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

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 corpo...

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Autores principales:	Guangxun Jin, Ohbyung Kwon
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/16261ef392e24660be94f1a554572d09
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id	oai:doaj.org-article:16261ef392e24660be94f1a554572d09
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spelling	oai:doaj.org-article:16261ef392e24660be94f1a554572d092021-12-02T20:10:14ZImpact of chart image characteristics on stock price prediction with a convolutional neural network.1932-620310.1371/journal.pone.0253121https://doaj.org/article/16261ef392e24660be94f1a554572d092021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253121https://doaj.org/toc/1932-6203Stock 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.Guangxun JinOhbyung KwonPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0253121 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Guangxun Jin Ohbyung Kwon Impact of chart image characteristics on stock price prediction with a convolutional neural network.
description	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.
format	article
author	Guangxun Jin Ohbyung Kwon
author_facet	Guangxun Jin Ohbyung Kwon
author_sort	Guangxun Jin
title	Impact of chart image characteristics on stock price prediction with a convolutional neural network.
title_short	Impact of chart image characteristics on stock price prediction with a convolutional neural network.
title_full	Impact of chart image characteristics on stock price prediction with a convolutional neural network.
title_fullStr	Impact of chart image characteristics on stock price prediction with a convolutional neural network.
title_full_unstemmed	Impact of chart image characteristics on stock price prediction with a convolutional neural network.
title_sort	impact of chart image characteristics on stock price prediction with a convolutional neural network.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/16261ef392e24660be94f1a554572d09
work_keys_str_mv	AT guangxunjin impactofchartimagecharacteristicsonstockpricepredictionwithaconvolutionalneuralnetwork AT ohbyungkwon impactofchartimagecharacteristicsonstockpricepredictionwithaconvolutionalneuralnetwork
_version_	1718374985910714368

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

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