Edward L. Owens

Measuring the Probability of Financial Covenant Violation in Private Debt Contracts

We examine the measurement of financial covenant strictness in private debt contracts. Based on descriptive analysis of detailed underlying covenant definitions, for each Dealscan covenant type we specify a “standard” covenant definition that can be computed using readily available data in Compustat. For most covenants, the average error induced by using our standard definition rather than using the precise covenant definitions is insignificant. Applying these findings, we compute a single Dealscan-based comprehensive measure of financial covenant strictness that utilizes information about slack, volatility of underlying covenant parameters, and their covariance across the entire set of financial covenants included in a contract. We provide evidence that this measure is superior to alternative measures of covenant strictness used in prior literature. Although measurement error undoubtedly exists, our evidence endorses a comprehensive approach to measuring covenant strictness using the full breadth of covenant slack data available in Dealscan.

How Are Analysts’ Forecasts Affected by High Uncertainty?

This study examines whether key characteristics of analyst forecasts — timeliness, accuracy, and bias — change at a time when investor demand for information is likely to be especially high, i.e., during periods of high market uncertainty. Findings reveal that when market uncertainty is high, analysts’ forecasts are less timely as aggregate news between forecasts increases and forecasts are issued with a greater delay following earnings announcements. In contrast, when idiosyncratic firm uncertainty is high, there is only a slight increase in aggregate news between forecasts and forecasts are issued sooner following earnings announcements. Findings also reveal that, when market uncertainty is high, forecasts are less accurate and more biased. This result is larger for market uncertainty than firm uncertainty, and only for market uncertainty holds in both down and up markets. We also provide evidence that analysts are less sensitive to news when market uncertainty is high, which could explain this decline in timeliness and accuracy and increase in bias. However, despite the changes in characteristics of analysts’ forecasts when market uncertainty is high, evidence indicates that these forecasts nevertheless reduce bid-ask spreads, even to a greater extent than forecasts made when market uncertainty is low or when firm uncertainty is high.

Debt Contracts, Loss Given Default and Accounting Information

We investigate an unexplored channel — loss given default — through which accounting information can shape the design of debt contracts. Using a sample of defaulted bonds, we find that borrower accounting measures available at contract initiation possess significant power for predicting realized loss given default at the subsequent default date. We then use this prediction model to construct an accounting-based measure of expected loss given default at the contracting date for a large sample of bond issuances. We find that this measure is positively associated with the issuance date interest spread and covenant use. We further document that the relation between accounting-based expected loss given default and contract terms is not an artifact of its potential association with probability of default. Our results increase our understanding of both the informational role and contracting role of accounting information.

Heterogeneity in Firm Underlying Economics and Abnormal Accrual Models

Basic economics challenge the specification of discretionary accrual models. Since rent seeking firms pursue differentiated business strategies, firms in the same industry have heterogeneous accrual generating processes. Moreover, technological innovation, regulatory changes, and entry of new firms force existing firms to revise their extant business models. We present evidence that such business model shocks are widespread, propagate through several years of financial statements, reduce accrual models’ goodness of fit, and result in unrealistically large unsigned “abnormal” accruals. Further, there is a spillover effect among firms in the same industry in that one firm’s abnormal accrual is affected by business model shocks experienced by the other firms in the industry. We show that business model shocks not only add noise to abnormal accruals, but can also introduce biases into both unsigned and signed discretionary accruals. Our results suggest that removing observations contaminated by business model shocks leads to better specified accrual models, and reduces both Type I and Type II errors in tests of earnings management. We gratefully acknowledge the financial support provided by the Simon School at the University of Rochester and the comments from Dan Amiram, Dan Collins, Mark Evans, Wayne Guay, Shane Heitzman, Robert Resutek, Jerry Warner, Charles Wasley and seminar participants at Dartmouth University, University of Rochester, the MIT Asia Conference in Accounting, and Penn State Accounting Research Conference.Economics challenge the specification of discretionary accrual models. Since rent-seeking firms pursue differentiated business strategies, firms in the same industry have heterogeneous economic fundamentals and hence different accrual generating processes. Business model shocks amplify a firm’s underlying economic heterogeneity and thereby exacerbate accrual model misspecification. We present evidence that business model shocks are widespread, propagate through multiple years of financial statements, and reduce accrual models’ goodness of fit. This not only affects abnormal accrual estimates for the firm experiencing shocks, but also affects measurement of abnormal accruals for other firms in the industry. We show that underlying economic heterogeneity not only adds noise to abnormal accruals, but can also exacerbate bias in both unsigned and signed abnormal accruals. We propose ways to reduce accrual model misspecification.

Equity-Market Trading Restrictions and Credit Prices: Evidence from Short-Sale Constraints Around the World

We examine how equity-market trading restrictions affect credit prices. Using short-sale constraints (SSCs) as a proxy for trading restrictions, we examine two specific sources of variation — the randomized Regulation SHO experiment and time-series variation in short-selling bans during the 2008 financial crisis. In both analyses, we find that greater SSCs are associated with significantly higher credit-default-swap (CDS) spreads. Changes in the term structure of CDS spreads suggest that this increase is attributable to a decrease in the availability of default-risk-relevant information. Further, using a default-model-prediction framework, we document that it is more difficult to accurately assess default risk when short selling is constrained, which corroborates information risk as a channel through which SSCs lead to higher credit prices.

Pessimistic-Trading Restrictions and the Information Environment: Evidence from Short-Sale Constraints and Default Prediction around the World

We examine how constraints on pessimistic trading affect the ability to assess a firm’s likelihood of default using publicly available sources of information. Using cross-country differences in short selling as our primary empirical proxy for the ability of market participants to trade pessimistically, our results indicate that a dynamic multiperiod logit model accurately predicts 18 percentage points more actual occurrences of default in countries where short-selling is widely practiced. We find little evidence that short selling restrictions reduce the proportion of inaccurately classified non-default observations. These associations are further identified using time-series variation in the introduction of put option trading. Finally, in countries that face significant pessimistic trading constraints, we document that the direct incorporation of accounting information leads to a greater improvement in default prediction accuracy, particularly where financial reporting transparency is relatively high.We examine how pessimistic-trading restrictions, such as short-sale constraints, affect firms’ information environments. Although prior research finds that such restrictions reduce equity price informativeness, other non-equity-market-based sources of information, such as firm’s financial statements, could offset this loss of information. We assess the availability of firm-specific information based on the accuracy of a public-information-based default prediction model. We find that, while short-sale constraints both decrease the usefulness of equity-market variables for predicting firm default and increase the usefulness of equity-market variables for predicting non-defaults, the net effect of pessimistic-trading restrictions is a reduction in the informativeness of equity-market-based default predictors. Accounting and other non-equity-market-based sources of information significantly mitigate this informational cost. However, using an exogenous shock, we document that short-sale constraints are associated with higher credit spreads, which suggests that the net effect of pessimistic-trading restrictions is a reduction in the availability of firm-specific information.

Asymmetric Effects of Default Probability on Earnings Informativeness

I provide evidence that bankruptcy contagion reduces the earnings informativeness of surviving firms in the wake of an intra-industry bankruptcy. Further, I interact insights from accounting conservatism and earnings-persistence-based valuation theory to develop the prediction and show that the effect of default risk in general, and bankruptcy contagion in particular, on earnings informativeness is concentrated in good news earnings announcements. That is, whereas bankruptcy contagion decreases the informativeness of good news earnings, bankruptcy contagion has little effect on the informativeness of bad news earnings. These contagion effects are more pronounced during industry bankruptcy waves, when the surviving firms have relatively weak financial health, and when the industry is more competitive.