Investors who are male, or above the age of 45, or married, or have more dependents, or who self-identify as having excellent investment experience or knowledge tend to freak out with greater frequency

Elkind, Daniel and Kaminski, Kathryn and Lo, Andrew W. and Siah, Kien Wei and Wong, Chi Heem, When Do Investors Freak Out?: Machine Learning Predictions of Panic Selling (August 4, 2021). SSRN: http://dx.doi.org/10.2139/ssrn.3898940

Abstract: Despite standard investment advice to the contrary, individuals often engage in panic selling, liquidating significant portions of their risky assets in response to large losses. Using a novel dataset of 653,455 individual brokerage accounts belonging to 298,556 households, we document the frequency, timing, and duration of panic sales, which we 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. We find that a disproportionate number of households make panic sales when there are sharp market downturns, a phenomenon we call ‘freaking out’. We show that panic selling and freak outs are predictable and fundamentally different from other well-known behavioral patterns such as over trading or the disposition effect. Investors who are male, or above the age of 45, or married, or have more dependents, or who self-identify as having excellent investment experience or knowledge tend to freak out with greater frequency. We use a five-layer neural network model to predict freak out events one month in advance, given recent market conditions and an investor’s demographic attributes and financial history, which exhibited true negative and positive accuracy rates of 81.5% and 69.5%, respectively, in an out-of-sample test set. We measure the opportunity of cost of panic sales and find that, while freaking out does protect investors during a crisis, such investors often wait too long to reinvest, causing them to miss out on significant profits when markets rebound.

Keywords: Panic Selling; Stop-Loss; Tactical Asset Allocation; Freaking Out; Deep Learning; Behavioral Finance

JEL Classification: G11, G01, G02, D14, D91