School of Economics and Trade, Guangzhou Xinhua University, Dongguan, China.
Lingnan College, Sun Yat-Sen University, Guangzhou, China.
PLoS One. 2022 Aug 17;17(8):e0272637. doi: 10.1371/journal.pone.0272637. eCollection 2022.
Modeling and forecasting stock prices have been important financial research topics in academia. This study seeks to determine whether improvements can be achieved by forecasting a stock index using a hybrid model and incorporating financial variables. We extend the literature on stock market forecasting by applying a hybrid model that combines wavelet transform (WT), long short-term memory (LSTM), and an adaptive genetic algorithm (AGA) based on individual ranking to predict stock indices for the Dow Jones Industrial Average (DJIA) index of the New York Stock Exchange, Standard & Poor's 500 (S&P 500) index, Nikkei 225 index of Tokyo, Hang Seng Index of Hong Kong market, CSI300 index of Chinese mainland stock market, and NIFTY50 index of India. The results indicate an overall improvement in forecasting of the stock index using the AGA-LSTM model compared to the benchmark models. The evaluation indicators prove that this model has a higher prediction accuracy when forecasting six stock indices.
股票价格建模和预测一直是学术界重要的金融研究课题。本研究旨在探讨通过使用混合模型并纳入财务变量来预测股票指数是否可以提高预测精度。我们通过应用一种混合模型来扩展股票市场预测的文献，该模型结合了小波变换（WT）、长短时记忆（LSTM）和基于个体排名的自适应遗传算法（AGA），以预测纽约证券交易所道琼斯工业平均指数（DJIA）、标准普尔 500 指数（S＆P 500）、东京日经 225 指数、香港恒生指数、中国大陆沪深 300 指数和印度 NIFTY50 指数的股票指数。结果表明，与基准模型相比，AGA-LSTM 模型在股票指数预测方面总体上有所提高。评估指标证明，该模型在预测六个股票指数时具有更高的预测精度。
