When Do Investors Freak Out?: Machine Learning Predictions of Panic Selling

40 Pages Posted: 9 Aug 2021

See all articles by Daniel Elkind

Daniel Elkind

affiliation not provided to SSRN

Kathryn Kaminski

Massachusetts Institute of Technology (MIT)

Andrew W. Lo

Massachusetts Institute of Technology (MIT) - Laboratory for Financial Engineering; Santa Fe Institute

Kien Wei Siah

Massachusetts Institute of Technology (MIT) - Laboratory for Financial Engineering

Chi Heem Wong

Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL); Massachusetts Institute of Technology (MIT) - Sloan School of Management

Date Written: August 4, 2021

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 wedefine as a decline of 90% of a household account’s equity assets over the course of onemonth, of which 50% or more is due to trades. We find that a disproportionate numberof households make panic sales when there are sharp market downturns, a phenomenonwe call ‘freaking out’. We show that panic selling and freakouts are predictable andfundamentally different from other well-known behavioral patterns such as overtradingor the disposition effect. Investors who are male, or above the age of 45, or married, orhave more dependents, or who self-identify as having excellent investment experience orknowledge tend to freak out with greater frequency. We use a five-layer neural networkmodel to predict freakout events one month in advance, given recent market conditionsand an investor’s demographic attributes and financial history, which exhibited truenegative 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, whilefreaking out does protect investors during a crisis, such investors often wait too longto 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

Suggested Citation

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). Available at SSRN: https://ssrn.com/abstract=3898940 or http://dx.doi.org/10.2139/ssrn.3898940

Daniel Elkind

affiliation not provided to SSRN

Kathryn Kaminski

Massachusetts Institute of Technology (MIT) ( email )

50 Memorial Drive
Cambridge, MA 02139-4307
United States

Andrew W. Lo (Contact Author)

Massachusetts Institute of Technology (MIT) - Laboratory for Financial Engineering ( email )

100 Main Street
E62-618
Cambridge, MA 02142
United States
617-253-0920 (Phone)
781 891-9783 (Fax)

HOME PAGE: http://web.mit.edu/alo/www

Santa Fe Institute

1399 Hyde Park Road
Santa Fe, NM 87501
United States

Kien Wei Siah

Massachusetts Institute of Technology (MIT) - Laboratory for Financial Engineering ( email )

Cambridge, MA
United States

Chi Heem Wong

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL) ( email )

Stata Center
Cambridge, MA 02142
United States

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
Cambridge, MA 02142
United States

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