Integration of Effective Models to Provide a Novel Method to Identify the Future Trend of Nikkei 225 Stocks Index

Abstract

The stock market refers to a financial market in which individuals and institutions engage in the buying and selling of shares of publicly listed firms. The valuation of stocks is influenced by the interplay between the forces of supply and demand. The act of allocating funds to the stock market entails a certain degree of risk, while it presents the possibility of substantial gains over an extended period. The task of predicting stock prices in the securities market is further complicated by the presence of non-stationary and non-linear characteristics in financial time series data. While traditional techniques have the potential to enhance the accuracy of forecasting, they are also associated with computational complexities that might lead to an elevated occurrence of prediction mistakes. This is the reason why the financial industry has seen a growing prevalence of novel methods, particularly in the stock market. This work introduces a novel model that effectively addresses several challenges by integrating the random forest methodology with the artificial bee colony algorithm. In the current study, the hybrid model demonstrated superior performance and effectiveness compared to the other models. The proposed model exhibited optimum performance and demonstrated a significant degree of effectiveness with low errors. The efficiency of the predictive model for stock price forecasts was established via the analysis of data obtained from the Nikkei 225 index. The data included the timeframe from January 2013 to December 2022. The results reveal that the proposed framework demonstrates efficacy and reliability in evaluating and predicting the price time series of equities. The empirical evidence suggests that, when compared to other current methodologies, the proposed model has a greater degree of accuracy in predicting outcomes.