Latest Research Publications

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.

This study examines a reputation-concerned entrepreneur’s incentives to provide disaggregated information about a project’s future performance when the entrepreneur seeks to increase both the market price of the project and the market assessment of the entrepreneur’s ability as a project manager. Two factors determine equilibrium: (i) the informational quality of the signal related to the entrepreneur’s ability and (ii) the magnitude of reputational concerns. If the former is relatively low, the entrepreneur with moderate reputational concerns is more likely to provide disaggregated information when the signal about the project’s overall performance is intermediate than when it is sufficiently good or bad. Also, given any value of the signal about the overall performance, this entrepreneur withholds disaggregated information when the signal about the entrepreneur’s ability is intermediate rather than sufficiently good or bad. The comparative static results provide novel empirical predictions about disclosure of aggregate versus disaggregated information.

This study documents the effect of CEO's identification with their hometown on corporate social responsibility (CSR). We propose that firms headquartered in their CEOs’ hometowns tend to do more CSR. This is because identification with their hometown activates CEOs’ altruistic tendency to be more prosocial and makes them more likely to have long-term goals, both of which are compatible with the nature of CSR. This hometown identity effect is stronger when the firm is more locally connected and is weaker when the firm is located in a region with more diverse dialects. Analyzing a large sample of publicly listed Chinese firms for 2009–2016, we found strong support for our predictions. The robustness of our findings is confirmed by a field survey, a difference-in-differences (DID) approach, the Heckman two-stage model, the impact threshold of confounding variables (ITCV), and alternative measures of CSR and CEO hometown identity.

We investigate how the mandatory adoption of International Financial Reporting Standards (IFRS) by publicly listed firms in the European Union affects peer private firms. We find that private firms’ capital investment decreases significantly after the IFRS mandate, relative to public firms. Private firms also display decreased investment when benchmarked against firms relatively insulated from the impact of the IFRS mandate, but the magnitude of the effect is smaller in this case. These results are consistent with the hypothesis that mandatory IFRS reporting (combined with other reforms), while increasing public firms’ financing and investment, crowds out funding for private firms. The effect is more pronounced for larger private firms and in industries where public peers have greater external financing needs. Our evidence suggests that financial reporting regulations cause shifts in resource allocation in an economy.

A central problem in planning production capacity is how to effectively manage demand risk. We develop a model that integrates capacity planning and risk hedging decisions under a popular risk measure, conditional value at risk (CVaR). The CVaR objective generalizes the usual risk-neutral objective (such as the expected payoff) and allows for explicit modeling of the degree of aversion to downside risk (associated with low demand). The starting point of our model is to incorporate the impact on demand from a financial asset (including for instance, a tradable market index as a proxy for the general economy). This way, in addition to the capacity decision at the beginning of the planning horizon, there is also a dynamic hedging strategy throughout the horizon, and the latter plays the role of both mitigating demand risk and supplementing the payoff. The hedging strategy is restricted to partial information and constrained with a cap on loss (pathwise). To find the optimal hedging strategy, we construct and solve a dual problem to derive the optimal terminal wealth from hedging; the real-time hedging strategy is then mapped out via the martingale representation theorem. With the hedging strategy optimized, we show that optimizing the production quantity is a concave maximization problem. With both production and hedging (jointly) optimized, we provide a complete characterization of the efficient frontier and quantify the improvement over the production-only model. Furthermore, via sensitivity and asymptotic analyses, we spell out the impacts of the loss cap and the risk aversion level, along with other qualitative insights.

Over the past decades the number of news outlets has increased dramatically, but the quality of news products has declined. We propose a model to reconcile these facts where consumers' attention allocation decisions not only depend on but also affect news outlets' quality choices. When competition is intensified by new entries, the informativeness of the news industry rises. Thus, attention is diverted from existing outlets, reducing their incentives to improve news quality, which begets a downward spiral. Furthermore, when attention becomes cheaper, a larger number of news outlets can be accommodated in equilibrium, but news quality still falls.

Problem definition: Shared micromobility vehicles provide an eco-friendly form of short-distance travel within an urban area. Because customers pick up and drop off vehicles in any service region at any time, such convenience often leads to a severe imbalance between vehicle supply and demand in different service regions. To overcome this, a micromobility operator can crowdsource individual riders with reward incentives in addition to engaging a third-party logistics provider (3PL) to relocate the vehicles. Methodology/results: We construct a time-space network with multiple service regions and formulate a two-stage stochastic mixed-integer program considering uncertain customer demands. In the first stage, the operator decides the initial vehicle allocation for the regions, whereas in the second stage, the operator determines subsequent vehicle relocation across the regions over an operational horizon. We develop an efficient solution approach that incorporates scenario-based and time-based decomposition techniques. Our approach outperforms a commercial solver in solution quality and computational time for solving large-scale problem instances based on real data. Managerial implications: The budgets for acquiring vehicles and for rider crowdsourcing significantly impact the vehicle initial allocation and subsequent relocation. Introducing rider crowdsourcing in addition to the 3PL can significantly increase profit, reduce demand loss, and improve the vehicle utilization rate of the system without affecting any existing commitment with the 3PL. The 3PL is more efficient for mass relocation than rider crowdsourcing, whereas the latter is more efficient in handling sporadic relocation needs. To serve a region, the 3PL often relocates vehicles in batches from faraway, low-demand regions around peak hours of a day, whereas rider crowdsourcing relocates a few vehicles each time from neighboring regions throughout the day. Furthermore, rider crowdsourcing relocates more vehicles under a unimodal customer arrival pattern than a bimodal pattern, whereas the reverse holds for the 3PL.

Problem definition: We study the use of nonmonetary incentives based on reciprocity to facilitate capacity sharing between two service providers that have limited and substitutable service capacity. Academic/practical relevance: We propose a parsimonious game theory framework, in which two firms dynamically choose whether to accept each other’s customers without the capability to perfectly monitor each other’s capacity utilization state. Methodology: We solve the continuous-time imperfect-monitoring game by focusing on a class of public strategy, in which firms’ real-time capacity-sharing decision depends on an intuitive and easy-to-implement accounting device, namely the current net number of transferred customers. We refer to such an equilibrium as a trading-favors equilibrium. We characterize the condition in which capacity sharing takes place in such an equilibrium. Results: We find that some degree of efficiency loss (as compared with a central planner’s solution) is necessary to induce reciprocity. The efficiency loss is small when the two firms have similar traffic intensity even if they are different in service-capacity scale, whereas the efficiency loss can be considerably large when the two firms have significantly different traffic intensities. The trading-favors mechanism, surprisingly, can outperform the perfect-monitoring benchmark when the two firms exhibit high asymmetry in terms of service-capacity scale or traffic intensity because the smaller firm tends to deviate from collaboration. Managerial implications: Firms should consider engaging in nonmonetary reciprocal capacity sharing if regulations, transaction costs, or other market and operational frictions make it difficult to use a capacity-sharing contract based on monetary payments. The trading-favors collaboration can improve the firms’ payoff close to the centralized upper bound when the firms have similar traffic intensities. However, when their traffic intensities are highly different, firms are better off with a monetary-payment contract to induce more capacity sharing and are worse off investing in increasing their visibility to each other’s real-time available capacity, namely investing in perfect monitoring.

Non-advertising-based mobile apps face several critical challenges when trying to monetize their free services—among them, the choice of pricing strategies (hard landing vs. soft landing; i.e., a “pay or churn” paywall vs. continuing to offer limited free services to existing users after monetization) and aspects of product design (whether to provide exclusive secondary offerings to paying users). The authors implemented a large-scale randomized field experiment with an app firm to test the causal effects of such pricing and product design strategies. Results show that both soft landing and exclusive secondary offerings decrease existing app users’ willingness to subscribe, but there is a positive interaction between these two strategies on subscriptions. The authors propose a theoretical framework, discuss potential mechanisms that might be at play, and conduct robustness checks to rule out several alternative explanations. A customer survey by the firm and an experiment on Prolific provide further support for the theoretical mechanism. To assess generalizability, the authors conducted a second field experiment and obtained consistent results. They also report the results from the actual implementation of the best-performing strategy by the firm. This research provides guidance on possible theoretical underpinnings of users’ responses and important managerial implications for app monetization.