Prediction of the Change Points in Stock Markets Using DAE-LSTM
| Autor: | Hosun Ryou, Yeonji Choi, Hee Soo Lee, Seunghwan Jeong, Sangyong Jeon, Sanghyuk Yoo, Kyongjoo Oh, Tae Hyun Park |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Index (economics)
Geography Planning and Development TJ807-830 Management Monitoring Policy and Law Discount points TD194-195 Renewable energy sources denoising autoencoder Econometrics Economics GE1-350 Time series Stock (geology) Denoising autoencoder Environmental effects of industries and plants Renewable Energy Sustainability and the Environment business.industry Deep learning change-point detection Environmental sciences Russell 2000 index Change points Artificial intelligence business long short-term memory Change detection |
| Zdroj: | Sustainability Volume 13 Issue 21 Sustainability, Vol 13, Iss 11822, p 11822 (2021) |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su132111822 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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