We demonstrate improvements in predictive power when introducing spline functions to take account of highly nonlinear relationships between firm failure and leverage, earnings, and liquidity in a logistic bankruptcy model. Our results show that modeling excessive nonlinearities yields substantially improved bankruptcy predictions, on the order of 70%–90%, compared with a standard logistic model. The spline model provides several important and surprising insights into nonmonotonic bankruptcy relationships. We find that low-leveraged as well as highly profitable firms are riskier than those given by a standard model, possibly a manifestation of credit rationing and excess cash-flow volatility.

The voluntary nature of hedge fund database reporting creates strategic listing opportunities for hedge funds. However, little is known about how managers list funds across multiple databases or whether investors are fooled by funds’ listing decisions. In this paper, we find that hedge funds strategically list their small, best-performing funds in multiple outlets immediately while preserving the option to list their other funds in additional databases later. We generally find that investors react rationally to these fund listings based on the predictability of performance. Finally, our results lead to specific guidelines on handling backfilled returns to minimize biases.

This paper examines whether one of the most important participants in the takeover market, the institutional investors of target companies, suffers from the disposition effect and, if so, how this selling bias influences the takeover outcomes. I report robust evidence that target institutional investors are reluctant to realize losses. This bias further allows their sunk cost to affect both the takeover price and the deal success. My results are explained by neither the undervalued targets nor the 52-week-high price effect. They are most pronounced among targets whose investors have a strong propensity to hold on to loser stocks.

We study the value gains from takeovers of firms with poor acquisition histories. We document that the premium received by target shareholders is higher when the value loss from the targets’ prior acquisitions is larger. However, the gains to target shareholders seem to be offset by losses to acquiring shareholders. The average announcement return to acquiring shareholders is negative and decreasing in the value loss from the targets’ prior acquisitions. Additionally, the combined acquirer-target value created in these takeovers is insignificant. These results suggest that the value lost from targets’ prior acquisitions is not recovered through changes in corporate control.

I empirically investigate the relation between chief executive officer (CEO) inside debt holdings and mergers and acquisitions (M&As), and find evidence consistent with the agency theory’s prediction of a negative relation between CEO inside debt holdings and corporate risk taking. Further analysis shows that CEO inside debt holdings are positively correlated with M&A announcement abnormal bond returns and long-term operating performance, but negatively correlated with M&A announcement abnormal stock returns. Finally, I find evidence that acquirers restructure the postmerger composition of CEO compensation that mirrors their capital structure in order to alleviate incentives for wealth transfer from shareholders to bondholders or vice versa.

A growing number of studies use dollar-weighted (DW) returns as evidence that bad timing substantially reduces investor returns, and that consequently the equity risk premium must be considerably lower than previously thought. This paper demonstrates that this method is subject to a hindsight effect (as prior returns influence levels of new investment) and derives a technique that corrects it. The results show that for mainstream U.S. equities, DW returns are low because of this hindsight effect (bad investor timing had very little impact). Thus, low DW returns do not imply that the risk premium is correspondingly low.

We examine shareholder litigation and the price and nonprice terms of bank loan contracts. After filing a lawsuit, defendant firms pay higher loan spreads and up-front charges, experience more financial covenants, and are more likely to have a collateral requirement. These findings are consistent with reputational losses associated with shareholder litigation. The magnitude of a firm’s lost market value when the lawsuit is filed is positively related to the increase in the firm’s future borrowing costs. We investigate whether the lawsuit allegations and its merit affect future bank loan terms. Our results do not appear to be affected by self-selection.

Numerous hedge funds stop reporting each year to commercial databases, wreaking havoc with analyzing investment strategies that incur the unobserved delisting return. We use estimated portfolio holdings for funds-of-funds to back out estimated hedge-fund delisting returns. For all exiting funds, the estimated mean delisting return is insignificantly different from the average monthly return for live hedge funds. However, funds with poor prior performance and no clearly stated delisting reason had a significantly negative estimated mean delisting return of -5.97%, suggesting that a shock to their returns “tips them over the edge” and leads to delisting.

We develop a multivariate credit risk model that accounts for joint defaults of banks and allows us to disentangle how much of banks’ credit risk is systemic. We find that the United States and United Kingdom differ not only in the evolution of systemic risk but, in particular, in their banks’ systemic exposures. In both countries, however, systemic credit risk varies substantially, represents about half of total bank credit risk on average, and induces high risk premia. The results suggest that sovereign and bank systemic risk are particularly interlinked in the United Kingdom.

A spurious positive relation between exponential generalized autoregressive conditional heteroskedasticity (EGARCH) estimates of expected month t idiosyncratic volatility and month t stock returns arises when the month t return is included in estimation of model parameters. We illustrate via simulations that this look-ahead bias is problematic for empirically observed degrees of stock return skewness and typical monthly return time series lengths. Moreover, the empirical idiosyncratic risk-return relation becomes negligible when expected month t idiosyncratic volatility is estimated using returns only up to month t − 1.