Hedging, Cash Flows, and Firm Value: Evidence of an ... · PDF fileHEDGING, CASH FLOWS, AND...

Hedging, Cash Flows, and Firm Value: Evidence of an Indirect Effect

Muhammed Altuntas,1 Andre P. Liebenberg,2

Ethan D. Watson,3 and Serhat Yildiz4

Abstract: This paper extends and tests the predictions of Froot, Scharfstein, and Stein’s(1993) model of the relation between hedging, cash flows, and firm value. Specifically,we model the impact of derivatives hedging on firm value both directly and alsoindirectly through its effect on cash flow volatility. We test the model’s predictionsusing a sample of publicly traded life insurers who report detailed information on boththe extent and purpose of derivatives use. We find that both derivatives hedging andcash flow volatility are negatively related to firm value. However, consistent with ourtheoretical predictions, we find that hedging mitigates the negative value effect of cashflow volatility.

INTRODUCTION

he value relevance of corporate risk management via the use ofderivatives has drawn considerable attention in the literature

(Allayannis and Weston, 2001; Carter, Rogers, and Simkins, 2006; Allayan‐nis, Lel, and Miller, 2003; Berrospide, Purnanandam, and Rajan, 2010;Fung, Wen, and Zhang, 2012). Under the assumption of frictionless capitalmarkets, the propositions of Modigliani and Miller (1958; Miller andModigliani, 1961) suggest that risk management by a firm should be

1Department of Risk Management and Insurance, University of Cologne, muhammed.altun‐tas@uni‐koeln.de2Contact author; School of Business Administration, University of Mississippi, alieben‐[email protected] School of Business, University of North Carolina Wilmington, [email protected] 4School of Business Administration, University of Mississippi, [email protected]

T

1Journal of Insurance Issues, 2017, 40 (1): 1–22.Copyright © 2017 by the Western Risk and Insurance Association.All rights reserved.

2 ALTUNTAS, LIEBENBERG, WATSON, AND YILDIZ

irrelevant, and should therefore be of no value to a firm or investors.However, Smith and Stulz (1985) and others argue that hedging may addvalue via its impact on expected taxes, bankruptcy costs, and investmentdecisions. According to Froot, Scharfstein, and Stein (1993) hedging addsvalue to the extent that it helps to ensure that a corporation has sufficientinternal funds available to take advantage of attractive investment oppor‐tunities. If a firm does not hedge, there will likely be greater variability inthe cash flows generated by assets in place, which leads to an underinvest‐ment problem. Thus, hedging may be beneficial to the firm via a channelof reducing cash flow volatility and the likelihood of underinvestment.

The extant literature has separately examined the impact of hedgingon firm value and the impact of cash flow volatility on firm value. Forexample, Allayannis and Weston (2001), Carter, Rogers, and Simkins(2006), Allayannis, Lel, and Miller (2003), Berrospide, Purnanandam, andRajan (2008), Bartram, Brown, and Conrad (2011), and Fung, Wen, andZhang (2012) all study the impact of hedging on firm value, but do notconsider cash flow volatility. While Rountree, Weston, and Allayannis(2008) examine the relation between firm value and cash flow volatility,they do not investigate the role hedging plays in reducing cash flowvolatility. There is an absence of research that considers the effect ofhedging on firm value via its impact on cash flow volatility.5 In this study,we model and test the relations between hedging policy, cash flow volatil‐ity, and firm value.

We begin our analysis by extending the theoretical model of Froot,Scharfstein, and Stein (1993) to develop our hypothesis. Our extension oftheir model predicts that the firm value will be impacted via an interactionterm of hedging activity and cash flow volatility. Specifically, our modelextension predicts that firm value will be greater for hedgers than non‐hedgers due to a reduction in the adverse effect of cash flow volatility.

We use the life insurance industry as a setting for testing our model’spredictions. This industry is an ideal setting for testing our research ques‐tion due to the prevalence of derivatives use (especially among large, publiclife insurers that we study) and the uniquely detailed regulatory reportingrequirements that apply to licensed insurers. In the U.S. life insuranceindustry derivatives play a key role in managing risk—as evidenced by the$1.9 trillion notional value of insurance industry derivatives holdings forlife insurers in 2014 (NAIC, 2015). In terms of regulation, U.S. licensed

5Scordis, Barrese, and Wang (2008) argue that if hedging reduces cash flow volatility itshould also affect systematic risk. For a sample of U.S. insurers they find that the effect ofcash flow volatility on systematic risk is dependent on the relative importance of growthopportunities.

HEDGING, CASH FLOWS, AND FIRM VALUE 3

companies are required to report the type (forwards, options, etc.), details(e.g., trade date, maturity date, notional amount) and purpose (e.g., hedg‐ing or other) of all derivatives instruments that they hold. We use thesedata to measure whether or not an insurer used derivatives, whether thederivative instrument was used for hedging or some other purpose, andthe extent of derivatives use. We then model firm value as a function ofderivatives use, cash flow volatility, and their interaction.

Our results indicate that it is not simply the act of hedging that addsvalue to a firm. In fact, we report a negative direct relation between hedgingactivity and firm value. We also find a negative relation between cash flowvolatility and firm value. However, we find that the interaction of hedgingand cash flow volatility has a positive value effect. Thus, consistent withthe predictions of our extension of Froot et al. (1993), derivatives hedgingmitigates the negative value effect of cash flow volatility. Although hedginghas a negative direct effect on value for sample life insurers, it enhancesvalue by reducing cash flow volatility.

We proceed as follows. In the next section (Section 2) we discuss theprior literature on hedging activity, and describe how our paper extendsthis literature. Section 3 develops our hypothesis by extending the Froot,Scharfstein, and Stein (1993) model. A description of the sample and howvariables are calculated are presented in Section 4. Empirical methodologyand the results are found in Section 5, and Section 6 concludes.

PRIOR LITERATURE

Our paper contributes to the literature that examines the question ofwhy firms engage in hedging activity. According to Modigliani and Miller(1958; Miller and Modigliani, 1961) hedging should be irrelevant in fric‐tionless markets. However, subsequent theoretical literature has focusedon the beneficial aspects that hedging might have on a firm. For example,according to Smith and Stulz (1985) if the hedging policy affects the valueof the firm, it must do so through its effect on taxes, contracting costs, andthe firm’s investment decisions. Their study implies that the benefits ofhedging arise due to reducing variability of pre‐tax firm values due to taxfunction convexity. Smith and Stulz (1985) also argue hedging may affectfirm value through management compensation, taxes, and reduction ofagency conflicts. In addition, they emphasize that if managers’ expectedutilities heavily depend on, and are concave functions of, accountingearnings, then managers may hedge even if hedging increases volatility infirm value.

Froot, Scharfstein, and Stein (1993) focus on costly capital, arguing thatinternal capital is less costly than external capital. In their model if a firm


does not hedge, there may be more variability in cash flows, i.e., internalcapital. If there is more variability, then there will be more variability in thefirm’s external capital raised and the amount of investment. Therefore,Froot, Scharfstein, and Stein (1993) demonstrate that the value of hedgingarises from its ability to preserve internal capital and avoid an underinvest‐ment problem.

Several empirical papers study the predictions of the aforementionedtheoretical models. Consistent with the Smith and Stulz (1985) theory,Nance, Smith, and Smithson (1993) find that firms hedge to reduceexpected tax liabilities, to lower expected transactions costs, and to controlagency problems. Mian (1996) also finds evidence for the tax incentive forhedging. However, Graham and Rogers (2002) use an explicit measure oftax function convexity, and document no evidence that firms hedge inresponse to tax function convexity. Additionally, Whidbee and Wohar(1999) find empirical evidence that managerial compensation is an impor‐tant factor in the hedging decision.

Our paper is most closely related to the literature that tests the predic‐tions that hedging reduces cash flow volatility (Froot, Scharfstein and Stein,1993). Minton and Schrand (1999) examine the predictions of Froot, Scharf‐stein, and Stein (1993) and test the effects of cash flow volatility on invest‐ment and the costs of accessing capital and show that higher cash flowvolatility is associated with lower average levels of investment in capitalexpenditures, R&D, and advertising. Minton and Schrand (1999) also findthat cash flow volatility increases both the likelihood and the cost ofaccessing capital markets.

Allayannis and Mozumdar (2000) and Deshmukh and Vogt (2005)study the impact of cash flow on investment decisions and find thathedgers’ investments are less sensitive to cash flows compared to non‐hedgers. Together these two studies support that hedging reduces theunderinvestment problem. Moreover, Rountree, Allayannis, and Weston(2008) examine the impacts of cash flow volatility and earnings volatilityon firm value and present empirical evidence that volatility of both earn‐ings and cash flow are negatively valued by investors. Their results suggestthat investors value managerial actions that result in lower cash flowvolatility.

Our paper is also related to the literature that examines the impact ofhedging on firm value, without specifying the mechanism of the valuebenefit. For example, Allayannis and Weston (2001), Carter, Rogers, andSimkins (2006), Allayannis, Lel, and Miller (2003), and Berrospide, Pur‐nanandam, and Rajan (2008) examine the impact of hedging on firm valueand find that hedging increases firm value.6 There is opposing research thatquestions the impact of hedging activity on firm value. For example,


Bartram, Brown, and Conrad (2011) find that the statistical significance ofa hedging premium is weak, and Jin and Jorion (2006) find that hedgingdoes not affect market value of firms in the oil and gas industry. Similar toour analysis, Fung, Wen, and Zhang (2012) examine the relation betweeninsurers’ credit default swap usage and firm value, and find a negativerelation between insurers’ income generating use of credit default swapand firm value.7

Although empirical tests of hedging theories are abundant, the extantliterature lacks an empirical analysis of the direct impact of hedging onfirm value, as well as the indirect impact of hedging on value via its effecton cash flow volatility. We provide such an analysis by extending thetheoretical models of Froot, Scharfstein, and Stein (1993) and subsequentlytesting our theoretical predictions using detailed financial data for U.S.insurers. The main focus of our study is the value relevance of hedging viathe mechanism of its impact on cash flow volatility.

HYPOTHESIS DEVELOPMENT

AND MODEL EXTENSION

In this section we develop our hypothesis by modifying the modelsprovided by Froot, Scharfstein, and Stein (1993) (hereafter FSS) to show thevalue impact of cash flow volatility for a firm in two cases: namely, whenthe firm hedges and when the firm does not hedge. We build on the modelsof FSS and make similar assumptions. In the FSS model, the agent is a firmthat faces a decision to hedge or not to hedge over two periods and

. FSS define internal funds for a linear hedging strategy as follows inequation (1):

, (1)

where h is the is the hedge ratio chosen by the firm, and is the primitivesource of uncertainty. The variable, w, is the internal cash flow in period

, and is the internal cash flow in period . Further, FSSpropose that firm value is a function of risk in at least two ways—its direct

6Scordis and Steinorth (2012) study the effect of hedging via reinsurance on insurer firmvalue and find a positive relation.7We generally follow the empirical models of Fung, Wen, and Zhang (2012) in our empiricalanalysis. However, we differ from them in that we take a broader focus of hedging activityand look at all types of derivative use, not just CDSs. Additionally, our model specifies themechanism (cash flow volatility) through which firm value is affected.

t 0=t 1=

w w0 h 1 h– + =

t 1= w0 t 0=


effect on investment opportunities, , conditional on internal funds (w)and also indirectly through its impact on internal cash flow (w) holdingconstant investment opportunities (I ). Accordingly, FSS recommend thefollowing model (equation [2]) for firm value (P).

, (2)

where t denotes the time, i denotes the firm, are constants, and is theerror term. We extend the FSS model as follows.

Assume that firm i hedges, then substituting equation (1) for inequation (2) yields firm value (P) in period as per equation (3) below:

. (3)

Assume that firm i does not hedge (i.e., h = 0), then the firm value in is given by (4):

. (4)

We now assume that the hedge ratio is bounded such that ,and it follows that .

When we compare the coefficients of in equations (3) and (4), wearrive at (5) below:

(5)

Thus, our extension of the model predicts that the value of firms thathedge should be less sensitive to changes in cash flows relative to non‐hedgers. We form and test the hypothesis that hedging impacts firm valuevia the mechanism of cash flow volatility.

SAMPLE AND VARIABLE CONSTRUCTION

Sample

In order to test the value relevance of hedging we focus on the U.S.insurance industry where derivatives play a key role in managing risk—asevidenced by the $2 trillion notional value of insurance industry derivativesholdings in 2014 (NAIC, 2015). We believe that the insurance industry is an

I

Pt i, a0 wt i, a1 a2I+ a3I vt i,+ + +=

ai vt i,

wt i,t 1=

P1 a0 a1 a2I+ w0h a1 a2I+ w0 1 h– a3I vt i,+ + + +=

t 1=

P1 a0 a1 a2I+ w0 a3I vt i,+ + +=

0 h 1 0 1 h 1–

hedger , 1 h– a1 a2I+ w0=

non hedger ,– a1 a2I+ w0=

non hedger ,– hedger .,


ideal setting for testing our research question due to the prevalence ofderivatives use (especially among large, public companies that we study)and the uniquely detailed regulatory reporting requirements that apply tolicensed insurers. In terms of regulation, U.S. licensed companies arerequired to report the type (forwards, options, etc.), details (e.g., trade date,maturity date, notional amount) and purpose (e.g., hedging or other) of allderivatives instruments that they hold. Within the insurance industry, lifeinsurers are the primary users of derivatives, accounting for 94% of totalindustry derivatives exposure (NAIC, 2015). We therefore focus our anal‐ysis on life insurers rather than property or health insurers. Finally, becausewe are concerned with measuring the effect of derivatives use on marketvalue, we further confine our sample to publicly traded life insurers asindicated in the SNL financial database. We supplement the NAIC datawith data from Compustat and the Center for Research in Security Prices(CRSP). As reported in Table 1, our final sample consists of an unbalancedpanel of 55 publicly‐traded life insurers (367 firm‐year observations) withcomplete data for all variables used in our main models. During our sampleperiod of 2002 to 2012, we have 158 firm‐year insurer observations (27insurers) that report using derivatives for the purpose of hedging in Sched‐ule DB of the NAIC filing.

Table 1. Sample Description

Number of publicly traded life insurers 55

Number that hedge in any sample year 27

Number that never hedge during the sample period 28

# of firm‐year observations (hedgers) 158

# of firm‐year observations (non‐hedgers) 209

# of firm‐year observations (total) 367

Hedging_binary (firm‐year mean) 0.43

Hedging_Amount (mean) $4.40 billion

OtherThan Hedging_Amount (mean) $417 million

Notes: This table summarizes the sample of data used in this study. We confine our sample to the publicly traded life insurers as indicated in the SNL financial database. Hedging_binary is a dummy variable equal to one for insurers that reported derivatives holdings in a given year for the purpose of hedging. Hedging_Amount is equal to the notional amount of derivatives holdings in a given year for the purpose of hedging. OtherThan Hedging_Amount is equal to the notional amount of derivatives holdings for non‐hedging purposes, in a given year. The sample period is from 2002 to 2012.


Variable Construction

Since our goal in this analysis is to determine if hedging impacts firmvalue via the mechanism of cash flow volatility, we specify the followingrelationship in equation (6) to guide our discussion. The following pro‐vides rationale and defines how we measure the variables that we will usethroughout our analysis.

Firm Value = f (Hedging, Cash Flow Volatility, Hedging CF Volatility, controls) (6)

Firm Value measure: According to Smithson and Simkins (2005), moststudies of the value‐relevance of risk management use Tobin’s Q as proxyfor firm value.8 Thus, we follow the extant literature and define Tobin’s Qas the market value of equity plus the book value of liabilities divided bythe book value of assets. Following Allayannis and Weston (2001), in ourregression analysis we use the natural logarithm of Tobin’s Q (ln(Tobin’sQ)) to deal with the skewness of the ratio.

Hedging measures: We define two measures of hedging: one binary andone continuous. First, consistent with Allayannis and Weston (2001),Carter, Rogers, and Simkins (2006), and Jin and Jorion (2006), we use anindicator variable, Hedging_binary, that is equal to one for insurers thatreported derivatives holdings in Schedule DB in year t for the purpose ofhedging, and zero otherwise. Second, we follow Cummins, Phillips, andSmith (2001) and define the extent of hedging with a continuous measure(Hedging_Amount) equal to the aggregate notional amount of derivativeshedging reported in Schedule DB. Previous literature that examines thevalue relevance of hedging has reported mixed results. Allayannis andWeston (2001), and Carter, Rogers, and Simkins (2006) find a positivecoefficient for a binary hedging measure, while Jin and Jorion (2006) findan insignificant coefficient for this variable. Jin and Jorion (2006) find thathedging is not always a positive‐value proposition and argue that thehedging premium depends on the types of risks to which the firm isexposed. When the type of risk is easy to identify and easy to hedge by theindividual investors, the hedging premium should disappear. Accordinglywe expect that if the risks in the insurance industry are not easy to diversifyor understand, then Hedging_Binary (and Hedging_Amount) should bepositively related to ln(Tobin’s Q). However, if the risks are easy for

8See for example Allayannis and Weston (2001), Cummins, Lewis, and Wei (2006), Carter,Rogers, and Simkins (2006), Allayannis, Lel, and Miller (2003), Berrospide, Purnanandam,and Rajan (2008), and Fung, Wen, and Zhang (2012)


shareholders understand or diversify then our hedging proxies should benegative or insignificant.

Cash Flow Volatility measure: According to Froot, Scharfstein, and Stein(1993), cash flow volatility may result in value‐reducing underinvestment.We follow Jayaraman (2008) and calculate cash flow volatility (CashFlow‐Volt) as the variance of five years’ operating cash flows, scaled by totalassets. Minton and Schrand (1999) find that cash flow volatility decreasesinvestment in capital expenditures, R&D costs, and advertising expenses.Since cash flow volatility can cause expensive external funding and anunderinvestment problem, we expect CashFlowVolt to be negatively relatedto ln(Tobin’s Q).

Interaction between Hedging and Cash Flow Volatility: Our variable ofinterest is the interaction term Hedging*CashFlowVolt.9 If hedging reducescash flow volatility, then we expect this coefficient to be positive, so thatoverall impact of cash flow volatility on firm value of hedgers will besmaller than that of non‐hedgers. This coefficient will show us the differ‐ence between the impact of cash flow volatility on firm value for hedgersand non‐hedgers.

Control Variables: We control for other determinants of firm value usinga set of variables drawn primarily from Allayannis and Weston (2001) andFung, Wen, and Zhang (2012). Specifically, we control for leverage, firm size,geographic diversification, line of business diversification, reinsurance, andinvestment opportunities. These control variables are defined below.

Leverage: According to Allayannis and Weston (2001), a firm’s capitalstructure may be related to firm value. Furthermore, Disatnik, Duchin, andSchmidt (2014) model the relationship between a firm’s hedging activitiesand liquidity choices. Their model predicts that firms that hedge may relymore on externally provided capital. Therefore, we define Leverage to bethe ratio of total liabilities to total assets.

Firm Size: To control the impact of size on firm value, we use the log oftotal assets (Size). Lang and Stulz (1994) and Allayannis and Weston (2001)find a negative relation between firm value and size. Thus, we expect thatSize will be negatively related to firm value.

Geographic diversification: We follow Fung, Wen, and Zhang (2012) andcontrol for the extent of geographic diversification by including a variable(GeoDiv) equal to the complement of the Herfindahl index of premiumswritten across all states.

Line of Business diversification: Similar to our approach for geographicdiversification (Fung, Wen, and Zhang, 2012) we include a line of business

9The variable, Hedging, is either the binary measure (Hedging_binary) or the continuous mea‐sure (Hedging_Amount).


diversification measure, LobHerf, calculated as the complement of theHerfindahl index of premiums written across all lines of business.

Reinsurance: According to Colquitt and Hoyt (1997), reinsurance andhedging activity serve similar purposes by reducing the variance of firm’svalue and taxable income. Reinsurance may be seen as a substitute forhedging. Following Fung, Wen, and Zhang (2012), we include Reinsuranceas a control variable. Reinsurance is defined as the ratio of reinsurance cededto the sum of direct business written and reinsurance assumed as reportedin the NAIC data.

Investment Opportunity: Since we wish to isolate the effect that hedginghas on firm value via the mechanism of reducing cash flow volatility, it isimportant that we control for the level of firm investment. FollowingCummins, Phillips, and Smith (2001) and Fung, Wen, and Zhang (2012),we proxy for insurers’ investment opportunities with the variable Premi‐umGrowth, which is equal to the one‐year premium growth.

Table 2 provides descriptive statistics of our sample of life insurers forthe variables described above with percentiles τ = 0.05, 0.25, 0.5, 0.75, 0.95.As seen in the table, life insurers have substantial variation in cash flows,with a mean of 7.67 (standard deviation 11.32). Life insurers have, onaverage, a leverage of 0.82. According to Colquitt and Hoyt (1997) highlylevered firms may benefit more from hedging activities. Geographic andLine of Business diversification can provide natural hedges, so we reportthe complement of Herfindahl indices for both factors. On average wedocument that life insurers are fairly diversified geographically, but some‐what less diversified across lines of business. There is also at least someamount of variation across insurers for both measures. Since Colquitt andHoyt (1997) report that reinsurance and hedging activity serve similarpurposes, we report that approximately twenty percent (.2126) of lifeinsurers use reinsurance on average. Finally, we report the operating returnon equity is, on average, 0.16.

EMPIRICAL METHOD AND RESULTS

Univariate Analysis:

We start our analysis by partitioning the data into hedgers and non‐hedgers. We then conduct a univariate means test of the two groups. Ourresults suggest that insurers in these two groups are statistically differentin terms of size, leverage, their geographic diversification, and their busi‐ness mix. The results are presented in Table 3.

Table 3 shows that, on average, non‐hedgers have larger firm value,measured by Tobin’s Q, and have higher operating return on equity. Also


Table 2. D

escriptive Statistics

Variable

Mean

Std. dev.

5th pctl.

25th pctl.

50th pctl.

75th pctl.

95th pctl.

N

Tobin’s Q

1.1214

0.3573

0.9081

0.9706

1.0133

1.1320

1.6813

367

CashFlowVolt

7.6678

11.3157

0.0681

0.6530

3.1412

10.1443

28.9218

367

Leverage

0.8158

0.1368

0.5556

0.7613

0.8606

0.9203

0.9511

367

Size

9.4587

2.1020

6.0467

7.9163

9.6389

10.8673

12.9426

367

GeoDiv

0.8139

0.2670

0.1445

0.8737

0.9416

0.9528

0.9602

367

LOBDiv

0.2776

0.2818

0.0000

0.0680

0.3031

0.5015

0.5949

367

Reinsurance

0.2126

0.2654

0.0030

0.0427

0.1277

0.2810

0.7775

367

PremiumGrowth

0.0621

0.3626

–0.1965

–0.0533

0.0224

0.0805

0.3696

367

OROE

0.1560

0.1668

–0.0195

0.0827

0.1594

0.2283

0.3377

367

Notes: N denotes firm‐year observations. The sample period is from 2002 to 2012. Tobin’s Q is a common proxy for firm value an

d is equal to

market value of equity plus the book value of liabilities divided by the book value of assets. CashFlowVolt is a proxy for the volatility of cash

flows calculated as the variance of five years’ operating cash flows, scaled by total assets. Leverage is equal to the ratio of total liabilities to total

assets. Size is equal to the natural logarithm of total assets. GeoDiv is equal to the complement of the Herfindah

l index of premiums written

across all states. LOBDiv is equal to the complement of the Herfindah

l index of premiums written across all lines of business. Reinsurance is

defined as the ratio of reinsurance ced

ed to the sum of direct business written and reinsurance assumed

. PremiumGrowth is equal to the one‐

year premium growth, in percent. OROE is a proxy for accounting perform

ance and is calculated as earnings before interest and taxes (EBIT)

divided by total shareholders’ equity.


worth noting is that hedgers have higher leverage compared to non‐hedgers, which is consistent with the argument of Disatnik, Duchin, andSchmidt (2014), who argue that hedgers may rely on more external funds.Although, hedgers are larger than non‐hedgers, their operations are morediverse with respect to line of business diversification than non‐hedgers.Hedgers are also more geographically diverse. Thus, there are differenceswith respect to natural hedging between hedgers and non‐hedgers. Tocontrol for other firm‐specific factors, we next specify a multivariate test ofhedging impact on firm value.

Table 3. Univariate Comparison of Non‐Hedgers and Hedgers

(1) Non‐hedger(N = 209)

(2) Hedger(N = 158) Difference

(1) – (2)Variable Mean Mean

Tobin’s Q 1.2120 1.0016 0.2104***

CashFlowVolt 8.4499 6.6332 1.8167

Leverage 0.7506 0.9020 –0.1515***

Size 8.4872 10.7439 –2.2567***

GeoDiv 0.7604 0.8847 –0.1243***

LOBDiv 0.2188 0.3554 –0.1366***

Reinsurance 0.2105 0.2154 –0.0050

PremiumGrowth 0.0775 0.0417 0.0358

OROE 0.1698 0.1378 0.0320*

Notes: This table presents the univariate comparison of means of the descriptive statisticsbetween non‐hedgers and hedgers. Non‐hedgers are firm‐year observations whereHedging_binary = 0 and Hedgers are firm‐year observations where Hedging_binary =1. N denotes firm‐year observations. Tobin’s Q is a common proxy for firm value and isequal to market value of equity plus the book value of liabilities divided by the bookvalue of assets. CashFlowVolt is a proxy for the volatility of cash flows calculated as thevariance of five years’ operating cash flows, scaled by total assets. Leverage is equal tothe ratio of total liabilities to total assets, in percent. Size is equal to the natural logarithmof total assets. GeoDiv is equal to the complement of the Herfindahl index of premiumswritten across all states. LOBDiv is equal to the complement of the Herfindahl index ofpremiums written across all lines of business. Reinsurance is defined as the ratio ofreinsurance ceded to the sum of direct business written and reinsurance assumed.PremiumGrowth is equal to the one‐year premium growth, in percent. OROE is a proxyfor accounting performance and is calculated as earnings before interest and taxes (EBIT)divided by total shareholders’ equity. Statistical significance is based on a t‐test. ***, **,and * denote statistical significance at the 1, 5, and 10 percent level, respectively.


Multivariate Analysis

To test the effect of hedging on firm value (both directly and alsoindirectly via its impact on cash flow volatility) we specify the followingmodel:

ln (Tobin’s Q)i,t = 0 + 1Hedgingi,t + 2CashFlowVolti,t +

3Hedgingi,t CashFlowVolti,t + 4Leveragei,t + 5Sizei,t + 6GeoDivi,t +

7LOBDivi,t + 8Reinsurancei,t + 9PremiumGrowthi,t + ui,t (7)

where Hedging is a measure for hedging activity, either an indicator vari‐able, Hedging_binary, or a continuous measure, Hedging_Amount. Priorliterature suggests that empirical tests of the value effects of corporatehedging may suffer from endogeneity problems (Bartram, Brown andConrad, 2011). In unreported results, we test for endogeneity of the hedg‐ing decision by applying a regression‐based Hausman test.10 We are unableto reject the null of exogeneity and therefore treat hedging as exogenousin our firm value fixed effects regressions. The other variables in equation(7) have been defined previously.

We estimate equation (7) via ordinary least squares (OLS) with firm‐level fixed effects that control for unobservable value determinants such asmanagerial skill (Allayannis and Weston, 2001). T‐statistics are based onrobust standard errors clustered at the firm level. Estimation results arepresented in Table 4.

In column (1) of Table 4, we report a negative and significant coefficientfor Hedging (where Hedging is Hedging_binary—an indicator variable). Thisresult indicates that the act of hedging alone reduces firm value and isconsistent with the findings of Jin and Jorion (2006). The results of Jin andJorion (2006) refute the hypothesis that hedging is always a positive‐valueproposition. According to Jin and Jorion, the hedging premium depends onthe types of risks to which the firm is exposed. When the type of risk is easyto identify and easy to hedge by the individual investors, the hedgingpremium should disappear. It is possible that the risks faced by life insurersare easy to understand by the investors, who prefer to diversify these risks

10To conduct the Hausman test, we estimate a model of insurer hedging choice by estimat‐ing a Logit model. The determinants of insurer hedging are chosen from Cummins, Phillips,and Smith (1997; 2001), Colquitt and Hoyt (1997), and Cummins and Song (2008). We usethree risk‐related instruments (the stock ratio, the bond ratio, and an overall measure ofrisky assets) and conduct the Hausman test accordingly (Cummins and Song, 2008; andFung, Wen, and Zhang, 2012).


Table 4. The Impact of Hedging and Cash Flow Volatility on Firm Value

Hedging Proxy: (1) (2)

Hedging_binary Hedging_Amount

Hedging –0.0252* –0.0009**(–1.7740) (–2.3847)

Hedging*CashFlowVolt 0.0036** 0.0001**(2.5312) (2.1666)

CashFlowVolt –0.0067** –0.0052**(–2.3802) (–2.0999)

Leverage 0.0647 0.0539(0.2071) (0.1704)

Size –0.1369*** –0.1351***(–2.9039) (–2.8750)

GeoDiv 0.0459 0.0435(0.1384) (0.1305)

LOBDiv 0.0114 0.0113(0.7456) (0.7501)

Reinsurance –0.0564 –0.0557(–1.2927) (–1.2666)

PremiumGrowth 0.0672* 0.0669*(1.7149) (1.7020)

Constant 1.3455*** 1.3261***(3.0031) (2.9886)

R‐squared 0.2345 0.2332Observations 367 367

Notes: The dependent variable is ln(Tobin’s Q), a common proxy for firm value. Tobin’s Q isequal to market value of equity plus the book value of liabilities divided by the book valueof assets. In column (1), hedging choice is proxied by Hedging_binary—an indicator variablethat is equal to one for insurers that reported derivatives holdings in year t for the purposeof hedging, and zero otherwise. In column (2), hedging extent is proxied by Hedging_Amount—a continuous variable equal to the notional amount of derivatives holdings for hedgingpurposes. CashFlowVolt is the proxy for the volatility of cash flows calculated as the varianceof five years’ operating cash flows, scaled by total assets. In column (1), Hedging*CashFlow‐Volt is the interaction term of CashFlowVolt and Hedging_binary. In column (2), Hedging*Cash‐FlowVolt is the interaction term of CashFlowVolt and Hedging_Amount. Leverage is equal tothe ratio of total liabilities to total assets, in percent. Size is equal to the natural logarithm oftotal assets. GeoDiv is equal to the complement of the Herfindahl index of premiums writtenacross all states. LOBDiv is equal to the complement of the Herfindahl index of premiumswritten across all lines of business. Reinsurance is defined as the ratio of reinsurance cededto the sum of direct business written and reinsurance assumed. PremiumGrowth is equal tothe one‐year premium growth, in percent. Models are estimated using firm fixed‐effectregressions. T‐statistics are based on standard errors clustered at the firm level and arepresented in the parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10percent level, respectively. The sample period is from 2002 to 2012.


themselves. An alternative explanation for the negative sign is the manage‐rial compensation hypothesis of Smith and Stulz (1985), which states that ifmanagers’ expected utilities heavily depend on accounting earnings andthe expected utilities are concave functions of accounting earnings, thenmanagers may hedge even if hedging increases volatility in firm value. It ispossible that insurance firms’ managerial compensation schemes may pro‐vide incentives for hedging activities that are not valued by shareholders.

Additionally, we report that CashFlowVolt is associated with a reduc‐tion in firm value for life insurers. Our result is consistent with the argu‐ment of Froot, Scharfstein, and Stein (1993) that cash flow volatility couldimpair investment and therefore negatively impact firm value. The nega‐tive sign on the coefficient is also consistent with the empirical findings ofRountree, Weston, and Allayannis (2008), who show that cash flow vola‐tility negatively impacts firm value.

In our extension of the Froot, Scharfstein, and Stein (1993) model, weshow that there should be a benefit to firms who hedge relative to thosewho do not by reducing the sensitivity of firm value to changes in cash flowvolatility. Therefore, we are interested in the interaction term of hedgingactivity and cash flow volatility (Hedging*CashFlowVolt). Given the negativecoefficient on CashFlowVolt, a positive sign on the interaction term impliesa reduction in the negative effect that cash flow volatility has on firm value.For non‐hedgers, the effect of cash flow volatility on firm value is –0.0067,while for hedgers the positive interactive effect (of hedging and cash flowvolatility) reduces the overall effect to –0.003. Therefore, our result isconsistent with the prediction of our model extension that firm values ofhedgers are less affected by their cash flow volatilities than those of non‐hedgers.11

Next, we alter the model slightly to use a continuous measure ofhedging (defined to be the notional amount of hedging listed in ScheduleDB) instead of a binary indicator variable. In column (2) of Table 4,Hedging_Amount denotes a continuous variable that aggregates thenotional amount reported by each insurer in an insurer group where theinsurer has indicated that the derivative usage is for hedging purposes. Allother variables are the same as previously defined. In column (2), we notethat using a continuous measure that isolates derivatives used for hedgingpurposes, we find that the firm value of hedgers is less sensitive to changesin cash flow volatility relative to that of non‐hedgers via the channel of

11We test for multicollinearity among the explanatory variables in all regression modelsusing variance inflation factors. The mean variance inflation factors are well below thebenchmark of 10, indicating that multicollinearity does not appear to be a concern (see, e.g.,Belsley, Kuh, and Welsch, 1980; Chatterjee, Hadi, and Price, 2000).


hedging reducing cash flow volatility. This result is consistent with ourresults using the binary hedging variable.

Additionally, since recent studies (see Chernenko and Faulkender,2011) have documented the importance of distinguishing between deriva‐tive usage for hedging versus non‐hedging and speculative activities, weexamine whether or not non‐hedging activity determines firm value. Wespecify the following model in equation (8). As before, we estimate themodel via ordinary least squares (OLS) with firm‐level fixed effects, andreport T‐statistics that are based on robust standard errors clustered at thefirm level.

ln (Tobin’s Q)i,t = 0 + 1Nonhedgingi,t + 2CashFlowVolti,t +

3Nonhedgingi,t CashFlowVolti,t + 4Leveragei,t + 5Sizei,t + 6GeoDivi,t +

7LOBDivi,t + 8Reinsurancei,t + 9PremiumGrowthi,t + ui,t. (8)

In equation (8), we define a continuous variable, Nonhedging, which isthe notional amount of derivative usage where the insurer has indicatedthat the purpose is something other than hedging. All other variables arethe same as defined previously. Table 5 holds the results of estimatingequation (8). We fail to find evidence that derivatives used for non‐hedgingpurposes (such as speculative derivative trades) impacts firm value at all,as neither the hedging variable nor the interaction term is significant. Thus,the market seems to ignore derivative usage by life insurers that is notrelated to hedging. This may be due to the fact that most of the derivativeusage (between 70 and 80%) of all reported derivatives transactions isindicated by the insurer to be for the purpose of hedging; non‐hedgingcould be listed as for the purposes of “replication,” “income generation,”or “other.” The “other” category makes up the majority of the “non‐hedging” transactions. Therefore, the market may view the amount ofderivatives for non‐hedging purposes as trivial.

Since theoretical models such as Smith and Stulz (1985) suggest thathedging may impact the pre‐tax financial performance of the firm, we alsoconsider operating return on equity (OROE) as a dependent variable.Following the empirical approach of Fung, Weng, and Zhang (2012), whofind that the use of credit default swap can affect insurers’ financialperformance, we estimate the following model (equation [9]) via OLS withfirm‐level fixed effects. As before, T‐statistics are based on robust standarderrors clustered at the firm level.


Table 5. The Impact of Non‐Hedging Derivative Usage on Firm Value

NonHedging 0.0002

(0.4449)

NonHedging*CashFlowVolt 0.0000

(0.0518)

CashFlowVolt –0.0056*

(–1.9724)

Leverage 0.0817

(0.2638)

Size –0.1365***

(–2.9439)

GeoDiv –0.0004

(–0.0013)

LOBDiv 0.0102

(0.6010)

Reinsurance –0.0536

(–1.2542)

PremiumGrowth 0.0697*

(1.7321)

Constant 1.3593***

(3.0628)

R‐squared 0.2254

Observations 367

Notes: The dependent variable is ln(Tobin’s Q), a common proxy for firm value. Tobin’sQ is equal to market value of equity plus the book value of liabilities divided by the bookvalue of assets. NonHedging is a continuous variable equal to the notional amount ofderivatives holdings for purposes other than hedging, as indicated by the insurer.CashFlowVolt is the proxy for the volatility of cash flows calculated as the variance of fiveyears’ operating cash flows, scaled by total assets. NonHedging*CashFlowVolt is theinteraction term of CashFlowVolt and NonHedging. Leverage is equal to the ratio of totalliabilities to total assets, in percent. Size is equal to the natural logarithm of total assets.GeoDiv is equal to the complement of the Herfindahl index of premiums written acrossall states. LOBDiv is equal to the complement of the Herfindahl index of premiumswritten across all lines of business. Reinsurance is defined as the ratio of reinsuranceceded to the sum of direct business written and reinsurance assumed. PremiumGrowthis equal to the one‐year premium growth, in percent. Models are estimated using firmfixed‐effect regressions. T‐statistics are based on standard errors clustered at the firmlevel and are presented in the parentheses. ***, **, and * denote statistical significance atthe 1, 5, and 10 percent level, respectively. The sample period is from 2002 to 2012.


OROEi,t = 0 + 1Hedgingi + 2CashFlowVolti,t +

3Hedgingi CashFlowVolti,t + 4Leveragei,t + 5Sizei,t + 6GeoDivi,t +

7LOBDivi,t + 8Reinsurancei,t + 9PremiumGrowthi,t + ui,t. (9)

OROE in equation (9) is calculated as earnings before interest and taxes(EBIT) divided by total shareholders’ equity. Other variables are the sameas defined previously. The results of the estimation are shown in Table 6.

We follow the same approach as in Table 4 and estimate two differentmodels. In column (1), Hedging is an indicator variable, while for column(2) Hedging denotes a continuous variable. In both models, we fail to findevidence that the act of hedging has an impact on firm’s pre‐tax operatingperformance (measured by OROE), as the coefficients for the Hedgingvariables are insignificant. The lack of significance is suggestive of a firmnot considering pre‐tax financial performance as a concern for hedging,which is consistent with the findings of Graham and Rogers (2002). Thecoefficient for CashFlowVolt is weakly positive and significant in all fourmodels.

CONCLUSION

In this paper we examine, theoretically and empirically, the direct andindirect effect of derivatives hedging on firm value. The extant literaturehas separately examined the effect of hedging on firm value and cash flowvolatility (e.g., Bartram, Brown, and Conrad, 2011), and the effect of cashflow volatility on firm value (Rountree, Weston, and Allayannis, 2008).However, there is an absence of research that considers the effect ofhedging on firm value via its impact on cash flow volatility, which may, inpart, explain the mixed results in the extant literature with regards to theeffect hedging has on firm value.

Our paper extends and tests the predictions of Froot, Scharfstein, andStein’s (1993) model of the relation between hedging, cash flows, and firmvalue. Specifically, we model the impact of derivatives hedging on firmvalue both directly and indirectly through its effect on cash flow volatility,i.e., via an interaction effect. We test the predictions of our model using asample of publicly traded life insurers that are required to report details oftheir insurance activities as well as their investment activities—includingcontract‐specific data for all derivatives transactions and holdings.


Table 6. The Impact of Hedging and Cash Flow Volatility on OROE

(1) (2)

Hedging proxy: Hedging_binary Hedging_Amount

Hedging –0.0174 –0.0001(–1.0888) (–0.3525)

Hedging*CashFlowVolt 0.0011 0.0000(1.1130) (0.4931)

CashFlowVolt 0.0019 0.0023**(1.6509) (2.4489)

Leverage –0.1767 –0.1671(–0.8925) (–0.8506)

Size –0.1063*** –0.1084***(–3.3991) (–3.6318)

GeoDiv 0.0740 0.0650(0.8852) (0.7831)

LOBDiv 0.0278 0.0273(1.3174) (1.2916)

Reinsurance 0.0521 0.0515(0.7980) (0.7828)

PremiumGrowth 0.0136 0.0145(1.4446) (1.5543)

Constant 1.2152*** 1.2278***(3.8474) (3.9759)

R‐squared 0.1791 0.1767Observations 367 367

Notes: The dependent variable is Operating Return on Equity (OROE = EBIT / totalshareholders’ equity), a proxy for accounting based performance measure. In column(1), Hedging is an indicator variable that is equal to one for insurers that reportedderivatives holdings in year t for the purpose of hedging, and zero otherwise. In column(2), Hedging is equal to the notional amount of derivatives holdings for hedging purposes.CashFlowVolt is the proxy for the volatility of cash flows calculated as the variance of fiveyears’ operating cash flows, scaled by total assets. In column (1), Hedging*CashFlowVoltis the interaction term of CashFlowVolt and the hedging indicator. In column (2), Hedging*CashFlowVolt is the interaction term of CashFlowVolt and the notional amount of deriva‐tives use. Leverage is equal to the ratio of total liabilities to total assets, in percent. Size isequal to the natural logarithm of total assets. GeoDiv is equal to the complement of theHerfindahl index of premiums written across all states. LOBDiv is equal to the comple‐ment of the Herfindahl index of premiums written across all lines of business. Reinsur‐ance is defined as the ratio of reinsurance ceded to the sum of direct business written andreinsurance assumed. PremiumGrowth is equal to the one‐year premium growth, inpercent. Models are estimated using firm fixed‐effect regressions. T‐statistics are basedon standard errors clustered at the firm level and are presented in the parentheses. ***,**, and * denote statistical significance at the 1, 5, and 10 percent level, respectively. Thesample period is from 2002 to 2012.


Overall, we find that derivatives usage alone decreases firm value andperformance for life insurers; however, when we consider the impact ofhedging via the impact on cash flow volatility, we document that hedgers’firm values are less sensitive to cash flow volatility compared to non‐hedgers. Given the mixed results of the impact of hedging on firm valuefound in prior studies, our results highlight the importance of consideringthe mechanism through which a firm’s derivative usage impacts firm value.

REFERENCES

Allayannis, G. and A. Mozumdar (2000) “Cash Flow, Investment, and Hedging.”Working paper.

Allayannis, G. and J. P. Weston (2001) “The Use of Foreign Currency Derivativesand Firm Market Value,” Review of Financial Studies 14(1): 243–276.

Allayannis, G., U. Lel, and D. P. Miller (2003) “Corporate Governance and theHedging Premium around the World.” Working paper, Darden School.

Bartram, S. M., G. W. Brown, and J. Conrad (2011) “The Effects of Derivatives onFirm Risk and Value,” Journal of Financial and Quantitative Analysis 46(04): 967–999.

Belsley, D. A., E. Kuh, and R. E. Welsch (1980) Regression Diagnostics, IdentifyingInfluential Data and Sources of Collinearity, Wiley.

Berrospide, J., A. Purnanandam, and U. Rajan (2010) “Corporate Hedging, Invest‐ment and Value,” in EFA 2008 Athens Meetings Paper, December 2010: 2008–2016.

Carter, D. A., D. A. Rogers, and B. J. Simkins (2006) “Does Hedging Affect FirmValue? Evidence from the US Airline Industry,” Financial Management 35(1): 53–86.

Chatterjee, S., A. S. Hadi, and B. Price (2000) Regression Analysis by Example, Wiley.Chernenko, S. and M. Faulkender (2011) “The Two Sides of Derivatives Usage:

Hedging and Speculating with Interest Rate Swaps, Journal of Financial andQuantitative Analysis 46(6): 1727–1754.

Colquitt, L. L. and R. E. Hoyt (1997) “Determinants of Corporate Hedging Behavior:Evidence from the Life Insurance Industry, Journal of Risk and Insurance 64(4):649–671.

Cummins, J. D. and Q. (F.) Song (2008) “Hedge the Hedgers: Usage of Reinsuranceand Derivatives by PC Insurance Companies.” Working paper, available athttp://ssrn.com/abstract=1138028.

Cummins, J. D., C. Lewis, and R. Wei (2006). The Market Impact of OperationalRisk Events for US Banks and Insurers. Journal of Banking and Finance 30: 2605–2634.


Cummins, J. D., R. D. Phillips, and S. D. Smith (2001) “Derivatives and CorporateRisk Management: Participation and Volume Decisions in the Insurance Indus‐try,” Journal of Risk and Insurance 68(1): 51–91.

Cummins, J. D., R. D. Phillips, and S. D. Smith (1997) “Corporate Hedging in theInsurance Industry: The Use of Financial Derivatives by US Insurers,” NorthAmerican Actuarial Journal 1(1): 13–40.

Deshmukh, S. and S. C. Vogt (2005) “Investment, Cash Flow, and CorporateHedging,” Journal of Corporate Finance 11(4): 628–644.

Disatnik, D., R. Duchin, and B. Schmidt (2014) “Cash Flow Hedging and LiquidityChoices,” Review of Finance 18(2): 715–748.

Froot, K. A., D. S. Scharfstein, and J. C. Stein (1993) “Risk Management: Coordinat‐ing Corporate Investment and Financing Policies,” The Journal of Finance 48(5):1629–1658.

Fung, H. G., M. M. Wen, and G. Zhang (2012) “How Does the Use of Credit DefaultSwaps Affect Firm Risk and Value? Evidence from US Life and Property/Casualty Insurance Companies,” Financial Management 41(4): 979–1007.

Graham, J. and D. Rogers (2002) “Do Firms Hedge in Response to Tax Incentives?”The Journal of Finance 57(2): 815–839.

Jayaraman, S. (2008) “Earnings Volatility, Cash Flow Volatility, and InformedTrading,” Journal of Accounting Research 46(4): 809–851.

Jin, Y. and P. Jorion (2006) “Firm Value and Hedging: Evidence from US Oil andGas Producers,” The Journal of Finance 61(2): 893–919.

Lang, L. H. and R. M. Stulz (1994) “Tobin’s q, Corporate Diversification, and FirmPerformance,” Journal of Political Economy 102(6): 1248–1280.

Mian, S. L. (1996) “Evidence on Corporate Hedging Policy,” Journal of Financial andQuantitative Analysis 31(3): 419–439.

Miller, M. H. and F. Modigliani (1961) “Dividend Policy, Growth and the Valuationof Shares,” Journal of Business 34(4): 411–433.

Minton, B. A. and C. Schrand (1999) “The Impact of Cash Flow Volatility onDiscretionary Investment and the Costs of Debt and Equity Financing,” Journalof Financial Economics 54(3): 423–460.

Modigliani, F. and M. H. Miller (1958) “The Cost of Capital, Corporation Financeand the Theory of Investment,” The American Economic Review 48: 261–297.

NAIC (2015) “NAIC Capital Markets Special Report: Update on the InsuranceIndustry’s Use of Derivatives and Exposure Trends,” August 7, 2015, lastaccessed 07/17/2016 at www.naic.org/capital_markets_archive/150807.htm.

Nance, D. R., C. W. Smith, and C. W. Smithson (1993) “On the Determinants ofCorporate Hedging,” The Journal of Finance 48(1): 267–284.

Rountree, B., J. P. Weston, and G. Allayannis (2008) “Do Investors Value SmoothPerformance?” Journal of Financial Economics 90: 237–251.

Scordis, N. A. and P. Steinorth (2012) “Value from Hedging Risk with Reinsurance,”Journal of Insurance Issues 35(2): 210–231.

Scordis, N. A., J. Barrese, and P. Wang (2008) “The Impact of Cash Flow Volatilityon Systematic Risk,” Journal of Insurance Issues 31(1): 43–71.

Smith, C. W. and R. M. Stulz (1985) “The Determinants of Firms’ Hedging Policies,”Journal of Financial and Quantitative Analysis 20(4): 391–405.


Whidbee, D. A. and M. Wohar (1999) “Derivative Activities and ManagerialIncentives in the Banking Industry,” Journal of Corporate Finance 5(3): 251–276.

Hedging, Cash Flows, and Firm Value: Evidence of an ... · PDF fileHEDGING, CASH FLOWS, AND...

Documents

Transcript of Hedging, Cash Flows, and Firm Value: Evidence of an ... · PDF fileHEDGING, CASH FLOWS, AND...