Regression and Forecasting of U.S. Stock Returns Based on LSTM

TL;DR

This study evaluates the effectiveness of various factor models in explaining U.S. stock sector returns and demonstrates that LSTM models can improve prediction accuracy by capturing industry-specific factors.

Contribution

It introduces the application of LSTM neural networks to enhance stock return forecasting and compares its performance with traditional factor models across different market sectors.

Findings

Fama-French five-factor model shows superior validity for the sectors studied.

LSTM models outperform traditional models in predicting stock returns.

LSTM captures industry-specific factors influencing stock performance.

Abstract

This paper analyses the investment returns of three stock sectors, Manuf, Hitec, and Other, in the U.S. stock market, based on the Fama-French three-factor model, the Carhart four-factor model, and the Fama-French five-factor model, in order to test the validity of the Fama-French three-factor model, the Carhart four-factor model, and the Fama-French five-factor model for the three sectors of the market. French five-factor model for the three sectors of the market. Also, the LSTM model is used to explore the additional factors affecting stock returns. The empirical results show that the Fama-French five-factor model has better validity for the three segments of the market under study, and the LSTM model has the ability to capture the factors affecting the returns of certain industries, and can better regress and predict the stock returns of the relevant industries. Keywords- Fama-French model; Carhart model; Factor model; LSTM model.