Stock Return Serial Dependence and Out-of-Sample Portfolio Performance
研究了投资者能否利用股票收益的序列依赖来提升样本外投资组合表现，发现基于向量自回归模型的套利组合在交易成本低于10个基点时优于传统无条件组合。
We study whether investors can exploit serial dependence in stock returns to improve out-of-sample portfolio performance. We show that a vector-autoregressive (VAR) model captures stock return serial dependence in a statistically significant manner. Analytically, we demonstrate that, unlike contrarian and momentum portfolios, an arbitrage portfolio based on the VAR model attains positive expected returns regardless of the sign of asset return cross-covariances and autocovariances. Empirically, we show, however, that both the arbitrage and mean-variance portfolios based on the VAR model outperform the traditional unconditional portfolios only for transaction costs below ten basis points.