Download Choices and Best Practice in Corporate Risk Management Disclosure

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
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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.
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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