Abstract

LSTM is a special type of Recurrent Neural Networks (RNN) with a broad range of applications including time series analysis, document classification, speech, and voice recognition. In this study we employ LSTM for predicting out-ofsample style factor returns and group/sector stock returns derived from the countries, industries, and style explanatory variables of a cross-section factor model. The data considered for the analysis is from September 2013 to June 2023 (approx. 10 years) of 4 style factor returns and 9 stock market groups/sectors for the South African stock market. One of the challenges of using factor models to forecast returns is the assumption that the prior consecutive observations are independent of each other as a result they do not account for the previous observations. Deep learning models like the LSTM are more accurate in predicting these sources of expected returns with their time-series behavior they can accurately predict markets where the effects of multiple market variables have interdependence. The results show that LSTM model is a powerful tool that can be used to predict returns which can help investors and portfolio managers who make investment decisions by grouping stocks into style, countries, or sectors.

Keywords: Neural Networks, LSTM, RNN, Investment style analysis, Factor returns, Stock returns, Deep learning, Machine learning