Latest Research Publications

We propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high-dimensional problems. For a (potentially misspecified) stand-alone model, it provides reliable price of risk estimates for both tradable and nontradable factors, and detects those weakly identified. For competing factors and (possibly nonnested) models, the method automatically selects the best specification—if a dominant one exists—or provides a Bayesian model averaging–stochastic discount factor (BMA-SDF), if there is no clear winner. We analyze 2.25 quadrillion models generated by a large set of factors and find that the BMA-SDF outperforms existing models in- and out-of-sample.

We study the economic value of market timing in foreign exchange (FX) markets, that is, using information about the conditional Sharpe ratio to adjust the notional value of a conditionally mean–variance efficient currency portfolio. Our strategy trades more (less) aggressively when the conditional risk-return trade-off is more (less) favorable. This leads to a significant improvement in the out-of-sample unconditional Sharpe ratio, skewness, and maximum drawdown per 1% expected excess return. The strategy’s market timing predicts returns, volatility, and skewness in FX markets. Popular currency pricing factors do not explain the strategy’s high average excess returns. Our findings suggest that it is costly to impose leverage or risk (i.e., conditional volatility) limits or other inferior market timing policies when constructing currency trading strategies.

We study the effects of mark-to-market accounting (MTM) for banks following the originate-to-distribute lending model. Banks have expertise in originating loans, but it is costly for them to retain the loans on their books. We study how the accounting measurement of the retained loans affects the banks’ origination and retention decisions. We show that, relative to historic cost accounting (HC), MTM has three consequences. First, it improves the accuracy of loan measurement ex post. Second, it forces banks to retain more risk exposure on their own books. Finally, it can reduce ex ante origination efforts and lower the average quality of loans in the economy.

Although the significance of relational ties with supply chain partners to firms' operations management has been frequently studied, it is unclear how political ties, which represent another form of relational ties that is especially important in emerging economies, relate to operational efficiency. Drawing on the political embeddedness perspective, we propose a negative association between political ties and operational efficiency and examine how this association is moderated by environmental and firm-level factors. Using panel data of listed private firms in China, we show that political ties are negatively correlated with operational efficiency. In addition, this negative relationship is stronger when firms operate in regions with less developed factor markets and in highly competitive industries. However, the negative relationship is weaker for firms with high levels of foreign shareholding and customer concentration. These findings caution against the downsides of political ties for operations management and highlight strategies for reducing their negative effect.

A firm manager is concerned about both the firm value and the market assessments of his abilities. When investing in a project, he has private knowledge of his project-related ability that interacts with the project investment, and his general ability that produces a cash flow independent of the project cash flow. The concerns about the general (project-related) ability assessment create a signaling incentive to decrease (increase) investment. In the presence of underinvestment (overinvestment), higher-quality earnings information reduces (improves) equilibrium efficiency. When the manager issues an earnings forecast as an additional signaling device, the forecast is upwardly biased, and the equilibrium investment is smaller than that without a forecast. The latter is because the signaling incentive to decrease investment is strengthened. When the manager’s concerns about the general ability assessment are relatively large, he is better off by committing to no forecast. Novel empirical predictions about investment and earnings forecast emerge.

Peer-to-peer (P2P) sharing marketplaces enable sharing of idle resources. When a renter requests an owner’s resource, the owner needs to decide whether to accept the request: accepting it helps the owner fill up the idle periods of the resource and generate a payoff but reduces the flexibility to serve a future request for a longer duration. This paper develops a framework to uncover the tradeoffs faced by owners on these platforms when making acceptance decisions, which can be used by owners to optimize their decisions and by platforms to improve their operations. The model explicitly accommodates two types of owners: some are attentive to the availability states of their cars and forward-looking, whereas others myopically make the acceptance decisions. Applying the model to unique data from a leading peer-to-peer car sharing platform in China, we obtain similar sizes of both types of owners and find that female, experienced, and younger owners are more likely to be strategic. The results also reveal the differentiated preferences of the two types of owners toward their renters. Building on model estimates, we calibrate the option value of each day in the future (i.e., the value of having the day available) for strategic owners and find it to first increase, then decrease. Two counterfactual analyses are conducted. The first analysis shows that if the platform imposes a minimum rental duration, strategic owners may become more reluctant to accept requests, even if the current availability state entails a higher expected payoff. The second analysis shows that with better understanding of its owners, the platform can greatly improve the matching efficiency by optimal (re)allocation of rental requests, a move that benefits almost all participants in the business.

Problem definition: Firms heavily invest in big data technologies to collect consumer data and infer consumer preferences for price discrimination. However, consumers can use technological devices to manipulate their data and fool firms to obtain better deals. We examine how a firm invests in collecting consumer data and makes pricing decisions and whether it should disclose its scope of data collection to consumers who can manipulate their data. Methodology/results: We develop a game-theoretic model to consider a market in which a firm caters to consumers with heterogeneous preferences for a product. The firm collects consumer data to identify their types and issue an individualized price, whereas consumers can incur a cost to manipulate data and mimic the other type. We find that when the firm does not disclose its scope of data collection to consumers, it collects more consumer data. When the firm discloses its scope of data collection, it reduces data collection even when collecting more data is costless. The optimal scope of data collection increases when it is more costly for consumers to manipulate data but decreases when consumer demand becomes more heterogeneous. Moreover, a lower cost for consumers to manipulate data can be detrimental to both the firm and consumers. Lastly, disclosure of data collection scope increases firm profit, consumer surplus, and social welfare. Managerial implications: Our findings suggest that a firm should adjust its scope of data collection and prices based on whether the firm discloses the data collection scope, consumers’ manipulation cost, and demand heterogeneity. Public policies should require firms to disclose their data collection scope to increase consumer surplus and social welfare. Even without such a mandatory disclosure policy, firms should voluntarily disclose their data collection scope to increase profit. Moreover, public educational programs that train consumers to manipulate their data or raise their awareness of manipulation tools can ultimately hurt consumers and firms.

Consumers often need to schedule different activities. While consumers who adopt a clock-time scheduling style decide when to transition from one activity to the next according to external temporal cues (e.g., clock), those who adopt an event-time scheduling style tend to perform each activity until they feel internally that it is completed. This research showed that consumers' scheduling style (clock-time vs. event-time) could influence their satiation with repeated consumption. Four studies involving actual consumption across various domains (e.g., music, artwork, food) demonstrated that an event-time scheduling style leads to more rapid satiation with repeated consumption than a clock-time scheduling style because event-timers (vs. clock-timers) have higher private self-focus. The results further revealed that the satiation effect of scheduling style is mitigated when consumers are distracted from their private self or informed of additional sensitization cues in the consumption stimuli.

Information sets, expectations, and preferences of politicians are fundamental, but unobserved determinants of their policy choices. Employing repeated votes in the US House of Representatives on China's normal trade relations (NTR) status during the two decades straddling China's World Trade Organization (WTO) accession, we apply a moment inequality approach designed to deliver consistent estimates under weak informational assumptions on the information sets of members of Congress. This methodology offers a robust way to test hypotheses about what information politicians have at the time of their decision and to estimate the weight that constituents, ideology, and other factors have in policy making and voting.