Market Return Around the Clock: A Puzzle

  • Oleg Bondarenko and Dmitriy Muravyev
  • Working Paper
  • A version of this paper can be found here
  • Want to read our summaries of academic finance papers? Check out our Academic Research Insight category

What are the research questions?

Get your popcorn ready, the quants are about to do battle…

As with all good questions in academic research, there are two sides to the story. We recently published Matthew Bartolini’s blog post explaining the impacts of trading costs on the “Overnight Return Anomaly.” This paper, takes the opposing view, providing evidence that equity market returns are positive overnight and close to zero during regular trading hours. To explain this puzzling fact, the authors study how the excess market return depends on the time of the day using E-mini S&P 500 futures with tick-level data from 2004 to 2018. These futures are actively traded for almost 24 hours: the bid-ask spread is almost always one tick, and trading volume is $2.4 billion per hour during the Asian session.

What are the Academic Insights?

The authors document several stylized facts about high-frequency returns:

  1. The four hours around the Asian markets’ close and the European open account for 100% of average market return: a 7.6% annualized return with a Sharpe ratio of 1.67 and a t-statistic of 6.4. The authors call this period between 11:30 pm and 3:30 am ET – “EU-open.” The average return for the rest of the day is slightly negative: -0.8%.

  2. EU-open returns are extremely robust:
    • They are positive in every year, every calendar month and weekday.

    • Returns computed with trade prices and quote midpoints are nearly identical.
    • If the sample is split in half, a trajectory for cumulative returns during EU-open and the rest of the overnight session are almost identical.
    • The Bonferroni procedure and other statistical tests rule out data mining.
    • The Sharpe ratio around the European open is several times higher than during any other one-hour period.
  3. The authors suggest a simple trading strategy to earn market returns: buy futures right before EU-open and close position after its end. This trading strategy:
    • is profitable net of conservative estimates of trading costs.
    • has enough capacity to earn $50 million per year.
    • can be greatly improved if one only trades after VIX and Asian volatility increases.
    • this timing strategy has a high out-of-sample R2 and a Sharpe ratio of 1.2 after costs.

  4. The authors also consider VIX futures and find that their returns are positive during the Asian session, highly negative around European open, and mildly negative during the rest of the day. VIX changes are often used as a proxy for price uncertainty; thus, the uncertainty decreases during EU-open.

  5. The authors attribute high returns around European open to the uncertainty resolution: European investors help process information accumulated during Asian trading hours. They also rule out several alternative explanations: news arrival, data mining, price pressure/inventory risk, carrying costs, and investor sentiment.

Why does it matter?

  1. These results help us better understand when and why equity returns are positive.
  2. The disappearing alpha in conventional anomalies pushes investors to look for new strategies that often involve frequent trading. The authors suggest a promising trading strategy to exploit positive EU-open returns.
  3. Long term investors can also benefit from positive EU-open returns by timing their purchases before EU-open (and sales after its end).
  4. The overnight session is becoming increasingly important. The average daily volume for E-mini S&P 500 futures doubled in 2020!!

The most important charts from the paper.

Annualized average cumulative log returns for S&P 500 E-mini futures as a function of time of the day. Line arrows denote open and close times for major equity markets (Japan, China, Hong Kong, Frankfurt, London, and the US). The market is mostly closed from 4:15 pm to 6:00 pm. EU-open period is in grey. The time of day is in Eastern Time (ET). Annualized return equals period return times 252, the number of trading days in a year.

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index.

Cumulative market return for EU-open (blue) period and the rest of the day (RoD, green). Vertical dashed lines indicate the 2008 financial crisis.

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index.

Abstract

We study how the excess market return depends on the time of the day using E-mini S&P 500 futures that are actively traded for almost 24 hours. Strikingly, four hours around Asian markets’ close and European open account for the entire average market return. This period’s Sharpe ratio is extra-high as overnight volatility is low. Its returns are positive in every year and survive transaction costs. Remarkably, average returns are zero during the remaining 20 hours and almost all sub-intervals. We attribute high returns around European open to the uncertainty resolution as European investors help process information accumulated during Asian trading hours. Consistent with this hypothesis, VIX future returns are positive during the Asian session and highly negative around European open.

About the Author: Dmitriy Muravyev

Dmitriy Muravyev
Dmitriy Muravyev is an Associate Professor of Finance at the Eli Broad College of Business, Michigan State University. His research focuses on using derivative securities and big data methods to answer important questions in financial economics. His research has been published in leading academic journals including the Journal of Finance, the Review of Financial Studies, the Journal of Financial Economics, Management Science, and the Journal of Financial and Quantitative Analysis. Professor Muravyev received his Ph.D. in Finance from the University of Illinois at Urbana-Champaign. He also holds an M.Sc. in applied mathematics from Moscow State University and an M.A. in economics from the New Economic School. In his previous life, he was a competitive chess player.

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For informational and educational purposes only and should not be construed as specific investment, accounting, legal, or tax advice. Certain information is deemed to be reliable, but its accuracy and completeness cannot be guaranteed. Third party information may become outdated or otherwise superseded without notice.  Neither the Securities and Exchange Commission (SEC) nor any other federal or state agency has approved, determined the accuracy, or confirmed the adequacy of this article.

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