Latest Research Publications

What capital allocation role can China’s stock market play? Counter to perception, stock prices in China have become as informative about future profits as they are in the US. This rise in stock price informativeness has coincided with an increase in investment efficiency among privately owned firms, suggesting the market is aggregating information and providing useful signals to managers. However, price informativeness and investment efficiency for state-owned enterprises fell below that of privately owned firms after the postcrisis stimulus, perhaps reflecting unpredictable subsidies and state-directed investment policy. Finally, evidence from realized returns suggests Chinese firms face a higher cost of equity capital than US firms.

We empirically examine the impact of industry exchange-traded funds (IETFs) on informed trading and market efficiency. We find that IETF short interest spikes simultaneously with hedge fund holdings on the member stock before positive earnings surprises, reflecting long-the-stock/short-the-ETF activity. This pattern is stronger among stocks with high industry risk exposure. A difference-in-difference analysis on the ETF inception event shows that IETFs reduce post-earnings-announcement drift more among stocks with high industry risk exposure, suggesting that IETFs improve market efficiency. We also find that the short interest ratio of IETFs positively predicts IETF returns, consistent with the hedging role of IETFs.

This article addresses the challenges in classifying textual data obtained from open online platforms, which are vulnerable to distortion. Most existing classification methods minimize the overall classification error and may yield an undesirably large Type I error (relevant textual messages are classified as irrelevant), particularly when available data exhibit an asymmetry between relevant and irrelevant information. Data distortion exacerbates this situation and often leads to fallacious prediction. To deal with inestimable data distortion, we propose the use of the Neyman–Pearson (NP) classification paradigm, which minimizes Type II error under a user-specified Type I error constraint. Theoretically, we show that the NP oracle is unaffected by data distortion when the class conditional distributions remain the same. Empirically, we study a case of classifying posts about worker strikes obtained from a leading Chinese microblogging platform, which are frequently prone to extensive, unpredictable and inestimable censorship. We demonstrate that, even though the training and test data are susceptible to different distortion and therefore potentially follow different distributions, our proposed NP methods control the Type I error on test data at the targeted level. The methods and implementation pipeline proposed in our case study are applicable to many other problems involving data distortion. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

From employees’ point of view, changes in ethical leadership perceptions can signal important changes in the nature of the employment relationship. Guided by social exchange theory, this study proposes that changes in ethical leadership perceptions shape how employees appraise their exchange relationship with the organization and affect their pride in or contempt for the organization. Changes in these associative/dissociative emotions, in turn, precipitate changes in behaviors that serve or hurt the organization, notably voice and turnover. Experimental data collected from 900 subjects (Study 1) and field data collected from 470 employees across 4 waves over 14 months (Study 2) converged to show that changes in ethical leadership perceptions were related to same-direction changes in employees’ pride in the organization and to opposite-direction changes in their contempt for the organization above and beyond the effect of the present ethical leadership level. Changes in pride were in turn related to same-direction changes in functional voice, whereas changes in contempt were related to same-direction changes in dysfunctional voice. The field study also provided evidence that when pride increased (decreased), employees were less (more) likely to leave the organization 6 months after. These results suggest that changes in ethical leadership perceptions are meaningful on their own, that they may alter employees’ organization-targeted behaviors, and that changes in associative/dissociative emotions are the mediating mechanism.

We identify an important channel, acquisitions of public targets, via which the governance through trading (GTT) improves firm values. The disciplinary effect of GTT is more pronounced for firms with higher managerial wealth-performance sensitivity and moderate institutional ownership concentration. Firms with higher GTT also have higher subsequent ROA, ROE, Tobin's Q, analysts forecasted EPS growth rate, and lower expected default risk. The effect is stronger after Decimalization and robust to using two instrumental variables. We conduct several exercises to rule out alternative explanations, such as institutional superior information, investor activism, and momentum. Additional tests show that the disciplinary effect of GTT only exists for less financially-constrained firms and non-all-cash M&As where the agency problem is more likely to be prevalent.

We investigate how communication within banks affects small business lending. Using travel times between a bank’s headquarters and its branches to proxy for the costs of communicating soft information, we exploit shocks to these travel times—the introduction of new airline routes—to evaluate the impact of within-bank communication costs on small business loans. We find that reducing headquarters-branch travel time boosts small business lending in the branch’s county. Several extensions suggest that new airline routes facilitate in-person communications that boost small-firm lending.

Does the predeal geographic overlap of the branches of two banks affect the probability that they merge, postannouncement stock returns, and postmerger performance? We compile information on U.S. bank acquisitions from 1984 through 2016, construct several measures of network overlap, and design and implement a new identification strategy. We find that greater predeal network overlap (1) increases the likelihood that two banks merge; (2) boosts the cumulative abnormal returns of the acquirer, target, and combined banks; and (3) reduces employment, boosts revenues, reduces the number of branches, improves loan quality, and expedites executive turnover.

Macroeconomists failed to predict the Great Recession, suggesting that the existing macroeconomic models may have been misspecified. Bearing in mind this potential misspecification or “model uncertainty,” how do agents’ optimal decisions change? Furthermore, how large are the welfare costs of model misspecification? To shed light on these questions, we develop a tractable continuous-time general equilibrium model to show that a fear of model misspecification reduces both the equilibrium interest rate and the relative inequality of consumption to income, making the model’s predictions closer to the data. Our quantitative analysis shows that the welfare costs of model uncertainty are sizable.

Prior studies have shown that social media discussions can be helpful in predicting price movements in financial markets. With the increasingly large amount of social media data, how to effectively distinguish value-relevant information from noise remains an important question. We study this question by investigating the role of network cohesion in the relationship between social media sentiment and price changes in the Bitcoin market. As network cohesion is associated with information correlation within the discussion network, we hypothesize that less cohesive social media discussion networks are better at predicting the next-day returns than more cohesive networks. Both regression analyses and trading simulations based on data collected from Bitcointalk.org confirm our hypothesis. Our findings enrich the literature on the role of social media in financial markets and provide actionable insights for investors to trade based on social media signals.