Does Overnight News Explain Overnight Returns?

Paper: arXiv 2507.04481

Authors: Paul Glasserman, Kriste Krstovski, Paul Laliberte, Harry Mamaysky

Abstract

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.

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs not modelled; see penalty flags)
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper employs advanced NLP and statistical methods (topic modeling, supervised selection, OLS regressions with clustering) but focuses heavily on empirical backtesting with 2.4M articles and out-of-sample forecasts, making it data- and implementation-heavy rather than mathematically dense.

Research Flowchart

  flowchart TD
  A["Research Goal: <br>Explain the 'overnight premium' <br>(why stock gains occur overnight)"] --> B["Data Collection & Processing"]
  B --> C["Novel Supervised Topic Analysis"]
  C --> D["Identify Market-Explanatory Topics"]
  
  B -->|Input:| B1["2.4M News Articles"]
  B -->|Input:| B2["Stock Price Data (Overnight/Intraday)"]
  
  C -->|Method:| C1["Select topics maximizing <br>return explanation"]
  C -->|Method:| C2["Measure topic prevalence & <br>market response"]
  
  D --> E["Out-of-Sample Forecasting"]
  E --> F["Key Findings/Outcomes"]
  
  F --> F1["News explains significant <br>portion of overnight premium"]
  F --> F2["Intraday vs. Overnight <br>News patterns differ"]
  F --> F3["Topics predict specific <br>stock overnight performance"]

Cited References (40)

1. Overnight returns and firm-specific investor sentiment (2018)
2. Overnight returns, daytime reversals, and future stock returns (2022)
3. Illiquidity and stock returns: Cross-section and time-series effects (2002)
4. Price discovery and trading after hours (2003)
5. What drives momentum and reversal? Evidence from day and night signals (2022)
6. The night and day of A mihud’s (2002) liquidity measure (2021)
7. Paying attention: overnight returns and the hidden cost of buying at the open (2012)
8. A Lottery-Demand-Based Explanation of the Beta Anomaly (2017)

0 of 40 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.