Abstract
I develop and test an investor demand-driven explanation for why one firm’s change in voluntary disclosure behavior is emulated by some firms in the industry but not others. I focus on the overlap in institutional investor ownership between two firms as a mechanism by which a first-mover firm’s increase in disclosure prompts investors to seek a similar increase from a follower firm. Using 10-K market risk disclosures as my empirical setting, I find that a firm’s decision to follow a first mover in providing more quantitative information than is required by the SEC is positively associated with an increase in investor overlap from the prior year. I also find that the association is stronger for overlap in large institutional investors, consistent with their greater influence over managers, and for firms where investor uncertainty is high. This association is found after controlling for the herding effect documented in prior studies and after addressing potential endogeneity concerns. Overall, this evidence provides new insight into patterns of intra-industry disclosure behavior and highlights investor overlap as a communication channel and feedback mechanism that helps facilitate the diffusion of disclosure practices.
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Notes
I focus on institutional investors, rather than individual investors, because a large literature has shown they are more sophisticated at processing information (Jiambalvo et al. 2002) and are the primary targets when CFOs set voluntary disclosure policies (Graham et al. 2005). Also, I do not focus on sell-side analysts because analyst coverage tends to follow institutional ownership (O’Brien and Bhushan 1990; Bushee and Miller 2012), and many small firms lack sufficient analyst coverage to compute overlap. Nonetheless, in empirical tests, I control for overlap in analyst coverage but do not anticipate any incremental association.
Buy-side analysts repeatedly meet with managers in private meetings (Brennan and Tamarowski 2000; Hong and Huang 2005), over phone calls (Carleton et al. 1998), at investor conferences (Bushee et al. 2011), and at the headquarters of either the firm or the buy-side analyst (Abramowitz 2006; Jackson 2009).
See Linsmeier and Pearson (1997) for a full discussion of these formats and Appendix 1 and 2 for examples and discussions of the three types: tabular presentation, sensitivity analysis, and value-at-risk. These disclosures are “sticky” because, once added, firms tend not to remove them in future filings.
For example, the synchronicity literature examines the association between firms’ stock returns and industry returns and how market participants affect the association (e.g., Piotroski and Roulstone 2004).
I use the terms “institutions,” “institutional investors,” “buy-side analysts,” “portfolio managers,” and “buy-siders” interchangeably to refer to the representatives of Form 13F filers (entities with over $100 million of investment discretion) who regularly meet with managers of firms. They are not presumed to be insiders or board members.
Legal considerations also restrict the number of firms that certain institutional investors may consider for investment. For example, prudent person laws cause financial institutions to tilt the composition of their portfolios toward stocks that are viewed by courts as prudent (Del Guercio 1996).
The full title of the rule is “Disclosure of Accounting Policies for Derivative Financial Instruments and Derivative Commodity Instruments and Disclosure of Quantitative and Qualitative Information about Market Risk Inherent in Derivative Financial Instruments, Other Financial Instruments, and Derivative Commodity Instruments.”
Although market risk includes foreign currency exchange risk and equity price risk, such risks are less relevant to the sample firms, and such disclosures are not used in the empirical tests of this paper.
Using 4-digit SIC codes to define industries assumes that a buy-side analyst who follows the industry first mover also follows other firms that are primarily within the same 4-digit SIC code. This is the assumption that motivates the conjecture that overlap in institutional ownership is a mechanism that facilitates intra-industry disclosure demand. I assess the reasonableness of this assumption in the following manner. While data about the span of coverage for buy-side analysts is not publicly available, I use IBES data on the span of coverage for sell-side analysts to proxy for buy-side coverage. I find that, for the 40 sell-side analysts who covered Apache Corp. in 1999, the first mover in the Crude Oil & Natural Gas industry (SIC 1311), roughly 90 % of the other firms that those analysts covered were also in SIC code 1311. The percentage is over 80 % for analysts covering petroleum refining firms (SIC 2911), 60 % for national banks (the other firms were primarily state banks under SIC 6022), and 55 % for brokerages. A lack of sufficient sell-side analysts covering real estate firms (SIC 6798) prevents a similar analysis. Therefore, to the extent that the span of coverage for sell-side analysts proxies for the span of coverage for buy-side analysts, basing industries on 4-digit SIC codes sufficiently captures the hypothesized overlap mechanism.
The first mover in the banking industry, Bank of America, was the first to disclose three formats.
Thomson-Reuters also provides data at the mutual fund level. However, overlap in mutual fund ownership is a noisier measure because each institution can manage multiple mutual funds yet rely on the same buy-side analyst. For example, if Firms A and B are owned by the same institution but within different mutual funds, then a measure of overlap in mutual fund ownership would not capture the common ownership. Nonetheless, I test whether overlap in mutual fund ownership (US equity growth funds) is associated with overlap in firms’ disclosure decisions and find weaker but still significantly positive results (not tabulated).
In the section on additional analyses, I repeat the tests using a weighted-average measure of investor overlap that incorporates all firms that increased their market risk, not just the first mover.
Partitioning institutions by the top quintile and bottom four quintiles allows for sufficient variation in both the variables for overlap in large and small institutional investors, which increases the statistical power of the test for H2. However, similar results are found when partitioning institutions by the top decile and tercile.
Specifying the variable as the proportion of firms (PRIOR_PCT) yields similar results as LPRIOR. Using the nonlogarithm specification of PRIOR yields an insignificant coefficient for the control variable, but the variable of interest, ∆OVLPII, remains significant.
I do not include total analysts as a control variable because in preliminary tests, that variable had over a 70 % correlation with firm size and its inclusion did not change the results for the variables of interest.
Omitting the requirement that the auditors must be from the same office location does not change the main results.
For 142 of the 150 sample firms (95 %), the fiscal year ends in December.
In preliminary tests, a levels analysis was performed, and a positive association was found between the level of investor overlap and a follower’s decision to include multiple formats. However, for brevity and to focus more on examining the relative timing of disclosure changes and investor overlap changes, the levels analysis is not included in the paper.
Using the prior year change in LPRIOR, ∆LPRIORt-1, as the control variable results in an insignificant coefficient for the herding variable and does not alter the results for the investor overlap variable.
I also try a specification where the dependent variable is an indicator variable for whether next year’s change in investor overlap is positive. Results (not tabulated) and inferences are similar to those reported for Eq. (2).
Results are quantitatively similar when interaction terms using the continuous variables are used.
To keep the correlation tables to a manageable size, I omit the hedging and trading indicators (∆HEDGING and ∆TRADING), oil price and volatility variables (∆OILPRC and ∆OILVOL), and the interest rate and volatility variables (∆PRIMERATE and ∆PRIMERATEVOL). Pair-wise correlations of these variables with the variable of interest (∆OVLPII) do not exceed |0.15|.
To check for industry effects, I run the regression on the sample of energy and financial firms separately. I find that the coefficients for the prior year change in investor overlap (∆OVLPIIt−1) remain significantly positive in each regression but only at the 10 % level, likely due to the loss of statistical power.
In computing the hit ratio, a firm-year is classified as INCREASEi,t = 1 (0 otherwise) if the predicted value exceeds the cutoff of 0.0916, which is the probability that INCREASE = 1 in the sample (77/841). A benchmark hit ratio is 56.1 % (472/841).
Managers may make this attempt but simply fail; my test does not distinguish between a nonattempt and a failed attempt. This result also raises the questions of whether followers gain institutional investors or nonfollowers lose institutional investors conditional on the disclosure increase, even if investor overlap does not change. In untabulated results, I do not find any significant difference in the change in the number of institutional investors between followers and nonfollowers, but there is a difference in the average holdings of each institution. Institutional investors of the followers increased their percentage ownership by 0.02 % in the calendar quarter immediately following the 10-K filing, while investors of nonfollowers increased their percentage ownership by 0.004 %. The difference of 0.016 % is significant at p value = 0.04 (one-sided in the hypothesized direction). Economically, this translates into roughly an average of 25,000 more shares owned by each institutional investor in the quarter immediately after an increase in market risk disclosure by a follower, based on average shares outstanding of 154 million for the sample firms.
In untabulated results, a logistic regression of INCREASEt on contemporaneous changes in investor overlap (ΔOVLPIIt) yields a negative but insignificant coefficient (p value = 0.52).
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Acknowledgments
This paper is based on my dissertation conducted at the University of Pennsylvania. I greatly appreciate the invaluable guidance and support from my dissertation committee members, Brian Bushee, Robert Holthausen, and Catherine Schrand. I also thank the following people for their helpful comments and suggestions: Chris Armstrong, Russell Lundholm (the editor), Stephanie Sikes, Robert Verrecchia, Holly Yang, an anonymous referee, and workshop participants at the University of Pennsylvania, University of Michigan, Northwestern University, University of Chicago, New York University, Dartmouth College, Harvard University, University of Washington and Stanford University. I gratefully acknowledge the financial support from the Wharton School, the Stern School, the Deloitte Foundation, and the Robert R. Nathan Memorial Foundation.
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Appendices
Appendix 1: Examples of market risk disclosures using three possible quantitative formats
1.1 Panel A: Tabular presentation (from Apache Corp.’s 1999 10-K filing, dated 3/29/2000)
Commodity Price Hedges—Apache periodically enters into commodity derivative contracts and fixed-price physical contracts to manage its exposure to oil and gas price volatility. Commodity derivatives contracts, which are usually placed with major financial institutions that the Company believes are minimal credit risks, may take the form of futures contracts, swaps or options. The derivative contracts call for Apache to receive, or make, payments based upon the differential between a fixed and a variable commodity price as specified in the contract. As a result of these activities, Apache recognized hedging losses of $6.7 million in 1999 and hedging gains of $1.3 million and $14.5 million in 1998 and 1997, respectively. The hedging gains and losses are included in oil and gas production revenues in the statement of consolidated operations.
The following table and note thereto cover the Company’s pricing and notional volumes on open commodity derivative contracts as of December 31, 1999:
2000 | 2001 | 2002 | 2003 | 2004 | Thereafter | |
|---|---|---|---|---|---|---|
Natural gas swap positions (FERC indexes) | ||||||
Pay fixed price—January 2000 to July 2008 (thousand MMBtu/d)a | 50 | 30 | 30 | 30 | 30 | 32 |
Average swap price, per MMBtua | $2.27 | $2.27 | $2.31 | $2.35 | $2.39 | $2.51 |
Oil swap positions (NYMEX) | ||||||
Receive fixed price—January–August 2000 (Mbbl/d) | 5 | – | – | – | – | – |
Swap price, per bbl | $19.42 | – | – | – | – | – |
Oil swap positions (NYMEX) | ||||||
Receive fixed price—January 2000–June 2002 (Mbbl/d) | 10 | 9 | 8 | – | – | – |
Average swap price, per bbl | $20.52 | $18.82 | $18.45 | – | – | – |
Oil collar positions (NYMEX) | ||||||
Volume—January–August 2000 (Mbbl/d) | 13 | – | – | – | – | – |
Average ceiling price, per bbl | $23.00 | – | – | – | – | – |
Average floor price, per bbl | $17.73 | – | – | – | – | – |
Gas collar positions (NYMEX) | ||||||
Volume—January–August 2000 (thousand MMBtu/d) | 80 | – | – | – | – | – |
Average ceiling price, per MMBtu | $3.31 | – | – | – | – | – |
Average floor price, per MMBtu | $2.06 | – | – | – | – | – |
1.2 Panel B: Sensitivity analysis (from Apache Corp.’s 1999 10-K filing, dated 3/29/2000)
1.2.1 Commodity risk
The Company’s major market risk exposure is in the pricing applicable to its oil and gas production. Realized pricing is primarily driven by the prevailing worldwide price for crude oil and spot prices applicable to its United States and Canadian natural gas production. Historically, prices received for oil and gas production have been volatile and unpredictable and price volatility is expected to continue. Monthly oil price realizations ranged from a low of $10.09 per barrel to a high of $24.11 per barrel during 1999. Gas price realizations ranged from a monthly low of $1.60 per Mcf to a monthly high of $2.74 per Mcf during the same period.
The Company periodically enters into hedging activities on a portion of its projected oil and natural gas production through a variety of financial and physical arrangements intended to support oil and natural gas prices at targeted levels and to manage its exposure to oil and gas price fluctuations. Apache may use futures contracts, swaps, options and fixed-price physical contracts to hedge its commodity prices. Realized gains or losses from the Company’s price risk management activities are recognized in oil and gas production revenues when the associated production occurs. Apache does not hold or issue derivative instruments for trading purposes. In 1999, Apache recognized a net loss of $3.1 million from hedging activities that decreased oil and gas production revenues. The net loss in 1999 includes $6.7 million in derivatives losses and $3.6 million in gains from fixed-price physical gas contracts. Gains or losses on derivative contracts are expected to be offset by sales at the spot market price or to preserve the margin on existing physical gas contracts.
At December 31,1999, the Company had open natural gas price swap positions with a positive fair value of $11.1 million. A 10 % increase in natural gas prices would increase the fair value by $19.7 million. A 10 % decrease in prices would decrease the fair value by $19.7 million. The Company also had open oil price swap positions at December 31, 1999 with a negative fair value of $(9.4) million. A 10 % increase in oil prices would decrease the fair value by $18.3 million. A 10 % decrease in oil prices would increase the fair value by $18.3 million. Discount rates used in arriving at fair values range from 6.5 % for 2000 to 7.3 % for 2008.
At December 31, 1999, the Company also had natural gas commodity collars with a fair value of $0.8 million and oil commodity collars with a fair value of $(4.9) million. A 10 % increase in oil and gas prices would change the fair values of the gas collars and the oil collars by $(0.9) million and $(5.2) million, respectively. A 10 % decrease in oil and gas prices would change the fair values of the gas collars and the oil collars by $1.6 million and $3.9 million, respectively. The model used to arrive at the fair values for the commodity collars is based on the Black commodity pricing model. Changes in fair value, assuming 10 % price changes, assume nonconstant volatility with volatility based on prevailing market parameters at December 31,1999.
Notional volumes associated with the Company’s derivative contracts are shown in Note 9 to the Company’s consolidated financial statements.
1.3 Panel C: Value-at-risk (from Unocal Corp.’s 2001 10-K filing, dated 3/15/2002)
1.3.1 Commodity price risk
The Company is a producer, purchaser, marketer and trader of certain hydrocarbon commodities such as crude oil and condensate, natural gas and refined products and is subject to the associated price risks. The Company uses hydrocarbon price-sensitive derivative instruments (hydrocarbon derivatives), such as futures contracts, swaps, collars and options to mitigate its overall exposure to fluctuations in hydrocarbon commodity prices. The Company may also enter into hydrocarbon derivatives to hedge contractual delivery commitments and future crude oil and natural gas production against price exposure. The Company also actively trades hydrocarbon derivatives, primarily exchange regulated futures and options contracts, subject to internal policy limitations.
The Company uses a variance–covariance value at risk model to assess the market risk of its hydrocarbon derivatives. Value at risk represents the potential loss in fair value the Company would experience on its hydrocarbon derivatives, using calculated volatilities and correlations over a specified time period with a given confidence level. The Company’s risk model is based upon historical data and uses a 3-day time interval with a 97.5 % confidence level. The model includes offsetting physical positions for hydrocarbon derivatives related to the Company’s fixed price pre-paid crude oil and pre-paid natural gas sales. The model also includes the Company’s net interests in its subsidiaries’ crude oil and natural gas hydrocarbon derivatives and forward sales contracts. Based upon the Company’s risk model, the value at risk related to hydrocarbon derivatives held for purposes other than hedging was approximately $11 million at December 31, 2001 and approximately $12 million at December 31, 2000. The value at risk related to hydrocarbon derivatives held for nonhedging purposes was approximately $5 million at December 31, 2001 and approximately $13 million at December 31, 2000.
Appendix 1 provides examples of the three quantitative formats prescribed by the SEC in Financial Reporting Release No. 48 (1997). Firms are required to disclose their exposures to market risk, to the extent that the risk is material, using one of three possible quantitative formats: tabular presentation, sensitivity analysis, and value-at-risk. Market risk includes interest rate risk, foreign currency exchange risk, commodity price risk, and equity price risk. Examples are from the Crude Oil and Natural Gas industry (SIC 1311). Apache Corp. was the first firm in the industry to include multiple formats in its market risk disclosure, having done so in its 1999 10-K filing. Panel A illustrates its tabular presentation and Panel B shows its sensitivity analysis. Unocal Corporation included multiple formats beginning with its 2001, 10-K filing; panel C illustrates its value-at-risk estimates.
Appendix 2: Evolution of market risk disclosures and their use by investors
2.1 The evolution of market risk disclosures and literature
In the early 1990s, a wave of companies reporting highly publicized derivative losses (e.g., Proctor and Gamble, Gibson Greetings, Metallgesellschaft AG) led to a call from investors, creditors, and regulators for improvement in the financial reporting and disclosure of companies’ risk exposures and use of derivatives. In October 1994, the Financial Accounting Standards Board (FASB) issued Statement of Financial Accounting Standards No. 119 (SFAS 119), Disclosure about Derivative Financial Instruments and Fair Value of Financial Instruments. The new rule increased the general level of disclosure about derivatives, but researchers and regulators still felt there was insufficient quantitative information about market risk and how the effects of derivatives flowed through the financial statements (Herz et al. 1996).
To address this problem, in January 1997 the Securities and Exchange Commission (SEC) issued Financial Reporting Release No. 48 (FRR No. 48), which required companies to disclose qualitative and quantitative information about market risk. Market risk is defined as risk to earnings, cash flows, or fair values arising from fluctuations in foreign exchange rate, interest rates, commodity prices, and equity prices. Companies are required to use one of three quantitative formats—a tabular presentation, sensitivity analysis, or value-at-risk estimate—to disclose how market risk can affect earnings, cash flow, or fair values of financial instruments. The flexibility was intended to give each company the discretion to disclose its market risk exposure in a manner consistent with internal reporting.
Since then, a number of studies have examined the effectiveness of the market risk disclosures and whether investors find them useful. A year after the SEC rule came into effect, a survey by Roulstone (1999) indicated that market risk disclosures improved greatly under FRR No. 48, but there was significant room for improvement as the disclosures varied widely in detail and clarity. An internal staff report by the SEC made similar conclusions (SEC Staff Report 1998). Even before a sufficient time series of market risk disclosures was available, a number of academic studies used proxies for FRR No. 48 disclosures to provide early evidence that the disclosures were useful to investors in certain industries, such as the oil and gas industry (Rajgopal 1999) and commercial banking industry (Ahmed et al. 2004). Later studies examining actual FRR No. 48 disclosures also concluded that they were useful to investors in reducing uncertainty (Linsmeier et al. 2002) and predicting future revenue (Jorion 2002; Liu et al. 2004).
However, market risk disclosures appear to be more important in certain industries, such as those in which market risk is highly correlated with operating risk. Accordingly, to increase statistical power, many of the above-mentioned studies (with the exception of Linsmeier et al. 2002) used a sample of firms in the energy or financial industries, where operations are greatly affected by commodity prices and interest rates, respectively. Guay and Kothari (2003) question whether the magnitude of derivative use is economically significant and find that derivative use is modest for a broad sample of large firms. As a result, this paper uses a sample of firms in which market risk is expected to be highly correlated with operating risk to ensure that the disclosures are important to investors and other stakeholders.
2.2 The use of market risk disclosures by investors
The methods by which institutional investors use market risk disclosures to analyze and value firms depend upon many factors, including but not limited to (i) the type of firm, (ii) the specific operations of the firm, (iii) the type of quantitative format disclosed by the firm, and (iv) the number of formats disclosed. While a full discussion for all the possible scenarios is beyond the scope of this paper, I provide an overview of the methods that investors can use disclosures specifically about interest rate risk to help analyze financial firms and disclosures about commodity price risk to help analyze energy firms.
A textbook approach to valuing a financial firm is to analyze its portfolio of financial instruments carried on the balance sheet at fair value, as well as to analyze its future stream of net interest earnings from financial operations on a discounted cash flow basis (Ryan 2007, 16–17). Within this framework, information disclosed in the tabular format can be used to estimate the duration of the firm’s portfolio of financial instruments and the firm’s re-pricing gap (interest-earning assets due to be re-priced minus interest-paying liabilities due to be re-priced) at different time intervals. The investor can then assess the expected changes in fair values of financial instruments and net interest earnings based on possible changes in the level, slope, and shape of the yield curve, although the investor would need to make some simplifying assumptions about the timing of fixed-rate coupon payments and floating-rate re-pricing schedules (among other assumptions). Information disclosed in a sensitivity analysis already incorporates these assumptions from management (which are usually more accurate than the investor’s assumptions) and often provides the assessment of value or earnings change in a simple and concise manner, typically for only a limited (one or two) number of interest rate scenarios. Information disclosed in a value-at-risk incorporates the covariances of different classes of assets and liabilities and provides an estimate of one particular bad-case scenario over a specific period and with a specific probability of occurrence. In summary, information contained in each format is complementary and gives an investor a more precise assessment of the potential impact on fair values and net interest earnings for a financial firm from realizations of future interest rate moves.
For energy firms, the information contained in a commodity price risk disclosure provides the investor an estimate of how revenues or earnings may change given a change in oil and gas spot prices. For example, information about commodity derivatives (e.g., notional amounts, maturities, and average strike prices) disclosed in the tabular format can be used by an investor to estimate the proportion of the firm’s production that is hedged at different time intervals and at different strike prices. The investor can then assess potential gains and losses from derivative contracts, which often flow through the firm’s top or bottom line, based on hypothetical or realized changes in commodity prices. Similarly, the firm may summarize such an assessment of their derivative positions or even production revenues in a sensitivity analysis for a limited number of commodity price scenarios. Information disclosed in a value-at-risk estimate incorporates the covariances of price movements for different classes of commodities for firms with several energy businesses, trading operations, or both. Overall, the commodity price risk disclosures provide an investor with a more precise assessment of how sensitive the firm’s revenues or earnings are to fluctuations in energy prices.
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Jung, M.J. Investor overlap and diffusion of disclosure practices. Rev Account Stud 18, 167–206 (2013). https://doi.org/10.1007/s11142-012-9209-4
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DOI: https://doi.org/10.1007/s11142-012-9209-4