Option Return Predictability with Machine Learning and Big Data
Journal
Review of Financial Studies
ISSN
0893-9454
Type
journal article
Date Issued
2023-02
Author(s)
Abstract
Drawing upon more than 12 million observations over the period from 1996 to 2020,
we nd that allowing for nonlinearities signi cantly increases the out-of-sample
performance of option and stock characteristics in predicting future option returns.
The nonlinear machine learning models generate statistically and economically sizeable
pro ts in the long-short portfolios of equity options even after accounting for
transaction costs. Although option-based characteristics are the most important
standalone predictors, stock-based measures o er substantial incremental predictive
power when considered alongside option-based characteristics. Finally, we provide
compelling evidence that option return predictability is driven by informational
frictions and option mispricing.
we nd that allowing for nonlinearities signi cantly increases the out-of-sample
performance of option and stock characteristics in predicting future option returns.
The nonlinear machine learning models generate statistically and economically sizeable
pro ts in the long-short portfolios of equity options even after accounting for
transaction costs. Although option-based characteristics are the most important
standalone predictors, stock-based measures o er substantial incremental predictive
power when considered alongside option-based characteristics. Finally, we provide
compelling evidence that option return predictability is driven by informational
frictions and option mispricing.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
None
Refereed
Yes
Publisher
Oxford University Press
Volume
36
Number
9
Start page
3548
End page
3602
Subject(s)
Eprints ID
264255
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open.access
Name
SSRN-id3895984-2.pdf
Size
2.84 MB
Format
Adobe PDF
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