![]() ![]() Leung, Daouk hold the view that trading could be made profitable by an accurate prediction of the direction of movement of the stock index. Hence, precise forecasting of the trends of the stock price index can be extremely advantageous for investors. Accurate forecast of the trends of the stock index can help investors to acquire opportunities for gaining profit in the stock exchange. Predicting the direction is a practical issue that heavily influences a financial trader’s decision to buy or sell an instrument. The direction of the stock market index refers to the movement of the price index or the trend of fluctuation in the stock market index in the future. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately. We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). The main contribution of this study is the ability to predict the direction of the next day’s price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. ![]() In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. ![]() There are numerous research studies that use similar indicators to forecast the direction of the stock market index. Many factors such as political events, general economic conditions, and traders’ expectations may have an influence on the stock market index. In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. ![]()
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