Parameter uncertainty in multiperiod portfolio optimization with transaction costs

We study the impact of parameter uncertainty in the expected utility of a multiperiod investor subject to quadratic transaction costs. We characterize the utility loss associated with ignoring parameter uncertainty, and show that it is equal to the product between the single-period utility loss and another term that captures the effects of the multiperiod mean-variance utility and transaction cost losses. To mitigate the impact of parameter uncertainty, we propose two multiperiod shrinkage portfolios and demonstrate with simulated and empirical datasets that they substantially outperform portfolios that ignore parameter uncertainty, transaction costs, or both

Maximum Mispricing on Announcement Days

I study the role of macroeconomic announcements in the multidimensional challenge posed by Cochrane (2011). Recent work argues that market betas explain average returns well on announcement days, however I document that the exposure to a multi-signal factor contributes to explain the cross-section on those days. A long-short strategy exploiting these exposures can improve the market Sharpe ratio by 30% on announcement days. I argue that macroeconomic announcements can exacerbate CAPM-mispricing of certain stocks, and its large economic significance can be illustrated through simple trading rules. A parsimonious rational expectations model with multiple signals about fundamentals can reconcile these results

Shrinking Against Sentiment: Exploiting Behavioral Biases in Portfolio Optimization

High sentiment predicts lower market returns, higher arbitrage returns, and lower transaction costs. We propose a shrinkage methodology that exploits this empirical evidence to construct mean-variance portfolios. Exploiting the eigenvalue decomposition of the covariance matrix of stock returns, we show that mean-variance portfolio performance is the sum of two components: a market and an arbitrage component. Shrinking the sample covariance matrix toward the identity in the construction of mean-variance portfolios gives more relevance to the market component as the shrinkage intensity increases. We time the exposure to each component byshrinking more (less) when sentiment is low (high), which provides sizable economic gains even net of transaction costs

Shrinking against sentiment: Exploiting latent asset demand in portfolio selection

We examine how sentiment-driven demand, a key component of latent asset demand, can be used to build mean-variance portfolios. We decompose these portfolios into an equally weighted component and an arbitrage component that captures the asset mispricing unexplained by the equally weighted component. Our approach shrinks mean-variance portfolios toward the equally weighted component when investor sentiment is low, i.e., shrinks against sentiment, reducing estimation risk and imposing a tighter bound on the amount of asset mispricing the arbitrage component exploits. The significant economic gains offered by our approach highlight the importance of considering latent demand in building robust investment strategies