Prediction of Financial Markets Utilizing an Innovatively Optimized Hybrid Model: A Case Study of the Hang Seng Index

Abstract

Stock trading is a highly consequential and frequently discussed subject in the realm of financial markets. Due to the volatile and unpredictable nature of stock prices, investors are perpetually seeking methods to forecast future trends in order to minimize losses and maximize profits. Nevertheless, despite the ongoing investigation of various approaches to optimize the predictive efficacy of models, it is indisputable that a method for accurately forecasting forthcoming market trends does not yet exist. A multitude of algorithms are currently being employed to forecast stock prices due to significant developments that have occurred in recent years. An innovative algorithm for predicting stock prices are examined in this paper which is a Gated Recurrent Unit combined with the Aquila optimizer. A comprehensive data implementation utilizing the Hang Seng Index stock price was executed as a dataset of this research which was collected between the years of 2015 and the end of June 2023. In the study, several additional methods for predicting stock market movements are also detailed. A comprehensive comparative analysis of the stock price prediction performances of the aforementioned algorithms has also been carried out to offer a more in-depth analysis and then the results are displayed in an understandable tabular and graphical manner. The proposed model obtained the values of 0.9934, 0.71, 143.62, and 36530.58, for 𝑹 𝟐 , MAPE, MAE, and MSE, respectively. These results proved the efficiency and accuracy of the suggested method and it was determined that the proposed model algorithm produces results with a high degree of accuracy and performs the best when it comes to forecasting a time series or stock price.