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
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
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.