Regression and Forecasting of U.S. Stock Returns Based on LSTM
This work addresses stock return prediction for investors and analysts, but it is incremental as it applies existing models to specific sectors without major methodological breakthroughs.
This paper tested the validity of Fama-French and Carhart factor models for three U.S. stock sectors and used an LSTM model to explore additional factors affecting returns, finding that the Fama-French five-factor model performed better and the LSTM could capture industry-specific factors for improved regression and prediction.
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.