We explicitly consider financial leverage in a simple equity valuation model and study the cross-sectional implications of potential shareholder recovery upon resolution of financial distress. Our ...model is capable of simultaneously explaining lower returns for financially distressed stocks, stronger book-to-market effects for firms with high default likelihood, and the concentration of momentum profits among low credit quality firms. The model further predicts (i) a hump-shaped relationship between value premium and default probability, and (ii) stronger momentum profits for nearly distressed firms with significant prospects for shareholder recovery. Our empirical analysis strongly confirms these novel predictions.
We develop a model for an investor with multiple priors and aversion to ambiguity. We characterize the multiple priors by a "confidence interval" around the estimated expected returns and we model ...ambiguity aversion via a minimization over the priors. Our model has several attractive features: (1) it has a solid axiomatic foundation; (2) it is flexible enough to allow for different degrees of uncertainty about expected returns for various subsets of assets and also about the return-generating model; and (3) it delivers closed-form expressions for the optimal portfolio. Our empirical analysis suggests that, compared with portfolios from classical and Bayesian models, ambiguity-averse portfolios are more stable over time and deliver a higher out-of sample Sharpe ratio.
We provide a general framework for finding portfolios that perform well out-of-sample in the presence of estimation error. This framework relies on solving the traditional minimum-variance problem ...but subject to the additional constraint that the norm of the portfolio-weight vector be smaller than a given threshold. We show that our framework nests as special cases the shrinkage approaches of Jagannathan and Ma (Jagannathan, R., T. Ma. 2003. Risk reduction in large portfolios: Why imposing the wrong constraints helps. J. Finance 58 1651–1684) and Ledoit and Wolf (Ledoit, O., M. Wolf. 2003. Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. J. Empirical Finance 10 603–621, and Ledoit, O., M. Wolf. 2004. A well-conditioned estimator for large-dimensional covariance matrices. J. Multivariate Anal. 88 365–411) and the 1/ N portfolio studied in DeMiguel et al. (DeMiguel, V., L. Garlappi, R. Uppal. 2009. Optimal versus naive diversification: How inefficient is the 1/ N portfolio strategy? Rev. Financial Stud. 22 1915–1953). We also use our framework to propose several new portfolio strategies. For the proposed portfolios, we provide a moment-shrinkage interpretation and a Bayesian interpretation where the investor has a prior belief on portfolio weights rather than on moments of asset returns. Finally, we compare empirically the out-of-sample performance of the new portfolios we propose to 10 strategies in the literature across five data sets. We find that the norm-constrained portfolios often have a higher Sharpe ratio than the portfolio strategies in Jagannathan and Ma (2003), Ledoit and Wolf (2003, 2004), the 1/ N portfolio, and other strategies in the literature, such as factor portfolios.
We develop a model of portfolio choice to nest the views of Keynes, who advocates concentration in a few familiar assets, and Markowitz, who advocates diversification. We use the concepts of ...ambiguity and ambiguity aversion to formalize the idea of an investor's "familiarity" toward assets. The model shows that for any given level of expected returns, the optimal portfolio depends on two quantities: relative ambiguity across assets and the standard deviation of the expected return estimate for each asset. If both quantities are low, then the optimal portfolio consists of a mix of familiar and unfamiliar assets; moreover, an increase in correlation between assets causes an investor to increase concentration in familiar assets (flight to familiarity). Alternatively, if both quantities are high, then the optimal portfolio contains only the familiar asset(s), as Keynes would have advocated. In the extreme case in which both quantities are very high, no risky asset is held (nonparticipation).
This paper was accepted by Brad Barber, Teck Ho, and Terrance Odean, special issue editors.
This paper studies whether house prices reflect belief differences about climate change. We show that in an equilibrium model of housing choice in which agents derive utility from ownership in a ...neighborhood of similar agents, prices exhibit different elasticities to climate risk. We use comprehensive transaction data to relate prices to inundation projections of individual homes and measures of beliefs about climate change. We find that houses projected to be underwater in believer neighborhoods sell at a discount compared to houses in denier neighborhoods. Our results suggest that house prices reflect heterogeneity in beliefs about long-run climate change risks.
We evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1/N portfolio. Of the 14 models we ...evaluate across seven empirical datasets, none is consistently better than the 1/N rule in terms of Sharpe ratio, certainty-equivalent return, or turnover, which indicates that, out of sample, the gain from optimal diversification is more than offset by estimation error. Based on parameters calibrated to the US equity market, our analytical results and simulations show that the estimation window needed for the sample-based mean-variance strategy and its extensions to outperform the 1/N benchmark is around 3000 months for a portfolio with 25 assets and about 6000 months for a portfolio with 50 assets. This suggests that there are still many "miles to go" before the gains promised by optimal portfolio choice can actually be realized out of sample.
Capital utilization and market power crucially affect asset prices in an economy exposed to shocks that improve real investment opportunities through capital-embodied technological innovations. We ...embed these two mechanisms in a standard general equilibrium model and show that (i) the price of risk for investment shocks is negative under fixed capital utilization, but positive under sufficiently flexible capital utilization, and (ii) the equity return exposure to investment shocks is negative under perfect competition, but positive under high market power. We further show that, high market power, persistent components in technology growth, and a strong preference for early resolution of uncertainty are jointly important to quantitatively match the observed equity risk premium.
Using two macro-based measures and one return-based measure of investment-specific technology (IST) shocks, we find that over the 1964–2012 period, exposure to IST shocks cannot explain ...cross-sectional return spreads based on book-to-market, momentum, asset growth, net share issues, accrual, and price-to-earnings ratio. Only one of the two macro-based measures can explain a sizable portion of the value premium over the longer 1930–2012 period. We also find that the IST risk premium estimates are sensitive to the sample period, the data frequency, the test assets, and the econometric model specification. Impulse responses of aggregate investment and consumption indicate potential measurement problems in IST proxies, which may contribute to the sensitivity of IST risk premium estimates and the failure of IST shocks to explain cross-sectional returns.
This paper was accepted by Neng Wang, finance
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We propose a new approach, based on investment data, to determine firms’ return exposure to investment-specific technology (IST) shocks. When applied to U.S. data, we find that, in contrast to the ...pattern estimated from empirical IST proxies, value firms have higher exposure to IST shocks than growth firms. When applied to simulated data from existing theoretical models, our approach reveals that existing empirical findings may result from measurement errors in the IST proxies. Importantly, our simulation analysis uncovers the key role played by investment data in determining the economic mechanism through which IST shocks affect cross-sectional asset prices.
I analyze the impact of competition on the risk premia of R&D ventures engaged in a multiple-stage patent race with technical and market uncertainty. After solving in closed form for the case of a ...two-stage race in continuous time, I show that a firm's risk premium decreases as a consequence of technical progress and increases when a rival pulls ahead. Compared to the case where firms collude, R&D competition erodes the option value to mothball a project, reduces the completion time and the failure rate of R&D, and causes higher and more volatile risk premia. Numerical simulations reveal that competition can generate risk premia up to 500 annual basis points higher and up to three times more volatility than in a collusive industry.
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