Abstract
We provide new evidence on the determinants of performance pricing provisions in bank loan contracts. We find that firms that are easier to monitor, such as those with better accounting quality, lower information opacity, or a stronger relationship with the lender are more likely to have performance pricing loans. The use of performance pricing is less likely after financial restatement events. Furthermore, we find that the likelihood of using accounting-based (as opposed to credit-rating-based) pricing provisions increases as the firm’s accounting quality increases, and as the strength of the prior lending relation increases. Our results are robust to alternative measures of accounting quality, information opacity, and bank monitoring, and suggest that monitoring costs have a significant impact on the design of debt contracts.
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Notes
Anecdotal evidence also supports this viewpoint. For instance, in 2006, creditors sued Adelphia Communications (which had performance-priced debt tied to leverage) to recover up to $187 million in interest payments. They claimed that Adelphia paid less interest than it should have because the leverage ratio it reported to lenders was lower than its true leverage ratio.
We examine all performance pricing loans with performance measure of Debt/EBITDA ratio in Dealscan during 1990-2007, and find that the median grid in Debt/EBITDA is 0.5, i.e., the maximum distance of a borrower’s current Debt/EBITDA ratio to the performance pricing thresholds is 0.25. In contrast, the median distance between borrowers’ initial Debt/EBITDA ratio to their covenant thresholds for our sample firms is 1.00.
Lenders care about managerial effort (or real firm performance) as well as financial reporting quality. In particular, lenders are worried about the effect of moral hazard on managerial effort. Being able to credibly contract on ex post performance should mitigate this concern. Therefore, an argument could be made here that lenders are more likely to issue a loan with pricing provisions tied to borrowers’ future performance if the borrowers’ managerial efforts are harder to monitor. However, lenders would worry about the effect of moral hazard on reporting quality as well. We argue here that if the borrowers’ financial reporting quality is harder to monitor (because of poorer accounting quality, more informational opacity, no prior lending relationship), then accounting numbers are less easily verifiable, and, therefore, less reliable. So the lender will not offer a loan with spreads tied to the borrower’s accounting numbers.
We verify the robustness of our results using alternative accrual-based as well as non-accrual based measures of accounting quality. In particular, we use the standard deviation of residuals based on the original Dechow–Dichev model, the absolute value of residuals from both the Dechow–Dichev as well the modified Dechow–Dichev models, abnormal cash flows as in Roychowdhury (2006), and financial restatement events as alternative measures of accounting quality. These results are discussed in detail later in Sect. 4.
Bharath et al. (2011) similarly capture opacity using firm size, availability of credit rating, S&P 500 inclusion, microstructure based measures, and analyst following. Flannery et al. (2004) use two proxies for firm opacity: the stock’s market microstructure properties and the ability of analysts to forecast firm earnings. Their market measures of the stock’s information opacity are the bid–ask spreads, trading activity, or return volatility.
We do not differentiate between interest-increasing and interest-decreasing pricing provisions as do Asquith et al. because most of our loans are at the midpoint of the pricing grid and the loans could be interpreted as either interest- increasing or interest-decreasing.
Roberts and Sufi (2009), however, find little evidence that the presence of performance pricing provision reduces the likelihood of renegotiation.
We start our sample in 1990 because the data before 1990 are not comprehensive and do not cover a majority of commercial loans.
We keep the first restatement for each firm because we aim to examine the use of performance pricing provisions in loan contracts issued before and after restatement events, and the post-restatement window of the first restatement is likely to overlap with the pre-restatement window of (any) second restatement. This would be problematic in interpreting the results..
Gomes and Phillips (2010), Maskara and Mullineaux (2011) construct information asymmetry index in a similar manner. It is computed as the average of the quintile ranking of a firm based on six information asymmetry measures (analyst forecast errors, dispersion of analyst opinions, volatility of residual returns, volatility of abnormal returns around earnings announcements, firm age, and bid-ask spreads).
Similar to Anderson et al., to make data manageable, we focus on one-trading day every month (third Wednesday) and aggregate these 12 observations to get yearly bid-ask spread.
The vast majority of our sample loans are syndicated loans, which typically involve two types of lenders: lead arrangers and participant lenders. Lead arrangers establish and maintain a relationship with the borrower, and take on the primary responsibility of information collection and monitoring. In contrast, participant lenders rarely directly negotiate with the borrowing firm. Lead arrangers are compensated with a fee for arranging and managing the syndicated loan, in addition to the loan interest and commitment fee income.
Participant lenders rarely directly negotiate with the borrowing firm and usually hold a relatively small share of the loan.
For example, in April 1998, First Union Corp. acquired CoreStates Financial Corp. with the merged entity being called ‘First Union Corp.’ Thus for the purpose of computing lead bank-borrower relation after April 1998, we assume that First Union Corp. inherited CoreStates’ entire lending history prior to April 1998.
Although we call this variable BIG5 for convenience, the variable equals one in a given year if the firm’s auditors were any of the following: Arthur Andersen, Arthur Young, Coopers & Lybrand (this merged with Price Waterhouse on July 1, 1998), Ernst & Young, Deloitte & Touche, KPMG, PricewaterhouseCoopers (Price Waterhouse prior to July 1, 1998 merger with Coopers and Lybrand), or Touche Ross.
Among the 7381 performance pricing loans, 422 loans involve more than one performance measure. This leaves us with 6959 performance pricing loans involving only one performance measure.
We also find (results not tabulated in the interests of brevity) that performance pricing loans have longer maturity, are more likely to be a revolver, more likely to be secured and senior debt, more likely to have a financial or general covenant, and more likely to be syndicated relative to non-performance pricing loans.
We get similar results when we use MDD_AQ 5, DD_AQ5, AMDD_AQ5, and ADD_5.
Restatements might have positive or negative effect on income, and negative restatements are likely more indicative of low accounting quality. To investigate this conjecture, we identify whether a restatement has “Adverse” effect on financial statement from the Audit Analytics database. However we do not find any significant difference between restatements with and without “Adverse” effect on the likelihood of receiving performance pricing loans.
Our results here are consistent with Bharath et al. (2008) who find that firms with poorer accounting quality take private rather than public debt. This is because public debt is associated with higher monitoring costs relative to private debt, and therefore, borrowers that are opaque or that have poorer accounting quality will find it costlier to take public debt..
The Rating Dummy is equal to one if a firm has access to the public debt market, i.e., any type of S&P debt ratings is available in the Compustat database, and zero otherwise.
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Acknowledgments
Yan Hu is grateful to the financial support from University of Minnesota Grant-in-Aid of Research, Artistry, and Scholarship. We thank Sudipta Basu, Naveen Daniel, Elyas Elyasiani, Lily Li, Xi Li, Jagan Krishnan, Jayanthi Krishnan, Mihir Mehta, Lalitha Naveen, David Reeb, Ryan Williams, and seminar participants at Temple University, University of Minnesota, Duluth, and the 2010 FMA Annual Meeting in New York for their helpful comments and discussions. All errors are solely ours.
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Appendix: Variable definitions
Appendix: Variable definitions
Variable | Definition |
|---|---|
Performance pricing type | |
Debt to EBITDA | Outstanding debt divided by (net income plus depreciation and other non-cash charges) |
Senior Rating | Rating of outstanding debt on a senior basis |
Leverage ratio | Debt divided by capitalization (or equity) |
Senior Debt to EBITDA | Outstanding debt on a senior basis divided by (net income plus depreciation and other non-cash charges) |
Fixed Charge Coverage | Equal to EBITDA divided by (interest charges paid plus long-term lease payments) |
Debt to Tangible Net Worth | Total debt divided by (net worth minus intangible assets) |
Interest Coverage | Equal to EBITDA divided by interest expense |
Debt Service Coverage | Equal to EBITDA divided by (interest expense plus the quantity of principal repayments) |
Loan characteristics | |
Ln(loan size) | Natural logarithm of loan amount in US dollars |
Maturity | Time to maturity of the loan in years |
Revolver | Dummy = 1 if the loan is a revolver (364-Day Loan, Revolver/Line < 1 Yr., Revolver/Line > = 1 Yr., Revolver/Term Loan) |
Secured | Dummy = 1 if the loan is secured |
Senior | Dummy = 1 if the loan is senior |
Covenant | Dummy = 1 if the loan has any financial covenant or general covenant |
Syndicate | Dummy = 1 if the loan is syndicated |
Borrower characteristics | |
Firmsize | Natural logarithm of book value of assets |
Market-to-book | The sum of market value of equity and book value debt divided by book value of total assets |
ROA | Net income divided by total assets |
Rating dummy | A dummy variable that equals one if any type of S&P debt rating of a firm is available in Compustat |
Tangibility | Net PP&E divided by total assets |
Big5 | Indicator variable equal one if auditor code (Compustat variable AU) is 01 (Arthur Anderson), 02 (Arthur Young), 03, 04,05,06, 07, or 08 |
Accounting quality | |
DD_AQ10 | The standard deviation of residuals of following equation over the years t−9 through t: |
\(CA_{j,t} = c + \varphi_{1} CFO_{j,t - 1} + \, \varphi_{2} CFO_{j,t} + \varphi_{3} CFO_{j,t + 1} + \upsilon_{j,t} .\) | |
All variables are scaled by assets of that year. Estimation of Dechow and Dichev (2002, hereafter DD) model involves two steps. First, we estimate above equation annually for each firm for each of the 10 years t−9 through t. Then we calculate the standard deviation of firm j’s residuals across the 10 years, i.e., υ j,t through υ j,t−9 | |
DD_AQ5 | The same as DD_AQ10 except that it is the standard deviation of residuals over the 5 years t−4 through t |
MDD_AQ10 | Following McNichols (2002), we estimate the modified DD model (hereafter MDD) as follows: |
\(CA_{j,t} = c + \varphi_{1} CFO_{j,t - 1} + \, \varphi_{2} CFO_{j,t} + \varphi_{3} CFO_{j,t + 1} + + \, \varphi_{4} \Delta Sales_{j,t} + + \, \varphi_{5} PPE_{j,t} + \upsilon_{j,t} .\) | |
MDD model is equivalent to the DD model except that changes in sales and PPE are added. All variables are scaled by assets of that year. MDD_AQ10 is the standard deviation of residuals of the above equation over the years t−9 through t. MDD model involves two steps. First, we estimate above equation annually for each of the Fama and French (1997) 48 industry groups having at least 20 firms for each of the 10 years t−9 through t. Then we calculate the standard deviation of firm j’s residuals across the 10 years, i.e., υ j,t through υ j,t−9 | |
MDD_AQ5 | The same as MDD_AQ10 except that it is the standard deviation of residuals over the 5 years t−4 through t |
ADD_AQ10 | It is the average of the absolute value of υ j,t through υ j,t−9 of DD model |
ADD_AQ5 | It is the average of the absolute value of υ j,t through υ j,t−4 of DD model |
AMDD_AQ10 | It is the average of the absolute value of υ j,t through υ j,t−9 of MDD model |
AMDD_AQ5 | It is the average of the absolute value of υ j,t through υ j,t−4 of MDD model |
Abnormal CFO | Following Dechow et al. (1998) and Roychowdhury (2006), we measure abnormal CFO as deviation from the predicted values from the corresponding industry-year regression \(\frac{{CFO_{t} }}{{A_{t - 1} }} = \alpha_{0} + \alpha_{1} \frac{1}{{A_{t - 1} }} + \beta_{1} \frac{{S_{t} }}{{A_{t - 1} }} + \beta_{2} \frac{{\Delta S_{t} }}{{A_{t - 1} }} + \varepsilon_{t}\) |
Information opacity | |
Opacity index | Opacity index based on Anderson, Duru and Reeb (2008). It is an index based on four individual proxies: trading volume, bid-ask spread, analyst following and analyst forecast errors. Each of the four variables is ranked into deciles, with the most opaque firm taking a value of 10 and the least one taking a value of 1. The four ranks are summed and divided by 40 |
Trading volume | Natural logarithm of the average daily dollar volume during a fiscal year |
Bid-ask spread | The difference of ask price and bid price divided by the average of bid and ask prices |
# of analysts | The number of analysts providing EPS fiscal year-end estimate three quarters before the end of fiscal year |
Forecast error | The absolute value of the difference between the mean analysts’ EPS forecast (EPS fiscal year-end estimate provided three quarters before the end of fiscal year) and actual EPS scaled by the firm’s stock price |
Syndicate concentration | Sum of square of lead bank shares or sum of square of 100 divided by the number of lead banks if lead bank share data are missing |
Lead bank share | The largest share of lead bank in a particular loan or 100 divided by the number of lead banks if lead bank share data are missing |
Strength of bank-borrower relationship | |
Strength of lead bank-borrower prior lending relation ($) | It is computed as the dollar amount of loans arranged by a particular lead bank and its predecessors for a firm during the previous 5 years divided by the total dollar amount of loans borrowed by the firm during the same period |
Strength of lead bank-borrower prior lending relation (N) | It is computed as the number of loans arranged by a particular lead bank and its predecessors for a firm during the previous 5 years divided by the total number of loans borrowed by the firm during the same period |
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Hu, Y., Mao, C. Accounting quality, bank monitoring, and performance pricing loans. Rev Quant Finan Acc 49, 569–597 (2017). https://doi.org/10.1007/s11156-016-0601-1
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DOI: https://doi.org/10.1007/s11156-016-0601-1