Essays in Corporate Finance

This dissertation consists of three chapters in corporate finance and private equity. Chapter 1, “Incentives of Private Equity General Partners from Future Fundraising”, co-authored with Ji-Woong Chung, Berk Sensoy and Michael Weisbach, studies the incentives of private equity general partners (GPs). Lifetime incomes of GPs are affected by their current funds’ performance not only directly, through carried interest profit-sharing provisions, but also indirectly by the effect of the current fund’s performance on GP’s abilities to raise capital for future funds. In the context of a rational learning model, which we show better matches the empirical relations between future fundraising and current performance than behavioral alternatives, we estimate that indirect pay for performance from future fundraising is of the same order of magnitude as direct pay for performance from carried interest. Consistent with the learning framework, indirect pay for performance is stronger when managerial abilities are more scalable and weaker when current performance is less informative about ability. Specifically, it is stronger for buyout funds than for venture capital funds, and declines in the sequence of a partnership’s funds. Total pay for performance in private equity is both considerably larger and much more heterogeneous than implied by the carried interest alone. Our framework can be adapted to estimate indirect pay for performance in other asset management settings. Uncertainty is ubiquitous in financial markets, and market participants form expectations and learn about parameters, which may be the ability of general partners or the quality of a firm’s governance structure. Assessing the quality of a firm\u27s governance is valuable, which might explain the recent growth of the governance industry. Yet, governance indices have been criticized by researchers and practitioners alike, mainly on the grounds of overlooking firms\u27 heterogeneity and their specific governance needs. Chapter 2, “D&O Insurance and IPO Performance: what can we learn from insurers?”, co-authored with Martin Boyer, provides new insights into the ability of directors’ and officers’ (D&O) insurers to price risk, and in particular risk related to governance characteristics. Therefore, learning by investors about governance quality could be facilitated by providing investors with a market-based assessment of governance as reflected in the D&O insurance premium. We investigate whether a firm’s D&O liability insurance contract at the time of the IPO is related to insured firms’ first year post-IPO performance. We find that insurers charge a higher premium per dollar of coverage to protect the directors and officers of firms that will subsequently have poor first year post-IPO stock performance. A higher price of coverage is also associated with a higher post-IPO volatility and lower Sharpe ratio. Our results are robust to various econometric specifications and suggest that even when the high level of information asymmetry inherent to the IPO context prevails, insurers have information about the firms’ prospects that should be valuable to outside investors. In Chapter 3, “A Learning-Based Approach to Evaluating Boards of Directors”, I develop a general framework based on a theoretical model of learning to assess how investors react to the appointment of new directors. Using predictions from a learning model, this chapter exploits the cross-sectional variation in the learning-induced decline in stock return volatility over director tenure to infer the marginal value of different kinds of directors. This new framework confirms prior empirical findings and documents new results. For example, directors joining better compensated boards have higher marginal value while the marginal value of a director joining an entrenched board is muted. Furthermore, the estimates imply that governance related uncertainty associated with the arrival of a new director accounts for 7% of return volatility, shedding light on the extent to which governance matters