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The impact of cash flow management versus accruals management on credit rating performance and usage


Corporate bond issuers attempt to influence bond ratings through their discretion over reported numbers, which could diminish credit rating quality. I find that cash flow management is negatively associated with rating quality, while accruals management is not. These results suggest that rating agencies fail to adjust for cash flow management, but they undo accruals management. The differential results for cash flow management versus accruals management could be due to the rating agencies’ more skeptical attitude towards accruals and the lower cost of adjusting accruals management. The relation between cash flow management and rating quality is weaker for issuers with high leverage and issuers with prior ratings around the investment-/speculative-grade cutoff. Overall, the evidence suggests that rating management through managerial discretion over reported numbers has a detrimental impact on rating quality, but only via the cash flow component. Fortunately, the bond market understands the implications of cash flow management on rating quality and relies less on ratings in response to cash flow management.

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  1. 1.

    A credit rating provides information about the creditworthiness of a security. It can reduce information asymmetry between borrowers and capital providers, help monitor debt contracts, and facilitate compliance with regulations (e.g., Listokin and Taibleson 2010; Becker and Milbourn 2011; Kisgen 2006).

  2. 2.

    Similar to Lee (2012), I define CFO management as managerial use of discretion over financial reporting or real transactions to manage cash flow from operations. Some forms of CFO management affect earnings; others don’t. For instance, firms can boost their reported CFO—without affecting earnings—by postponing supplier payments and hastening customer collections or by moving items within the categories of cash flow statements. Firms can also reduce discretionary expenses, which increases both CFO and earnings (Dechow and Sloan 1991; Roychowdhury 2006).

  3. 3.

    Caton et al. (2011) document that accruals management surrounding a seasoned bond offering (SBO) is associated with worse ratings. In contrast, Alissa et al. (2013) show that when firm ratings deviate from the expected ratings, accruals management helps move the ratings towards the expected ratings.

  4. 4.

    A favorable rating does not speak to rating quality unless some benchmarks like correct ratings are used to gauge rating quality. Jiang et al. (2012) document that S&P’s ratings are worse than Moody’s while S&P bills investors and Moody’s bills issuers, but they still acknowledge that “we also cannot infer whether S&P’s rating for a particular bond is too optimistic or pessimistic with regard to a true objective rating.” Plus, rating quality has multiple dimensions (e.g., rating accuracy, rating timeliness, rating informativeness), it is challenging to infer which dimension is influenced based on rating levels.

  5. 5.

    Note that lack of effort is not equivalent to catering. CRAs could expend less effort on cash flow analysis because they take reported cash flow as fact or becuase it is too costly for them to adjust CFO management. Catering occurs when CRAs intentionally curry favor with issuers and reduce their scrutiny due to economic bonding with issuers.

  6. 6.

    I estimate my measure of CFO management using cross-sectional data, while Lee (2012) estimates a measure of CFO management using time-series data. I have more discussion in Sect. 3 on why I choose the cross-sectional approach over the time-series approach.

  7. 7.

    Lee (2012) has done several tests to validate a similar metric estimated using time-series data as a proxy for CFO management.

  8. 8.

    Similarly, Rountree et al. (2008) show that investors value smooth financial statements, but only through the cash flow part of earnings as opposed to the accruals part of earnings. Recently, Kothari et al. (2016) indicate that financial statement management through managerial discretion over expenses leads to overvaluation at the time of a seasoned equity offering (SEO), while accruals management does not.

  9. 9.

    My research is related to but differs from prior studies on earnings management and credit ratings such as Caton et al. (2011) and Alissa et al. (2013). First, these studies look at rating levels, while mine examines rating quality. The results on rating levels do not clearly imply whether and which dimension of rating quality are influenced. Even though more favorable ratings could indicate bias, rating bias does not necessarily lead to lower rating quality (Narayanan 1985; Stein 1989; Verrecchia 1986; Bonsall 2014; deHaan 2017). Second, prior studies mainly focus on accruals management and do not examine CFO management. Given that CFO management could substitute for, or complement, accruals management, it is essential to investigate the effect of CFO management in addition to that of accruals management. Third, I discuss the cross-sectional variations in the CFO management-ratings quality relation. Finally, my study also investigates the bond market usage of ratings in response to rating management through managerial discretion over reported numbers. Overall, my study complements and extends prior research on earnings management and credit ratings.

  10. 10.

    Also, Smith and Walter (2001) document that the CRAs' reputation plays a critical role in influencing the demand of bond issuers for rating services.

  11. 11.

    See articles by Fink (2000), Glassman (2002), Henry (2004), and Lauricella (2008), and books by Schilit (2002), Wild et al. (2004), Libby et al. (2008), and Dyckman et al. (2011).

  12. 12.

    Moody’s (2006, 6) notes the following: “Measures of cash flow (e.g., free cash flow or operating cash flow) are useful because they are more difficult to manage or manipulate than are earnings or EPS (e.g., through the timing of recognition of accounting costs or, in the case of EPS, share buybacks).”

  13. 13.

    Moody’s (2006, 6) notes the following: “However, Moody’s views EBITDA as a flawed metric and a poor measure of cash flow to the extent it is used for that purpose, particularly for healthy companies in good periods. This is true in part because EBITDA can easily be manipulated through aggressive accounting.”

  14. 14.

    I do not make formal predictions on the cross-sectional variation of the association between accruals management and rating quality because I expect no association between accruals management and rating quality.

  15. 15.

    Using the ratings of mortgage-backed securities, He et al. (2012, 2097) document that investors understand that “large issuers received more inflated ratings than small issuers, especially during boom periods.”

  16. 16.

    I start the sample in the fiscal year 1994 because FISD provides insufficient coverage of bond issues before 1994. I end the sample in 2010 due to data availability constraints.

  17. 17.

    This approach of performance adjustment is very similar to the performance adjustment that Kothari et al. (2005) used in estimating performance-adjusted abnormal accruals..

  18. 18.

    I implement the cross-sectional form of Dechow et al.’s (1998) model rather than the time-series equivalent employed by Lee (2012). My five reasons for this are similar to those of prior studies that utilize the cross-sectional version of Jones’s (1991) model rather than its time-series counterpart (Subramanyam 1996): First, the cross-sectional version allows me to use a larger sample. Second, the parameter estimates are more precise and better specified in the cross-sectional version than in the time-series version, because there are more observations available (a median value of 183 for the cross-sectional version compared to 10 or less for the time-series version), and because the coefficients’ average standard errors are lower, with fewer outliers. Third, the lengthy (up to 10 years) term during which the time-series version is estimated could lead to a non-stationarity issue, in which case the time-series version could be mis-specified. Fourth, time-series analysis over an extended period using firm-level data could bias the sample toward stable, mature firms (Lee 2012). Lastly, due to overlapping estimation, using a time-series model diminishes the power of tests examining the time-series behavior in unexpected CFO.

  19. 19.

    Specifically, Allison stated, “The problem is not specifically the rarity of events, but rather the possibility of a small number of cases on the rarer of the two outcomes. If you have a sample size of 1000 but only 20 events, you have a problem. If you have a sample size of 10,000 with 200 events, you may be OK. If your sample has 100,000 cases with 2000 events, you’re golden.” For details, refer to In my sample of 56,653 observations, there are 2464 observations in defaults. Thus, the rarity of corporate defaults will not cause bias in my estimation of traditional logit regression.

  20. 20.

    When I use leverage as a conditional variable, I take it out as a control variable.

  21. 21.

    As sensitivity analyses, I test the effect of CFO management on the ability of ratings to predict defaults in two years and three years, respectively. The untabulated results show that CFO management is not associated with the ability of ratings to predict defaults in two (three) years, suggesting that rating agencies could undo CFO management over a longer term. There are two possible reasons for these results. First, given that the fundamental role of bond ratings is to predict long-term credit risk, rating agencies may be more cautious about CFO management over a longer term and hence exert more effort to undo CFO management over a longer term. Second, over a longer term, rating agencies gain access to more information and have more time to distinguish between fundamental CFO and manipulated CFO.

  22. 22.

    Note that the significance level in column (2) drops down to 10% level because the additional interaction terms introduce severe multicollearity into the model.

  23. 23.

    Campbell et al. (2008) show that their model is more precise in predicting default than measures of distance to default (Merton 1974; Hillegeist et al. 2004; Bharath and Shumway 2008).

  24. 24.

    Since TRACE started on July 1, 2002, the tests in columns (1) through (3) have shorter sample periods than those in columns (4) through (6). I follow Bessembinder et al. (2009) to clean up TRACE data.

  25. 25.

    I keep the last restatement if there are multiple restatements for a given firm-year. As a sensitivity check, I alternatively keep the first restatement and find similar results.

  26. 26.

    Alternatively, I reestimate performance-adjusted CFO management measures using the concurrent form of all four alternative performance metrics used in the paper. All the results (untabulated) resemble those in Table 3.

  27. 27.

    Specifically, I reestimate Eq. (4) by introducing firm fixed effect using OLS procedure because the non-linear method has parameter-estimation problems. Woodridge (2002) suggests that when estimating the fixed-effects coefficients in a nonlinear model such as a logit or probit model, an incidental parameter problem can be introduced. The maximum likelihood parameter estimates in nonlinear panel data models that include fixed effects are biased and inconsistent when the panel has small and fixed length.

  28. 28.

    Kedia et al. (2015) make a similar argument and provide evidence supporting the argument in the context of contagion in earnings management. They show that the contagion in earnings management ceases from 2003 through 2005, but reappears during 2006–2008. They argue that this pattern exists because, in the 2003–2005 period, the potential enforcement from SOX constrains earnings management by peers in the industry or neighborhood, but the sting associated with SOX wears off in the 2006-2008 period.

  29. 29.

    I follow Cheng and Neamtiu (2009) to define the period before SOX and the period right after SOX.


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I owe a debt of gratitude to Inder Khurana and Raynolde Pereira for their tremendous help and guidance on this project. I am grateful to C.-F. Lee (the editor) and one anonymous referee for valuable comments and suggestions. I also thank Brian Akins, Sam Bonsall, Mei Cheng, Jeff Coulton (Discussant), Ed deHaan (Discussant), Ilia Dichev, Matt Glendening, Leslie Hodder, Mike Penn, Doug Skinner, K. Philip Wang, Qiuhong Zhou, and workshop participants at the American Accounting Association (annual and mid-year FARS) meetings, UNSW annual accounting conference, University of Missouri-Columbia, and Georgetown University for their helpful comments. I also thank William Maxwell for sharing the SAS program in cleaning up TRACE data.

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Appendix A: Rating schemes definitions

Credit risk Moody’s Standard & Poor’s Fitch’s Code assigned
Highest grade Aaa AAA AAA 1
Aa1 AA+ AA+ 2
High grade Aa2 AA AA 3
Aa3 AA− AA− 4
A1 A+ A+ 5
Upper medium grade A2 A A 6
A3 A− A− 7
Baa1 BBB+ BBB+ 8
Medium grade Baa2 BBB BBB 9
Baa3 BBB− BBB− 10
Ba1 BB+ BB+ 11
Lower medium grade Ba2 BB BB 12
Ba3 BB− BB− 13
B1 B+ B+ 14
Low grade B2 B B 15
B3 B− B− 16
Caa1 CCC+ CCC+ 17
Caa2 CCC CCC 18
Caa3 CCC− CCC− 19
Ca CC CC 20
C C C 21
Default   D DDD/DD/D 22

Appendix B: Variable definitions

Variables of interest

  • DEFAULT is an indicator that takes one if there is a default event within one year from the rating date, 0 otherwise.

  • RATING is assigned numeric rating score, following Cheng and Neamtiu (2009).

  • UCFO is the difference between actual CFO and the performance-adjusted expected (fitted) CFO, where the expected CFO (ECFO) is estimated by running the modified Dechow et al. (1998) model cross-sectionally for industry-years with at least 15 observations:

    $$CFO_{t} /TA_{t - 1} =\uplambda_{0} +\uplambda_{1} (1 /TA_{t - 1} ) +\uplambda_{2} (SALE_{t} /TA_{t - 1} ) +\uplambda_{3} (\Delta SALE_{t} /TA_{t - 1} ) +\uplambda_{4} (ROA_{t - 1} ) +\upvarepsilon$$

    where CFO is the operating cash flow for the period t, TA is the total assets for the period t − 1, SALE is the sales during period t, ∆SALE is the change in sales during period t, and ROA is the return on assets during period t − 1.

  • UACC is the difference between actual accruals and the performance-adjusted (fitted) normal accruals based on the modified Jones (1991) model. When constructing the performance-adjusted normal accruals, I first estimate the following model cross-sectionally for industry-years with at least 15 observations:

    $$TACC_{t} /TA_{t - 1} =\upbeta_{0} +\upbeta_{1} (1/TA_{t - 1} ) +\upbeta_{2} (\Delta Sales_{t} /TA_{t - 1} ) +\upbeta_{3} (PPE_{t} /TA_{t - 1} ) +\upbeta_{4} (ROA_{t - 1} )$$

    where TACC is total accruals for the period t, TA is the total assets for the period t − 1, ΔSALES is change in sales revenues for the period t, PPE is gross property and equipment for period t, and ROA is the return on assets during period t − 1. Then I use the estimated coefficients and the following model to generate the performance-adjusted normal and abnormal accruals:

    $$TACC_{t} /TA_{t - 1} =\upbeta_{0} +\upbeta_{1} \left( {1/TA_{t - 1} } \right) +\upbeta_{2} [(\Delta Sales_{t} - \Delta AR_{t} ) /TA_{t - 1} ] +\upbeta_{3} (PPE_{t} /TA_{t - 1} ) +\upbeta_{4} (ROA_{t - 1} )$$

    where ΔAR is the change in accounts receivable.

Control variables

  • SIZE is the natural logarithm of an issuer’s total assets.

  • LEV is long-term debts divided by total assets.

  • COV is operating income before depreciation divided by interest expense.

  • ROA is income before extraordinary items divided by lagged total assets.

  • CFOVOL is the standard deviation of cash flow from operations scaled by lagged total assets over the four previous years ending in the current fiscal year.

  • ISSUESIZE is the natural logarithm of the face value of the bond issue.

  • MATURITY is time until the maturity of the bond in years.

  • SENIOR is a binary variable set equal to one if a bond has seniority status and zero otherwise.

  • SECURE is a binary variable set equal to one if a bond is secured with collateral and zero otherwise.

  • ASSETB is an indicator variable that takes a value of 1 if the issue is an asset-based issue, 0 otherwise.

  • CONV is an indicator variable that takes a value of 1 if the issue can be converted to the common stock of the issuer, 0 otherwise.

  • ENHANCE is an indicator variable that takes a value of 1 if the issue has the credit enhancement feature, 0 otherwise.

  • PUT is an indicator variable that takes a value of 1 if the issue has the option, but not the obligation, to sell the security back to the issuer under certain circumstances, 0 otherwise.

  • REDEEM is an indicator variable that takes a value of 1 if the issue is redeemable under certain circumstances, 0 otherwise.

Other variables

FAILSCORE is the measure of default risk adopted from Campbell et al. (2008). Specifically, I estimate the following model using logit regression:

$$\begin{aligned} FAILURE_{t + 1} & = \alpha_{0} + \beta_{1} NIMTAAVG_{t} + \beta_{2} TLMTA_{t} + \beta_{3} EXRETAVG_{t} + \beta_{4} SIGMA_{t} \\ & \quad + \,\beta_{5} RSIZE_{t} + \beta_{6} CASHMTA_{t} + \beta_{7} MB_{t} + \beta_{8} PRICE_{t} + \varepsilon \\ \end{aligned}$$

where FAILURE is an indicator variable that takes a value of 1 if there is a default in year t + 1, and 0 otherwise; NIMTA is net income divided by market-valued total assets, the sum of market equity and book liabilities; NIMTAAVG is geometric weighted average of NIMTA over year t; TLMTA is total liabilities divided by the sum of market equity and book liabilities; EXRET is the monthly log excess return on each firm’s equity relative to the S&P 500 index; EXRETAVG is geometric weighted average of EXRET over year t; SIGMA is the standard deviation of each firm’s daily stock return over the past 3 months; RSIZE is the natural log ratio of the market capitalization to that of the S&P 500 index; CASHMTA is the ratio of a company’s cash and short-term assets to the market value of its assets; MB is the market-to-book ratio; and PRICE is the natural log of price per share, truncated above at $15. To avoid look-ahead bias, I re-estimate the above model every year using only historically available data.

  • WRATE is the sum of all rating levels outstanding during the one year leading to default multiplied by the number of days each rating has been outstanding and then scaled by 365.

  • DAYAHEAD is the number of days between the default date and the downgrade date.

  • RATINGLAG is the lagged numerical rating (RATING).

  • LOSS is an indicator variable that takes a value of 1 if income before extraordinary items is negative in the current and prior fiscal year, 0 otherwise.

  • CAP_INTEN is gross PPE divided by total assets.

  • HIGHLEV is an indicator variable that takes a value of 1 if the leverage is above the sample median, and 0 otherwise, where leverage is the sum of short-term and long-term debt divided by total assets.

  • AROUNDIG is an indicator variable that takes a value of 1 if the ratings outstanding at the end of the prior year are BB+ or BBB−, and 0 otherwise.

  • YSPREAD is the difference between a bond’s yield-to-maturity and the yield on a U.S. Treasury bond with the closest maturity.

  • RTINIT is the numeric ratings (RATING) outstanding for the newly issued bonds.

  • BUBBLE is an indictor variable that takes one if ratings are issued between March 11, 2000, and July 24, 2002 and zero otherwise.

  • SOX is an indictor variable that takes one if ratings are issued between July 25, 2002, and December 31, 2005 and zero otherwise.

  • CRISIS is an indictor variable that takes one if ratings are issued between August 9, 2007, and December 31, 2009 and zero otherwise.

  • RECOVERY is an indictor variable that takes one if ratings are issued between January 1, 2010, and December 31, 2012 and zero otherwise.

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Zhang, E.X. The impact of cash flow management versus accruals management on credit rating performance and usage. Rev Quant Finan Acc 54, 1163–1193 (2020).

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  • Cash flow management
  • Accruals management
  • Corporate bond rating quality
  • Credit rating agencies
  • Bond yield spread

JEL Classification

  • M41
  • G24
  • G18