Causality-Inspired Models for Financial Time Series Forecasting
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910570313154560 |
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| author | Oliveira, Daniel Cunha Lu, Yutong Lin, Xi Cucuringu, Mihai Fujita, Andre |
| author_facet | Oliveira, Daniel Cunha Lu, Yutong Lin, Xi Cucuringu, Mihai Fujita, Andre |
| contents | We introduce a novel framework to financial time series forecasting that leverages causality-inspired models to balance the trade-off between invariance to distributional changes and minimization of prediction errors. To the best of our knowledge, this is the first study to conduct a comprehensive comparative analysis among state-of-the-art causal discovery algorithms, benchmarked against non-causal feature selection techniques, in the application of forecasting asset returns. Empirical evaluations demonstrate the efficacy of our approach in yielding stable and accurate predictions, outperforming baseline models, particularly in tumultuous market conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09960 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Causality-Inspired Models for Financial Time Series Forecasting Oliveira, Daniel Cunha Lu, Yutong Lin, Xi Cucuringu, Mihai Fujita, Andre Computational Finance We introduce a novel framework to financial time series forecasting that leverages causality-inspired models to balance the trade-off between invariance to distributional changes and minimization of prediction errors. To the best of our knowledge, this is the first study to conduct a comprehensive comparative analysis among state-of-the-art causal discovery algorithms, benchmarked against non-causal feature selection techniques, in the application of forecasting asset returns. Empirical evaluations demonstrate the efficacy of our approach in yielding stable and accurate predictions, outperforming baseline models, particularly in tumultuous market conditions. |
| title | Causality-Inspired Models for Financial Time Series Forecasting |
| topic | Computational Finance |
| url | https://arxiv.org/abs/2408.09960 |