This paper examines the relation between portfolio concentration and investment performance in corporate bond mutual funds. Using detailed holdings data, we construct portfolio concentration measures at the firm, industry, and credit rating levels. We find that portfolio concentration is significantly positively related to expected abnormal returns of corporate bond funds, and this relation is mainly driven by investment-grade funds. High-yield funds, however, do not exhibit such a relation, possibly due to the erosion of the value of portfolio concentration by liquidity costs. In support of this conjecture, we document that the concentration-performance relation is less pronounced among funds with higher sensitivities to market-wide illiquidity innovations and during periods of bond market illiquidity shocks and fund-level net money outflows. Finally, we show that more concentrated funds demonstrate stronger performance persistence, and investors appear to consider portfolio concentration in making investment decisions.

Using data from 2002 to 2013, we show that algorithmic trading has a positive impact on firm value. Most of this positive impact flows through the channels of stock liquidity, idiosyncratic volatility, and idiosyncratic skewness, but algorithmic trading also has a large economic effect outside those channels. We use the advent of auto quotation on the New York Stock Exchange as an exogenous shock to algorithmic trading to rule out reverse causality. The positive effects of algorithmic trading on firm value are stronger for larger firms and in the post-2007 period when algorithmic trading intensity is higher.