On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2023
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910295008477184 |
|---|---|
| author | Dong, Xinshuai Dai, Haoyue Fan, Yewen Jin, Songyao Rajendran, Sathyamoorthy Zhang, Kun |
| author_facet | Dong, Xinshuai Dai, Haoyue Fan, Yewen Jin, Songyao Rajendran, Sathyamoorthy Zhang, Kun |
| contents | Financial data is generally time series in essence and thus suffers from three fundamental issues: the mismatch in time resolution, the time-varying property of the distribution - nonstationarity, and causal factors that are important but unknown/unobserved. In this paper, we follow a causal perspective to systematically look into these three demons in finance. Specifically, we reexamine these issues in the context of causality, which gives rise to a novel and inspiring understanding of how the issues can be addressed. Following this perspective, we provide systematic solutions to these problems, which hopefully would serve as a foundation for future research in the area. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05414 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors Dong, Xinshuai Dai, Haoyue Fan, Yewen Jin, Songyao Rajendran, Sathyamoorthy Zhang, Kun Statistical Finance Machine Learning Methodology Financial data is generally time series in essence and thus suffers from three fundamental issues: the mismatch in time resolution, the time-varying property of the distribution - nonstationarity, and causal factors that are important but unknown/unobserved. In this paper, we follow a causal perspective to systematically look into these three demons in finance. Specifically, we reexamine these issues in the context of causality, which gives rise to a novel and inspiring understanding of how the issues can be addressed. Following this perspective, we provide systematic solutions to these problems, which hopefully would serve as a foundation for future research in the area. |
| title | On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors |
| topic | Statistical Finance Machine Learning Methodology |
| url | https://arxiv.org/abs/2401.05414 |