[We develop a new solution method for a broad class of discrete-time dynamic portfolio choice problems. The method efficiently approximates conditional expectations of the value function by using (i) a decomposition of the state variables into a component observable by the investor and a stochastic deviation; and (ii) a Taylor expansion of the value function. We illustrate the accuracy of the method in handling several realistic features of portfolio choice problems such as intermediate consumption, multiple assets, multiple state variables, portfolio constraints, non-time-separable preferences, and nonredundant endogenous state variables. We finally use the method to solve a realistic large-scale life-cycle portfolio choice and consumption problem with predictable expected returns and recursive preferences.]

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.

[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.]

[This paper examines the relationship between default probability and stock returns. Using the Expected Default Frequency (EDF) of Moody' s KMV, we document that higher default probabilities are not associated with higher expected stock returns. Within a model of bargaining between equity holders and debt holders in default, we show that the relationship between default probability and equity return is (i) upward sloping for firms where shareholders can extract little benefit from renegotiation (low "shareholder advantage") and (ii) humped and downward sloping for firms with high shareholder advantage. This dichotomy implies that distressed firms with stronger shareholder advantage should exhibit lower expected returns in the cross section. Our empirical evidence, based on several proxies for shareholder advantage, is consistent with the model's 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.]

Among U.S. public firms, technological innovation is concentrated on a small set of large players, with innovation “leaders” having considerably lower systematic risk than “laggards.” To understand this fact, we build a winner-takes-all patent race model and show that a firm’s expected return decreases in its innovation output and increases in that of its rivals. Using a comprehensive firm-level panel of information on patenting activity by fields of technology in 1950–2010, we find strong support for the model’s predictions. Our results highlight that strategic interactions among firms competing in innovation are an important determinant of risk and expected returns. (JEL G12, G31) Received August 6, 2018; editorial decision October 19, 2019 by Andrew Ellul.

We study the implications of creative destruction on asset prices. We develop a general equilibrium model of endogenous firm creation and destruction in which “incremental” innovation by incumbents and “radical” innovation by entrants drive productivity improvements. Firms’ incentives to innovate generate time-varying economic growth and countercyclical economic uncertainty. The model matches key properties of consumption and asset prices, as well as novel facts on the process of creative destruction in the United States obtained using a sample of patents from 1975–2013. We show that the interplay between incumbents and entrants is an important determinant of risks priced in the financial markets.

We study a dynamic corporate investment problem where decisions have to be made collectively by a group of agents holding heterogeneous beliefs and adhering to a “utilitarian” governance mechanism in which each agent has a given influence in the decision. In this setting we show that: (i) group decisions are typically dynamically inconsistent, (ii) dynamic inconsistency leads to inefficient underinvestment, and (iii) the ability to trade securities among insiders or with outsiders may restore efficient investment decisions but it may, in some cases, lead to inefficient overinvestment. Our theory can help explain the empirical evidence on the effect of diversity of groups, such as corporate boards, on firms’ outcomes and, more generally, on the difference between group and individual behavior.

We study a standard real-option problem in which sequential decisions are made through voting by a group of members with heterogeneous beliefs. We show that, when facing both investment and abandonment timing decisions, the group behavior cannot be replicated by that of a representative “median” member. As a result, members’ disagreement generates inertia—the group delays investment relative to a single-agent case—and underinvestment—the group rejects projects that are supported by a majority of members, acting in autarky. These coordination frictions hold in groups of any size, for general voting protocols, and are exacerbated by belief polarization.