Mispricing in leveraged value small-capitalization stocks
| Autor: | Mgr. Jan Picálek |
|---|---|
| Rok: | 2022 - letní |
| Vedoucí: | Mgr. Martin Hronec |
| Konzultant: | |
| Typ práce: | Diplomová Finance, finanční trhy a bankovnictví |
| Jazyk: | Anglicky |
| Stránky: | 83 |
| Ocenění: | Ocenění DOT Award za vynikající magisterskou práci. |
| Odkaz: | https://dspace.cuni.cz/handle/20.500.11956/173956 |
| Abstrakt: | 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 |
