Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading Strategies

Thiago Christiano Silva,Benjamin Miranda Tabak,Idamar Magalhães Ferreira

doi:10.1155/2019/4325125

Thiago Christiano Silva, Benjamin Miranda Tabak + Show 1 more

Open Access

https://doi.org/10.1155/2019/4325125

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Abstract

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.

Highlights

To mitigate potential concerns due to subjective decisions by the analyst—and to prevent discarding a potentially relevant predictor—we opt to use an objective approach to identify those horizons that best explain investors’ buy or sell operations
We test whether investors use a mean-reversal or momentum strategy as follows: (i) If investors use a mean-reversal strategy, increases in the IBOVESPA index—i.e., ΔIBOVESPAt > 0 —are followed by sell operations in such a way that the investment volume of investors, on average, decreases (Δyit < 0). erefore, a mean-reversal strategy is translated by a negative β coefficient (β < 0)
We employ machine learning techniques together with econometrics techniques to model investor behavior using a unique dataset for investors that focus on stock market investments

Summary

Type High school Higher education

Complexity with time fixed effects to purge out macroeconomic components as follows: Δyit αt + εit,. To select the most important attributes, we use the residual εit, the investment volume variation of investor i at time t not due to common time factors, as dependent variable and different IBOVESPA index time aggregations and investors’ biological and education characteristics as independent variables as follows: εit βT · Xit + errorit,. Is procedure is cycled k times such that each fold appears exactly once for testing Such methodology enables us to tune the regularization parameters while preventing overfitting of the model. E attribute “1-day IBOVESPA variation” is the most powerful predictor for explaining investors’ behavior, followed by “2-day IBOVESPA variation” and “5-day IBOVESPA variation.” is suggests that investors prefer to base their investment decisions using short-term variations of the stock market index.

Highly educated investor Male investor

Yes Yes

Interactions of ΔIBOVESPAt with gender

Interactions of ΔIBOVESPAt with academic degree

Findings

Conclusions