Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading Strategies
We model investor behavior by training machine learning techniques with financial data comprising more than 13,000 investors of a large bank in Brazil over 2016 to 2018. We take high-frequency data on every sell or buy operation of these investors on a daily basis, allowing us to fully track these i...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/4325125 |
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| _version_ | 1850172233608069120 |
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| author | Thiago Christiano Silva Benjamin Miranda Tabak Idamar Magalhães Ferreira |
| author_facet | Thiago Christiano Silva Benjamin Miranda Tabak Idamar Magalhães Ferreira |
| author_sort | Thiago Christiano Silva |
| collection | DOAJ |
| description | We model investor behavior by training machine learning techniques with financial data comprising more than 13,000 investors of a large bank in Brazil over 2016 to 2018. We take high-frequency data on every sell or buy operation of these investors on a daily basis, allowing us to fully track these investment decisions over time. We then analyze whether these investment changes correlate with the IBOVESPA index. We find that investors decide their investment strategies using recent past price changes. There is some degree of heterogeneity in investment decisions. Overall, we find evidence of mean-reverting investment strategies. We also find evidence that female investors and higher academic degree have a less pronounced mean-reverting strategy behavior comparatively to male investors and those with lower academic degree. Finally, this paper provides a general methodological approach to mitigate potential biases arising from ad-hoc design decisions of discarding or introducing variables in empirical econometrics. For that, we use feature selection techniques from machine learning to identify relevant variables in an objective and concise way. |
| format | Article |
| id | doaj-art-cf14dcb770084318ab8a129318b49cea |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-cf14dcb770084318ab8a129318b49cea2025-08-20T02:20:08ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/43251254325125Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading StrategiesThiago Christiano Silva0Benjamin Miranda Tabak1Idamar Magalhães Ferreira2Universidade Católica de Brasília, Distrito Federal, BrazilFGV/EPPG Escola de Políticas Públicas e Governo, Fundação Getúlio Vargas, School of Public Policy and Government, Getulio Vargas Foundation, Distrito Federal, BrazilFGV/EPPG Escola de Políticas Públicas e Governo, Fundação Getúlio Vargas, School of Public Policy and Government, Getulio Vargas Foundation, Distrito Federal, BrazilWe model investor behavior by training machine learning techniques with financial data comprising more than 13,000 investors of a large bank in Brazil over 2016 to 2018. We take high-frequency data on every sell or buy operation of these investors on a daily basis, allowing us to fully track these investment decisions over time. We then analyze whether these investment changes correlate with the IBOVESPA index. We find that investors decide their investment strategies using recent past price changes. There is some degree of heterogeneity in investment decisions. Overall, we find evidence of mean-reverting investment strategies. We also find evidence that female investors and higher academic degree have a less pronounced mean-reverting strategy behavior comparatively to male investors and those with lower academic degree. Finally, this paper provides a general methodological approach to mitigate potential biases arising from ad-hoc design decisions of discarding or introducing variables in empirical econometrics. For that, we use feature selection techniques from machine learning to identify relevant variables in an objective and concise way.http://dx.doi.org/10.1155/2019/4325125 |
| spellingShingle | Thiago Christiano Silva Benjamin Miranda Tabak Idamar Magalhães Ferreira Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading Strategies Complexity |
| title | Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading Strategies |
| title_full | Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading Strategies |
| title_fullStr | Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading Strategies |
| title_full_unstemmed | Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading Strategies |
| title_short | Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading Strategies |
| title_sort | modeling investor behavior using machine learning mean reversion and momentum trading strategies |
| url | http://dx.doi.org/10.1155/2019/4325125 |
| work_keys_str_mv | AT thiagochristianosilva modelinginvestorbehaviorusingmachinelearningmeanreversionandmomentumtradingstrategies AT benjaminmirandatabak modelinginvestorbehaviorusingmachinelearningmeanreversionandmomentumtradingstrategies AT idamarmagalhaesferreira modelinginvestorbehaviorusingmachinelearningmeanreversionandmomentumtradingstrategies |