We examine whether industry product market conditions are important in assessing the benefits and costs of chief executive officer (CEO) power. We find that firms are more likely to have powerful CEOs in high demand product markets where firms are facing entry threats. In these markets, investors react favorably to announcements granting more power to CEOs, and CEO power is associated with higher market value, sales growth, investment, advertising, and the introduction of more new products. Our results remain significant when addressing the endogeneity of CEO power by instrumenting CEO power with past non-CEO executive and director sudden deaths.

In an effort to increase transparency, the chair of the Federal Reserve now holds a press conference (PC) following some, but not all, Federal Open Market Committee (FOMC) announcements. Evidence from financial markets shows that investors lower their expectations of important decisions on days without PCs and that these announcements convey less price-relevant information. Correspondingly, we show that investors pay more attention to upcoming announcements with PCs. This coordination of attention can reduce welfare in models of the social value of public information. Consistent with theories of investor attention, the market risk premium is larger on days with PCs.

We examine liquidity transformation by funds of hedge funds (FoFs) by developing a new measure, illiquidity gap, that captures the mismatch between the liquidity of their portfolios and the liquidity available to their investors. We find that higher liquidity transformation is driven by FoFs’ incentives to attract more capital and earn higher compensation. Greater liquidity transformation is associated with higher exposure to investor runs and worse performance during crisis periods. Finally, FoFs mitigate the risks associated with liquidity transformation by maintaining higher cash buffers.

Using panel data on top management characteristics and a management quality factor constructed using common factor analysis on individual management quality measures, we analyze the relation between top firm management quality and corporate innovation input and output. We show that top management quality is an important determinant of corporate innovation, with individual aspects of management quality affecting innovation in younger and older firms differently. Further, firms with higher top management quality engage in more risky (“explorative”) innovation strategies. Finally, hiring more and higher-quality inventors is an important channel through which firms with higher management quality achieve greater innovation output.

We show that market volatility of volatility is a significant risk factor that affects index and volatility index option returns, beyond volatility itself. The volatility and volatility of volatility indices, identified model-free as the VIX and VVIX, respectively, are only weakly related to each other. Delta-hedged index and VIX option returns are negative on average and are more negative for strategies that are more exposed to volatility and volatility-of-volatility risks. Further, volatility and volatility of volatility significantly negatively predict future delta-hedged option payoffs. The evidence suggests that volatility and volatility-of-volatility risks are jointly priced and have negative market prices of risk.

I investigate how distraction affects the trading behavior of professional asset managers. Exploring detailed transaction-level data, I show that managers with a large fraction of portfolio stocks that have an earnings announcement are significantly less likely to trade in other stocks, suggesting that these announcements divert attention from trading decisions for other stocks. This distraction effect is more pronounced for nonpassive managers who engage in active stock selection choices. Finally, I identify three channels through which distraction hurts managers’ performance: Distracted managers trade less profitably, incur slightly higher transaction costs, and are less likely to close losing positions.

Post-earnings announcement drift (PEAD) is stronger in firms that release earnings on days when market returns are higher in magnitude. This drift remains robust after controlling for previously documented factors such as Friday releases, the number of simultaneous releases, and price delay measure. Negative earnings surprises drive this drift, and the drift is more pronounced among small stocks, value stocks, and stocks that have low analyst following. Slower analyst response to earnings contributes to the drift. These findings are consistent with investors paying more attention to market information and less attention to firm-specific information due to attention constraints.

This article proposes the entropy of m2 (m is the stochastic discount factor) as a metric to evaluate asset-pricing models. We develop a bound on the entropy of m2 when m correctly prices a finite number of returns and consider models that pass the lower bound on m, yet fail the lower bound on m2. Interpreting our results, we elaborate on the distinction between the entropy of m2 versus the entropy of m. We further show that the entropy of m2 represents an upper bound on the expected excess (log) return of the security with the payoff of m.

Insurance companies often follow highly correlated investment strategies. As major investors in corporate bonds, their investment commonalities subject investors to fire sale risk when regulatory restrictions prompt widespread divestment of a bond following a rating downgrade. Reflective of fire sale risk, the clustering of insurance companies in a bond has significant explanatory power for yield spreads, controlling for liquidity, credit risk, and other factors. The effect of insurer clustering on bond yield spreads is more evident for bonds held to a greater extent by capital-constrained insurance companies, those with ratings closer to National Association of Insurance Commissioners risk categories with larger capital requirements, and during the financial crisis.

We propose a new approach based on a generalization of the logit model to improve prediction accuracy in U.S. bank failures. Mixed-data sampling (MIDAS) is introduced in the context of a logistic regression. We also mitigate the class-imbalance problem in data and adjust the classification accuracy evaluation. In applying the suggested model to the period from 2004 to 2016, we show that it correctly classifies significantly more bank failure cases than the classic logit model, in particular for long-term forecasting horizons. Some of the largest recent bank failures in the United States that had been previously misclassified are now correctly predicted.

Traders can reduce search costs in dealership markets by entering relationships with dealers. However, dealers draw little informational benefit from these relationships in Treasury markets, due to low risk and information asymmetry. We investigate the extent, duration, effects on pricing, and potential benefits of client–dealer relationships. We find that relationship strength leads to higher execution costs for clients, more so during stressed market conditions but less so in the presence of information asymmetry and when trading in corporate bonds. Relationship traders are compensated with immediacy at times when search is costly.

This article introduces the impact of debt misvaluation on merger and acquisition activity. We show the potential for debt misvaluation to help explain the shifting dominance of financial acquirers (private equity firms) relative to strategic acquirers (operating companies). Fundamental differences in governance and project coinsurance between the two types of acquirer would interact with debt misvaluation, resulting in variation in how assets are owned that depends on debt market conditions. We find support for our theory in merger data using a novel measure of debt misvaluation.