Stock Market Dynamics Through Deep Learning Context
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911878459949056 |
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| author | Aminimehr, Amirhossein Aminimehr, Amin Kamali, Hamid Moradi Eetemadi, Sauleh Hoseinzade, Saeid |
| author_facet | Aminimehr, Amirhossein Aminimehr, Amin Kamali, Hamid Moradi Eetemadi, Sauleh Hoseinzade, Saeid |
| contents | Studies conducted on financial market prediction lack a comprehensive feature set that can carry a broad range of contributing factors; therefore, leading to imprecise results. Furthermore, while cooperating with the most recent innovations in explainable AI, studies have not provided an illustrative summary of market-driving factors using this powerful tool. Therefore, in this study, we propose a novel feature matrix that holds a broad range of features including Twitter content and market historical data to perform a binary classification task of one step ahead prediction. The utilization of our proposed feature matrix not only leads to improved prediction accuracy when compared to existing feature representations, but also its combination with explainable AI allows us to introduce a fresh analysis approach regarding the importance of the market-driving factors included. Thanks to the Lime interpretation technique, our interpretation study shows that the volume of tweets is the most important factor included in our feature matrix that drives the market's movements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09932 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Stock Market Dynamics Through Deep Learning Context Aminimehr, Amirhossein Aminimehr, Amin Kamali, Hamid Moradi Eetemadi, Sauleh Hoseinzade, Saeid Computational Engineering, Finance, and Science Studies conducted on financial market prediction lack a comprehensive feature set that can carry a broad range of contributing factors; therefore, leading to imprecise results. Furthermore, while cooperating with the most recent innovations in explainable AI, studies have not provided an illustrative summary of market-driving factors using this powerful tool. Therefore, in this study, we propose a novel feature matrix that holds a broad range of features including Twitter content and market historical data to perform a binary classification task of one step ahead prediction. The utilization of our proposed feature matrix not only leads to improved prediction accuracy when compared to existing feature representations, but also its combination with explainable AI allows us to introduce a fresh analysis approach regarding the importance of the market-driving factors included. Thanks to the Lime interpretation technique, our interpretation study shows that the volume of tweets is the most important factor included in our feature matrix that drives the market's movements. |
| title | Stock Market Dynamics Through Deep Learning Context |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2405.09932 |