Stock Return Serial Dependence and Out-of-Sample Portfolio Performance

• 成果类型: Article
• 署名作者: DeMiguel, Victor; Nogales, Francisco J.; Uppal, Raman
• 署名单位: University of London; London Business School; Universidad Carlos III de Madrid; Universite Catholique de Lille; EDHEC Business School
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhu002
• 发表日期: 2014
• 页码: 1031
• 关键词: cross-autocorrelations; MARKET; INVESTMENT; momentum; predictability; optimization; selection; MODEL; cost

摘要

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.