Forecasting stock price movement: new evidence from a novel hybrid deep learning model
Purpose This study explores whether a new machine learning method can more accurately predict the movement of stock prices. Design/methodology/approach This study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RC...
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University of Economics Ho Chi Minh City
2023
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| Truy cập trực tuyến: | https://www.emerald.com/insight/content/doi/10.1108/JABES-05-2021-0061/full/html https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/115468 |
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oai:scholar.dlu.edu.vn:DLU123456789-1154682023-03-08T03:56:19Z Forecasting stock price movement: new evidence from a novel hybrid deep learning model Zhao, Yang Chen, Zhonglu Purpose This study explores whether a new machine learning method can more accurately predict the movement of stock prices. Design/methodology/approach This study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model. Findings The hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016. Originality/value This study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices. 2023-03-08T03:56:19Z 2023-03-08T03:56:19Z 2022 Article 2515-964X https://www.emerald.com/insight/content/doi/10.1108/JABES-05-2021-0061/full/html https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/115468 10.1108/JABES-05-2021-0061 en Journal of Asian Business and Economic Studies, Volume 29, Issue 2; p. 91-104 application/pdf University of Economics Ho Chi Minh City |
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Thư viện Trường Đại học Đà Lạt |
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Thư viện số |
| language |
English |
| description |
Purpose This study explores whether a new machine learning method can more accurately predict the movement of stock prices. Design/methodology/approach This study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model. Findings The hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016. Originality/value This study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices. |
| format |
Article |
| author |
Zhao, Yang Chen, Zhonglu |
| spellingShingle |
Zhao, Yang Chen, Zhonglu Forecasting stock price movement: new evidence from a novel hybrid deep learning model |
| author_facet |
Zhao, Yang Chen, Zhonglu |
| author_sort |
Zhao, Yang |
| title |
Forecasting stock price movement: new evidence from a novel hybrid deep learning model |
| title_short |
Forecasting stock price movement: new evidence from a novel hybrid deep learning model |
| title_full |
Forecasting stock price movement: new evidence from a novel hybrid deep learning model |
| title_fullStr |
Forecasting stock price movement: new evidence from a novel hybrid deep learning model |
| title_full_unstemmed |
Forecasting stock price movement: new evidence from a novel hybrid deep learning model |
| title_sort |
forecasting stock price movement: new evidence from a novel hybrid deep learning model |
| publisher |
University of Economics Ho Chi Minh City |
| publishDate |
2023 |
| url |
https://www.emerald.com/insight/content/doi/10.1108/JABES-05-2021-0061/full/html https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/115468 |
| _version_ |
1819821051753791488 |