Working Paper: NBER ID: w31719
Authors: Martin Lettau
Abstract: This paper proposes a new approach to the “factor zoo” conundrum. Instead of applying dimension-reduction methods to a large set of portfolio returns obtained from sorts on characteristics, I construct factors that summarize the information in characteristics across assets and then sort assets into portfolios according to these “characteristic factors”. I estimate the model on a data set of mutual fund characteristics. Since the data set is 3-dimensional (characteristics of funds over time), characteristic factors are based on a tensor factor model (TFM) that is a generalization of 2-dimensional PCA. I find that parsimonious TFM captures over 90% of the variation in the data set. Pricing factors derived from the TFM have high Sharpe ratios and capture the cross-section of fund returns better than standard benchmark models.
Keywords: tensor factor model; factor zoo; asset pricing; mutual funds; high-dimensional data
JEL Codes: C38; G12; G0
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| tensor factor model (TFM) (F16) | variation in mutual fund characteristics (G23) |
| tensor factor model (TFM) (F16) | asset pricing performance (G19) |
| TFM factors (F16) | cross-sectional variation in fund returns (G23) |
| characteristic factors (C38) | mutual fund returns (G23) |
| TFM factors (F16) | pricing errors (D49) |