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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2507.04481 |
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| _version_ | 1866911041965785088 |
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| author | Glasserman, Paul Krstovski, Kriste Laliberte, Paul Mamaysky, Harry |
| author_facet | Glasserman, Paul Krstovski, Kriste Laliberte, Paul Mamaysky, Harry |
| contents | Over the past 30 years, nearly all the gains in the U.S. stock market have been earned overnight, while average intraday returns have been negative or flat. We find that a large part of this effect can be explained through features of intraday and overnight news. Our analysis uses a collection of 2.4 million news articles. We apply a novel technique for supervised topic analysis that selects news topics based on their ability to explain contemporaneous market returns. We find that time variation in the prevalence of news topics and differences in the responses to news topics both contribute to the difference in intraday and overnight returns. In out-of-sample tests, our approach forecasts which stocks will do particularly well overnight and particularly poorly intraday. Our approach also helps explain patterns of continuation and reversal in intraday and overnight returns. We contrast the effect of news with other mechanisms proposed in the literature to explain overnight returns. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04481 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Does Overnight News Explain Overnight Returns? Glasserman, Paul Krstovski, Kriste Laliberte, Paul Mamaysky, Harry Trading and Market Microstructure Computation and Language Machine Learning Over the past 30 years, nearly all the gains in the U.S. stock market have been earned overnight, while average intraday returns have been negative or flat. We find that a large part of this effect can be explained through features of intraday and overnight news. Our analysis uses a collection of 2.4 million news articles. We apply a novel technique for supervised topic analysis that selects news topics based on their ability to explain contemporaneous market returns. We find that time variation in the prevalence of news topics and differences in the responses to news topics both contribute to the difference in intraday and overnight returns. In out-of-sample tests, our approach forecasts which stocks will do particularly well overnight and particularly poorly intraday. Our approach also helps explain patterns of continuation and reversal in intraday and overnight returns. We contrast the effect of news with other mechanisms proposed in the literature to explain overnight returns. |
| title | Does Overnight News Explain Overnight Returns? |
| topic | Trading and Market Microstructure Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2507.04481 |