Asian Stock Index Price Prediction Analysis Using Comparison of Split Data Training and Data Testing

Abstract

This study implements stock index price predictions using the LSTM method, where one of the processes in data management before running with the LSTM method is data split. This study also looks for the most appropriate split data ratio in predicting stock index prices to minimize error rates and differences in forecasted prices and original prices because in previous studies there were several rules of thumb in dividing data, so it is necessary to compare the most appropriate ratios in this research. Based on the evaluation process, the error value was found from nine split data ratios that were run by five ratios which produced a predictive graph line shape that resembled the validation line. Three datasets, namely split data ratios of 80:20, 70:30, and 60:40, are the ratios that get the lowest error values based on the RMSE, MSE, MAPE, and MAE values in the five stock index datasets. The three ratios are then compared again by looking at the average percentage difference between the validation price and the predicted price for the next working day, and it is found that the ratio of 80:20 is the most suitable split data ratio for predicting the stock index price for the next working day, with a level of difference in the average value between the original price and the predicted price on the stock index of 1.3%. While the ratio of 70:30 has an average predicted value of five stock index datasets of 1.9% and a ratio of 60:40 of 1.8%.

The Application of Neural Network Models to Explain the Relationship Between Stock Value, Returns Value, and Information on Prices

“…A neural network is then given this data along with the time series to enhance predictions. Furthermore, studies have been conducted to detect and classify investment structures using public stock information and market transaction records and to predict market trends using a combination of sentiment and market data [9]. The findings demonstrate how grouping factors might enhance artificial neural network predictions.…”