Prediction of the Change Points in Stock Markets Using DAE-LSTM

Since the creation of stock markets, there have been attempts to predict their movements, and new prediction methodologies have been devised. According to a recent study, when the Russell 2000 industry index starts to rise, stocks belonging to the corresponding industry in other countries also rise...

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Autores principales:	Sanghyuk Yoo, Sangyong Jeon, Seunghwan Jeong, Heesoo Lee, Hosun Ryou, Taehyun Park, Yeonji Choi, Kyongjoo Oh
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	change-point detection denoising autoencoder long short-term memory Russell 2000 index Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/9c173e41ffc844b3a9b8125ee05783e6
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id	oai:doaj.org-article:9c173e41ffc844b3a9b8125ee05783e6
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spelling	oai:doaj.org-article:9c173e41ffc844b3a9b8125ee05783e62021-11-11T19:31:19ZPrediction of the Change Points in Stock Markets Using DAE-LSTM10.3390/su1321118222071-1050https://doaj.org/article/9c173e41ffc844b3a9b8125ee05783e62021-10-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/11822https://doaj.org/toc/2071-1050Since the creation of stock markets, there have been attempts to predict their movements, and new prediction methodologies have been devised. According to a recent study, when the Russell 2000 industry index starts to rise, stocks belonging to the corresponding industry in other countries also rise accordingly. Based on this empirical result, this study seeks to predict the start date of industry uptrends using the Russell 2000 industry index. The proposed model in this study predicts future stock prices using a denoising autoencoder (DAE) long short-term memory (LSTM) model and predicts the existence and timing of future change points in stock prices through Pettitt’s test. The results of the empirical analysis confirmed that this proposed model can find the change points in stock prices within 7 days prior to the start date of actual uptrends in selected industries. This study contributes to predicting a change point through a combination of statistical and deep learning models, and the methodology developed in this study could be applied to various financial time series data for various purposes.Sanghyuk YooSangyong JeonSeunghwan JeongHeesoo LeeHosun RyouTaehyun ParkYeonji ChoiKyongjoo OhMDPI AGarticlechange-point detectiondenoising autoencoderlong short-term memoryRussell 2000 indexEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 11822, p 11822 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	change-point detection denoising autoencoder long short-term memory Russell 2000 index Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	change-point detection denoising autoencoder long short-term memory Russell 2000 index Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Sanghyuk Yoo Sangyong Jeon Seunghwan Jeong Heesoo Lee Hosun Ryou Taehyun Park Yeonji Choi Kyongjoo Oh Prediction of the Change Points in Stock Markets Using DAE-LSTM
description	Since the creation of stock markets, there have been attempts to predict their movements, and new prediction methodologies have been devised. According to a recent study, when the Russell 2000 industry index starts to rise, stocks belonging to the corresponding industry in other countries also rise accordingly. Based on this empirical result, this study seeks to predict the start date of industry uptrends using the Russell 2000 industry index. The proposed model in this study predicts future stock prices using a denoising autoencoder (DAE) long short-term memory (LSTM) model and predicts the existence and timing of future change points in stock prices through Pettitt’s test. The results of the empirical analysis confirmed that this proposed model can find the change points in stock prices within 7 days prior to the start date of actual uptrends in selected industries. This study contributes to predicting a change point through a combination of statistical and deep learning models, and the methodology developed in this study could be applied to various financial time series data for various purposes.
format	article
author	Sanghyuk Yoo Sangyong Jeon Seunghwan Jeong Heesoo Lee Hosun Ryou Taehyun Park Yeonji Choi Kyongjoo Oh
author_facet	Sanghyuk Yoo Sangyong Jeon Seunghwan Jeong Heesoo Lee Hosun Ryou Taehyun Park Yeonji Choi Kyongjoo Oh
author_sort	Sanghyuk Yoo
title	Prediction of the Change Points in Stock Markets Using DAE-LSTM
title_short	Prediction of the Change Points in Stock Markets Using DAE-LSTM
title_full	Prediction of the Change Points in Stock Markets Using DAE-LSTM
title_fullStr	Prediction of the Change Points in Stock Markets Using DAE-LSTM
title_full_unstemmed	Prediction of the Change Points in Stock Markets Using DAE-LSTM
title_sort	prediction of the change points in stock markets using dae-lstm
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9c173e41ffc844b3a9b8125ee05783e6
work_keys_str_mv	AT sanghyukyoo predictionofthechangepointsinstockmarketsusingdaelstm AT sangyongjeon predictionofthechangepointsinstockmarketsusingdaelstm AT seunghwanjeong predictionofthechangepointsinstockmarketsusingdaelstm AT heesoolee predictionofthechangepointsinstockmarketsusingdaelstm AT hosunryou predictionofthechangepointsinstockmarketsusingdaelstm AT taehyunpark predictionofthechangepointsinstockmarketsusingdaelstm AT yeonjichoi predictionofthechangepointsinstockmarketsusingdaelstm AT kyongjoooh predictionofthechangepointsinstockmarketsusingdaelstm
_version_	1718431482951761920

Prediction of the Change Points in Stock Markets Using DAE-LSTM

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