A Hybrid Framework for Evaluating Financial Market Price: An Analysis of the Hang Seng Index Case Study

The accurate prediction of financial outcomes presents a considerable challenge as a result of the intricate interaction of economic fundamentals, market dynamics, and investor psychology. The task of accurately forecasting stock prices in the securities market is a challenging undertaking owing to the presence of non-stationary, non-linearity, and significant volatility in the time series data of stock prices. The utilization of conventional approaches possesses the potential to enhance the precision of predictive modeling. It is crucial to acknowledge that these methodologies also encompass computational intricacies, hence potentially augmenting the likelihood of prediction inaccuracies. This work introduces a methodology that addresses many issues by integrating support vector regression technology with the Aquila optimizer procedure. The results of this investigation suggest that, when compared to the other models, the hybrid model performed better and had more efficacy. The proposed model performed at an ideal level and demonstrated a significant level of effectiveness, with a low number of errors. The Hang Seng Index data was analyzed in order to assess the predictive model's accuracy in stock price forecasting. The data was accessible for the years 2015 through 2023. The results show that the proposed framework performs well and is reliable when analyzing and predicting the price time series of equities. Empirical data suggests that, in comparison to other methods presently in use, the suggested model forecasts outcomes with a higher degree of accuracy.