Impact of chart image characteristics on stock price prediction with a convolutional neural network

Abstract

Stock price prediction has long been the subject of research because of the importance of accuracy of prediction and the difficulty in forecasting. Traditionally, forecasting has involved linear models such as AR and MR or nonlinear models such as ANNs using standardized numerical data such as corporate financial data and stock price data. Due to the difficulty of securing a sufficient variety of data, researchers have recently begun using convolutional neural networks (CNNs) with stock price graph images only. However, we know little about which characteristics of stock charts affect the accuracy of predictions and to what extent. The purpose of this study is to analyze the effects of stock chart characteristics on stock price prediction via CNNs. To this end, we define the image characteristics of stock charts and identify significant differences in prediction performance for each characteristic. The results reveal that the accuracy of prediction is improved by utilizing solid lines, color, and a single image without axis marks. Based on these findings, we describe the implications of making predictions only with images, which are unstructured data, without using large amounts of standardized data. Finally, we identify issues for future research.

Forecasting of NIFTY 50 Index Price by Using Backward Elimination with an LSTM Model

“…In recent years, there has been a growing interest in research employing artificial intelligence-based techniques for stock market prediction using the NIFTY 50 data, several machine learning models, including logistic regression (LR), support vector machine (SVM), random forest, etc., have been used for solving specific difficulties in time series forecasting (Abraham et al 2022;Jin and Kwon 2021;Parmar et al 2018;Vijh et al 2020). However, predicting the real-time market requires models to detect hidden data patterns in order to analyze such time-series data.…”