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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.10449 |
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| _version_ | 1866909205821128704 |
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| author | Ardia, David Bluteau, Keven |
| author_facet | Ardia, David Bluteau, Keven |
| contents | We propose an approach to construct text-based time-series indices in an optimal way--typically, indices that maximize the contemporaneous relation or the predictive performance with respect to a target variable, such as inflation. We illustrate our methodology with a corpus of news articles from the Wall Street Journal by optimizing text-based indices focusing on tracking the VIX index and inflation expectations. Our results highlight the superior performance of our approach compared to existing indices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10449 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimal Text-Based Time-Series Indices Ardia, David Bluteau, Keven Econometrics Artificial Intelligence Computational Finance We propose an approach to construct text-based time-series indices in an optimal way--typically, indices that maximize the contemporaneous relation or the predictive performance with respect to a target variable, such as inflation. We illustrate our methodology with a corpus of news articles from the Wall Street Journal by optimizing text-based indices focusing on tracking the VIX index and inflation expectations. Our results highlight the superior performance of our approach compared to existing indices. |
| title | Optimal Text-Based Time-Series Indices |
| topic | Econometrics Artificial Intelligence Computational Finance |
| url | https://arxiv.org/abs/2405.10449 |