Philippines Stock Exchange Prediction Using Hybrid Neural Network

The stock market is dynamic and highly chaotic due to its complicated nature. The prediction of future stock prices has gained the interest of investors and researchers. Numerous conventional and hybrid approaches were put forth. The projections performed poorly and strangely did not get any better. This study aimed to predict Philippine Stock Exchange closing prices using a proposed hybrid method. This proposed study aimed to improve the Artificial Neural Network (ANN) performance using the Kepler Optimization Algorithm, a metaheuristic algorithm, in conjunction with the Artificial Neural Network (ANN-KOA). ANN is a widely accepted technique in predicting. Still, the Kepler Optimization Algorithm can provide random initial inputs for the method through effective feature selection. It can identify superior subsets of input variables to integrate into ANN, enabling more accurate prediction. In this study, the ups and downs were grouped, and 12 technical indicators with varying lengths of days 3, 5, 10, 15, and 20 were used to decompose the original historical data, which are regular and smoother than the original data. The historical data was normalized into [-1, 1], so the predicted result would be 0 (decrease) or 1 (increase) otherwise. Finally, to obtain the train and test data, it was fused into five groups and tested using the ANN and ANN-KOA. The experiment's outcome of 0 (a declining stock price) indicates two alternative courses of action based on the two scenarios: holding if investors have already purchased it or purchasing if they haven't made a decision. In this proposed study, ANN-KOA resulted in higher accuracy than ANN. However, regarding the number of elapsed times, KOA-ANN provided a slower time than ANN. On the other hand, in terms of variations of lengths of the days, 3-5-10-KOA-ANN outperformed the variations of lengths of days: 3-5-10-ANN, 3-5-10-15-KOA-ANN, 3-5-10-15-20-KOA, and 3-5-10-15-20-KOA-ANN. In conclusion, this study suggested that it be utilized for other problems, such as predicting academic performance, diseases, floods, etc.