Capital Market Frictions, Leasing and Hedging

This paper examines the hitherto unexplored effect of lease intensity on hedging. Using a sample of 218 small and large non-financial firms drawn from 2006 to 2010, we find that firms leasing more of their Property, Plant and Equipment (PPE) use less financial derivatives, consistent with the theoretical predictions of Rampini and Viswanathan (2010). Further, using broad market microstructure based measures of information asymmetry, we offer empirical evidence consistent with theory that firms with higher information asymmetry hedge more. These results are robust to several alternative measurements of key variables, different regression specifications, estimation techniques and corrections for endogeneity.

Consider the risk management rationale for the use of lease financing. Lease financing is often preferable in cases where firms want to mitigate the risk of capital expenditures incurred with respect to positive Net Present Value (NPV) projects. Should such investment underperform expectations, leased assets have an embedded put option that can be exercised without risking the balance sheet assets.
Further, firms might view leases as a way to transfer the risk of fluctuations in the value of the asset. Bessembinder (1991) argues that hedging improves debt capacity (i.e., the ability borrow additional debt) by reducing the probability of financial distress. Eisfeldt and Rampini (2009) argue that leased capital has higher debt capacity than secured debt capital because of the superior ability of a lessor to repossess a leased asset compared to foreclosure on the collateral of a secured loan in case of financial distress of the lessee firm. They further argue that financially constrained firms value the additional debt capacity more and hence lease more of their capital than less constrained firms. Since poorly capitalized firms lease more of their assets (see Eisfeldt and Rampini (2009)), firms that lease more should hedge less as the need for financing the investments and conserving the debt capacity dominate the hedging concerns. Further, in case of lease financing, the leased asset itself acts as collateral to the lessor and the lessee firm does not have to pledge its own assets as collateral, unlike secured debt which could pose collateral constraints for borrowers. The superior repossession rights allow the lessor to extend more credit than a secured lender for the same asset. Therefore, leasing may not only enhance the debt capacity of financially constrained firms but also serve as an effective mechanism to mitigate the risks associated with capital expenditures. Rampini and Viswanthan (2010) note that both lease financing and hedging serve to improve debt capacity and relax financial constraints. They highlight a fundamental trade-off between hedging and lease financing and predict that firms that rely more on leasing tend to hedge less. Based on these arguments, we propose the following empirical relation between leasing and hedging:

H2: The extent of corporate hedging is negatively related to the ratio of leased capital deployed by firms.
Other Theories of Hedging: Smith and Stulz (1985) argue that if taxes are a convex function of earnings, then it is optimal for firms to hedge in order to reduce expected tax liability. Further, Stulz (1996), Ross (1996), andLeland (1998) suggest that tax shields associated with debt financing provide an incentive for risk management. They argue that by reducing risk, hedging enables the firm to increase debt capacity and to reduce tax liabilities due to increases in leverage. Graham and Rogers (2002) report that firms hedge to increase debt capacity and interest deductions and find no evidence that firms hedge in response to tax convexity.

C. Tax Incentives
They also identify an important link between hedging and the capital structure decisions and argue that hedging-leverage causality can go both ways. On the other hand, using a different sample and control variables, Geczy, Minton, and Schrand (1997) find no support for this hypothesis. 48 www.scholink.org/ojs/index.php/ijafs International Journal of Accounting and Finance Studies Vol. 3, No. 2, 2020 However, with the adoption of Statement of Financial Accounting Standards (SFAS) 133 the derivative reporting requirements have substantially changed. This Statement requires that firms recognize all derivatives as either assets or liabilities and measure those instruments at fair market value (Note 3).
Under this standard, fair value is defined as the price that would be received to sell an asset or paid to transfer a liability (i.e., the "exit price") in an orderly transaction between market participants at the measurement date.
Another important issue acknowledged in prior studies on hedging is the inability of the researcher to distinguish the hedging motivation of an entity from risk management to speculation. For the first time, FASB through SFAS 133 required hedging performance rather than hedging intent as the criterion for determining whether to apply deferral accounting for the derivative gain or loss. We follow Campello et al. (2010) and define notional hedge ratio (NHR) as the ratio of sum of notional values of both interest rate and foreign exchange derivatives to lease-adjusted total assets. Fair value hedge ratio (FHR) is defined as the ratio of sum of fair values of both interest rate and foreign exchange derivatives to lease-adjusted total assets. Hedging Dummy (HD) is defined as equal to 1 if either FHR or NHR is greater than zero, else it is equal to zero. Lease-adjusted total assets are defined as the sum of total assets and capitalized value of operating leases, i.e., (rental expense + present value of future rental commitments for the next 5 years + present value of thereafter portion). Past studies on leasing (Cornaggia et al. (2013), Yan (2002, and Graham, Lemmon and Schallheim (1998)) use 10% as the typical discount rate. Hence, we also use 10% as the discount rate in computing the present value of rental commitments and the thereafter portion. We define Operating Lease Ratio (OLR) as the ratio of capitalized value of operating leases to lease-adjusted total assets (Note 4).
Based on an extensive literature survey, Bharath et al. (2009) argue that adverse selection is an important determinant of market liquidity, when liquidity is proxied by either bid-ask spread or trading volume (Note 5). Following Amihud (2002), we use a market microstructure-based measure of stock illiquidity, viz. "ILLIQ", measured as the average ratio of daily absolute return to the dollar trading volume on that day, to proxy for information asymmetry (Note 6).
Following Adam and Goyal (2008), we use Tobin's Q, i.e., market to book ratio of assets as a proxy for the firm's growth opportunities (Note 7). Debt Ratio (DR) is measured as the ratio of book value of long term debt to the book value of lease adjusted total assets. Recently, Hadlock and Pierce (2010) conclude that firm size and age are particularly useful predictors of financial constraint levels and question the validity of commonly used measures of financial constraints such as Kaplan and Zingales (KZ) and Whited and Wu (WW) indices. Accordingly, we use firm size and firm age as proxies for financial constraints. We measure firm size as a natural logarithm of net sales. Firm age in any given year is measured as ln (1+difference between that year and the year of incorporation). Research and Development intensity (RD) is defined as ratio of annual Research & Development expenditure to lease-adjusted total assets. We measure exposure to foreign exchange rate risk (foreign) as the ratio of foreign sales to total sales as per Geczy et al. (1997). Taxrate is defined as the ratio of taxes paid to pretax income. Volatility (VOLA) in any sample year is defined as the standard deviation of EBITDA over the 5 years preceding the sample year.
We follow Campello et al. (2010) and Acharya et al. (2007)  assets. Another source of market frictions is agency problems. One widely available proxy for firm-level agency issues is Anti-Takeover Provisions (ATPs). More the number of ATPs adopted by a firm, higher are the agency frictions. These structures are created by managers to insulate themselves 51 from the discipline of the market for corporate control and to seek private benefits of control. Since the pursuit of private benefits through more ATPs aggravates the severity of capital market frictions between managers and investors, agency frictions might increase the incentives of managers to resort to lease financing. This line of argument suggests that lease intensity would be positively related to ATPs. Robicheaux et al. (2008) offer empirical evidence that agency cost reducing measures such as corporate governance and lease financing are complements. We use the Entrenchment Index (EIndex), a proxy for corporate governance, of Bebchuk, Cohen and Ferrell (2009). The level of EIndex for any given firm in a given year is computed as a sum of points by assigning one point for each of the six components of the index that the firm has: staggered boards, limits to shareholder bylaw amendments, poison pills, golden parachutes, and super majority requirements for mergers and charter amendments. We collect the data on these variables from Governance data set of RiskMetrics. By definition poorly governed firms have higher EIndex value.
We measure managerial stock incentive compensation (MSIC) as a ratio of value of restricted stock granted during the year to total compensation as in Rogers (2002). Total compensation includes salary, bonus, other annual, total value of restricted stock granted, total value of stock options granted (using Black-Scholes option pricing model), long-term incentive payouts, and all other total (Note 8). We collect the data on these variables from Compustat Execucomp database. The descriptive statistics for all variables mentioned above are reported in Table 1  Similarly, bid-ask spread (BASPRD) has a minimum of 0.041, a maximum of 0.89 and a median value of 0.029. The median firm-year size is 21.12 (in log of sales), and the median (log) age is 3.56.      The pairwise correlations among the key variables are reported in Table 3 and briefly discussed here. It is reassuring to note that the Fair value Hedge Ratio (FHR) has a significant correlation coefficient with  notional hedge ratio and hedging dummy but positive and insignificant with respect to fair value hedge ratio. Both of these estimates are consistent with the hedging puzzle-that older and larger firms (likely subject to less financial constraints) hedge more. In line with the distress-cost based theories of hedging, Debt Ratio (DR) is positively and significantly related to notional and fair value hedge ratios as well as the hedging dummy. This positive relation between hedging and debt financing is in sharp contrast to the negative correlation between hedging and lease financing. Not surprisingly, long-term debt financing is significantly negatively correlated with lease financing. Debt Capacity (DC) is negatively correlated to notional and fair value hedge ratios as well as the hedging dummy but it is not significant.  we find that small firms hedge less compared to large firms. While the mean difference of the notional hedge ratio between small and large firms is significant, that based on the fair value hedge ratio is not significant. Further, consistent with our second hypothesis, firms with high operating lease ratios tend to have low hedge ratios, see Panels C and D. The mean differences are significant for both notional and fair value hedge ratios. Overall, we find suggestive evidence to support our arguments that hedging intensity increases with information frictions and that hedging decreases with lease financing.

Information Asymmetry, Leasing and Hedging
In the comprehensive hedging model, presented below, Information Asymmetry (IA) and Operating Lease Ratio (OLR) are test variables. We lag the independent variables to search for potentially causal influences and mitigate spurious correlations among the regressors and the regressand. We correct for time-series dependence among the error terms by clustering the residuals based on firm id (gvkey).
Also, we control for industry fixed effects and include year dummies to remove any cross-sectional dependence between observations in the same time period (Note 9). We use these heteroskedasticity and autocorrelation robust standard errors for inference.
HR represents either fair value or nominal hedge ratio. IA denotes information asymmetry proxied by either ILLIQ (Amihud, 2002) or percent bid-ask spread (BASPRD). OLR is operating lease ratio. DR is debt ratio. Q is M/B ratio of total assets, a proxy for growth opportunities. AGE is the firm age, since 56 the date of incorporation, and proxies financial constraints. SIZE is ln (net Sales) a proxy for the financial constraints. MZ is modified Altman's Z-score and proxies financial distress. DC is debt capacity. Foreign is foreign sales/Total sales and proxies currency exposure. VOLA represents standard deviation of cash flows over the past five years. TAXRATE is income taxes paid as a fraction of pretax income. RD is research and development as a fraction of lease adjusted total assets. MSIC is managerial stock incentive compensation and proxies for managerial risk aversion. Tufano (1996) finds industry patterns in hedging practices-while some industries tend to use financial derivatives to hedge their exposures, others rarely use these instruments. We use industry and year fixed effects to control for unobserved heterogeneity that is constant over time and across industries.  This evidence supports the arguments by Rampini and Viswanathan (2010) that there is a fundamental trade-off between firms' financing and risk management policies and financing needs can override hedging concerns for financially constrained firms. The coefficient on information asymmetry (0.0199), proxied by ILLIQ, is positive and significant, supporting our first hypothesis that firms with high information asymmetry should hedge more to reduce information asymmetry between managers and markets and to mitigate agency costs/costly external financing (see Demarzo & Duffie, 1991).

The coefficient on Managerial Stock Incentive Compensation (MSIC) is negative and significant,
indicating that firms that rely more on incentive compensation to mitigate shareholder-manager agency issues would rely less on hedging. The coefficient on Debt Ratio (DR) is positive and significant as predicted. This is consistent with Graham and Rogers (2002) that hedging increases leverage and vice-versa. The coefficient on Debt Capacity (DC) is negative as expected but not significant. The coefficient on R&D (RD) is positive and significant. This finding is in line with the idea that firms with high R&D tend to have high growth opportunities and firms with high growth opportunities should hedge more to mitigate the agency costs of underinvestment (see Nance, Smith, Smithson (1997), Lin (2007)) (Note 10). We find a positive (but not significant) coefficient on firm size (SIZE), consistent with the argument that large firms are less constrained and hence should hedge more. Another explanation is due to the economies of scale associated with hedging which suggests that large firms should hedge more as they have less cost of setting up and running an in house hedging program, see.
The coefficient on firm age (AGE) is positive as expected, because older and established firms are less constrained and hence should hedge more, but it is not significant. Supporting the idea that firms with higher investment opportunities suffer the most from the costs of underinvestment and should hedge more to mitigate underinvestment costs, the coefficient on Q is positive and significant. The coefficient on cash flow volatility (VOLA) is positive as expected but not significant. The coefficient on Modified Z-score (MZ) is negative, indicating that firms with high financial distress hedge more to reduce the probability of bankruptcy. Consistent with the notion that firms with higher exposure to exchange rate risk (measured as the ratio of foreign sales to total sales) should hedge more, the coefficient on FOREIGN is positive and significant. Finally the coefficient on TAXRATE is negative and significant. would hedge less, ceteris paribus. The Probit estimation results confirm that the operating lease ratio is negative and highly significant. The coefficient on illiquidity is positive and highly significant as predicted.
Turning to the economic significance of the coefficient estimates on information asymmetry and operating lease ratio. Consider Model (1). A one standard deviation (= 0.0846) increase in illiquidity ILLIQ increases the notional hedge ratio by 0.168% (= 0.0199*0.0846) from its expected value. This is considerable given that the mean notional hedge ratio is 2.8%. Similarly, one standard deviation  We repeat the analysis using the Fair value Hedge Ratio (FHR) and report the estimation results in  Vol. 3, No. 2, 2020 solve the endogeneity bias completely, so we offer several other ways of dealing with endogeneity, in robustness checks, in section IV below.

Instrumental Variable Regressions
Our first robustness test involves estimating equation (1) with an instrumental variable for the Operating Lease Ratio (OLR). We use Fixed Asset Ratio (FAR), measured as a ratio of netPPE to total assets, and EIndex Dummy (EID) coded as 1 if EIndex is above median else equal to zero, a proxy for corporate governance, as instruments for the Operating Lease Ratio (OLR). These instruments are selected on the basis of past studies on the determinants of leasing as well as the pair-wise correlation of these two variables with operating lease ratio in the sample data. The economic intuition is as follows: Robicheaux et al. (2008) offer empirical evidence that agency cost reducing measures such as corporate governance and lease financing are complements. Sharpe and Nguyen (1995) argue that fixed asset ratio can serve as a proxy for capital intensity and that firms with high capital intensity lease more of their capital stock, i.e., PPE. The correlation between OLR and FAR is 0.2274 with a p-value of 0.
The correlation between OLR and EIndex dummy is 0.1042 with a p-value of 0.001. We overidentify the system, by including more than one instrument for the endogenous variable, as it is a necessary condition to test instrument exogeneity.
The estimation is carried out using two-step Generalized Method of Moments (GMM). This estimator also produces both Heteroskedasticity and Autocorrelation (HAC) consistent estimates of both the slope coefficients and the corresponding standard errors. Only the second-stage regression results are reported in Table VII for brevity. The sign on the predicted OLR is negative (-0.0228) as expected and also highly significant confirming our second hypothesis that the firm-level hedge ratio varies inversely with the intensity of leasing. Based on this estimate, a one standard deviation increase in the predicted value of the operating lease ratio decreases the notional hedge ratio by 0.46%. This is considerable given the mean notional hedge ratio is 2.8%. Further, the coefficient on bid-ask spread (BASPRD) is positive and significant confirming our first hypothesis that the extent of hedging is positively related to information asymmetry faced by the firms. We repeat the analysis using fair value hedge ratio and report the results in column (2). Again, the sign on the predicted OLR is negative as expected and significant. The coefficient on illiquidity (ILLIQ) is positive but not significant.  Vol. 3, No. 2, 2020 significant at 10%, ** significant at 5% and *** significant at 1% respectively. The estimation period is from 1974-2006. The number of firm-year observations and R 2 along with the Sargan statistic, for overidentification test, is also reported. To examine instrument validity, we report Sargan statistic for overidentification at the bottom of Table   7. The Sargan statistic has a value of 0.595 with a p-value of 0.44 thus failing to reject the null hypothesis that instruments are valid. We further check the relevance of instruments through a test of week instruments. The Cragg-Donald Wald F-statistic has a value of 24.18 with a critical F value (at IV size 10%) equal to 19.93. Since the test statistic is larger than the critical value, we reject the null that the equation is weakly identified. This further mitigates potential concerns whether our instruments are weakly correlated with the endogenous regressor.

Simultaneous Equation Modeling of Hedging and Leasing
Now we address the problem of joint determination of hedging and leasing. One can argue that the risk management and financing are strategic decisions and determined simultaneously by a firm as part of its business strategy. However, finance theory does not offer any clear theoretical models to address this problem. Hence, consistent with past empirical studies on hedging by Geczy et al. (1997), Graham and Rogers (2002), Lin and Smith (2007), and Purnanandam (2008) and past studies on leasing by Robicheaux et al. (2008), Eisfeldt and Rampini (2009) and Sharpe and Nguyen (1995), we model hedging and leasing decisions as a simultaneous system using the following structural equations:  (1)), the coefficient on operating lease ratio (-0.0442) is negative and highly significant, thus supporting our second hypothesis that firms deploying more leased capital tend hedge less of their risk exposures. In terms of economic significance, a one standard deviation increase in Operating Lease Ratio (OLR) from its predicted value would decrease the notional hedge ratio by 0.88% from its mean of 2.8%. Similarly, the coefficient on illiquidity (ILLIQ) is positive and significant, thus supporting our first hypothesis that the intensity of corporate hedging is increasing in formation frictions. The coefficients on most controls are consistent with the prior studies.  In the leasing equation, the coefficient on the notional hedge ratio (NHR) is negative and highly significant. This indicates that hedging and leasing causality can go both ways. We repeat the analysis using fair value hedge ratios and report the results in Table 9. The main result that firms that lease more hedge less is robust to this alternative measure of hedging intensity.

Conclusions
Theoretical models of risk management predict that firms subject to financial constraints have greater incentives to hedge their risk exposures to ensure that they have sufficient internal funds to exploit investment opportunities. In practice, however, large firms, which are arguably less financially constrained, hedge whereas small firms, which are likely more financially constrained, often do not engage in risk management which is considered as a hedging puzzle in the literature.  Minton and Schrand (1997); Graham and Rogers (2002);Rogers (2002); Dionne andTriki (2004, 2005)]. It is important to recognize that a negative coefficient reported for this variable in regressions with hedge ratio as the dependent variable could have another explanation than being an indication of a lower incentive to hedge in order to reduce information asymmetry costs. In fact, institutions are usually well diversified and might find it less useful to manage the risk at the firm level. Consequently, they may encourage a reduction in the hedging ratio.
The number of financial analysts following the firm [Géczy, Minton and Schrand (1997)] is another proposed measure in the literature for information asymmetry. When the firm is under greater public scrutiny, it should suffer less from information asymmetry. Consequently, information asymmetry should decrease with the number of analysts following its operations and so does the incentive to hedge.
However, a positive coefficient for this variable could be interpreted either as evidence supporting the reduction of information asymmetry cost motive or as indication that analysts choose to follow firms with fewer earnings surprises.
DaDalt, Gay and Nam (2002) use earnings related measures of information asymmetry. The first measure they consider is "the analysts forecast accuracy" and is defined as the absolute value of the average earnings forecast error. A limitation of this measure is that it captures the magnitude of information asymmetry under the premise that managers disclose unanticipated firm specific information only around earnings announcements. The second measure used in DaDalt, Gay and Nam (2002) is the dispersion in analysts' earnings forecast. According to them, analysts are unable to provide a precise and unanimous forecast of the firm's earnings when there is a lack of information about it. The concern when using this measure is that one never knows whether the dispersion in forecasts is caused by a higher level of information asymmetry or by other factors such as inherent forecasting errors caused by different forecasting models used by analysts.
Note 2. Tufano (1996) finds evidence that CFO tenure is negatively correlated with the probability of hedging but he finds CEO tenure has no significant effect on the derivative usage decision.
Note 3. SFAS #133 requires special accounting for two types of hedges, viz. fair value hedges and cash flow hedges. In a fair value hedge, a derivative is used to hedge or offset the exposure to changes in the fair value of a recognized asset or liability or of an unrecognized firm commitment. Cash flow hedges are used to hedge exposures to cash flow risk, i.e., exposure to the variability of cash flows. The accounting for fair value hedges records the derivative at its fair value in the balance sheet with any gains or losses recorded in income. However, derivatives used in cash flow hedges are accounted for at fair value on the balance sheet, but gains or losses are recorded in equity as part of other 72 www.scholink.org/ojs/index.php/ijafs International Journal of Accounting and Finance Studies Vol. 3, No. 2, 2020 Note 12. In an unreported analysis, in addition to controlling for size and age among other variables, I also interact high size (above median size) dummy with OLR and high age (above median age) dummy with OLR in equation-1 to see the effect of OLR on both NHR and FHR for high vs. low financial constraints. I find that the coefficients on the interaction terms are not significant. Hence, it appears once I control for the financial constraints proxied by size and age, the interaction variables created using dummies based on these two variables have no significant explanatory power. The results are available from the author.
Note 13. Wooldridge (2002) argues that in applied econometrics, endogeneity usually arises in three ways viz. omitted variables, measurement error, and simultaneity. He mentions that the distinctions among the three forms are not always sharp and an equation can in fact have more than one source of endogeneity. The use of lagged dependent variables in dynamic models could be yet another source of endogeneity!

Appendix: Variable Definitions
Hedging Variables: • NHR = Notional value of both interest and currency derivatives scaled by lease-adjusted total assets.
• FHR = Fair value of both interest and currency derivatives scaled by lease-adjusted total assets.
• HD = Hedging dummy equal to 1 if either FHR or NHR is greater than zero, else equal to 0.
Leasing Variable: • Operating Lease Ratio (OLR) = (rental expenses plus present value of future rental commitments for the next 5 years and present value of the thereafter portion)/(rental expenses + present value of future rental commitments for the next 5 years and present value of the thereafter portion + Total Assets).
Financial Constraints: • AGE = ln(1+difference between that year and the year of incorporation) • SIZE = ln(Sales).
Information Frictions: • BASPREAD = yearly average of difference between daily closing bid and ask prices reported as a percentage of midpoint of bid ask quotes.
• ILLIQ = yearly average of ratio of daily absolute return to the dollar trading volume on that day.
Other Control Variables: • DR = long-term debt/ lease-adjusted total assets.