Latest Research Publications

The prior literature on analyst forecasts has focused almost exclusively on firms that just meet or beat the mean or median consensus analyst forecast, without much regard to alternative benchmarks within the forecast distribution. Anecdotal evidence suggests that there is institutional significance to the lowest (minimum) and highest (maximum) analyst earnings forecast. We rigorously explore whether these two new benchmarks actually have incremental significance and, if so, whether there are differences in how managers and investors perceive the importance of these three benchmarks (i.e., minimum, mean, and maximum). Consistent with the theory of investor ambiguity aversion, which predicts an asymmetric market response to good and bad news, our results support the notion that of the three benchmarks we explore, firms act most aggressively to exceed the minimum forecast, followed by the mean, and then finally the maximum. This order is consistently supported by the following evidence: the existence of higher incentives to beat the benchmark; the likelihood of earnings management to beat the benchmark; accrual reversal after firms just barely achieve each benchmark; accrual mispricing around each benchmark; and, finally, a faster incorporation into the stock price of the bad news that a firm misses the minimum than of the good news that a firm meets or beats the maximum. These findings fill a void in academic research on these two new benchmarks and offer a consistent explanation as to why the popular press and managers frequently highlight and discuss beating these benchmarks as a separate and notable achievement.

Sharing economy platforms are pressed to rapidly grow user bases at the early stage by aggressively targeting potential users through competitive actions. Due to the volatile nature of the sharing economy and its disruption to industry norms, these platforms encounter legitimacy challenges that impede user base growth. This paper integrates competitive repertoire and institutional legitimacy theories to develop a research model that explains early-stage user base development in the sharing economy. We posit that the early-stage user base is associated with structural characteristics of the competitive repertoire, whose effects are moderated by a platform's socio-political legitimation efforts that address stakeholders’ regulatory and normative concerns. Using a comprehensive sample of 4644 monthly observations of 129 sharing economy platforms in China, we find that the volume of two context-specific competitive actions, offering economic incentives and staging high-visibility events, along with competitive repertoire complexity, are positively related to the platform's early-stage user base. We also identify a significant negative relationship between repertoire differentiation and user base. Direct relationships are moderated by socio-political legitimation, however, such that legitimation weakens the positive impact of context-specific action volume but enhances those of repertoire complexity and differentiation. Managerial and practical implications are discussed in light of the findings.

This paper investigates the economic costs of the recent United Nations sanctions on North Korea. Exploiting a novel data set on North Korean firms, we construct measures of regional exposure to export and intermediate input sanctions and show that trade sanctions cause sharp declines in local nighttime luminosity. Additional analysis of newly available product-level price data reveals that import sanctions led to significant increases in market prices. We then estimate a quantitative spatial equilibrium model using cross-region variations. The model implies that the sanctions reduced the country's manufacturing output by 12.9% and real income by 15.3%. We further quantify the potential impact of alternative sanction scenarios.

We study how adverse economic shocks influence political outcomes in strong authoritarian regimes, by examining the export slowdown in China during the mid-2010s. We first show that prefectures that experienced a more severe export slowdown witnessed a significant increase in incidents of labor strikes, using a shift-share instrumental variables strategy. The prefecture party secretary was subsequently more likely to be replaced by the central government, particularly if the rise in strikes was greater than in other prefectures that saw comparable export slowdowns. These patterns are consistent with a simple framework we develop, where the central government makes strategic use of a turnover decision to induce effort from local officials in preserving social stability, and to screen them for retention. In line with the framework’s predictions, we find a heightened emphasis by local party secretaries—particularly younger officials whose career concerns are stronger—on upholding stability following negative export shocks. This is evident in both words (from textual analysis of official speeches) and deeds (from expenditures on public security and social spending).

Investors have a finite capacity to organize all information they receive from financial disclosures. Under rational inattention, we show that investor processing capacity affects the probability of disclosure. Our main result is that the likelihood of disclosure is inverse-U shaped in investor attention. For low levels of attention, more attention facilitates communication and increases disclosure; for high levels of attention, more attention better identifies, and therefore deters, unfavorable voluntary disclosures. We provide empirical evidence that the relationship between investor attention and management forecast follows the predictions of the theory, using institutional ownership as a proxy for investor attention as well as exogenous shocks to fund manager distraction.

Investors' individual arbitrage models introduce idiosyncratic risk into complex asset strategies, driving up average returns and Sharpe ratios. However, despite the attractive risk-return trade-off, participation is limited. This is because effective Sharpe ratios in complex asset markets vary with investors' expertise. Investors with higher expertise, better models, and lower resulting idiosyncratic risk exposures realize higher Sharpe ratios. Their demand deters entry by less sophisticated investors. As predicted by our model, market dislocations are characterized by an increase in idiosyncratic risk, investor exit, and persistently elevated alphas and Sharpe ratios. The selection effect from higher expertise agents' more favorable Sharpe ratios is unique to our model and key to our main results.

We collaborated with a leading fast-moving consumer goods (FMCG) manufacturer to investigate how intelligent image processing (IIP)-based shelf monitoring aids manufacturers’ shelf management by using data from a quasi-experiment and a field experiment. We discovered that such artificial intelligence (AI) assistance significantly and consistently improves product sales. Several underlying mechanisms were revealed by our quantitative and qualitative analysis. First, retailers are more likely to comply due to the greater monitoring effectiveness enabled by AI assistance. Second, the positive effect of IIP-based shelf monitoring partially persists after it is terminated, implying that human learning takes place. Third, the value of IIP-based shelf monitoring can be attributed to independent retailers rather than chain retailers. Since the degree of contract heterogeneity is the major difference between these retailers in terms of monitoring, this finding further suggests that AI is relatively more scalable when coping with more heterogeneous instances. Apart from these great benefits, we demonstrate the low marginal costs of implementing IIP-powered shelf monitoring, which indicates its long-term applicability and potential to generate incremental value. Our research contributes to several literature streams and provides managerial insights for practitioners who consider AI-assisted operational models.

The ease of customer data collection has enabled the widespread personalization of content and services in digital platforms. We examine personalization in a hitherto unaddressed context: that of mobile app distribution. Specifically, we develop a comprehensive framework for the personalized ranking of app impressions, leveraging revealed preferences embedded in consumer clickstream data. To improve platform revenues, the framework jointly accounts for consumer utility and cost per action (CPA) margin, which is the revenue earned by the platform per app installation. To this end, we specify a structural model of click and installation choices, jointly estimated as a function of a comprehensive set of numerical (screen rank, quality, and popularity) and textual (titles, descriptions, and reviews) covariates. Our novel data set is at the granular user-impression level and uniquely includes app CPA margins paid to the platform. We conduct a series of policy experiments to quantify the value of personalization. Specifically, we show that a personalized hybrid margin and utility margin ranking scheme outperforms other personalized methods, including those based on utilities alone or a combination of utilities and margins. Overall, our analysis demonstrates how platforms can leverage routine consumer clickstream data to personalize the ranking of app impressions, thereby more effectively monetizing mobile app distribution.

Host-generated property images as a visual channel reveal substantial information about properties. Selecting proper images to display can lead to higher demand and increased rental revenue. In this paper, we define, estimate, and optimize the impacts of Airbnb photos on customers’ renting decisions. We apply ResNet-50, a convolutional neural network model, to build two separate, supervised learning models to evaluate the image quality and room types posted by Airbnb hosts. Then, we characterize the overall impacts of photo layout by the room type featured in the photo, photo quality, and order of display on the listings’ web pages. To address two estimation challenges in the Airbnb setting, namely, censored demand and changing consideration sets, we propose a novel pairwise comparison model that utilizes customers’ booking sequence data to consistently estimate the impact of photo layout on customers’ renting decisions. Our estimation results suggest that the cover image has a significantly larger impact than noncover photos and a high-quality bedroom cover image leads to the largest increase in demand. Furthermore, we build a nonlinear integer programming optimization problem and develop an algorithm to determine the optimal photo layout. Our counterfactual analysis suggests that a listing’s unilateral adoption of optimal photo layout leads to 11.0% more bookings on average. Moreover, depending on the neighborhood and market size, when listings simultaneously switch to the optimal photo layout, they get booked for two to five additional days in a year on average, which boosts revenue by $500 to $1,100.