Zhiyong Yang, Yuxi Ye, and Yu Zhou, Journal of Information Processing Systems Vol. 21, No. 2, pp. 125-138, Apr. 2025  

https://doi.org/10.3745/JIPS.04.0340
Keywords: LSTM, Attention Mechanism, Extreme Gradient Progression Tree, Stock Forecasting
Fulltext: PDF Full Text PubReader

Abstract

Forecasting is a popular topic in the stock market. In recent years, many scholars have utilized machine- and deep-learning models in this field. However, many stock forecasting models suffer from problems of information overlap in stock trading data and a relatively simple structure of the prediction model. To overcome these issues, we built a stock forecasting model based on extreme gradient boosting (XGBoost), long shortterm memory (LSTM), and attention (XGBoost-LSTM-Attention). XGBoost is used to extract important information from stock data, and the LSTM combined with the attention mechanism can enhance stock prediction performance. To verify the feasibility and effectiveness of XGBoost-LSTM-Attention, we selected 14 Chinese stocks from different industries for the prediction experiments and compared their performance with those of existing models. The experimental results showed that the average root-mean-square error value of the XGBoost-LSTM-Attention model for the different stock datasets was the smallest (0.012); the average R2 value (0.96) and average accuracy (66.1%) were the highest.

Statistics
Cite this article