hedging effectiveness, cash flow volatility, and …1 hedging effectiveness, cash flow volatility,...

1

Hedging Effectiveness, Cash Flow Volatility, and Firm Value

Nancy Beneda

Professor

University of North Dakota

Gamble Hall

Grand Forks, ND 58202

[email protected]

701 777-4690

Key Words: hedging, effective hedging, optimal hedging, risk management, derivative use

mailto:[email protected]

2

Abstract: The current study suggests that lower than expected cash flow volatility and firm use

of derivatives, which are designated as hedging instruments under SFAS 133, are indicators of

the intent to use derivatives to hedge and effectively manage risk. Alternatively, higher than

expected cash flow volatility and non-designated derivative use indicates a firm may be hedging

ineffectively or selectively using derivatives to take active positions. Also firms with debt are

more likely to benefit from hedging effectiveness from derivative use. The study sample

includes 91 oil and gas exploration and production companies and 704 firm observations for the

years 2003 through 2011. The results of this study also indicate that lower than expected cash

flow volatility combined with the use of designated derivatives is associated with reduced stock

price sensitivity to changes in crude oil prices for this sample of oil and gas explorations

companies.

Introduction and hypothesis development

The current study suggests that differences between expected and actual cash flow volatilities

combined with firm use of derivatives which are designated for hedging under SFAS 1331 have

implications for the intent and effectiveness of derivative use. It is expected that a firm which

has reduced cash flow volatility and primarily uses derivatives designated for hedging, under

SFAS 133, would have the intent to use derivatives to effectively hedge to manage risk.

Alternatively, higher than expected cash flow volatility and non-designated derivative use would

indicate a firm may be ineffectively hedging or selectively using derivatives to take active

positions. This study contributes to the literature on hedging effectiveness and firm decision-

making with regard to derivative use. The study finds that cross-sectional differences in cash

flow volatilities and derivative designation have implications for intent and hedging

effectiveness.

The current study hypothesizes that lower than expected cash flow volatility and the use of

derivatives designated for hedging under SFAS 133 should be associated with hedging

effectiveness, as measured by Tobin’s Q. The study also finds that cash flow volatility and

designated derivative use have implications for risk exposure (i.e. stock price sensitivity to

3

changes in crude oil prices). The current study includes 91 oil and gas exploration and

production companies; 542 derivative use observations and 162 non-derivative use observations

(the sample contains 704 total firm year observations).

Froot et al. (1993) developed a theoretical model which shows that financial hedging should

reduce the volatility of internal cash flows and thus increase a firm’s ability to invest in available

profitable projects. Jin and Jorion (2006) use a hedging delta2 to measure hedging intent and do

not find that commodity derivative use has any impact on firm value. However, Jin and Jorion

(2006) do not differentiate between designated versus non-designated derivatives. The payoffs

for certain non-designated derivatives, such as options and complex derivatives, may not

correlate well with the changes in prices of the underlying assets, and thus could result in

hedging ineffectiveness. The use of cash flow volatility and derivative designation may be

useful as measures of hedging intent. Consistent with Froot et al. (1993) I find that lower than

expected cash flow volatilities are associated with hedging effectiveness, as measured by Tobin’s

Q. I further find that derivative designation, under SFAS 133, has implications for intent of use

and hedging effectiveness, as well.

1Statement of Financial Accounting Standard No. 133 (SFAS 133), Accounting for Derivative Instruments and

Hedging Activities (implemented in 2001), requires that the gains and losses, and the fair values for all derivatives

be reported in the financial statements. Derivatives designated for hedging receive preferential accounting treatment

under SFAS 133 and are required to have a high correlation between the derivative payoffs and the changes in prices

of the underlying asset. Further hedging effectiveness must be disclosed for designated derivatives. Derivatives

which have not been designated for hedging and hedge accounting must be disclosed separately. Non-designated

derivatives include those which were not elected to be designated by the derivative user, those which do not qualify

for hedge accounting, and those derivatives which had qualified and designated but which became ineffective.

Commodity designated derivatives may be classified as ineffective, for example, when actual production falls short

of the hedged amount causing commodity designated derivatives to become ineffective and be reclassified as non-

hedging derivatives, under SFAS 133. A derivative may not qualify for hedge accounting, or may become

ineffective under SFAS 133, for example, when the payoffs for certain types of derivatives, such as options, do not

correlate well with the changes in prices of the underlying assets.

2The hedging delta used by Jin and Jorion (2006) is computed as the net short position of volume hedged divided by

the total actual production of oil and gas.

4

A theoretical model developed by Melumad et al. (1999) suggests that the use of derivatives

designated for hedge accounting results in a higher degree of transparency, and thus promotes

optimal hedging. SFAS 133 requires a high correlation between the derivative payoffs and the

changes in prices of the underlying asset and any ineffectiveness must be disclosed. The intent

and effectiveness of non-designated derivative use may be less transparent. The current study

suggests that derivative users may be more likely not to designate derivatives for hedging and

hedge accounting, and the associated increased disclosure, if their intent is to use ineffective

hedging instruments or take active positions. The association between non-designated derivative

use and higher than expected cash flow volatility, with hedging effectiveness would thus be less

predictable. The expectation, of the current study, is that if the intent of derivative use is to

effectively manage risk, then one would expect there to be an association between derivative use

and hedging effectiveness.

Liu et al. (2011) examine the gains and losses from hedging ineffectiveness for users of hedge

accounting and find that the hedging ineffectiveness measure for users of hedge accounting,

under SFAS 133, is useful in evaluating a firm’s risk management activities. However, Liu et al.

(2011) do not examine the impact of non-designated (i.e. for hedge accounting) derivatives on

hedging effectiveness. In contrast with Liu et al. (2011), the current study examines the

implications of designated verus non-designated and ineffective derivatives. According to

Melumad’s et al. (1999) model, if transparency is higher for designated derivatives, under SFAS

133, then it would be expected that the use of derivatives designated for hedging should motivate

firms to hedge more effectively. Consistent with Melumad et al. (1999) the current study finds

that firms, which use derivatives which are designated for hedging under SFAS 133 exhibit

5

implications for hedging effectiveness, in contrast with firms which primarily use non-designated

derivatives. Hedging effectiveness is measured as Tobin’s Q.

Consistent with Liu et al. (2011), Zhang (2009), Ahmed et al. (2006), Glaum and Klocker

(2011), and Lins et al. (2010), the current study suggests that fair value reporting and disclosure

requirements can impact decisions regarding the purpose of derivative use. Zhang (2009) finds

collectively that certain firms engage in more conservative use of derivatives after the

implementation of SFAS 133. Further in international surveys, Lins et al.( 2010); and Glaum

and Klocker (2011) concluded that a significant number of firms do not choose optimal hedging

because of the required disclosures under SFAS 133. Demarzo and Duffie (1995) developed a

model which supports the contention that managers choose a hedging policy based on required

accounting disclosures versus effective hedging. Marshall and Weetman (2007) find that when

given the choice, managers would choose not disclose their risk strategies. The current study is

different from these previous studies because I examine the implications of the differences

between expected and actual cash flow volatilities and designated derivative use on intent and

hedging effectiveness, as measured by Tobin’s Q.

The current study also indicates increased hedging effectiveness for firms with debt. The model

developed by Froot et al. (1993) incorporates the idea that hedging will be more valuable to

firms with more costly external financing. Mayers and Smith (1982) and Smith and Stulz (1985)

develop hedging theories which suggest that levered firms benefit more from hedging and that

hedging reduces the expected cost of financial distress resulting in higher firm value. The

current study suggests that firms with debt have more to gain from effective risk management.

Other studies, which examine the benefits of hedging for firms with debt, include Mayers and

Smith (1982); Smith and Stulz (1985); Watts and Zimmermann (1990); DeFond and Jiambalvo

6

(1994); Guay (1999); Haushalter (2000); Graham and Rogers (2002); Borokhovich et al. (2004);

and Bartram et al. 2009).

Contribution to literature

Other previous studies on hedging effectiveness have examined the impact of hedging on firm

value and have, primarily, used a dummy variable, where a value of 1 indicates hedging. These

previous studies include Nance et al. (1993), DeMarzo and Duffie (1995), Tufano (1996), Geczy

et al. (1997), Allayannis and Ofek (2001), and Choi et al. (2013), Geczy et al. (1997), Allayannis

and Weston (2001), Mackay and Moeller (2007), and Carter et al. (2006). The results of these

studies are mixed. Allaynis and Ofek (2001) find results that suggest that firms with more

growth opportunities use hedging more and may benefit more from derivative use. Geczy et al.

(1997) and Nance et al. (1993) find an association between hedging use and higher research and

development. Choi et al. (2013) find that financial hedging is more likely to increase firm value

for firms which exhibit both information asymmetries and growth opportunities.

Since the implementation of SFAS 133 in 2001, the empirical literature has provided little

evidence about the implications of the use of derivatives which are designated versus non-

designated, for hedging under SFAS 133, for transparency, intent of derivative use, and hedging

effectiveness. The current study is the first to empirically differentiate between non-designated

and designated derivative use, under SFAS 133, and the implications for decisions regarding

effective risk management. The current study provides significant evidence that lower than

expected cash flow volatility and designated derivative use indicate hedging effectiveness.

Zhang 2009; Lins et al. 2011; Geczy et al. 2007 find that the new rules under SFAS 133 have an

impact on the intent of derivative use. Firms, which ineffectively hedge or selectively use

7

derivatives to speculate, may choose not to designate any of their derivatives for hedging and

hedge accounting, to minimize disclosure about their risk management activities (Melumad’s et

al. 1999; Glaum and Klocker 2011). Under SFAS 133, for derivatives not designated for hedge

accounting, the only reporting requirement is that gains and losses must be included in ordinary

income3, and the fair values must be reported on the balance sheet. Although firms are required

to disclose derivatives which are not designated for hedge accounting, there is no required test

which proves the intent (i.e. effectively hedging, ineffectively hedging, vs. speculative) of non-

designated derivatives. Further, firms are not required to report ineffectiveness for non-

qualifying derivatives under SFAS 133. Thus it is difficult to determine, from observing the

financial statements, the intent of non-designated derivative use.

Zhang (2009) examines changes in derivative use, risk exposure, and hedging effectiveness, after

SFAS 133 using a broad sample of 225 non-financial firms. Risk exposure is measured as the

stock price sensitivity to changes in commodity prices, interest rates, and foreign exchange rates.

Zhang (2009) suggests that decisions regarding the amount and purpose of derivative use are

affected by increased transparency, under SFAS 133. However Zhang (2009) does not examine

the implications for transparency, intent, and hedging effectiveness for designated versus non-

designated derivative use, as does the current study. Further, the current study compares firm

characteristics cross-sectionally within the same time frame (2003 through 2011) and includes all

oil and gas firms in the sample (N=704). Zhang’s (2009) sample includes only firms which

initiated a derivative program during the period 1996 to 1999.

3Brown et al. (2006) and Adam and Fernando (2006) suggest that active hedging positions are not associated with

higher profitability. Other research conducted, regarding extreme derivative positions, include DeMarzo and Duffie

(1995) and Sapra (2002).

8

Liu et al. (2011) examine the gains and losses from hedging ineffectiveness and find that the

reported hedging ineffectiveness, as required under SFAS 133, has important implications for

measuring the effectiveness of derivatives designated and qualifying for hedge accounting. The

current study is different, in two ways, from Lu et al. (2011). First, the current study examines

the intensity of derivative use as measured by the reported fair gains and losses of designated,

non-designated, and ineffective derivative use. Liu et al. (2011) examine only the gains and

losses which result from hedging ineffectiveness of designated instruments and do not consider

non-designated derivatives. In contrast with Liu et al. (2011), the current study also

differentiates firms which primarily use designated derivatives versus those which don’t, under

SFAS 133. Second, Liu et al. (2011) uses the Compustat variables AOCIDERGL and

CIDERGL to identify probable use of hedge accounting. These variables identify users of cash

flow hedging but do not identify users of fair value hedge accounting. Further these variables do

not distinguish among hedging for commodity, interest rate, and foreign exchange rate risk. The

current study is more specific and uses a key word search of annual 10-Ks for each firm year

observation to identify commodity derivative users and users of designated derivatives.

Also in contrast with Liu et al. (2011), the current study is also more narrowly focused and

examines only oil and gas commodity derivative use, which also may mitigate endogeneity

issues. The intensity of designated and non-designated derivative use is measured as the absolute

value of the level of reported net gains and losses of commodity (oil and gas) derivatives. The

data is obtained from the annual 10K reports, by performing a key word search. The intensity of

use of non-qualifying derivatives is used as a measure of hedging ineffectiveness and is expected

to negatively impact firm value, as measured by Tobin’s Q, for all derivative users. The

intensity of qualifying derivative use is used as a measure of hedging effectiveness and is

9

expected to positively impact Tobin’s Q for derivative users which use hedge accounting. For

the current study, data was obtained for 704 firm year observations, over the years 2003 through

2011, for 91 oil and gas production companies.

Methodology issues

One of the primary challenges in attempting to ascertain hedging effectiveness is endogeneity.

Firms which choose not to hedge, versus those which choose to hedge, may inherently possess

certain characteristics such as higher firm value, higher cash flow volatility, or higher risk

exposure. Thus it may not be correct to reach a conclusion about causality among these

variables. Also, firms which are all equity tend to have higher firm values (Strebulaev and Yang

(2013). Studies have also shown that hedging use may be associated with debt, and debt may

have implications for hedging effectiveness4. To alleviate endogeneity issues in the current

study, I estimate the expected differences between actual and expected values for 1) firm value,

2) cash flow volatility, and 3) stock price sensitivity to changes in crude oil prices (risk

exposure), as measures of interest. Second, I examine associations among the variables of

interest across sub-samples of firms, based on derivative use, use of designated derivatives, and

use of debt.

Another inherent problem faced by studies on hedging effectiveness, is that it is difficult to

determine from archival data if the use of derivatives has increased or decreased as a result of

SFAS 133, and why. Prior to SFAS 133 (in 2001), many derivative positions (i.e. fair values,

and gains and losses) were not required to be reported. Zhang (2009) addresses this issue,

4 See Mayers and Smith (1982), Smith and Stulz (1985), Watts and Zimmermann (1990), DeFond and Jiambalvo

(1994), Guay (1999), Graham and Rogers (2002), Borokhovich et al. (2004), Bartram et al. (2009), Haushalter

(2000) and Froot et al. (1993).

10

empirically, by examining broadly focused sample of firms, and the associated changes in risk

exposure Zhang (2009) finds that certain firms are more prudent in their risk management

strategies after SFAS 133, but does not attempt to measure with accuracy the level of derivative

use.

Consistent with Zhang (2009), two survey studies (Lins et al. 2011; and Glaum and Klocker

2011) revealed that the use of derivatives by a significant number of firms in over 36 countries

has decreased under SFAS 133 and IAS 395. The survey responses by many firms indicated

lower derivative use because many of the hedging techniques historically used by some firms no

longer qualify for hedge accounting, under SFAS 133 (Glaum and Klocker 2011 and Lins et al.

2011). Other reasons cited for lower derivative use were: 1) the required ineffective and non-

qualifying disclosures and 2) complexity of use (Glaum and Klocker 2011 and Lins et al. 2011).

The current study compares hedging effectiveness within the same time frame (after SFAS 133)

and does not examine what has changed about firms and their risk-taking (since the

implementation of SFAS 133).

A third issue associated with empirical studies, which examine derivative use, is determining the

intent and purpose of use. Both prior to and after SFAS 133, when hedge accounting is not used,

it is difficult to determine from archival data whether a firm is taking an active position (i.e.

speculative) in a derivative instrument or utilizing it specifically for hedging and risk

management (Zhang 2009; Lins et al. 2011; Geczy et al. 2007).6 After SFAS 133, the fair

values, and gains and losses for both derivatives designated as hedging instruments and non-

5International Accounting Standard (IAS) 39, Financial Instruments: Recognition and Measurement, also requires

firms to report the gains and losses and fair values for qualifying and non-qualifying derivative instruments and has

similar disclosure requirements as SFAS 133.

11

designated derivatives are required to be reported in the financial statements. However, no test is

required to differentiate the intent of derivative use (i.e. hedging versus speculative) for non-

qualifying derivatives. Jin and Jorion (2006) assume that firms with a hedging delta, less than 1,

have the intent to effectively hedge. However hedge positions, such as options and/or complex

derivatives, which have payoffs that potentially may not correlate with movements in prices of

the underlying asset, were included in the ratio. The current study indicates hedging success is

cross-sectionally associated with lower than expected cash flow volatilities and firm designation

of derivative use, under SFAS 133.

Data, hypotheses, and research design

From the Compustat database, I obtain a sample of 91 oil and gas price sensitive firms (sic code

1311) and obtain 704 firm observations during the study period 2003 through 2011. The study

excludes 2002 observations, since it is the first year for the required reporting, under SFAS 133.

To be included in the sample, each firm must have at least five years of annual 10-K reports

available on the web site http://sec.gov, and have at least five years of matching Compustat and

CRSP data, during the study period, 2003 to 2011, and be non-foreign. For all firm year

observations, I then conduct a key word search on the 10-K financial statements using the web

site http://sec.gov. The key words used in each search to obtain information about derivative use

include ‘derivative’, ‘designated’, ‘non-designated’, ‘qualifying’, ‘non-qualifying’, ‘fair value’

‘hedge’, ‘hedging’, and ‘risk management’. I obtain the reported net gains and losses, for

6The results of Lins et al. (2011) and Glaum and Klocker (2011) suggest that the majority of firms use derivatives

for risk management. Further Jin and Jorion (2006), find that all of the oil and gas firms in their sample have

hedging deltas of less than 1. Research conducted regarding active positions include Brown et al. (2006), Adam and

Fernando (2006), DeMarzo and Duffie (1995) and (Sapra 2002). The general findings of these studies are that

speculative derivative positions do not lead to higher profitability.

http://sec.gov/

http://sec.gov/

12

designated derivatives, non-designated derivatives, and the ineffective gains and losses for oil

and gas commodity derivatives. The data related to interest rate and foreign exchange

derivatives are dis-regarded in the current study.

The current study uses Tobin’s Q as an indicator of firm value and hedging effectiveness.

Previous studies which have used Tobin’s Q as a measure of firm value and hedging

effectiveness include Choi et al. (2013), Mackay and Moeller (2007), Jin and Jorion (2006),

Carter et al. (2006), Allaynnis and Weston (2001), and Tufano (1996). Perez-Gonzalez and Yun

(2013) use market-to-book ratio as a measure of hedging effectiveness for weather derivatives.

Extreme values for Tobin’s Q are deleted from the sample. The final sample contains 704 firm

year observations for 91 firms. Consistent with Jin and Jorion (2006) and Zhang (2009), I

estimate the risk exposure as the beta in regression Model A. Model A regresses the 12 monthly

CRSP value weighted market returns on the monthly computed returns on crude oil prices.

Model A: Rit = a0it + a1RMt + a2Macrot + eit

R is the monthly stock return for firm i and for month t; RM is the CRSP value-weighted market

return for month t; Macro is the monthly percentage change in the New York Mercantile

Exchange (NYMEX) historical prices for Crude Oil (Cushing). Monthly prices for both crude oil

were obtained at http://www.eia.gov/dnav/pet/pet_pri_fut_s1_d.htm. Oil and gas firms may

exhibit price sensitivity to changes in natural gas prices. However, the current study uses the

sensitivity to changes in oil prices as the measure of risk exposure, for two reasons. First a high

correlation was found between monthly oil and gas price changes for study period. Second, oil

derivatives were found to have been predominantly used for the sample firm year observations

http://www.eia.gov/dnav/pet/pet_pri_fut_s1_d.htm

13

The actual risk exposure in relation to changes in oil prices is represented by the absolute value

of the estimated coefficient, a2.

To mitigate endogeneity issues, I selectively use the differences between the expected (Elntob,

Eocfvol, and Ecorisk) and the actual values as measures of interest for firm value, cash flow

volatility, and risk exposure. First, using firm year observations in which no derivative use is

present, I use the following regression (Model B) to estimate the coefficients for the purpose of

computing estimated amounts for firm value (i.e. log of Tobin’s Q). See Appendix 1 for

regression results.

Model B: Lntobinit = b0 + b1Lnsizeit + b2Bklevit + b3Quikit + b4Intanit + b5Cfbveit + year

dummies

Lntobin is the dependent variable, in the regression Model B, and is used as a measure of

hedging effectiveness. It is computed as the natural log of Tobin’s Q, computed as the sum of

long-term debt (dltt) plus debt in current liabilities (dlc) plus market value of common equity

(prrc_f * csho) divided by the sum of long-term debt (dltt) plus debt in current liabilities (dlc)

plus book value of common equity (bve). This measure of Tobin’s Q is similar to that used in Jin

and Jorion (2006) and Choi et al. (2013)7

and is derived from the definition of firm value from

Strebulaev and Yang (2013). Lnsize is the natural log of the firm’s total assets (at) for each firm

year observation. Bklev is the book value of debt (dltt plus dlc) divided by total assets (at) for

each firm year observation. Intan is intangible assets (intan) divided by total assets (at) for each

firm year observation. Quik is the quick ratio (qr) for each firm year observation. Ocfbve is the

7Jin and Jorion (2006) and Choi et al. (2013) compute Tobin’s Q as the sum of book value of total assets (at) minus

book value of common equity (bve) plus the market value of common equity (prrc_f * csho) divided by the sum of

book value of total assets (at).

14

annual operating cash flow (oanc) divided by the book value of equity (bve) for each firm year

observation. The variable construction is modeled after previous studies including Jin and Jorion

(2006), Choi et al. (2013), and Allaynis and Weston (2001). Compustat variable names are

shown italicized and in parentheses.

Using firm year observations in which no hedging is present, I then use the following regressions

(Models C and D), to estimate the coefficients for the purpose of computing estimated values for

Ocfvol and AbsCOexp, respectively. See Appendix 1 for regression results.

Model C: Ocfvolit = d0 + d1Lnsizeit + d2Bklevit + d3Quikit + d5Intanit + d7Cfbveit + year

dummies

Model D: AbsCOexpit = c0 + c1Lnsizeit + c2Bklevit + c3Quikit + c5Intanit + c6Ocfvolit + year

dummies

Ocfvol is the standard deviation of quarterly reported amounts for operating cash flow (oancq)

divided by total assets (at) for each firm year observation. AbsCOexp is the estimated

coefficient, a2, from Model A (see above). Using the estimated coefficients from Models B, C,

and D, I then calculate the expected firm values (Elntob), expected cash flow volatility (Eocfvol),

and expected risk exposure (Ecorisk), for the sub-samples (See Table 1 Panel B

Models E, F, and G are used to compute the differences (Lntobininc, Ocfvolred, and COexpred)

between expected and actual values for each firm year observation in each of the three sub-

samples.

Model E: Lntobinincit = LnTobinit - Elntobit

Model F: Ocfvolredit = Eocfvolit - Ocfvolit

Model G: COexpredit =Ecoriskit - AbsCOexpit

15

The current study hypothesizes that reduced cash flow volatility and the use of derivatives

designated for hedging under SFAS 133 should be associated with increased hedging

effectiveness. To test the hypothesis, I first perform the following regressions, Models 1A, 1B,

and 1C on the sample overall and the sub-sample, DEBT.

Model 1A: Lntobinincit = e0 + e1EffcfvDerDit + e2DerivDit + e3Lnsizeit + e4Bklevit + e5Intanit +

e6Quikit + e7Cfbveit + + year dummies

Model 1B: Lntobinincit = e0 + e1 DesigDerDit + e2DerivDit + e3Lnsizeit + e4Bklevit + e5Intanit +

e6Quikit + e7Cfbveit + + year dummies

Model 1C: Lntobinincit = e0 + e1 EffcfvDerD_DesigDerDit + e2DerivDit + e3Lnsizeit + e4Bklevit

+ e5Intanit + e6Quikit + e7Cfbveit + + year dummies

EffcfvDerD is a dummy variable that takes a value of 1 for each firm year observation if the firm

is a consistent user of derivatives and exhibits a positive value for Ocfvolred. A consistent use of

derivatives is indicated if the firm exhibits no more than 1 year in which derivatives are not used.

DerivD is a dummy variable that takes a value of 1 for each firm year observation if the firm

exhibits a consistent use of derivatives (no more than 1 year in which derivatives are not used).

DerivD is a control variable in this model to mitigate the endogeneity issues between firms

which do not use derivatives versus firms which do. Firms which do not use derivatives

endogenously may have higher firm values. DesigDerD is a dummy variable which takes a

value of 1 for each firm year observation if the firm exhibits a consistent program of using

designated derivatives. A consistent use of designated derivatives is indicated by a firm which

has no more than one year of no use of designated derivatives. EffcfvDerD_DesigDerD is a

dummy variable that takes a value of 1 for each firm year observation if the firm took a value of

1 for both of the variables, EffcfvDerD and DesigDerD.

16

The regressions are also applied to the sub-samples, DERIV and DERIV_DEBT (the control

variable DerivD is excluded) using Models 2A, 2B, and 2C.

Model 2A: Lntobinincit = e0 + e1EffcfvDerDit + e2Lnsizeit + e3Bklevit + e4Intanit + e5Quikit +

e6Cfbveit + year dummies

Model 2B: Lntobinincit = e0 + e1DesigDerDit + e2Lnsizeit + e3Bklevit + e4Intanit + e5Quikit +


Model 2C: Lntobinincit = e0 + e1EffcfvDerD_DesigDerDit + e2Lnsizeit + e3Bklevit + e4Intanit +

e5Quikit + e6Cfbveit + year dummies

To further test the hypothesis, I examine the levels of 1) lower than expected cash flow

volatilities and 2) the absolute values of the net gains (losses) of designated versus non-

designated derivatives, on hedging effectiveness. It is expected that lower than expected cash

flow volatility and the gains (losses) of designated derivatives should be positively associated

with hedging effectiveness. It is also expected that the gains (losses) of non-designated

derivatives should be negatively associated with hedging effectiveness. I apply the following

regression (Model 3) to the sample overall and to the sub-samples, DEBT, DERIV and

DERIV_DEBT, DESIG, and DESIG_DEBT.

Model 3: Lntobinincit = e0 + e1Ocfvolred_DerDit + e2Ntgldesigit + e3Ntglnondesigit + e4Lnsizeit +

e5Bklevit + e6Intanit + e7Quikit + e8Cfbveit + year dummies

Ocfvolred_DerD is the [expected cash flow volatility (Eocfvol) minus the actual cash flow

volatility] times DerivD. Thus this variable contains the value for Ocfvolred for all derivative

users and 0 for all others. Ntgldesig is the absolute value of the reported dollar amount of the net

gains (losses) of designated commodity derivatives divided by the firm’s total assets for each

firm year observation. Ntglnondesig is the absolute value of the reported dollar amount of the

17

net gain/loss of non-designated commodity derivatives divided by the firm’s total assets for each

firm year observation. (The absolute value of the reported dollar amount of the net gain/loss of

ineffective commodity derivatives are included in Ntglnondesig for firms which use designated

derivatives). DerivD the dummy variable for derivative use is excluded from Model 3, since the

three primary variables are all associated only with derivative users and inclusion of DerivD

would contribute too much multi-collinearity to these variables.

To isolate the impact of reduced cash flow volatility on hedging effectiveness to derivative users

and designated derivative users, I apply regressions (Models 4A and 4B) to the sub-sample

DEBT.

Model 4A: Lntobinincit = e0 + e1Ocfvolredit + e2DerivDit + e3Lnsizeit + e4Bklevit + e5Intanit +

e6Quikit + e7Cfbveit + year dummies

Model 4B: Lntobinincit = e0 + e1Ocfvolredit + e2DerivDit + e3Ocfvolred_DerDit + e4Lnsizeit +

e5Bklevit + e6Intanit + e7Quikit + e8Cfbveit + year dummies

Model 4C: Lntobinincit = e0 + e1Ocfvolredit + e2DerivDit + e3Ocfvolred_DesigDerDit + e4Lnsizeit

+ e5Bklevit + e6Intanit + e7Quikit + e8Cfbveit + year dummies

It is expected that the significance on the variable Ocfvolred will lose its significance when either

the variable Ocfvolred_DerD is included in the regression (Model 4B) or the variable

Ocfvolred_DesigDerD is included (Model 4C).

To further isolate the impact of designated derivative use on hedging effectiveness, to reduced

cash flow volatility, I apply Model 5A to derivative users with lower than expected versus higher

than expected cash flow volatilities (sub-samples DER_EFFCFV and DER_INEFFCFV). To

isolate the impact of reduced cash flow volatility on hedging effectiveness, to designated

18

derivative use, I apply Model 5B to designated versus non-designated derivative users (sub-

samples DESIG and NONDESG). I also apply both regression models to corresponding sub-

samples which contain only firms with debt.

Model 5A: Lntobinincit = e0 + e1DesigDerD + e2Lnsizeit + e3Bklevit + e4Intanit + e5Quikit +


Model 5B: Lntobinincit = e0 + e1 EffcfvDerD + e2Lnsizeit + e3Bklevit + e4Intanit + e5Quikit +


Model 6A is applied to the sub-samples DER_EFFCFV and DER_INEFFCFV and Model 6B is

applied to DESIG and NONDESG to examine the effects of lower than expected cash flow

volatility and designated derivative use on reduced risk exposure. I also apply the regression

models to corresponding sub-samples which contain only firms with debt.

Model 6A: COexpredit = e0 + e1DesigDerD + e2Lnsizeit + e3Bklevit + e4Intanit + e5Quikit +


Model 6B: COexpredit = e0 + e1EffcfvDerD + e2Lnsizeit + e3Bklevit + e4Intanit + e5Quikit +


Consistent with Watts and Zimmermann (1990); DeFond and Jiambalvot (1994); Guay (1999);

Graham and Rogers (2002); Borokhovich et al. (2004); and Bartram et al. (2009) I expect

leveraged firms to have a more positive impact on hedging effectiveness from the use of

derivatives.

I. Results

Table 2 presents descriptive statistics for the study sample and by sub-sample. Lntobin is

inherently lower for firms which have debt across the sample overall, derivative users, and

19

designated derivative users. Ntglnondesig is slightly higher for the sample overall (1.1% of total

assets) than Ntgldesig (0.9%). Ocfvol is slightly lower for users of debt.

From Table 3, Panels A and B, for the regressions (Models 1A and 2A) applied to the sample

overall and the sub-samples, the coefficient for EffcfvDerD is only significant for firms with debt

and derivative users with debt. This finding suggests that firms with debt benefit more from

lower than expected cash flow volatility than firms without debt. Also from Table 3, Panels A

and B, the coefficients for DesigDerD, in all the regressions (Models 1B and 2B) are not

significant. For the regressions (Models1C and 2C) applied to the sample overall and all three

sub-samples the coefficients for EffcfvDerD_DesigDerD are higher and very significant. This

indicates, robustly, that firms which have lower than expected cash flow volatilities and

designate their derivatives for hedging, under SFAS 133, are effectively hedging, and have

higher Tobin’s Q than all other firms in the sample overall. Also, the coefficients for

EffcfvDerD_DesigDerD for firms with debt and derivative users with debt are slightly higher and

more significant than for unleveraged firms.

To further test the hypothesis, I examine the level of lower than expected cash flow volatilities

(Table 4 Panel A). I apply the regression (Model 3) across the sample overall and five sub-

samples. Consistent with the results indicated in Table 3, Panels A and B, the coefficients for

Ocfvolred_DerD are significant for firms with debt and derivative users with debt, but not for the

sample overall nor for derivative users overall. This further indicates that firms with debt benefit

more from lower than expected cash flow volatility. Also, as expected the coefficient is much

higher and more significant for designated derivative users and designated derivate users with

20

debt. This indicates that hedging effectiveness from lower cash flow volatility is more

transparent for designated derivative users.

The coefficient for net gains (losses) of designated derivatives is only significant for designated

derivatives with debt (Table 4 Panel A). The coefficient for net gains (losses) of non-designated

derivatives is significant for the sample overall and all five sub-samples. However this

coefficient is higher and more significant for designated derivative users. These results are

consistent with higher hedging transparency for designated derivative users.

To isolate the indicated lower than expected cash flow volatility on hedging effectiveness to

firms which use derivatives or to firms which primarily use designated derivatives I apply the

regressions (Models 4A, 4B, and 4C) to the sub-sample of firms with debt. The results are

presented in Table 4 Panel B. The coefficient for Ocfvolred is positive and significant in Model

4A. However the coefficient for Ocfvolred, loses its significance, both, when Ocfvolred_DerD

and Ocfvolred_DesigDerD are added to the regressions (Models 4B and 4C, respectively).

Further when both variables Ocfvolred_DerD and Ocfvolred_DesigDerD are added to the

regressions (Models 4D), the coefficient for Ocfvolred_DerD becomes insignificant. This

indicates that the hedging effectiveness from lower than expected cash flow volatility is

primarily associated with designated derivative use.

To further isolate hedging effectiveness to firms with lower than expected cash flow volatilities

and users of designated derivatives, first, I apply Model 5A to derivative users with lower than

expected cash flow volatilities versus higher than expected cash flow volatilities. See Table 5

21

Panel A. The model is also applied to firms, in these two sub-samples, with debt. Consistent

with the hypothesis, the coefficients for DesigDerD are positive and significant only for

derivative users that have lower than expected cash flow volatilities. Second, I apply Model 5B

to designated derivative users versus non-designated derivative users. See Table 5, Panel B.

Again, consistent with the hypothesis, the coefficients for EffcfvDerD are positive and significant

only for designated derivative users and designated derivative users with debt.

Jin and Jorion (2006) find that a sample of oil and gas exploration firms which use derivatives

for hedging have lower stock price sensitivity to changes in oil and gas prices. In contrast with

Jin and Jorion(2006) this study finds that the stock price sensitivity to changes in commodity

prices is reduced only for firms which exhibit hedging effectiveness. The results of the

regressions (Models 6A and 6B) are presented in Tables 6, Panels A and B. These models

regress DesigDerD (Model 6A) and EffcfvDerD (Model 6B) on reduced risk exposure

(COexpred). Consistent with the hypothesis, the coefficients for DesigDerD are positive and

significant only for derivative users that have lower than expected cash flow volatilities. Also

consistent with the hypothesis, the coefficients for EffcfvDerD are positive and significant only

for designated derivative users and designated derivative users with debt.

II. Conclusion

The current study documents that lower than expected cash flow volatility and designated

derivative use, in combination, are associated with hedging effectiveness (i.e. higher firm value).

The current study fills an important void in the literature by differentiating between 1) firms

which are effective derivative users and 2) firms which are ineffective derivative users or

22

speculators. Because of increased transparency of designated derivative use under, decisions

about derivative use and the effectiveness of hedging can be affected.

This study is different from previous studies, because I focus on the association between intent of

derivative use and the difference between expected and actual cash flow volatilities with firm

value cross-sectionally. This study should add insight into how decisions are made with regard

to derivative use and how hedging effectiveness can be achieved.

23

Table 1 Panel A Sub-samples

Sub-sample name Sub-sample descriptions

DEBT contains all firm year observations for firms from the entire sample which are leveraged as defined by Strebulev

and Yang (2013).1

DERIV contains the all firm year observations for firms which have a strong and consistent use of derivatives. The firm must exhibit no

more than 1 year in which derivatives are not used.

DERIV_DEBT contains all firm year observations included the sub-sample DERIV which are leveraged as defined by Strebulev

and Yang (2013).

DER_EFFCFV contains all firm year observations in the sub-sample DERIV which have cash flow volatilities which are lower than expected

DER_EFFCFV_DBT contains firm year observations included in the sub-sample, DER_EFFCFV which are leveraged as defined by

Strebulev and Yand (2013)

DER_INEFFCFV contains all firm year observations in the sub-sample DERIV which have cash flow volatilities which are higher than expected

DER_INEFFCFV_DBT contains firm year observations included in the sub-sample, DER_INEFFCFV which are leveraged as defined by

Strebulev and Yand (2013)

DESIG contains all firm observations for firms which have a strong and consistent use of designated derivatives. The firm must exhibit no

more than 1 year in which designated derivatives are not used.

DESIG_DBT contains all firm observations included the sub-sample DESIG which are leveraged as defined by Strebulev

and Yang (2013).

NONDESIG contains all firm observations included the sub-sample DERIV, but not included in DESIG

NONDESIG_DBT contains all firm observations included the sub-sample NONDESIG which are leveraged as defined by Strebulev and Yang (2013).

1Strebulev and Yang (2013) define zero leverage as those firms having a debt ratio which is 5% or lower. Firms with debt ratios greater than 5% are categorized

as leveraged, for inclusion in the sub-sample DEBT.

24

Table 1 Panel B Variables

Variable name Variable descriptions

Lntobin natural log of Tobin’s Q

Lntobininc Lntobin minus expected natural log of Tobin’s Q (Model B)

Tobin’s Q computed as the sum of long-term debt (dltt)2 plus debt in current liabilities (dlc) plus market value of common equity (prrc_f *

csho) divided by the sum of long-term debt (dltt) plus debt in current liabilities (dlc) plus book value of common equity (bve).

DerivD takes a value of 1 for firm year observations for firms which have a strong and consistent use of derivatives (firms must exhibit no

more than 1 year in which derivatives are not used), and zero otherwise.

EffcfvDerD takes a value of 1 for firm year observations in the sub-sample DERIV which have cash flow volatilities which are lower than

expected, and zero otherwise

DesigDerD takes a value of 1 for firm year observations in the sub-sample DERIV for firms which have strong and consistent use of designated

derivatives, (firms must exhibit no more than 1 year in which designated derivatives are not used), and zero otherwise.

EffcfvDerD_DesigDerD takes a value of 1 for firm year observations included in both sub-samples, DER_EFFCFV and DESIG

Ntgldesig the absolute value of the reported dollar amount of the net gains (losses) of designated commodity derivatives divided

by the firm’s total assets for each firm year observation.

Ntglnondesig the absolute value of the reported dollar amount of the net gain/loss of non-designated commodity derivatives divided

by the firm’s total assets for each firm year observation. (The absolute value of the reported dollar amount of the net

gain/loss of ineffective commodity derivatives are included in Ntglnondesig for firms which use designated derivatives).

Ocfvol the standard deviation of quarterly reported amounts for operating cash flow (oancq) divided by total assets (at) for each

firm year observation.

Ocfvolred Estimated operating cash flow volatility from regression (Model C) minus Ocfvol

AbsCOexp crude oil risk exposure, the estimated coefficient, a2, from Model A (Model A: Rit = a0it + a1RMt + a2Macrot + eit). It is the stock

price sensitivity to changes in crude oil prices.

COexpred Estimated crude oil risk exposure obtained from regression (Model D) minus AbsCOexp

25

Firm Size ($billion) total firm assets (at) in $billion

Bklev book value of debt (dltt plus dlc) divided by total assets (at) for each firm year observation.

Intan intangible assets (intan) divided by total assets (at) for each firm year observation.

Quik the quick ratio (qr) for each firm year observation.

Ocfbve the annual operating cash flow (oanc) divided by the book value of equity (bve) for each firm year observation.

2Compustat variables shown in parentheses

26

Table 2 Descriptive Statistics

Variables Overall DEBT DERIV DERIV_DEBT DESIG DESIG_DEBT EFFCFVDER EFFCFVDER_DEBT

N 704 573 543 498 182 166 308 298

Lntobin 0.559 0.440 0.482 0.429 0.484 0.434 0.381 0.370

(0.528) (0.413) (0.446) (0.389) (0.407) (0.370) (0.408) (0.392)

Lntobininc -0.264 -0.335 -0.342 -0.370 -0.351 -0.371 -0.330 -0.331

(0.501) (0.447) (0.453) (0.591) (0.450) (0.443) (0.446) (0.436)

Tobin’s Q 2.046 1.695 1.811 1.660 1.773 1.658 1.593 1.564

(1.426) (0.784) (1.072) (0.721) (0.849) (0.677) (0.705) (0.642)

Ntgldesig 0.009 0.010 0.012 0.012 0.020 0.021 0.011 0.011

(0.023) (0.024) (0.026) (0.025) (0.033) (0.030) (0.025) (0.026)

Ntglnondesig 0.011 0.013 0.014 0.014 0.004 0.004 0.016 0.016

(0.024) (0.026) (0.027) (0.027) (0.011) (0.011) (0.029) (0.029)

Ocfvol 0.054 0.053 0.056 0.054 0.0056 0.054 0.037 0.037

(0.030) (0.029) (0.030) (0.029) (0.028) (0.028) (0.017) (0.017)

Ocfvolred 0.001 0.003 0.001 0.003 0.001 0.003 0.020 0.021

(0.029) (0.028) (0.030) (0.029) (0.027) (0.027) (0.015) (0.015)

AbsCOexp 0.518 0.504 0.476 0.470 0.409 0.408 0.449 0.447

(0.473) (0.469) (0.421) (0.425) (0.333) (0.338) (0.407) (0.416)

COexpred 0.047 0.041 0.060 0.050 0.119 0.110 0.044 0.043

(0.442) (0.443) (0.415) (0.420) (0.342) (0.344) (0.417) (0.416)

Firm Size ($billion) $11.236 $9.117 $10.107 $10.227 $12.743 $11.746 $13,434 $13,378

(35.6) (24.3) (26.9) (25.8) (32.9) (28.8) (26.1) (30.6)

Bklev 0.272 0.327 0.314 0.338 0.286 0.310 0.343 0.354

(0.209) (0.19) (0.202) (0.193) (0.173) (0.161) (0.189) (0.183)

Intan 0.031 0.036 0.036 0.038 0.035 0.037 0.045 0.046

(0.066) (0.069) (0.069) (0.070) (0.065) (0.068) (0.071) (0.071)

Quik 0.008 -0.023 -0.024 -0.030 -0.032 -0.036 -0.020 -0.025

(0.151) (0.109) (0.104) (0.095) (0.076) (0.073) (0.110) (0.094)

Ocfbve 0.026 0.027 0.028 0.026 0.020 0.021 0.050 0.048

(0.398) (0.430) (0.333) (0.343) (0.116) (0.115) (0.393) (0.395)

Assets are reported in $billion. Firms with negative debt and negative total assets were dropped from the original sample. Values for Tobin’s Q and

Ntglnondesig which exceeded three standard deviations from the mean were deleted from the sample.

27

Table 3 Panel A Regressions of EffcfvDerD and DesigDerD on Lntobininc; Models 1A, 1B, 1C; sample overall and sub-sample DEBT

Sample overall Firms with debt

DEBT

Model 1A 1B 1C 1A 1B 1C

Variables N=704 N=704 N=704 N=573 N=573 N=573

Intercept 0.000*** 0.000*** 0.000*** -0.000 0.000*** 0.000

(2.73) (2.71) (2.85) (-1.24) (-2.88) (-1.25)

EffcfvDerD 0.015 0.098**

(0.37) (2.43)

DesigDerD 0.012 0.029

(0.34) (0.78)

EffcfvDerD_DesigDerD 0.100*** 0.151***

(2.79) (4.16)

DerivD -0.327*** -0.326*** -0.335*** -0.236*** -0.215*** -0.222***

(-7.30) (-7.44) (7.93) (-5.42) (-4.99) (5.32)

Lnsize -0.211*** -0.209*** -0.221*** -0.219*** -0.202*** -0.222***

(-5.19) (-5.21) (-5.51) (-4.93) (-4.59) (-5.09)

Bklev 0.152*** 0.156*** 0.152*** 0.301*** 0.319*** 0.314**

(3.89) (4.06) (3.99) (7.88) (8.39) (8.41)

Intan 0.164*** 0.166*** 0.157*** 0.233*** 0.239*** 0.227

(4.54) (4.59) (4.37) (6.23) (6.40) (6.14)

Quik -0.092** -0.090** -0.085** -0.190*** -0.186*** -0.176***

(-2.31) (-2.35) (-2.23) (-5.25) (-5.11) (-4.89)

Cfbve 0.083** 0.084** 0.085** 0.109*** 0.114*** 0.115***

(2.39) (2.43) (2.46) (3.04) (3.18) (3.27)

F-value 10.916*** 10.915*** 11.546*** 16.778*** 16.269*** 1.790*

R2 0.175 0.175 0.184 0.293 0.286 0.307

Year dummies included; p<.01*** p<.05** p<.10*

Model 1A: Lntobinincit = e0 + e1EffcfvDerDit + e2DerivDit + e3Lnsizeit + e4Bklevit + e5Intanit + e6Quikit + e7Cfbveit + year dummies

Model 1B: Lntobinincit = e0 + e1DesigDerDit + e2DerivDit + e3Lnsizeit + e4Bklevit + e5Intanit + e6Quikit + e7Cfbveit + year dummies

Model 1C: Lntobinincit = e0 + e1EffcfvDerD_ DesigDerDit + e2DerivDit + e3Lnsizeit + e4Bklevit + e5Intanit + e6Quikit + e7Cfbveit + year dummies

28

Table 3 Panel B Regressions of EffcfvDerD and DesigDerD on Lntobininc; Models 2A, 2B, 2C; sub-samples DERIV and DERIV_DEBT

Derivative users Derivative users with debt

DERIV DERIV_DEBT

Model 1A 1B 1C 1A 1B 1C


Intercept 0.000 0.000 0.000 -0.000*** 0.000*** 0.000***

(-0.50) (-0.15) (0.16) (-4.69) (-4.72) (-4.59)

EffcfvDerD 0.033 0.106***

(0.82) (2.66)

DesigDerD 0.019 0.027

(0.49) (0.72)

EffcfvDerD_DesigDerD 0.131*** 0.166***

(3.38) (4.36)

Lnsize -0.297*** -0.291*** -0.312*** -0.216*** -0.194*** -0.218***

(-7.03) (-7.03) (-7.53) (-5.02) (-4.56) (-5.17)

Bklev 0.166*** 0.175*** 0.168*** 0.281*** 0.304*** 0.297***

(3.99) (4.32) (4.19) (6.71) (7.34) (7.33)

Intan 0.226*** 0.229*** 0.218*** 0.240*** 0.248*** 0.233***

(5.70) (5.80) (5.55) (6.14) (6.31) (6.01)

Quik -0.177*** -0.173*** -0.164** -0.244*** -0.236*** -0.224***

(-4.39) (-4.29) (-4.09) (-6.25) (-6.01) (-5.81)

Cfbve 0.154*** 0.156*** 0.157** 0.162*** 0.168*** 0.171***

(3.89) (3.94) (4.02) (4.20) (4.33) (4.48)

F-value 11.918*** 10.915*** 12.926*** 16.643*** 15.957*** 17.889*

R2 0.220 0.219 0.236 0.306 0.296 0.322


Model 2A: Lntobinincit = e0 + e1EffcfvDerDit + e2Lnsizeit + e3Bklevit + e4Intanit + e5Quikit + e6Cfbveit + year dummies

Model 2B: Lntobinincit = e0 + e1DesigDerDit + e2Lnsizeit + e3Bklevit + e4Intanit + e5Quikit + e6Cfbveit + year dummies

Model 2C: Lntobinincit = e0 + e1EffcfvDerD_DesigDerDit + e2Lnsizeit + e3Bklevit + e4Intanit + e5Quikit + e6Cfbveit + year dummies

29

Table 4 Panel A Regression of OcfvolredDerD, Ntglnondesig, and Ntgldesig on Lntobininc; Model 3

Sample Firms Derivative Derivative Designated Designated

Overall with debt users users with derivative derivative

debt users users with

debt DEBT DERIV DERIV_DEBT DESIG DESIG_DEBT

Model 3 3 3 3 3 3


Intercept 0.000** 0.000 -0.000 0.000*** 0.000 0.000

(2.44) (-0.73) (-0.49) (-3.07) (1.34) (0.85)

Ocfvolred_DerD 0.064* 0.146*** 0.065 0.146*** 0.427*** 0.478***

(1.66) (3.79) (1.52) (3.52) (5.45) (6.48)

Ntgldesig -0.024 0.029 -0.010 0.041 0.127* 0.240***

(-0.66) (0.77) (-0.25) (1.05) (1.74) (3.36)

Ntglnondesig -0.128*** -0.128*** -0.106*** -0.100** -0.222*** -0.234***

(-3.41) (-3.42) (-2.61) (-2.50) (3.35) (-3.69)

Lnsize -0.336*** -0.238*** -0.324*** -0.251*** -0.398*** -0.393***

(-8.54) (-5.27) (-7.42) (-5.69) (-5.27) (-5.31)

Bklev 0.065* 0.303*** 0.162*** 0.268*** -0.081 -0.039

(1.68) (7.88) (3.84) (6.36) (-1.11) (-0.54)

Intan 0.165*** 0.238 0.235*** 0.255*** 0.260*** 0.313***

(4.40) (6.41) (5.93) (6.54) (3.79) (4.77)

Quick -0.044 -0.199*** -0.192*** -0.250*** 0.010 -0.026

(-1.12) (-5.50) (-4.63) (-6.28) (0.13) (-0.35)

Cfbve 0.066* 0.099*** 0.147*** 0.146*** 0.084 0.059

(1.85) (2.77) (3.71) (3.80) (1.20) (0.90)

F-value 7.068*** 15.274*** 11.021*** 15.575*** 5.881*** 8.433***

R2 0.121 0.285 0.228 0.319 0.301 0.419


Model 3: Lntobinincit = e0 + e1Ocfvolred_DerDit + e2Ntgldesigit + e3Ntglnondesigit + e4Lnsizeit + e5Bklevit + e6Intanit + e7Quikit + e8Cfbveit + year dummies

30

Table 4 Panel B Regression of Ocfvolred_DerD and Ocfvolred_DesigDerD on Lntobininc; Model 4

__________Firms with debt DEBT______________

Model 4A 4B 4C 4D

Variables N=573 N=573 N=573 N=573

Intercept 0.000 0.000** 0.000 0.000

(-1.18) (-2.20) (-1.00) (-0.78)

Ocfvolred 0.088** -0.162 -0.019 -0.158

(2.31) (-1.43) (-0.43) (-1.42)

DerivD -0.193*** -0.201*** -0.189*** -0.194***

(-4.53) (-4.73) (-4.52) (-6.63)

Ocfvolred_DerD_ 0.266** 0.155

(2.34) (1.35)

Ocfvolred_DesigDerD 0.203*** 0.190***

(4.87) (4.46)

Lnsize -0.226*** -0.228*** -0.234*** -0.239***

(-5.01) (-5.07) (-5.16) (-5.41)

Bklev 0.298*** 0.293*** 0.294*** 0.297***

(7.71) (7.62) (7.56) (7.84)

Intan 0.235*** 0.233*** -0.193*** 0.220***

(6.30) (6.27) (-5.25) (5.99)

Quick -0.193*** -0.197*** 0.104*** -0.180***

(-5.32) (-5.44) (3.47) (-5.04)

Cfbve 0.103*** 0.106*** 0.233*** 0.118***

(2.86) (2.95) (6.25) (3.35)

F-value 16.721*** 16.145*** 17.795*** 16.881***

R2 0.292 0.298 0.320 0.321


Model 4A: Lntobinincit = e0 + e1Ocfvolredit + e2DerivDit + e3Lnsizeit + e4Bklevit + e5Intanit + e6Quikit + e7Cfbveit + year dummies

Model 4B: Lntobinincit = e0 + e1Ocfvolredit + e2DerivDit + e3Ocfvolred_DerDit + e4Lnsizeit + e5Bklevit + e6Intanit + e7Quikit + e8Cfbveit + year dummies

Model 4C: Lntobinincit = e0 + e1Ocfvolredit + e2DerivDit + e3Ocfvolred_DesigDerDit + e4Lnsizeit + e5Bklevit + e6Intanit + e7Quikit + e8Cfbveit + year dummies

Model 4D: Lntobinincit = e0 + e1Ocfvolredit + e2DerivDit + e3Ocfvolred_DerDit + e4Ocfvolred_DesigDerDit + e5Lnsizeit + e6Bklevit + e7Intanit + e8Quikit + e9Cfbveit + year

dummies

31

Table 5 Panel A Regression of DesigDerD on Lntobininc; Model 5A

Effective Ineffective Effective Ineffective

Derivative users Derivative users Derivative users Derivative users

(Low CFV) (High CFV) with debt with debt

(Low CFV) (High CFV)

DER_EFFCFV DER_INEFFCFV DER_EFFCFV_DBT DERIV_ INEFFCFV_DBT

Variables N=308 N=235 N=298 N=200

Intercept 0.000* 0.000*** 0.000* 0.000*

(-1.68) (2.36) (-1.75) (-1.73)

DesigDerD 0.180*** -0.212*** 0.181*** -0.238***

(3.63) (-3.58) (3.61) (-4.21)

Lnsize -0.259*** -0.343*** -0.256*** -0.160**

(-4.97) (-5.38) (-4.72) (-2.49)

Bklev 0.249*** -0.005 0.265*** 0.232***

(4.88) (-0.07) (4.97) (3.56)

Intan 0.293*** 0.066 0.277*** 0.103*

(5.95) (1.08) (5.51) (1.80)

Quik -0.114** -0.237*** -0.155*** -0.325***

(-2.24) (-3.67) (-3.04) (-5.46)

Cfbve 0.208*** 0.070 0.204*** 0.088

(4.18) (1.15) (4.09) (1.55)

F-value 10.437*** 6.296*** 10.353*** 11.548***

R2 0.301 0.241 0.306 0.426


Model 5A: Lntobinincit = e0 + e1DesigDerD + e2Lnsizeit + e3Bklevit + e4Intanit + e5Quikit + e6Cfbveit + year dummies

32

Table 5 Panel B Regression of EffcfvD on Lntobininc; Model 5B

Designated Non-designated Designated Nondesignated


with debt with debt

DESIG NONDESG DESIG_DBT NONDESG_DBT

Variables N=182 N=361 N=166 N=332

Intercept 0.000 0.000 0.000 0.000***

(0.26) (0.31) (-0.48) (-4.03)

EffcfvDerD 0.340*** -0.093* 0.397*** -0.027

(4.25) (-1.91) (5.18) (-0.58)

Lnsize -0.316*** -0.322*** -0.289*** -0.207***

(-4.23) (-6.25) (-3.84) (-3.94)

Bklev -0.052 0.242*** 0.037 0.374***

(-0.68) (4.90) (0.50) (7.50)

Intan 0.255*** 0.192*** 0.278*** 0.188***

(3.56) (4.00) (3.94) (3.96)

Quik -0.018 -0.212*** -0.097 -0.280***

(-0.23) (-4.33) (-1.32) (-5.93)

Cfbve 0.095 0.205*** 0.081 0.221***

(1.31) (4.32) (1.14) (4.78)

F-value 5.131*** 10.164*** 6.878*** 13.492***

R2 0.242 0.263 0.333 0.346


Model 5B: Lntobinincit = e0 + e1 EffcfvDerD + e2Lnsizeit + e3Bklevit + e4Intanit + e5Quikit + e6Cfbveit + year dummies

33

Table 6 Panel A Regression of DesigDerD on COexpred; Model 6A

Effective Ineffective Effective Ineffective


(Low CFV) (High CFV) with debt with debt

(Low CFV) (High CFV)

DER_EFFCFV DER_INEFFCFV DER_EFFCFV_DBT DERIV_ INEFFCFV_DBT

Variables N=308 N=235 N=298 N=200

Intercept 0.000*** 0.000*** 0.000*** 0.000***

(3.22) (3.07) (3.42) (3.87)

DesigDerD 0.120** -0.008 0.113** -0.026

(2.23) (-0.13) (2.07) (-0.38)

Lnsize -0.178*** -0.089 -0.197*** -0.144*

(-3.14) (-1.29) (-3.32) (-1.79)

Bklev -0.217*** -0.269 -0.239*** -0.312***

(-3.91) (-3.74) (-4.12) (-3.85)

Intan 0.079 0.137 0.097* -0.147**

(1.48) (-2.09) (1.78) (-2.06)

Quik -0.013 0.066 0.039 0.077

(-0.23) (0.94) (0.71) (1.04)

Cfbve -0.127** -0.108* -0.124** -0.113

(-2.34) (-1.65) (-2.28) (-1.60)

F-value 5.508*** 3.041*** 5.568*** 2.801***

R2 0.171 0.109 0.177 0.112


Model 6A: COexpredit = e0 + e1DesigDerD + e2Lnsizeit + e3Bklevit + e4Intanit + e5Quikit + e6Cfbveit + year dummies

34

Table 6 Panel A Regression of DesigDerD on COexpred; Model 6B

Designated Non-designated Designated Nondesignated


with debt with debt

DESIG NONDESG DESIG_DBT NONDESG_DBT

Variables N=182 N=361 N=166 N=332

Intercept 0.000*** 0.000 0.000*** 0.000***

(3.62) (1.42) (3.17) (3.75)

EffcfvDerD 0.157* -0.003 0.153* 0.002

(1.92) (-0.06) (1.80) (0.04)

Lnsize -0.348*** -0.061 -0.331*** -0.110*

(-4.56) (-1.09) (-3.98) (-1.82)

Bklev -0.107 -0.266*** -0.108*** -0.295***

(-1.37) (-4.94) (-1.31) (-5.12)

Intan 0.108 -0.071 0.109 -0.058

(1.47) (-1.35) (1.40) (-1.06)

Quik 0.077 0.019 0.091 0.060

(0.98) (0.35) (1.13) (1.11)

Cfbve -0.131* -0.118* -0.110 -0.120**

(-1.76) (-2.27) (-1.41) (-2.25)

F-value 4.405*** 4.606*** 3.63*** 4.415***

R2 0.208 0.123 0.182 0.126


Model 6B: COexpredit = e0 + e1EffcfvDerD + e2Lnsizeit + e3Bklevit + e4Intanit + e5Quikit + e6Cfbveit + year dummies

35

Appendix 3. Regressions used to compute expected values for Lntobin, Abs_COexp, and Ocfvol

Dependent variable Lntobin Abs_COexp Ocfvol

Intercept 0.542*** 1.186*** 0.041***

(2.98) (6.51) (5.47)

Lnsize 0.063*** -0.085*** 0.003***

(3.09) (-4.59) (3.13)

Bklev -0.145 -0.042 0.001

(-0.45) (-0.146) (0.04)

Quik 1.098*** -0.224 0.004

(4.62) (-1.07) (0.36)

Intan 1.974* 0.404 -0.082*

(-1.87) (0.43) (-1.90)

Ocfbve -0.017 0.012***

(-0.18) (3.04)

Ocfvol -0.416

(0.24)

F-value 2.847*** 3.175*** 3.556***

R2

0.131 0.151 0.173

Model B: Lntobinit = b0 + b1Lnsizeit + b2Bklevit + b3Quikit + b4Intanit + b5Cfbveit + year dummies

Model C: Abs_COexpit = c0 + c1Lnsizeit + c2Bklevit + c3Quikit + c5Intanit + c6Ocfvolit + year dummies

Model D: Ocfvolit = d0 + d1Lnsizeit + d2Bklevit + d3Quikit + d5Intanit + d7Cfbveit + year dummies

Unstandardized coefficients presented

Sample consists of firm observations for non-hedging firms, N=160

Year dummy variables not shown

36

References

Adam, T. and C. Fernando, 2006. Hedging, speculation, and shareholder value. Journal of Financial Economics 81, 283-309.

Ahmed, A., E. Kilic, and G. Lobo, 2006. Does recognition versus disclosure matter? Evidence from value-relevance of banks’

recognized and disclosed derivative financial instruments. The Accounting Review 81, 567–588.

Allayannis, G. and E. Ofek, 2001. Exchange rate exposure, hedging, and the use of foreign currency derivatives. Journal of

International Money and Finance 20, 273–296.

Allayannis, G. and J. Weston, 2001. The Use of Foreign Currency Derivatives and Firm Market Value. The Review of Financial

Studies 14, 243-76.

Barron, O.E., C.O. Kile, and T.B. O’Keefe, 1999. MD&A quality as measured by the SEC and analysts’ earnings forecasts.

Contemporary Accounting Research 16, 75–109.

Barry, C.B. and S.J. Brown, 1985. Differential information and security market equilibrium. Journal of Financial and Quantitative

Analysis 20, 407–422.

Barth, M.E., A. Elliott, and M.W. Finn, 1999. Market rewards associated with patterns of increasing earnings. Journal of Accounting

Research, 37 (2), 387–413.

Bartram, S.M., G.W. Brown, and F.R. Fehle, 2009. International evidence on financial derivatives usage. Financial Management 38,

185-206.

Borokhovich, K.A., K. R. Brunarski, C.E. Crutchley, B.J. Simkins, 2004. Board composition and corporate use of interest rate

derivatives. Journal of Financial Research, 27, 199–216.

Brown, G., P. Crabb, and D. Haushalter, 2006. Are firms successful at selective hedging? Journal of Business 79, 2925-2949.

Carter, D.A., D. Rogers and B.J. Simkins, 2006. Does Hedging Affect Firm Value? Evidence from the U.S. Airline Industry.

Financial Management, Vol. 35, pp. 53-86.

Choi, J. C., C. X. Mao, and A. D. Upadhyay, 2013. Corporate risk management under information asymmetry. Journal of Business

Finance and Accounting 40, 239-271.

37

DeFond, M.L. and J. Jiambalvo, 1994. Debt covenant violation and manipulation of accruals. Journal of accounting and economics 17,

145–176.

DeFond, M. and C. Park, 1997. Smoothing income in anticipation of future earnings, Journal of Accounting and Economics 23, 115-

139.

DeMarzo, P.M. and D. Duffie,1995. Corporate Incentives for Hedging and Hedge Accounting, Review of Financial Studies 8, 743-71.

FASB Statement No. 133, Accounting for Derivative Instruments and Hedging Activities,1998. Financial Accounting Standards Board

(FASB),Statement No. 133 (Stamford, CT; FASB, 1998)

Froot, K., D. Scharfstein, and J. Stein, 1993. Risk-management: coordinating corporate investment and financing policies, Journal of

Finance 48, 1629-1648.

Geczy, C., B. Minton, and C. Schrand, 1997. Why firms use currency derivatives, Journal of Finance 52, 1323-1354.

Glaum, M. and A. Klocker, 2011. Hedge accounting and its influence on financial hedging: when the tail wags the dog, Accounting

and Business Research, 459-489.

Graham, J., and D. Rogers, 2002. Do firms hedge in response to tax incentives? Journal of Finance 57, 815-839.

Guay W., 1999. The impact of derivatives on firm risk: an empirical examination of new derivative users. Journal of Accounting and

Economics 26, 319-351.

Haushalter, D., 2000. Financing policy, basis risk, and corporate hedging: Evidence from oil and gas producers. Journal of Finance

55, 107-152.

Jin, Y. and P. Jorion, 2006. Firm value and hedging: evidence from U.S. oil and gas producers’, Journal of Finance 61, 893-919.

Liu, Z. G. Seow, and H Xie, 2011. Does the accounting hedge ineffectiveness measure under SFAS 133 capture the economic

ineffectiveness of a firm’s hedging activities, Working paper, University of Connecticut and University of Kentucky.

Lins, K.V., Servaes, H., and Tamayo, A., 2011. Does fair value reporting affect risk management? International survey evidence,

Financial Management 40, 525-551.

Mackay, P. and S.B. Moeller, 2007. The Value of Corporate Risk Management, Journal of Finance 62, 1379-419.

38

Marshall, A. and P. Weetman, 2007. Modelling transparency in disclosure: the case of foreign exchange risk management, Journal of

Business Finance & Accounting 34, 705-39.

Mayers, D. and C.W. Smith Jr., 1982. On the Corporate Demand for Insurance, The Journal of Business 55, 281-96.

Melumad, N. D., G. Weyns, and A. Ziv, 1999. Comparing alternative hedge accounting standards; shareholders perspective, Review of

Accounting Studies 5, 265-292.

Nance, D.R., C.W. Smith Jr. and C.W. Smithson, 1993. On the Determinants of Corporate Hedging, Journal of Finance 48, 267-84.

Sapra H., 2002. Do mandatory hedge disclosures discourage or encourage excessive speculation? Journal of Accounting Research 40,

933-964.

Smith, C.W. and R.M. Stulz, 1985. The determinants of firms hedging policies, Journal of Financial and Quantitative Analysis 20,

391-405.

Strebulaev, I. A. and B. Yang, 2013. The mystery of zero-leverage firms, Journal of Financial Economics 109, 1-23.

Tufano, P., 1996. Who manages risk? An empirical examination of risk-management practices in the gold mining industry, Journal of

Finance 51, 1097-1137.

Watts, R.L. and J.L. Zimmerman, 1990. Positive accounting theory: a ten year perspective. Accounting Review 65, 131–156.

Wong, F., 2000. The association between SFAS No. 119 derivatives disclosures and the foreign exchange risk exposure of

manufacturing firms. Journal of Accounting Research 38, 387-417.

Zhang, H., 2009. Effect of derivative accounting rules on corporate risk-management behavior. Journal of accounting and economics

47, 244–264.

hedging effectiveness, cash flow volatility, and …1 hedging effectiveness, cash flow volatility,...

Documents