PUBLICATIONS

The Executive Turnover Risk Premium
The Journal of Finance, vol. 69, no. 4, pp.
1529-1563, 2014.
(with Alexander Wagner)
Presented at the AFA Meetings 2010.

Abstract: We establish that CEOs of companies experiencing volatile industry conditions are more likely to be dismissed. At the same time, industry risk is, accounting for various other factors, unlikely to be associated with CEO compensation other than through dismissal risk. Using this identification strategy, we document that CEO turnover risk is significantly positively associated with compensation. This finding is important because job-risk compensating wage differentials arise naturally in competitive labor markets. By contrast, the evidence rejects an entrenchment model according to which powerful CEOs have lower job risk and at the same time secure higher compensation.

Winning by Losing: Evidence on the Long-Run Effects of Mergers

The Review of Financial Studies, vol. 31, no. 8, pp. 3212-3264, 2018.
(with Ulrike Malmendier and Enrico Moretti)

Presented at AFA Meetings 2009, EFA Meeting 2011, FIRS Meeting 2014, , Herzliya Summer Finance Conference 2014, Chicago Booth, DePaul, LSE, MIT Sloan, NBER Summer Institute 2013, NYU, Ohio State, Princeton, Tinbergen Institute, Yale.
Media mentions: CNN Money (May 2, 2012), Wall Street Daily (May 2, 2012)

Abstract: We propose a novel approach to measuring long-run returns to mergers. In a new data set of close bidding contests we use losers‘ post-merger performance to construct the counterfactual performance of winners had they not won the contest. Stock returns of winners and losers closely track each other over the 36 months before the merger, and bidders are also very similar in terms of Tobin’s Q, profitability and other accounting measures. Over the three years after the merger, however, losers outperform winners by 24 percent (14 percent internationally). Commonly used methodologies such as announcement returns fail to identify acquirors‘ underperformance.

WORKING PAPERS


 

Measuring Biases in Expectation Formation, 2018

(with Simas Kucinskas)

Presented at Bank of Lithuania, Dutch National Bank, HEC Paris, University of Amsterdam, University of Bonn, Vrije Universiteit Amsterdam.

Abstract: We develop a general framework for measuring biases in expectation formation. The method is based on the insight that biases can be inferred from the response of forecast errors to past news. Empirically, biases are measured by flexibly estimating the impulse response function of forecast errors. The framework does not require precise knowledge of the true data-generating process, and it nests all major existing models of expectations. Monte Carlo simulations show that the method is able to detect biases in empirically relevant settings. We illustrate the methodology using inflation expectations and find underreaction in both individual- and consensus-level forecasts.

Additional material: SlidesReplication files. See also my co-author’s blog for a non-technical summary: link.

Optimism Propagation, 2017

(with Torsten Jochem)

Miami Behavioral Finance Conference 2017, AFA 2018, EFA 2016, Research in Behavioral Finance Conference 2016, Columbia University,  Dutch National Bank, Norwegian School of Economics, U Amsterdam, U Bonn, U Michigan.

Abstract: We develop an empirical framework to identify bias correlation between agents using forecasts as a measure of subjective expectations. We document that managerial optimism spreads across firms along supply chains, and that these propagated beliefs cause changes in corporate policies of connected firms. Indicating causality, biases in supplier forecasts are only affected by previously issued customer forecasts, not by those issued in the near future. Propagation is stronger when suppliers are less confident about their own forecasts, and it increases in the perceived precision and salience of the customer forecast. In addition, propagated optimism causes changes in the financial policies of suppliers, suggesting that contagious sentiment contributes to fluctuations of business and credit cycles via production networks. Conceptually, we argue that propagation of irrational beliefs occurs even if suppliers are perfectly Bayesian; hence the transmission of biased beliefs is not per se irrational.

Product Market Peers and Relative Performance Evaluation, 2017

(with Sudarshan Jayaraman, Todd Milbourne, and Hojun Seo)

Revise and resubmit at The Accounting Review
Presented at 2015 Conference on Convergence of Financial and Managerial Accounting Research, AFA 2016, 2016 MIT Asia Accounting Conference, 2016 FMA conference, Washington University in St. Louis.
Abstract: Relative Performance Evaluation (RPE) theory predicts that firms filter out common shocks (i.e., those affecting the firm and its peers) while evaluating CEO performance and that the extent of filtering increases with the number of firms in the peer group. Despite the intuitive appeal of the theory, previous tests of RPE find weak and inconsistent evidence. We hypothesize that one reason for the mixed evidence is the inaccurate classification of peers. Rather than using static, pre-defined Standard Industry Classifications (SIC), we exploit recent advances in textual analysis and define peers based on firms’ product descriptions in their 10-K filings (Hoberg and Phillips, 2016). This alternative classification not only captures common shocks to firms’ product markets more effectively but also tracks the evolving nature of these markets, as 10-Ks are updated annually. Using product market peers, we find three pieces of evidence consistent with RPE in relation to CEO pay  – (i) firms on average filter out common shocks to stock returns, (ii) the extent of filtering increases with the number of peers, and (iii) firms completely filter out common shocks in the presence of a large number of peers. We also examine forced CEO turnover decisions and find evidence consistent with RPE theory. Overall, our results suggest that a key identification strategy to testing RPE theory lies in accurately defining the peer group.

Risk Premia in Executive Compensation: A Life-Cycle Perspective, 2010

Presented at the Mitsui Finance Symposium, 2010, UC Berkeley Macro Lunch, UC Berkeley
Corporate Finance Lunch

Abstract: How much of the rise in CEO pay can be explained by the increased risk that CEOs are exposed to? This paper employs a life-cycle model of consumption and saving to answer this question, and, more broadly, to study the relationship between the risk and level of CEO compensation. The model incorporates the main types of risk that executives of public corporations face: option- and stock-based pay, pay-performance sensitivity, dismissal risk, and stock return volatility. I use the model to compute risk premia in pay levels, and analyze how well they explain the observed variation in CEO pay. A calibration to a large panel of CEOs shows that, for realistic degrees of risk aversion, risk premia explain about 20 percent of the variation in CEO pay, both in the cross-section and the time-series. The model captures the higher moments of the cross-sectional pay distribution particularly well. The structural framework provided in this paper is robust to some sources of endogeneity typically encountered in reduced-form empirical research, and allows for the welfare analysis of policy interventions such pay limits for executives.

WORK IN PROGRESS


 

  • Common Ownership, Competition, and Top Management Turnover (with Martin Schmalz)
  • Bias Contagion Among Sell-Side Analysts