Insider Purchase Signals in Microcap Equities: Gradient Boosting Detection of Abnormal Returns

TL;DR

This study uses gradient boosting to analyze SEC insider purchase filings in U.S. microcap stocks, finding that certain patterns, especially after price increases, predict abnormal returns and reflect slower market information incorporation.

Contribution

It introduces a machine learning approach to predict microcap stock returns based on insider trading signals, highlighting the importance of post-price appreciation disclosures.

Findings

Gradient boosting classifier achieves 0.70 AUC in prediction.

Price appreciation over 10% before disclosure yields higher returns.

Slower information flow in illiquid markets influences insider signal effectiveness.

Abstract

This paper examines whether SEC Form 4 insider purchase filings predict abnormal returns in U.S. microcap stocks. The analysis covers 17,237 open-market purchases across 1,343 issuers from 2018 through 2024, restricted to market capitalizations between \$30M and \$500M. A gradient boosting classifier trained on insider identity, transaction history, and market conditions at disclosure achieves AUC of 0.70 on out-of-sample 2024 data. At an optimized threshold of 0.20, precision is 0.38 and recall is 0.69. The distance from the 52-week high dominates feature importance, accounting for 36% of predictive signal. A momentum pattern emerges in the data: transactions disclosed after price appreciation exceeding 10% yield the highest mean cumulative abnormal return (6.3%) and the highest probability of outperformance (36.7%). This contrasts with the simple mean-reversion intuition often applied to post-run-up entries. The result is robust to winsorization and holds across subsamples. These patterns are consistent with slower information incorporation in illiquid markets, where trend confirmation may filter for higher-conviction insider signals.