Portfolio performance measures using holdings data are panel regressions. The returns of a fund’s stocks are regressed on its lagged portfolio weights. Stock fixed effects isolate average performance from time-series predictive ability. Control variables condition for fund performance on the characteristics of the stocks held. The long-term performance of average holdings drives some of the classical measures, while predictive ability drives others. A “buy-and-hold drift,” where portfolio weights increase over time in the higher alpha stocks, affects performance measures. Investor flows respond to average performance net of the buy-and-hold drift.

We propose a dynamic equilibrium model with stochastic interest rates in which agents hold heterogeneous valuations for the same asset and take on positions against each other. The model shows that interest rate uncertainty and investor heterogeneity are key determinants of price dispersion. Higher search intensity reduces price dispersion, while raising volume, leading to a negative volatility-volume relation. The sensitivity of volatility to volume is high when liquidity is low, interest rate variations are high and investors’ valuations are more heterogeneous. Evidence supports our model’s predictions and shows that search frictions play an important role in driving the volatility-volume relation.

This paper investigates the roles of illiquidity and credit risk in determining the relations between price volatility of a bond and its trading frequency and trade size based on a large transaction dataset from October 2004 to June 2012. We find a positive relation between volatility and trading frequency and a negative relation between volatility and trade size. Consistent with the prediction of the search-based theory, the relations are much stronger for illiquid and risky bonds. Furthermore, both liquidity and credit risk become more important in times of stress and their effects are reinforcing. Results strongly suggest that search frictions and credit risk are important factors driving the relation between volatility and trading activity in the corporate bond market.

We study the underpricing and long-term performance of A- and B-share initial public offerings (IPOs) issued in China during the 1993–1998 period. The average underpricing for A- and B share IPOs are 178% and 11.6%, respectively. The underpricing of A-share IPOs is positively related to the number of days between the offering and the listing and the number of stock investors in the province from which the IPO comes, and negatively related to the number of shares being issued. None of these characteristics explain the underpricing of B-share IPOs. In the long run, A-share IPOs slightly underperform the size- and/or book/market (B/M)-matched portfolios while B-shares outperform the benchmark portfolios.

Existing term structure models of defaultable bonds have often underestimated corporate bond spreads. A potential problem is that investors’ taxes are ignored in these models. We propose a pricing model that accounts for stochastic default probability and differential tax treatments for discount and premium bonds. By estimating parameters directly from bond data, we obtain significantly positive estimates for the income tax rate of a marginal corporate bond investor after 1986. This contrasts sharply with the previous finding that the implied tax rates for Treasury bonds are close to zero. Results show that taxes explain a substantial portion of corporate bond spreads.

Exploring the role of social proximity in bank lending using a proprietary data set, we uncover evidence of a significant social proximity effect on private information acquisition. Local loan officers have better information to identify higher-quality borrowers through improved screening. This advantage is greater when firms have higher information asymmetry and lower ratings. Results suggest that sociocultural resources drive locals’ informational advantages and improve their lending performance. Importantly, we find that the social proximity advantage interacts with loan officer incentives. Strong evidence suggests that loan officers can be incentivized to use their social proximity advantage to make better loans.