Work detail

Mispricing in leveraged value small-capitalization stocks

Author: Mgr. Jan Picálek
Year: 2022 - summer
Leaders: Mgr. Martin Hronec
Consultants:
Work type: Finance, Financial Markets and Banking
Masters
Language: English
Pages: 83
Awards and prizes: Nomination Deloitte Outstanding Thesis Award.
Link: https://dspace.cuni.cz/handle/20.500.11956/173956
Abstract: We study returns in the universe of leveraged value small-capitalization stocks,
a universe with historically significant exposure to common risk factors. We separate future winners and losers within this universe of risky stocks by adopting
machine-learning-based mispricing strategy. The strategy considers 34 stocklevel characteristics to predict 1-month-ahead returns and construct a longshort portfolio accordingly. The portfolio yields abnormal risk-adjusted returns of 0.42% per month out-of-sample, uncovering statistically significant
mispricing. The machine-learning algorithm is trained on leveraged value smallcapitalization stocks, so it captures universe-specific nonlinearities and variable
interactions. The nonlinear effects and predictive power of individual variables
are extracted and presented as well. We found no evidence of a relationship
between the magnitude of the mispricing and credit cycles, or market volatility
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