Asymmetric information and securitization design in Islamic capital markets

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Asymmetric information and securitization design in Islamic capital markets Zairihan Abdul Halima, , Janice Howb, Peter Verhoevenb, M. Kabir Hassanc ⁎

a b c

School of Social and Economic Development, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia School of Economics and Finance, Queensland University of Technology, Gardens Point Campus, 2 George St., Brisbane, Queensland 4000, Australia Department of Economics and Finance, University of New Orleans, New Orleans, LA 70148, United States

ARTICLE INFO

ABSTRACT

Keywords: Tranching Securitization Subordination Sukuk Adverse selection Asymmetric information Market segmentation

Motivated by religious prescriptions and severe informational problems in the corporate sukuk market, we investigate how capital market imperfections influence sukuk securitization design, focusing on tranching and subordination practices. Employing negative binomial regressions and a 2SLS regression framework for a sample of 335 corporate sukuk offerings in Malaysia between 2001 and 2014, we find Islamic finance principles matter to sukuk securitization design. For private firms, lease-based sukuk have less tranching, consistent with the significance of collateral value resilience (asset tangibility) in reducing investment risk. Contrary to expectations, equitybased sukuk have fewer tranches and lower levels of subordination – findings which we attribute to the principal and profit guarantee under this structure. We also find support for the stylized view that securitization stems from asymmetric information and market segmentation arguments. An implication of our research is that investors should pay attention to how sukuk structure, information asymmetry, and credit risk impact subordination to avoid deals with poor credit enhancement.

JEL classifications: G11 G20 G32 Z12 O16

1. Introduction Asset securitization, the process of taking an illiquid asset and through financial engineering transforming it into a liquid security that is attractive to investors, is a ubiquitous and important financial innovation that has substantially enhanced the financing flexibility of borrowers. Securitization thus facilitates funding accessibility for all types of borrowers, allowing them to time the issuance of securities to coincide with their financing needs. It also lowers the overall cost of financing, provided that the issuer can sell the securities at an acceptable price. In this paper, we examine sukuk securitization, an emerging Islamic financial instrument that represents an interest in an underlying funding arrangement structured according to Islamic finance norms. Specifically, we test how capital market imperfections influence tranching and subordination decisions in corporate sukuk offerings.1 Our investigation is set in Malaysia, which continues

Corresponding author. E-mail addresses: [email protected] (Z. Abdul Halim), [email protected] (J. How), [email protected] (P. Verhoeven), [email protected] (M.K. Hassan). 1 Tranching is the process whereby a risky financial deal is sliced up (tranched) into smaller portions, with various characteristics. Subordination is the process whereby junior tranches are placed in a lower priority to senior tranches, required to absorb initial credit losses. See Section 2 for further details. ⁎

https://doi.org/10.1016/j.pacfin.2019.101189 Received 5 January 2019; Received in revised form 27 May 2019; Accepted 7 August 2019 0927-538X/ © 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Zairihan Abdul Halim, et al., Pacific-Basin Finance Journal, https://doi.org/10.1016/j.pacfin.2019.101189

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to dominate the global sukuk market.2 Sukuk involve the structuring of pools of Shariah-compliant assets where investors jointly own the assets or their usufructs and are entitled to share the related returns. This asset-backing principle distinguishes sukuk from conventional bonds, which are typically not asset-based and are primarily concerned with the return on the investment rather than the actual cash flows generated by the object that is being financed. In principle, the volatility of the value of the underlying asset determines the risk to sukuk investors. Unlike conventional debt securities, sukuk are subject to religious rulings3 which require the structuring and securitization process to conform to Islamic principles. In particular, “loans” can be made and the profits are gained through the application of three Islamic finance structures: lease-based, sale-based, and equity-based contracts.4 The key feature which differentiates these structures is the tangibility of the underlying assets. We use the Arabic nomenclature contrasting tangible assets ('ayn) from intangible assets (dayn) to investigate whether values emanating from Islamic rulings can help mitigate the investment risk of sukuk securitization. We expect lease-based sukuk to have the lowest investment risk since the pool of lease-based assets is predominantly tangible in nature, providing greater cushion against uncertainty in the market. Equity-based sukuk, in contrast, have the lowest threshold for tangible asset backing in securitization and are thus expected to have the highest investment risk. Sale-based sukuk fall between lease-based and equity-based sukuk in the spectrum of investment risk. Therefore, we predict that the number of tranches and the level of subordination are lowest for lease-based sukuk and highest for equity-based sukuk. The corporate finance literature proposes two hypotheses on the securitization design of conventional debt, which we also consider in our paper. One major appeal of securitization, as expounded in the literature, is the subordination mechanism which allows each tranche to carry a successively lower rating, supporting the tranches senior to it.5 According to the information hypothesis, the number of tranches and the level of subordination increase in the degree of information asymmetry, allowing firms to offer nearly default-free (senior) tranches. This subordination alleviates adverse selection and moral hazard concerns of investors (Boot and Thakor, 1993; Riddiough, 1997; Plantin, 2003). Accordingly, we predict that sukuk tranching and subordination increase in the degree of information asymmetry. Another appeal of securitization, as proposed by the market segmentation hypothesis, is that it allows firms to complete the market for their issuance by catering to segmented investor demand (Boot and Thakor, 1993; Riddiough, 1997; Plantin, 2003; Franke and Weber, 2009). Therefore, we predict the number of sukuk tranches increases with the variation in issuance terms. The information and segmentation hypotheses are not mutually exclusive. For example, while the creation of a senior tranche might be driven by asymmetric information, multiple junior tranches might be created to suit the tastes of different classes of investors (market segmentation) (Riddiough, 1997). We perform our empirical analysis on a sample of 335 corporate sukuk offerings comprising 3491 individual tranches issued in Malaysia between 2001 and 2014. Our results show Islamic finance principles matter to sukuk securitization design but only for the subsample of highly opaque (private) firms. For these firms, lease-based sukuk have fewer tranches than sale-based sukuk, consistent with lease-based sukuk being less risky since only claims backed by real tangible assets are securitized. Contrary to our expectation, equity-based sukuk are associated with fewer tranches and are less likely to use credit enhancement through subordination relative to sale-based sukuk. We attribute this finding to equity-based sukuk being frequently offered with a principal and profit guarantee (Dusuki, 2010; Zada, 2017). We also find support for the information hypothesis − sukuk deals that have relatively low quality and high information-sensitive asset pools have more extensive tranching and offer better credit protection through subordination − and the market segmentation hypothesis for sukuk tranching. In the latter case, our results show issuers offer tranches to meet the different maturity and credit risk preferences of sukuk buyers. These results confirm previous findings documented for conventional debt securities (Franke and Weber, 2009; An et al., 2015). Our study contributes to the literature in several ways. First, we provide the first empirical evidence on the determinants of securitization in the context of Islamic finance. Our research thus complements the theoretical work of Ebrahim et al. (2016), who demonstrate that Islamic guidelines, if faithfully followed, can mitigate ex ante and ex post informational problems in debt

2 From just 95 sukuk issues between 1990 and 2001, the total number of sukuk offerings in Malaysia has escalated to 5655 (total value of US$618 billion) by the fourth quarter of 2015. However, these offerings are predominantly sovereign sukuk. Domestic corporate sukuk issuance, which is the focus of our study, takes a smaller slice of the pie at US$116 billion (see https://medium.com/fitch-blog/why-the-corporate-sukuk-market-could-beripe-for-blossoming-1ec95342f193). 3 In Islam, all contracts are permissible unless prohibited in the religious texts. Shariah bans the practice of riba (debt usury) and gharar (excessive risk) and the funding of unlawful activities. Therefore, sukuk are structured using commercial-based contracts that conform to these rulings. 4 Lease-based sukuk (ijarah) are certificates representing undivided ownership of the leased asset (or services). In legal terms, a lease contract is the sale of the usufruct, or the right of using the object that is leased for a predetermined time and fee while the object remains in the possession of the lessor. The lease payments provide investors with a relatively predictable stream of returns. At the end of the specified lending time, the asset may be sold to the lessee, but not at a predetermined price. In sale-based sukuk (murabahah and istisna), the investor acquires a commodity/good and then sells it to the issuer by disclosing the cost incurred as well as the mark-up (profit). The profit is determined by mutual consent either by lump sums or through an agreed ratio of the profit to be charged over the cost. Finally, equity-based sukuk (mudarabah and musharakah) is a form of joint venture/partnership where the returns are determined based on profit and loss sharing in the underlying investment. Upon maturity of the contract, the investor receives the investment plus a predetermined percentage of the profit made. 5 Credit enhancement in the form of subordination is the subject of ongoing debate in Islamic finance. Shariah scholars differ in their opinions on the acceptability of ranking the rights of different financiers over the same asset or collateral. See http://www.ifre.com/tranching-of-risk-andshariah/566037.fullarticle

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securitization – a proposition which our results show is far from being universal in its application. We document evidence showing that the values emanating from Islamic rulings help mitigate the investment risk of sukuk securitization but only for highly opaque (private) firms. Our research also adds to the security design literature by showing that religious values can influence security design beyond the factors identified in the conventional debt securitization literature. In particular, our analysis of sukuk structure underscores the significance of collateral value resilience (asset tangibility) in reducing investment risk to sukuk investors of private firms. In the case of equity-based sukuk, our findings suggest that third-party guarantee, which is a typical feature of this sukuk structure, may provide an effective substitute for the lack of tangible asset backing. The rest of this paper is structured as follows. The next section develops the hypotheses, followed by data and methodology in Section 3. We present and discuss the empirical results in Section 4 and robustness tests in Section 5. Section 6 summarizes and concludes. 2. Literature review and hypotheses A focal point for much discussion in the security design literature is the impact of information asymmetries on security offerings (Peña-Cerezo et al., 2016). Gorton and Pennacchi (1990) argue that pooling security assets in a single portfolio complicates the evaluation of assets' quality and monitoring, hence exacerbating information asymmetries. An adverse selection problem arises when investors are wary about the quality of the securities offered and are reluctant to buy them. Tranching, or splitting cash flow claims according to their respective risk or priority structure, provides a solution to this problem as it enables uninformed investors to value each slice of the securities “independently of the information known only by the informed” (Gorton and Pennachi, 1990, p. 50). Several studies provide theoretical explanations for the benefits of tranching to the issuers of debt securities. Boot and Thakor (1993) argue it is optimal to split securities into senior (informationally insensitive) and junior (informationally sensitive) classes as doing so reduces noise in pricing. The former is less risky and is thus targeted strictly at uninformed investors. Sophisticated investors who are better able to monitor the investment risk are more likely to take up the riskier tranches. Riddiough (1997) extends this insight by proposing that issuers will circulate a larger number of tranches to diversify away the adverse selection component of the underlying assets. DeMarzo (2005) also proposes tranching as a risk diversification tool. His model suggests that tranching is optimal when the information asymmetry is severe; the payoff is maximized because issuers are able to sell assets based on their private information, i.e., diversification effects. He notes that the benefit of creating a large number of tranches may offset the liquidity and transaction costs associated with the issuance of smaller tranches. Maskara (2010) consistently illustrates in his model that the total cost of borrowing is reduced if loans are tranched to two parts with different credit risks, and this benefit accrues primarily to issuers with high credit risk. Empirical evidence provides support for the above arguments. Firla-Cuchra and Jenkinson (2005) test the asymmetric information argument for tranching on a sample of 1605 European securitized transactions from 1987 to 2003. They find a positive relation between various proxies of information asymmetry and the number and ratings of tranches. Similarly, for a large sample of 23,721 U.S. syndicated loan tranches, Maskara (2010) finds that unlisted firms and firms with poor credit ratings are more likely to issue loans with multiple tranches. Another aspect of securitization design focuses on the level of subordination, i.e., the number of subordinated (junior) tranches to be offered to support senior tranches. Subordination plays an important role in the senior-subordinated structure of securitized transactions. Subordination is about credit risk, and the degree of subordination reflects the number of tranches required to reach a desired level of rating. A higher level of subordination provides a larger cushion to high-rated securities against default losses (Demiroglu and James, 2012). Accordingly, the lower risk level of the tranches which are senior to the junior tranches helps reduce the average differential spread for the overall securitization issue (DeMarzo, 2005). It is in this sense that subordination serves as an internal credit enhancement for securitized assets. Previous studies show that concerns about adverse selection problems are mitigated by issuing subordinated tranches that absorb default losses (Agarwal et al., 2012; An et al., 2015; Franke et al., 2007). Supporting this idea, An et al. (2015) find commercial mortgage-backed securities (CMBS) with greater adverse selection concerns have higher levels of subordination. Similarly, Franke and Weber (2009) report the first loss position (subordinated tranche offerings) in European collateralized debt obligations (CDOs) is negatively related to securitized assets' quality (information asymmetry). The above discussion leads to the following predictions for sukuk offerings H1a. The number of sukuk tranches increases with information asymmetry. H1b. The level of sukuk subordination increases with information asymmetry. Failure to mitigate asymmetric information is not the only driver of investment risk. We predict that, unique to the sukuk market, the level of tranching and subordination also varies according to how the sukuk deal is being structured. The key feature which differentiates the three main sukuk structures (i.e., leased-based, sale-based, and equity-based) is the tangibility of the underlying asset. We argue that the Arabic nomenclature contrasting tangible assets ('ayn) from intangible assets (dayn) determines the riskiness, and thus the level of tranching and subordination, of the three sukuk structures. This is because financial claims that are inadequately collateralized by intangible assets cannot mitigate not only information asymmetry problems but also agency cost of debt (risk shifting and/or underinvestment) due to the risk of default (Ebrahim et al., 2016).

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Shariah compliance standards6 on lease-based financing contracts emphasize that the return paid to investors should correspond to the value of the underlying assets (van Wijnbergen and Zaheer, 2013). Apart from the pricing condition, the assets underlying lease-based sukuk must be specified precisely and have a valuable use, i.e., be leasable. These requirements, as stipulated by Shariah Standard No. 9 of the Accounting and Auditing Organization for Islamic Financial Institutions (AAOIFI), suggest that leased-based sukuk are the least risky of all the sukuk structures since the pool of lease-based assets tends to be predominantly tangible in nature, providing greater cushion against uncertainty in the market. In an exposition of Islamic rulings which can help alleviate the structural flaws of securitization, Ebrahim et al. (2016) explain why claims backed by tangible assets are less risky and more bankruptcyremote. First, lenders have greater access to the asset's historical (ex post) risk and return, suggesting that the distribution of information about the quality of lease-based sukuk is nearly symmetric between the various agents (Boot and Thakor, 1993). Second and importantly, financing the purchase of tangible assets where funds are released in the escrow process when the title of the tangible asset changes hands mitigates adverse selection. The sale-based (murabahah) structure is prevalent in corporate sukuk offerings that aim at acquiring assets or creating credits. Under the sale-based structure, the special purpose vehicle (SPV) or trustee acquires the assets identified and sells them to the originator at the cost price plus a pre-agreed markup. As with the lease-based structure, the return on sale-based sukuk is fixed and pre-determined. However, the assets underlying sale-based sukuk are mainly intangible since this sukuk structure is a deferred debt obligation (receivable) and does not represent an ownership interest in the assets. The lack of tangibility of the underlying assets makes them harder to collateralize because of the associated low redeployability (Williamson, 1988), higher information asymmetry, and uncertain liquidation value (Holthausen and Watts, 2001). This contention suggests that sale-based sukuk, which are inadequately collateralized by tangible assets, have relatively higher investment risk than lease-based sukuk (Ebrahim et al., 2016) The requirement of tangible asset backing in sukuk securitization is further eroded in equity-based sukuk (mudarabah and musharakah). Adverse selection and moral hazard concerns are also greater because equity-based sukuk are structured based on the principal-agent framework (Bashir, 1996). Since such an arrangement grants the issuer (agent) discretion over project management and cash flow distribution, ex post informational problems for equity-based sukuk are more severe than lease-based and sale-based sukuk. Research shows the equity-based structure tends to attract issuers with poor business prospects (Godlewski et al., 2013). Under the equity-based principle (musharakah), profit and loss realized from the project are shared between the issuing firm and the investors. The adverse selection argument thus predicts that firms with poor quality projects would prefer equity-based sukuk which allow them to minimize losses in bad times. Unlike lease-based and sale-based sukuk, the return to equity-based sukuk investors cannot be fixed and are paid based on a pre-agreed profit sharing ratio, i.e., the return depends on business venture performance (Abdul Halim et al., 2017; Azmat et al., 2014). Since managerial efforts cannot be observed without cost, investors will rationally anticipate ex post adverse investment incentives and accordingly demand better credit enhancements. In light of the above differences underlying the sukuk structures, we provide the following hypotheses: H2a. The number of tranches is smallest for lease-based sukuk. H2b. The number of tranches is largest for equity-based sukuk. H3a. The level of subordination is lowest for lease-based sukuk. H3b. The level of subordination is highest for equity-based sukuk. The existence of market segmentation due to heterogeneity in investors' yield/risk preferences, private information, or capacity to assess investments provides an alternative explanation for tranching. The creation of multiple tranches with diverse characteristics such as in the degree of sensitivity to information, risk exposure, and yield allows the securitized issues to be attuned to the various investor profiles, thus completing the market for the offering (Franke and Weber, 2009; Schaber, 2008). That is, by creating tranches of varying characteristics (such as credit ratings and maturity), the issuer can cater to investors with heterogeneous private information and screening abilities (Firla-Cuchra and Jenkinson, 2005). Wang et al. (2016) illustrate that offering multiple tranches of varying risk profiles increases originators' revenues; senior security tranches are offered to investors who, due to regulatory constraints, are allowed to invest only in investment-grade rated tranches, while more risky tranches are sold to investors with fewer portfolio constraints. Firla-Cuchra and Jenkinson (2005), Franke and Weber (2009), and Schaber (2008) present early evidence of market segmentation motivations for tranching. If the gains from catering to segmented market demand motivate the issuer's decision on tranching, we predict there will be more tranches of the same offering when there is a greater variation in issuance characteristics. Therefore, we hypothesize the following: H4. The number of sukuk tranches is positively related to the variation in issuance terms.

3. Data and methodology 3.1. Data Our sample consists of Malaysia-domiciled corporate sukuk offerings issued between 2001 and 2014. We collect issuance data at 6 Shariah compliance standards for sukuk offerings are issued by market authorities such as Accounting and Auditing Organization for Islamic Financial Institutions (AAOIFI) and Islamic Financial Services Board (IFSB).

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both the offering and tranche levels. Data on sukuk tranches are collected from Bloomberg Professional Service, which provides issuance details for each tranche, including the issue price, coupon, proceeds, payment rank, maturity, rating, and arranger(s). Information on the payment rank allows us to classify tranches based on their seniority in claims. Tranches are classified as senior if they are labelled as “first lien” or “senior”. Since our analysis is concerned with issuance-specific factors rather than firm-specific factors, our dataset includes sukuk offerings by both privately held and publicly listed firms. Given the substantial differences between public and private firms, including both types of firms in the sample will substantially increase the generalizability of our results. We match tranche-level data with offering-level data obtained from the Securities Commission Malaysia's website (https://www. sc.com.my/). Tranches issued by government agencies or financial institutions are excluded from our analysis since information asymmetries are unlikely to be a concern. We also exclude issues with missing issuance details as well as those from other domiciles; in the latter case, the offerings tend to originate from government agencies and financial institutions. These filters result in a final sample of 3491 tranches from 335 corporate sukuk offerings with total proceeds of US$35 billion. 3.2. Empirical design To test our hypotheses, we estimate the following negative binomial regression model:

Tranchei =

0

+

1

Sukuk Structurei +

2 Information

Asymmetryi +

3 Segmentationi

+

4 Controlsi

+ e1, i

(1)

where Tranche is the number of tranches in an offering; Sukuk Structure represents indicator (dummy) variables for the three structures of sukuk (lease-based, sale-based, and equity-based contracts); Information Asymmetry and Segmentation are vectors of variables proxying information asymmetry and market segmentation respectively; and Control is a vector of other determinants of the tranching decision. We note (untabulated) that the standard deviation of Tranche is larger than its mean, indicating an over-dispersion in the data. Long (1997) and Cameron and Trivedi (2013) contend that the unobserved heterogeneity that can cause over-dispersion can lead to “excess zeros”. Although the percentage of zero observations in our count response variable is small (13.5%), we nevertheless perform zero-inflated negative binomial regressions with Vuong's (1989) test option to account for potential separate processes driving the zero and non-zero counts as well as between-subject heterogeneity. This model requires a set of variables that determines the decision to tranche. Our specification for this so-called “inflation” model follows Maskara (2010), who tests the determinants of tranching in syndicated loans. Specifically, we include Amount, Maturity, and Private firm to account for cross-sectional differences in the size of the offer proceeds and maturity, as well as whether the issuer is a private or listed firm, respectively. The Vuong test (untabulated) indicates that the standard negative binomial regression fits the data well. Next, we analyze the determinants of the subordination decision by sukuk issuers. About half the sukuk offerings in our sample offer subordinated tranches. Since the subordination decision is conditional on the tranche decision, there is a potential bias arising from non-randomness in the decision to subordinate sukuk tranches. To address this potential sample selection bias, we employ the two-step Heckman procedure. In the first-stage, we estimate a selection model where the dependent variable is Subordinate, which takes a value of one if the firm issues subordinated tranches, and zero otherwise. We use Maturity variation and Rating class as exclusion restrictions in the first step regression. The inverse Mills ratio (Lambda) obtained from the first-step probit regression is then included in the second-step ordinary least squares (OLS) regression which takes the following form:

Sub _leveli =

0

+

1

Sukuk Structurei +

2

Information Asymmetryi +

3 Lambda

+

4 Controlsi

+ e2, i

(2)

where Sub_level is the cumulative level of subordination proxied by the value of non-AAA rated tranches as a percentage of the initial principal balance (An et al., 2015; Vink and Thibeault, 2008).7 The other variables are as defined previously. We do not test the market segmentation hypothesis in Eq. (2) since the primary aim of subordination is in mitigating adverse selection and moral hazard problems (Agarwal et al., 2012; An et al., 2015; Demiroglu and James, 2012; Firla-Cuchra and Jenkinson, 2005). 3.3. Variable measurement Sukuk structures is a vector of indicators for each of the three sukuk structures: lease-based, equity-based, and sale-based. Asset and pricing requirements for the various sukuk structures suggest that lease-based are the least risky (highest tranching and subordination levels) and equity-based sukuk the most risky (lowest tranching and subordination levels). Since sale-based sukuk fall between these two extremes in the expected number of tranches and subordination level, they form the base case in our regressions. We include a number of proxies for Information Asymmetry, as proposed by the literature. The first proxy is the weighted average credit rating (WACR), which is frequently used in the empirical literature on securitization to represent the quality of the pool of underlying assets (Firla-Cuchra and Jenkinson, 2005; Franke and Weber, 2011; Schaber, 2008). WACR is the weighted (by amount) average credit rating of the tranches in a deal. A numerical scale is used for the rating class, with the highest rating (i.e., AAA) assigned a value of 6 and the lowest 1. Since tranching is less beneficial for high quality assets (high WACR) due to low adverse selection concerns, a negative coefficient on WACR is predicted. Our next proxy for information asymmetry is the fraction of senior tranches to the total number of tranches in a deal (Senior). The 7

The cumulative level of subordination is a credit risk indicator frequently used in securitized debt markets (Gan and Mayer, 2006). 5

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information hypothesis predicts that the greater the number of senior tranches offered, the greater the number of junior tranches that need to be offered in order to support the senior ones. Variation in spreads has also been linked to information asymmetries in the literature (Bernardo and Cornell, 1997; Firla-Cuchra and Jenkinson, 2005). We measure this asset sensitivity indicator using the standard deviation of launch spreads per offering (Spread variation). A greater variation in launch spreads suggests greater information sensitivity of the assets. The information hypothesis predicts that issuers generate a larger number of tranches for highly information-sensitive assets to avoid asset-specific information destruction, hence the lemon problem's discount (DeMarzo, 2005; Firla-Cuchra and Jenkinson, 2005). Since our dataset includes sukuk offerings by both private and public listed firms, our firm-specific proxy for information asymmetry is therefore the issuing firm's status, as captured by Private firm, which takes the value of one for privately held firms, and zero otherwise. Privately held firms tend to be more informationally opaque than public listed firms because they are less regulated, in terms of financial reporting, and have less publicly available information about them. Since private firms pose greater information risk concerns to investors, we expect a larger number of tranches and a higher level of subordination for private firms. To capture the influence of market Segmentation on the number of tranches, we use the standard deviation of tranche maturities (Maturity variation) and the number of credit rating classes per deal (Rating class). A larger number of tranches is predicted to be created if there is greater heterogeneity in investors' preferences in bond maturity and risk. Hence, we predict a positive sign for these proxies of market segmentation. We include a large set of control variables (Controls) in the regressions. The first control variable is the deal amount (Amount). Since producing multiple tranches is cost-effective only if the issuance is sufficiently large (Cumming et al., 2015; Franke and Weber, 2009), we expect economies of scale in tranching would manifest in a positive sign on Amount. Second, we control for Maturity8 since issuers of longer maturity sukuk, which are associated with higher risk, are incentivized to issue more tranches to diversify the risk away. Third, we control for whether the sukuk offerings are backed by collateral or security interests (Collateral) because secured securities have lower adverse selection and moral hazard concerns (Franke et al., 2007; Scott, 1997). We also control for sukuk issuances with a special purpose vehicle (SPV). A large number of tranches is expected for these sukuk since the function of an SPV is to transform illiquid asset pools into (liquid) marketable securities that can be readily sold off to investors. Finally, we control for the reputation of the arranging bank and Shariah advisor since these independent agents can mitigate adverse selection concerns (Abdul Halim et al., 2019). Following Abdul Halim et al. (2019), we include Top5 bank and Top5 advisor, which are dummy variables that respectively take the value of one for sukuk offerings arranged by a top five bank and approved by a top five Shariah advisor, and zero otherwise. Following previous studies (Agarwal et al., 2012; Peña-Cerezo et al., 2013), we include issuers' industry classification dummies to capture unobserved industry effects.9 As in Azmat et al. (2017), we account for the potential impact the global financial crisis may have on sukuk securitization by including a GFC dummy in the regressions. GFC takes the value of one if the issue was offered during the 2008–2009 period, and zero otherwise. Variable definitions are summarized in Appendix A. 4. Results 4.1. Offering characteristics and univariate tests Descriptive statistics for our test variables are presented in Table 1. The proceeds of our sample of sukuk offerings (Amount) average US$104.5 million, ranging from US$300,000 to US$3.1 billion (untabulated). The largest corporate sukuk offering in Malaysia during our sample period is equity-based (i.e., musharakah) and issued by PLUS Berhad in 2012. Sukuk offerings have a median of seven tranches, ranging from zero to 105 tranches. Multi-tranche offerings (> 1 tranches) dominate, representing nearly 90% of our sample. The average maturity of the sukuk offerings is six years and the weighted average credit rating (WACR) is 1.21 (out of a maximum of six), suggesting a high level of information asymmetry. Half of the sample deals have subordinated (junior) tranches, with an average subordination level of 68% to support an average of 7% senior tranches; these figures are comparable to previous studies on commercial mortgage-backed securities (CMBS) (An et al., 2011; An et al., 2015; Downing and Wallace, 2005; Gan and Mayer, 2006). Thus, subordination in sukuk securitization closely resembles that of conventional asset-backed securities. The average variation in spread and maturity is 1.70% and 2.21 years, respectively. The most prominent Islamic structure is sale-based sukuk, comprising 62% (208) of the deals, followed by equity-based sukuk at 23% (78) and lease-based sukuk at 14% (49). Nearly half the offerings have collateral and 18% have an SPV. The median number of rating classes per deal is one. Further, 64% of the sukuk offerings are arranged by a top-five bank and 81% are approved by a top-five Shariah advisor. Half of the sukuk are issued by private firms. Comparing subordinated sukuk with non-subordinated sukuk, we find the former have larger average deal size (US$117.8 vs. US $90.9 million); higher median number of tranches (8 vs. 6); lower average WACR (0.56 vs. 1.47); and higher average spread variation (1.97 vs. 1.41). These differences are both statistically and economically significant, suggesting greater informational risk of the asset pool for subordinated sukuk. To better understand sukuk securitization design, we partition the sample by credit rating class and payment rank (Table 2); deal 8 We use the weighted average maturity per offering, which is more meaningful than the nominal measure due to structured cashflows and embedded options in securitization transactions (Firla-Cuchra, 2005). 9 We do not control for firms' accounting variables since the decision on the number of tranches to be issued is issuance-specific rather than firmspecific (Schaber, 2008; Firla-Cuchra and Jenkinson, 2005).

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Table 1 Descriptive statistics for sukuk offerings by subordination. Variable

Lease-based Equity-based Sale-based Amount (million US$) Maturity Tranche Subordinate Sub_level Senior WACR Spread variation Maturity variation Rating class Collateral SPV Top5 bank Top5 advisor Private firm

Full sample (N = 335)

Subordinated (N = 169)

Non-subordinated (N = 166)

Diff. of means

Mean

Std.

Med.

Mean

Std.

Med.

Mean

Std.

Med.

0.14 0.23 0.62 104.47 5.99 10.82 0.50

0.35 0.42 0.50 255.25 5.04 12.48 0.50

0.00 0.00 1.00 30.65 5.00 7.00 1.00

0.12 0.22 0.65 117.76 6.08 12.29

0.33 0.38 0.48 325.39 6.30 13.75

0.00 0.00 1.00 27.54 4.50 8.00

0.16 0.24 0.59 90.94 5.89 9.33

0.36 0.45 0.50 153.99 3.30 10.89

0.00 0.00 1.00 31.90 5.50 6.00

−0.87 −2.83⁎⁎⁎ 2.52⁎⁎ 1.15 0.38 2.10⁎⁎

0.07 1.21 1.70 2.21 1.76 0.46 0.18 0.64 0.81 0.51

0.46 1.47 3.10 2.05 0.72 0.50 0.38 0.48 0.37 0.50

0.00 0.46 0.72 1.73 1.00 0.00 0.00 1.00 1.00 1.00

0.68 0.08 0.56 1.97 2.05 2.01 0.49 0.19 0.57 0.82 0.51

0.37 0.19 0.78 3.08 2.31 0.79 0.50 0.39 0.50 0.38 0.50

0.88 0.00 0.32 0.90 1.41 2.00 0.00 0.00 1.00 1.00 1.00

0.07 1.47 1.41 2.37 1.52 0.43 0.17 0.72 0.84 0.51

0.19 1.73 3.10 1.71 0.48 0.50 0.38 0.45 0.35 0.50

0.00 0.71 0.60 2.19 1.00 0.00 0.00 1.00 1.00 1.00

0.13 −5.94⁎⁎⁎ 1.69⁎ −1.28 3.48⁎⁎⁎ 0.99 0.58 −3.02⁎⁎⁎ −0.63 0.00

This table presents the descriptive statistics (mean and median values) and univariate tests of differences between subordinated and non-subordinated sukuk. The sample consists of 335 Malaysian corporate sukuk offerings issued between 2001 and 2014. t-test is used for the test of difference in means. ⁎⁎⁎, ⁎⁎, ⁎ denote two-tailed significance at the 1, 5, and 10% level, respectively. Variable definitions are provided in Appendix A.

size and number of tranches (Table 3); and Islamic principles (Table 4). As reported in Table 2, more than half the sukuk tranches are assigned with a high credit rating (AA to AAA). In terms of payment rank, one in five tranches are first lien and senior secured, while two in five tranches are junior (subordinated). Table 3 depicts a positive association between sukuk deal size and the number of tranches, as expected. About 15% of small sukuk offerings (less than US$10 million in proceeds) and 56% of large offerings (proceeds over US$80 million) have > 10 tranches per deal. Table 4 shows that across the Islamic finance structures, lease-based sukuk and sale-based sukuk have on average 12 tranches per deal, compared to eight tranches per deal for equity-based sukuk. Lease-based sukuk have the largest average deal size (US$180 million), followed by equity-based sukuk (US$144 million) and sale-based sukuk (US$72 million). Sale-based sukuk have the highest average level of subordination (0.12), followed by equity-based sukuk (0.08) and lease-based sukuk (0.06). Spread variation is smallest for the equity-based structure, which is consistent with this Islamic structure having the highest weighted average credit rating (WACR). Spearman's rank correlation coefficients (unreported) show a high correlation between the various explanatory variables. For example, Senior is strongly correlated with Spread variation and Collateral, while Maturity is strongly correlated with Maturity Table 2 Sukuk offerings by rating class and payment rank. Panel A: rating distribution 6 - AAA and equivalent class rating 5 - AA and equivalent class rating 4 - A and equivalent class rating 3 - BBB and equivalent class rating 2 - BB and equivalent class rating 1 - B and lower rated tranches 0 - Rating is not available 0–6 Percentage of offerings with at least 1 AAA-rated tranche Average size of AAA-rated tranches (where present) (% of the offering size) Average size of BBB and below tranches (% of offering size)

1180 967 516 117 33 18 660 3491 38.7% 8.28% 9.59%

(33.8%) (27.7%) (14.8%) (3.4%) (0.9%) (0.5%) (18.9%) (100%)

Panel B: subordination Total number of ‘most senior’ tranches Total number of ‘most junior’ tranches Average number of ‘most senior’ tranches per offering Average number of ‘most junior’ tranches per offering

659 1341 8 10

(18.9%) (38.4%)

This table presents the distribution of tranches across ratings class and payment rank. The sample consists of 335 Malaysian corporate sukuk offerings issued between 2001 and 2014. 7

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Table 3 Sukuk offerings by deal size and number of tranches. Tranches #

< 10 million US$

10–30 million US$

30–80 million US$

> 80 million US$

Total deals #

0 1 2 3 4 5 6 7 8 9 > 10 Deals # Tranches (mean)

14 10 12 11 6 5 1 4 3 4 12 82 4.7

14 6 8 5 8 6 2 5 8 2 20 84 6.7

11 7 4 4 0 3 1 2 4 5 38 79 11.5

5 8 2 9 0 4 3 5 2 2 50 90 16.3

44 31 26 29 14 18 7 16 17 13 120 335

(17%) (12%) (15%) (13%) (7%) (6%) (1%) (5%) (4%) (5%) (15%) (100%)

(17%) (7%) (10%) (6%) (10%) (7%) (2%) (6%) (10%) (2%) (24%) (100%)

(14%) (9%) (5%) (5%) (0%) (4%) (1%) (3%) (5%) (6%) (48%) (100%)

(6%) (9%) (2%) (10%) (0%) (4%) (3%) (6%) (2%) (2%) (56%) (100%)

(13%) (9%) (8%) (9%) (4%) (5%) (2%) (5%) (5%) (4%) (36%) (100%)

This table presents the distribution of the number of tranches per deal by issuance amount. The sample consists of 335 Malaysian corporate sukuk offerings issued between 2001 and 2014. Table 4 Tranches by Islamic principle. Variable

Lease-based

Equity-based

Sale-based

# Deals Amount (million US$) Maturity Tranche Subordinate Sub_level Senior WACR Spread variation Maturity variation Rating class Collateral SPV Top5 bank Top5 advisor Private firm

49 179.65 6.60 11.69 0.45 0.06 0.79 1.05 1.48 2.30 1.47 0.57 0.22 0.76 0.84 0.57

78 144.29 8.45 8.36 0.37 0.08 0.79 1.64 0.78 2.82 1.37 0.44 0.13 0.78 0.88 0.53

208 72.05 4.90 11.83 0.56 0.12 0.49 1.14 2.10 1.98 1.58 0.44 0.18 0.57 0.84 0.49

This table presents sample statistics (mean values) by Islamic sukuk structure. The sample consists of 335 Malaysian corporate sukuk offerings issued between 2001 and 2014. Variable definitions are provided in Appendix A.

variation. A moderate degree of negative correlation between WACR and Amount, and between Maturity and Collateral suggests substitutability between these sets of variables. To address the potential multicollinearity problem, we include explanatory variables that are highly correlated with each other in the regressions one at a time.10 4.2. Sukuk structure, asymmetric information, segmentation, and tranching We begin our analysis by running a negative binomial regression where the dependent variable is the number of tranches (Tranche). The results are reported in Table 5. Our key variable of interest is the sukuk structure. The base case in these specifications is sale-based sukuk. Contrary to expectations, we find no evidence supporting the hypotheses that lease-based sukuk have fewer tranches than salebased sukuk (H2a), or that equity-based sukuk have more tranches than other structures (H2b). While the equity-based structure is theoretically more risky than other sukuk structures, the significant negative coefficient on this variable implies otherwise. To be precise, our results suggest that equity-based sukuk have lower risk concerns, thus requiring less extensive tranching. These results resonate with those in Azmat et al. (2017), who find that equity-based sukuk have the highest rating of the three structures. We attribute these findings to the fact that equity-based sukuk typically involve principal and profit guarantee by either the originator or a third party, such as a bank (Dusuki, 2010; Zada, 2017).11 10 Our collinearity check using variance inflation factor (VIF) in the post-estimation test finds a VIF of about 3, well below the generally accepted threshold value of 10. Multicollinearity is unlikely to be a serious problem in our analysis. 11 While such practice falls foul of Shariah principles, some principles of Islamic jurisprudence provide a rationale for some kind of leniency (Zada, 2017).

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Table 5 Determinants of sukuk tranching.

Sukuk structure Lease-based Equity-based Information asymmetry Senior WACR

(1)

(2)

(3)

(4)

−0.113 (−1.014) −0.436⁎⁎⁎ (−3.536)

−0.0983 (−1.023) −0.271⁎⁎ (−2.360)

−0.114 (−0.987) −0.373⁎⁎⁎ (−3.295)

−0.106 (−0.95) −0.425⁎⁎⁎ (−3.39)

0.289⁎⁎⁎ (2.91)

0.255 (1.292)

Spread variation Private firm Market segmentation Maturity variation Rating class Control variables Ln(Amount) Maturity Collateral SPV Top5 bank Top5 Advisor GFC Constant Industry fixed effects # Obs. (Pseudo) R-sq

−0.704⁎⁎⁎ (−8.005)

0.287⁎⁎⁎ (2.862)

0.0322 (0.392)

0.0533⁎⁎⁎ (2.668) 0.259⁎⁎⁎ (2.629)

0.101⁎⁎ (2.301) 0.237⁎⁎⁎ (4.308)

0.113⁎⁎ (2.235) 0.179⁎⁎⁎ (3.982)

0.0864⁎⁎ (2.085) 0.244⁎⁎⁎ (4.426)

0.107⁎⁎ (2.46) 0.240⁎⁎⁎ (4.41)

0.244⁎⁎⁎ (7.087) −0.0233⁎⁎ (−2.081)

0.207⁎⁎⁎ (7.817) −0.0315⁎⁎⁎ (−2.960) 0.0579 (0.617) −0.0729 (−0.802) −0.154⁎⁎ (−2.150) −0.156 (−1.324) 0.182⁎ (1.941) −5.760⁎⁎⁎ (−29.793) Yes 294 0.144

0.242⁎⁎⁎ (7.132) −0.0158 (−1.409) 0.307⁎⁎⁎ (3.142) −0.0235 (−0.206) −0.204⁎⁎ (−2.112) −0.131 (−0.911) 0.250⁎⁎ (2.481) −6.721⁎⁎⁎ (−28.689) Yes 294 0.091

0.245⁎⁎⁎ (7.09) −0.022⁎⁎ (−1.97) 0.267⁎⁎⁎ (2.73) −0.005 (−0.04) −0.260⁎⁎⁎ (−2.76) −0.123 (−0.84) 0.300⁎⁎⁎ (2.89) −6.480⁎⁎⁎ (−27.53) Yes 294 0.081

0.00171 (0.014) −0.264⁎⁎⁎ (−2.812) −0.122 (−0.839) 0.288⁎⁎⁎ (2.758) −6.474⁎⁎⁎ (−27.301) Yes 294 0.081

This table presents the results of negative binomial regressions. The dependent variable is Tranche, the number of tranches per deal. Each specification includes a GFC dummy, industry fixed effects, and year exposure. Standard errors are clustered at the firm-level. The sample consists of 335 Malaysian corporate sukuk offerings with 3491 tranches issued between 2001 and 2014. Z-statistics are in parentheses. ⁎, ⁎⁎, and ⁎⁎⁎ denote significance at the 1, 5, and 10% level, respectively. Variable definitions are provided in Appendix A.

We find supporting evidence for the information hypothesis (H1a) using conventional proxies of information asymmetry. WACR, Spread variation, and Private firm all enter the regressions with the expected sign and are statistically significant. All else constant, a one unit increase in WACR decreases the difference in the logs of expected number of tranches by −0.704. Thus, sukuk deals with higher asset quality have fewer tranches, consistent with previous studies for conventional bonds (Franke and Weber, 2011; Schaber, 2008). Spread variation is positively related to the number of tranches, with a one unit increase in spread variation increasing the difference in the logs of expected number of tranches by 0.053. Relative to public firms, private firms are associated with more extensive tranching, consistent with the findings of Maskara (2010). Hence, by creating default-free tranches which are supported by junior tranches, more informationally opaque firms are able to raise funds through sukuk. Although the coefficient on Senior is positive, suggesting that deals with senior tranches have more tranches created to support the junior ones, it is not statistically significant. There is also support for the market segmentation hypothesis, as shown by the positive and significant estimated coefficients on Maturity variation. All else constant, a one unit increase in Maturity variation increases the difference in the logs of expected number of tranches by 0.11. Hence, issuers offer more tranches to meet the different maturity preferences of sukuk buyers. The number of Rating classes is also positive and significant, both economically and statistically. In economic terms, we find a one unit increase in the number of rating classes increases the difference in the logs of expected number of tranches by 0.20. Together, these results lend credence to the view that issuers opt for multi-tranching to meet the heterogenous preferences of investors (Franke and Weber, 2009) relating to, in this case, short vs. long maturity and high vs. low risk securities. Focusing on the control variables, deal size (Amount) is positively related to the number of tranches, in line with past findings 9

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(Cumming et al., 2015; Franke and Weber, 2009; Schaber, 2008). The coefficient on Maturity is significantly negative; thus, sukuk deals that offer a longer term to maturity have fewer tranches, consistent with the findings of Firla-Cuchra and Jenkinson (2005) for conventional debt. The authors infer that long-term securities complicate the creation of default-free (senior) tranches because they appear less attractive to unsophisticated investors. We also find evidence consistent with the proposition that sukuk deals that are secured with the originator's assets (Collateral) have more tranches. This finding implies that collateral assets in sukuk mitigate adverse selection concerns (Abdul Halim et al., 2017) and may thus improve the marketability of the security. Sukuk deals that are associated with a lead arranger from a top-five bank have less tranching, supporting the notion that reputable arrangers can mitigate adverse selection concerns. Finally, we find tranching has increased significantly in the post-crisis period, as shown by the positive and significant coefficient on the GFC dummy. 4.3. Sukuk structure, asymmetric information, market segmentation, and the level of subordination This section examines the determinants of the level of subordination. Specifications (1) to (4) of Table 6, which report first-stage regression results (the selection model), show the coefficient on the lease-based structure is negative but statistically insignificant. Thus, there is an insignificant difference in the likelihood of subordination between lease-based sukuk and sale-based sukuk (base case). Interestingly, equity-based sukuk are less likely to be subordinated than either sale-based sukuk or lease-based sukuk, corroborating the results for tranching in Table 5. These findings for equity-based sukuk suggest that other credit enhancement mechanisms, such as third-party guarantee, can mitigate the investment risk associated with securitizing claims that are not backed by tangible assets. Consistent with our main results, offerings with a better-quality asset pool (WACR) are less likely to be subordinated. Results for the second stage (outcome) model based on OLS estimations are provided in specifications (5) to (8). Lambda is positive and significant, indicating that sample selection bias exists. This is confirmed by the Ramsey (1969) regression specification error test (RESET) for omitted variables. Not addressing this bias may lead to invalid inferences. In the outcome model, we find no statistically significant evidence supporting hypotheses H3a and H3b, which respectively predict that lease-based sukuk have lower subordination levels than sale-based sukuk (base case), and that equity-based sukuk have higher subordination levels than other structures. Instead, we again find equity-based sukuk are associated with a lower level of subordination, driven perhaps by the principal and profit guarantee embedded in this sukuk structure. Senior has a significantly negative coefficient in specification (5) − a 1% increase in the number of senior tranches is associated with a 0.4% lower subordination level. This finding is contrary to the notion that having a larger proportion of senior tranches in a sukuk deal provides a greater level of credit protection. The results for the remaining information asymmetry proxies are mostly as predicted. Specifically, offerings with a poorer quality asset pool require more extensive subordination − a 0.1 unit decrease in the quality of the pool of underlying assets, as proxied by WACR, is associated with 2.46% higher subordination levels. The subordination level is positively associated with the degree of information-sensitivity of the assets, as proxied by Spread variation. Results show a one unit increase in Spread variation is associated with 1.40% higher subordination. Control variables directly associated with credit risk also have significant explanatory power (Maturity, Collateral and Amount). Long-term sukuk, which tend to exhibit higher credit risk, are associated with greater credit support through a higher level of subordination. Deals that are larger and secured with the originator's assets require less protection in the form of subordination. Sukuk issued in the post-crisis period (GFC) have on average a 13.3% higher level of subordination, suggesting greater internal credit protection. This finding is consistent with the concerns raised during the height of the crisis about the lack of adequate subordination protection. 5. Robustness check We perform several robustness checks of our main tests. First, to address the high collinearity between our asymmetric information variables, we orthogonalize these variables to examine their combined effects on tranching design. The orthogonalized variables (with an “Ortho” prefix) are generated from the fitted values of WACR, Spread variation, and Senior.12Table 7 shows that Ortho_WACR retains its negative coefficient and the positive sign on Ortho_Spread variation is preserved in the tranching regression. We obtain qualitatively similar results for Ortho_WACR, Ortho_Senior, and Ortho_Spread variation in the second-stage regression. Our results for the sukuk structure remain intact with this adjustment. Second, in an attempt to disentangle the effect of information asymmetry from market segmentation motivations for sukuk tranching, we partition our data to form subsamples of private and public firms, and repeat the analyses. Private firms face a greater adverse selection problem when raising external capital due to limited publicly available information. Therefore, we expect a more significant influence of asymmetric information proxies on the tranching decision for private firms than for public firms. Table 8 shows that the coefficients on lease-based and equity-based sukuk structures are negative and significant only for the subsample of private firms. The former is as expected since the pool of lease-based assets is predominantly tangible in nature, providing greater cushion against market uncertainty. Results for equity-based sukuk echo those reported earlier, confirming the importance of principal and profit guarantee embedded in this type of sukuk structure. The results for the subsample of private firms 12 We use the orthog routine in STATA to generate a set of orthogonalized asymmetric information variables. The order of the variables determines orthogonalization, i.e., the variable that is the most significant is listed first. Based on our main results, orthogonalization is done with the following order: WACR, Spread variation, and Senior.

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Table 6 Two-step regressions of subordination level. First-stage logit regression (Subordinate)

Sukuk structure Lease-based Equity-based Information asymmetry Senior WACR

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

−0.239 (−0.98) −0.536⁎⁎ (−2.30)

−0.238 (−0.95) −0.395⁎ (−1.67)

−0.255 (−1.05) −0.513⁎⁎ (−2.19)

−0.285 (−1.17) −0.553⁎⁎ (−2.38)

0.044 (0.62) −0.241⁎⁎ (−2.58)

0.068 (0.95) −0.203⁎⁎ (−2.07)

0.076 (1.04) −0.252⁎⁎ (−2.58)

0.040 (0.55) −0.268⁎⁎⁎ (−2.66)

0.031 (0.54)

−0.045⁎⁎ (−2.42) 0.019⁎⁎⁎ (3.93) −0.157⁎⁎⁎ (−2.66) −0.037 (−0.52) −0.111⁎ (−1.70) 0.085 (1.09) 0.163⁎⁎ (2.46) 0.399⁎⁎⁎ (5.31) 0.563⁎⁎⁎ (4.16) Yes 151 0.29

−0.003 (−0.01)

Spread variation Private firm Market segmentation Maturity variation Rating class Control variables Ln(Amount) Maturity Collateral SPV Top5 bank Top5 advisor GFC Lambda Constant Industry fixed effects # Obs. (Pseudo) R-sq

Second-stage OLS regression (Sub_level)

−0.628⁎⁎⁎ (−4.26)

−0.438⁎⁎ (−2.64)

0.250 (1.33)

−0.014 (−0.07)

0.011 (0.37) 0.203 (1.03)

−0.100 (−1.33) 1.159⁎⁎⁎ (7.73)

−0.099 (−1.14) 1.201⁎⁎⁎ (7.54)

−0.106 (−1.36) 1.160⁎⁎⁎ (7.71)

−0.106 (−1.37) 1.184⁎⁎⁎ (7.90)

0.001⁎ (1.86) 0.035⁎ (1.68) 0.239 (1.07) −0.234 (−1.18) −0.130 (−0.53) 0.461⁎⁎ (2.42)

0.001 (1.53) 0.021 (1.02) −0.050 (−0.23) 0.239 (1.02) −0.193 (−0.93) −0.178 (−0.71) 0.345⁎ (1.74)

0.001⁎ (1.80) 0.036⁎ (1.67) 0.170 (0.81) 0.212 (0.95) −0.219 (−1.09) −0.143 (−0.57) 0.444⁎⁎ (2.31)

0.001⁎ (1.83) 0.029 (1.44) 0.117 (0.58) 0.184 (0.84) 0.183 (0.95) −0.107 (−0.42) 0.463⁎⁎ (2.44)

−1.977⁎⁎⁎ (−3.81) Yes 294 0.254

−1.318⁎⁎ (−2.49) Yes 294 0.303

−2.041⁎⁎⁎ (−3.93) Yes 294 0.256

−1.896⁎⁎⁎ (−3.76) Yes 294 0.252

0.183 (0.95)

−0.246⁎⁎⁎ (−4.90)

0.032 (0.57)

0.022 (0.37)

0.014⁎ (1.76) 0.090 (1.53)

−0.044⁎⁎ (−2.28) 0.021⁎⁎⁎ (4.78)

−0.067⁎⁎⁎ (−3.43) 0.018⁎⁎⁎ (4.23) −0.186⁎⁎⁎ (−3.13) 0.018 (0.24) −0.093 (−1.56) 0.078 (0.98) 0.133⁎⁎ (2.06) 0.407⁎⁎⁎ (4.89) 0.862⁎⁎⁎ (5.99) Yes 151 0.352

−0.058⁎⁎⁎ (−3.12) 0.021⁎⁎⁎ (4.40) −0.147⁎⁎ (−2.44) 0.003 (0.04) −0.076 (−1.18) 0.095 (1.21) 0.157⁎⁎ (2.42) 0.398⁎⁎⁎ (4.82) 0.565⁎⁎⁎ (4.14) Yes 151 0.336

−0.066 (−0.96) −0.087 (−1.38) 0.086 (1.09) 0.173⁎⁎ (2.51) 0.388⁎⁎⁎ (5.17) 0.529⁎⁎⁎ (3.79) Yes 151 0.324

This table presents the results from two-step regressions. The dependent variable is Subordinate in the first-stage regression and Sub_level in the second-stage regression. Lambda is the inverse Mills ratio estimated from the first-stage regression. Each specification includes a GFC dummy and industry classification dummies. Standard errors are clustered at firm-level. The sample consists of 335 Malaysian corporate sukuk offerings with 3491 tranches issued between 2001 and 2014. Z-statistics are in parentheses. ⁎, ⁎⁎, and ⁎⁎⁎ denote significance at the 1, 5, and 10% level, respectively. Variable definitions are provided in Appendix A.

also provide strong support for the asymmetric information hypothesis, with Ortho_WACR and Ortho_Spread variation being both economically and statistically significant with the expected sign. We note that the effects of sukuk structure and information asymmetry on tranching are somewhat muted for the subsample of public firms. For these firms, the market segmentation argument (captured by Maturity variation and Rating class) underlies the motivation for sukuk tranching. Finally, we consider the role of bargaining power in our analysis. He et al. (2010) note that issuers with bargaining power in the structured bond market can exert greater influence on the rating process. In Malaysia, firms with substantial holdings by governmentlinked investment companies (GLICs) are in such a position.13 We argue that these firms are likely to issue a larger fraction of senior (high-rated) tranches with less subordination. To test this prediction, we repeat the main regressions controlling for GLIC

13 Intimate ties between business and politics in Malaysia have been well-documented (Gomez and Jomo, 1999; Fraser et al., 2006). In the relationship-based capitalism of Malaysia, firms with political patronage such as GLICs have emerged as the principal rent-seeking group in the corporate sector.

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Table 7 Estimation using orthogonalized asymmetric information variables.

Information asymmetry Ortho_WACR Ortho_Spread variation Ortho_Senior Private firm Sukuk structure Lease-based Equity-based Market segmentation Maturity variation Rating class Control variables Ln(Amount) Maturity Collateral SPV Top5 bank Top5 advisor GFC Lambda Constant Industry fixed effects # Obs. (Pseudo) R-sq

Sukuk structure Lease-based Equity-based Information asymmetry Ortho_Senior Ortho_WACR Ortho_Spread variation Private firm Market segmentation Maturity variation Rating class

Tranching

First-stage probit regression (Subordination dummy)

Second-stage OLS regression (Subordination level)

(1)

(2)

(3)

−0.505⁎⁎⁎ (−7.85) 0.087⁎⁎ (2.22) 0.027 (0.83) 0.059 (0.70)

−0.461⁎⁎⁎ (−4.24) −0.013 (−0.14) −0.041 (−0.46) −0.017 (−0.08)

−0.170⁎⁎⁎ (−4.84) 0.007 (0.28) −0.079⁎⁎⁎ (−2.66) 0.018 (0.31)

−0.087 (−0.89) −0.261⁎⁎ (−2.39)

−0.239 (−0.95) −0.392 (−1.63)

0.077 (1.11) −0.181⁎⁎ (−2.05)

0.106⁎⁎ (2.10) 0.180⁎⁎⁎ (3.96)

−0.095 (−1.08) 1.214⁎⁎⁎ (7.64)

0.204⁎⁎⁎ (7.66) −0.027⁎⁎ (−2.52) 0.091 (0.97) −0.050 (−0.55) −0.128⁎ (−1.72) −0.158 (−1.35) 0.158⁎ (1.69)

0.001 (1.50) 0.021 (1.03) −0.061 (−0.28) 0.234 (1.00) −0.186 (−0.91) −0.176 (−0.70) 0.363⁎ (1.78)

−6.314⁎⁎⁎ (−35.17) Yes 294 0.147

−1.783⁎⁎⁎ (−3.48) Yes 294 0.303

Tranching

Second-stage OLS regression (Sub_level)

(1)

First-stage probit regression (Subordinate) (2)

−0.087 (−0.89) −0.261⁎⁎ (−2.39)

−0.239 (−0.95) −0.392 (−1.63)

0.077 (1.11) −0.181⁎⁎ (−2.05)

0.027 (0.83) −0.505⁎⁎⁎ (−7.85) 0.087⁎⁎ (2.22) 0.059 (0.70)

−0.041 (−0.46) −0.461⁎⁎⁎ (−4.24) −0.013 (−0.14) −0.017 (−0.08)

−0.079⁎⁎⁎ (−2.66) −0.170⁎⁎⁎ (−4.84) 0.007 (0.28) 0.018 (0.31)

0.106⁎⁎ (2.10) 0.180⁎⁎⁎ (3.96)

−0.095 (−1.08) 1.214⁎⁎⁎ (7.64)

−0.065⁎⁎⁎ (−3.28) 0.020⁎⁎⁎ (4.42) −0.176⁎⁎⁎ (−3.10) −0.000 (−0.00) −0.067 (−1.09) 0.077 (0.98) 0.133⁎⁎ (2.00) 0.403⁎⁎⁎ (4.83) 0.622⁎⁎⁎ (4.72) Yes 152 0.385

(3)

(continued on next page) 12

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Table 7 (continued)

Control variables Ln(Amount) Maturity Collateral SPV Top5 bank Top5 advisor GFC Lambda Constant Industry fixed effects # Obs. (Pseudo) R-sq

Tranching

First-stage probit regression (Subordination dummy)

Second-stage OLS regression (Subordination level)

(1)

(2)

(3)

0.204⁎⁎⁎ (7.66) −0.027⁎⁎ (−2.52) 0.091 (0.97) −0.050 (−0.55) −0.128⁎ (−1.72) −0.158 (−1.35) 0.158⁎ (1.69)

0.001 (1.50) 0.021 (1.03) −0.061 (−0.28) 0.234 (1.00) −0.186 (−0.91) −0.176 (−0.70) 0.363⁎ (1.78)

−6.314⁎⁎⁎ (−35.17) Yes 294 0.147

−1.783⁎⁎⁎ (−3.48) Yes 294 0.303

−0.065⁎⁎⁎ (−3.28) 0.020⁎⁎⁎ (4.42) −0.176⁎⁎⁎ (−3.10) −0.000 (−0.00) −0.067 (−1.09) 0.077 (0.98) 0.133⁎⁎ (2.00) 0.403⁎⁎⁎ (4.83) 0.622⁎⁎⁎ (4.72) Yes 152 0.385

This table presents the results for negative binomial regressions of the tranching model and two-step regressions of subordination level. Ortho_WACR, Ortho_Spread variation, and Ortho_Senior are orthogonal fitted asymmetric information variables. Lambda is the inverse Mills ratio estimated from the first-stage regression. Each specification includes a GFC dummy, industry fixed effects, and year exposure. Standard errors are clustered at firm-level. The sample consists of 335 Malaysian corporate sukuk offerings with 3491 tranches issued between 2001 and 2014. Z-statistics are in parentheses. ⁎, ⁎⁎, and ⁎⁎⁎ denote significance at the 1, 5, and 10% level, respectively. Variable definitions are provided in Appendix A.

blockholding (> 5%). Results (untabulated) show GLIC blockholding is significantly negatively associated with tranching, but does not significantly explain issuers' subordination decision. Controlling for the role of bargaining power in our analysis does not change the overall conclusion of our paper. 6. Conclusion Motivated by religious prescriptions and severe informational problems in the corporate sukuk market, we examine the influence of capital market imperfections on the securitization design of sukuk, focusing on tranching and subordination practices. Our research shows that Islamic finance principles matter to sukuk securitization design, particularly for highly opaque (private) firms. For these firms, we find lease-based sukuk have significantly less intensive tranching, supporting Ebrahim et al.'s (2016) proposition that default-free loans that are completely collateralized by tangible assets can be structured to curtail risk shifting. However, contrary to our prediction, equity-based sukuk are associated with lower tranching and subordination levels – findings that we attribute to the principal and profit guarantee typically used to support this form of sukuk. While this practice is questionable from the ideal Islamic finance viewpoint, it reflects the practical approach to managing credit risk in the absence of tangible assets supporting the equitybased structure. Our research also shows that traditional determinants of securitization design for conventional debt, specifically those relating to information asymmetry and market segmentation, are equally applicable to corporate sukuk. An implication of our research is that investors should pay close attention to how sukuk structure, information asymmetry, and credit risk impact subordination in order to avoid deals that have poor credit enhancement. Declaration of Competing Interest Earlier version of this research was presented at the 20th Malaysian Finance Association Conference, and won the PBFJ Best Paper Award. The paper was previously titled “Asymmetric Information and Securitization Design in Islamic Finance”. This research is financially supported by Universiti Malaysia Terengganu, Grant Number 55184. Co-authors of this research have no competing interest to declare.

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Table 8 Sukuk tranching by private firms and public firms.

Sukuk structure Lease-based Equity-based Information asymmetry Ortho_Senior Ortho_WACR Ortho_Spread variation Market segmentation Maturity variation Rating class Control variables Ln(Amount) Maturity Collateral SPV Top5 bank Top5 advisor GFC Constant Industry Fixed Effects # Obs. Pseudo R-sq

Private firm

Public firm

(1)

(2)

−0.217⁎ (−1.76) −0.350⁎⁎ (−2.49)

0.105 (1.03) −0.122 (−0.83)

0.056 (1.38) −0.379⁎⁎⁎ (−3.77) 0.198⁎⁎ (1.97)

−0.031 (−0.63) −0.626⁎⁎⁎ (−12.23) 0.055 (1.63)

0.075 (0.96) 0.094 (1.25)

0.077⁎⁎ (2.11) 0.216⁎⁎⁎ (4.12)

0.194⁎⁎⁎ (5.20) −0.007 (−0.23) 0.103 (0.89) −0.145 (−1.00) −0.096 (−0.89) −0.009 (−0.06) 0.147 (1.19) −6.400⁎⁎⁎ (−29.36) Yes 156 0.186

0.236⁎⁎⁎ (6.55) −0.036⁎⁎⁎ (−4.38) 0.055 (0.55) 0.035 (0.29) −0.195⁎⁎ (−2.11) −0.243 (−1.43) 0.075 (0.65) −6.524⁎⁎⁎ (−28.40) Yes 138 0.226

This table presents the results of negative binomial regressions for the subsamples of private and public firms. The dependent variable is Tranche. Ortho_WACR, Ortho_Spread variation, and Ortho_Senior are orthogonal fitted asymmetric information variables. Each specification includes a GFC dummy and industry fixed effects. Standard errors are clustered at firm-level. The sample consists of 335 Malaysian corporate sukuk offerings with 3491 tranches issued between 2001 and 2014. Z-statistics are in parentheses. ⁎, ⁎⁎, and ⁎⁎⁎ denote significance at the 1, 5, and 10% level, respectively Variable definitions are provided in Appendix A.

Acknowledgement We thank Afifah Husin and Najib Samsudin for their research assistance. We are grateful for constructive comments given by Dr. Hassilah Salleh and participants at the 20th Malaysian Finance Association Conference, August 1–2, 2018. This research is supported financially by Universiti Malaysia Terenganu, Malaysia Grant Number 55184. Appendix A. Summary of variable definitions Variable

Definition

Dependent variable Tranche Number of tranches per deal Subordinate Dummy variable equal to 1 if tranches of the sukuk deal are subordinated, 0 otherwise Sub_level Value of non-AAA rated tranches as a fraction of the offering initial outstanding amount Sukuk structure

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Dummy variable equal to 1 if the sukuk deal is structured using lease-based principle, 0 otherwise Dummy variable equal to 1 if the sukuk deal is structured using equity-based principle, 0 otherwise A dummy variable equal to 1 if the sukuk deal is structured using sale-based principle, 0 otherwise

Information asymmetry Senior Number of senior tranches as a fraction of the total number of tranches within a deal. Tranches are classified as senior if they are labelled as “first lien” or “senior”. WACR Weighted (by size) average rating of all tranches in a single deal Spread variation Standard deviation of tranche spreads within a deal Private firm Dummy variable equal to 1 if the issuer is a privately held firm, 0 otherwise Market segmentation Maturity variaStandard deviation of tranche maturities within a deal tion Rating class Standard deviation of tranche ratings within a deal Control variables Amount Maturity Collateral SPV Top5 bank Top5 advisor GFC

Sum amount of all tranches within a deal (million US$) Average maturity of the tranches within a deal Dummy variable equal to 1 if the sukuk deal is secured, 0 otherwise Dummy variable equal to 1 if the sukuk deal involves an SPV, 0 otherwise Dummy variable equal to 1 if the sukuk deal is arranged by a top-five bank, 0 otherwise Dummy variable equal to 1 if the sukuk deal is approved by a top-five Shariah advisor, 0 otherwise Dummy variable equal to 1 if the tranche is issued during 2008–2009, 0 otherwise

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