A Method for Assessing Financial Market Price Behavior: An Analysis of the Shanghai Stock Exchange Index

A stock market is a venue where the shares of publicly traded companies are available for purchase and sale by individuals. The financial markets exert a substantial influence on various domains, including technology, employment, and business. Given the substantial rewards and risks associated with stock trading, investors are exceedingly concerned with the precision of future stock value forecasts. They modify their investment strategies in an effort to achieve even greater returns. Accurate stock price forecasting can be challenging in the securities industry due to the complex nature of the problem and the requirement for a comprehensive understanding of various interconnected factors. The stock market is influenced by a variety of factors, including politics, society, and economics. A multitude of interrelated factors contribute to these behaviors, and stock price fluctuations are capricious. In order to tackle a range of these difficulties, the present investigation proposes an innovative framework that integrates a Grasshopper optimization method with the gated recurrent unit model, a machine-learning approach. The research used data from the Shang Hai Stock Exchange Index for the period of 2015-2023. The proposed hybrid model was also tested on the 2013-2022 S&P 500 and Nikkei 225. The proposed model demonstrated optimal performance, exhibiting a minimal error rate and exceptional effectiveness. The study's findings demonstrate that the proposed model is more suitable for the volatile stock market and surpasses other existing strategies in terms of predictive accuracy.