1 option
Complexity in Factor Pricing Models / Antoine Didisheim, Shikun (Barry) Ke, Bryan T. Kelly, Semyon Malamud.
- Format:
- Book
- Author/Creator:
- Didisheim, Antoine.
- Series:
- Working Paper Series (National Bureau of Economic Research) no. w31689.
- NBER working paper series no. w31689
- Language:
- English
- Physical Description:
- 1 online resource: illustrations (black and white);
- Place of Publication:
- Cambridge, Mass. National Bureau of Economic Research 2023.
- Summary:
- We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance--in terms of SDF Sharpe ratio and test asset pricing errors--is improving in model parameterization (or "complexity"). Our empirical findings verify the theoretically predicted "virtue of complexity" in the cross-section of stock returns. Models with an extremely large number of factors (more than the number of training observations or base assets) outperform simpler alternatives by a large margin.
- Notes:
- Print version record
- September 2023.
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.