A systematic equity fund wants to build a signal from SEC Form 4 insider transaction filings. Their data science team is debating whether to use raw dollar transaction amounts or a normalized metric. Which approach to signal construction produces the most robust predictive alpha, and what confound must be controlled for?
Select an answer to reveal the explanation.
Short Explanation and Infographic
A CEO buying $100,000 of stock in a $2 trillion company is the equivalent of you buying a vending machine snack — it means almost nothing. But a CEO who doubles their personal stake in an open-market transaction with no 10b5-1 plan in place? That's someone putting real skin in the game, and that signal is genuinely informative.
Full explanation below image
Full Explanation
Option B is correct because effective insider signal construction requires both normalization and transaction-type segmentation. Normalizing by existing holdings (the 'conviction ratio') adjusts for the wealth effect — a $500,000 purchase means far more when it represents 15% of an insider's net worth in company stock than when it represents 0.1%. Academic research by Lakonishok and Lee (2001) and Jeng, Metrick, and Zeckhauser (2003) consistently finds that the most predictive insider transactions are those where the purchase represents a meaningful fraction of existing holdings.
The 10b5-1 plan distinction is equally important. Pre-scheduled trading plans allow insiders to trade on a fixed schedule established months in advance during an open window, mechanically executing regardless of subsequent developments. These trades carry no informational content about current business conditions. In contrast, open-market discretionary purchases in the absence of an existing 10b5-1 plan represent genuine expressions of insider optimism and have significantly stronger predictive power for future returns.
Option expiration-driven sales (conversions of in-the-money options into shares and immediate sale) are mechanically motivated and should be excluded entirely from the signal. Failure to exclude them inflates the apparent sell-signal count and suppresses the historically observed asymmetry where purchases outperform sales as predictors.
Option A's dollar-amount threshold is an imprecise proxy for conviction that penalizes legitimate high-conviction buys by insiders at small-cap companies. Option C's breadth signal has some validity in the literature but ignores the normalization problem — ten insiders each buying trivially small amounts in relative terms do not create the same signal as one insider making a concentrated open-market purchase. Option D's equal-weighting across transaction types is precisely what erodes the signal, as extensively documented in the insider trading literature.