Multi-source aggregated classification for stock price movement prediction
Predicting stock price movements is a challenging task. Previous studies mostly used numerical features and news sentiments of target stocks to predict stock price movements. However, their semantics-based sentiment analysis is sub-optimal to represent real market sentiments. Moreover, only consider...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/170546 |
| _version_ | 1826114766939095040 |
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| author | Ma, Yu Mao, Rui Lin, Qika Wu, Peng Cambria, Erik |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Ma, Yu Mao, Rui Lin, Qika Wu, Peng Cambria, Erik |
| author_sort | Ma, Yu |
| collection | NTU |
| description | Predicting stock price movements is a challenging task. Previous studies mostly used numerical features and news sentiments of target stocks to predict stock price movements. However, their semantics-based sentiment analysis is sub-optimal to represent real market sentiments. Moreover, only considering the information of target companies is insufficient because the stock prices of target companies can be affected by their related companies. Thus, we propose a novel Multi-source Aggregated Classification (MAC) method for stock price movement prediction. MAC incorporates the numerical features and market-driven news sentiments of target stocks, as well as the news sentiments of their related stocks. To better represent real market sentiments from the news, we pre-train an embedding feature generator by fitting the news to real stock price movements. Embeddings given by the pre-trained sentiment classifier can represent the sentiment information in vector space. Moreover, MAC introduces a graph convolutional network to capture the news effects of related companies on the target stock. Finally, MAC can predict stock price movements for the next trading day based on the aforementioned features. Extensive experiments prove that MAC outperforms state-of-the-art baselines in stock price movement prediction, Sharpe Ratio, and backtesting trading incomes. |
| first_indexed | 2024-10-01T03:44:32Z |
| format | Journal Article |
| id | ntu-10356/170546 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:44:32Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1705462023-09-19T02:40:52Z Multi-source aggregated classification for stock price movement prediction Ma, Yu Mao, Rui Lin, Qika Wu, Peng Cambria, Erik School of Computer Science and Engineering Engineering::Computer science and engineering Stock Prediction Multi-Source Aggregating Predicting stock price movements is a challenging task. Previous studies mostly used numerical features and news sentiments of target stocks to predict stock price movements. However, their semantics-based sentiment analysis is sub-optimal to represent real market sentiments. Moreover, only considering the information of target companies is insufficient because the stock prices of target companies can be affected by their related companies. Thus, we propose a novel Multi-source Aggregated Classification (MAC) method for stock price movement prediction. MAC incorporates the numerical features and market-driven news sentiments of target stocks, as well as the news sentiments of their related stocks. To better represent real market sentiments from the news, we pre-train an embedding feature generator by fitting the news to real stock price movements. Embeddings given by the pre-trained sentiment classifier can represent the sentiment information in vector space. Moreover, MAC introduces a graph convolutional network to capture the news effects of related companies on the target stock. Finally, MAC can predict stock price movements for the next trading day based on the aforementioned features. Extensive experiments prove that MAC outperforms state-of-the-art baselines in stock price movement prediction, Sharpe Ratio, and backtesting trading incomes. Agency for Science, Technology and Research (A*STAR) This research is supported by the Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme (Project #A18A2b0046). The paper is also supported by the National Natural Science Foundation of China (project numbers are 72274096 and 71774084), Program for Jiangsu Excellent Scientific and Technological Innovation Team, China (project number is [2020]10). 2023-09-19T02:40:52Z 2023-09-19T02:40:52Z 2023 Journal Article Ma, Y., Mao, R., Lin, Q., Wu, P. & Cambria, E. (2023). Multi-source aggregated classification for stock price movement prediction. Information Fusion, 91, 515-528. https://dx.doi.org/10.1016/j.inffus.2022.10.025 1566-2535 https://hdl.handle.net/10356/170546 10.1016/j.inffus.2022.10.025 2-s2.0-85141923315 91 515 528 en A18A2b0046 Information Fusion © 2022 Elsevier B.V. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Stock Prediction Multi-Source Aggregating Ma, Yu Mao, Rui Lin, Qika Wu, Peng Cambria, Erik Multi-source aggregated classification for stock price movement prediction |
| title | Multi-source aggregated classification for stock price movement prediction |
| title_full | Multi-source aggregated classification for stock price movement prediction |
| title_fullStr | Multi-source aggregated classification for stock price movement prediction |
| title_full_unstemmed | Multi-source aggregated classification for stock price movement prediction |
| title_short | Multi-source aggregated classification for stock price movement prediction |
| title_sort | multi source aggregated classification for stock price movement prediction |
| topic | Engineering::Computer science and engineering Stock Prediction Multi-Source Aggregating |
| url | https://hdl.handle.net/10356/170546 |
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