machine learning in behavioral finance a systematic literature review pdf

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Machine learning in finance: a metadata-based systematic review of the literature.

1. Introduction

2. materials and methods, 2.1. methodology, 2.2. descriptive statistics, 3. conceptual structures of our sample, 3.1. keywords analyses, 3.2. keywords co-occurrences network analyses, 3.3. topic modeling-based analyses, 4. intellectual structures of our sample, 4.1. authors, 4.2. articles, 5. social structures of our sample, 5.1. co-citations of authors, 5.2. co-citations of articles, 5.3. co-citations of journals, 5.4. co-citations of institutions, 6. conclusions, author contributions, institutional review board statement, informed consent statement, acknowledgments, conflicts of interest.

• Aria, Massimo, and Corrado Cuccurullo. 2017. bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics 11: 959–75. [ Google Scholar ] [ CrossRef ]
• Aria, Massimo, V. Della Corte, and A. Piscitelli. 2017. Business Orientation and Governance Choices in Cultural Firms: A Survey Research in Area of Naples. Italian Journal of Applied Statistics 29. [ Google Scholar ] [ CrossRef ]
• Atsalakis, George S., Ioanna G. Atsalaki, Fotios Pasiouras, and Constantin Zopounidis. 2019. Bitcoin price forecasting with neuro-fuzzy techniques. European Journal of Operational Research 276: 770–80. [ Google Scholar ] [ CrossRef ]
• Bekiros, Stelios D., and Dimitris Georgoutsos. 2008. Direction-of-change forecasting using a volatility-based recurrent neural network. Journal of Forecasting 27: 407–17. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Berardi, Michele. 2011. Fundamentalists vs. chartists: Learning and predictor choice dynamics. Journal of Economic Dynamic Control 35: 776–92. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Blake, Andrew P., and George Kapetanios. 2000. A radial basis function artificial neural network test for ARCH. Economics Letters 69: 15–23. [ Google Scholar ] [ CrossRef ]
• Chung-Ming, Kuan, and Halbert White. 1994. Artificial neural networks: An econometric perspective. Econometric Reviews 13: 1–91. [ Google Scholar ]
• Day, Richard H., and Weihong Huang. 1990. Bulls, bears and market sheep. Journal of Economic Behavior & Organization 14: 299–329. [ Google Scholar ]
• De Spiegeleer, Jan, Dilip B. Madan, Sofie Reyners, and Wim Schoutens. 2018. Machine learning for quantitative finance: Fast derivative pricing, hedging and fitting. Quantitative Finance 18: 1635–43. [ Google Scholar ] [ CrossRef ]
• del Amo, Iñigo Fernández, John Ahmet Erkoyuncu, Rajkumar Roy, Riccardo Palmarini, and Demetrius Onoufriou. 2018. A systematic review of Augmented Reality content-related techniques for knowledge transfer in maintenance applications. Computers in Industry 103: 47–71. [ Google Scholar ] [ CrossRef ]
• Dixon, Matthew F., Igor Halperin, and Paul Bilokon. 2020. Machine Learning in Finance: From Theory to Practice . Cham: Springer. [ Google Scholar ]
• Donaldson, Glen R., and Mark Kamstra. 1997. An artificial neural network-GARCH model for international stock return volatility. Journal of Empirical Finance 4: 17–46. [ Google Scholar ] [ CrossRef ]
• Fernandez-Rodrıguez, F., C. Gonzalez-Martel, and S. Sosvilla-Rivero. 2000. On the profitability of technical trading rules based on artificial neural networks: Evidence from the Madrid stock market. Economics Letters 69: 89–94. [ Google Scholar ] [ CrossRef ]
• Fischer, Thomas, and Christopher Krauss. 2018. Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270: 654–69. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Frankel, Jeffrey A., and Kenneth A. Froot. 1990. Chartists, Fundamentalists, and Trading in the Foreign Exchange Market. The American Economic Review 80: 181–85. [ Google Scholar ]
• Garcia, Rene, and Ramazan Gencay. 2000. Pricing and hedging derivative securities with neural networks and a homogeneity hint. Journal of Econometrics 94: 93–115. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Gavrishchaka, Valeriy, and Supriya Banerjee. 2006. Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting. Computational Management Science 3: 147–60. [ Google Scholar ] [ CrossRef ]
• Gencay, Ramazan, and Thanasisa Stengos. 1998. Moving average rules, volume, and the predictability of security returns with feedforward networks. Journal of Forecasting 17: 401–14. [ Google Scholar ] [ CrossRef ]
• Gerritsen, Dirk F., Elie Bouri, Ehsan Ramezanifar, and David Roubaud. 2020. The profitability of technical trading rules in the Bitcoin market. Finance Research Letters 34: 101263. [ Google Scholar ] [ CrossRef ]
• Glasserman, Paul, Kriste Krstovski, Paul Laliberte, and Harry Mamaysky. 2020. Choosing News Topics to Explain Stock Market Returns. In Proceedings of the ACM International Conference on A.I. in Finance (ICAIF’ 20), New York, NY, USA, October 15–16; New York: ACM. [ Google Scholar ] [ CrossRef ]
• Gradojevic, Nikola, and Ramazan Gencay. 2013. Fuzzy logic, trading uncertainty and technical trading. Journal of Banking and Finance 37: 578–86. [ Google Scholar ] [ CrossRef ]
• Grant, Maria J., and Andrew Booth. 2009. A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information & Libraries Journal 26: 91–108. [ Google Scholar ] [ CrossRef ]
• Gu, Shihao, Bryan Kelly, and Dacheng Xiu. 2020. Empirical Asset Pricing via Machine Learning. The Review of Financial Studies 33: 2223–73. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Hans, Franses P., and Kasper van Griensven. 1998. Forecasting Exchange Rates Using Neural Networks for Technical Trading Rules. Studies in Nonlinear Dynamics & Econometrics 2: 1–8. [ Google Scholar ]
• Hsieh, David A. 1989. Testing for nonlinear dependence in daily foreign exchange rates. The Journal of Business 62: 339–68. [ Google Scholar ] [ CrossRef ]
• Huang, Jian, Junyi Chai, and Stella Cho. 2020. Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China 14: 1–24. [ Google Scholar ] [ CrossRef ]
• Hutchinson, James M., Andrew W. Lo, and Tomaso Poggio. 1994. A nonparametric approach to pricing and hedging derivative securities via learning networks. The Journal of Finance 49: 851–89. [ Google Scholar ] [ CrossRef ]
• Jiang, Xinxin, Shirui Pan, Jing Jiang, and Guodong Long. 2018. Cross-domain deep learning approach for multiple financial market predictions. In Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, July 8–13; pp. 1–8. [ Google Scholar ]
• Kaucic, Massimiliano. 2010. Investment using evolutionary learning methods and technical rules. European Journal of Operational Research 207: 1717–27. [ Google Scholar ] [ CrossRef ]
• Kraus, Mathias, and Stefan Feuerriegel. 2017. Decision Support from Financial Disclosures with Deep Neural Networks and Transfer Learning. Available online: https://arxiv.org/pdf/1710.03954.pdf (accessed on 18 March 2021).
• Krauss, Christopher, Xuan Anh Do, and Nicolas Huck. 2017. Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research 259: 689–702. [ Google Scholar ]
• Kristjanpoller, Werner, and Marcel C. Minutolo. 2018. A hybrid volatility forecasting framework integrating GARCH, artificial neural network, technical analysis, and principal components analysis. Expert Systems with Applications 109: 1–11. [ Google Scholar ] [ CrossRef ]
• Lee, Cheng-Fee, and John Lee, eds. 2010. Handbook of Quantitative Finance and Risk Management . New York: Springer. [ Google Scholar ]
• Lo, Andrew W. 2004. The adaptive markets hypothesis. The Journal of Portfolio Management 30: 15–29. [ Google Scholar ] [ CrossRef ]
• Lo, Andrew W., Harry Mamaysky, and Jiang Wang. 2000. Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. The Journal of Finance 55: 1705–65. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Loughran, Tim, and Bill McDonald. 2011. When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks. Journal of Finance 66: 35–65. [ Google Scholar ] [ CrossRef ]
• Malinauskaite, Laura, David Cook, Brynhildur Davíðsdóttir, Helga Ögmundardóttir, and Joe Roman. 2019. Ecosystem services in the Arctic: A thematic review. Ecosystem Services 36: 100898. [ Google Scholar ] [ CrossRef ]
• Markowitz, Harry M. 1952. Portfolio Selection. The Journal of Finance 7: 77–91. [ Google Scholar ]
• Matsubara, Takashi, Ryo Akita, and Kuniaki Uehara. 2018. Stock price prediction by deep neural generative model of news articles. IEICE Transactions on Information and Systems 4: 901–8. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Mengist, Wondimagegn, Teshome Soromessa, and Gudina Legese. 2020. Method for conducting systematic literature review and meta-analysis for environmental science research. MethodsX 7: 11. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Menkhoff, Lukas. 1997. Examining the use of technical currency analysis. International Journal of Finance & Economics 2: 307–18. [ Google Scholar ]
• Menkhoff, Lukas. 2010. The use of technical analysis by fund managers: International evidence. Journal of Banking & Finance 34: 2573–86. [ Google Scholar ]
• Minh, Dang, Abolghasem Sadeghi-Niaraki, Huy Huynh, Kyungbok Min, and Hyeonjoon Moon. 2017. Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network. IEEE Access 6: 55392–404. [ Google Scholar ] [ CrossRef ]
• Mitra, Gautam, and Xiang Yu, eds. 2016. The Handbook of Sentiment Analysis in Finance . New York: Albury Books. [ Google Scholar ]
• Mitra, Leela, and Gautam Mitra. 2011. The Handbook of News Analytics in Finance . Hoboken: John Wiley & Sons. [ Google Scholar ]
• Neely, Christopher, David E. Rapach, Jun Tu, and Guofu Zhou. 2014. Forecasting the equity risk premium: The role of technical indicators. Management Science 60: 1772–1791. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Neely, Christopher, Paul Weller, and Joshua Ulrich. 2009. The Adaptive Markets Hypothesis: Evidence from the Foreign Exchange Market. Journal of Financial and Quantitative Analysis 44: 467–88. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Perevochtchikova, Maria, José Álvaro Hernández Flores, Wilmer Marín, Alfonso Langle Flores, Arturo Ramos Bueno, and Iskra Alejandra Rojo Negrete. 2019. Systematic review of integrated studies on functional and thematic ecosystem services in Latin America, 1992–2017. Ecosystem Services 36: 100900. [ Google Scholar ] [ CrossRef ]
• Popper, Karl Raimund. 1962. Conjectures and Refutations; The Growth of Scientific Knowledge . New York: Basic Books. [ Google Scholar ]
• Prasch, Robert, and Thierry Warin. 2016. Systemic Risk and Financial Regulations: A Theoretical Perspective. Journal of Banking Regulation 17: 188–99. [ Google Scholar ] [ CrossRef ]
• Schumaker, Robert P., and Hsinchun Chen. 2010. A discrete stock price prediction engine based on financial news. Computer 43: 51–56. [ Google Scholar ] [ CrossRef ]
• Sharpe, William F. 1963. A Simplified Model for Portfolio Analysis. Management Science 9: 277–93. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Sharpe, William F. 1964. Capital Asset Prices—A Theory of Market Equilibrium under Conditions of Risk. Journal of Finance 19: 425–42. [ Google Scholar ]
• Taylor, Mark, and Hellen Allen. 1992. The use of technical analysis in the foreign exchange market. Journal of International Money and Finance 11: 304–14. [ Google Scholar ] [ CrossRef ]
• Warin, Thierry. 2005. Popper’s Falsifiability and Mises’ a-Priorism: Is Dogmatism Everywhere? Epistemologia 28: 121–38. [ Google Scholar ]

Share and Cite

Warin, T.; Stojkov, A. Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature. J. Risk Financial Manag. 2021 , 14 , 302. https://doi.org/10.3390/jrfm14070302

Warin T, Stojkov A. Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature. Journal of Risk and Financial Management . 2021; 14(7):302. https://doi.org/10.3390/jrfm14070302

Warin, Thierry, and Aleksandar Stojkov. 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature" Journal of Risk and Financial Management 14, no. 7: 302. https://doi.org/10.3390/jrfm14070302

Article Metrics

Article access statistics, supplementary material.

• Externally hosted supplementary file 1 Link: https://warin.ca/posts/article-machine-learning-finance/ Description: Dataset of 5,053 references on Machine Learning in Finance

Further Information

Mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

• DOI: 10.3390/JRFM14070302
• Corpus ID: 235958246

Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature

• Thierry Warin , Aleksandar Stojkov
• Published in Journal of Risk and Financial… 10 June 2021
• Computer Science, Economics, Business

Ask This Paper

By using this feature, you agree to AI2's terms and conditions and that you will not submit any sensitive or confidential info.

AI2 may include your prompts and inputs in a public dataset for future AI research and development. Please check the box to opt-out.

Ask a question about " "

Supporting statements, figures and tables from this paper.

14 Citations

Machine learning methods in climate finance: a systematic review, climate finance innovation: the value of machine learning, benchmarking biologically-inspired automatic machine learning for economic tasks, small data machine learning in materials science, imperative role of artificial intelligence and big data in finance and banking sector, a study of the machine learning approach and the mgarch-bekk model in volatility transmission, artificial intelligence enabled project management: a systematic literature review, testing the efficient market hypothesis and the model-data paradox of chaos on top currencies from the foreign exchange market (forex), advanced analytics in central banks: basic assumptions and preliminary results of a research project, deformable protopnet: an interpretable image classifier using deformable prototypes, 61 references, deep learning in finance and banking: a literature review and classification, decision support from financial disclosures with deep neural networks and transfer learning, deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network, deep neural networks, gradient-boosted trees, random forests: statistical arbitrage on the s&p 500, cross-domain deep learning approach for multiple financial market prediction, deep learning with long short-term memory networks for financial market predictions, empirical asset pricing via machine learning, stock price prediction by deep neural generative model of news articles, investment using evolutionary learning methods and technical rules, on the profitability of technical trading rules based on artificial neural networks:: evidence from the madrid stock market, related papers.

Showing 1 through 3 of 0 Related Papers

Bahareh Abedin

Machine Learning in Behavioral Finance: A Systematic Literature Review

S. navid hojaji mahmood yahyazadehfar bahareh abedin.

The Journal of Financial Data Science

10.3905/jfds.2022.1.100

• Download PDF To download content, you need to upgrade your trial to full subscription. Please contact your account manager to do this.
• Add to Favorites Please log-in to or register for your personal account in order to save a bookmark. Log-in/register

Share this article

• Labels Please log-in to or register for your personal account in order to apply a label. Log-in/register

Cite this article

• Alerts You must be logged in as an individual apply an alert. Log-in/register

Latest issue

Log in to access this content or Request a Demo

This study endeavors to investigate the application of machine learning in behavioral economics and behavioral finance to represent a profile of studies conducted in this field. To accomplish this task, 90 scientific studies were systematically extracted between 2000 and June 1, 2020. Utilizing the text analysis techniques and related statistical methods, the abstracts of the extracted studies were reviewed and analyzed. First, it was found that attention to this field has developed in recent years with an accelerating trend. Second, it was demonstrated that specialized journals have also bestowed more curiosity in these studies than in the past by publishing more relevant studies. Third, results revealed that machine learning has been applied in areas such as investor sentiment, decision making, consumer behavior, trading strategies, game theory, and other areas in the field of behavioral economics and behavioral finance. In this regard, the application of machine learning has included techniques such as support vector machine, regression, neural networks, random forest, and so on. Despite the expanding consideration adjusted to this field by researchers and specialized journals, there are still many research gaps in this field. Accordingly, there is a relatively significant distance until fully unleashing the superior powers of machine learning, like prediction and classification in behavioral economics and behavioral finance. Finally, this research completed its mission by suggesting implications for the future of this field based on the acquired outcomes.

• is a master of business administration in finance from the University of Mazandaran in Iran. ( snavidhojaji{at}gmail.com )
• is a professor of finance at the University of Mazandaran in Iran. ( m.yahyazadeh{at}umz.ac.ir )
• is an assistant professor, Department of Executive Management, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran. ( b.abedin{at}umz.ac.ir )

Related Topics

• Quantitative Finance
• Statistical Methods
• Machine Learning and Data Science
• Economics and Financial History

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

A Literature Review of Behavioural Finance

Related Papers

Muhammad Mubin , Sumra Mughal

The deliberation in theoretical finance among the Efficient Market Hypothesis (EMH) and the subject of the behavioral finance is of immense interest. from the time when its emerge, the EMH has been the most significant theory which describes the behavior of the diverse agents in the financial markets and overlooks more or less any prospective impact of human behavior in the investment method. From the end of 1970s and the commencement of 1980s, a rising number of researchers and scholars showed the irregularity of this theory. The anomalies of the recent portfolio models and theories have provoked the development of behavioral finance. Behavioral finance assimilates psychology and economics in finance theory and has its heredity in theground-breaking work of great psychologists Tversky and Daniel Kahneman (1979). The rationale of this study is to present a synthesis of the behavioral finance literature over the last two decades.

Nicholas C. Barberis

This article has been informed by many discussions over the years with Robin Greenwood, Ming Huang, Lawrence Jin, Matthew Rabin, Andrei Shleifer, Richard Thaler, and Wei Xiong, as well as with my students and my colleagues in the fields of behavioral finance and behavioral economics. I am grateful to Douglas Bernheim, Stefano DellaVigna, and David Laibson for their comments on an early draft, and to Pedro Bordalo, Erik Eyster, Shane Frederick, Sam Hanson, Philipp Krueger, Alan Moreira, Tobias Moskowitz, Charles Nathanson, Cameron Peng, David Thesmar, and Baolian Wang for their help with questions that came up during the writing process. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.

Micaela Emídio

Mustafa Okur

DeBondt, W.F.M. and Thaler, R.H. (1995), “Financial decision making in markets and firms: a behavioral perspective”,Handbooks inOperations Research and Management Science, Vol. 9 No. 13, pp. 385-410.

Asif Ali 8221-FMS/MBA/F19

Purpose-This paper aims to review the theory and empirical evidence of institutional investor behavioral biases in the lenses of behavioral finance paradigm. It surveys the research specifically focusing on behavioral biases among institutional investors in investment management activities worldwide. Design/methodology/approach-A literature survey is done to gather and synthesize evidence on behavioral biases of institutional investors. Findings-The survey and analysis reveal the following findings. First, the theoretical underpinning of investors' irrational behavior has been neglected in behavioral finance research. Second, the behavioral heuristics and biases are dynamic and complex. Third, understanding behavioral biases' origin, causes and effects requires interdisciplinary perspectives from the fields of psychology, sociology and biology. Originality/value-The analysis and alternative perspectives drawn in this paper provide new insights into the field of behavioral finance and aims to suggest researchers, practitioners and regulators on the next course of actions.

Spanish Journal of Finance and Accounting / Revista Española de Financiación y Contabilidad

Rosa M Mayoral , Eleuterio Vallelado , Werner Bondt

ABSTRACT Everyday financial decisions are the product of diverse factors, including instinct, habit, emotion, reason, and social interaction. Psychologists have long aspired to unravel the determinants of intuitive judgment and choice. Slowly but surely, they have identified various psychological mechanisms that cause predictable decision biases. This survey puts special emphasis on behavioral research in finance that investigates information overload, emotion, social influence, and ambiguity aversion. It also discusses selected cognitive models that attempt to integrate the way individuals interpret and act upon information. In general, behavioral research casts serious doubts on the validity of some of the key insights of mainstream finance such as portfolio theory, the positive risk-return trade-off, and efficient markets.

Journal of Asset Management

Paul Woolley , Satish B Thosar

Alexander Decker

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Pedro Valls

SSRN Electronic Journal

Yongcheol Shin

Fabrice Rousseau

The Journal of Finance

David Hirshleifer

Magdalena Mikołajek-Gocejna

Concetta Sorropago

Raja Asad Azad

Worapot "Warren Wu" Ongkrutaraksa

Journal of Monetary Economics

Mohamad Saleh

Journal of Economic Dynamics and Control

Cars Hommes

Amos Nadler , Peiran Jiao

Working Papers of LaRGE ( …

Marie-Hélène BROIHANNE

Gaby Blanka

Wing-keung Wong

IAEME Publication

Working Papers

Robert Merton

IJAR Indexing

Mark Salmon

Prashant Kotwal

IOSR Journals

Alex Plastun , Ronald H Mynhardt

Sana Jameel

David Mygind

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Machine learning in accounting and finance research: a literature review

• Review of Quantitative Finance and Accounting

• University of Thessaly

• This person is not on ResearchGate, or hasn't claimed this research yet.

Abstract and Figures

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations
• Rev Quant Finance Account

• Tangtang He

• Luu Duc Toan Huynh

• Emmanouil Platanakis
• Hoang Hiep Nguyen
• Jean-Laurent Viviani

• Georgios Chortareas
• Apostolos G. Katsafados

• J FINANC ECON

• Int Rev Financ Anal

• Muhammad Atif Khan Achakzai
• Geoffrey Frost
• Stewart Jones

• Johannes Kriebel
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up