Modeling opening price spread of Shanghai Composite Index based on ARIMA-GRU/LSTM hybrid model.

ABSTRACT: In the dynamic landscape of financial markets, accurate forecasting of stock indices remains a pivotal yet challenging task, essential for investors and policymakers alike. This study is motivated by the need to enhance the precision of predicting the Shanghai Composite Index's opening price spread, a critical measure reflecting market volatility and investor sentiment. Traditional time series models like ARIMA have shown limitations in capturing the complex, nonlinear patterns inherent in stock price movements, prompting the exploration of advanced methodologies. The aim of this research is to bridge the gap in forecasting accuracy by developing a hybrid model that integrates the strengths of ARIMA with deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurre

SUBMITTER: Si Y 

PROVIDER: S-EPMC10936816 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature