Related Experiment Video
Updated: Oct 1, 2025

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
Published on: September 4, 2019
Testing the Insider Trading Anomaly in FTSE-350
Jinxia Meng1, Leping Huang2, Zhou Lu3
1Jiaxing Vocational and Technical College, Jiaxing, China.
This study examines insider trading anomalies, finding that after excluding small firms, insider sales yield higher abnormal returns than purchases. The findings suggest stronger market efficiency, even under the strong-form of the efficient market hypothesis (EMH).
Area of Science:
- Financial Economics
- Market Microstructure
Background:
- Efficient Market Hypothesis (EMH) anomalies are often insignificant when controlling for the small-firm effect.
- The strong-form EMH, which posits that all information is reflected in prices, is particularly challenged by insider trading anomalies.
Purpose of the Study:
- To investigate if the insider trading anomaly persists after excluding small firms.
- To analyze insider purchase and sale patterns and their associated abnormal returns using a novel FTSE-350 dataset.
Main Methods:
- Analysis of insider trading data from the FTSE-350 index.
- Comparison of abnormal returns generated by insider purchases versus sales.
- Assessment of trading costs associated with insider transactions.
Main Results:
- Insider sales generated higher abnormal returns than insider purchases.
- Abnormal returns from insider trading were lower than previously documented in the literature.
- Trading costs associated with insider trades were also found to be lower.
Conclusions:
- The insider trading anomaly's significance is reduced when small firms are excluded.
- Market efficiency, particularly the strong-form EMH, may hold to a greater extent than previously recognized, especially for larger firms.
More Related Videos
Related Concept Videos
Quantifying and Rejecting Outliers: The Grubbs Test
Detection of Gross Error: The Q Test
Unusual Results
According to the range rule of thumb, any value above or below two standard deviations, 2σ from the mean, μ is considered unusual.
Maximum unusual value =...
Significance Testing: Overview
Testing a Claim about Standard Deviation
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Equity Theory

