A Practical Machine Learning Approach for Dynamic Stock Recommendation

TL;DR

This paper presents a machine learning-based dynamic stock recommendation system for the S&P 500, outperforming traditional strategies in terms of risk-adjusted returns and cumulative gains.

Contribution

It introduces a practical, multi-model machine learning scheme for stock selection and demonstrates its effectiveness through empirical testing on the S&P 500.

Findings

Outperforms long-only strategies in Sharpe ratio

Achieves higher cumulative returns

Utilizes multiple ML models for dynamic stock ranking

Abstract

Stock recommendation is vital to investment companies and investors. However, no single stock selection strategy will always win while analysts may not have enough time to check all S&P 500 stocks (the Standard & Poor's 500). In this paper, we propose a practical scheme that recommends stocks from S&P 500 using machine learning. Our basic idea is to buy and hold the top 20% stocks dynamically. First, we select representative stock indicators with good explanatory power. Secondly, we take five frequently used machine learning methods, including linear regression, ridge regression, stepwise regression, random forest and generalized boosted regression, to model stock indicators and quarterly log-return in a rolling window. Thirdly, we choose the model with the lowest Mean Square Error in each period to rank stocks. Finally, we test the selected stocks by conducting portfolio allocation methods such as equally weighted, mean-variance, and minimum-variance. Our empirical results show that the proposed scheme outperforms the long-only strategy on the S&P 500 index in terms of Sharpe ratio and cumulative returns. This work is fully open-sourced at \href{https://github.com/AI4Finance-Foundation/Dynamic-Stock-Recommendation-Machine_Learning-Published-Paper-IEEE}{GitHub}.