Stock index trend prediction based on TabNet feature selection and long short-term memory

ABSTRACT: In this study, we propose a predictive model TabLSTM that combines machine learning methods such as TabNet and Long Short-Term Memory Neural Network (LSTM) with a complete factor library for stock index trend prediction. Our motivation is based on the notion that there are numerous interrelated factors in the stock market, and the factors that affect each stock are different. Therefore, a complete factor library and an efficient feature selection technique are necessary to predict stock index. In this paper, we first build a factor database that includes macro, micro and technical indicators. Successively, we calculate the factor importance through TabNet and rank them. Based on a prespecified threshold, the optimal factors set will include only the highest-ranked factors. Finally, using t

SUBMITTER: Wei X 

PROVIDER: S-EPMC9746941 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature