When Do Investors Freak Out? Machine Learning Predictions of Panic Selling
Name
2022_Freakout_JFDS.pdf
Description
Main article
Size
2.04 MB
Format
Adobe PDF
Checksum (MD5)
6fe12af1a65d4064f6fc172a27470afc
Author(s)
Elkind, Daniel
Kaminski, Kathryn
Lo, Andrew W.
Siah, Kien Wei
Wong, Chi Heem
Date Issued
2022
Publisher
Journal of Financial Data Science
Citation
Elkind, Daniel, Kathryn Kaminski, Andrew W. Lo, Kien Wei Siah, and Chi Heem Wong. “When Do Investors Freak Out? Machine Learning Predictions of Panic Selling.” Journal of Financial Data Science 4(1), 11–39.
Abstract
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 overtrading or the disposition effect.
Subjects
deep learning, freaking out, panic selling, stop-loss, tactical asset allocation
MIT Department
Sloan School of Management. Laboratory for Financial Engineering
Sloan School of Management
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