PT  - JOURNAL ARTICLE
AU  - Daniel Elkind
AU  - Kathryn Kaminski
AU  - Andrew W. Lo
AU  - Kien Wei Siah
AU  - Chi Heem Wong
TI  - When Do Investors Freak Out? Machine Learning Predictions of Panic Selling
AID  - 10.3905/jfds.2021.1.085
DP  - 2022 Jan 31
TA  - The Journal of Financial Data Science
PG  - 11--39
VI  - 4
IP  - 1
4099  - https://pm-research.com/content/4/1/11.short
4100  - https://pm-research.com/content/4/1/11.full
AB  - 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.