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Using the January Effect to Demonstrate an Event Study

Article Quant Q&A · Author: Constantin
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Summary

The document asks for a stock market anomaly that is straightforward to reproduce in an event study. The motivating constraint is that some candidate anomalies, such as post-earnings-announcement drift, are harder to demonstrate because company events occur on different dates. A calendar-based effect offers a simpler setup: researchers can align observations across stocks using the same recurring dates.

The response recommends the January effect, since the relevant calendar window is shared across multiple stocks. That makes it convenient for a basic demonstration of event-study design and cross-sectional comparison. The note gives no sample definition, return window, dataset, statistical test, or empirical results, so it does not establish that the effect is present or persistent. Researchers would still need to specify the event window and test whether any observed returns survive appropriate controls and statistical scrutiny.

Key ideas

  • A useful demonstration anomaly should have event dates that align across securities.
  • Company earnings announcements complicate event studies because their dates vary.
  • The January effect is suggested because the calendar timing can be shared across stocks.
  • The response proposes a simple example but supplies no data, test design, or evidence that the anomaly persists.

Tags

EquitiesEvent-drivenBacktestingStatistics
Full text
# Which anomalies are easy to replicate in an event study?


# Which anomalies are easy to replicate in an event study?












In order to examine different approaches to event studies, I am looking for a market anomaly which is simple to replicate in an event study for demonstration purposes. For example, post-earnings-announcemet drift is made tricky by the different points in time over the year at which companies disclose their earnings. I am thus looking for an anomaly based on a stock characteristic that is available at a frequency common for price data (i.e. daily, monthly etc.).

## Answer by QuantK (score 2)

https://quant.stackexchange.com/a/16179

The January effect is easy to demonstrate. Always the same dates for multiple shares.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.