Differences of Prediction Accuracy between Various Charts in Stock Price Forecasting

Abstract

In recent years, there has been a lot of research on stock price forecasting. Technical analysis, which uses opening and closing prices to make forecasting, has become the mainstream method for stock price forecasting. Since this method grasps the trend of data and makes forecasting, the ease of making forecasting differs depending on the data. Traditionally, daily-charts have been widely used; and depending on the research, other charts such as minute-charts have been selected by hand. Currently, there is little evidence that the selected scales are the most appropriate, and the results could be further improved. In this study, we propose a method to relate the results of machine learning and statistical methods. In the experiment, we use USDJPY and multiple time series. First, we investigate easily predictable scales by using a LSTM model. The aim is to gain a foothold in explainability by providing statistical support for the results. As a result of the experiment, we find that there are differences of the predictive accuracy on each scale. In addition, the correlation with the data is confirmed. Finally, we discuss how to use this research to expand explainability.