学术论文

In finance, economics and many other fields, observations in a matrix form are often generated over time. For example, a set of key economic indicators are regularly reported in different countries every quarter. The observations at each quarter neatly form a matrix and are observed over consecutive quarters. Dynamic transport networks with observations generated on the edges can be formed as a matrix observed over time. Although it is natural to turn the matrix observations into long vectors, then use the standard vector time series 2 models for analysis, it is often the case that the columns and rows of the matrix represent different types of structures that are closely interplayed. In this paper we follow the autoregression for modeling time series and propose a novel matrix autoregressive model in a bilinear form that maintains and utilizes the matrix structure to achieve a substantial dimensional reduction, as well as more interpretability. Probabilistic properties of the models are investigated. Estimation procedures with their theoretical properties are presented and demonstrated with simulated and real examples.

We explore a large sample of analysts' estimates of the cost of equity capital (CoE) to evaluate their usefulness as expected return proxies (ERP). We find that the CoE estimates are significantly related to a firm's beta, size, book-to-market ratio, leverage, and idiosyncratic volatility but not other risk proxies. Even after controlling for the popular return predictors, the CoE estimates incrementally predict future stock returns. This predictive ability is better explained as the CoE estimates containing ERP information rather than reflecting stock mispricing. When evaluated against traditional ERPs, including the implied costs of capital, the CoE estimates are found to be the least noisy. Finally, we document CoE responses around earnings announcements, demonstrating their usefulness to study discount-rate reactions of market participants. We conclude that analysts' CoE estimates are meaningful ERPs that can be fruitfully employed in a variety of asset pricing contexts.

Product price risk is a potentially important factor for firms’ liquidity management. A natural place to evaluate the impact of this risk on liquidity management is the electricity industry, because producing firms face substantial price volatility in wholesale markets. Empirically, higher volatility of electricity prices leads to an increase in cash holdings, and this effect is robust to instrumenting for price risk using weather volatility. Cash increases more with price risk in firms using inflexible production technologies and those that cannot easily hedge electricity prices, indicating that operating flexibility and hedging are substitutes for liquidity management.

We use staggered share repurchases legalization from 1985 to 2010 across the world to examine its impact on corporate behaviors. We find that share-repurchasing firms do not cut dividends as a substitution. The cash for repurchasing shares comes more from internal cash than external debt issuance, leading to reductions in capital expenditures and R&D expenses. While this strategy boosts stock prices, it results in lower long-run Tobin's Q, profitability, growth, and innovation, accompanied by lower insider ownership. Tax benefits and paying out temporary earnings are two primary reasons that firms repurchase.

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.