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Related Experiment Video

Updated: Jul 30, 2025

Measuring Relative Insulin Secretion using a Co-Secreted Luciferase Surrogate
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Insider trading.

Attila Balogh1

  • 1Department of Finance, University of Melbourne, Melbourne, 3010, Australia. balogh@unimelb.edu.au.

Scientific Data
|May 15, 2023
PubMed
Summary
This summary is machine-generated.

This study presents a new dataset of insider trading activity, sourced directly from regulatory filings. This offers a transparent and reliable resource for investors and analysts studying corporate finance.

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Area of Science:

  • Finance
  • Economics
  • Data Science

Background:

  • Insider trading data is crucial for investors and analysts.
  • Executives' trades may signal non-public information and predict stock movements.
  • Current academic practices often rely on opaque commercial databases, hindering replication.

Purpose of the Study:

  • To introduce a novel dataset of insider trading activity.
  • To provide a transparent and replicable data source derived from original regulatory filings.
  • To enhance the study of corporate finance and market behavior.

Main Methods:

  • Data collected directly from original Securities and Exchange Commission (SEC) filings.
  • Dataset updated daily to reflect the latest insider trading reports.
  • Inclusion of all reported insider information without alteration.

Main Results:

  • A comprehensive dataset of insider trading activity is now available.
  • The dataset overcomes limitations of proprietary databases, ensuring data integrity.
  • Facilitates direct analysis of regulatory filings for academic research.

Conclusions:

  • The new dataset enhances transparency and replicability in financial research.
  • Provides a valuable tool for understanding insider behavior and its market impact.
  • Supports more accurate analysis of corporate finance and investment strategies.