The Journal of Computational Finance is the leading forum for research and analysis into computational methods. Edited by Cornelis W. Oosterlee from CWI - Dutch Center for Mathematics and Computer Science, the Journal offers the latest cutting edge technical papers written by leading academics in the field. Papers published in the journal are ...
11494 PDFs
Explore the latest full-text research PDFs, articles, conference papers, preprints and more on COMPUTATIONAL FINANCE. Find methods information, sources, references or conduct a literature review ...
Research in Mathematical & Computational Finance
The Oxford Mathematical and Computational Finance Group is one of the leading academic research groups in the world focused on mathematical modeling in finance and offers a thriving research environment, with experts covering multiple areas of quantitative finance. Our group maintains close links with the Data Science, Stochastic Analysis and Numerical Analysis groups as well as the Institute ...
Computational Finance: An Introduction
In the subfield of finance known variously as "mathematical finance", "financial engineering", or "computational finance", a major objective is to develop and study models of the movement of the market price of basic financial assets. A second important objective in computational finance is to develop useful models of the fair price ...
New Advances in Computational Finance and Computational ...
We welcome submissions advancing cutting-edge research and novel ideas for computational methods with practical applications in finance, as well as computational intelligence methods as an alternative to statistical and econometric approaches to financial market analysis. Contributions primarily focused on the following topics are encouraged:
Mathematical and Computational Finance @ Oxford
The Oxford Mathematical and Computational Finance Group is one of the world's leading research groups in the area of mathematical modeling in finance.. Research Topics include stochastic processes, derivative pricing, multi-level Monte Carlo methods, computational methods for PDEs, credit risk modelling, quantitative risk management, data-driven modeling and machine learning, market ...
Recent Advancements in Computational Finance and Business ...
By embracing computational finance and business analytics, organizations can gain a competitive edge in an increasingly data-driven and complex business environment. This book has explored the latest developments and breakthroughs in this rapidly evolving domain, providing a comprehensive overview of the current state of computational finance ...
Mathematical and Computational Finance
Background. The Mathematical and Computational Finance Program at Stanford University ("MCF") is one of the oldest and most established programs of its kind in the world. Starting out in the late 1990's as an interdisciplinary financial mathematics research group, at a time when "quants" started having a greater impact on finance in ...
Special issue—Computational and algorithmic finance
Nowadays, extensive research in computational finance is performed, both in the academic and financial worlds. ... These topics are relatively new for the Journal of Computational Science, and are usually discussed in the financial journals like the Journal of Computational Finance, Quantitative Finance, etc. For more details, see every paper ...
Computational finance
risk-neutral measure. Computational finance is a branch of applied computer science that deals with problems of practical interest in finance. [1] Some slightly different definitions are the study of data and algorithms currently used in finance [2] and the mathematics of computer programs that realize financial models or systems. [3]
Computational Finance: An Introductory Course with R
The book covers a wide range of topics, yet essential, in Computational Finance (CF), understood as a mix of Finance, Computational Statistics, and Mathematics of Finance. In that regard it is unique in its kind, for it touches upon the basic principles of all three main components of CF, with hands-on examples for programming models in R.
Artificial intelligence and machine learning in finance: Identifying
This is particularly true for both Quantitative Finance and Computational Economics. Interestingly, about 84% of the most significant journals for emphasizing AI and ML in finance are ranked 'A*' or 'A' in the Australian Business Deans Council (ABDC) 2019 Journal Quality List. ... Table 10 research topics in finance enlisted against ...
Algorithms
Algorithmic trading is a hot topic in finance as over 70% of trades by volume is generated by trading programs. Algorithms are also used to analyse data for detecting changes in the market. ... This Special Issue aims to attract submissions that report state-of-the-art research in algorithms in computational finance. The scope of this Special ...
Quantitative Finance
Categories within Quantitative Finance. q-fin.CP - Computational Finance (new, recent, current month) Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling ... international economics, theory of the firm, labor economics, and other economic topics outside finance. q-fin.GN ...
Supervised deep learning in computational finance
T1 - Supervised deep learning in computational finance. AU - Liu, S. PY - 2021. Y1 - 2021. N2 - Mathematical modeling and numerical methods play a key role in the field of quantitative finance, for example, for financial derivative pricing and for risk management purposes.
Introduction to Computational Finance and Financial Econometrics with R
Hence, this book is truly an "introduction" to the methods of computational finance and financial econometrics and is appropriate for undergraduate economics and finance majors at Universities worldwide. Another feature that distinguishes this book from others is the extensive use of R and illustrated R code throughout the book.
DPhil (PhD) studies in Mathematical Finance @ Oxford
The Mathematical and Computational Finance Group (MCFG) at Oxford is one of the largest and most dynamic research environments in mathematical finance in the world. ... We welcome students with their own particular ideas of research topic as well as students with a broad interest in the field of Mathematical Finance. You have an opportunity to ...
Journals
arXiv.org is one of the largest open access article archives in the world. Topics in the Quantitative Finance section include Computational Finance, General Finance, Portfolio Management, Pricing of Securities, Risk Management, Statistical Finance, and Trading and Market Microstructure. Last Updated: Sep 12, 2022 3:48 PM.
The use of predictive analytics in finance
This paper reviews and explores the way that predictive analytic methods are used by scholars in finance, and in computational fields of study, to make forecasts. A systemic identification of methods and use cases is presented, specifically drawn from data sourced either internally or externally from financial Information Systems (IS).
Computational Mathematical Finance MSc
The MSc in Computational Mathematical Finance (CMF) is a dynamic new programme with the aim to deliver high quality training in the theory of Mathematical Finance with strong emphasis on computational methods. Currently graduates in this field are expected to have a working knowledge of advanced computational finance (including construction of ...
Computational Thinking in Economics and Finance ...
For instance, ABMs are increasingly used to model complex interactions in Economics and Finance (Tesfatsion and Judd 2006; Chen 2017; Chen et al. 2012; LeBaron 2006).Algorithmic rationality and algorithmic game theory are at the frontiers of research topics today (Halpern and Pass 2015; Roughgarden 2016; Schmid et al. 2021).Computational methods and simulations are heavily relied upon to ...
120+ Research Topics In Finance (+ Free Webinar)
Corporate Finance. These research topic ideas explore a breadth of issues ranging from the examination of capital structure to the exploration of financial strategies in mergers and acquisitions. Evaluating the impact of capital structure on firm performance across different industries.
Research
Computational statististics and machine learning algorithms for estimation and prediction; Decision analysis via operational research and financial methods; Research Themes: The department's methodological and applied research is organised into six areas. Follow the links for information on research activities, groups and members.
Compugen Publishes Paper in Cancer Immunology Research ...
Compugen Ltd. (Nasdaq: CGEN) (TASE: CGEN) a clinical-stage cancer immunotherapy company and a pioneer in computational target discovery, today announced the online publication of a peer reviewed ...
Utility of Computational Toxicology at FDA Center for Tobacco Products
The NCTP seeks to advance regulatory science of tobacco products by including computational toxicology tools, training, and research in the synthesis of evidence regarding respiratory, mutagenic ...
Connecting Computational and Systems Biology for Biodefense
The article "Computational and Systems Biology Advances to Enable Bioagent Agnostic Signatures" explores the computational data challenges of threat-agnostic biodefense, or the ability to characterize an unknown agent's likely impact to human, animal, and plant health. The researchers discuss how the biodefense community can make the ...
New work extends the thermodynamic theory of computation
Taken together, their research point to a new avenue for finding the lowest energy needed for computation in any system, no matter how it's implemented. "It's exposing a vast new set of issues ...
AI intervention mitigates tension among conflicting ethnic groups
Now, a research team including Kyoto University has shown that interactive AI programs may help reduce prejudice and anxiety among historically divided ethnic groups in Afghanistan during online ...
Computational Finance with R
About this book. This book prepares students to execute the quantitative and computational needs of the finance industry. The quantitative methods are explained in detail with examples from real financial problems like option pricing, risk management, portfolio selection, etc. Codes are provided in R programming language to execute the methods.
Network traffic anomaly detection based on deep learning: a review
Network traffic anomaly detection has become an important research topic with the increasing prevalence of network attacks. Deep learning, with its ability to analyse large-scale datasets, has emerged as a powerful tool for network traffic anomaly detection.
COMMENTS
The Journal of Computational Finance is the leading forum for research and analysis into computational methods. Edited by Cornelis W. Oosterlee from CWI - Dutch Center for Mathematics and Computer Science, the Journal offers the latest cutting edge technical papers written by leading academics in the field. Papers published in the journal are ...
Explore the latest full-text research PDFs, articles, conference papers, preprints and more on COMPUTATIONAL FINANCE. Find methods information, sources, references or conduct a literature review ...
The Oxford Mathematical and Computational Finance Group is one of the leading academic research groups in the world focused on mathematical modeling in finance and offers a thriving research environment, with experts covering multiple areas of quantitative finance. Our group maintains close links with the Data Science, Stochastic Analysis and Numerical Analysis groups as well as the Institute ...
In the subfield of finance known variously as "mathematical finance", "financial engineering", or "computational finance", a major objective is to develop and study models of the movement of the market price of basic financial assets. A second important objective in computational finance is to develop useful models of the fair price ...
We welcome submissions advancing cutting-edge research and novel ideas for computational methods with practical applications in finance, as well as computational intelligence methods as an alternative to statistical and econometric approaches to financial market analysis. Contributions primarily focused on the following topics are encouraged:
The Oxford Mathematical and Computational Finance Group is one of the world's leading research groups in the area of mathematical modeling in finance.. Research Topics include stochastic processes, derivative pricing, multi-level Monte Carlo methods, computational methods for PDEs, credit risk modelling, quantitative risk management, data-driven modeling and machine learning, market ...
By embracing computational finance and business analytics, organizations can gain a competitive edge in an increasingly data-driven and complex business environment. This book has explored the latest developments and breakthroughs in this rapidly evolving domain, providing a comprehensive overview of the current state of computational finance ...
Background. The Mathematical and Computational Finance Program at Stanford University ("MCF") is one of the oldest and most established programs of its kind in the world. Starting out in the late 1990's as an interdisciplinary financial mathematics research group, at a time when "quants" started having a greater impact on finance in ...
Nowadays, extensive research in computational finance is performed, both in the academic and financial worlds. ... These topics are relatively new for the Journal of Computational Science, and are usually discussed in the financial journals like the Journal of Computational Finance, Quantitative Finance, etc. For more details, see every paper ...
risk-neutral measure. Computational finance is a branch of applied computer science that deals with problems of practical interest in finance. [1] Some slightly different definitions are the study of data and algorithms currently used in finance [2] and the mathematics of computer programs that realize financial models or systems. [3]
The book covers a wide range of topics, yet essential, in Computational Finance (CF), understood as a mix of Finance, Computational Statistics, and Mathematics of Finance. In that regard it is unique in its kind, for it touches upon the basic principles of all three main components of CF, with hands-on examples for programming models in R.
This is particularly true for both Quantitative Finance and Computational Economics. Interestingly, about 84% of the most significant journals for emphasizing AI and ML in finance are ranked 'A*' or 'A' in the Australian Business Deans Council (ABDC) 2019 Journal Quality List. ... Table 10 research topics in finance enlisted against ...
Algorithmic trading is a hot topic in finance as over 70% of trades by volume is generated by trading programs. Algorithms are also used to analyse data for detecting changes in the market. ... This Special Issue aims to attract submissions that report state-of-the-art research in algorithms in computational finance. The scope of this Special ...
Categories within Quantitative Finance. q-fin.CP - Computational Finance (new, recent, current month) Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling ... international economics, theory of the firm, labor economics, and other economic topics outside finance. q-fin.GN ...
T1 - Supervised deep learning in computational finance. AU - Liu, S. PY - 2021. Y1 - 2021. N2 - Mathematical modeling and numerical methods play a key role in the field of quantitative finance, for example, for financial derivative pricing and for risk management purposes.
Hence, this book is truly an "introduction" to the methods of computational finance and financial econometrics and is appropriate for undergraduate economics and finance majors at Universities worldwide. Another feature that distinguishes this book from others is the extensive use of R and illustrated R code throughout the book.
The Mathematical and Computational Finance Group (MCFG) at Oxford is one of the largest and most dynamic research environments in mathematical finance in the world. ... We welcome students with their own particular ideas of research topic as well as students with a broad interest in the field of Mathematical Finance. You have an opportunity to ...
arXiv.org is one of the largest open access article archives in the world. Topics in the Quantitative Finance section include Computational Finance, General Finance, Portfolio Management, Pricing of Securities, Risk Management, Statistical Finance, and Trading and Market Microstructure. Last Updated: Sep 12, 2022 3:48 PM.
This paper reviews and explores the way that predictive analytic methods are used by scholars in finance, and in computational fields of study, to make forecasts. A systemic identification of methods and use cases is presented, specifically drawn from data sourced either internally or externally from financial Information Systems (IS).
The MSc in Computational Mathematical Finance (CMF) is a dynamic new programme with the aim to deliver high quality training in the theory of Mathematical Finance with strong emphasis on computational methods. Currently graduates in this field are expected to have a working knowledge of advanced computational finance (including construction of ...
For instance, ABMs are increasingly used to model complex interactions in Economics and Finance (Tesfatsion and Judd 2006; Chen 2017; Chen et al. 2012; LeBaron 2006).Algorithmic rationality and algorithmic game theory are at the frontiers of research topics today (Halpern and Pass 2015; Roughgarden 2016; Schmid et al. 2021).Computational methods and simulations are heavily relied upon to ...
Corporate Finance. These research topic ideas explore a breadth of issues ranging from the examination of capital structure to the exploration of financial strategies in mergers and acquisitions. Evaluating the impact of capital structure on firm performance across different industries.
Computational statististics and machine learning algorithms for estimation and prediction; Decision analysis via operational research and financial methods; Research Themes: The department's methodological and applied research is organised into six areas. Follow the links for information on research activities, groups and members.
Compugen Ltd. (Nasdaq: CGEN) (TASE: CGEN) a clinical-stage cancer immunotherapy company and a pioneer in computational target discovery, today announced the online publication of a peer reviewed ...
The NCTP seeks to advance regulatory science of tobacco products by including computational toxicology tools, training, and research in the synthesis of evidence regarding respiratory, mutagenic ...
The article "Computational and Systems Biology Advances to Enable Bioagent Agnostic Signatures" explores the computational data challenges of threat-agnostic biodefense, or the ability to characterize an unknown agent's likely impact to human, animal, and plant health. The researchers discuss how the biodefense community can make the ...
Taken together, their research point to a new avenue for finding the lowest energy needed for computation in any system, no matter how it's implemented. "It's exposing a vast new set of issues ...
Now, a research team including Kyoto University has shown that interactive AI programs may help reduce prejudice and anxiety among historically divided ethnic groups in Afghanistan during online ...
About this book. This book prepares students to execute the quantitative and computational needs of the finance industry. The quantitative methods are explained in detail with examples from real financial problems like option pricing, risk management, portfolio selection, etc. Codes are provided in R programming language to execute the methods.
Network traffic anomaly detection has become an important research topic with the increasing prevalence of network attacks. Deep learning, with its ability to analyse large-scale datasets, has emerged as a powerful tool for network traffic anomaly detection.