Knowledge that Transforms

We exploit a unique natural experiment—recent restrictions of dark trading in Canada and Australia—and proprietary trade-level data to analyze the effects of dark trading. Disaggregating two types of dark trading, we find that dark limit order markets are beneficial to market quality, reducing quoted, effective, and realized spreads and increasing informational efficiency. In contrast, we do not find consistent evidence that dark midpoint crossing systems significantly affect market quality. Our results support recent theory that dark limit order markets encourage aggressive competition in liquidity provision. We discuss implications for the regulation of dark trading and tick sizes.

The performance of portfolio managers depends on market timing, volatility timing, and security selection. We develop holdings-based performance measures that adjust for risk using stochastic discount factors, display all three components in a consistent framework, and avoid strong assumptions about managers’ behavior. Previous models leave out some of the components of performance, and correcting for this we deliver better measures of selectivity. Sorting stocks held by funds on selectivity produces a quintile spread in four-factor alphas greater than 2.5% per year before costs and more than 1.7% greater than found using the Daniel, Grinblatt, Titman, and Wermers (1997) measure.

We show that short interest is arguably the strongest known predictor of aggregate stock returns. It outperforms a host of popular return predictors both in and out of sample, with annual R2 statistics of 12.89% and 13.24%, respectively. In addition, short interest can generate utility gains of over 300 basis points per annum for a mean-variance investor. A vector autoregression decomposition shows that the economic source of short interest’s predictive power stems predominantly from a cash flow channel. Overall, our evidence indicates that short sellers are informed traders who are able to anticipate future aggregate cash flows and associated market returns.

How best to discern trading intentions from market data? We examine the accuracy of three methods for classifying trade data: bulk volume classification (BVC), tick rule and aggregated tick rule. We develop a Bayesian model of inferring information from trade executions and show the conditions under which tick rules or bulk volume classification predominates. Empirically, we find that tick rule approaches and BVC are relatively good classifiers of the aggressor side of trading, but bulk volume classifications are better linked to proxies of information-based trading. Thus, BVC would appear to be a useful tool for discerning trading intentions from market data.

This article examines managers’ incentive to play it safe. We find that, after managers are insulated by the adoption of an antitakeover law, they take value-destroying actions that reduce their firms’ stock volatility and risk of distress. To illustrate one such action, we show that managers undertake diversifying acquisitions that target firms likely to reduce risk, have negative announcement returns, and are concentrated among firms with managers who gain the most from reducing risk. Our findings suggest that instruments typically used to motivate managers, such as greater financial leverage and larger ownership stakes, exacerbate risk-related agency challenges.

We examine the dynamic relation between credit risk and liquidity in the Italian sovereign bond market during the eurozone crisis and the subsequent European Central Bank (ECB) interventions. Credit risk drives the liquidity of the market. A 10% change in the credit default swap (CDS) spread leads to a 13% change in the bid-ask spread, the relation being stronger when the CDS spread exceeds 500 basis points. The Long-Term Refinancing Operations of the ECB weakened the sensitivity of market makers’ liquidity provision to credit risk, highlighting the importance of funding liquidity measures as determinants of market liquidity.

We propose a simple methodology to evaluate a large number of potential explanations for the negative relation between idiosyncratic volatility and subsequent stock returns (the idiosyncratic volatility puzzle). Surprisingly, we find that many existing explanations explain less than 10% of the puzzle. On the other hand, explanations based on investors’ lottery preferences and market frictions show some promise in explaining the puzzle. Together, all existing explanations account for 29–54% of the puzzle in individual stocks and 78–84% of the puzzle in idiosyncratic volatility-sorted portfolios. Our methodology can be applied to evaluate competing explanations for other asset pricing anomalies.

Using audit-trail data from the Toronto Stock Exchange, we find that market makers scale back in unison when market conditions are unfavorable, which contributes to covariation in liquidity supply, both within and across stocks. Market conditions lower aggregate participation via their impact on trading profits and risk. Contrary to regulatory view, higher stock volatility is associated with more participation and higher profits, even after controlling for other market conditions, including stock volume. Fragility concerns extend to larger stocks and to active participants. The designated market maker mitigates periodic illiquidity created by synchronous withdrawal of market makers in large and small stocks.

The paper presents a new theory of trade credit in which firms buy inputs on credit from suppliers to restore the benefits of secured bank financing impaired by contract incompleteness. In a setting where investment is endogenous and unobservable to financiers, we show that a bank-secured credit contract is time-inconsistent. Upon being granted credit, the entrepreneur has an incentive to alter the original input combination, jeopardizing the bank’s revenues. Anticipating the entrepreneur’s opportunism, the bank offers an unsecured credit contract, reducing the surplus from the venture. One way for the entrepreneur to commit to the contract terms is to purchase inputs on credit from the supplier. The supplier observes the input investment and acts as a guarantor that inputs will be purchased as contracted, thus facilitating access to secured bank financing. The commitment role of trade credit still holds in a multi-period extension that investigates the impact of bank relationship lending on secured debt and trade credit. Our model provides novel testable predictions on optimal financial contracts in both one-period and repeated lending relationships.

This paper examines post-revision return drift, or PRD, following analysts’ revisions of their stock recommendations. PRD refers to the finding that the analysts’ recommendation changes predict future long-term returns in the same direction as the change (i.e., upgrades are followed by positive returns, and downgrades are followed by negative returns). During the high-frequency algorithmic trading period of 2003–2010, average PRD is no longer significantly different from zero. The new findings agree with improved market efficiency after declines in real trading cost inefficiencies. They are consistent with a reduced information production role for analysts in the supercomputer era.