Working Paper: CEPR ID: DP9456
Authors: Victor Demiguel; Francisco J. Nogales; Raman Uppal
Abstract: We study whether investors can exploit stock return serial dependence to improve out-of- sample portfolio performance. To do this, we first show that a vector-autoregressive (VAR) model estimated with ridge regression captures daily stock return serial dependence in a stable manner. Second, we characterize (analytically and empirically) expected returns of VAR-based arbitrage portfolios, and show that they compare favorably to those of existing arbitrage portfolios. Third, we evaluate the performance of VAR-based investment (positive-cost) portfolios. We show that, subject to a suitable norm constraint, these portfolios outperform the traditional (unconditional) portfolios for transaction costs below 10 basis points.
Keywords: out-of-sample performance; portfolio choice; serial dependence; vector autoregression
JEL Codes: G11
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Today's returns (G12) | Tomorrow's expected returns (G17) |
| Past returns (G12) | Future returns (G17) |
| VAR-based arbitrage portfolios (G11) | Traditional arbitrage portfolios performance (G19) |
| Expected returns of VAR-based portfolios (G17) | Improved portfolio performance (G11) |