TY - JOUR T1 - When Do Investors Freak Out? Machine Learning Predictions of Panic Selling JF - The Journal of Financial Data Science SP - 11 LP - 39 DO - 10.3905/jfds.2021.1.085 VL - 4 IS - 1 AU - Daniel Elkind AU - Kathryn Kaminski AU - Andrew W. Lo AU - Kien Wei Siah AU - Chi Heem Wong Y1 - 2022/01/31 UR - https://pm-research.com/content/4/1/11.abstract N2 - Using a novel dataset of 653,455 individual brokerage accounts belonging to 298,556 households, the authors document the frequency, timing, and duration of panic sales, which they define as a decline of 90% of a household account’s equity assets over the course of one month, of which 50% or more is due to trades. The authors find that a disproportionate number of households make panic sales when there are sharp market downturns, a phenomenon they call freaking out. The authors also show that panic selling and freak-outs are predictable and fundamentally different from other well-known behavioral patterns such as overtrading or the disposition effect. ER -