Challenges and Opportunities in Disclosure Research*

Keynote: Tsinghua International Corporate Governance Conference

Phil BergerUniversity of Chicago Booth School of Business

June 14th, 2014

*This presentation builds on and updates my Journal of Accounting & Economics (2011) article Challenges and opportunities in

disclosure research: a discussion of ‘the financial reporting environment: review of the recent literature’

Introduction and Overview of Talk

• Introduction– Disclosure plays a key role in governance and valuation– It has been widely studied in both modeling and empirical research– Still, large challenges and opportunities for future work in the

disclosure area remain

• Overview of Talk– Voluntary disclosure models– Empirical voluntary disclosure research– Mandatory disclosure models– Empirical mandatory disclosure research– Concluding thoughts

Voluntary Disclosure Models

• Most disclosure choice models use pure exchange economy

• i.e., no real effects of disclosure on things like production as a result of feedback from capital markets to firm managers

• Until recently, only real effect typically considered in voluntary disclosure models was effect on product market competitors

• Considering effect on investment decisions seems a fruitful path to pursue

• A nice recent example that does this is Beyer & Guttman (JAR, Dec. 2012)

Voluntary Disclosure Models (continued)

• Beyer & Guttman (2012) adds voluntary disclosure to a Myers & Majluf (1984) setting

– M&M setting has no ability to disclose or signal value of existing assets– Firm with assets in place considers equity issue to fund new project– Owners have private information about value of assets in place– In Myers & Majluf, firm gives up new opportunity to avoid equity issue

when value of assets in place sufficiently high

• Beyer & Guttman setting allows disclosure or signals about value of assets in place and they then solve for the equilibrium disclosure and investment policies that result:– Find cost of credibly disclosing value of assets in place is biggest for

intermediate values (i.e., nonlinear relation)

– Thus, in contrast to M&M, firms pursue opportunity and issue equity when value of assets in place is high, but not for intermediate values

Empirical – Proprietary Information Costs

• Consensus view of empirical evidence in this area was that it generally shows that product market competition affects disclosure

• Issues I have with extant findings on product market competition:

– Conflicting evidence

– Measurement error in product market competition proxies

– What if proprietary costs of disclosure are correlated with capital market benefits of disclosure?

– Difficulties in disentangling proprietary cost hypothesis from agency/entrenchment cost hypothesis

Conflicting Evidence on Proprietary Costs

• Verrecchia & Weber (2006) find firms are less likely to ask the SEC to withhold information from filings when they operate in more concentrated industries

• Bamber & Cheon (1998) find firms are less likely to provide earnings forecasts in more concentrated industries

• VW result is consistent with more informative disclosure in more concentrated industries, but BC result is consistent with less informative disclosure in these industries

Measurement Error in Competition Proxies

• Proxies usually calculated using Compustat data, which include publicly-traded (but generally not privately-held) firms

• Ali, Klasa and Yeung (2009) show that using U.S. Census industry concentration measures (i.e., including private and public firms) often reverses prior results in the literature that had been based on Compustat concentration metrics:– Correlation of Compustat & Census concentration measures just 13%– Compustat concentration measures proxy for declining industries– Harris (1998) found segment disclosures less likely in more

concentrated industries, measured per Compustat– Ali et al. show result goes away using Census data

• Compustat-based concentration measure used in numerous discretionary disclosure papers – Bamber & Cheon (98), Harris (98), Botosan & Harris (00), Botosan & Stanford (05), Rogers & Stocken (05) and Verrecchia & Weber (06)

Costs Correlated with Benefits• Models often assume away any correlation between proprietary

costs and capital market benefits– E.g., Verrecchia (1983) - proprietary cost is fixed (and exogenous)

• Empirical papers attempt to identify variables that capture variation in proprietary costs

• These papers, however, often make no attempt to measure variation in the capital market benefits of disclosure

• If the proxy for proprietary costs is also a proxy for the (omitted) capital market benefits of disclosure, a serious correlated omitted variables problem makes inferences difficult

• Costs and benefits are likely to be correlated (e.g., if product market competition affects cost to reveal info to competitors, it likely also affects benefit of info to stock market investors)

Proprietary vs. Agency Cost Explanations

• Berger and Hann (2007) argue much evidence consistent with the proprietary cost hypothesis also consistent with an agency costs

• Bens, Berger and Monahan (2011) expand on this by arguing:– Proprietary cost motive needed for agency cost motive to exist

– If agency costs were a plausible motive for nondisclosure and proprietary costs were not, than “unraveling” would occur

– Agency and proprietary cost motives are reasonable only if outsiders can’t use public disclosures to unravel these motives for aggregation

– These observations imply: (1) agency and proprietary cost motives should be studied simultaneously and (2) data from public financial statements have limitations for making inferences about either motive

Empirical – Disclosure & Cost of Capital

• Two main arguments for disclosure to affect equity cost of capital – pricing of information risk and pricing of information quality

• Empirical researchers should likely admit that whether or not information risk is priced is an open issue

• Empiricists may also be too quick to appeal to “direct effects” theory papers such as Lambert, Leuz & Verrecchia’s (2007)– i.e., empiricists may seek theoretical justification for predictions relating info quality to cost of capital without fully understanding the necessary conditions for the prediction to hold

Cost of Capital Estimation Challenges

• Easton & Monahan (2005):– Use variance decomposition to evaluate implied cost of capital metrics

– Absolute magnitude of noise in each measure is large

– Aggregating across firms may not help much

– Instrumental variables approach may not help much

• Dealing with the pervasive estimation problems difficult because:– Alternatives to implied cost of capital have their own major problems:

– Target price problems:– The target price applies only to a specific time horizon; – Target price is a reliable proxy for the market’s (as opposed to the

analyst’s) expected return only for Hold recommendations; – Target price is of questionable accuracy (Bradshaw & Brown)

– Realized returns are likely biased and noisy measures of expected returns

Example of Addressing Problems Well

• Hail & Leuz (2006):

– Context matters – in identifying problems and deciding how to address – Cross-country study – long-term earnings growth assumptions less

accurate in some countries and analysts’ earnings forecasts capture long-term cash flow growth less well in some accounting systems

– HL assess the influence of growth differences on their results

– Also take 4 steps to address error in implied cost of capital measures:• Per Gode & Mohanram (2003) and Botosan & Plumlee (2005), show implied cost

of capital estimates are related to traditional risk and country factors

• Robustness checks address deficiencies in analyst forecasting behavior

• Compute accuracy-weighted country-year means of their cost of capital estimates to give more weight to observations with higher forecast accuracy

• Use dividend yields and expected returns derived from country credit-risk ratings and country-index returns as alternatives to analyst-based cost of capital proxies

Measuring Disclosure & Reporting Quality:Limitations of Deterministic Measures

• Deterministic approach to measure accrual quality has limits:

– Jones (1991) assumes non-discretionary accruals are a deterministic and linear function of change in sales and the level of PP&E

– Accruals unexplained by the Jones (1991) are assumed to represent discretionary (and generally lower quality) accruals

– Dechow & Dichev (2002) specify a deterministic intertemporal decomposition of operating cash flow (OCF)

– Measure accruals quality as estimation error from regression of changes in working capital on past, current, and future OCF

– Deterministic benchmarking has the fundamental limitation of treating reporting bias as noise

– Thus, reporting bias is entangled with stochastic variation arising from volatility in the firm’s operating environment

– Grouping volatility and bias into a common regression error term may lead to excessive identification of manipulated or low quality earnings

An Alternative to Deterministic Measures

Gerakos & Kovrijnykh (JAE 2013):

•Stochastic model of earnings that separately accounts for economic shocks and reporting bias

•Assumptions: (1) True earnings are not totally transitory

•(2) Goal of reporting bias is to mask the true impact of economic shocks. G&K argue this results in strategic manipulation that is generally in the opposite direction to the performance shock

•Implies reporting bia leads to negative second lag autocorrelation in residuals from regression of current on lagged earnings

Use of Multiple Earnings Quality Measures

Concerns with using multiple measures in one paper:

•When using multiple quality metrics, a consistent directional effect on all of them for some disclosure or governance variable may not indicate a robust finding

•Ewert and Wagenhofer (2010) define earnings quality as the amount of reduction in the market’s uncertainty about the firm’s terminal value due to the earnings report

•They compare this measure with widely used metrics such as value relevance, persistence, predictability, smoothness, and accrual quality

•Each measure captures different effects and several of the common metrics even vary non-monotonically with the benchmark earnings quality measure

Computational Linguistics Measures of Disclosure• Li (2008) brings natural language processing to accounting• Shows computational linguistics can be used to assess how basic

aspects of disclosure such as readability may be associated with managers’ motives to obfuscate

• Li’s (2008) paper also points to some of the challenges and limitations of using natural language processing techniques to examine disclosure motives and consequences

• Bloomfield (2008) notes that Li’s evidence, while consistent with his obfuscation hypothesis, is also consistent with alterntaive explanations such as attribution

• Raises the following issue: What if main issue becomes distinguishing among various discretionary managerial motives related to language choices?

• Then need better grounding in underlying literatures in linguistics, psychology and natural language processing [a good e.g., is Larcker and Zakolyukina (JAR 2012), “Detecting deceptive discussions in conference calls”]

Role of the Media in Capital Market Disclosure

• Dyck and Zingales (2002), Miller (2006), Bushee et al. (2010), and Soltes et al. (JFE forthcoming) examine role of the press as a mechanism for transmitting firm or mutual fund news

• Last month’s JAR Conference illustrates the variety of issues being studied that link media and disclosure:

– Brown, Stice & White, “Mobile communication and local trading activity: evidence from distracted driving laws”

– Dai, Parwada & Zhang, “The governance role of the media through news dissemination: evidence from insider trading”

– Kim, Li & Liu, “Who benefits more from XBRL adoption? Evidence from breadth of ownership”

– Lee, Hutton & Shu, “The role of social media in the capital market: evidence from consumer product recalls”

• Similarly, an example of one scholar focusing on these issues:– Blankespoor, “The impact of information processing costs on firm disclosure:

evidence from the XBRL mandate”– Blankespoor et al. (RAST 2015) “Initial evidence on the impact of the XBRL

mandate”– Blankespoor et al. (TAR 2014 ) “The role of dissemination in market liquidity:

evidence from firms’ use of Twitter”

Measuring More Subtle Aspects of Disclosure

• Disclosure measures to date have focused on:– Did disclosure occur? [simple]– What was the quality or transparency of the disclosure? [complex]– What were the linguistic characteristics of the disclosure, such as the

tone, readability, etc. [very complex]

• Other complex dimensions of disclosure seem promising to explore if new measures can be developed:– One example is the “horizon” of the disclosure– Horizon construct means something like this: Does the disclosure

provide information about long-term aspects of firm strategy versus short-term aspects (such as current or near-term operating results)?

– Horizon of disclosure might be linked to investor clienteles, horizon of investment/production decisions, etc.

– Ball and Milian (working paper) make a promising attempt to construct and use a disclosure horizon measure based on the term structure of implied equity volatilities (i.e., duration of different implied volatilities from exchange-traded options)

Mandatory Disclosure Regulation

• Traditional rationales for disclosure regulation

– Four traditional rationales for disclosure regulation are financial externalities; real externalities; agency costs; and economies of scale

– These rationales result in disclosure regulation being difficult to justify, as is true with respect to regulation in a host of areas according to standard economic theory

– Healthy skepticism of the potential benefits from regulation of disclosure (and other activities) is understandable, but casual empiricism suggests heavy regulation of disclosure (and other activities) is common in successful economies

– Question arises on whether something is missing from existing models given that they often tend to indicate no net benefit from regulation

Mandatory Disclosure Models• How to model the choice among alternative rules?

– As with voluntary disclosure, I think models that allow for real effects of disclosure have considerable promise in the mandated disclosure setting

– An insight that emerges from recent papers allowing for real effects of mandated accounting rules is that more transparency can be harmful

– Allen and Carletti (2008) and Plantin et al. (2008) both model situations where decreasing prices combine with fair value accounting (often advocated by standard setters on transparency grounds) to force asset fire sales which then drive down (transparent) prices even further

– Qi Chen, in co-authored work with Zeqiong Huang & Yun Zhang (JAR, June 2014) examines impact of public information in a setting of short-horizon traders who have varying levels of private information precision

– In Chen et al., more public information can increase or decrease price informativeness in this setting, as it not only directly endows price with more public information but also can indirectly effect the extent to which price reveals private information

Empirical Mandatory Disclosure Research• Empirical tests suggested by models that allow for real effects of

accounting rules are worthwhile, but difficult

• An example is Bhat et al. (2010), who examine how changes in commercial bank mortgage-backed-securities (MBS) holdings relate to liquidity-driven changes in MBS prices and mark-to-market accounting during the 2007 financial crisis

• Define feedback as tendency of banks to sell in the face of liquidity-driven price declines and interpret their evidence as consistent with such a feedback effect from mark-to-market accounting rules

• Difficulty with interpreting tests is that liquidity in the MBS market is likely endogenously related to the behavior of commercial banks

• Disentangling cause from consequence and identifying the role of any feedback effect thus becomes considerably more complicated

Challenges in Using Research to Inform Regulation

• Lack of robust findings across studies:– My discussion of the difficulties in studies of the impact of proprietary

information costs on discretionary disclosure illustrates that even fairly widely accepted results (like disclosure is reduced by proprietary costs) do not seem to hold up very well to close scrutiny

• Scarce instances of major regulatory change:– There are very few sweeping changes in broad regulation along the lines of

the U.S. securities acts of 1933 and 1934; SOX; or the mandated adoption of IFRS

– Empirical research cleverly exploits these major regime changes and the variation observable within any given major regime change in terms of different affected groups, differences in adoption timing by affected groups, and so on

– Nevertheless, it seems reasonable to wonder if even the best empirical research about these infrequent past major regime changes can really inform debate about how best to proceed when the next major reform opportunity arises

Concluding Thoughts

• Traditional approaches to disclosure research have provided a basis for initial exploration of questions related to disclosure

• Many of these approaches are now long in the tooth and significant progress in both models and empirical papers seems to require significant innovation

• On the modeling side, models based on pure exchange economies seem less likely to produce important new insights than those that allow disclosure to have real effects on things like investment decisions

• On the empirical side, improved measures are needed for constructs such as proprietary information costs, earnings or accruals quality, overall disclosure or overall disclosure quality, and cost of capital. Recognition of our currently very limited ability to inform disclosure regulation policy also appears warranted