. This method includes stochastic dominance, linear programming, semi-parametric method, and non-parametric method for European option. Then option bounds for American and Asian options are discussed. Finally, we discuss empirical applications in equities and equity indices, index futures, foreign exchange rates, and real options.

Chapter 28: Economic Policy Uncertainty and Short-term Reversals

• Andy C.W. Chui
• Pages: 923–949

https://doi.org/10.1142/9789811269943_0028

This chapter finds that short-term reversals become more profound in the current month when economic policy uncertainty is larger in the prior month. There is evidence that the economic policy uncertainty influences return reversals through the liquidity channel. Short-term reversal profits are also positively related to the VIX index, the Baker–Wurgler (2007) investor sentiment index, and the Aruoba–Diebold–Scotti (2009) business conditions index in the prior month. Though, the predictability of the latter two indexes is less robust. However, adding these indexes and other variables does not weaken the relationship between economic policy uncertainty and return reversals.

Chapter 29: Time Aggregation and the Estimation of the Market Model: Revision and Extension

• Fu-Lai Lin , and 
• Phillip Cartwright
• Pages: 951–978

https://doi.org/10.1142/9789811269943_0029

Data for heavily and lightly traded firms are used to evaluate the effects of temporal aggregation on beta estimates, t values, and R 2 estimates. In addition to our analysis of the standard market model, dynamic market and error correction models are estimated. This study evaluates differences in the short-term and long-term dynamic relationships between the market and each type of firm. It is found that temporal aggregation has important effects on both the specification of a market model and the stability of beta estimates.

Chapter 30: Leases on Balance Sheets

• Peter Chinloy ,
• Matthew Imes , and 
• Pages: 979–1006

https://doi.org/10.1142/9789811269943_0030

Leasing transfers effective ownership of physical assets to specialists. Firms not specializing in asset ownership focus on core competence and scale. In asset ownership including real estate, specialists have low or zero corporate tax rates and use depreciation to shield dividends. With operating leases, non-specialist firms operate the assets with no entry on balance sheets. On income statements, non-specialists deduct rent as an expense. On specialist income statements, the rent is exempt from double taxation of distributed dividends. Accounting reform proposes to undo and upend these arbitrage provisions. All leases have capitalized present values and are reflected as right-to-use assets and liabilities on balance sheets, expensed straight line. Reported liabilities rise, potentially undermining debt covenants. Sale and leaseback provisions are at risk of being reversed, notably if proceeds have been already distributed.

Chapter 31: Financial Econometrics, Mathematics, Statistics, and Financial Technology: An Overall View

• Pages: 1007–1075

https://doi.org/10.1142/9789811269943_0031

Based upon my experience in research, teaching, writing textbooks, and editing handbooks and journals, this review paper discusses how financial econometrics, mathematics, statistics, and financial technology can be used in research and teaching for students majoring in quantitative finance.

A major portion of this paper discusses essential content of Lee and Lee (2020). Then Lee et al. (2019) and Lee et al. (2017), and Lee and Lee (2015) are used to enhance the content of this paper. In addition, important and relevant papers, which have been published in different journals are also used to support the issues discussed in this paper.

I have found the applications of financial econometrics, mathematics, statistics, and technology have improved drastically over the last five decades. Therefore, both practitioners and academicians need to update their skills in this area to compete in both financial market and academic research.

Chapter 32: Entropic Two-Asset Option

• Tumellano Sebehela
• Pages: 1077–1128

https://doi.org/10.1142/9789811269943_0032

This chapter extends the Margrabe formula such that it is suitable for accounting for any type jump of stocks. Despite the fact that prices of an exchange option are characterized by jumps, it seems no study has explored those price jumps of an exchange option. The jump in this chapter is illustrated by a Poisson process. Moreover, the Poisson process can be extended into Cox process in case there is more than one jump. The results illustrate that incompleteness in an exchange option leads to a premium which in turn increases an option value while hedging strategies reveal mixed-bag type of results.

Chapter 33: Joint Normality Test for the Returns on the Futures and Spot

• Keshab Shrestha
• Pages: 1129–1158

https://doi.org/10.1142/9789811269943_0033

It is well known that the optimal hedge ratios derived based on the mean-variance approach, the expected utility-maximizing approach, the mean extended-Gini approach, and the generalized semivariance approach will all converge to the minimum-variance hedge ratio if the futures price follows a pure martingale process and if the spot and futures returns are jointly normal. In this chapter, we perform empirical tests to see if the pure martingale and joint normality hypotheses hold using 25 different futures contracts and five different hedging horizons. Our results indicate that the pure martingale hypothesis holds for all commodities and all hedging horizons except for three stock index futures contracts. As for joint normality, we propose two new tests based on the generalized method of moments, which allow for calculating multivariate test statistics that take account of the contemporaneous correlation across spot and futures returns. Our findings show that the joint normality hypothesis generally does not hold except for a few contracts and relatively long hedging horizons.

Chapter 34: Analysis of Theoretical and Empirical Relationships between the Treasury Bills and Eurodollar

• Keshab Shrestha , and 
• Robert L. Welch
• Pages: 1159–1187

https://doi.org/10.1142/9789811269943_0034

In this paper, we derive an equilibrium relationship between the yields on Eurodollar and Treasury bills based on equivalent martingale results derived by Harrison and Kreps (1979) and Harrison and Pliska (1981, 1983), and corporate debt pricing model developed by Merton (1974). The derived equilibrium relationship incorporates the models used by Booth and Tse (1995) and Shrestha and Welch (2001) as special cases. The equilibrium relationship indicates that the conditional volatility of yield on Eurodollar explains the variation in TED spread. We empirically test the equilibrium relationship using a GARCH-M model and the concept of fractional cointegration. We use both the ex ante data implied by the respective futures contracts as well as the ex post spot data with daily, weekly and monthly frequencies. We find empirical support for the Equilibrium relationship.

Chapter 35: Volatility Risk Measures and Banks’ Leverage

• Giulio Anselmi
• Pages: 1189–1207

https://doi.org/10.1142/9789811269943_0035

In this chapter, we investigate how different measures of volatility influence bank’s capital structure beside mandatory capital requirements. We study the relationship between four volatility risk measures (volatility skew and spread, variance risk premia, and realized volatility) and bank’s market leverage and we analyze if banks adjust their capital needs in response to significant increase of risk premia discounting from traders. Among the four volatility measures, volatility skew (defined as the difference between OTM put and ATM call implied volatility and representing the perceived tail risk by traders) affects bank’s leverage the most. As volatility skew increases — hence OTM put became more expensive than ATM call — banks deleverage their assets structure. One plausible explanation relates to the higher costs of equity issuance that a bank will face during a period of distress. As the possibility to incur in expensive equity issuance increases the bank prefers to deleverage its balance sheet and create a capital buffer.

Chapter 36: The Reactions to On-Air Stock Reports: Prices, Volume, and Order Submission Behavior

• Chaoshin Chiao ,
• Tung-Ying Lin , and 
• Pages: 1209–1252

https://doi.org/10.1142/9789811269943_0036

The purpose of this chapter is to analyze the real-time responses of stock prices, volume, and order submission behavior across investor groups to 2,894 on-air stock reports from 9:16 a.m. to 1:15 p.m. during regular trading hours from October 11, 2010 to December 31, 2010 in Taiwan. First, positive (negative) reports move stock prices upward (downward) in real time, accompanied by increasing trading volume. However, the observed price movements are short-lived and vanish 14 days afterward. Second, responding to the reports, individual investors trade more actively and aggressively than institutional investors do. The overreaction of individual investors are responsible for the observed price movements.

Chapter 37: Mutual Fund Competition for Ranking: When Risk-Taking Comes with Managerial Effort

• Thi Thanh Huyen Nguyen ,
• Duc De Ngo , and 
• Mouloud Tensaout
• Pages: 1253–1276

https://doi.org/10.1142/9789811269943_0037

This study investigates theoretically and empirically mutual fund managers’ risk-taking behavior due to ranking objectives. We argue that managers can not only choose the riskiness of their portfolio but can also determine how hard to work (their effort). The combination of risk and effort depends on the interim performance gap and the effort cost level. Both interim winner and loser gamble by taking high risk and spending low effort when the interim performance gap is below a certain threshold. Only the interim loser gambles when the interim performance gap is small and the effort cost is sufficiently high. Otherwise, managers adopt the same choice of risk-effort. In many cases, high (low) risk-taking induces higher (lower) effort. Empirically, we find that managerial effort is strongly and positively linked to their risk-shifting level. The worst-performers behave differently from the others but are not necessarily riskier and lazier.

Chapter 38: Hedge Ratios: Theory and Applications

• Pages: 1277–1328

https://doi.org/10.1142/9789811269943_0038

This chapter first presents a review of various theoretical models and six estimation methods to the optimal futures hedge ratios. Then we use data to show how some of the hedge ratios can be applied to estimate hedge ratio in terms of S&P 500 future. We also show the estimation procedure on how to apply OLS, GARCH, and CECM models to estimate optimal hedge ratios through R language. These approaches are theoretically derived in terms of minimum variance, mean-variance, expected utility, and Value-at-Risk. Various ways of estimating these hedge ratios are also discussed, ranging from simple ordinary least squares to complicated heteroskedastic cointegration methods. Under martingale, joint-normality conditions, and some other conditions, different hedge ratios can be shown that this different ratio can be converted to the minimum variance hedge ratio. Otherwise, the optimal hedge ratios based on the different approaches are in general different. Finally, our empirical findings suggest the importance of capturing the heteroskedastic error structures including the long-run equilibrium error term in conventional regression model.

Chapter 39: A Note on Stock Market Seasonality: The Impact of Stock Price Volatility on the Application of Dummy Variable Regression Model

• Chin-Chen Chien ,
• Andrew M. L. Wang
• Pages: 1329–1338

https://doi.org/10.1142/9789811269943_0039

This article provides both statistical analysis and empirical evidence that the dummy variable regression models extensively employed in the market seasonality literature may wind-up misleading results. We show that the estimates of the said model tend to reject the null hypothesis incorrectly once the stock returns exhibit higher volatility for the specified period under examination. Our empirical results suggest that the so-called “January effect” could be attributed to the application of inappropriate statistical method.

Chapter 40: Time-Changed GARCH versus GARJI Model for Extreme Events: An Empirical Study

• Pages: 1339–1356

https://doi.org/10.1142/9789811269943_0040

In literature, a GARCH-jump mixture model, namely, the GARCH-jump model with autoregressive conditional jump intensity (GARJI) model, in which two conditional independent processes, i.e., a diffusion and a compounded Poisson process are used to account for stock price movements caused by normal and extreme event news arrivals, individually, is developed by Chan and Maheu (2002, 2004) to describe the volatility clustering and leverage effect phenomenon. The resulting model is less efficient and provides only ex post filter for the probability of the occurrences of large price movements. A more informative and parsimonious model, however, the VG NGARCH model, is proposed and calibrated in this study. Being an extension of the variance-gamma model developed by Madan et al . (1998), the proposed VG NGARCH model incorporates an autoregressive structure on the conditional shape parameters, which describes the news arrival rates of different impact sizes on the price movements, and an ex ante prediction for the occurrences of large price movements is provided. The performance of the proposed VG NGARCH model is compared to the GARJI model based on daily stock prices of five component financial companies in S&P 500, namely, Bank of America, Wells Fargo, J.P. Morgan Chase, CitiGroup, and AIG, respectively, from January 3, 2006 to December 31, 2009. The goodness of fit of the VG NGARCH model and its ability to predict the probabilities of large price movements are demonstrated by comparing with the benchmark GARJI model.

Chapter 41: Corporate Financial Hedging and the Cost of Equity Capital

• Hany B. Ahmed  and 
• Yilmaz Guney
• Pages: 1357–1402

https://doi.org/10.1142/9789811269943_0041

Using a large panel of UK public firms, we examine the relationship between the financial risk hedging and the cost of equity capital. We hypothesize that firms utilizing financial derivative instruments reduce the stock return volatility which is priced in investors’ expectations. While financial risk hedging serves as a vehicle for firms to alleviate cash flows volatility, it also leads to economic benefits to the firm value in case of the presence of increasing asymmetric information. In addition, we hypothesize and test whether the nature of relation between financial risk hedging and cost of equity capital varies and is more negative or more ambiguous with economic shocks. Our results show that engaging in financial risk hedging enables firms to have a lower cost of capital. Consistent with the extant literature, we control for potential endogeneity problems and sample selection bias using instrumental variables and treatment effects approaches. Thus, our results are robust to a battery of sensitivity checks, including the use of multiple estimation methods and alternative proxies of cost of equity measures. Overall, our findings suggest that the value of financial hedging decisions increases during economic shocks, and if financial constraints become more severe and if cash flows volatility increases.

Chapter 42: Does Trading Volume Contain Information to Predict Stock Returns? Evidence from China’s Stock Markets

• Oliver M. Rui
• Pages: 1403–1429

https://doi.org/10.1142/9789811269943_0042

This paper examines empirical contemporaneous and causal relationships between trading volume, stock returns and return volatility in China’s four stock exchanges and across these markets. We find that trading volume does not Granger-cause stock market returns on each of the markets. As for the cross-market causal relationship in China’s stock markets, there is evidence of a feedback relationship in returns between Shanghai A and Shenzhen B stocks, and between Shanghai B and Shenzhen B stocks. Shanghai B return helps predict the return of Shenzhen A stocks. Shanghai A volume Granger-causes return of Shenzhen B. Shenzhen B volume helps predict the return of Shanghai B stocks. This paper also investigates the causal relationship among these three variables between China’s stock markets and the U.S. stock market and between China and Hong Kong. We find that U.S. return helps predict returns of Shanghai A and Shanghai B stocks. U.S. and Hong Kong volumes do not Granger-cause either return or volatility in China’s stock markets. In short, information contained in returns, volatility, and volume from financial markets in the U.S. and Hong Kong has very weak predictive power for Chinese financial market variables.

Chapter 43: Financial Statement Analysis

• Orla Lenihan
• Pages: 1431–1460

https://doi.org/10.1142/9789811269943_0043

This chapter provides readers with the skills and insights to enable a fuller understanding of a public company’s underlying financial statements. Financial statement analysis is a form of fundamental analysis that identifies and analyzes the key financial information relevant to a company, for the purpose of determining the company’s intrinsic value. This chapter concentrates on the calculation and application of the principal financial ratios for profitability, efficiency, liquidity, solvency, and investment potential. There is an effort to make the content accessible to readers on a practical level, by applying the various ratios to the real financial statements of Johnson & Johnson. While the chapter outlines in detail the quantitative calculations required to undertake financial statement analysis, there is a firm emphasis on the importance of context in properly applying these techniques. The establishment of trends, the comparison of data to benchmarks, and the consideration of strategic factors all form part of this holistic contextual analysis of financial statement information.

Chapter 44: Expected Credit Losses under IFRS 9: Concept, Models, and Disclosures

• Alessandra Allini ,
• Bikki Jaggi ,
• Annamaria Zampella , and 
• Martina Prisco
• Pages: 1461–1511

https://doi.org/10.1142/9789811269943_0044

The IFRS 9 on Financial Instruments has made an important contribution to the credit loss recognition process and financial reporting by replacing the existing Incurred Credit Loss (ICL) model with the Expected Credit Losses (ECL) model. The ECL model applies to all financial instruments whether they are recognized at the amortized cost or at fair value. Firms are required to estimate and recognize loan loss allowances based either on the 12-month or lifetime ECL, depending on whether there has been a significant increase in the credit risk since initial recognition. In this chapter, we first briefly explain the scope of IFRS 9 and then discuss the main characteristics of ECL model and also present mathematical models that can be used to estimate credit loan losses. The mathematical models can be based either on the capital market, discounted cash flow, or weighted losses approach. Finally, we discuss ECL disclosures that are expected to provide greater transparency on credit risk and loan loss provisions, and also present economic implications of the ECL model on firm performance.

Chapter 45: Hedging with the International Equity Index Futures: The Conventional Model versus the Error Correction Model

• Win-Lin Chou , and 
• Dennis Kin-Keung Fan
• Pages: 1513–1524

https://doi.org/10.1142/9789811269943_0045

This chapter estimates and compares the hedge ratios of the conventional and the error correction models using three advanced international stock markets with different time intervals. Comparisons of out-of-sample hedging performance reveal that the error correction model outperforms the conventional model, suggesting that the hedge ratios obtained by using the error correction model do a better job in reducing the risk of the cash position than those from the conventional model. In addition, this chapter evaluates the effects of temporal aggregation on hedge ratios. It is found that temporal aggregation has important effects on the hedge ratio estimates.

Chapter 46: Technical Analysis in Investing

• Pages: 1525–1547

https://doi.org/10.1142/9789811269943_0046

Technical analysis helps investors to better time their entry and exit from financial asset positions. This methodology relies solely on past information of the prices and volumes of financial assets to predict the future price trend of a financial asset. Modern research has established that combined with other sentiment measures such as social media, it can outperform the standard buy and hold strategy. Moreover, it has been documented that novice and professional investors apply technical analysis in their investing strategy. An experienced investor should combine fundamental analysis and technical analysis for better trading results. Programmers use technical analysis to create algorithmic trading systems that learns and adopts to the changing trading environments and performs trading accordingly without human involvement. There are hundreds of technical tools offered by known trading platforms. Investors must use specific tools that fits his trading style and risk adoption. Moreover, different financial assets such as stocks, ETFs, cryptocurrency, futures, and commodities demand different set of tools. Furthermore, investors should use these tools according to the time frame they use for trading. This chapter will discuss different technical tools that are used to help traders of different time frames and different financial assets to achieve better returns over the traditional buy and hold strategy.

Chapter 47: A Comparative Static Analysis Approach to Derive Greek Letters: Theory and Applications

• Pages: 1549–1581

https://doi.org/10.1142/9789811269943_0047

Based upon comparative analysis, we first discuss different kinds of Greek letters in terms of Black–Scholes option pricing model, then we show how these Greek letters can be applied to perform hedging and risk management. The relationship between delta, theta, and gamma is also explored in detail.

Chapter 48: A Correlation-Based Portfolio Choice Algorithm

• Jonathan Ross ,
• Joshua Madsen , and 
• Gordon Alexander
• Pages: 1583–1600

https://doi.org/10.1142/9789811269943_0048

Analyzing the correlation matrix of listed stocks, we identify “singletons” that table minimal cross-sectional correlations. Portfolios comprising 100–500 singletons all have lower betas and standard deviations and, correspondingly, higher average Sharpe and Treynor ratios than the Center for Research in Security Prices (CRSP) universe over the sample time period 1950–2017. Portfolios of singletons chosen from subsets of the CRSP universe, including small-value, low-variability, and momentum stocks, similarly realize lower portfolio standard deviations and higher risk-adjusted returns. These well-diversified portfolios suggest that the positive abnormal returns to low-beta portfolios are driven by their component stocks having low average cross-sectional correlation. One of the authors invested $20,000 of his own money in the algorithm-chosen 240 stock singleton portfolio over a 4-year period (2015–2018) and beat the market year-by-year on a risk-adjusted basis just as our results predicted.

Chapter 49: Stock Returns and Volatility on China’s Stock Markets

• Pages: 1601–1627

https://doi.org/10.1142/9789811269943_0049

We examine time-series features of stock returns and volatility, as well as the relation between return and volatility in four of China’s stock exchanges. Variance-ratio tests reject the hypothesis that stock return follows a random walk. We find evidence of long memory of returns. Application of GARCH and EGARCH models provides strong evidence of time-varying volatility and shows volatility is highly persistent and predictable. The results of GARCH-M do not show any relation between expected returns and expected risk. Daily trading volume used as a proxy for information arrival time has no significant explanatory power for the conditional volatility of daily returns.

Chapter 50: Value Line Investment Survey Rank Changes and Beta Coefficients

• Hun Y. Park
• Pages: 1629–1635

https://doi.org/10.1142/9789811269943_0050

By using a regression relationship, this chapter investigates the relationship between Value Line rank changes and beta changes in an attempt to explain the Value Line enigma.

Chapter 51: International Hedge Ratios for Index Futures Market: A Simultaneous Equations Approach

• Mei-Ling Chen
• Pages: 1637–1647

https://doi.org/10.1142/9789811269943_0051

The main purpose of this chapter is to investigate hedge ratios in terms of the international index futures markets. Instead of looking at hedging in a single market, we here construct a simultaneous equations system to study the index hedging in the light of the cross-country linkage and interaction. The three-stage least squares (3SLS) estimating procedure is then applied to S&P500, FTSE100, and NIKKEI225 indices over the period 1990–2020. The empirical results indicate that the cross-country hedging strategy in both markets is feasible and the investors can bring down the holding position in own futures market. Moreover, the hedging effectiveness of cross-country hedging strategy performs better than the traditional single market hedging strategy in terms of the percentage reduction in variance.

Chapter 52: Empirical Studies of Structural Credit Risk Models and the Application in Default Prediction: Review and New Evidence

• Han-Hsing Lee ,
• Ren-Raw Chen , and 
• Pages: 1649–1706

https://doi.org/10.1142/9789811269943_0052

This paper first reviews empirical evidence and estimation methods of structural credit risk models. Next, an empirical investigation of the performance of default prediction under the down-and–out barrier option framework is provided. In the literature review, a brief overview of the structural credit risk models is provided. Empirical investigations in extant literature papers are described in some detail, and their results are summarized in terms of subject and estimation method adopted in each paper. Current estimation methods and their drawbacks are discussed in detail. In our empirical investigation, we adopt the Maximum Likelihood Estimation method proposed by Duan (1994). This method has been shown by Ericsson and Reneby (2005) through simulation experiments to be superior to the volatility restriction approach commonly adopted in the literature. Our empirical results surprisingly show that the simple Merton model outperforms the Brockman and Turtle (2003) model in default prediction. The inferior performance of the Brockman and Turtle model may be the result of its unreasonable assumption of the flat barrier.

Chapter 53: Predicting Stock Return Movement Directions with Sentiment Analysis of News Headlines: A Machine Learning Approach

• Hanxin Hu  and 
• Pages: 1707–1734

https://doi.org/10.1142/9789811269943_0053

This chapter combines the sentiment features of news headlines, stock market data, and macroeconomic factors to predict the direction of stock return movements in one month after the release of the news article. The sentiment features are extracted with Flair, an advanced library for Natural Language Processing (NLP). The stock market data of a company contains a comprehensive collection of 94 variables used by a prior literature (Gu et al ., 2020) for the prediction of stock return. To construct the prediction model, this chapter applies seven machine learning algorithms (including Gradient Boosting, XGBoosting, Random Forest, Artificial Neural Networks, Support Vector Machine, Naïve Bayes, and Logistic Regression). The out-of-sample tests show that tree-based ensemble methods (i.e., Random Forest, XGBoosting, and Gradient Boosting) provide the most accurate predictions, with the maximum AUC-ROC of 0.74. Furthermore, this study provides evidence for the effectiveness of the sentiment features of news headline for the prediction of future stock returns as the median of the sentiment score of the news headline is listed as one of the most important predictors in the model.

Chapter 54: Style Investing, Momentum, and Co-movement

• Chunchi Wu  and 
• Xinyuan Tao
• Pages: 1735–1753

https://doi.org/10.1142/9789811269943_0054

Investors and professional money managers typically categorize assets into different styles to facilitate portfolio management and capital allocations. As these market participants move funds among assets of different styles based on their relative performance, correlated trading generates return co-movement and style momentum. This chapter reviews existing theories on style investing and important findings. In particular, it presents new evidence in a large bond market and demonstrates that behavioral finance theory can help explain return co-movement and momentum in the bond market traditionally dominated by institutional and long-term investors who are thought to be less behaviorally biased.

Chapter 55: Mining for “Green Diamonds” — Value Relevance of Greenhouse Gas Emissions

• Carsten Homburg ,
• Laurens O. J. Lapp , and 
• Roman Schick
• Pages: 1755–1794

https://doi.org/10.1142/9789811269943_0055

Using an international dataset of 5,861 firm-year observations between 2009 and 2016 obtained from the Carbon Disclosure Project (CDP), we analyze the effect of firms’ Greenhouse Gas (GHG) emissions on stock price performance. To this end, we first discuss former research which finds an equity discount entailed by high levels of GHG emissions. We then focus on additional metrics of stock price performance, namely stock price return and stock price risk. Interestingly, we do not find any significant impact of GHG emissions on these metrics. A possible explanation is that investors are not yet able to quantify the GHG emission risk due to insufficient disclosure.

Chapter 56: Risk Estimation, Diversification, and Optimal Weights

• Pages: 1795–1833

https://doi.org/10.1142/9789811269943_0056

The main purposes of this chapter are (i) to discuss risk classification and estimation; (ii) to show how to use minimum-variance and Sharpe performance measure approach to estimate optimal weights for a two-security portfolio; (iii) to discuss applications of performance measures; and (iv) to use concepts discussed in this chapter to show how banking lending rate can be estimated.

Chapter 57: The Role of Founder Presence in Investment Analysis

• Bin Srinidhi
• Pages: 1835–1851

https://doi.org/10.1142/9789811269943_0057

Extant studies and my own work show that in US listed corporations, the presence of a firm’s founder adds value to the firm. The incremental value increases with the extent of decision rights controlled by the founder. Furthermore, the value addition is higher if the founder CEO is younger at the time of the initial public offering and decreases with the founder’s tenure in the firm. Further investigation reveals that the founders add value by improving operating performance and being more transparent than similar non-founder firms. Moreover, the founders are more focused on the strategic positioning of the firm in improving operating performance — they improve profit margins in differentiated firms while improving efficiency in firms with cost-leadership strategy. Analysts and investors can benefit by incorporating these insights into their analysis.

Chapter 58: Financial Statement Analyses and Firm Valuation: Johnson & Johnson as a Case Study

• Wen-Chi Yeh
• Pages: 1853–1892

https://doi.org/10.1142/9789811269943_0058

The main purpose of this chapter is to use Johnson & Johnson’s (JNJ) accounting information and market information to discuss the following three aspects concerning investment analysis: (i) financial ratio analyses, (ii) impact of intangible assets on Tobin Q estimates, and (iii) use of simultaneous model to forecast pro forma financial statements. Peter and Taylor (2017) have theoretically shown that intangible asset is one of the important factors in calculating Tobin Q . We use accounting information to calculate Tobin Q with and without considering intangible assets. Since JNJ’s intangible assets account for about 51% of its total assets, we review the Tobin Q ’s alternative estimation and show how the intangible assets affect Tobin Q of JNJ. By using financial ratio and market information, we perform 20 equation models to forecast pro forma balance sheet and pro forma income statement.

Chapter 59: Technical Analysis in the Stock Market: A Review

• Yufeng Han ,
• Guofu Zhou , and 
• Pages: 1893–1928

https://doi.org/10.1142/9789811269943_0059

Technical analysis is the study of forecasting future asset prices with past data. In this survey, we review and extend studies on not only the time-series predictive power of technical indicators on the aggregated stock market and various portfolios but also the cross-sectional predictability with various firm characteristics. While we focus on reviewing major academic research on using traditional technical indicators, we also discuss briefly recent studies that apply machine learning approaches, such as lasso, neural network, and genetic programming, to forecast returns both in the time series and on the cross section.

Chapter 60: The Sovereign Rating Channel in the European Debt Crisis: Spillover Effects on Sovereign CDS and Other Systemic Risk Indicators

• Dimitris Georgoutsos  and 
• George Moratis
• Pages: 1929–1959

https://doi.org/10.1142/9789811269943_0060

This chapter investigates the effect of sovereign debt ratings on credit default swap (CDS) spreads during the Eurozone sovereign debt crisis. The empirical investigation is conducted by means of panel vector autoregressive models which allow the analysis of multidirectional relationships in a dynamic context. Our main findings, based on directional spillover effects, are that credit rating announcements did not have any impact on the CDS spreads of the Eurozone periphery countries. However, when we limit our analysis on the sovereign debt ratings of Portugal, and Ireland to a minor extent, we obtain some evidence for spillover effects on the CDS spreads of other countries. Furthermore, the quantitative contribution of sovereign ratings has been of secondary importance when calculating their spillover impact on other “systemic” risk indicators. Overall, the results indicate that during the recent sovereign debt crisis, credit rating announcements on the Eurozone’s periphery countries did not contribute to the global financial crisis.

Chapter 61: Interest Rate Sensitivity and Investor Disagreement: How to Explain Bank Stock Turnover

• Mark Iarovyi ,
• Sasson Bar-Yosef , and 
• Itzhak Venezia
• Pages: 1961–1990

https://doi.org/10.1142/9789811269943_0061

Maturity mismatches (MMs) expose banks to interest rate sensitivity, adding to the uncertainty of banks’ performances. Since information regarding MMs is usually not readily available, considering the high correlation between the two, interest rate sensitivities could serve as proxies to these mismatches for short periods. Therefore, we label them as implied MMs. Our analyses of the correlation between banks’ interest rate sensitivity and the trading volume of the banks’ equity reveal positive correlations. The heightened increase in volume suggests that implied MMs increase disagreement among banks’ investors.

Chapter 62: A Novel Semi-Static Method for the Index Tracking Problem

• Chun-Chong Fu ,
• Chuan-Hsiang Han , and 
• Pages: 1991–2002

https://doi.org/10.1142/9789811269943_0062

With the rapid growth of index investing in wealth management, such as the exchange traded fund (ETF) market, tracking the reference index of ETF regains considerable attention because the corresponding tracking error is one of the key measurements of a fund’s performance. To improve accuracy and efficiency in minimizing tracking errors, we propose a novel approach that combines the generalized singular value decomposition (GSVD) and the constrained Kalman filter method for estimation of portfolio weights and their dynamical updates, respectively. GSVD reveals all feasible weights under a static constrained regression model for the index tracking error minimization problem. The constrained Kalman filter is applied to update the GSVD-solved weights of the optimal port-folio along with the dynamical time series of return data. This two-stage approach is semi-static. Our empirical studies demonstrate that sufficient low tracking error can be obtained under a robust top-down stratified framework that consists of 11 sector indices defined by the Global Industry Classification Standard.

Chapter 63: Fundamental Analysis: A Practical Approach

• Andreas G. Koutoupis  and 
• Leonidas G. Davidopoulos
• Pages: 2003–2021

https://doi.org/10.1142/9789811269943_0063

Fundamental analysis is a simple concept which tries to isolate investing choices from sentiment. Moreover, it is a mixture of science and art that breaks down to simple math. Great value investors such as Warren Buffet, Benjamin Graham, and John C. Bogle very vividly state that in order to successfully value a business, it does not take more than common sense and simple math. In this chapter, we approach the very fundamentals in a practical manner, showcasing a simple way to estimate the intrinsic value of a business.

Chapter 64: Lessons on Risk, Return, and Portfolio Construction from the Great Investors

• John M. Longo
• Pages: 2023–2050

https://doi.org/10.1142/9789811269943_0064

There is often a wide divergence between academic and practitioner views on risk, return, and portfolio construction. For example, academics focus primarily on purely quantitative measures or factors. Initially, the focus was on dividends, free cash flow, standard deviation, and beta. Later, additional factors analyzed by the academic community came into focus, such as size, style, liquidity, momentum, and quality. Practitioners, in contrast, often focus on a company’s products, its history, and the competitive dynamics of its industry. Furthermore, practitioners “discovered” anomalies, such as momentum, decades before they were rigorously analyzed and published by academics. The current distinction between the two groups is not merely quantitative versus qualitative. This chapter summarizes the viewpoints of the two camps — academic and practitioner — and suggest steps that may effectively combine the two schools of thought, at least to a certain degree using the Black–Litterman model and other qualitative techniques, such as stratifying asset pricing models. This analysis may result in a more robust investment, risk management, and portfolio construction process.

Chapter 65: Sources of Liquidity Premium: Risk or Mispricing?

• Pin-Huang Chou ,
• Kuan-Cheng Ko , and 
• K.C. John Wei
• Pages: 2051–2088

https://doi.org/10.1142/9789811269943_0065

We study three widely used liquidity measures and find that they all carry significant premiums beyond the size, book-to-market, and momentum effects. Although liquidity as a risk factor bears a significant return premium, it is better characterized by a characteristic-based model. Further analysis shows that (1) although the premium persists for up to five years following formation, it diminishes over time and becomes insignificant in the post-1960 period; (2) the premium is larger for stocks with higher idiosyncratic risk. Thus, the empirical results provide some evidence that supports the mispricing argument.

Chapter 66: Analysis of IBEX-35 Listed Companies: Recent CSR Reports and Behavior of the Main Indicators. Existence of a Proportional Relationship between Greenwashing and Deficient CSR Reports

• Cristina Chueca Vergara  and 
• Luis Ferruz Agudo
• Pages: 2089–2120

https://doi.org/10.1142/9789811269943_0066

Currently, environmental concern, as well as other issues such as corporate social responsibility (CSR) or socially responsible investment, is leading many companies to implement “greenwashing” behaviors. In this chapter, the factors concerning “greenwashing” will be analyzed, and the relationship between those behaviors and deficient sustainability reports will be examined. This study will be approached from the point of view of the Spanish reality, paying special attention to listed companies, specifically to those which are listed on the IBEX 35 Index. In order to achieve that objective, this chapter analyzes the sustainability reports of six companies listed on the IBEX 35 Index belonging to different sectors: energy, oil, consumer goods, financial services, basic materials, industry and construction, and technology and telecommunications. Moreover, greenwashing in plastic industry will be analyzed. Finally, we draw conclusions about the relationship between “greenwashing” and CSR.

Chapter 67: Return Volatility, Skewness, and Momentum Effects

• Alex YiHou Huang  and 
• Ming-Che Hu
• Pages: 2121–2150

https://doi.org/10.1142/9789811269943_0067

This study identifies nonlinear patterns of momentum profits across stocks with different levels of return volatility and skewness. It finds that momentum profits are the largest from mildly volatile and skewed stocks; this phenomenon is consistently observed for different formation/holding periods, types of examined returns, and momentum grouping. Based on the patterns, this chapter proposes a sample filtering criterion for momentum investment; the profits accrued through the sample filtering are economically enlarged. Such an enhancement of momentum profits by the filtering process is documented in various strategies, including the conventional, 52-week high, and risk-managed momentum strategies.

Chapter 68: Predicting Implied Volatility with Historical Volatility

• Xinjie Wang ,
• Ge Wu , and 
• Suyang Zhao
• Pages: 2151–2175

https://doi.org/10.1142/9789811269943_0068

In this study, we document a novel lead–lag relation between historical and implied volatilities based on China’s CSI300 Index options. We show that historical volatilities have incremental information when we predict implied volatilities, and this pattern tends to be more stable for put options than for call options. Moreover, we reveal that this lead– lag relation is relevant to option terms and time horizons of historical volatilities, which means implied volatilities of long-term options are more likely to be properly predicted by long-term historical volatilities on average. Finally, we find that speculative trading might explain our results.

Chapter 69: Estimating Binomial and Black & Scholes Option Pricing Models: Excel, R Language, and SAS Program Approach

• LiJane Kao ,
• John Lee , and 
• Pages: 2177–2195

https://doi.org/10.1142/9789811269943_0069

In this chapter, we show how Excel and R language programming can be used to estimate the European call/put prices based on Black–Scholes model as well as binomial option pricing model. Different underlings are considered, including individual stocks, currency, and stock indices. SAS language programming to estimate the European call/put prices based on binomial option pricing model and Black–Scholes option pricing model are given in the appendices.

Chapter 70: Value Contributions

• Peter Chinloy  and 
• Matthew Imes
• Pages: 2197–2236

https://doi.org/10.1142/9789811269943_0070

When firms increase the size of their net balance sheet, they earn a higher return as value. Value is earned from different holdings. An examination of holdings compares which earn value. Holdings of US traded firms are divided into categories from their balance sheet. As reported on the asset side are cash, receivables, physicals, and intangibles. On the liability side, in order are payables, short- and long-term debt. In value, the sum of book equity is divided by market capitalization. For individual value, each is divided by market capitalization. Then, the return is regressed on the individual values and their interactions. Negative value is when increasing a holding reduces the return. Positive value comes from return increasing with a holding. The rank order of contributors to value, for US firms over 1980–2016, is

Replacing holdings by their risk-weight adjustments and adding up, the resulting book to market has no effect on returns. Returns are fully priced by firms’ balance sheet holdings. The results potentially explain why value or overall book to market fails to outperform growth. Value is a return to holding certain risky holdings. Not all balance sheet holdings are risky.

Chapter 71: Using Computational Science Methods in Accounting and Finance Research

• David A. Ziebart ,
• Mark Cheng ,
• Sohee Kim ,
• Wenyin Li ,
• Anh Pham , and 
• Darren Woodward
• Pages: 2237–2264

https://doi.org/10.1142/9789811269943_0071

Computational modeling is being used in more research and applications in accounting and finance. These approaches include simulations and computer-intensive methods that are different from traditional methods in most prior research. This chapter explains the underlying philosophy of these methods and discusses several research studies using them. Inferences of the studies are discussed and both benefits and concerns with the methods are addressed.

Chapter 72: Stock Buybacks and Financial Turmoil: Pros and Cons for Investors

• Foued Hamouda
• Pages: 2265–2292

https://doi.org/10.1142/9789811269943_0072

Buyback programs are often used by firms for different purposes, including distributing excess cash to shareholders and signal that the stock price is underpriced. The first purpose of this chapter is to review studies of buyback programs and to highlight that fundamentals-based hypotheses are problematic in financial turmoil. We will show how buyback programs add value to shareholders while also identifying some situations in which they can destroy value. The second purpose is to present the pros and cons of buyback programs to shareholders, particularly during financial turmoil.

Chapter 73: The Roles of Financial Analysts in the Stock Market

• Guanming He  and 
• April Zhichao Li
• Pages: 2293–2308

https://doi.org/10.1142/9789811269943_0073

Investors lacking ample time, professional knowledge, and sufficient ability may find it difficult to understand the implication of complex corporate information and figure out the clear trend of future corporate performance. Financial analysts who provide earnings forecasts and stock recommendations could help investors with investment decision-making. This chapter explores the roles that analysts play in the stock market, the determinants of the effectiveness of their roles, and how well they play the roles.

Chapter 74: Funding Liquidity and CDS-Bond Basis: Evidence from the CDS Big Bang

• Xinjie Wang  and 
• Zhaodong (Ken) Zhong
• Pages: 2309–2331

https://doi.org/10.1142/9789811269943_0074

The CDS Big Bang increased the upfront funding requirements for trading CDS contracts, especially for those with credit spreads further away from 100 and 500 basis points. Exploiting this regulatory change, we document that a higher funding requirement reduces market liquidity and increases the absolute value of the CDS-bond basis. The funding effects are stronger for smaller reference entities. Our findings highlight the importance of the tradeoff between standardization and the funding cost of upfront payments.

Chapter 75: Issues and Challenges of Weather and Freight Derivatives: Impact of Pandemic Situation

• G.V. Satya Sekhar
• Pages: 2333–2348

https://doi.org/10.1142/9789811269943_0075

During the past couple of months, the pandemic situation raised the need for assessment of the impact on derivatives, particularly weather and freight derivatives, as an innovative financial product. There are several issues and challenges faced by weather and freight derivatives in the financial market. This chapter aims to appreciate innovative financial derivatives and also address issues relating to the functioning of weather and freight derivatives. We have also examined the pricing models of weather derivatives across the globe. In addition, we examine the impact of the COVID-19 pandemic situation on weather derivatives.

Chapter 76: On a Long-Term Investment Strategy in a Stock Market

• Guanming He ,
• April Zhichao Li , and 
• Dongxiao Shen
• Pages: 2349–2391

https://doi.org/10.1142/9789811269943_0076

Against the backdrop of increasingly fierce industrial competition nowadays, firms tend to have substantive business risk and/or information risk, increasing the estimation risk and limit of arbitrage for investors in their short-term investments in a stock market. It is thus important for investors to hold a long-term horizon for at least part of their investments in the stock market. This chapter aims to introduce a long-term investment strategy that is practically feasible and potentially profitable for investors. To this end, we first develop a parsimonious model in which we identify the major determinants of a firm’s value and long-term growth. This model is used to select high-value firms from each industry for further fundamental analysis and valuation. We next expatiate on how to perform strategy analysis, accounting analysis, financial analysis, and prospective analysis and therein apply the residual operating income valuation model, in the best possible manner, to further value the selected firms and their long-term investment potential. Lastly, we expound the strategy of forming and adjusting a long-term investment portfolio in a way that potentially maximizes long-term portfolio return.

Chapter 77: European Option, American Option, and Option Bounds: Theory, Method, and Some Empirical Results

• Pages: 2393–2429

https://doi.org/10.1142/9789811269943_0077

It is well known that both normal and log-normal distributions are important to understand Black & Scholes-type European and American options. Therefore, we first review the basic theory of normal and log-normal distributions and their relationship, then bivariate and multivariate normal density functions are analyzed in detail. Next, we discuss American options in terms of random dividend payment. We then use bivariate normal density function to analyze American options with random dividend payment. Excel programs are used to show how American co-options can be evaluated. Finally, pricing option bounds are analyzed in some detail.

Chapter 78: Improving the Stock Market Prediction with Social Media via Broad Learning

• Xi Zhang  and 
• Philip S. Yu
• Pages: 2431–2500

https://doi.org/10.1142/9789811269943_0078

This chapter discusses how to exploit various information on the web to improve stock market prediction. We first discuss the impacts of investors’ social network on the stock market and then propose several information fusion methods, that is the tensor-based model and the multiple-instance learning model, to integrate web information and quantitative information to improve the prediction capability.

Chapter 79: Bond Portfolio Management, Swap Strategy, Duration, and Convexity

• Pages: 2501–2539

https://doi.org/10.1142/9789811269943_0079

This chapter first focuses on the bond strategies of riding the yield curve and structuring the maturity of the bond portfolio in order to generate additional return. This is followed by a discussion on swapping, which is essentially interest-rate swapping. Next is an analysis of duration or the measure of the portfolio sensitivity to changes in interest rates with and without convexity, after which immunization is the focus. The convexity is essentially discussed as a nonlinear relationship between bond price and duration. Finally, a case study of bond-portfolio management is presented in the context of portfolio theory. Overall, this chapter presents how interest rates change affect bond price and how maturity and duration can be used to manage portfolios.

Chapter 80: Do CFA Charterholders Make Better Hedge Fund Managers?

• Yao Zheng  and 
• Pages: 2541–2564

https://doi.org/10.1142/9789811269943_0080

In this chapter, we investigate whether hedge fund managers possessing advanced professional education, as represented by the CFA@ designation, have better managerial ability and fund performance compared to managers lacking such education. We define managerial ability in terms of market, volatility, and liquidity timing ability. In general, we find that CFA designated managers have better volatility and liquidity timing ability and earn better risk-adjusted returns than non-CFA managers. We also find that the CFA designation does not contribute to the fund flow–performance relation. The rise of high frequency and algorithmic trading may partially explain these findings.

Chapter 81: Impact of Bank Activity and Funding Strategies on Liquidity Management: International Evidence

• Yu-Li Huang  and 
• Pages: 2565–2600

https://doi.org/10.1142/9789811269943_0081

This study examines the implications of a bank’s activity mix and funding strategy for its liquidity management as defined in the Basel III accords or when they create more liquidity as measured by Berger and Bouwman (2009). Using an international sample of 624 banks in 65 countries, we find that, at low levels of noninterest income and nondeposit funding, there could be some risk diversification benefits in increasing these shares; however, at higher levels of noninterest income and nondeposit funding shares, additional increases result in higher illiquidity. Finally, better accounting disclosure will improve the effect on bank liquidity by both bank’s activity mix and funding strategy, but at the same time, they worsen the bank liquidity by stricter capital stringency and more market power.

Chapter 82: Accounting Information and Firm Valuation

• Cathy Zishang Liu ,
• Kai-Cheung Kenneth Chu , and 
• C. S. Agnes Cheng
• Pages: 2601–2641

https://doi.org/10.1142/9789811269943_0082

Our chapter aims to elucidate the theoretical relation and empirical observation between accounting information and contemporaneous firm value (measured as a firm’s stock price and/or stock return). Grounded on the intricate links of future expected cash flow and current stock price, future expected cash flow and future earnings, and future earnings and realized earnings, our work provides constitutional understanding of the residual income valuation model (Ohlson, 1995) and the abnormal earnings growth model (Ohlson and Juettner-Nauroth, 2003). The dynamics of the three intricate links and its implications for the valuation models has untangled a long-time myth in the literature of value relevance of financial reporting. Our chapter also presents the mediation effect of market efficiency on the empirical observation between accounting information and contemporaneous firm value.

Chapter 83: Developments in CDS Markets: A Review on Recent CDS Studies

• Xingyi Hu  and 
• Pages: 2643–2681

https://doi.org/10.1142/9789811269943_0083

The credit default swap (CDS) market has experienced tremendous changes over the last two decades. This chapter reviews recent studies on the CDS contract and the CDS market, highlighting recent developments in CDS contracts and market structures. Characteristics of CDS contracts make them outstanding from other derivatives products, and the rapid development of CDS markets shows the popular use and importance of CDS contracts in general. Policies and regulations on CDSs have brought huge changes to the market and the availability of publicly reported data. The relationship between firms and CDS has long been a focus among academics. Moreover, CDSs are also commonly used among mutual funds, hedge funds, and banks, affecting the trading activities of various financial intermediaries. While the sovereign CDS and index CDS are shown to be more and more significant in financial markets, other CDS products, including CDS options and tranches, also experience changes. We summarize the evolution in CDS markets and the structure of CDS studies, aiming to gain a better understanding of CDS markets and provide insights for future studies on the CDS market.

Chapter 84: Decision Tree and Microsoft Excel Approach for Option Pricing Model

• Jow-Ran Chang  and 
• Pages: 2683–2726

https://doi.org/10.1142/9789811269943_0084

The main aims of this chapter are (i) to use the decision tree approach to derive binomial option pricing model (OPM) in terms of the method used by Rendleman and Barter (RB, 1979) and Cox, Ross, and Rubinstein (CRR, 1979) and (ii) to use Microsoft Excel to show how decision tree model can be converted to Black–Scholes model when the number period increases to infinity. In addition, we develop binomial tree model for American option and trinomial tree model. The efficiency between binomial and trinomial tree is also compared. In sum, this chapter shows how binomial OPM can be converted step by step to Black–Scholes OPM.

Chapter 85: Comparisons between the Markowitz Model and the Black–Litterman Model

• Huei-Wen Teng
• Pages: 2727–2749

https://doi.org/10.1142/9789811269943_0085

Model risk is critical in constructing a portfolio. To avoid model risk, the Black–Litterman model is an approach that allows to adjust the original estimated parameters using the implied market equilibrium returns and investors’ views (Black and Litterman, 1991). This chapter contrasts the standard approach of Markowitz (1952) with the Black–Litterman model and reviews different investment philosophies by Longo (2021). For empirical demonstrations, we consider a predictive regression to form investors’ views, where asset returns are regressed against their lagged values and the market return. Motivated by stylized features of historical returns, we employ heteroscedastic time-series models. Empirical analysis using five industry indexes in the Taiwan stock market shows that the proposed Black–Litterman portfolio outperforms the 1/ N portfolio and Markowitz portfolio.

Chapter 86: Empirical Performance of the Constant Elasticity Variance Option Pricing Model

• Ren Raw Chen ,
• Han-Hsing Lee
• Pages: 2751–2793

https://doi.org/10.1142/9789811269943_0086

In this essay, we empirically test the Constant–Elasticity-of-Variance (CEV) option pricing model by Cox (1975, 1996) and Cox and Ross (1976), and compare the performances of the CEV and alternative option pricing models, mainly the stochastic volatility model, in terms of European option pricing and cost-accuracy based analysis of their numerical procedures.

In European-style option pricing, we have tested the empirical pricing performance of the CEV model and compared the results with those by Bakshi, Cao and Chen (1997). The CEV model, introducing only one more parameter compared with Black-Scholes formula, improves the performance notably in all of the tests of in-sample, out-of-sample and the stability of implied volatility. Furthermore, with a much simpler model, the CEV model can still perform better than the stochastic volatility model in short term and out-of-the-money categories. When applied to American option pricing, high-dimensional lattice models are prohibitively expensive. Our numerical experiments clearly show that the CEV model performs much better in terms of the speed of convergence to its closed form solution, while the implementation cost of the stochastic volatility model is too high and practically infeasible for empirical work.

In summary, with a much less implementation cost and faster computational speed, the CEV option pricing model could be a better candidate than more complex option pricing models, especially when one wants to apply the CEV process for pricing more complicated path-dependent options or credit risk models.

Chapter 87: Asset Allocation with Cryptocurrencies

• Han-Hsing Lee  and 
• Ken-Kuan Su
• Pages: 2795–2858

https://doi.org/10.1142/9789811269943_0087

This research discusses the role of cryptocurrencies in portfolio investment and observes the timing within which the cryptos provide benefit to investors in a traditional financial market. We first use a mean-variance spanning test to check for any improvement that cryptos bring to a well-diversified portfolio and find a significant difference between port-folios with and without cryptos. Second, we analyze the weight dynamics of cryptos in the minimum-variance portfolio and the tangent portfolio to examine if cryptos present a hedging property in the mean-variance viewpoint. The finding shows that the optimal weights of cryptos increase distinctly in a market distress period, which shows their hedging property in a mean-variance view. Finally, we include cryptos in a well-diversified portfolio composed of common assets to check their weight dynamics in both tangent portfolio and minimum-variance portfolio. Consequently, we found that the cryptos take more weights in the tangent portfolio rather than in the minimum-variance portfolio, while the weights of cryptos increased in both portfolios during the COVID-19 pandemic; we thus conclude that cryptocurrencies can bring some hedging effect even in a portfolio with very common traditional assets. We also compare gold and cryptos and find that they have a similar pattern of weight dynamics, although gold has a slightly better effect in eliminating the downside risk of a minimum-variance portfolio.

Chapter 88: Market-Based, Accounting-Based, and Composite-Based Beta Forecasting

• Pages: 2859–2899

https://doi.org/10.1142/9789811269943_0088

This chapter uses the concepts of basic portfolio analysis and the dominance principle to derive the CAPM. A graphical approach is first utilized to derive the CAPM, after which a mathematical approach to the derivation is developed that illustrates how the market model can be used to decompose total risk into two components. This is followed by a discussion of the importance of beta in security analysis and further exploration of the determination and the forecasting of beta. The discussion closes with the applications and implications of the CAPM, and the appendix offers empirical evidence of the risk–return relationship.

In this chapter, we define both market beta and accounting beta and how they are determined by different accounting and economic information. Then, we forecast both market beta and accounting beta. Finally, we propose a composite method to forecast beta.

Chapter 89: Utility Theory, Capital Asset Allocation, and Markowitz Portfolio Selection Model

• Pages: 2901–2943

https://doi.org/10.1142/9789811269943_0089

In this chapter, we first discuss utility theory and utility function in detail, then we show how asset allocation can be done in terms of quadratic utility function. Based upon these concepts, we show that Markowitz’s portfolio selection model can be executed by the constrained maximization approach. Real-world examples in terms of three securities are also demonstrated. In the Markowitz selection model, we consider that short sale is both allowed and not allowed.

Chapter 90: Single-Index Model, Multiple-Index Model, and Portfolio Selection

• Pages: 2945–2981

https://doi.org/10.1142/9789811269943_0090

This chapter offers some simplifying assumptions that reduce the overall number of calculations of Markowitz models through the use of the Sharpe single-index and multiple-index models. Besides the single-index model, we also discuss how the multiple-index model can be applied to portfolio selection. We have theoretically demonstrated how single-index and multiple-index portfolio selection model can be used to replace the Markowitz portfolio selection model. An Excel example of how to apply the single-index model approach is also demonstrated.

Chapter 91: Sharpe Performance Measure and Treynor Performance Measure Approach to Portfolio Analysis

• Paul W. Chiou
• Pages: 2983–3018

https://doi.org/10.1142/9789811269943_0091

The main points of this chapter show how Markowitz’s portfolio selection method can be simplified by either the Sharpe performance measure or the Treynor performance measure. These two approaches do not need to use constrained optimization procedures; however, these methods do require the existence of a risk-free rate. Overall, this chapter has mathematically demonstrated how the Sharpe measure and Treynor measure can be used to determine optimal portfolio weights.

Chapter 92: Modeling Different REIT Cash Flows

• Tamala Amelia Manda
• Pages: 3019–3075

https://doi.org/10.1142/9789811269943_0092

The accuracy and appropriateness of discounted cash flows (DCFs) have been debated for years in academia and industry. Broadly, for most valuations, appraisers use accounting cash flows (ACFs) in determining present values (PVs) of firms. Some unique industries such as the real estate investment trusts (REITs) require customized DCFs to account for their capital structure among other factors. Fernández (2004, 2007) suggested that customized DCFs include debt cash flows, equity cash flows, free cash flows, and capital cash flows. The customized cash flows reveal different PVs for REIT firms. Furthermore, robustness results illustrate that customized DCFs are sensitive to selected macroeconomic (debt, equity, and funds from operations) and management (board and executive) variables. Fundamentally, the results are generalizable to the global REIT industry.

Chapter 93: Bayesian Portfolio Mean-Variance Efficiency Test with Sampling Error of Sharpe Ratio

• Huei Ching Soo , and 
• Pages: 3077–3098

https://doi.org/10.1142/9789811269943_0093

This study proposes a Bayesian test for a test portfolio p ’s mean-variance efficiency that takes into account the sampling errors associated with the ex-post Sharpe ratio ŜR of the test portfolio p . The test is based on the Bayes factor that compares the joint likelihoods under the null hypothesis H 0 and the alternative H 1 . Using historical monthly return data of ten industrial portfolios and a test portfolio, namely, the CRSP value-weighted index, from January 1941 to December 1973 and January 1980 to December 2012, the power function of the proposed Bayesian test is compared to the conditional multivariate F test by Gibbons et al. (1989) and the Bayesian test by Shanken (1987). In an independent simulation study, the performance of the proposed Bayesian test is also demonstrated.

Chapter 94: Fundamental Analysis, Technical Analysis, and Mutual Fund Performance

• Pages: 3099–3157

https://doi.org/10.1142/9789811269943_0094

This chapter discusses methods and applications of fundamental analysis and technical analysis. In addition, it investigates the ranking performance of the Value Line and the timing and selectivity of mutual funds. A detailed investigation of technical versus fundamental analysis is first presented. This is followed by an analysis of regression time series and composite methods for forecasting security rates of return. Value Line ranking methods and their performance are then discussed, leading finally into a study of the classification of mutual funds and the mutual fund managers’ timing and selectivity ability. In addition, the hedging ability is also briefly discussed. Sharpe measure, Treynor measure, and Jensen measure are defined and analyzed. All of these topics can help improve performance in security analysis and portfolio management.

Chapter 95: Synthetic Options, Portfolio Insurance, and Contingent Immunization

• Pages: 3159–3202

https://doi.org/10.1142/9789811269943_0095

This chapter discusses how futures, options, and futures options can be used in portfolio insurance (dynamic hedging). Four alternative portfolio insurance strategies are discussed in this chapter. These strategies are as follows: (i) stop-loss orders, (ii) portfolio insurance with listed put options, (iii) portfolio insurance with synthetic options, and (iv) portfolio insurance with dynamic hedging. In addition, the techniques of combining stocks and futures to derive synthetic options are explored in detail. Finally, important literature related to portfolio insurance is also reviewed.

Chapter 96: Global International ELM versus Momentum

• Robert Snigaroff  and 
• David Wroblewski
• Pages: 3203–3223

https://doi.org/10.1142/9789811269943_0096

We construct liquidity and earnings-based factors and combine with the Market to describe stock returns. Liquidity and Liquidity Growth are significant factors across markets. Intercept tests show that the IELM (International Earnings, Liquidity, and Market) model fits the cross section in various country groupings. As previous research showed, a Liquidity Growth factor subsumes momentum in the U.S., and we test this across international markets. From 2001 through 2019, the momentum factor has a high mean and is significant in Europe and in the Asia-Pacific, except Japan. For this time period, however, momentum is not significant in North American and Japan. While the IELM model reduces the momentum intercept in North America, both IELM and Fama and French (2017) have trouble explaining momentum in Europe and Asia where momentum is pervasive.

Chapter 97: Estimating European and American Option Pricing Models: Excel and SAS Language Approach

• Jow-Ran Chang ,
• Pages: 3225–3253

https://doi.org/10.1142/9789811269943_0097

The main purpose of this chapter is to show how Excel and SAS language can be used to estimate European options and American options. In this chapter, we use bivariate normal distribution to derive American options with one dividend payment. Both Excel program and SAS program are presented in the appendices.

Chapter 98: Estimating the Probabilities of Default under the Assumption of Unobserved Heterogeneity

• Jacob Oded  and 
• Pages: 3255–3276

https://doi.org/10.1142/9789811269943_0098

Bond default and rank transition are often modeled as a Markov chain with an absorbing state. However, recent studies have shown that the theory does not match the empirical data. We suggest that this mismatch possibly arises from unobserved heterogeneity and we examine via a numerical example whether increased heterogeneity reduces, as expected, the accuracy of the estimated defaults. The extent to which this reduction is economically significant is also considered. We then suggest a methodology for identifying the heterogeneity parameters and for testing their explanatory power.

Chapter 99: A Factor Model for Graph Data

• Wei-Fang Niu  and 
• Henry Horng-Shing Lu
• Pages: 3277–3298

https://doi.org/10.1142/9789811269943_0099

Graph data have become an important channel in exploring relations among a large number of subjects in the big data era. In past decades, community structures have been found in many complex real-world networks and play a key role in the modeling of graph data, for example, the stochastic block model (SBM) and its extensions. However, recent studies have unveiled more sophisticated modules, and typical examples include star and bipartite structures. In most graph models, these link-pattern modules are piled up in terms of multiple communities. This paper proposes a graph factor model in which each node is endowed with several (latent) features. Factors are channels for edge connections and can be characterized by link functions that map features of pairs of nodes to the edge probabilities. This model may naturally incorporate different kinds of link-pattern modules including communities, stars, and bipartite structures. The inference for the model can be carried out through an Markov Chain Monte Carlo (MCMC) procedure. Both synthetic data and real-world networks are used for numerical illustrations.

Chapter 100: A Dynamic CAPM with Supply Effect: Theory and Empirical Results

• Chiung-Min Tsai , and 
• Alice C. Lee
• Pages: 3299–3328

https://doi.org/10.1142/9789811269943_0100

Breeden (1979), Grinols (1984 Cox et al. [Cox, J. C., Ingersoll, J. E., Jr., & Ross, S. A. (1985). An intertemporal general equilibrium model of asset prices. Econometrica 53, 363–384] have described the importance of supply side for the capital asset pricing. Black [Black, S. W. (1976). Rational response to shocks in adynamic model of capital asset pricing. American Economic Review66, 767–779] derives a dynamic, multiperiod CAPM, integrating endogenous demand and supply. However, Black’s theoretically elegant model has never been empirically tested for its implications in dynamic asset pricing. We first theoretically extend Black’s CAPM. Then we use price, dividend per share and earnings per share to test the existence of supply effect with U.S. equity data. We find the supply effect is important in U.S. domestic stock markets. This finding holds as we break the companies listed in the S&P 500 into ten portfolios by different level of payout ratio. It also holds consistently if we use individual stock data.

A simultaneous equation system is constructed through a standard structural form of a multi-period equation to represent the dynamic relationship between supply and demand for capital assets. The equation system is exactly identified under our specification. Then, two hypotheses related to supply effect are tested regarding the parameters in the reduced-form system. The equation system is estimated by the Seemingly Unrelated Regression (SUR) method, since SUR allow one to estimate the presented system simultaneously while accounting for the correlated errors.

Chapter 101: Indices Herding Behavior and Its Impact on Listed Real Estate and Two Other Asset Classes: A Case of Developed versus Emerging Markets

• Sibongile Zwane
• Pages: 3329–3368

https://doi.org/10.1142/9789811269943_0101

The literature on indices herding behavior among bonds, equities, and real estate is very scant. When one compares developed and emerging markets, specifically the United States, the United Kingdom, Taiwan, and South Africa, such studies are hard to find. This study uses principal component analysis to extract and illustrate parameters driving herding investment behavior for the indices of the mentioned countries. Thereafter, the vector autoregressive model is used for robustness tests. The results reveal the following: First, governmental relationships and similarities among countries influenced herding behavior in the selected capital markets indices. Second, most of the herding occurs in the bond indices for the four countries. Finally, the robustness results reveal spillover opportunities in between and across countries irrespective of the index analyzed. The results are generalizable as they are consistent with prior studies such as Zaremba et al. (2021).

Chapter 102: Price Momentum, Earnings Forecasting, and Valuation: Implications for Inefficient Markets

• Christopher C. Geczy  and 
• John B. Guerard, Jr.
• Pages: 3369–3386

https://doi.org/10.1142/9789811269943_0102

Financial anomalies have been studied in the U.S. However, recent evidence suggests that what were initially identified as return anomalies have diminished in U.S. data. Have the identified regularities changed or are they persistent? Have historical and earnings forecasting data been a consistent and highly statistically significant source of excess returns? We test a number of financial anomalies of the 1980s–1990s and report that several models and strategies continue to produce statistically significant excess returns not absorbed by then-known factor models. We report that earnings forecasts, revisions, and breadth and price momentum have maintained their statistical significance during the May 1995–December 2017 time period. More importantly, we use expected return models and multi-factor models that are estimated and known at the start of our current analysis, assuring our readers of out-of-sample and post-publication verification of the models.

Chapter 103: Advancement of Optimal Portfolio Models with Short Sales and Transaction Costs: Methodology and Effectiveness

• Pages: 3387–3410

https://doi.org/10.1142/9789811269943_0103

This paper presents the advancement of several widely applied portfolio models to ensure flexibility in their applications: Mean-variance (MV), Mean-absolute deviation (MAD), Linearized value-at-risk (LVaR), Conditional value-at-risk (CVaR), and Omega models. We include short sales and transaction costs in modeling portfolios and further investigate their effectiveness. Using the daily data of international ETFs over 15 years, we generate the results of the rebalancing portfolios. The empirical findings show that the MV, MAD, and Omega models yield a higher realized return with lower portfolio diversity than the LVaR and CVaR models. The outperformance of these risk–return-based models over the downside-risk-focused models comes from efficient asset allocation and not only the saving of transaction costs.

Chapter 104: Implied Variance Estimates for Black–Scholes and CEV OPM: Review and Comparison

• Pages: 3411–3444

https://doi.org/10.1142/9789811269943_0104

The main purpose of this chapter is to demonstrate how to estimate implied variance for both the Black–Scholes option pricing model (OPM) and the constant elasticity of variance (CEV) OPM. For the Black–Scholes OPM model, we classify them into two different estimation routines: numerical search methods and closed-form derivation approaches. Both the MATLAB approach and approximation method are used to empirically estimate implied variance for American and Chinese options. For the CEV model, we present the theory and demonstrate how to use a related Excel program in detail.

Chapter 105: On the Treatment of the Momentum Factor in Accounting-Based Anomalies: A Discussion

• Philip Keejae Hong ,
• Kyonghee Kim , and 
• Sukesh Patro
• Pages: 3445–3461

https://doi.org/10.1142/9789811269943_0105

We survey the literature on anomalies in accounting research with a specific focus on whether and how they account for the momentum effect (Jegadeesh and Titman, 1993, 2001). Even though accounting academics recognize treatment of the momentum effect via inclusion in an extended Fama-French model to be appropriate, most extant empirical studies of accounting anomalies either do not account for the momentum effect or do so as a robustness check. Where included in the analysis, the momentum factor substantially reduces returns to portfolio strategies which exploit market underreaction. We argue that this treatment is in part due to the normal lag in the incorporation of research innovations but also likely due to persisting differences of opinion in the finance and accounting literature on whether to treat momentum as an anomaly or an asset pricing factor. More recent studies in accounting, however, seem to account for and treat the momentum effect more uniformly.

Chapter 106: Constant Elasticity of Variance Option Pricing Model: Integration and Detailed Derivation

• Y. L. Hsu ,
• T. L. Lin , and 
• Pages: 3463–3481

https://doi.org/10.1142/9789811269943_0106

In this chapter we review the renowned constant elasticity of variance (CEV) option pricing model and give the detailed derivations. There are two purposes of this article. First, we show the details of the formulae needed in deriving the option pricing and bridge the gaps in deriving the necessary formulae for the model. Second, we use a result by Feller to obtain the transition probability density function of the stock price at time T given its price at time t with. In addition, some computational considerations are given for the facilitation of computing the CEV option pricing formula.

Chapter 107: Options, Put–Call Parities, and Option Strategies: Theory and Empirical Results

• Pages: 3483–3546

https://doi.org/10.1142/9789811269943_0107

This chapter aims to establish basic knowledge of options and the markets in which they are traded. It begins with the most common types of options, calls, and puts, explaining their general characteristics, and discussing the institutions where they are traded. In addition, the concepts relevant to the new types of options on indexes and futures are introduced. The next focus is the basic pricing relationship between puts and calls, known as put–call parity. The final section concerns how options can be used as investment tools. An alternative option strategies theory has been presented. Excel is used to demonstrate how different option strategies can be executed.

Chapter 108: A Cross-sectional Asset Pricing Test with More Power: An Instrumental Variable Approach

• Jungshik Hur
• Pages: 3547–3581

https://doi.org/10.1142/9789811269943_0108

I find that downward bias of the estimated coefficient of betas in the Fama-MacBeth cross-sectional regression is caused by endogeneity of the estimated betas due to measurement errors. I propose an instrumental variable methodology that purges the endogeneity. The purged betas have a 95% correlation with the original betas and retain the relation with firm size. I document that controlling for the purged betas in the Fama-MacBeth cross-sectional regression has higher statistical power to correctly reject the null hypothesis of nonexistence of size premium and also has higher R-squared and higher total sum of squares than the Brennan-Chordia-Subrahmanyam (1998) methodology.

Chapter 109: Current vs. Permanent Earnings for Estimating Alternative Dividend Payment Behavioral Model: Theory, Methods, and Applications

• Alice Lee , and 
• Pages: 3583–3626

https://doi.org/10.1142/9789811269943_0109

Marsh and Merton (1987) and Garrett and Priestley (2000) have used aggregated permanent instead of current earnings to estimate aggregated dividend behavior models which were developed by Lintner (1956). Lee and Primeaux (1991) used permanent instead of current EPS to estimate Lintner’s dividend payment behavior model for individual companies. Most recently, Lambrecht and Myer (2012) have theoretically shown that permanent, instead of current, EPS should be used to estimate the dividend payment behavior model for individual companies to avoid measurement error and misspecification of the model.

The main purposes of this paper are as follows: (1) theoretically explain why firms generally allocate permanent earnings and transitory earnings between dividend payments and retained earnings; (2) develop alternative methods for decomposing current earnings into permanent and transitory components; (3) empirically estimate alternative dividend payment behavior models by using two alternative permanent EPS estimates for both individual firms and pooled data; and (4) test Lambrecht and Myer’s (2012) theoretical results related to alternative dividend payment behavior models. We find that the average long-term payout ratio is downward biased and the average estimated intercept is generally upward biased when current instead of permanent EPS is used. We also find that the combined model performs well to deal with both measurement errors and specification errors in describing the dividend payment behavior model.

Chapter 110: Differential Effect of Inside Debt, CEO Compensation Diversification, and Firm Investment

• Chengru Hu , and 
• Maggie Foley
• Pages: 3627–3680

https://doi.org/10.1142/9789811269943_0110

The main purposes of this paper are to study (1) a differential effect of inside debt on components of firm risk, and (2) how it relates to CEO portfolio diversification to reduce firm risk exposure. We find that compensating CEOs with inside debt (e.g., pensions and other deferred compensation plans) leads to reductions in firms’ systematic risk and idiosyncratic risk, but to disproportionate degrees. CEOs with larger inside debt draft and implement policies, which lead to a significantly larger reduction in the idiosyncratic firm risk and investment. We then show that the differential effect is the result of an asymmetry in CEOs’ perceived benefits of diversifying exposures to individual firm risk components. We further show that granting excessive debt-based pay may divert executives from firm specific but productive activities (e.g., R&D investments), therefore may compromise longrun corporate success.

Chapter 111: Optimal Payout Ratio under Uncertainty and the Flexibility Hypothesis: Theory, Empirical Evidence, and Implications

• Manak C. Gupta ,
• Hong-Yi Chen , and 
• Pages: 3681–3731

https://doi.org/10.1142/9789811269943_0111

Following the dividend flexibility hypothesis used by DeAngelo and DeAngelo (2006), Blau and Fuller (2008), and others, we theoretically extend the proposition of DeAngelo and DeAngelo’s (2006) optimal payout policy in terms of the flexibility dividend hypothesis. We also introduce growth rate, systematic risk, and total risk variables into the theoretical model. In addition, based upon Lee and Alice (2021), we discuss the implication of the existence of optimal payout ratio in financial analysis and decision for a company.

To test the theoretical results derived in this chapter, we use data collected in the US from 1969 to 2009 to investigate the impact of growth rate, systematic risk, and total risk on the optimal payout ratio in terms of the fixed-effect model. We find that based on flexibility considerations, a company will reduce its payout when the growth rate increases. In addition, we find that a nonlinear relationship exists between the payout ratio and the risk. In other words, the relationship between the payout ratio and risk is negative (or positive) when the growth rate is higher (or lower) than the rate of return on total assets. Our theoretical model and empirical results can therefore be used to identify whether flexibility or the free cash flow hypothesis should be used to determine the dividend policy.

Chapter 112: Sustainable Growth Rate, Optimal Growth Rate, and Optimal Payout Ratio: A Joint Optimization Approach

• Pages: 3733–3779

https://doi.org/10.1142/9789811269943_0112

A large number of studies have examined issues of dividend policy, while they rarely consider the investment decision and dividend policy jointly from a non-steady state to a steady state. We extend Higgins’ (1977, 1981, and 2008) sustainable growth rate model and develops a dynamic model which jointly optimizes the growth rate and payout ratio. We optimize the firm value to obtain the optimal growth rate in terms of a logistic equation and find that the steady state growth rate can be used as the benchmark for the mean-reverting process of the optimal growth rate. We also investigate the specification error of the mean and variance of dividend per share when introducing the stochastic growth rate. Empirical results support the mean-reverting process of the growth rate and the importance of covariance between the profitability and the growth rate in determining dividend payout policy. In addition, the intertemporal behavior of the covariance may shed some light on the fact of disappearing dividends over decades.

BACK MATTER

• Pages: 3781–3800

https://doi.org/10.1142/9789811269943_bmatter

Professor Cheng Few Lee is a Distinguished Professor of Finance at Rutgers Business School, Rutgers University and was chairperson of the Department of Finance from 1988–1995. He has also served on the faculty of the University of Illinois (IBE Professor of Finance) and the University of Georgia. He has maintained academic and consulting ties in Taiwan, Hong Kong, China, and the United States for the past three decades. He has been a consultant to many prominent groups including the American Insurance Group, the World Bank, the United Nations, the Marmon Group Inc., Wintek Corporation, and Polaris Financial Group.

Professor Lee founded the Review of Quantitative Finance and Accounting (RQFA) in 1990 and the Review of Pacific Basin Financial Markets and Policies (RPBFMP) in 1998, and serves as managing editor for both journals. He was also previously a co-editor of the Financial Review (1985–1991) and the Quarterly Review of Economics and Finance (1987–1989).

In the past 42 years, Professor Lee has written numerous textbooks ranging in subject matters from financial management to corporate finance, security analysis and portfolio management to financial analysis, planning and forecasting, and business statistics. In addition, he edited five popular books, Encyclopedia of Finance (with Alice C Lee); Handbook of Quantitative Finance and Risk Management (with Alice C Lee and John Lee); Handbook of Financial Econometrics and Statistics ; Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning ; and Handbook of Investment Analysis, Portfolio Management, and Financial Derivatives . Professor Lee has also published more than 250 articles in more than 20 different journals in finance, accounting, economics, statistics, and management. Professor Lee was ranked the most published finance professor worldwide during the period 1953–2008.

Professor Lee was the intellectual force behind the creation of the new Masters of Quantitative Finance program at Rutgers University. This program began in 2001 and has been ranked as one of the top 15 quantitative finance programs in the United States. Professor Lee also started the Conference on Financial Economics and Accounting in 1989. This conference is a consortium of Rutgers University, New York University, Temple University, University of Maryland, Georgia State University, Tulane University, Indiana University, and University of Toronto. This conference is the most well-known conference in finance and accounting.

John C Lee is a Microsoft Certified Professional in Microsoft Visual Basic and Microsoft Excel VBA. He has a bachelor's and master's degree in accounting from the University of Illinois at Urbana-Champaign.

John has worked in both the business and technical fields as an accountant, auditor, systems analyst, and business software developer for over 20 years. He is the author of the book on how to use MINITAB and Microsoft Excel for statistical analysis, which is a companion text to Statistics of Business and Financial Economics , 2nd and 3rd edition, of which he is one of the co-authors. In addition, he has also coauthored the textbooks Financial Analysis, Planning and Forecasting , 3rd ed (with Cheng F Lee and Alice C Lee), and Security Analysis, Portfolio Management, and Financial Derivatives (with Cheng F Lee, Joseph Finnerty, Alice C Lee, and Donald Wort). He also coauthored two forthcoming books entitled Microsoft Excel VBA, Python and R for Financial Statistics and Portfolio Analysis and Microsoft Excel VBA, Python and R for Financial Derivatives, Financial Management, and Machine Learning . John has been a Senior Technology Officer at the Chase Manhattan Bank and Assistant Vice President at Merrill Lynch. Currently, he is the Director of the Center for PBBEF Research.

Sample Chapter(s) Preface Chapter 1: Introduction to Investment Analysis, Portfolio Management, and Financial Derivatives

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27 Research Issues in Commodities and Commodity Derivatives

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Commodity derivatives allow hedgers to smooth wealth across different states of the world and provide a direct link between financial markets and the real economy. This chapter describes recent research related to core concepts for these products: hedging behavior, the provision of liquidity in commodity markets, and the “financialization” debate related to the recent inflow of institutional investments into commodities. Intermediation activity in these markets has evolved in recent years. Modern commodity markets now incorporate extensive intermediation by swap dealers to facilitate over-the-counter swap dealing activity and execute the associated hedging activity linking swap and futures markets. The emergence of highly computerized, automated trading and the trend toward liquidity provision by commercial firms are intermediation issues that require better understanding. The chapter also provides insights into the types of topics faced by researchers who consider policy-related issues.

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50+ Best Finance Dissertation Topics For Research Students

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Finance Dissertation Made Easier!

Embarking on your dissertation adventure? Look no further! Choosing the right finance dissertation topics is like laying the foundation for your research journey in finance, and we're here to light up your path. In this article, we will be diving deep into why dissertation topics in finance matter so much. We've got some golden writing tips to share with you! We're also unveiling the secret recipe for structuring a stellar finance dissertation and exploring intriguing topics across various finance sub-fields. Our buffet of finance dissertation topics will surely set your research spirit on fire!

What is a Finance Dissertation?

Finance dissertations are academic papers that delve into specific finance topics chosen by students, covering areas such as stock markets, banking, risk management, and healthcare finance. These dissertations require extensive research to create a compelling report and contribute to the student's confidence and satisfaction in the field of finance. Now, let's understand why these dissertations are so important and why choosing the right finance dissertation topics is crucial!

Why are Finance Dissertation Topics Important?

Choosing the dissertation topics for finance students is essential as it will influence the course of your research. It determines the direction and scope of your study. You must make sure that the finance dissertation topics you choose are relevant to your field of interest. Here are a few reasons why finance thesis topics are important:

1. Relevance

Opting for relevant finance thesis topics ensures that your research contributes to the existing body of knowledge and addresses contemporary issues in finance. Choosing a dissertation topic relevant to the industry can make a meaningful impact and advance understanding in your chosen area.

2. Personal Interest

Selecting finance dissertation topics that align with your interests and career goals is vital. When genuinely passionate about your research area, you are more likely to stay motivated during the dissertation process. Your interest will drive you to explore the subject thoroughly and produce high-quality work.

3. Future Opportunities

Well-chosen finance dissertation topics can open doors to various future opportunities. They can enhance your employability by showcasing your expertise in a specific finance area. They may also lead to potential research collaborations and invitations to conferences in your field of interest.

4. Academic Supervision

Your choice of topics for dissertation in finance also influences the availability of academic supervisors with expertise in your chosen area. Selecting a well-defined research area increases the likelihood of finding a supervisor to guide you effectively throughout the dissertation. Their knowledge and guidance will greatly contribute to the success of your research.

Writing Tips for Finance Dissertation

Writing a dissertation requires a lot of planning, formatting, and structuring. It starts with deciding on topics for a dissertation in finance, conducting tons of research, deciding on methods, and so on. Below are some tips to assist you along the way, and here is a blog on the 10 tips on writing a dissertation that can give you more information, should you need it!

1. Select a Manageable Topic

It is important to choose finance research topics within the given timeframe and resources. Select a research area that interests you and aligns with your career goals. This will help you stay inspired throughout the dissertation process.

2. Conduct a Thorough Literature Review

A comprehensive literature review forms the backbone of your research. After choosing the finance dissertation topics, dive deep into academic papers, books, and industry reports. Gain a solid understanding of your chosen area to identify research gaps and establish the significance of your study.

3. Define Clear Research Objectives

Clearly define your dissertation's research questions and objectives. It will provide a clear direction for your research and guide your data collection, analysis, and overall structure. Ensure your objectives are specific, measurable, achievable, relevant, and time-bound (SMART).

4. Collect and Analyse Data

Depending on your research methodology and your finance dissertation topics, collect and analyse relevant data to support your findings. It may involve conducting surveys, interviews, experiments, and analysing existing datasets. Choose appropriate statistical techniques and qualitative methods to derive meaningful insights from your data.

5. Structure and Organization

Pay attention to the structure and organisation of your dissertation. Follow a logical progression of chapters and sections, ensuring that each chapter contributes to the overall coherence of your study. Use headings, subheadings, and clear signposts to guide the reader through your work.

6. Proofread and Edit

Once you have completed the writing process, take the time to proofread and edit your dissertation carefully. Check for clarity, coherence, and proper grammar. Ensure that your arguments are well-supported, and eliminate any inconsistencies or repetitions. Pay attention to formatting, citation styles, and consistency in referencing throughout your dissertation.

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Finance Dissertation Topics

Now that you know what a finance dissertation is and why they are important, it's time to have a look at some of the best finance dissertation topics. For your convenience, we have segregated these topics into categories, including cryptocurrency, risk management, internet banking, and so many more. So, let's dive right in and explore the best finance dissertation topics:

Dissertation topics in Finance related to Cryptocurrency

1. The Impact of Regulatory Frameworks on the Volatility and Liquidity of Cryptocurrencies.

2. Exploring the Factors Influencing Cryptocurrency Adoption: A Comparative Study.

3. Assessing the Efficiency and Market Integration of Cryptocurrency Exchanges.

4. An Analysis of the Relationship between Cryptocurrency Prices and Macroeconomic Factors.

5. The Role of Initial Coin Offerings (ICOs) in Financing Startups: Opportunities and Challenges.

Dissertation topics in Finance related to Risk Management

1. The Effectiveness of Different Risk Management Strategies in Mitigating Financial Risks in Banking Institutions.

2. The Role of Derivatives in Hedging Financial Risks: A Comparative Study.

3. Analysing the Impact of Risk Management Practices on Firm Performance: A Case Study of a Specific Industry.

4. The Use of Stress Testing in Evaluating Systemic Risk: Lessons from the Global Financial Crisis.

5. Assessing the Relationship between Corporate Governance and Risk Management in Financial Institutions.

Dissertation topics in Finance related to Internet Banking

1. Customer Adoption of Internet Banking: An Empirical Study on Factors Influencing Usage.

Enhancing Security in Internet Banking: Exploring Biometric Authentication Technologies.

2. The Impact of Mobile Banking Applications on Customer Engagement and Satisfaction.

3. Evaluating the Efficiency and Effectiveness of Internet Banking Services in Emerging Markets.

4. The Role of Social Media in Shaping Customer Perception and Adoption of Internet Banking.

5. Fraud and Identity Theft are Accomplished via Internet Banking.

Dissertation topics in Finance related to Microfinance

1. The Impact of Microfinance on Poverty Alleviation: A Comparative Study of Different Models.

2. Exploring the Role of Microfinance in Empowering Women Entrepreneurs.

3. Assessing the Financial Sustainability of Microfinance Institutions in Developing Countries.

4. The Effectiveness of Microfinance in Promoting Rural Development: Evidence from a Specific Region.

5. Analysing the Relationship between Microfinance and Entrepreneurial Success: A Longitudinal Study.

Dissertation topics in Finance related to Retail and Commercial Banking

1. The Impact of Digital Transformation on Retail and Commercial Banking: A Case Study of a Specific Bank.

2. Customer Satisfaction and Loyalty in Retail Banking: An Analysis of Service Quality Dimensions.

3. Analysing the Relationship between Bank Branch Expansion and Financial Performance.

4. The Role of Fintech Startups in Disrupting Retail and Commercial Banking: Opportunities and Challenges.

5. Assessing the Impact of Mergers and Acquisitions on the Performance of Retail and Commercial Banks.

Dissertation topics in Finance related to Alternative Investment

1. The Performance and Risk Characteristics of Hedge Funds: A Comparative Analysis.

2. Exploring the Role of Private Equity in Financing and Growing Small and Medium-Sized Enterprises.

3. Analysing the Relationship between Real Estate Investments and Portfolio Diversification.

4. The Potential of Impact Investing: Evaluating the Social and Financial Returns.

5. Assessing the Risk-Return Tradeoff in Cryptocurrency Investments: A Comparative Study.

Dissertation topics in Finance related to International Affairs

1. The Impact of Exchange Rate Volatility on International Trade: A Case Study of a Specific Industry.

2. Analysing the Effectiveness of Capital Controls in Managing Financial Crises: Comparative Study of Different Countries.

3. The Role of International Financial Institutions in Promoting Economic Development in Developing Countries.

4. Evaluating the Implications of Trade Wars on Global Financial Markets.

5. Assessing the Role of Central Banks in Managing Financial Stability in a Globalised Economy.

Dissertation topics in Finance related to Sustainable Finance

1. The Impact of Sustainable Investing on Financial Performance.

2. The Role of Green Bonds in Financing Climate Change Mitigation and Adaptation.

3. The Development of Carbon Markets.

4. The Use of Environmental, Social, and Governance (ESG) Factors in Investment Decision-Making.

5. The Challenges and Opportunities of Sustainable Finance in Emerging Markets.

Dissertation topics in Finance related to Investment Banking

1. The Valuation of Distressed Assets.

2. The Pricing of Derivatives.

3. The Risk Management of Financial Institutions.

4. The Regulation of Investment Banks.

5. The Impact of Technology on the Investment Banking Industry.

Dissertation topics in Finance related to Actuarial Science

1. The Development of New Actuarial Models for Pricing Insurance Products.

2. The Use of Big Data in Actuarial Analysis.

3. The Impact of Climate Change on Insurance Risk.

4. The Design of Pension Plans That Are Sustainable in the Long Term.

5. The Use of Actuarial Science to Manage Risk in Other Industries, Such as Healthcare and Finance.

Dissertation topics in Finance related to Corporate Finance

1. Study the Relations Between Corporate Governance Structures and Financial Performance

2. Testing the Effects of Capital Structure on Firm Performance Across Different Industries

3. Effectiveness of Financial Management Practices in Emerging Markets

4. Integrating Sustainability and CSR Initiatives Impacts a Corporation’s Financial Performance and Enhances its Brand Reputation.

5. A Comparative Study of the Financing Strategies Employed in Mergers and Acquisitions.

Tips To Find Good Finance Dissertation Topics 

Embarking on a dissertation report on finance topics journey requires careful consideration of various factors. Your choice of topic in finance research topics is pivotal, as it sets the stage for the entire research process. We suggest the following tips that can help you pick the perfect dissertation topic:

1. Identify your interests and strengths 

2. Check for current relevance

3. Feedback from your superiors

4. Finalise the research methods

5. Gather the data

6. Work on the outline of your dissertation

7. Make a draft and proofread it

Lastly, we have discussed the importance of finance thesis topics and provided valuable writing tips and tips for finding the right topic. We have also presented a list of thesis topics for finance students within various subfields. With this, we hope you have great ideas for finance dissertations. Good luck with your finance research journey!

Frequently Asked Questions

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More competition in banking, less information—research reveals the impacts on potential borrowers

by Ezio Renda, Bocconi University

A new study conducted by Filippo De Marco of Bocconi University, Milan, and Silvio Petriconi of the Catolica Lisbon School of Business and Economics, published in the Journal of Financial and Quantitative Analysis reveals that competition among banks significantly reduces the production of information about potential borrowers. This result emerges against a backdrop of deregulation of interstate branching in the United States, which has seen a decline in positive yields of loan announcements, especially for opaque and bank-dependent firms.

According to the study, increased bank competition has reduced banks' incentive to collect detailed information on borrowers, a phenomenon reflected in lower yields of loan announcements, especially in states that have deregulated interstate branches. This phenomenon is particularly pronounced for smaller, bank-dependent firms, which need more detailed information for accessing credit.

"Our study clearly shows that increased competition reduces the quality of information produced by banks about borrowers," said Filippo De Marco. "This is particularly worrisome for opaque firms that rely heavily on banks to obtain credit."

Silvio Petriconi adds, "The reduction in information production not only makes it more difficult for firms to obtain loans, but also increases the risk of covenant violations, undermining financial stability ."

At a time when bank competition is incentivized to improve access to credit and stimulate economic growth , this study offers a crucial counterpoint, underscoring that excessive competition can lead to decreased credit quality and increased financial risks. The current environment, marked by increasing deregulation, makes these findings particularly relevant to financial regulators and legislators.

The study is notable for its use of an innovative approach in measuring information production through cumulative abnormal returns to borrowers' shares after a loan announcement. It also uses detailed data from DealScan and a variety of robust methodologies to ensure the reliability of the results.

The implications of this study are far-reaching. As pointed out by De Marco and Petriconi, existing banks reduce information production to prevent their best customers from switching to competitors. This behavior, while understandable, has negative consequences for the credit market and economic stability.

"Our research suggests that balanced regulation is essential to maintain a healthy and informed banking system," De Marco concludes.

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Derivatives pose thorny problem for banks, regulators in resolution plans

NEW YORK — Federal regulators want large banks to get specific about their contingency plans for their derivatives holdings.

The Federal Deposit Insurance Corp. and the Federal Reserve cited four of the country's largest banks last week for weaknesses in their resolution plans related to derivatives — a broad and varied market of financial contracts that include swaps, options and futures. The move was the latest and most direct move by the agencies to encourage banks to step up their practices around the handling of these contracts. 

Some experts say regulators still have not gone far enough. Mayra Rodriguez Valladares, managing principal of the financial regulatory consulting firm MRV Associates, said the findings show that some banks do not have the personnel or the procedures in place to safely engage in such a critical component of the financial market.

"If you don't know where these things are housed, if you don't know how much money you could lose during a period of upheaval and if you don't know how you can unwind, then you cannot be in this business," Rodriguez Valladares said. "If you can't explain, in plain English, to regulators the issues they're asking you to answer in these living wills then you cannot be in that business."

Derivatives are bilateral agreements tied to the performance of specific market factors, such as interest rates, commodity prices or foreign exchange rates. 

Regulators flagged derivatives shortcomings with Citigroup Inc., Bank of America, JPMorgan Chase and Goldman Sachs. The FDIC went so far as to call Citi's weakness a "deficiency," rendering its overall resolution plan "not credible." 

"The performance of Citigroup's resolution forecasting tools and systems demonstrated that the firm lacks the capability to incorporate updated stress scenarios and assumptions, and that ongoing weaknesses regarding data reliability and the firm's control environment contributed to materially inaccurate calculations of the resources required to execute its preferred resolution strategy," said FDIC chair Martin Gruenberg in a prepared statement. "This weakness could undermine the feasibility of the company's resolution plan and requires urgent attention by the firm's senior management and board of directors."

The Fed took a less harsh view of the New York-based bank's plan, deeming it a "shortcoming" rather than a "deficiency" and thus sparing it a full overhaul. Citi, for its part, defended itself by saying that its firm-wide stress testing and resolution planning processes are "rigorous" and its balance sheet "strong."

In his statement, Gruenberg said the firms examined in the biannual review have improved their performance cycle to cycle. He also noted that the firms have the proper playbooks to handle resolutions in theory, but not the capabilities to implement them in practice.

Rodriguez Valladares, who consults both banks and bank regulators on resolution planning,  said there is a lot to keep track of related to derivatives agreements, including where the contracts are domiciled, the specific provisions of the agreements and the legal regimes under which they operate. 

She said the findings demonstrate that some institutions are not making the investments necessary to keep track of all these moving parts. 

quants to model asset allocation, the hotshot traders and the big time lenders because those are revenue-generating people," Rodriguez Valladares said. "They tend to cut corners when it comes to the people who have to do documentation [and] the people who do risk reporting … because those individuals are just seen as a cost center. They're not seen as a revenue generator, and that, in itself, is a big problem."

Globally, there were $667 trillion outstanding over-the-counter derivatives on a notional basis, according to the Bank for International Settlements. Over-the-counter derivatives are those made between two or more entities directly, as opposed to exchange-traded derivatives that are subject to more standardization and are bought on an exchange. The vast majority of those OTC derivatives are interest rate swaps, in which one party effectively benefits when rates rise and the other benefits when rates fall. Such contracts are typically used to hedge risks present in other parts of an entity's business or portfolio.

Edward Ivey, head of derivatives for the law firm Moore & Van Allen, said most large banks have "risk neutral books" of derivatives, meaning the various hedging positions they take on are offset and are profitable not because they beat the market but because they make incremental profit from the spread. But, he noted, they achieve this balance across a wide range of agreements and must manage it constantly.

Ivey said the recent findings from the Fed and FDIC show that regulators would like to see more granularity in resolution plans and explanations about how such interconnected portfolios could be pulled apart, rather than assuming they could be taken on wholesale by a bridge bank or acquirer. 

"The regulators want to know they're putting thought into this, because it's about constantly improving the process — they want to know if there's some way to improve beyond just planning to transfer the whole book," Ivey said. "Regulators want to see some thoughtful analysis here, because they are also trying to find the best way to do this."

Derivatives have been a focal point for regulators since the passage of the Dodd-Frank Act of 2010, which requires large banks to devise resolution plans — also known as living wills — to detail how they would be wound down safely. They have issued guidance around the subject and updated the rules around resolution plans several times during the past decade.

Yet because of their complexity and the role they play in financial markets, derivatives receive different legal treatment than other assets on a bank's balance sheet, said Karen Petrou, managing partner at Federal Financial Analytics. 

Deemed "qualified financial contracts" by Dodd-Frank, derivatives held by systemically large banks — known as "covered entities" — are shielded from typical default provisions in cases of failure. This means that, unlike in a traditional bankruptcy process, when these banks fail, their counterparties cannot simply close out their derivative positions. This arrangement is meant to protect banks, the FDIC and broader financial stability by mitigating losses for large failed banks and preventing counterparties from having to quickly seek out hedging alternatives.

But, because these contracts are treated differently under the special resolution regime than they are under the traditional bankruptcy code, Petrou said there is ambiguity and confusion about how they should be handled.

"Without clarification of the bankruptcy code, this is a particularly thorny area for resolvability," she said. "And there's only so much the banks can do about it because there is a fundamental statutory confusion."

Two days after the Fed released the results of its annual stress tests, the nation's eight largest banks all announced plans to supplement their payouts to shareholders. At the same time, most of the banks also said that their capital requirements are expected to rise.

The Tennessee bank is the latest to be punished for lapses in oversight of fintech partners. Unlike most FDIC consent orders, the filing liberally uses the terms "fintech" and "BaaS."

The trade groups said Friday that a proposed rule from CISA would burden firms with overly broad reporting requirements as they scramble to respond to incidents.

New top executives were lined up for lenders across the West and Midwest, including Bank of North Dakota.

Executives from Citi and TDECU teased their plans for digital lending at American Banker's Digital Banking conference this week.

Bloom Credit Union and West Michigan Credit Union aim to join forces; Long Island-based New York Community Bancorp plans a reverse stock split; Providence, Rhode Island-based Citizens Financial hires longtime California banker to lead its middle-market team; and more in this week's banking news roundup.

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The High Cost of Misaligned Business and Analytics Goals

• Preethika Sainam,
• Seigyoung Auh,
• Richard Ettenson,
• Bulent Menguc

Findings from research on more than 300 companies undergoing data and analytics transformations.

How and where do companies’ investments in new and improved data and analytic capabilities contribute to tangible business benefits like profitability and growth? Should they invest in talent? Technology? Culture? According to new research, the degree of alignment between business goals and analytics capabilities is among the most important factors. While companies that are early in their analytics journey will see value creation even with significant internal misalignment, at higher levels of data maturity aligned companies find that analytics capabilities create significantly more value across growth, financial, and customer KPIs.

Business leaders are feeling acute pressure to ramp up their company’s data and analytics capabilities — and fast — or risk falling behind more data-savvy competitors. If only the path to success were that straightforward! In our previous research, we found that capitalizing on data and analytics requires creating a data culture, obtaining senior leadership commitment, acquiring data and analytics skills and competencies, as well as empowering employees. And each of these dimensions is necessary just to start the analytics journey.

• PS Preethika Sainam is an Assistant Professor of Global Marketing at Thunderbird School of Global Management, Arizona State University.
• SA Seigyoung Auh is Professor of Global Marketing at Thunderbird School of Global Management, Arizona State University, and Research Faculty at the Center for Services Leadership at the WP Carey School of Business, Arizona State University.
• RE Richard Ettenson is Professor and Keickhefer Fellow in Global Marketing and Brand Strategy, The Thunderbird School of Global Management, Arizona State University .
• BM Bulent Menguc is a Professor of Marketing at the Leeds University Business School in the U.K.

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The asymmetric role of financial commitments to renewable energy projects, public r&d expenditure, and energy patents in sustainable development pathways.

1. Introduction

• RQ1: How do environment quality and economic growth respond to the ascending and descending movements in financial commitments to the investment in renewable energy enterprises?
• RQ2: What is the impact of the rise and fall in the public R&D budget on economic and environmental sustainability?
• RQ3: How do positive and negative shocks in energy technology innovations affect environmental quality?

2. Literature Review

2.1. green investment, environmental sustainability, and economic growth, 2.2. technological innovation and environmental sustainability, 2.2.1. total patent index and environmental impact, 2.2.2. public r&d expenditure and environmental sustainability, 2.2.3. energy patent index and environmental impact, 3. theoretical framework and model construction, 4. materials and methods, 5. results and discussion, 5.1. preliminary analysis, 5.2. panel unit root and cointegration tests, 5.3. nonlinear panel ardl results, 5.3.1. environmental sustainability model, 5.3.2. economic growth model, 6. conclusions and policy implications, 6.1. concluding remarks, 6.2. policy insights, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Alnour, M.; Önden, A.; Hasseb, M.; Önden, İ.; Rehman, M.Z.; Esquivias, M.A.; Hossain, M.E. The Asymmetric Role of Financial Commitments to Renewable Energy Projects, Public R&D Expenditure, and Energy Patents in Sustainable Development Pathways. Sustainability 2024 , 16 , 5503. https://doi.org/10.3390/su16135503

Alnour M, Önden A, Hasseb M, Önden İ, Rehman MZ, Esquivias MA, Hossain ME. The Asymmetric Role of Financial Commitments to Renewable Energy Projects, Public R&D Expenditure, and Energy Patents in Sustainable Development Pathways. Sustainability . 2024; 16(13):5503. https://doi.org/10.3390/su16135503

Alnour, Mohammed, Abdullah Önden, Mouad Hasseb, İsmail Önden, Mohd Ziaur Rehman, Miguel Angel Esquivias, and Md. Emran Hossain. 2024. "The Asymmetric Role of Financial Commitments to Renewable Energy Projects, Public R&D Expenditure, and Energy Patents in Sustainable Development Pathways" Sustainability 16, no. 13: 5503. https://doi.org/10.3390/su16135503

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