Predicting stock returns with financial ratios: A new methodology incorporating machine learning techniques to beat the market

Abstract

ABSTRACT This study proposes a methodology incorporating machine learning algorithms to predict stock returns and construct portfolios that beat the market. The performance evaluation is based on the statistical metrics as well as the return and Sharpe ratios of the portfolios. Additionally, a new performance evaluation metric, Safe-Side, is introduced to address the needs of conservative portfolio managers and investors. The results provide strong evidence that the machine learning algorithms can be used to predict the stock returns with approximately 86% classification accuracy. The proposed methodology also provides guidance for investors and portfolio managers for their portfolio selection problems.