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Review of Quantitative Finance and Accounting

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01.01.2013 | Original Research

The impact of multi-dimensional corporate transparency on us firms’ credit ratings and cost of capital

verfasst von: David Gregory DeBoskey, Peter R. Gillett

Erschienen in: Review of Quantitative Finance and Accounting | Ausgabe 1/2013

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Abstract

This study examines corporate transparency in the US market for a sample of 319 S&P 500 firms. We examine whether a number of disparate measures of corporate transparency used by other researchers are distinct, cohere as measures of a single factor of corporate transparency, or capture multiple different dimensions. Next, we begin to examine the impact of corporate transparency, conceived in the broadest sense, and not limited to financial reporting, on US firms. We develop a model of corporate transparency based on a broad definition and framework proposed by Bushman, Piotrowski and Smith, which we extend in several ways, and then study the effect of corporate transparency on cost of debt, credit rating, and cost of equity. First, we find that corporate transparency is neither a unitary concept nor merely an ambiguous term for multiple distinct concepts: factor analysis of ten corporate transparency variables identifies four independent underlying dimensions: public disclosure information, intermediary information, earnings quality information and insider information. Second, we find that corporate transparency has significant power to explain cross-sectional variation in credit rating and cost of capital. More specifically, (i) credit rating, cost of debt, and beta are significantly associated with disclosure information transparency; (ii) credit rating, cost of equity, and beta are significantly associated with intermediary information transparency; and (iii) cost of equity and beta are significantly associated with insider information transparency. Our findings offer a more comprehensive evaluation of corporate transparency than prior studies, and we demonstrate direct economic implications for both US firms and markets.

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CFO opinions documented during 2000, long before the Enron debacle, show their dissatisfaction with the extent of corporate financial statement disclosures (Barth et al. 2003). Thus, corporate transparency appears to be an important concept to understand in terms of its potential impact on financial markets in the US and abroad. In this study, financial statement disclosures are an important element of our corporate transparency model.

By definition these firms are best practice disclosers. Our primary conclusions are limited to the extent that the associations we document generalize to non-best practice discloser firms’ as well. However, the multi-dimensionality of our corporate transparency model offers significantly more explanatory power than disclosure by itself.

We add earnings quality and creditor leverage to our corporate transparency framework. See Sect. 2.1 for a detailed description of these measures.

We define information risk as the likelihood and potential magnitude of loss arising from inadequate corporate transparency.

There is an extensive literature on factors associated with variations in analyst coverage and disclosure (Bhushan 1989; 2003a, b). The firms in our sample are very large and have a mean (median) analyst following of 19 (18). The research has shown that analysts prefer to follow firms where the private information benefit is greatest. In firms with few analysts the potential for private information gains is higher than in firms with high analyst following.

This is based on a constant equity risk premium of 4.7 percent applied to the beta impact of 0.57. Section 4.3.5 presents our overall summary of the economic impact of corporate transparency on the various criterion variables: credit rating, cost of capital, and market model beta.

The implementation of the Sarbanes–Oxley Act may be expected to have had a significant impact on corporate transparency; however, the availability of the S&P Transparency and Disclosure data set for large firms (arguably including many best-practice disclosers) provides a unique opportunity to study the ramifications of corporate transparency independently of Sarbanes–Oxley requirements.

Measuring corporate transparency in a cross-country study is different from measuring it at the firm level in the US market, primarily because the US is a richer reporting environment with strong monitoring and regulation, and availability and accessibility of information intermediaries such as a free press; moreover, many corporate transparency measures are readily available at the firm level.

We do not list the detailed S&P T&D questions here, but they are fully documented in the literature (see for example, Khanna et al. (2004)).

Although this can vary across years for our sample firms, we expect it to remain relatively stable given that our sample firms are very large and come from the S&P. As a robustness check, timeliness was calculated for future years (2002–2004), and the mean difference between the base year (2001) and future years was not significant indicating that the timeliness appears to be consistent across time.

In summary, evidence exists that the timing of information disclosure is related to the quality of that information; e.g., firms delay releasing bad news. This timeliness variable is a common control variable in other empirical research studies on the determinants of financial reporting quality (See Kross and Schroeder 1984 and Chambers and Penman 1984).

In the US market the relatively high level of investor protection mitigates the incentives for earnings management (Leuz et al. 2003). This precludes us from using a more traditional measure such as discretionary accruals to operationalize our quality of earnings construct. Moreover, our goal is to use a measure that captures the quality of the disclosures in addition to the extent of disclosure captured by DISCLOSURE and GOVERNANCE.

A priori, we expect an inverse relationship between these two measures. As the quality of reported earnings decreases (i.e., more non-GAAP charges/credits are included in the firms’ earnings), audit quality increases (i.e., more effort is required). This results in less timely reporting of earnings, which is intuitive in that firms’ with more complicated accounting earnings transactions require more audit effort and more time to report their earnings.

Confirmatory factor analysis using EQS, and additional common factor analyses using just the variables that BPS were able to measure, not reported in detail here, also fail to confirm their two factor model with our US data.

This is similar to our study where we are examining the impact of multiple information dimensions including a corporate governance aspect, which is subsumed in our public disclosure information dimension.

Studies examining ex-ante proxies such as bond yields, bond prices etc. that are available in machine-readable forms are appearing with increasing frequency (e.g., Moerman-Wittenberg 2007; Easton et al. 2009; DeFranco et al. 2009). Moreover, there is an argument to be made that bond yields, as a measure of internal rates of return, contain ex-ante expected bond returns rather than realized cost of debt. However, the primary purpose of our study is to examine what are the various impacts of corporate transparency including, in particular, how corporate transparency affects subsequent realized cost of debt and credit rating. In the extensive literature, it appears that realized cost of debt and credit ratings are still being used in many published articles and working papers. Finally, our sample period covers the time frame of 2001–2002 and precludes us from using data sets such as Mergent and Trace. The Trace database was not available until July 2002 and covers only 498 bonds. This is outside the sampling period over which our main variables are measured.

The sample in the present study includes S&P 500 firms that are very large and that have large analyst following.

The sample firms in the present study include S&P 500 firms that are required to file frequent reports with the SEC as well as other required reporting that is necessary according to various SEC reporting regulations. These firms all file timely and frequent reports.

Informed traders impose significant liquidity costs on other market participants due to adverse selection. As liquidity decreases, investors potentially demand a higher expected return to compensate for this information imbalance. See for example, Admati and Pfleiderer (1988) and Kyle (1985).

In separate results not reported here, weighted average cost of capital is used as an alternative criterion variable and the results are qualitatively similar to those realized with cost of equity capital.

All tests are cross-sectional. Data limitations preclude the use of time series tests. A cross-sectional approach is preferred because our corporate transparency dimensions are not expected to vary much in the time-series.

1–3 = AAA; 4–6 = AA; 7–9 = A; 10–12 = BBB; 13–15 = BB; 16–18 = B; 19–21 = CCC; 23 = CC; 24 = C; 27 = D. This study uses each firm’s raw rating and does not recode each credit rating, as other studies have done (Anderson et al. 2003; Ashbaugh et al. 2006b).

This approach is similar to other studies that rely on Compustat for credit rating details (Ashbaugh et al. 2006b). Francis et al. (2005) use S&P Issuer Credit Ratings as an ex-ante proxy for cost of debt.

As a supplementary analysis, an ordinal logistic regression was run with a new dependent variable (CREDIT) based on combining adjacent credit categories, similar to Sengupta (1998). We omit details; however the results are similar to the OLS results and are available from the authors upon request. We also perform supplemental ordinal logistic regression analyses using as the dependent variable the raw S&P credit rating RATE. The results are qualitatively similar to the OLS results except that the assumption that the regression slopes is the same for each category of the dependent variable (i.e., proportional odds) is not meaningful. A test of this assumption failed to provide useful results, apparently because with so many categories for the dependent variable, 98% of the cells have zero frequencies. Consequently, we perform the ordinal logistic regression using a new dependent variable based on combining adjacent credit categories, not presented here. Supplementary generalized ordinal logistic regression and nominal multinomial regression analyses also not reported here suggest that the slopes are largely consistent.

As supplementary analysis, our OLS results are qualitatively similar when alternative credit ratings from Moodys and Fitch are substituted. Since, the transparency index is developed by S&P it is possible that using S&P’s credit rating may tend to favor a significant correlation between our dependent variable (credit rating) and our transparency measures (CTF1 and CTF2). We thank an anonymous reviewer for this suggestion.

Our measure of earnings quality captures the difference in reported earnings and the core earnings and doesn’t appear to be strongly correlated with firm characteristics such as size and profitability. Therefore, we argue that our earnings quality measure represents a reasonable proxy of earnings quality.

Due to space limitations weighted cost of capital (WCOC) models are not formally presented in this paper. The results are qualitatively similar to COD and COE OLS estimations.

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Titel: The impact of multi-dimensional corporate transparency on us firms’ credit ratings and cost of capital
verfasst von: David Gregory DeBoskey
Peter R. Gillett
Publikationsdatum: 01.01.2013
Verlag: Springer US
Erschienen in: Review of Quantitative Finance and Accounting / Ausgabe 1/2013
Print ISSN: 0924-865X
Elektronische ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-011-0266-8

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