Modeling News Interactions and Influence for Financial Market Prediction
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929541235081216 |
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| author | Wang, Mengyu Cohen, Shay B. Ma, Tiejun |
| author_facet | Wang, Mengyu Cohen, Shay B. Ma, Tiejun |
| contents | The diffusion of financial news into market prices is a complex process, making it challenging to evaluate the connections between news events and market movements. This paper introduces FININ (Financial Interconnected News Influence Network), a novel market prediction model that captures not only the links between news and prices but also the interactions among news items themselves. FININ effectively integrates multi-modal information from both market data and news articles. We conduct extensive experiments on two datasets, encompassing the S&P 500 and NASDAQ 100 indices over a 15-year period and over 2.7 million news articles. The results demonstrate FININ's effectiveness, outperforming advanced market prediction models with an improvement of 0.429 and 0.341 in the daily Sharpe ratio for the two markets respectively. Moreover, our results reveal insights into the financial news, including the delayed market pricing of news, the long memory effect of news, and the limitations of financial sentiment analysis in fully extracting predictive power from news data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10614 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modeling News Interactions and Influence for Financial Market Prediction Wang, Mengyu Cohen, Shay B. Ma, Tiejun Computational Engineering, Finance, and Science Artificial Intelligence Computation and Language Computational Finance The diffusion of financial news into market prices is a complex process, making it challenging to evaluate the connections between news events and market movements. This paper introduces FININ (Financial Interconnected News Influence Network), a novel market prediction model that captures not only the links between news and prices but also the interactions among news items themselves. FININ effectively integrates multi-modal information from both market data and news articles. We conduct extensive experiments on two datasets, encompassing the S&P 500 and NASDAQ 100 indices over a 15-year period and over 2.7 million news articles. The results demonstrate FININ's effectiveness, outperforming advanced market prediction models with an improvement of 0.429 and 0.341 in the daily Sharpe ratio for the two markets respectively. Moreover, our results reveal insights into the financial news, including the delayed market pricing of news, the long memory effect of news, and the limitations of financial sentiment analysis in fully extracting predictive power from news data. |
| title | Modeling News Interactions and Influence for Financial Market Prediction |
| topic | Computational Engineering, Finance, and Science Artificial Intelligence Computation and Language Computational Finance |
| url | https://arxiv.org/abs/2410.10614 |