I examine how the investment behavior of bond mutual funds affects corporate financing decisions. Mutual funds that hold a firm’s existing bonds have a high propensity to acquire additional new issuances from the same firm. I utilize this stylized fact to construct a firm-specific bond capital supply measure by aggregating flows from a firm’s existing bondholders. Firms with a higher flow-driven capital supply are more likely to issue bonds, enjoy lower yields, and substitute away from equity financing and bank loans. Information acquisition costs and underwriter relationship likely contribute to the impact of flow-driven capital supply.

This paper considers the nature of returns to scale in active management following Pástor et al. (2015) who fail to establish diseconomies of scale at the fund level. Using an enhanced empirical strategy, we find a significant negative impact of fund size on performance. This empirical evidence indicates that fund alpha and fund size are not independent entities. Consequently, skill, rather than being measured by the fund alpha, should be measured by the value that a fund extracts from capital markets. We also show that there exist sophisticated investors who correctly exploit positive net present value investment opportunities.

The Federal Reserve uses (reverse) auctions to implement its purchases of Treasury bonds in quantitative easing (QE). To evaluate dealers’ offers across multiple bonds, the Fed relies on its internal yield curve model, fitted to secondary market bond prices. From November 2010 to September 2011, a one standard deviation increase in the cheapness of a Treasury bond (how much the market price of the bond is below a model-implied value) increases the Fed’s purchase quantity of that bond by 276 million and increases the auction costs on that bond by 2.6 cents per $100 par value, controlling for standard covariates. Our results suggest that the Fed harvests gains from trades by purchasing undervalued bonds, but strategic dealers extract some profits because the Fed’s relative values can be partly inferred from price data.

In this paper, we analyze the usefulness of technical analysis, specifically the widely employed moving average trading rule from an asset allocation perspective. We show that, when stock returns are predictable, technical analysis adds value to commonly used allocation rules that invest fixed proportions of wealth in stocks. When uncertainty exists about predictability, which is likely in practice, the fixed allocation rules combined with technical analysis can outperform the prior-dependent optimal learning rule when the prior is not too informative. Moreover, the technical trading rules are robust to model specification, and they tend to substantially outperform the model-based optimal trading strategies when the model governing the stock price is uncertain.

Average skewness, which is the average of monthly skewness values across firms, performs well at predicting future market returns. This prediction still holds after controlling for the size or liquidity of the firms or for current business cycle conditions. Also, average skewness compares favorably with other economic and financial predictors of subsequent market returns. The asset allocation exercise based on predictive regressions also shows that average skewness generates superior performance.

Regulation Fair Disclosure, implemented on October 23, 2000, prohibits U.S. public companies from making selective, nonpublic disclosures to favored investment professionals. Regulation Fair Disclosure has a number of exclusions, however, including disclosure of nonpublic information to credit rating agencies. As a result, credit analysts at rating agencies have access to confidential information that is no longer made available to equity analysts, potentially increasing the information content of credit ratings. We examine the effect of credit rating changes on stock prices and find that the informational effect of downgrades and upgrades is much greater in the post-FD period.

The Dodd-Frank Act mandates that certain standard over-the-counter (OTC) derivatives must be traded on swap execution facilities (SEFs). Using message-level data, we provide a granular analysis of dealers’ and customers’ trading behavior on the two largest dealer-to-customer SEFs for index credit default swaps (CDS). On average, a typical customer contacts few dealers when seeking liquidity. A theoretical model shows that the benefit of competition through wider order exposure is mitigated by a winner’s curse problem and dealer-customer relationships. Consistent with the model, we find that order size, market conditions, and customer-dealer relationships are important empirical determinants of customers’ choice of trading mechanism and dealers’ liquidity provision.

Using jumps in stock prices as a proxy for large information shocks, we provide evidence consistent with short-term underreaction in the US equity market. Strategies long (short) stocks with positive (negative) lagged jump returns earn significantly positive returns over the next one- to three-month horizons. The results based on intraday jumps, especially overnight jumps, provide further evidence consistent with underreaction. The underreaction is robust to controlling for other firm characteristics, extends stock return momentum over intermediate to short horizons, and captures market underreaction to information shocks beyond earnings surprises. We further show that limited investor attention contributes to short-term underreaction.

In this paper, we provide a trend factor that captures simultaneously all three stock price trends: the short-, intermediate-, and long-term, by exploiting information in moving average prices of various time lengths whose predictive power is justified by a proposed general equilibrium model. It outperforms substantially the well-known short-term reversal, momentum, and long-term reversal factors, which are based on the three price trends separately, by more than doubling their Sharpe ratios. During the recent financial crisis, the trend factor earns 0.75% per month, while the market loses −2.03% per month, the short-term reversal factor loses −0.82%, the momentum factor loses −3.88%, and the long-term reversal factor barely gains 0.03%. The performance of the trend factor is robust to alternative formations and to a variety of control variables. From an asset pricing perspective, it also performs well in explaining cross-section stock returns.

We characterize the dynamic fragmentation of U.S. equity markets using a unique data set that disaggregates dark transactions by venue types. The “pecking order” hypothesis of trading venues states that investors “sort” various venue types, putting low-cost-low-immediacy venues on top and high-cost-high-immediacy venues at the bottom. Hence, midpoint dark pools on top, non-midpoint dark pools in the middle, and lit markets at the bottom. As predicted, following VIX shocks, macroeconomic news, and firms’ earnings surprises, changes in venue market shares become progressively more positive (or less negative) down the pecking order. We further document heterogeneity across dark venue types and stock size groups.