Abstract
Some firms voluntarily make disclosures about the controls and processes in place to ensure the reliability of fair value estimates. Consistent with these disclosures being driven by investors’ concerns about the reliability of their SFAS 157 estimates, we find that firms with more opaque estimates are more likely to provide such disclosures. We then examine whether these disclosures improve investors’ perception about the reliability of fair value estimates. We find that they are associated with higher market pricing and lower information risk for Level 3 estimates. Further analyses of the disclosures reveal that the following types of information are particularly important to investors: discussion of the external and independent pricing of fair value estimates and their proper classification according to the SFAS 157 hierarchy. Overall, our results suggest that the voluntary reliability disclosures that firms provide beyond SFAS 157’s three-level estimates help reduce investors’ uncertainty toward the more opaque fair value estimates.
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Notes
Specifically, Level 1 inputs are quoted prices in active markets and hence require no judgment. Level 2 inputs are data-adjusted from similar items traded in active markets or from identical or similar items in markets that are not active. Level 3 inputs are considered “unobservable” and are based on the models or assumptions of management, valuation specialists, or both. These inputs are the most subjective and are difficult to verify.
For example, Ryan (2008) contends that Level 3 fair value estimates that are supported by disclosures of critical inputs and the measurements’ sensitivity to these inputs would be more informative to investors. Laux and Leuz (2009) state that it would be interesting to study what determines fair value disclosure (or nondisclosure) as well as how investors respond to the additional disclosures that firms provide.
In its commentary “FAS 157—Fair Value Disclosures and Litigation Risk,” CRA International emphasizes the need for strong internal controls over the management processes that companies use to determine fair value reliably. Likewise, the International Swaps and Derivatives Association (ISDA), in its commentary on the IASB’s Discussion Paper on Fair Value Measurement, also notes that it is important for appropriate controls to be in place within an organization to ensure the consistent treatment and measurement of the fair values disclosed according to the three-level hierarchy. The commentary is available at http://www.isda.org/speeches/pdf/ISDA-response-to-IASB-DP-on-SFAS-157.pdf.
This motivation is consistent with prior studies in the management forecast literature, which show that managers provide additional disclosure in response to concerns about the credibility of the primary information (e.g., Hutton et al. 2003; Baginski et al. 2004; Lennox and Park 2006; Bagnoli and Watts 2007; Hirst et al. 2007).
A related paper by Blacconiere et al. (2011) studies the issue of fair value disclosures in the context of managers’ stock option expenses disclosed under SFAS 123. They find that voluntary disavowals of the reliability of the fair value of stock option expenses reflect legitimate reliability concerns, consistent with managers believing that these disavowals provide useful information. Unlike Blacconiere et al. (2011), we focus on fair value disclosures that potentially enhance, as opposed to refute, the reliability of fair value estimates. Taken together, the two papers suggest that voluntary disclosures have a role in enhancing the usefulness of reported fair value estimates.
For example, Bryan (1997) examines whether the narrative disclosures in the Management Discussion and Analysis (MD&A) section of 10-K reports are useful to investors, whereas Francis et al. (2002) examine whether price reactions to earnings releases relate to changes in the additional information disclosed in these earnings releases. Hutton et al. (2003) examine the impact of supplementary statements on the informativeness of management earnings forecasts.
Consider the example of Navigators Group in Appendix 1. In its reliability disclosures, the company discloses that any pricing where the input is based solely on a broker price is deemed a Level 3 price. More importantly, the company discloses: “all prices for its fixed maturities, short-term investments and equity securities valued as Level 1, Level 2 or Level 3, are received from independent pricing services utilized by one of our outside investment managers whom we employ to assist us with investment accounting services. This manager utilizes a pricing committee which approves the use of one or more independent pricing service vendors.” Note that our definition of “external and independent pricing” includes only pricing that is obtained from outside the firm but not pricing obtained from internal risk departments that are separate from trading activities.
Specifically, Muller and Riedl (2002) examine a sample of UK investment property firms and find that market makers perceive information asymmetry across traders to be lower for firms employing external appraisers than those employing internal appraisers. Dietrich et al. (2001) also provide evidence that external property appraisals are significantly less biased and are more accurate estimates of realized selling prices compared to director-based appraisals.
The example of Newalliance Bancshares Inc. in Appendix 1 notes that “management assessed the valuation techniques used by IDC based on a review of their pricing methodology to ensure proper hierarchy classifications.”
Furthermore, Blacconiere et al. (2011) find that voluntary disavowals about the reliability of fair value of stock option expenses disclosed under SFAS 123 reflect legitimate reliability concerns, consistent with managers believing that these disavowals provide useful information.
We view managers as voluntarily affirming the reliability of their reported fair value estimates and the fair value measurement processes. To the extent that these assurance disclosures overlap with mandatory disclosures under other requirements (e.g., CEO/CFO responsibility for financial statements under Sarbanes Oxley Section 302), this effect should work against our finding significant results for this test.
The FASB issued two additional updates during our sample period. The first update, ASU 2010-06, provides further clarification about disclosures relating to transfers in and out of Level 1 and 2 items as well as the reconciliation of Level 3 measurements during the year. The second update, ASU 2011-04, requires firms to provide more information relating to the valuation processes and the sensitivity analysis of the Level 3 measurements. However, neither of these updates requires firms to disclose information relating to the procedures in place to ensure the reliability of the SFAS 157 fair value estimates.
To identify reliability disclosures, we asked two research assistants to read the SFAS 157 footnote disclosure for each of these companies. The authors then read the reliability disclosures and verified that their identification was accurate. In cases of disagreement, the authors discussed the issue with the research assistants and made the final decision.
We choose FV_VolDiscl as our main disclosure variable because it is more objectively constructed and has possibly less noise. Furthermore, in this paper, we study many different outcomes and conduct several cross-sectional analyses. Nonetheless, when we use the total number of words in the reliability disclosures as an alternative measure, we obtain similar results. We report those results in Table 7.
Price is a complicated construct that reflects expected future cash flows and discount rate. We assume that the firm is making a reliability disclosure only if it indeed implemented the disclosed action to increase reliability. Hence an investor reading the disclosure could infer effects on both expected cash flows and discount rate. For example, the use of external pricing services could be costly and suggest complexities (e.g., higher risk) in the underlying assets such that fair valuation cannot be done internally. This, in turn, could lower the price. That said, reliability disclosures can also reduce information risk, and, to the extent that the reduction in information risk reduces the discount rate, the price will be increased. Hence there is tension as to the main effect. We focus on the interaction effect because our focus is on how reliability disclosures enhance investors’ confidence in the relatively more opaque fair value estimates. The idea here is that, while reliability disclosures may be costly, they may also have the additional effect of enhancing the credibility in other disclosures.
However, they note that the “equity beta is the weighted-average beta across a firm’s asset and liability structure.” They therefore estimate the asset-specific implied beta, which is “Beta_adj.”
Prior literature on stock return synchronicity suggests that the co-movement between market returns and firm-specific returns captures the firm’s information risk (Morck et al. 2000; Durnev et al. 2003; Jin and Myers 2006). The rationale is that, among firms with less firm-specific information, firm values are more affected by macroeconomic factors, resulting in a higher correlation between the firm’s stock price movement and that of the market.
We use the Fama and French 48-industry classification for banks (45) and insurance companies (46) from http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.
The sample size in the baseline regression differs slightly from those in the regressions with the interaction variables. This is because the studentized residuals are computed after running the regressions and the number of observations in which the studentized residuals are greater than two changes after each regression is run.
Although the median of FVA3 is tabulated as 0.000, consistent with the median of FVA3 s (0.056), the median is not zero. The actual median value is 0.00045. In our sample, approximately 57% of the observations have nonzero Level 3 assets.
In Column 3, after selecting the same number from the treatment group (with reliability disclosures) and the control group (without reliability disclosures) based on the propensity-score matching procedure, we perform t-tests to check whether those two groups have significantly different firm characteristics. None of the firm characteristic variables used in Equation (1) is significantly different between the treatment and control groups, indicating that the two groups are homogeneous in terms of characteristics except for reliability disclosure.
One might interpret the negative and significant coefficient on FV_VolDisc in Column (3) as an indication that reliability disclosure reduces firm value. However, the presence of interaction terms confounds the interpretation of an on-average effect for reliability disclosure. In untabulated analyses, when we remove the interaction terms, we find a significantly positive coefficient on FV_VolDisc. Alternatively, we compute the averages of FVA1 s, FVA2 s and FVA3 s in this PSM sample and multiply them with the coefficients in Column (3). The averages of FVA1 s, FVA2 s, and FVA3 s in the PSM sample are 7.231, 48.835, and 6.154, respectively. The on-average effect of the reliability disclosure is 2.346 (= -4.054 + 0.102*7.231 + 0.092*48.835 + 0.190*6.154).
We note that the coefficient on FVA3 in Column 3 is insignificant. Hence we should be cautious about interpreting the significant negative interaction effect between FVA3 and FV_VolDiscl as evidence that fair value reliability disclosures reduce the positive relation between information risk and Level 3 fair value assets.
Similar to how we identify reliability disclosures, we asked our research assistants to read the reliability disclosures to determine whether the specific components have been disclosed. We then checked their identification for accuracy.
In the case of Ally Financial Inc., although the firm discusses at length the numerous internal controls in place to ensure the appropriateness of fair value measurements, none of them pertains to the external and independent pricing of fair value estimates or the estimates’ proper classification according to the SFAS 157 three-level hierarchy, nor do any of them include an assurance of management’s responsibility in ensuring the reliability of the fair value estimates.
The consensus measures are constructed based on analysts’ one-year-ahead earnings forecasts issued within the 45-day window following the 10-K filing date. Consistent with the information risk tests, we include the ratio of the nonfair value assets to total assets (NFVA) and Leverage. We also control for information that is available before the 10-K filing dates by including the information released at annual earnings announcement dates. ABS Prior Return is the absolute value of the return from the next day of the fiscal year-ending date to the two trading days before the 10-K filing date. Because of the potential effect of management forecasts on analysts’ information sets, we include an indicator variable to capture the effect of management forecasting. Mgt Forecast is a dummy variable equal to one if the firm provides an annual earnings forecast for the next fiscal year before the filing date and zero otherwise. To control for the level of firm-specific consensus prior to the dissemination of the 10-K report, we also include Pre_consensus, which is measured by analysts’ most recent earnings forecasts for the next fiscal year issued in the period between the forty five and two days before the filing date.
We use Equation (1) as a selection model in this propensity score matching (PSM) procedure. Given that the initiation year of reliability disclosure varies by firms, we first select the one-on-one matched control firms using PSM and then use as a control sample the matched control firm’s fiscal year observation, which is the same as the reliability disclosure initiation year of the treatment firm. For example, firm A initiated reliability disclosure in 2009. Using PSM, we find a control firm and use this control firm’s 2009 observation as the control sample. To find the one-on-one matched control firms, we compute the averages of the determinant variables in Equation (1) during our sample period and run the equation with the averages.
We also examine whether a change analysis at the quarterly level will reveal substantial variation in the reliability disclosures. We hand-collect the 10-Q reports from five different firms for each year to compare with the disclosures in the 10-K reports. We find that there is little variation in disclosure. As with the 10-K reports, firms that started providing reliability disclosures in their 10-Q reports continue to provide similar disclosures in subsequent 10-Q reports.
4.7% of our firm-year observations used in the determinants regression have ICWs.
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We wish to thank Kristian Allee, Mary Barth, Holger Erchinger, Jim Leisenring, Tom Linsmeier, Andrew McMartin, Tricia O’Malley, Jonathan Rogers, Katherine Schipper, Holly Skaife, Donna Street, and participants at the IAAER Standard Setting Process workshops in Palm Springs and London, the 11th World Congress of Accounting Educators and Researchers, the 2011 American Accounting Association Annual Meeting, the 2011 Canadian Accounting Association Annual Meeting, the 2011 European Accounting Association Annual Congress, the 2015 SMU/SNU/KAIST/Korea/Yonsei Five School Conference, the 2016 FARS Midyear Meeting, Chinese University of Hong Kong, Hong Kong Polytechnic University, MIT, Peking University, Sun Yat-sen University and the University of Rochester for helpful comments and suggestions. We are grateful for the generous funding support from the International Association for Accounting Education and Research (IAAER), the KPMG Foundation, MIT and the School of Accountancy Research Center (SOAR) at Singapore Management University.
Appendix 1: Examples of reliability disclosures and identification of the content of reliability disclosures
Appendix 1: Examples of reliability disclosures and identification of the content of reliability disclosures
The following are some examples of reliability disclosures. In our content analysis, we further identify whether a reliability disclosure discusses the external and independent pricing of fair value estimates (External =1); proper classification of the estimates, according to the SFAS 157 three-level hierarchy (Classification =1); and an assurance of management’s responsibility in ensuring the reliability of the fair value estimates (Assurance =1). The use of italics refers to the content identified for each component.
Navigators Group, Form 10-K, 2010 (External =1)
All prices for our fixed maturities, short-term investments and equity securities valued as Level 1, Level 2 or Level 3 in the fair value hierarchy, as defined in the Financial Accounts Standards Board Accounting Standards Codification 820 (“ASC 820”), Fair Value Measurements, are received from independent pricing services utilized by one of our outside investment managers whom we employ to assist us with investment accounting services. This manager utilizes a pricing committee which approves the use of one or more independent pricing service vendors. The pricing committee consists of five or more members, one from senior management and one from the accounting group with the remainder from the asset class specialists and client strategists. The pricing source of each security is determined in accordance with the pricing source procedures approved by the pricing committee. The investment manager uses supporting documentation received from the independent pricing service vendor detailing the inputs, models and processes used in the independent pricing service vendors’ evaluation process to determine the appropriate fair value hierarchy. Any pricing where the input is based solely on a broker price is deemed to be a Level 3 price.
Newalliance Bancshares Inc., Form 10-K, 2009 (External =1; Classification =1)
The Company utilizes Interactive Data Corp., a third-party, nationally-recognized pricing service (“IDC”) to estimate fair value measurements for 98.7% of this portfolio. The pricing service evaluates each asset class based on relevant market information considering observable data that may include dealer quotes, reported trades, market spreads, cash flows, the U.S. Treasury yield curve, the LIBOR swap yield curve, trade execution data, market prepayment speeds, credit information and the bond’s terms and conditions, among other things, but these prices are not binding quotes. The fair value prices on all investment securities are reviewed for reasonableness by management through an extensive process. This review process was implemented to determine any unusual market price fluctuations and the analysis includes changes in the LIBOR / swap curve, the treasury curve, mortgage rates and credit spreads as well as a review of the securities inventory list which details issuer name, coupon and maturity date. The review resulted in no adjustments to the IDC pricing as of December 31, 2009. Also, management assessed the valuation techniques used by IDC based on a review of their pricing methodology to ensure proper hierarchy classifications.
Ace Ltd., Form 10-K, 2011 (Assurance =1; External =1)
While we obtain values for the majority of the investment securities we hold from one or more pricing services, it is ultimately management’s responsibility to determine whether the values obtained and recorded in the financial statements are representative of fair value. We periodically update our understanding of the methodologies used by our pricing services in order to validate that the prices obtained from those services are consistent with the GAAP definition of fair value as an exit price. Based on our understanding of the methodologies, our pricing services only produce an estimate of fair value if there is observable market information that would allow them to make a fair value estimate. Based on our understanding of the market inputs used by our pricing services, all applicable investments have been valued in accordance with GAAP valuation principles. We have controls to review significant price changes and stale pricing, and to ensure that prices received from pricing services have been accurately reflected in the consolidated financial statements. We do not typically adjust prices obtained from pricing services.
1.1 Appendix 2: Variable definitions
Dependent Variables
Share Price | 10-K filing month-end price per share. |
Beta_adj | Equity beta, which is the coefficient from a regression of firm-specific monthly returns on value-weighted stock market returns, multiplied by the ratio of common equity to total assets. |
Correl_adj | Correlation between firm-specific monthly returns and value-weighted stock market returns, multiplied by the ratio of common equity to total assets. The equity beta and the correlation are measured in the period from the month following the last fiscal year’s filing month to the current year’s filing month. |
Independent variables | |
FV_VolDiscl | Indicator variable that equals one if a company provides a reliability disclosure in a given fiscal year and zero otherwise. |
FVA1 | Level 1 fair value assets to total assets. |
FVA2 | Level 2 fair value assets to total assets. |
FVA3 | Level 3 fair value assets to total assets. |
FVA1 s | Level 1 fair value assets per share. |
FVA2 s | Level 2 fair value assets per share. |
FVA3 s | Level 3 fair value assets per share. |
External | Indicator variable that equals one if the reliability disclosures discuss the controls and procedures in place to obtain external and independent pricing of fair value estimates and zero otherwise. |
Classification | Indicator variable that equals one if the reliability disclosures discuss the controls and procedures for the proper classification of the estimates according to the SFAS 157 three-level hierarchy and zero otherwise. |
Assurance | Indicator variable that equals one if the reliability disclosures include an assurance of management’s responsibility in ensuring the reliability of fair value estimates and zero otherwise. |
FVL | Fair value liabilities to total assets. |
NFVA | Nonfair value assets to total assets. |
FVL s | Fair value liabilities per share. |
NFVA s | Nonfair value assets per share. |
NFVL s | Nonfair value liabilities per share. |
Leverage | Ratio of the total liabilities to total assets. |
Net Income | Earnings before extraordinary items per share. |
10-K Words | The total number of words in a 10-K, scaled by 1,000. |
Big 4 Auditor | Indicator variable that equals 1 if the firm is audited by a Big 4 auditor and zero otherwise. |
Early Adoption | Indicator variable that equals 1 if the firm adopts SFAS 157 before the mandated implementation date. |
Segment | Number of business segments in which the firm operates. |
Log Market Cap | Natural log of market cap (in $ mil). |
Inst Hold | Percentage of outstanding shares held by institutional investors. |
Coverage | Number of analysts covering the firm in the month before the month of the filing date. |
Litigation | Measure of litigation risk based on Rogers and Stocken (2005). |
Age | Number of years listed. |
Mgt Forecast | Indicator variable that equals one if, before the filing date, the firm provided an annual earnings-per-share management forecast for the next fiscal year and zero otherwise. |
Tobin’s Q | The sum of the equity market value and total liability book value to total assets. |
ROA | Earnings before extraordinary items divided by total assets. |
Loss | Indicator variable that equals 1 if a firm’s earnings before extraordinary items in a given year is negative and zero otherwise. |
Inverse Mills | Inverse Mills ratio computed with the selection model in Table 2, Panel B. |
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Chung, S.G., Goh, B.W., Ng, J. et al. Voluntary fair value disclosures beyond SFAS 157’s three-level estimates. Rev Account Stud 22, 430–468 (2017). https://doi.org/10.1007/s11142-016-9384-9
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DOI: https://doi.org/10.1007/s11142-016-9384-9