Latest Research Publications

Online auction markets host a large number of transactions every day. The transaction data in auction markets are useful for understanding the buyers and sellers in the market. Previous research has shown that sellers with different levels of reputation, as shown by the ratings and comments left in feedback systems, enjoy different levels of price premiums for their transactions. Feedback scores and feedback texts have been shown to correlate with buyers’ level of trust in a seller and the price premium that buyers are willing to pay (Ba and Pavlou 2002; Pavlou and Dimoka 2006). However, existing models do not consider the time-order effect, which means that feedback posted more recently may be considered more important than feedback posted less recently. This paper addresses this shortcoming by (1) testing the existence of the time-order effect, and (2) proposing a Bayesian updating model to represent buyers’ perceived reputation considering the time-order effect and assessing how well it can explain the variation in buyers’ trust and price premiums. In order to validate the time-order effect and evaluate the proposed model, we conducted a user experiment and collected real-life transaction data from the eBay online auction market. Our results confirm the existence of the time-order effect and the proposed model explains the variation in price premiums better than the benchmark models. The contribution of this research is threefold. First, we verify the time-order effect in the feedback mechanism on price premiums in online markets. Second, we propose a model that provides better explanatory power for price premiums in online auction markets than existing models by incorporating the time-order effect. Third, we provide further evidence for trust building via textual feedback in online auction markets. The study advances the understanding of the feedback mechanism in online auction markets.

We show that fraudulent firms allocate resources differently than honest companies. Resources obtained through fraudulent means are likely to be viewed as unearned gains and are less likely to be invested in productive activities, such as recruiting talent. We posit that honest and fraudulent companies also invest in different types of innovation: honest firms pursue technically significant innovations, while fraudulent companies are likely to make smaller investments in less challenging inventive opportunities that contribute to the appearance rather than the substance of innovation. We test these predictions in a longitudinal dataset tracking the personnel recruitment and patenting activities of 467 Chinese high technology firms, all of which applied for state-funded innovation grants. We identify fraud by comparing two sets of financial books prepared by each company in the data in the same fiscal year, which are legally required to be identical but are discrepant in over 50 percent of cases, in a direction that benefits the firm. We find that relative to honest companies, fraudulent firms are more likely to receive state grants and are less likely to recruit new employees or produce important inventions in the post-grant period.

We assess the impact of geographic diversification on a bank’s costs of interest-bearing liabilities. We employ a new identification strategy and discover that geographic expansion across U.S. states lowered funding costs. Consistent with expansion facilitating risk diversification, we find that (1) funding costs fall more when banks expand into states whose economies are less correlated with the banks’ state and (2) geographic diversification reduces the costs of uninsured, but not insured, deposits. Consistent with expansion intensifying agency frictions, which puts upward pressures on funding costs, we discover that geographic diversification reduces the costs of interest-bearing liabilities more in better-monitored and better-run banks.

Some media platforms earn their profits from both consumers and advertisers (e.g., Spotify, Hulu), whereas others earn their profits from either advertisers only (e.g., Jango, Tubi) or consumers only (e.g., Tidal, Netflix). Thus, media platforms adopt divergent strategies depending on how they allocate the limited space or bandwidth between content and advertising. In this paper, we examine media platforms’ content provision strategies and their implications for the profits of media platforms as well as content suppliers, taking into account the cross-side effects of a multisided media market and the nature of competition in the content supplier market. To facilitate the analysis, we propose a model where media platforms interact with three sides: content suppliers, consumers, and advertisers. First, our analysis of a perfectly competitive content market shows that though consumers’ desire for content raises the willingness to pay, it can hurt platforms’ profits. Second, counter to our intuition, platforms’ profits can increase with the cost of procuring content. Third, advertisers’ desire for consumers reduces a monopoly content supplier’s profits under a paid-content-with-ads strategy. Fourth, a monopoly content supplier cannot extract all the profits from competing platforms. Furthermore, competing content suppliers may even charge higher prices than a monopoly content supplier. Finally, we highlight how the nature of competition in the content market shapes platforms’ choice of a no-ad strategy.

Despite growing concerns regarding the increasing consumerism related to promotions, this research documents a positive effect of price promotions on consumers’ donation behavior. Specifically, the authors propose that price promotions increase consumers’ perceived resources, which in turn increase consumers’ donation behavior. A series of seven studies, combining field and experimental data, provide converging support for this proposition and its underlying mechanism of perceived resources. Furthermore, the authors show that the positive effect of price promotions on consumers’ donation behavior is attenuated when consumers focus on the amount of money spent (rather than saved), when consumers feel they have overspent their budget, and when the monetary savings cannot be realized immediately. Finally, the authors show that this effect is stronger when donation solicitation occurs immediately after the price promotion (vs. after a delay). This research documents a novel behavioral consequence of price promotions and uncovers a mechanism by which price promotions can lead to positive social consequences and contribute to a better world.

We investigate the effect of pre-IPO investments by public market institutional investors (institutions) on the exit of venture capitalists (VCs). Results indicate that institutions’ pre-IPO investments reduce IPO underpricing by mitigating VCs’ reliance on all-star analysts to boost market liquidity. We conclude that institutions facilitate VC exits in the secondary market. Supporting this view, our analysis reveals that the presence of institutions allows VCs to exit with a reduced price impact in the secondary market. Consistent with the ease of exit, VCs offer fewer shares at the IPO and are more likely to invest in institutionally backed startups.

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.