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

V. DeMiguel, F. Nogales, R. Uppal
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引用次数: 134

Abstract

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.
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股票收益序列依赖与样本外投资组合绩效
我们研究投资者是否可以利用股票收益的序列依赖来改善样本外投资组合的绩效。我们证明了向量自回归(VAR)模型以统计显著的方式捕获股票收益序列依赖性。通过分析,我们证明,与逆向投资组合和动量投资组合不同,基于VAR模型的套利投资组合无论资产收益交叉协方差和自协方差的符号如何,都能获得正的预期收益。然而,我们的经验表明,套利和基于VAR模型的均值-方差投资组合只有在交易成本低于10个基点时才优于传统的无条件投资组合。
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