Work

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

Year: 2025

Type: conference-paper

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.

Source: International Conference on Communication Networks and Machine Learning (CNML)

DOI: 10.1145/3728199.3728218

Authors: Shicheng Zhou, Zizhou Zhang, Rong Zhang, Yuchen Yin, Chia Hong Chang, Qinyan Shen

Institutions: University of Minnesota, University of Illinois Urbana-Champaign, University of California, Davis, Columbia University, University of Colorado Boulder

Language: English

Topic: Financial Markets and Investment Strategies

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: hybrid

OpenAlex ID: W4410813291

FWCI: 25.71

Cites: 24

Cited by: 4

Related to: 10