Working Papers

[5] "Subjective Beliefs and the Portfolio Allocations of Institutional Investors" (with Aleksandar Andonov, Andrei Goncalves,   Spencer Couts and Andrea Rossi)

We study how subjective beliefs shape the portfolio allocations of institutional investors. Linking the multi-asset allocations of U.S. public pension funds to the long-term capital market assumptions of their consultants, we examine the extent to which differences in subjective expected returns, volatilities, and correlations map into differences in portfolio weights. We embed these belief inputs in a mean-variance framework that incorporates fund-consultant belief wedges, heterogeneous risk aversion, non-negative weight constraints, and a benchmarking incentive due to frictions. We find that pension fund allocations are significantly linked to belief-implied mean-variance efficient allocations across pension funds, across asset classes, and over time. Accounting for frictions is essential: it dramatically increases the pass through and explanatory power of beliefs to portfolio allocations. Overall, our results show that beliefs play a central role in institutional portfolio decisions and that frictions critically shape their transmission into observed allocations.

[4] "A Tale of Two Market Returns: The Broad Market Factor and the Idiosyncratic Financial Factor" (with Sung Je Byun and Lawrence Schmidt)

We construct a Broad Market Factor (BMF), which is a proxy for the value-weighted equity return on all firms in the US economy (public and private). The BMF differs from the standard Value-weighted Market Factor (VMF), which reflects the value-weighted equity return on public firms. We define the difference between the VMF and the BMF to be the Idiosyncratic Financial Factor (IFF). The IFF carries no risk premium and is uncorrelated with all macroeconomic proxies for investor marginal utility we consider. CAPM betas and, consequently, discount rates are underestimated when measured with respect to the VMF compared to the BMF for most portfolios. Size factors become redundant and the size anomaly is resolved when the VMF is replaced by the BMF in standard factor models. The intertemporal risk-return relation is substantially stronger when one replaces the VMF with the BMF. The unifying explanation for these results is that the IFF adds unpriced risk to the VMF, distorting both cross-sectional and time-series estimates of exposure to priced market risk.

[3] "Institutional Investors' Subjective Risk Premia: Time Variation and Disagreement" (with Spencer Couts, Andrei Goncalves, and Yicheng Liu)

In this paper, we study the role of subjective risk premia in explaining subjective expected return time variation and disagreement using the long-term Capital Market Assumptions of major asset managers and investment consultants from 1987 to 2022. We find that market risk premia explain most of the expected return time variation, with the rest explained by alphas. The risk premia effect is almost entirely driven by time variation in risk quantities as opposed to risk price. Nevertheless, risk price explains about half of the transitory effect of risk premia on expected returns. Market risk premia also explain most of the expected return disagreement, but in this case alphas have a quantitatively significant effect, and risk price and risk quantities are roughly equally responsible for the risk premia effect. Our results provide benchmark moments that asset pricing models should match to be consistent with institutional investors' beliefs.

[2] "Out-of-Sample Alphas Post-Publication"  (with Andrei Goncalves and Richard Ogden)

Anomaly strategies generate positive and significant CAPM alphas post-publication. Existing explanations include non-market risks, trading costs, and investment frictions. This paper introduces a complementary and novel channel: when a new anomaly strategy is published, investors face uncertainty in identifying the optimal weight to allocate to the anomaly in order to achieve a positive alpha post-publication, making the strategy less appealing. Empirically, we find that the average post-publication alpha of anomaly strategies is close to zero when optimal weights are estimated out-of-sample using pre-publication data. This finding is robust across specifications, including those using empirical Bayesian shrinkage and machine learning to estimate weights. Conceptually, this suggests investors have little incentive to add a new anomaly strategy to their portfolios. While investors can generate positive out-of-sample alphas by combining multiple anomaly strategies via shrinkage methods, we show the demand from such investors is insufficient to eliminate alphas in equilibrium.

[1] "The Subjective Risk and Return Expectations of Institutional Investors" (with Spencer Couts and Andrei Goncalves)

We use the long-term Capital Market Assumptions of major asset managers and institutional investor consultants from 1987 to 2022 to study their subjective risk and return expectations across 19 asset classes. These beliefs demonstrate a strong positive risk-return tradeoff, with the vast majority of variability in expected returns coming from subjective risk premia (market beta compensation) rather than subjective alphas. Belief variation and the risk-return tradeoff are stronger across asset classes than across institutions. Also, subjective expected returns predict future realized returns across asset classes and over time, with most of this predictability driven by subjective risk premia, not alphas.