Using Machine Learning for Stock Return Prediction

Research Article
Open access

Using Machine Learning for Stock Return Prediction

Gongrun Zhang 1*
  • 1 Institute of Swift, Shenzhen University, Shenzhen, China    
  • *corresponding author 2022290204@email.szu.edu.cn
AEMPS Vol.185
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-141-9
ISBN (Online): 978-1-80590-142-6

Abstract

Traditional econometric models often struggle to address the non-linearity and uncertainty inherent in modern financial markets. This study proposes a machine learning framework integrating Gaussian Process Regression (GPR) for probabilistic forecasting and Bayesian Model Averaging (BMA) for ensemble-based robustness. Utilizing monthly stock return data (1980–2014) from CRSP and Compustat, we trained multiple models—including Lasso, Neural Networks, XGBoost, and GPR—and evaluated their performance under varying noise and complexity levels. Empirical results demonstrate that BMA consistently outperformed standalone models, achieving the lowest average RMSE (0.230) and highest R² (0.7505) across all cases. GPR enhanced risk assessment through prediction intervals, reducing RMSE by 15% in high-noise environments compared to point-estimate models. Notably, in small-sample, high-complexity scenarios (Case 4), BMA’s RMSE (0.355) was 26% lower than Neural Networks. Robustness tests—including subperiod analysis, sector-neutral portfolios, and transaction cost simulations—confirmed the framework’s stability, with BMA-GPR yielding 3.2% annualized alpha post-costs and minimal performance degradation under 30% missing data (8% RMSE increase).

Keywords:

Bayesian Model Averaging, Gaussian Process Regression, Probabilistic Forecasting

Zhang,G. (2025). Using Machine Learning for Stock Return Prediction. Advances in Economics, Management and Political Sciences,185,119-126.
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References

[1]. Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The Econometrics of Financial Markets. Princeton University Press.

[2]. Chen, G., et al. (2019). Deep Learning in Asset Pricing. Journal of Financial Data Science, 4(1), 1-25.

[3]. Fama, E. F., & French, K. R. (2015). A Five-Factor Asset Pricing Model. Journal of Financial Economics, 116(1), 1-22.

[4]. Kelly, B., & Pruitt, S. (2013). Market Expectations in the Cross-Section of Present Values. Journal of Finance, 68(5), 1721-1756.

[5]. Welch, I., & Goyal, A. (2008). A Comprehensive Look at The Empirical Performance of Equity Premium Prediction. Review of Financial Studies, 21(4), 1455-1508.

[6]. Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.

[7]. Kozak, S., Nagel, S., & Santosh, S. (2020). Shrinking the Cross-Section. Journal of Financial Economics, 135(2), 271-292.

[8]. Ghysels, E., Santa-Clara, P., & Valkanov, R. (2018). Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies. Journal of Econometrics, 204(1), 86-106.

[9]. Kumar, R., & Singh, P. (2022). Enhancing Forecast Robustness with Bayesian Model Averaging in Asset Pricing. Journal of Financial Data Science, 4(2), 150-175.

[10]. Wang, Q., & Zhang, L. (2023). Integrating Bayesian Model Averaging with Machine Learning for Improved Financial Predictions. Machine Learning in Finance, 8(1), 30-55.

[11]. Bianchi, D., Büchner, M., & Tamoni, A. (2021). Bond Risk Premiums with Machine Learning. Review of Financial Studies, 34(2), 1046-1089.

[12]. Feng, G., Polson, N. G., & Xu, J. (2018). Deep Learning in Asset Pricing. Review of Financial Studies, 31(11), 4214-4258.

[13]. Pettenuzzo, D., & Ravazzolo, F. (2016). Optimal Portfolio Choice under Decision-Based Model Combinations. Journal of Applied Econometrics, 31(7), 1312-1332.

[14]. Gu, S., Kelly, B., & Xiu, D. (2020). Empirical Asset Pricing via Machine Learning. Journal of Finance, 75(5), 2223-2273.

[15]. Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382-401.

Cite this article

Zhang,G. (2025). Using Machine Learning for Stock Return Prediction. Advances in Economics, Management and Political Sciences,185,119-126.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of ICEMGD 2025 Symposium: Innovating in Management and Economic Development

ISBN：978-1-80590-141-9(Print) / 978-1-80590-142-6(Online)
Editor：Florian Marcel Nuţă Nuţă, Ahsan Ali Ashraf
Conference website: https://2025.icemgd.org/Lahore.html
Conference date: 23 September 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.185
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The Econometrics of Financial Markets. Princeton University Press.

[2]. Chen, G., et al. (2019). Deep Learning in Asset Pricing. Journal of Financial Data Science, 4(1), 1-25.

[3]. Fama, E. F., & French, K. R. (2015). A Five-Factor Asset Pricing Model. Journal of Financial Economics, 116(1), 1-22.

[4]. Kelly, B., & Pruitt, S. (2013). Market Expectations in the Cross-Section of Present Values. Journal of Finance, 68(5), 1721-1756.

[5]. Welch, I., & Goyal, A. (2008). A Comprehensive Look at The Empirical Performance of Equity Premium Prediction. Review of Financial Studies, 21(4), 1455-1508.

[6]. Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.

[7]. Kozak, S., Nagel, S., & Santosh, S. (2020). Shrinking the Cross-Section. Journal of Financial Economics, 135(2), 271-292.

[8]. Ghysels, E., Santa-Clara, P., & Valkanov, R. (2018). Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies. Journal of Econometrics, 204(1), 86-106.

[9]. Kumar, R., & Singh, P. (2022). Enhancing Forecast Robustness with Bayesian Model Averaging in Asset Pricing. Journal of Financial Data Science, 4(2), 150-175.

[10]. Wang, Q., & Zhang, L. (2023). Integrating Bayesian Model Averaging with Machine Learning for Improved Financial Predictions. Machine Learning in Finance, 8(1), 30-55.

[11]. Bianchi, D., Büchner, M., & Tamoni, A. (2021). Bond Risk Premiums with Machine Learning. Review of Financial Studies, 34(2), 1046-1089.

[12]. Feng, G., Polson, N. G., & Xu, J. (2018). Deep Learning in Asset Pricing. Review of Financial Studies, 31(11), 4214-4258.

[13]. Pettenuzzo, D., & Ravazzolo, F. (2016). Optimal Portfolio Choice under Decision-Based Model Combinations. Journal of Applied Econometrics, 31(7), 1312-1332.

[14]. Gu, S., Kelly, B., & Xiu, D. (2020). Empirical Asset Pricing via Machine Learning. Journal of Finance, 75(5), 2223-2273.

[15]. Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382-401.

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