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Transcript
Choices and Best Practice in Corporate Risk Management Disclosure by Ekaterina E. Emm, Seattle University, Gerald D. Gay, Georgia State University, and Chen-Miao Lin, Clark Atlanta University* or the past decade, public U.S. companies have been required by the SEC to disclose key information about their risk management practices. Under Item 305 of SEC Regulation S-K, “Quantitative and Qualitative Disclosures about Market Risk,” companies must disclose in their 10-Ks information about their exposures to fluctuations in variables such as interest rates, foreign exchange, and commodity prices. While disclosure is mandatory, companies have the discretion to choose among three alternative methods: sensitivity analysis, valueat-risk (VaR), and the so-called “tabular” method. In our recent study of corporate practice, we read the 10-K risk disclosures of all non-financial S&P composite 1500 firms during the three-year period 2002-2004 with the immediate aim of documenting their choice of disclosure method and gathering other information about the nature and management of their risk exposures. The larger goal of our study was to investigate how both firm-specific and industry-level characteristics work together to shape corporate choices of disclosure method. Our analysis was guided by the recognition that disclosure entails significant costs as well as benefits and that corporate managers should—and typically do—strive to select the method that is best for their shareholders and maximizes firm value. How can disclosure affect company values? To begin with, disclosure is costly; it is not only costly to produce, but it could also reveal proprietary and strategic information to competitors and potential new entrants. Indeed, during the Item 305 rule-making, several companies expressed such concerns. In one letter to the SEC, General Motors stated, “If GM disclosed the volume of its commodity derivatives contracts and their anticipated cash flows, a competitor could calculate the purchase price of GM’s components.”1 On the other hand, as both theory and evidence suggest, disclosure can increase a company’s access to capital markets and enhance the attractiveness of their shares to current and prospective investors by reducing their costs of information gathering. This reduction in information costs in turn leads to an increase in the liquidity of the company’s securities and, possibly, a reduction in the cost of raising outside capital.2 While our main focus is on how managers should choose their disclosure method, our investigation also attempts to evaluate, at least to some extent, the quality of the disclosure given the method selected. To this point, our inspection of 10K risk disclosures revealed a large variation in the quality of companies’ disclosures. In the next section, we briefly describe each of the three disclosure methods and illustrate each with a company that, in our view, has made commendable and effective efforts to produce informative disclosures. These examples of best practice companies include UPS for sensitivity analysis, Microsoft for VaR, and Tyco International for the tabular method. Tyco’s choice of disclosure method as well as its implementation is especially revealing in light of its efforts to emerge from a widely publicized scandal over allegedly fraudulent accounting practices. To restore market confidence, its newly hired CEO in 2002 replaced most of the management team and the entire board of directors and took steps to increase transparency. In addition to its choice of the tabular method, which as we show below, provides the highest level of disclosure of the three methods, Tyco appears to have put considerable effort into producing detailed and comprehensive disclosures consistent with the CEO’s goal of “striving for corporate transparency…[to] give investors the necessary tools to make informed decisions about our business” and “to be an example of best practices in [regulatory] compliance.”3 The empirical part of our investigation provides evidence about the main factors that influence corporate choice of disclosure method. As already suggested, corporate managers, as agents of their shareholders, are expected to disclose information to the point where the company’s marginal benefit from additional disclosure equals the marginal cost. We show how the optimal disclosure choice for a given company will depend on three main considerations: (1) the nature and extent of its various market risk exposures; (2) its need * The authors gratefully acknowledge the helpful comments and guidance of Don Chew, Bob Daigler, Jayant Kale, and Isabel Tkatch, and the research assistance of Anna Agapova, Laetitia Dowd, and Tonmoy Navare. 1. See Merton H. Miller and Christopher L. Culp, “The SEC’s Costly Disclosure Rules,” Wall Street Journal, June 25, 1996, p. A14. 2. See Christine Botosan, “Evidence That Greater Disclosure Lowers the Cost of Equity Capital,” Journal of Applied Corporate Finance, Vol. 12 (2000), pp. 60-69. 3. See “Letter from Ed Breen”at:www.tyco.com/livesite/Page/Tyco/Our+Commitment/ Governance/Letter+from+Ed+Breen/??&DCRID=1977593717 B F Journal of Applied Corporate Finance • Volume 19 Number 4 A Morgan Stanley Publication • Fall 2007 17 to protect proprietary information; and (3) its demand for capital market access. The major findings from this part of our analysis are as follows: • Sensitivity analysis, which is the “middle of the road” method in terms of the amount of information disclosed, was the disclosure method most commonly selected by companies. At the same time, however, a significant number of companies used either the tabular or VaR methods—and in ways consistent with managers appropriately weighing and balancing the costs and benefits of disclosure. • A company’s propensity to use the tabular method, which is clearly the most revealing of the three alternatives, was positively related to the extent of its interest rate and commodity exposures and, perhaps even more important, to capital market considerations, notably its demand for external financing. • The propensity for companies to select VaR, the least revealing disclosure method, appeared to be driven mainly by scale economies, extensive derivatives use, and by competitive concerns about revealing proprietary information. With the aim of bringing our analysis together, the final section of this article provides a closer look at one particular industry, Computer and Office Equipment, to illustrate how apparently similar companies can arrive at different choices of disclosure method. While the majority of the companies in this industry used sensitivity analysis, several used the tabular or VaR methods. We discuss how these choices were consistent with our empirical findings, including evidence suggesting that the companies choosing the VaR method possessed relatively high levels of proprietary information, while those choosing the tabular method had a greater demand for capital market access. does not believe that there is any material market risk exposure with respect to derivative or other financial instruments that would require disclosure under this item.” Some Background on Item 305 The final rules amending SEC Regulation S-K to include Item 305 were approved in 1997. The rules require companies to disclose information about each type of market risk on 10-K forms in section 7A, “Quantitative and Qualitative Disclosures about Market Risk.”4 Under Item 305, companies may use one or more of the following three disclosure alternatives: sensitivity analysis, tabular, and VaR. Companies are required to provide separate disclosures for each type of market exposure and are allowed to use different reporting methods for different types of risk exposure (which several companies do). For those companies that deem their market risk exposure to be “immaterial,” disclosure is elective.5 For example, Walgreen Co., a consumer product and drugstore retailer, reported in its 2004 10-K filing that “management The Three Methods We now describe the three disclosure methods and briefly illustrate each by showcasing a company that we believe has done an effective job of not only complying with regulatory requirements, but providing information that could prove useful to investors. Sensitivity Analysis. The sensitivity analysis method expresses the potential loss in future earnings, fair values, or cash flows that could result from selected hypothetical changes in market rates and prices. Companies are required to provide a description of the model, assumptions, and parameters used. The disclosure provided by United Parcel Service, Inc. (UPS) is one application of the sensitivity analysis method that we found to be particularly well-implemented. UPS is one of the world’s largest couriers with operations in more than 200 countries. In its 2004 10-K filing, the company stated that it had multiple risk exposures, including those related to interest rates, exchange rates, commodity prices, and equity prices.6 For each exposure, the company provided a systematic and thorough discussion using the sensitivity analysis method. To illustrate, we present in Exhibit 1 an excerpt from UPS’s risk disclosure that contains statements describing its overall approach to managing market risk along with details of its foreign currency exposure. Due to its global operations, UPS had significant exposures to several foreign currencies, notably the Euro, British pound sterling, and Canadian dollar. Such exposures were managed using hedging strategies involving currency option and forward contracts. Dollar estimates of the sensitivity of these derivative instruments to movements in exchange rates were clearly presented, as in the following statement: “The potential loss in fair value for such instruments from a hypothetical 10% adverse change in quoted foreign currency exchange rates would be approximately $117 and $97 million at December 31, 2004 and 2003, respectively.” UPS also provided the key assumptions used in preparing its estimates—as well as an acknowledgment of the limitations of such estimates—to enable investors to better assess the extent of its risk exposures. For instance, it stated, “There are certain limitations inherent in the sensitivity analyses presented, primarily due to the assumption that exchange rates change in a parallel fashion and that interest rates change instantaneously. In addition, the analyses are unable to reflect the complex market reactions that normally would arise from the market shifts modeled.” 4. For a full description of the final rules, see Federal Register, February 10, 1997, Vol. 62, No. 27, “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.” pp. 6043-6079. 5. Materiality is determined through an assessment of (1) the fair values of all held market risk-sensitive instruments and (2) the potential losses in earnings, fair values, and cash flows in the near future attributable to “reasonably possible” changes in market prices or rates. 6. See UPS 10-K filing for the fiscal year ended on December 31, 2004. 18 Journal of Applied Corporate Finance • Volume 19 Number 4 A Morgan Stanley Publication • Fall 2007 Exhibit 1 Sensitivity Analysis Disclosure of Market Risk: UPS, Inc. Market Risk We are exposed to market risk from changes in certain commodity prices, foreign currency exchange rates, interest rates, and equity prices. All of these market risks arise in the normal course of business, as we do not engage in speculative trading activities. In order to manage the risk arising from these exposures, we utilize a variety of foreign exchange, interest rate, equity and commodity forward contracts, options, and swaps. The following analysis provides quantitative information regarding our exposure to commodity price risk, foreign currency exchange risk, interest rate risk, and equity price risk. We utilize valuation models to evaluate the sensitivity of the fair value of financial instruments with exposure to market risk that assume instantaneous, parallel shifts in exchange rates, interest rate yield curves, and commodity and equity prices. For options and instruments with non-linear returns, models appropriate to the instrument are utilized to determine the impact of market shifts. There are certain limitations inherent in the sensitivity analyses presented, primarily due to the assumption that exchange rates change in a parallel fashion and that interest rates change instantaneously. In addition, the analyses are unable to reflect the complex market reactions that normally would arise from the market shifts modeled. Foreign Currency Exchange Risk We have foreign currency risks related to our revenue, operating expenses, and financing transactions in currencies other than the local currencies in which we operate. We are exposed to currency risk from the potential changes in functional currency values of our foreign currency-denominated assets, liabilities, and cash flows. Our most significant foreign currency exposures relate to the Euro, the British Pound Sterling and the Canadian Dollar. We use a combination of purchased and written options and forward contracts to hedge cash flow currency exposures. These derivative instruments generally cover forecasted foreign currency exposures for periods up to one year. As of December 31, 2004 and 2003, the net fair value of the hedging instruments described above was a liability of $(28) and $(48) million, respectively. The potential loss in fair value for such instruments from a hypothetical 10% adverse change in quoted foreign currency exchange rates would be approximately $117 and $97 million at December 31, 2004 and 2003, respectively. This sensitivity analysis assumes a parallel shift in the foreign currency exchange rates. Exchange rates rarely move in the same direction. The assumption that exchange rates change in a parallel fashion may overstate the impact of changing exchange rates on assets and liabilities denominated in a foreign currency. Source: 10-K filing for the fiscal year ended on December 31, 2004. By disclosing losses resulting from potential market movements, sensitivity analysis provides a method for reporting a company’s risk exposures that is easy for investors to understand. On the other hand, as can also be seen from the UPS illustration, sensitivity analysis provides a total loss estimate that is aggregated across multiple currencies, which could make it difficult to identify potential losses stemming from individual currency movements.7 Tabular. Companies choosing the tabular method are required to make disclosures that are sufficiently detailed to enable investors to estimate expected cash flows related to market-sensitive instruments for each of the next five years and the aggregate cash flows expected for the years thereafter. Companies are also required to report related information about fair values and contract terms, such as notional amounts of derivative instruments and weighted-average interest rates. To illustrate a best practice in tabular disclosure, we present part of a statement provided by Tyco International, Ltd., a large conglomerate operating in more than 100 countries. In 2004, the company had approximately $33 billion in total liabilities, of which half represented long-term debt. Not surprisingly, the company held large quantities of interest rate-sensitive instruments, including fixed- and variable-rate debt and swaps, and in multiple currencies. In its 2004 10-K filing, Tyco’s disclosure of its risk exposures was extensive and contained separate, highly detailed presentations of its exposures to interest rates and foreign currency.8 In addition, to help investors understand the presented information, the company provided a helpful narrative. In Exhibit 2 we present an excerpt from Tyco’s tabular disclosure of its exposure to interest rate risk. For each maturity bucket of its various fixed and variable debt obligations, Tyco 7. What’s more, because different exchange rates or commodities can have significantly different volatilities, aggregating losses across currencies or commodities resulting from an assumed equal percentage change could provide a number that is difficult for an analyst or investor to interpret. For a discussion of this problem, see Thomas J. Lins- meier and Neil D. Pearson, “Quantitative Disclosures of Market Risk in the SEC Release,” Accounting Horizons, Vol. 11 (1997), pp. 107-135. 8. See Tyco International, Ltd. 10-K filing for the fiscal year ended on September 30, 2004. Journal of Applied Corporate Finance • Volume 19 Number 4 A Morgan Stanley Publication • Fall 2007 19 Exhibit 2 Tabular Disclosure of Interest Rate Risk: Tyco International, Ltd. The table below provides information about our financial instruments that are sensitive to changes in interest rates, including debt obligations and interest rate swaps. For debt obligations, the table presents cash flows of principal repayment and weighted-average interest rates. For interest rate and cross-currency swaps, the table presents notional amounts at the current market price rate and weighted average interest rates. Notional amounts are used to calculate the contractual payments to be exchanged under the contract. The amounts included in the table below are in U.S. Dollars, unless noted ($ in millions): 2005 2006 2007 2008 2009 Thereafter Total Fair Value 1,428 1,990 6.1% 6.0% 5 2,590 802 6.4% 2.9% 6.1% 5.8% — 624 6 — 742 2 833 2 2,209 2,323 4.4% 1 3.7% 6.1% 7.5% 5.5% 8.1% — — 1 — — — 54 56 56 2.9% 2.1% — — — 5.0% — — — — — — — 883 883 923 Average interest rate — — — — — 6.4% — — Fixed rate (Other) 11 9 5 2 — — 27 27 6.3% 6.1% 6.8% 6.4% — — — — 8 9 1 1 1 89 109 109 Average interest rate1 2.7% 3.0% 4.2% 3.4% 3.7% 8.0% — — Variable rate (Euro) 26 11 11 13 12 — 73 73 Average interest rate1 3.4% 3.4% 3.6% 3.7% 3.7% — — — Variable rate (Other) 18 — — — — — 18 18 11.4% — — — — — — — 71 Total Debt Fixed rate (US$) Average interest rate Fixed rate (Euro) Average interest rate Fixed rate (Yen) Average interest rate Fixed rate (British Pound) Average interest rate Variable rate (US$) Average interest rate1 6,543 13,358 15,864 Cross Currency Swap: Fixed to variable (British Pound) — — — — — 359 359 Average pay rate1 — — — — — 3.5% — — Average receive rate — — — — — 6.5% — — 55 Interest rate swaps: Fixed to variable (US$) — — — — — 2,750 2,750 Average pay rate1 — — — — — 3.6% — — Average receive rate — — — — — 6.3% — — Fixed to variable (British Pound) — — — — — 359 359 Average pay rate1 — — — — — 3.5% — — Average receive rate — — — — — 6.5% — — (2) 1. Weighted-average variable interest rates are based on applicable rates at September 30, 2004 per the terms of the contracts of the related financial instruments. Source: 10-K filing for the fiscal year ended on September 30, 2004. reported cash flows of expected principal repayments and weighted-average interest rates. Similarly, for its interest rate derivatives, which included both “plain vanilla” interest rate swaps and cross currency swaps, the company reported notional amounts and weighted-average pay and receive rates. Fair market values for each class of instruments were reported in the last column of the table. Among the three methods, the tabular method, with its multiple maturity buckets for assigning cash flows and other information, is likely to be most useful for companies challenged with disclosing interest rate exposures, but less so for firms with mainly foreign currency exposures. Interest rate instruments typically have well-defined cash flows and notional amounts, and broadly varying maturities. In contrast, instruments related to foreign exchange are typically shorter in maturity (often conducted as spot transactions or through contracts with maturities less than a year) and exposures are more difficult to quantify.9 9. A 2006 survey of the OTC derivatives market showed that 78% of foreign exchange instruments had maturities less than one year, 15% between one and five years, and 7% greater than five years. In contrast, for interest rate derivatives, the corresponding percentages were 34, 39, and 27%. See BIS Quarterly Review, March 2007. 20 Journal of Applied Corporate Finance • Volume 19 Number 4 A Morgan Stanley Publication • Fall 2007 Exhibit 3 Value-at-Risk Disclosure: Microsoft Corporation We use a value-at-risk (VAR) model to estimate and quantify our market risks. VAR is the expected loss, for a given confidence level, in fair value of our portfolio due to adverse market movements over a defined time horizon. The VAR model is not intended to represent actual losses in fair value, but is used as a risk estimation and management tool. The model used for currencies and equities is geometric Brownian motion, which allows incorporation of optionality with regard to these risk exposures. For interest rate risk, the mean reverting geometric Brownian motion is used to reflect the principle that fixed-income securities prices revert to maturity value over time. Value-at-risk is calculated by, first, simulating 10,000 market price paths over 20 days for equities, interest rates and foreign exchange rates, taking into account historical correlations among the different rates and prices. Each resulting unique set of equities prices, interest rates, and foreign exchange rates is then applied to substantially all individual holdings to re-price each holding. The 250th worst performance (out of 10,000) represents the value-at-risk over 20 days at the 97.5 percentile confidence level. Several risk factors are not captured in the model, including liquidity risk, operational risk, credit risk, and legal risk. The VAR numbers are shown separately for interest rate, currency, and equity risks. These VAR numbers include the underlying portfolio positions and related hedges. We use historical data to estimate VAR. Given the reliance on historical data, VAR is most effective in estimating risk exposures in markets in which there are no fundamental changes or shifts in market conditions. An inherent limitation in VAR is that the distribution of past changes in market risk factors may not produce accurate predictions of future market risk. The following table sets forth the VAR calculations for substantially all of our positions: (In millions) Risk Categories 2003 2004 Interest rates $ 448 $ 298 Currency rate $ 141 $ 207 Equity prices $ 869 $ 773 Year ended June 30, 2004 ––––––––––––––––––––––––––––––––––––––––––––––––––– Average High Low $ 625 $ 817 $ 298 $ 217 $ 326 $ 117 $ 969 $ 1,174 $ 770 The total VAR for the combined risk categories is $835 million at June 30, 2004 and $987 million at June 30, 2003. The total VAR is 35% less at June 30, 2004 and 32% less at June 30, 2003 than the sum of the separate risk categories for each of those years in the above table, due to the diversification benefit of the combination of risks. The change in the absolute value of VAR is primarily due to asset allocation shifts and portfolio growth. Source: 10-K filing for the fiscal year ended on June 30, 2004. Value-at-Risk. The third alternative available for quantifying potential losses resulting from movements in market rates or prices is the VaR method. VaR gained initial popularity among financial and trading institutions and has since been embraced by a number of corporate treasurers and risk officers of non-financial corporations. VaR measures the maximum loss that can be expected over a certain time frame, given a specified probability or level of confidence. Central to the implementation of VaR is the development of a forecast of the future values of the economic variable driving the risk exposure, whether it be interest rates, exchange rates, commodity prices, or some other. Companies typically use one of the following three modeling methods to form such forecasts: variance/covariance, historical simulation, and Monte Carlo simulation.10 As an example of risk disclosure using the VaR method, consider Microsoft Corporation’s report in its 2004 10-K filing. Microsoft had a high market-to-book value of assets (4.15 times), which generally is assumed to reflect the market’s favorable assessment of the company’s future growth opportunities. It also had exposure to several market risks, including foreign currency exchange rate, interest rate, and equity price risks.11 For each exposure category, as can be seen in Exhibit 3, Microsoft provided the average, high, and low VaR amounts for both the current and prior year. The company also provided detailed information about the modeling method and its parameters, assumptions, and limitations. Specifically, it clearly stated the model used (Monte Carlo simulation with geometric Brownian motion assumed to capture currencies and equities price movements, and mean reverting geometric Brownian motion for interest rate movements), the instruments included in the analysis (all portfolio positions and corresponding hedges), the time frame (20 days), and the 10. In the variance/covariance method, the distribution of future outcomes accounts for the variances of and covariances between the economic variables affecting the portfolio value. In the historical simulation method, this distribution is derived through a repeated re-sampling of the historic values of the variables. The Monte Carlo simulation method is similar to the historical simulation method, but instead uses randomly generated values based on an assumed probability distribution for each variable. 11. See Microsoft Corporation 10-K filing for the fiscal year ended on June 30, 2004. Journal of Applied Corporate Finance • Volume 19 Number 4 A Morgan Stanley Publication • Fall 2007 21 Table 1 2-digit SIC Code 01–09 10–14 15–17 20–39 40–49 50–51 52–59 70–89 99 Total Frequency of Disclosure Methods by Industry Industry Agriculture, forestry, and fisheries Mining Construction Manufacturing Transportation, Communications, Electric, Gas, and Sanitary Services Wholesale Trade Retail Trade Services Non-classifiable Establishments Number of Firms Disclosure method No Risk Exposure Sensitivity VaR Tabular Total Frequency 3 54 23 696 164 0 2 2 79 11 3 42 11 552 127 0 3 0 37 27 0 25 14 84 38 3 72 27 752 203 48 136 234 5 6 37 44 2 35 83 159 1 0 1 4 0 8 18 40 2 49 139 247 5 1,363 183 1,013 72 229 1,497 confidence interval (97.5%). The company also reported the VaR amount aggregated across risk exposures to indicate further diversification effects. There are a number of advantages of VaR, particularly for financial companies or firms with large derivatives portfolios. VaR provides a consistent measurement of risk across different financial instruments; by so doing, it allows companies to monitor their risks associated with portfolios of financial assets and liabilities and to manage them to a desired level.12 And, by accounting for the volatility and co-movement of multiple risk factors, VaR can aggregate risks into a single number for assessing a company’s overall risk exposure. It is thus, in effect, a “stock” measure of risk. But, as a stock as opposed to a single-period or “flow” measure, VaR has clear limitations for industrial companies. Although it may be useful for quantifying the exposures of a derivatives portfolio, VaR does not give corporate risk officers a way to assess, on a year-by-year basis, the market exposures of an industrial company’s operating assets that such derivatives are designed to hedge. That would require “flow” measures of risk, such as “cash flow at risk” (CFaR), that are much more consistent with the kinds of information provided by sensitivity analysis and, especially, the tabular method.13 And if most risk officers of industrial companies want much more information about corporate exposures than VaR can provide, the same is true of investors in such companies. Indeed, as should be clear from the above discussion, for most non-financial firms, VaR is likely to be the least informative of the three disclosure methods. In fact, although companies that use the VaR method are required to report the size of a risk exposure, they don’t have to specify its direction (i.e., long or short). 14 Sensitivity analysis provides information about both direction and magnitude, but as in the case of VaR, not enough to enable investors to derive a company’s annual cash flows for more than a year or two. Thus, only the tabular method appears to provide a meaningful “flow” measure of net exposures. And, as researchers have demonstrated, the information reported by companies using the tabular format can generally be used to derive VaR numbers and the results of sensitivity analysis—while neither VaR nor sensitivity analysis provides the “flow” content of tabular reporting.15 On the other hand, as already noted, many companies have expressed concerns that use of the tabular method in particular would result in the disclosure of proprietary information due to the highly detailed and disaggregated format in which data are presented. And so when disclosing even the direction of the exposure is potentially undesirable, companies are likely to prefer the VaR method.16 12. See Christopher Culp, Merton Miller, and Andrea Neves, “Value at Risk: Uses and Abuses,” Journal of Applied Corporate Finance, Vol. 10 (1998), pp. 26-38 and Stephen Godfrey and Ramon Espinosa, “Value-At-Risk and Corporate Valuation,” pp. 108-115 of the same issue. 13. What’s more, companies using a short time window to compute VaR may underestimate the potential for large market movements and therefore underestimate the true extent of risk. See Leslie Hodder and Mary Lea McAnally, “SEC Market Risk Disclosure: Enhancing Comparability,” Financial Analysts Journal, Vol. 57 (2001), pp. 62-78. 14. See Linsmeier and Pearson (1997), cited earlier. 15. See Hodder and McAnally (2001), cited earlier. 16. This view is also supported by Leslie Rahl who stated that “VAR is a single number as opposed to detailing your position, so you’re [revealing] less to the competition.” See Elizabeth Wine, “Disparaged VaR Now May Be the Risk Method of Choice,” CFO Alert, Vol. 5, Issue 17 (1998), p. 3. 22 Journal of Applied Corporate Finance • Volume 19 Number 4 Empirical Analysis of Disclosure Choices For all companies comprising the S&P 1500 during the period 2000-2004, we collected data for each of the three years 2002-2004. We focused on the practices of non-financial companies and thus excluded banks, financial service companies, and insurance companies. Companies that experi- A Morgan Stanley Publication • Fall 2007 Figure 1 Distribution of Firms Using Each Disclosure Method by Firm-size Quintile (a) Sensitivity Analysis 30.0 (b) Value-at-Risk 80.0 20.0 17.4% 20.1% 20.2% 20.9% 10.0 Percent of Firms Percent of Firms 69.4% 21.3% 0.0 2 3 4 Size Quintile 10.5% 25.3% 28.4% 14.4% 0.0 Percent of Firms 21.4% 0.0% 1.4% 1 (smallest) 2 40 5.6% 3 4 Size Quintile 43.72% 27.87% 20 17.49% 8.20% 0 1 (smallest) 2 3 Size Quintile 4 5 (largest) enced corporate events such as mergers and bankruptcies were also subsequently excluded when they no longer filed 10-Ks. This left 1,408, 1,391, and 1,363 companies in fiscal years 2002, 2003, and 2004, respectively. From each company’s annual 10-K filing, we collected a broad array of information related to its disclosure methods and risk management practices. Table 1 summarizes the disclosure methods used by the 1,363 companies in 2004 when broken down by industry. Sensitivity analysis was the most frequently chosen method and was highly prevalent across all industry categories. The choice of the tabular method, though less common, was still widespread. VaR was a distant third choice, with even the tabular method a more frequent choice than VaR in all industries. Of the close to 1,400 companies in our sample, 183 reported having no “material” risk exposures; these companies were concentrated in the retail, trade, and services industries. When a company’s choice of disclosure method was broken down by firm size, we observed several interesting patterns. Figure 1 shows breakdowns where all companies were assigned to one of five quintiles ranked by size (with quintile 1 containing the smallest 20% of companies and quintile 5 containing the largest 20%). As shown in panel (a) of the figure, the choice of sensitivity analysis was uniformly distributed across all size quintiles. By contrast, panel (b) shows that companies choosing VaR were mostly concenJournal of Applied Corporate Finance • Volume 19 Number 4 5 (largest) (d) No Risk Exposure 60 30.0 20.0 23.6% 20.0 5 (largest) (c) Tabular 40.0 Percent of Firms 40.0 0.0 1 (smallest) 10.0 60.0 1 (smallest) 2 3 Size Quintile 4 2.73% 5 (largest) trated in the largest two size quintiles, suggesting that larger companies are more likely to have the necessary skills and resources for its implementation. In fact, no company in the first quintile of smallest companies chose VaR and only one firm in the second quintile did. Panel (c) shows that the tabular method was represented in all size quintiles, with its frequency of use growing with size. Finally, panel (d) shows that the number of companies reporting no risk exposures decreased with firm size, consistent with the idea that larger companies tend to have greater scale and scope of operations, thus increasing their potential risk exposures. For the 72 companies that chose the VaR method, we also recorded the modeling method, confidence interval, and holding period. As shown in Table 2, the most popular modeling approach for computing VaR was the variance/ covariance method (used by 44% of companies), followed by Monte Carlo simulation (25%) and historical simulation (22%). Two additional companies used both the variance/ covariance and Monte Carlo simulation methods. As for the confidence interval, most companies (83%) chose a 95% confidence interval, with only a few selecting either the 97.5 or 99% confidence levels. With regard to the holding period over which potential losses were estimated, most companies (64%) used a time horizon of one day. The remaining companies used holding periods ranging from two days to one year. A Morgan Stanley Publication • Fall 2007 23 Table 2 Frequency of Implementation Practices of the Value-at-Risk Method The table presents frequency statistics for the choice of model, confidence interval, and holding period used in estimating value-at-risk. The statistics are based on a sample of 72 firms that used the VaR disclosure method in fiscal year 2004. ‘n/a’ means not available. Value-at-Risk Details Frequency Percent Model Variance/covariance 32 44.4% Monte Carlo simulation 18 25.0% Historical simulation 16 22.2% Variance/covariance & Monte Carlo simulation 2 2.8% n/a 4 5.6% Total 72 100.0% Confidence Interval 95 percent 60 83.3% 97.5 percent 3 4.2% 99 percent 1 1.4% 95 and 99 percent 4 5.6% n/a 4 5.6% Total 72 100.0% Holding Period 1 day 46 63.9% 2 days - 1 week 4 5.6% 2 weeks - 1 month 4 5.6% 1 quarter 1 1.4% 1 year 6 8.3% 1 day & 3 days 1 1.4% 1 day & 1 year 3 4.2% 1 week & 1 year 1 1.4% n/a 6 8.3% Total 72 100.0% Factors Influencing the Choice of Disclosure Method We conducted a multivariate regression analysis to investigate the joint influence of a set of variables on a company’s choice of disclosure method. In selecting our variables, we attempted to shed light on two questions. The first considers how the types and levels of various market risk exposures faced by a company influence its choice of method. The second attempts to identify the influence on disclosure choices of factors associated with the costs and benefits of disclosure, particularly those related to maintaining capital market access and avoiding the release of proprietary information.17 Market risk exposure: For each company, we computed measures of its risk exposure to interest rate, foreign currency, and commodity price movements. Interest rate exposure was measured as a company’s financial leverage, defined as the book value of total outstanding long-term debt as a percentage of the book value of total assets. Our measure of foreign currency exposure was the ratio of a company’s foreign sales to total sales while commodity exposure was measured by the ratio of its total inventories to total assets. Derivatives use: Companies pursue a number of strategies for managing their risk exposures, including the recognition of natural hedges, the establishment of operating hedges, and the use of exchange-traded and OTC derivatives. From our reading of 10-K filings, we discovered that, in 2004, 909 (or 67%) of the 1,363 companies were 17. Unless indicated otherwise, all information used to calculate the following variables were taken from the S&P’s Compustat Industrial Research files (“Compustat”). 24 Journal of Applied Corporate Finance • Volume 19 Number 4 A Morgan Stanley Publication • Fall 2007 users of derivatives. To capture derivatives use in our regression analysis, we included a dummy variable that was set equal to one if a company reported the use of derivatives, and zero otherwise. Firm size: A company’s size was also included in our analysis, which was measured by the logarithm of its total assets. On the one hand, size can act as an exposure variable since larger companies tend to have greater scale and scope of operations, thus potentially increasing risk exposures. But size can also serve as an indicator of disclosure costs; to the extent there are fixed costs associated with the burden of disclosure, larger companies are better positioned to bear these costs. Capital market considerations: Through greater disclosure, managers can reduce asymmetric information and uncertainty about their companies. This is particularly beneficial for companies requiring greater access to capital markets. Studies have shown that increased disclosure can enable companies to obtain lower issuing costs and a lower cost of capital.18 Disclosure has this effect by reducing the information costs of investors and analysts, which in turn enhances the liquidity and attractiveness of a company’s securities. To capture these dimensions of capital market considerations, we included the following three variables: External financing need: Companies with greater demand for external financing generally have stronger incentives to disclose more information. This demand will depend on the extent to which a company’s growth cannot be solely financed through its internally generated funds. Following prior research, we measured a firm’s external financing need as the difference between its actual growth rate and its “internal” growth rate. 19 A company’s internal growth rate is the rate that can be sustained by relying only on internally generated funds while maintaining its dividend. The actual growth rate was computed as the three-year geometric average of the annual growth rate in total assets, while internal growth rate was computed as the three-year average of ROA*b/(1-ROA*b), where ROA is a company’s net return on assets after interest and taxes, and b is its retention ratio. Analyst coverage: Studies have found that companies disclosing more information tend to have larger analyst followings.20 However, we are cautious in inferring the direction of causation because a larger analyst following could encourage managers to disclose more information. To measure analyst coverage, we collected data on the number of analysts following a firm from the Institutional Brokers Estimate System (IBES). Share liquidity: Greater disclosure reduces the information costs of investors and analysts, thus increasing the attractiveness of a company’s securities, promoting greater liquidity in the trading of its shares, and reducing the cost of capital. We hypothesize that a company will have a greater propensity to disclose more, the larger the number of investors seeking information about the company. Following earlier studies, we used as an indicator of investor demand for information a common measure of share liquidity—specifically, the logarithm of its annual trading volume of shares scaled by shares outstanding.21 Proprietary information considerations: Disclosure can be costly to the extent it risks revealing proprietary information to competitors and potential new entrants. We developed three variables to capture various dimensions of this concept: the degree of industry concentration; the extent of a company’s growth opportunities; and the capital intensity of an industry as a proxy of an entry barrier to potential competitors. Industry concentration: Companies in more concentrated industries will have greater incentives to protect profit margins and thus to select a method that discloses less of their proprietary information. Market concentration was defined according to the Herfindahl-Hirschman (HH) Index as: . ( ( )NDEX ¤ -ARKET3HARE I 18. See Botosan (2000), cited earlier, and also Richard Frankel, Maureen McNichols, and Peter G. Wilson, “Discretionary Disclosure and External Financing,” The Accounting Review, Vol. 70 (1995), pp. 135-150. 19. See, for example, Asli Demirgüç-Kunt and Vojislav Maksimovic, “Law, Finance, and Firm Growth,” Journal of Finance, Vol. 53 (1998), pp. 2107-2137. 20. See, for example, Mark H. Lang and Russell J. Lundholm, “Corporate Disclosure Policy and Analysts,” The Accounting Review, Vol. 71 (1996), pp. 467-492. 21. See Thomas Scott, “Incentives and Disincentives for Financial Disclosure: Voluntary Disclosure of Defined Benefit Pension Plan Information by Canadian Firms,” The Accounting Review, Vol. 69 (1994), pp. 26-43. 22. See Kee H. Chung, Mingsheng Li, and Linda Yu, “Assets in Place, Growth Opportunities, and IPO Returns,” Financial Management, Vol. 34 (2005), pp. 65-88. Journal of Applied Corporate Finance • Volume 19 Number 4 I where the market share of firm i is based on net sales, and N is the number of companies in the industry defined at the 4-digit SIC level, as reported in Compustat. Growth opportunities: Following common practice we proxied a company’s growth opportunities using its marketto-book value of assets. The market value of a company reflects not only the value of its existing assets in place, but also the value of growth opportunities related to its potential to make future investments in positive-NPV projects.22 These growth opportunities result from the company’s store of proprietary information as reflected in patent holdings, the use of innovative technologies, and its ability to develop unique product offerings. Disclosure of such information could lead to the loss of those opportunities. Thus, we hypothesized that companies with more growth opportunities could have a greater incentive to select a method that discloses less information. Capital intensity: We considered an industry’s capital intensity as a form of entry barrier. For a given level of industry competitiveness, the propensity for a company to disclose A Morgan Stanley Publication • Fall 2007 25 more should increase as the cost to potential new entrants increases, since the threats to profit margins are lower. Using all Compustat listed companies in an industry as defined at the 4-digit SIC level, an industry’s capital intensity was measured as the average ratio of each firm’s total investment in property, plant, and equipment to its total assets. Regression Procedure We performed a multinomial logit regression procedure that enabled us to estimate the change in the probability that a company would select one disclosure method versus a paired alternative when one of the explanatory variables is allowed to change. Since in this case there are three pair-wise combinations of disclosure methods (i.e., tabular versus sensitivity, VaR versus sensitivity, and VaR versus tabular), we jointly estimated three regressions. Using “tabular versus sensitivity” as an example, each of the three regressions had a specification similar to the following: ¥ Prob(Yi = Tabular) ln ¦ ¦Prob(Y = Sensitivity) i § ´ µ µ= ¶ f ( Market Exposure Variables i , Economic Disclosure Variables i ) In the above expression, Prob (·) is the probability that company i will select a specified disclosure method Y. Market Exposure Variables is the set of variables that includes interest rate exposure, foreign currency exposure, commodity exposure, derivatives use, and firm size. And Economic Disclosure Variables are those variables that capture factors related to the costs of revealing proprietary information as well as the benefits of improved capital market access. The results are presented in Table 3. The regression coefficients are interpreted as follows: The coefficients in the first column (Model 1) correspond to the relative change in the propensity of companies to select the tabular method over sensitivity analysis resulting from a change in one of the market exposure or economic disclosure variables. Similarly, Model 2 corresponds to the likelihood of a company selecting VaR over sensitivity analysis, and Model 3 corresponds to the likelihood of choosing VaR over the tabular method. Positive regression coefficients indicate an increase in the likelihood of choosing a particular disclosure method over a stated alternative, while negative values indicate a reduced likelihood. Our Findings Market Exposure Variables: As indicated in Table 3, when a company’s exposure to interest rates or commodity prices became larger, the probability that it selected the tabular method increased relative to choosing either sensitivity 26 Journal of Applied Corporate Finance • Volume 19 Number 4 analysis (Model 1) or VaR (Model 3). On the other hand, companies with larger foreign currency exposures had a reduced likelihood of selecting the tabular method relative to either the sensitivity analysis or VaR methods. Derivatives use increased the likelihood that a company selected either the tabular or VaR methods instead of sensitivity analysis; further, derivatives use increased the likelihood of using VaR relative to the tabular method. Finally, an increase in firm size raised the likelihood that a firm chose VaR relative to both the sensitivity analysis (Model 2) and tabular methods (Model 3). Economics of Disclosure Variables: Two broad findings emerge from our inspection of the coefficients on the variables related to the costs and benefits of disclosure. First, companies with a greater demand for capital market access tended to choose the tabular method, which again is the most revealing. Supporting this claim is the corresponding evidence that greater external financing needs reduced the likelihood of a company’s choosing the VaR method and instead increased the propensity to select either of the more revealing tabular or sensitivity analysis methods. Similar conclusions were reached for analyst coverage, as companies with greater analyst followings showed a greater propensity to select either the tabular or sensitivity analysis methods relative to VaR. Finally, companies with higher levels of share liquidity had a greater propensity to select the tabular method over sensitivity analysis. Our second broad finding is that companies that appeared to have higher potential costs from the revelation of proprietary information have a greater likelihood of choosing VaR, the least revealing method. As the level of industry concentration became greater (i.e., HHI becomes larger), companies had a marginally greater propensity to select VaR and a significantly lower propensity to choose the tabular method. Similarly, companies with higher growth opportunities (i.e., a higher market-to-book ratio) and thus higher holdings of proprietary information had a greater likelihood of choosing VaR. By contrast, companies in industries with higher entry barriers in the form of greater capital intensity showed a greater willingness to use the tabular method and disclose more. Choice of Disclosure Method in the Computer Industry In sum, our analysis provided evidence that a company’s choice of disclosure method is influenced by considerations related to its market exposures, capital market access, and proprietary information costs. We next explored in greater detail how these considerations have affected practice in the Computer and Office Equipment industry (SIC code 357). Like many industries, this one is also dominated by the choice of sensitivity analysis, but a number of companies also chose tabular or VAR. We investigated whether the characteristics of these latter companies were sufficiently A Morgan Stanley Publication • Fall 2007 Table 3 Results From Tests of the Importance of Market Exposure and Economic Disclosure Variables on Choice of Disclosure Method This table presents results from a multinominal logit estimation procedure. Models 1, 2, and 3 are estimated simultaneously, where the dependent variable is the probability that a firm chooses the first specified disclosure method relative to the second specified disclosure method. The three disclosure methods we consider are sensitivity analysis, tabular, and VaR. The sample consists of 3,501 S&P 1500 firm-year observations for fiscal years 2002-2004. p-values are in parentheses. ***, **, * indicate significance at 1%, 5%, and 10% levels. Explanatory Variables Model 1 Tabular vs Sensitivity Model 2 VaR vs Sensitivity Intercept -2.646*** -10.809*** (0.000) (0.000) Market Exposure Variables Interest Rate Exposure 1.184*** -1.020 (0.000) (0.126) Foreign Currency Exposure -0.538* 0.270 (0.010) (0.492) Commodity Exposure 1.515*** -3.838*** (0.000) (0.004) Derivatives Use 0.317*** 1.190*** (0.000) (0.000) Firm Size 0.055 0.944*** (0.241) (0.000) Economic Disclosure Variables Capital Market Considerations External Financing Need 0.531* -1.572** (0.075) (0.014) Analyst Coverage 0.009* -0.041*** (0.091) (0.000) Share Liquidity 0.177** 0.144 (0.031) (0.358) Proprietary Information Considerations Industry Concentration -1.540*** -0.656 (0.000) (0.193) Growth Opportunities -0.086** 0.138** (0.029) (0.018) Capital Intensity 0.610*** 0.767** (0.000) (0.015) different from those choosing sensitivity analysis to explain their use of the less common disclosure methods. In our sample of S&P 1500 firms, there were 33 companies identified as operating in the Computer and Office Equipment industry. Among these companies were such household names as Palm, Xerox, Dell, and Apple Computer, as well as companies with significantly lower market shares, such as Visual Networks and Exabyte. Of the 33 companies, four reported no material market risk exposure and were thus exempt from selecting a disclosure method. Of the remaining Journal of Applied Corporate Finance • Volume 19 Number 4 Model 3 VaR vs Tabular -8.163*** (0.000) -2.205*** (0.002) 0.807* (0.056) -5.353*** (0.000) 0.874*** (0.004) 0.889*** (0.000) -2.103*** (0.002) -0.049*** (0.000) -0.034 (0.841) 0.884 (0.119) 0.225*** (0.001) 0.156 (0.646) 29 companies, while the vast majority (22) used sensitivity analysis, five companies selected VaR, and two chose the tabular method. For each of the three sets of companies, we computed portfolio averages of key firm characteristics, which are presented in Table 4. Companies that chose the VaR method stand out on three dimensions from the firms that selected sensitivity analysis. First, companies selecting VaR were on average much larger. Specifically, the VaR companies had an average of $14.2 billion in total assets as compared to A Morgan Stanley Publication • Fall 2007 27 Table 4 Company Characteristics in the ‘Computer and Office Equipment Industry’ Summary statistics for S&P 1500 firms that had primary business operations in ‘Computer and Office Equipment Industry’ (SIC code 357) as of the end of fiscal year 2004. The statistics in the table show average values for companies within each disclosure Market Risk Exposure Factors Interest Rate Exposure Disclosure Method Foreign Commodity Percent of Firm Size Currency Exposure Derivatives Users Exposure Number Long-term Foreign Inventories of to total sales to debt Companies assets to total total sales assets VaR 5 10.8% 41.1% 2.4% Sensitivity 22 10.8% 48.7% 8.7% Tabular 2 19.3% 32.7% 10.2% Total assets (in $millions) 100.0% 14,202.3 Proprietary Information Share External Number of Financing Analysts Liquidity Need Growth Opportunities Market-to-book value of assets Actual growth rate minus internal growth rate Annual trading volume to shares outstanding 6.1% 35.4 2.5 4.2 54.5% 7,445.2 4.4% 18.9 2.6 2.8 100.0% 424.4 23.0% 11.5 2.9 2.2 $7.4 billion for the sensitivity analysis companies. Second, all of the VaR firms were active in hedging their market risk exposures using derivative instruments, whereas only about half of the sensitivity analysis companies were derivatives users. To illustrate, Dell, a VaR company, was an extensive derivatives user with aggregate notional holdings of $5.5 billion, which included a wide assortment of both linear and non-linear instruments. Third, companies choosing VaR had much larger growth opportunities and hence possibly higher levels of proprietary information. Specifically, the VaR firms had an average market-to-book value of assets ratio of 4.2, as compared to 2.8 for the sensitivity analysis companies. Consistent with our empirical findings, the two companies that selected the tabular method, Pinnacle Systems, Inc. and Coinstar, Inc., had higher levels of interest rate and commodity exposures (and a lower level of foreign currency risk) than the companies that used sensitivity analysis. Further, in terms of capital market considerations, these two companies had substantially greater reliance on external financing. The average external financing need for these companies (i.e., the difference between their actual and internal growth rate) was 23% versus a modest 4.4% for the companies using sensitivity analysis. Also, these companies had lower market-to-book ratios, suggesting they incurred lower costs from revealing proprietary information when having to access capital markets. 28 Capital Market Factors Journal of Applied Corporate Finance • Volume 19 Number 4 Concluding Comments The intent of SEC regulation “Quantitative and Qualitative Disclosures about Market Risk” is to better inform investors about companies’ market risk exposures and their risk management practices. In complying with this regulation, companies have a choice of three disclosure methods: sensitivity analysis, VaR, and the tabular method. Using a large sample of S&P 1500 companies, our analysis revealed a large variation in reporting practices across companies and industries, with companies appearing to weigh the costs and benefits of the various alternatives. Companies that required a greater access to financial markets were more likely to choose the tabular method, the method that reveals the most information. On the other hand, companies that appeared to have higher potential costs from revealing proprietary information to rivals and thus compromising their competitive position tended to choose VaR, the method that provides the most aggregated and least detailed information. ekaterina emm is Assistant Professor of Finance at Seattle University’s Albers School of Business and Economics. gerald gay is Professor of Finance at Georgia State University’s Robinson College of Business. chen-miao lin is Assistant Professor of Finance at Clark Atlanta University’s School of Business. A Morgan Stanley Publication • Fall 2007