Reprint of Director discretion and insider trading profitability

Abstract

Using a machine-learning algorithm, we classify over 60,000 director transactions into discretionary and non-discretionary purchases and sales based on the trading motive provided by the insider. We find that discretionary trades by company insiders are more informed than non-discretionary trades. Further, discretionary purchases generate higher abnormal returns (1) for larger purchases, or when the purchase is for (2) the stock of a smaller firm, or (3) a firm with greater information asymmetry.