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We propose a measure of managerial ability, based on managers' efficiency in generating revenues, which is available for a large sample of firms and outperforms existing ability measures. We find that our measure is strongly associated with manager fixed effects and that the stock price reactions to chief executive officer (CEO) turnovers are positive (negative) when we assess the outgoing CEO as low (high) ability. We also find that replacing CEOs with more (less) able CEOs is associated with improvements (declines) in subsequent firm performance. We conclude with a demonstration of the potential of the measure. We find that the negative relation between equity financing and future abnormal returns documented in prior research is mitigated by managerial ability. Specifically, more able managers appear to utilize equity issuance proceeds more effectively, illustrating that our more precise measure of managerial ability will allow researchers to pursue studies that were previously difficult to conduct. This paper was accepted by Mary E. Barth, accounting.

Power outages: catastrophic failure of linked networks On 28 September 2003, Italy suffered a near-nationwide power cut (Sicily was spared) that also brought down the Internet. Buldyrev et al . take this event, typical of a number that have occurred worldwide in recent years, and examine how such a cascade of failures involving independent networks can occur. They find that, surprisingly, a broader degree of distribution increases the vulnerability of interdependent networks to random failure — the opposite of what happens in a single network. This highlights the need to consider interdependent network properties when designing robust networks if a random failure is not to have catastrophic results. Modern networks are rarely independent, instead being coupled together with many others. Thus the failure of a small fraction of nodes in one network may lead to the complete fragmentation of a system of several interdependent networks. Here, a framework is developed for understanding the robustness of interacting networks subject to such 'cascading' failures. Surprisingly, a broader degree distribution increases the vulnerability of interdependent networks to random failure. Complex networks have been studied intensively for a decade, but research still focuses on the limited case of a single, non-interacting network 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 . Modern systems are coupled together 15 , 16 , 17 , 18 , 19 and therefore should be modelled as interdependent networks. A fundamental property of interdependent networks is that failure of nodes in one network may lead to failure of dependent nodes in other networks. This may happen recursively and can lead to a cascade of failures. In fact, a failure of a very small fraction of nodes in one network may lead to the complete fragmentation of a system of several interdependent networks. A dramatic real-world example of a cascade of failures (‘concurrent malfunction’) is the electrical blackout that affected much of Italy on 28 September 2003: the shutdown of power stations directly led to the failure of nodes in the Internet communication network, which in turn caused further breakdown of power stations 20 . Here we develop a framework for understanding the robustness of interacting networks subject to such cascading failures. We present exact analytical solutions for the critical fraction of nodes that, on removal, will lead to a failure cascade and to a complete fragmentation of two interdependent networks. Surprisingly, a broader degree distribution increases the vulnerability of interdependent networks to random failure, which is opposite to how a single network behaves. Our findings highlight the need to consider interdependent network properties in designing robust networks.

Are the attitudes and beliefs of chief executive officers (CEOs) linked to their firms' innovative performance? This paper uses a measure of overconfidence, based on CEO stock-option exercise, to study the relationship between a CEO's \"revealed beliefs\" about future performance and standard measures of corporate innovation. We begin by developing a career concern model where CEOs innovate to provide evidence of their ability. The model predicts that overconfident CEOs, who underestimate the probability of failure, are more likely to pursue innovation, and that this effect is larger in more competitive industries. We test these predictions on a panel of large publicly traded firms for the years from 1980 to 1994. We find a robust positive association between overconfidence and citation-weighted patent counts in both cross-sectional and fixed-effect models. This effect is larger in more competitive industries. Our results suggest that overconfident CEOs are more likely to take their firms in a new technological direction. This paper was accepted by Kamalini Ramdas, entrepreneurship and innovation.

Microloan markets allow individual borrowers to raise funding from multiple individual lenders. We use a unique panel data set that tracks the funding dynamics of borrower listings on Prosper.com, the largest microloan market in the United States. We find evidence of rational herding among lenders. Well-funded borrower listings tend to attract more funding after we control for unobserved listing heterogeneity and payoff externalities. Moreover, instead of passively mimicking their peers (irrational herding), lenders engage in active observational learning (rational herding); they infer the creditworthiness of borrowers by observing peer lending decisions and use publicly observable borrower characteristics to moderate their inferences. Counterintuitively, obvious defects (e.g., poor credit grades) amplify a listing's herding momentum, as lenders infer superior creditworthiness to justify the herd. Similarly, favorable borrower characteristics (e.g., friend endorsements) weaken the herding effect, as lenders attribute herding to these observable merits. Follow-up analysis shows that rational herding beats irrational herding in predicting loan performance. This paper was accepted by Pradeep Chintagunta, marketing.

In this paper we distinguish between two types of white lies: those that help others at the expense of the person telling the lie, which we term altruistic white lie s, and those that help both others and the liar, which we term Pareto white lies . We find that a large fraction of participants are reluctant to tell even a Pareto white lie, demonstrating a pure lie aversion independent of any social preferences for outcomes. In contrast, a nonnegligible fraction of participants are willing to tell an altruistic white lie that hurts them a bit but significantly helps others. Comparing white lies to those where lying increases the liar's payoff at the expense of another reveals important insights into the interaction of incentives, lying aversion, and preferences for payoff distributions. Finally, in line with previous findings, women are less likely to lie when it is costly to the other side. Interestingly though, we find that women are more likely to tell an altruistic lie. This paper was accepted by Teck Ho, decision analysis.

Contests are a historically important and increasingly popular mechanism for encouraging innovation. A central concern in designing innovation contests is how many competitors to admit. Using a unique data set of 9,661 software contests, we provide evidence of two coexisting and opposing forces that operate when the number of competitors increases. Greater rivalry reduces the incentives of all competitors in a contest to exert effort and make investments. At the same time, adding competitors increases the likelihood that at least one competitor will find an extreme-value solution. We show that the effort-reducing effect of greater rivalry dominates for less uncertain problems, whereas the effect on the extreme value prevails for more uncertain problems. Adding competitors thus systematically increases overall contest performance for high-uncertainty problems. We also find that higher uncertainty reduces the negative effect of added competitors on incentives. Thus, uncertainty and the nature of the problem should be explicitly considered in the design of innovation tournaments. We explore the implications of our findings for the theory and practice of innovation contests. This paper was accepted by Christian Terwiesch, operations management.

Increasingly, user-generated product reviews serve as a valuable source of information for customers making product choices online. The existing literature typically incorporates the impact of product reviews on sales based on numeric variables representing the valence and volume of reviews. In this paper, we posit that the information embedded in product reviews cannot be captured by a single scalar value. Rather, we argue that product reviews are multifaceted, and hence the textual content of product reviews is an important determinant of consumers' choices, over and above the valence and volume of reviews. To demonstrate this, we use text mining to incorporate review text in a consumer choice model by decomposing textual reviews into segments describing different product features. We estimate our model based on a unique data set from Amazon containing sales data and consumer review data for two different groups of products (digital cameras and camcorders) over a 15-month period. We alleviate the problems of data sparsity and of omitted variables by providing two experimental techniques: clustering rare textual opinions based on pointwise mutual information and using externally imposed review semantics. This paper demonstrates how textual data can be used to learn consumers' relative preferences for different product features and also how text can be used for predictive modeling of future changes in sales. This paper was accepted by Ramayya Krishnan, information systems.

Our society is increasingly relying on the digitized, aggregated opinions of others to make decisions. We therefore designed and analyzed a large-scale randomized experiment on a social news aggregation Web site to investigate whether knowledge of such aggregates distorts decision-making. Prior ratings created significant bias in individual rating behavior, and positive and negative social influences created asymmetric herding effects. Whereas negative social influence inspired users to correct manipulated ratings, positive social influence increased the likelihood of positive ratings by 32% and created accumulating positive herding that increased final ratings by 25% on average. This positive herding was topic-dependent and affected by whether individuals were viewing the opinions of friends or enemies. A mixture of changing opinion and greater turnout under both manipulations together with a natural tendency to up-vote on the site combined to create the herding effects. Such findings will help interpret collective judgment accurately and avoid social influence bias in collective intelligence in the future.

A fast fashion system combines quick response production capabilities with enhanced product design capabilities to both design \"hot\" products that capture the latest consumer trends and exploit minimal production lead times to match supply with uncertain demand. We develop a model of such a system and compare its performance to three alternative systems: quick-response-only systems, enhanced-design-only systems, and traditional systems (which lack both enhanced design and quick response capabilities). In particular, we focus on the impact of each of the four systems on \"strategic\" or forward-looking consumer purchasing behavior, i.e., the intentional delay in purchasing an item at the full price to obtain it during an end-of-season clearance. We find that enhanced design helps to mitigate strategic behavior by offering consumers a product they value more, making them less willing to risk waiting for a clearance sale and possibly experiencing a stockout. Quick response mitigates strategic behavior through a different mechanism: by better matching supply to demand, it reduces the chance of a clearance sale. Most importantly, we find that although it is possible for quick response and enhanced design to be either complements or substitutes, the complementarity effect tends to dominate. Hence, when both quick response and enhanced design are combined in a fast fashion system, the firm typically enjoys a greater incremental increase in profit than the sum of the increases resulting from employing either system in isolation. Furthermore, complementarity is strongest when customers are very strategic. We conclude that fast fashion systems can be of significant value, particularly when consumers exhibit strategic behavior. This paper was accepted by Yossi Aviv, operations management.

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.