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Abstract

The stock market is a financial marketplace that facilitates the purchasing and selling of shares linked with corporations listed on public exchanges. Additionally, it provides insight into the business environment in general and the performance of corporations, which allows it to indicate a nation's economic well-being. There is a direct relationship between a growing stock market and increased economic growth. Investing in the financial market is intended to boost returns; however, the market's future behavior is difficult to predict due to its complexity and the numerous events that influence it. Businesses and investors constantly try to increase profits and reduce losses, while simultaneously continuously pursuing precise techniques for evaluating stock prices. Researchers in machine learning have shown that Extreme Learning Machines can better identify obstacles and forecast market trends. This study employs historical trading data from the Nikkei 225 index to optimize the efficacy of ELM in forecasting long-term market patterns. In addition, feature selection methods are used to identify the exact combination of technical indicators that enhance the accuracy of predicting the Nikkei 225 index. This study demonstrates some optimization approaches including genetic algorithms, Grasshopper Optimization Algorithms, and War Strategy Optimization. The research proposes a hybrid model integrating the ELM with the WSO to enhance its performance. The testing method revealed that the chosen model had the greatest performance coefficient of determination value, which was 0.992. 5-fold cross-validation was employed to ensure the model's robustness and generalizability, yielding consistent performance results. The Friedman Chi-Square statistical test was conducted, indicating significant performance differences between the proposed model and other techniques. The study's findings indicate that the proposed model surpassed the other models in terms of accuracy and effectiveness.

Keywords

Nikkei 225 index technical indicator stock price extreme learning machine war strategy optimization

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Presenting a novel method in order to reduce the investment risks by identifying the future trend of stock price

Abstract

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