Hidden Champions or Black Sheep? Evidence from German Mini-Bonds Mark Mietzner *, Juliane Proelss †, and Denis Schweizer ‡
ABSTRACT This paper presents a first empirical examination of all available German mini-bond offerings between 2010 and 2015. We compare the default probability according to a mini-bond’s initial rating with that implied by credit risk models, and show that rating agencies can create ratings inflation by issuing overly favorable ratings. This creates a “window of opportunity” for lower-quality firms to compete for funding. In this environment, high-quality firms have an incentive to use mini-bond underpricing to signal their quality. Our data highlight that, according to information-based corporate finance theory, higher underpricing is correlated with higher-quality mini-bond issuers and lower early default rates.
Keywords: Credit Risk; Financing Gap; Mini-Bonds; Mittelstand; Rating Inflation; Small Medium Sized Enterprises (SMEs) JEL Classification: G12, G30, G32
*
†
‡
Mark Mietzner, Professor of Finance - FIF Institute, Zeppelin University, Am Seemoser Horn 20, 88045 Friedrichshafen, Germany, Tel.: +49 7541 6009-1232; fax: +49 7541 6009-1299. E-mail:
[email protected]; Department of Business Administration, Economics and Law, TU Darmstadt, Hochschulstrasse 1, 64289 Darmstadt, Germany.. Concordia University, Assistant Professor of Finance, John Molson School of Business Building, 1450 Guy, Montreal, Quebec, Canada H3H 0A1, Phone: +1 514-848-2424, ext. 2242, Fax: +1-514-848-4500, e-mail:
[email protected]. Corresponding Author: Concordia University, Associate Professor of Finance, John Molson School of Business Building, 1450 Guy, Montreal, Quebec, Canada H3H 0A1, Phone: +1 514-848-2424, ext. 2926, Fax: +1-514-848-4500, e-mail:
[email protected].
Acknowledgments: We are grateful to Jörn Block, Massimo Colombo, Douglas Cumming, Marc Deloof, Silvio Vismara, and the participants at Economics of Entrepreneurship and Innovation (Trier, Germany) for many helpful comments and suggestions. We especially thank Thomas Dierkes and Maxim Preminger (Börse Düsseldorf), Hendrik Janssen (Börsen Hamburg and Hannover), Dirk Kruwinnus (EUWAX Aktiengesellschaft), Vanessa Schuberth (Deutsche Börse AG), and Dr. Rainer Wienke (Bayerische Börse AG) for providing us with comprehensive lists of all listed mini-bonds at the respective exchanges. A previous version of this paper was titled “(Dis)advantages of Investing in German Mini-Bonds.”
1.
Introduction In the aftermath of the recent financial crisis, many banks became either unwilling or
unable to provide sufficient financing to companies. Over 300 German non-financial corporations responded by raising over €150 billion after the beginning of the recent financial crisis in 2008 through the issuance of straight bonds, an approximate doubling of the amount raised and number of corporations than in the three years prior to the crisis (own calculations based on Thomson Reuters SDC data). This development is especially striking given the traditionally close financing relationships between corporations and banks in the German bank-based financial system. It also highlights the growing importance of external capital market-based financing, especially when bank credit is tight. This desire by corporations to decrease their dependence on financing after a banking crisis has been well documented in the literature (see, e.g., Davis and Stone, 2004; Deutsche Bundesbank, 2012; Nassr and Wehinger, 2015). Therefore, it is not surprising that alternative financing methods such as accessing the bond market are gaining in importance. However, we note that many small and medium-size firms (SMEs) (referred to as “Mittelstand” in Germany) only recently entered the public bond market for the first time to meet their financing needs. These companies, often world market leaders, are the backbone of the German economy, and are considered “hidden champions” (see Simon, 2009). They play a pivotal role in modern knowledge-based economies because they are an important source of new jobs, radical innovations, and productivity growth. The Mittelstand classification has become an internationally recognized stamp of high quality. Nevertheless, German SMEs frequently face financing constraints, which limit their growth and threaten their survival (Audretsch and Elston, 1997). The rise of so-called “minibonds,” which are public bonds issued in special SME bond segments, is a response to this issue. One example was the bondm segment of the Stuttgart Stock Exchange, established
1
specifically for this purpose in May 2010 (but subsequently shut down). 1 Mini-bond issuers are predominantly first-time issuers, and thus inexperienced in raising debt publicly in these specialized market segments. Issue sizes vary considerably, from about €2 million to €200 million. Nominal values are rather small, at mostly €1,000, and therefore they generally attract private investors. 2 The majority of mini-bond issuers are rated at BB and BBB levels or higher at the time of issuance (see Table 5). According to, e.g., Standard & Poor’s default tables, these ratings would indicate 2%-9% probabilities of default over a five-year time horizon. However, ratings in the mini-bond segment are not usually provided by any of the big three agencies (Fitch, Moody’s, Standard & Poor’s). They are conducted instead by smaller, local agencies such as Scope, Euler Hermes, or Creditreform. Mini-bonds and their corresponding market segments have recently been the subject of extensive media coverage, primarily because issuers were increasingly defaulting on their liabilities. Given their initial ratings and their perceptions as hidden champions, this was somewhat surprising. In fact, approximately one-fifth of all mini-bonds issued in the bondm segment ultimately defaulted between May 2010 and November 2014, which may have contributed to its shutdown. 3 Even more importantly, with a nearly 20% default rate, the real default risk was substantially higher than indicated by the average initial issuer ratings, which corresponded to a nominal bond value of about €800 million of risk by the end of 2014. High default rates that deviate from the initially indicated credit risk not only raise doubts about the quality of the SMEs issuing mini-bonds, they also suggest that the SMEs raising
1
bondm was the first market segment that specifically focused on SME bonds. Nine months after its introduction, the German Stock Exchange opened the Entry Standard segment for small and medium-sized bonds. Other stock exchanges in Düsseldorf (Mittelstandsmarkt), Munich (m:access), and Hamburg-Hannover (Mittelstandsbörse) also subsequently established specialized mini-bond market segments. 2 A total of fourteen companies issued more than €200 million in the specialized mini-bond segment (for example, Deutsche Börse AG, Air Berlin). However, none were SMEs. 3 For prominent examples, see, e.g., FFK Environment, Centrosolar, Windreich I & II, and Getgoods. Specifically, 24 of the 118 mini-bonds in our sample, with a nominal volume of about €1.060 million, can be considered non-performing.
2
funds in this market segment differ widely in “quality.” Furthermore, the unreliability of the ratings can make it difficult for investors (particularly private investors) to differentiate between hidden champions and black sheep, or poorer performers. Ratings are an integral part of the bond market. They provide creditors and investors with information critical to their decision making processes, thereby reducing information asymmetries (see Weinstein, 1977; Griffin and Sanvicente, 1982). Some investors rely on ratings virtually unilaterally, without using other in-house judgments (see Kaplan and Urwitz, 1979); others are restricted from investing in so-called non-investment-grade bonds, meaning ratings below BBB for Standard & Poor’s or Baa for Moody's. Thus, with any reduction in accuracy, rating agencies fail, at least to some extent, to provide the certification that investors demand. Against this backdrop, information-based corporate finance theory suggests it can be advantageous for high-quality firms to use “credible” signals in an effort to better separate themselves from lower-quality firms. Allen and Faulhaber (1989) use a setting similar to ours, where companies go public in an initial public offering (IPO), and subsequently seek to issue further equity via seasoned equity offerings (SEOs). They show that high-quality firms tend to sell their equity “too cheaply” in IPOs. The underpricing gives investors a discount on the equity price, and is perceived as a positive and reliable signal of a high-quality firm offering. The rationale is that low-quality firms have almost no incentive for underpricing, because their primary desire is to raise the highest amount possible in an IPO. They assume investors will realize their low quality over time, and will consequently be unlikely to provide further capital in an SEO. 4
4
Other models also focus on information asymmetry levels, and place different weights on the objectives of the players involved. See, for example, Rock (1986), who explores information asymmetries among several types of investors, Benveniste and Spindt (1989), who focus on information asymmetries between underwriters and investors, and Baron (1982), who examines information asymmetries between issuing firms and underwriters.
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Similarly to IPO markets, when rating agencies fail to reduce asymmetric information, high-quality issuers interested in developing the bond market as a long-term financing channel will likewise have incentives to use underpricing to signal their quality. Thus, a firm’s bond underpricing can be viewed as a quality signal. To empirically test Allen and Faulhaber’s (1989) predictions in the market for German mini-bonds, we analyze a unique and hand-collected sample of all available mini-bonds from their time of inception in 2010 through May 2015. Based on our dataset of 118 issued minibonds, we first present corroborating evidence that the rating agencies tend to release overly favorable ratings or overly low probabilities of default, as measured by subsequent realized default rates. This leaves investors with considerable uncertainty about the true underlying levels of credit risk, and suggests an opportunity for black sheep to potentially mix undetected with hidden champions. Next, we measure underpricing as a signaling device for all mini-bond issues, and we find a statistically significant average underpricing of 0.68%. In multivariate analyses, we relate mini-bond underpricing to proxies for firm quality. The results are consistent with the notion that firm quality is positively correlated with mini-bond underpricing, which confirms the findings of Allen and Faulhaber’s (1989) model. Our results also suggest that low-quality German SME firms not only issue “overpriced” debt securities, but they may also time their bond issues to a temporal “window of opportunity” because of ambiguous signals about an issuer’s true credit risk. Finally, we test whether SMEs use underpricing as a credible signal for differentiation. We thus tie early mini-bond defaults, which we define as default within the first two years of issuance, to underpricing and several other controlling variables. If underpricing provides a definitive separation between high- and low-quality firms, we expect to find a negative relationship between early mini-bond defaults and underpricing. Our data supports this.
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In summary, our results are consistent with the notion that high-quality firms use underpricing as a credible but costly signal to differentiate themselves from low-quality firms. Several robustness checks confirm our results. To the best of our knowledge, this paper is the first to analyze mini-bond offerings in Germany as a possible response to the continuing difficulties SMEs are experiencing across Europe in obtaining sufficient financing (as documented in a panel survey on the Access to Finance of Small and Medium-sized Enterprises (SAFE) by the European Central Bank since the beginning of the recent financial crisis). We thus contribute to the understanding of how to construct appropriate policy and market designs in order to provide high-quality SMEs with financing. Mini-bonds as a financing channel are not unique to German SMEs. They have attracted the attention of several other EU countries, such as Italy, where similar start-up difficulties exist. The Italian government introduced so-called Decreto Sviluppo, or Development Decree, mini-bonds in October 2012 for unlisted companies, thereby expanding their non-bank-related financing opportunities. However, one year later, only twenty mini-bonds had been issued, and predominantly by large companies with an issue volume of more than €200 million (see Tuccio, 2014). This is surprising, given the level of financing need, and that approximately 35,000 Italian companies are qualified to issue mini-bonds. It seems that information asymmetries are again dampening investor interest, and causing the mini-bond market to lag behind expectations and potential. One response in Italy was to create investment funds such as the BNP Paribas Bond Italia PMI, which can diversify credit risk and presumably delegate investment decisions to professional asset managers who may be better able to identify high-quality firms. A fuller understanding of the mini-bond segment is critically important because the market for corporate financing is changing and developing rapidly in Germany and across Europe. Dependence on bank financing is concurrently decreasing. And, given that Germany plays a 5
fundamental role in the European economy, adverse developments there can potentially have negative spillover effects to other EU countries. Better and more complete knowledge would also be important for firms seeking financing, as well as for investors, rating agencies, (investment) banks, investment fund managers, and policy makers. The remainder of this paper is organized as follows. Section 2 discusses the related literature, while section 3 introduces the data. We describe the methodology of our empirical analysis in section 4. Section 5 presents our results, and section 6 explores the robustness of our findings. Section 7 concludes. 2.
Hypothesis Development As we noted earlier, credit ratings are assumed to signal firm creditworthiness, thus
helping reduce information asymmetries between issuers and investors (see, for example, Weinstein, 1977; Wansley and Clauretie, 1985; and White, 2010). Investors often use ratings to assess an investment’s (credit) riskiness, and to price debt securities. According to this view, and consistent with a general certification hypothesis, Faulkender and Petersen (2006) document that firms with credit ratings are able to increase their leverage by raising more external debt. Similarly, Sufi (2009) finds that firms that recently received a loan rating are able to raise (more) external funds from less informed investors, which supports the predictions of Boot et al.’s (2006) model. Furthermore, it is well documented in the literature that credit ratings are related to yield spreads, meaning that companies of lower creditworthiness issue speculative-grade bonds (also called junk bonds or high-yield bonds), and must bear higher interest payments than higher-quality firms due to higher default risks (see, e.g., Gabbi and Sironi, 2005; Creighton et al., 2007; and May, 2010).
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In summary, credit ratings are highly decision-relevant for investors because they can help bridge asymmetric information, and are a necessary condition for well-functioning bond markets. Nevertheless, several scandals, such as the bankruptcy of Enron in November 2001 and the role rating agencies played at the beginning of the recent financial crisis, have called into question the reliability and the information content provided by credit ratings (see, e.g., White, 2010; Bolton et al., 2012). In fact, credit rating agencies sometimes release and then update their ratings with a substantial time lag, which can reduce their value as timely and reliable signals of borrower quality (Galil and Soffer, 2011). Even more importantly, as per Bolton et al.’s (2012) model, rating agencies have a strong incentive to inflate their ratings quality when more uninformed investors are in the market, such as during an economic expansion. Becker and Milbourn (2011) find that an increase in competition in the market for credit ratings leads to a deterioration in ratings quality. The market for German mini-bonds began in 2010, a period characterized by solid economic growth. The vast majority of issuers at that time chose low nominal values of €1,000, thereby attracting uninformed and relatively inexperienced private investors, in addition to institutional investors. This occurrence fulfilled both conditions of Bolton et al.’s (2012) model predicting inflated credit ratings. Note also that the majority of mini-bond credit ratings are provided by smaller, local rating agencies, such as Creditreform, Euler Hermes, Scope, and Feri, not the big three agencies, Fitch, Moody’s, or Standard & Poor’s. This is presumably because issuers are aiming to keep rating costs down. As a result, the market for mini-bond ratings was hard-fought and resulted in intense competition between agencies, which is also a predictor of rating inflation according to Becker and Milbourn (2011).
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Thus, our first testable hypothesis is:
Hypothesis 1: German mini-bond ratings are inflated and of low quality (underestimation of default risk). If the assessment of creditworthiness is inflated, we expect that rating agencies will not accurately predict the probability of default. Consequently, more issuers will default on their liabilities than initially expected. Thus, we hypothesize that realized default rates in the respective rating class will be higher than those in Standard & Poor’s historical default tables. Our second hypothesis is therefore:
Hypothesis 2: Inflated German mini-bond ratings lead to higher default rates than the historical defaults in the respective rating classes. When credit ratings are potentially inflated, investors will value signals provided by minibond issuing firms more highly. A proven signal for this purpose in the IPO market is the underpricing of newly issued shares. 5 Many studies have analyzed the signaling power of IPO underpricing, and documented how it can overcome or at least reduce valuation uncertainty and subsequent asymmetric information problems (see, e.g., Ritter and Welch, 2002; Ljungqvist, 2007). According to signaling theory, high-quality firms underprice their shares to convince outside investors of their quality (see, e.g., Rock, 1986; Grinblatt and Hwang, 1989; Welch, 1989).
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Ritter (2003) provides an overview of the theoretical and empirical determinants of underpricing across different European markets. Khurshed et al. (2014) show that a transparent bookbuilding can take a certification role in Indian IPOs, thereby reducing information asymmetries, while Akyol et al. (2014) find that enhanced disclosure requirements reduce IPO underpricing that is associated with information uncertainty. See, for example, Vismara (2014) or Bonardo et al. (2011) for the effects of other signaling mechanisms on IPO underpricing.
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However, underpricing is not costless, and only high-quality firms can expect to recoup their costs in subsequent issues. Low-quality firms may thus refrain from imitating highquality firms because they risk detection before subsequent issues. In their model, Allen and Faulhaber (1989) conclude that underpricing can be a credible signal for distinguishing between high- and low-quality IPO firms in equity markets. We use the same rationale to explain underpricing in bond markets. Welch (2000) shows a 0.44% average underpricing for bond issues over the 1994-1995 time period. He also finds that underpricing is more pronounced for firms with lower credit ratings. 6 Cai et al. (2005) document that underpricing of initial bond offerings is larger for speculative-grade bonds. 7 These results suggest that riskier companies, with more private knowledge about their unobservable higher creditworthiness, signal to investors using higher levels of underpricing. Assuming credit ratings are not inflated, high-quality companies would not find it necessary to signal their quality by underpricing, simply because their ratings are observable and a true reflection of their quality. However, as soon as ratings become inflated and of low quality, high-quality firms are motivated to use underpricing as a signaling device to, e.g., reduce financing costs in subsequent financing rounds. This view is supported by the empirical findings of Hale and Santos (2006), who show that bond underpricing declines for subsequent offerings. It also provides support for Allen and Faulhaber’s (1989) signaling hypothesis.
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Chan and Lo (2001) and An and Chan (2008), among others, document that equity offerings of firms with credit ratings are less underpriced. This generally supports the view that ratings can reduce asymmetric information. However, in this paper, we assume ratings are inflated, thus overriding their certification ability. 7 Similar results are reported for the Swiss bond market by Wasserfallen and Wydler (1988). They find that newly issued bonds tend to be underpriced by 0.46%, which is positively correlated with maturity and negatively correlated with coupon size. Analyzing the same market but over a different time period (1999-2001), Kovács and Zeder (2003) find that straight bonds of foreign issuers quoted in CHF are overpriced, which may be attributable to the negative development of the Swiss bond market.
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Overall, the extant research indicates that the costs of entering the public bond market are lower if rating agencies provide accurate assessments of an issuer’s creditworthiness. However, a mini-bond market with inflated ratings of low quality will presumably attract not only high-quality firms, but also low-quality firms with restricted financing choices masquerading as high-quality firms (this is similar to Akerlof’s (1970) famous “market for lemons” problem). This idea is consistent with the view that low-quality firms have a window of opportunity to gain access to public debt markets with lower financing costs versus markets with symmetrical information. But, ironically, this may lead to risk-shifting problems, which credit ratings are believed to alleviate (Jensen and Meckling, 1976). Moreover, the favorable market conditions, in terms of strong demand from institutional and private investors for German Mittelstand bonds, further fuels the opportunity for lowquality firms to issue mini-bonds. However, because rating downgrades and mini-bond defaults have increased, we expect investors to become more sensitive to evaluating true underlying credit risk, thereby creating an incentive for high-quality firms to use underpricing as a signal of quality. Our third testable hypothesis is thus:
Hypothesis 3: High-quality firms use mini-bond underpricing to signal their quality. If high-quality firms use mini-bond underpricing to signal their quality, then the underpricing can be used for default prediction models. This leads to our final hypothesis:
Hypothesis 4: Mini-bond underpricing is negatively correlated with (early) mini-bond defaults.
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3.
Dataset To construct our German mini-bonds dataset, we first contacted all German stock
exchanges offering designated segments for bonds issued by the German Mittelstand (SMEs). We obtained lists of the complete history of all issued bonds from all stock exchanges, thereby ensuring the highest possible coverage and that all subsequent analyses would not be affected by survivorship bias. Our information came from the following stock exchanges (segment name in parentheses; number of bonds in brackets): Börse Stuttgart (bondm {33}), Börse Frankfurt am Main (Entry {62} and Prime Standard {16} for corporate bonds), Börse Düsseldorf (der Mittelstandsmarkt {19}), Börse München (m:access bonds {3}), Börse Hamburg/Börse Hannover (Mittelstandsbörse Deutschland {2}). Our initial sample consisted of 135 mini-bonds issued over the 2004 through March 2015 period (see panel A of Table 1 for more details). We obtained balance sheet information for the 113 distinct companies issuing the 135 bonds from Dafne (company information for Germany), and from Amadeus (company information for Europe). Both are databases from Bureau van Dijk, which provides private company information. 80 issuers are in both databases; 18 are available only in Dafne, and 15 are only in Amadeus. We use company information from Dafne when both or only Dafne provided information, and we complemented that with data for the fifteen companies available exclusively in Amadeus. The balance sheet items we use correspond to the year prior to bond issuance. We further checked all information available in both data bases for discrepancies. We found twenty-eight entries that exhibited small differences of less than 0.1%, which we attribute to rounding errors. For those cases, we used the data provided by Dafne. In unreported results, we instead used the data from Amadeus, and our results were virtually the same. However, for two issuers, HELMA Eigenheimbau Aktiengesellschaft and HAHNImmobilien- Beteiligungs AG, all the balance sheet information on liabilities, such as fixed 11
assets and short- and long-term liabilities, deviated substantially for the entire observation period. In those cases, we used the information provided in the annual report. To derive our final dataset, we excluded 1) two bonds that were not initially listed in the mini-bond segment and that had subsequently requested a change of segment, 2) one bond issued in 2004, which was six years before the others and which changed the segment afterward, 3) thirteen bonds with prospective issue volumes of greater than €200 million, because they are not considered mini-bonds, and 4) one bond with no information available in Dafne, Amadeus, Datastream, or Bloomberg. The final data set is comprised of 118 minibonds (see Table 1, panel A, for a detailed overview). However, we reduced the dataset further when calculating mini-bond underpricing, because prices were only available in Datastream for ninety-eight mini-bonds. Finally, we excluded any early mini-bond defaults, which occurred up to two years after issuance. Consequently, the latest possible issue date we can consider is March 31, 2013, which leaves us with seventy-three mini-bonds. For the multivariate analyses in Tables 8 and 9, we use, e.g., several balance sheet-related control variables that were not available for all mini-bonds in Dafne or Amadeus. This further reduced the number of observations, depending on which variables were used in the respective models (see again panel A of Table 1 for a detailed overview). Given our maximum sample size of 118, we decided to use the highest possible number of observations available for the respective specifications in the multivariate analyses, and not the least common factor, which would only be forty-two observations. 8 Table 2 shows the historical development of the number of mini-bond issuances and their prospective issue
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In an unreported robustness check, we also replicated all multivariate regressions with the least common factor of forty-two observations. We found that the signs as well as the magnitude of the coefficients on the main explanatory variables remained the same, but the overall significance was lower, presumably due to the sharp reduction in sample size.
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volumes for the various exchanges from 2010 through May 2015. The respective industries are in panel B of Table 1. —Please insert Tables 1 and 2 about here— From Table 2, we can infer that the average size of a German mini-bond is approximately €43.47 million, indicating a rather small issue volume in these market segments. This may also be why many issuers chose face values of €1,000 (although differences among the five German stock exchanges in terms of the mini-bonds issued and their volume are quite substantial). Thus, it is not surprising that most firms chose Stuttgart’s bondm, the first segment to focus on mini-bonds, or Frankfurt’s Entry Standard, because Frankfurt is by far the largest stock exchange. We obtain bond prices by using two independent gathering approaches. First, we used the unique identifiers (ISINs) provided by the exchanges and bond prices from Datastream’s variable Primary Exchange. In summary, we found prices for ninety-eight bonds in Datastream. Second, we used bond prices from the issuing exchanges as a robustness check. We were able to obtain prices for 116 bonds. Note that the analyses we present here are based on the first approach because it is directly replicable. When comparing both approaches, we find that the bond prices (when available in Datastream) are virtually the same. Bond characteristics, such as coupon, maturity, etc., come from Datastream, and are double-checked against information given in the bond prospectuses around the issuing date. Bond ratings are hand-collected from the respective bond prospectuses or from the related rating summaries from Euler Hermes, Scope, or CreditReform around the issuing date. All ratings are short term, within a twelve-month period.
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4.
Methodology We calculate the underpricing for mini-bond offerings similarly to how IPO underpricing
is calculated (see, e.g., Cai et al., 2007). Due to the illiquidity in corporate bond markets, we follow Cai et al. (2007) and calculate initial returns (underpricing) to investors if liquid bond prices available within the first six calendar days after the offering are as follows: 𝑈𝑛𝑑𝑒𝑟𝑝𝑟𝑖𝑐𝑖𝑛𝑔𝑖 =
𝑃𝑖,𝑛 𝑃𝑖,0
− 1,
(1)
where 𝑃𝑖,𝑛 is the closing price at the 𝑛’th trading day, which corresponds to the first trading
day after issuance with at least one trade, and 𝑃𝑖,0 is the bond offering price. This procedure is commonly used (see, e.g., Cai et al., 2007; Hale and Santos, 2006). 9
To estimate the following OLS regression models, we use the independent variable Underpricing, defined as in Equation (1). The explanatory variables are Equity Ratio, Relative Offering Size, Implied PD z-score, ∆ Implied PD, Coupon, and Intangible Assets, and other control variables (Time-to-Maturity, Listed, and ROA) (see the appendix for variable descriptions and calculation method). The basic structure of our regression equations is as follows: 10 𝑈𝑛𝑑𝑒𝑟𝑝𝑟𝑖𝑐𝑖𝑛𝑔 = 𝛼 + 𝛽1 ∙ 𝐸𝑞𝑢𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜 + 𝛽2 ∙ 𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑂𝑓𝑓𝑒𝑟𝑖𝑛𝑔 𝑆𝑖𝑧𝑒 + 𝛽3 ∙
𝐼𝑚𝑝𝑙𝑖𝑒𝑑 𝑃𝐷 𝑧 − 𝑠𝑐𝑜𝑟𝑒 + 𝛽4 ∙ ∆ 𝐼𝑚𝑝𝑙𝑖𝑒𝑑 𝑃𝐷 + 𝛽5 ∙ 𝐶𝑜𝑢𝑝𝑜𝑛 + 𝛽6 ∙ 𝐼𝑛𝑡𝑎𝑛𝑔𝑖𝑏𝑙𝑒 𝐴𝑠𝑠𝑒𝑡𝑠 +
+ ∑𝑗 𝛾𝑗 ∙ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑗 + 𝜀𝑡 .
(2)
We use two-way, firm and issue year, clustered standard errors, and we omit firm-level
notations for clarity.
Note that we are do not deduct a cumulative bond index return until the 𝑛’th trading day (such as the former Lehman Brothers index used in Cai et al., 2007). This is because we would need an index for the respective rating class given by the rating agencies, which is highly questionable, and we would need the same maturity, which is typically not provided. 10 For the different specifications in Table 8, we do not use Implied PD z-score and ∆ Implied PD jointly in the regression because both variables are highly correlated. If we consider them separately, we find no indication of multicollinearity as per the mean Variance Inflation Factor (VIF) of 1.21 and the maximum value of 1.50. 9
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The Implied PD z-score in Equation (2) is calculated using a two-step procedure. First, we calculate the Altman z-score as follows (see Altman et al., 1977; and Altman and Saunders, 1997): 𝑧 = 6.56 ∙ 𝑋1 + 3.26 ∙ 𝑋2 + 6.72 ∙ 𝑋3 + 1.05 ∙ 𝑋4,
(3)
where X1 is the ratio of working capital to total assets, X2 is the ratio of retained earnings to total assets, X3 is EBIT to total assets, and X4 is book value of equity to total liabilities (see the appendix for more details). Next, we transform the Altman z-score in a probability to default (Altman, 2010): 1
𝐼𝑚𝑝𝑙𝑖𝑒𝑑 𝑃𝐷 𝑧 − 𝑠𝑐𝑜𝑟𝑒 = 1+𝑒 𝑧 .
(4)
To determine whether an early default occurred within the first two years after bond issuance, we estimate the following logit model using the mini-bond Underpricing calculated in Equation (1) as follows: 𝐸𝑎𝑟𝑙𝑦 𝐷𝑒𝑓𝑎𝑢𝑙𝑡 (0/1) = 𝛼 + 𝛽1 ∙ 𝑈𝑛𝑑𝑒𝑟𝑝𝑟𝑖𝑐𝑖𝑛𝑔 + 𝛽2 ∙ 𝐸𝑞𝑢𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜 + 𝛽3 ∙
𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑂𝑓𝑓𝑒𝑟𝑖𝑛𝑔 𝑆𝑖𝑧𝑒 + 𝛽4 ∙ 𝐼𝑚𝑝𝑙𝑖𝑒𝑑 𝑃𝐷 𝑧 − 𝑠𝑐𝑜𝑟𝑒 + 𝛽5 ∙ ∆ 𝐼𝑚𝑝𝑙𝑖𝑒𝑑 𝑃𝐷 + 𝛽6 ∙
𝐶𝑜𝑢𝑝𝑜𝑛 + 𝛽7 ∙ 𝐼𝑛𝑡𝑎𝑛𝑔𝑖𝑏𝑙𝑒 𝐴𝑠𝑠𝑒𝑡𝑠 + + ∑𝑗 𝛾𝑗 ∙ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑗 + 𝜀𝑡 .
(5)
Similarly to the OLS regression, we use two-way, firm and issue year, clustered standard
errors, and we omit firm-level notations for clarity. In the robustness section, we change the Altman z-score to Ohlson’s (1980) o-score, calculated as follows: 𝑜 = −1.32 − 0.407 ∙ 𝑋1 + 6.03 ∙ 𝑋2 − 1.43 ∙ 𝑋3 + 0.757 ∙ 𝑋4 − 2.37 ∙ 𝑋5 − 1.83 ∙ 𝑋6 +
0.285 ∙ 𝑋7 − 1.72 ∙ 𝑋8 − 0.521 ∙ 𝑋9,
(6)
where X1 is size in terms of the log(total assets/GNP price-level index), assuming a base value of 100 in 1968, X2 is total liabilities divided by total assets, X3 is working capital divided by total assets, X4 is current liabilities divided by current assets, X5 is a dummy variable equal to 1 if total liabilities exceed total assets, and 0 otherwise, X6 is net income
15
divided by total assets, X7 is funds provided by operations 11 divided by total liabilities, X8 is a dummy variable equal to 1 if net income is negative, and 0 otherwise, and X9 is (𝑁𝐼𝑡 − 𝑁𝐼𝑡−1 )⁄(|𝑁𝐼𝑡 | + |𝑁𝐼𝑡−1 |), where 𝑁𝐼𝑡 is net income from the most recent period.
The coefficients in Equation (5) correspond to model 1 in Table 4 in Ohlson (1980), which
predicts bankruptcy within one year corresponding to the rating issued by the agencies. The Ohlson (1980) o-score is transformed in a probability of default by using the following calculation: 𝑒𝑜
5.
𝐼𝑚𝑝𝑙𝑖𝑒𝑑 𝑃𝐷 𝑜 − 𝑠𝑐𝑜𝑟𝑒 = 1+𝑒 𝑜 .
(7)
Results
5.1.
Univariate Analysis – Default Risk and Rating Migration
In this section, we first analyze the issuer and mini-bond offering characteristics, which are summarized in Table 3. Next, we investigate potential discrepancies in the assessment of issuer credit risk by rating agencies. With regard to issue characteristics, we find that Relative Offering Size is rather large and has a high standard deviation. On average, mini-bond issuers raise more capital than their existing long-term and short-term debt taken together would imply (see Table 3). A typical mini-bond issuance is comprised of an average time to maturity of five years, and no longer than seven years, which is lower than the average duration of U.S. corporate bonds (see, e.g., Welch, 2000). Moreover, we observe high average coupon rates and a rather low Equity Ratio, indicating that highly leveraged firms are the primary users of mini-bond issues. This view is also supported by the Implied PD z-score, which shows an average probability of 13.6% that issuers will default on their liabilities within the next year. Remarkably, we observe that the difference between the probability of default according to an issuer’s rating and the
11
We use EBIT to proxy for funds provided by operations.
16
probability of default implied by the z-score is on average -12.2%. This indicates that rating agencies estimated a much lower credit risk than Altman’s z-score would have implied. We also find that approximately one-third of all issuers are publicly listed companies. Because listed companies are generally covered by financial analysts and tend to be more mature, they are typically less prone to asymmetric information. We therefore expect to find a positive and statistically significant correlation coefficient between the variables Listed and Equity Ratio, and a negative correlation for Coupon (see Table 4). In contrast, privately held companies (when externally financed) rely almost exclusively on bank financing, and tend to be smaller than publicly listed companies. Higher default probabilities typically coincide with low operational profitability (ROA), which is supported by the negative correlation coefficient in Table 4. The histogram in Figure 1 shows the frequency of the implied probabilities of default for the initial mini-bond ratings for all categories. We compare the initial mini-bond ratings with the implied probabilities of default suggested by Altman’s z-score (panel A), z-score mapping (panel B), and Ohlson’s (1980) o-score (panel C). The figures suggest that mini-bond ratings do not concur with the estimated default risk of all applied credit risk models, and actually indicate dramatically lower credit risk. The underestimation of the one-year default probability is about 10% in the most frequently used rating categories, BBB and BB, which is statistically significant at the 1% level (see Table 5 and evidence for z-score mapping and Ohlson’s o-score in Table 1 in the appendix). This deviation is not only highly statistically significant, but also economically meaningful, because it provides a “back door” of sorts for issuing low-quality debt securities (assuming the applied credit risk models are reliably capturing true credit risk). Interestingly, we find that, on average, mini-bonds have a one-year default probability of 13.52%, which corresponds to a rating of at least B- or worse according to Standard & Poor’s (see again Table 5). In other words, the vast majority of German mini-bonds would be classified as junk 17
bonds, which are clearly not appropriate investments for uninformed private investors (again assuming the validity of the credit risk models). The results of our univariate analysis therefore provide support for our first hypothesis of rating inflation, meaning that initial minibond ratings have been too favorable. —Please insert Figure 1 and Tables 3, 4, and 5 about here— If the ratings provided by the rating agencies are inflated, and if the credit risk models are accurately capturing real credit risk, we would expect over time to find a more pronounced rating migration toward default than would otherwise be assumed by, e.g., Standard & Poor’s historical rating migrations. We analyze this development in Table 6, which shows the rating changes after two years and compares them to historically observed rating changes over that time period. Note first the strikingly high number of defaults (see column D, panels A and B), particularly for investment-grade rating classes. In fact, one-third of all mini-bonds issued with BBB ratings defaulted on their debt within two years (see panel B of Table 6). To test whether those initial ratings were inflated, panel D of Table 6 compares the rating migrations of mini-bonds with historical rating changes observed by Standard & Poor’s. The two rating categories for which we can formulate the most reliable statements are BBB and BB, because the majority of the eighty-nine mini-bonds fall into both categories. The results are thus less prone to any biased results caused by outliers. A comparison of the mini-bond rating migration with historical rating migrations reveals a markedly inferior development of the mini-bonds compared to the Standard & Poor’s benchmark (highlighted in red in panel D of Table 6). This means that BBB-rated (BB-rated) bonds have a -32.66% (-9.77%) higher probability of migrating from the initial rating to a default (D) rating within two years after issuance than the Standard & Poor’s benchmark would predict. Furthermore, mini-bonds in those categories are less likely to increase their ratings. 18
In summary, we interpret these findings as evidence for Hypothesis 2, that inflated ratings coincide with higher credit risk than would be indicated by the initial ratings. —Please insert Table 6 about here— 5.2. Mini-Bond Underpricing and Early Mini-Bond Default Against the background of our findings thus far, that initial ratings are inflated and do not measure credit risk adequately, we posit that high-quality issuers may have an incentive to underprice their mini-bonds to send creditworthy signals. To explore this question further, we calculate underpricing for every mini-bond issuance, and find a statistically significant 0.67% average underpricing (see Table 7). This is slightly smaller than the percent found for U.S. bonds (see Wasserfallen and Wydler, 1988; Welch, 2000; and Cai et al., 2007). 12 —Please insert Table 7 about here— However, note that we have not yet tied mini-bond underpricing to issuer quality. If highquality firms underprice their mini-bonds to signal creditworthiness, we posit that proxies for an issuer’s “financial strength” will be positively correlated with underpricing, after controlling for issuer and offering characteristics. The results of our cross-sectional regression analysis show that the coefficient for Equity Ratio is positive and highly statistically significant at the 1% level for all specifications in Table 8. This suggests that firms with lower leverage, i.e., less risky and financially healthier firms, are willing to leave more money on the table when issuing mini-bonds. We also find that the level of underpricing decreases statistically significantly at the 1% and 5% levels with larger Relative Offering Size. Thus, firms that raise higher debt volumes
12
Cai et al. (2007) show that bond issuances (unlike equity offerings) may suffer from insufficient or even nonexistent trading at the first trading day. Therefore, they recommend calculating underpricing only if liquid bond prices are available within the first seven calendar days after issuance. In our sample, the vast majority were traded on the first trading day (92). Only four were traded for the first time on the second day, and only two on the fifth trading day (see Table 7). This clearly indicates that illiquidity is unlikely to be affecting our results.
19
by one-time mini-bond issuance (compared to existing debt) tend to exhibit lower underpricing (see Table 8). This finding is consistent with the notion that low-quality firms (black sheep) are likely to take advantage of a “window of opportunity,” and raise comparably higher debt volumes. Knowing or expecting their quality to be revealed in the future reduces their chances to successfully issue follow-up mini-bonds, however. It also clearly reduces their incentive to imitate high-quality firms (hidden champions) and benefit from lower financing costs in the future. Furthermore, we find support for the idea that underpricing is less pronounced for companies with higher default risk (Implied PD z-score). This again implies that riskier and less stable financed companies will refrain from engaging in higher underpricing. More importantly, we find strong evidence for a positive and statistically significant relationship at the 1% and 5% levels in all specifications between the difference in the probability of default from initial ratings and the implied probability of default from Altman’s z-score (∆ Implied PD). Because the default risk assigned by the initial credit ratings tends to be lower than the predicted default probability from credit rating models (see Table 3), the difference must be negative. Thus, the positive coefficients in specifications 2 and 6 in Table 8 suggest that underpricing is lower for larger discrepancies between the initial default probabilities by rating agencies and the credit risk models. We interpret these results in accordance with Allen and Faulhaber’s (1989) model to mean that lower ∆ Implied PDs are a proxy for low-quality firms taking advantage of the “window of opportunity.” This is because initial credit ratings appear unable to identify them. The promised Coupon by mini-bond issuers also provides weak evidence that riskier firms have lower underpriced mini-bonds, because coupon size is commonly perceived to correlate positively with credit risk. However, the coefficient is statistically significant only in specification 7 of Table 8. Finally, we find that issuers with more Intangible Assets underprice
20
less. This is in line with the view that firms with higher levels of intangible assets have more trouble coming up with additional collateral during times of (extreme) financing pressures. In summary, we interpret the results of our multivariate regression as strong support for our third hypothesis, that high-quality firms have an incentive to use mini-bond underpricing to signal quality. The result also conforms with the predictions of information-based corporate finance theory. —Please insert Table 8 about here— In our final step, we empirically test whether underpricing has any predictive power to differentiate between low- and high-quality firms. If, as we expect, issuers with comparably low underpricing are revealed to be lower-quality firms, and all else is held equal, then those companies will have higher credit risk, which will result in higher default rates. To test for this relationship, we use multivariate logistic regressions, where the dependent variable is equal to 1 if a firm defaults on its debt within the first two years after mini-bond issuance, and 0 otherwise. In accordance with our expectations, we find that higher Underpricing is negatively correlated with early mini-bond defaults. This is statistically significant at the 1% and 5% levels, thus providing support for Hypothesis 4 (see Table 9). Furthermore, the proxy variables Relative Offering Size and Intangible Assets have interpretations similar to those in the previous analysis. Firms looking for a “window of opportunity” (high Relative Offering Size) and those with less collateral assets (high Intangible Assets) have higher probabilities of an early default. However, the coefficients for the Implied PD z-score and ∆ Implied PD might be surprising at first glance because they suggest that higher credit risk, and higher probability of default implied from credit risk models than from initial ratings, are negatively correlated with early mini-bond defaults. These results do not alter our conclusions about underpricing as a credible signal, however. They only suggest that in our sample more mini-bonds with 21
comparably lower credit risk (A, BBB+, and BBB) defaulted early (see Table 3), and that rating agencies do not systematically inflate ratings when credit risk models indicate high credit risk. —Please insert Table 9 about here— 6.
Robustness Checks Given the rather small size, we performed several robustness checks to validate the
robustness of our results. First, we increased the sample size from 98 to 116 when calculating mini-bond underpricing by using the data from the respective bond issuing exchange. As mentioned in the “Dataset” section, the prices from the bond issuing exchange and Datastream deviate only minimally (if prices are available in Datastream). In unreported results, we use bond prices from bond issuing exchanges, and find results that closely match those from Datastream. 13 All conducted analyses are highly similar in terms of magnitude, but the statistical significance is high, which can presumably be attributed to the higher sample size. We are therefore confident that our results are not subject to any bias from using Datastream data. To estimate the probability of default, we use primarily Altman’s z-score. This is the most widely used approach, and we use Equation (4) to transform it into a probability of default. However, the transformation ensures only that the domain is between 0 and 1, which could be interpreted as a probability of default. As an alternative approach, we use the mapping table from z-scores to probabilities of default presented in Altman and Saunders (1997). We can thus calculate bond rating equivalents using the z-score from Equation (3), and then adding a constant term of 3.25 (see Table 4 in Altman and Saunders, 1997). This approach has the advantage that the mapping does not rely on a transformation, and instead relies on economically meaningful criteria. 13
Tables are available from the authors upon request.
22
In summary, we find that the probabilities of default using the mapping approach are similar to those obtained from the transformation (see Figure 1 and Table 1 in the appendix). Furthermore, the results from the multivariate analyses using the mapping are also highly similar (see Table 10). For the next robustness check, we change Altman’s z-score to Ohlson’s o-score. The main differences between the scores are 1) “slightly” different factors, and 2) that the o-score uses nine instead of five factors. Thus, less weight is allocated to a single factor, which makes the o-score less sensitive. Again, we find that the probabilities of default are similar to those from the z-score transformation and the mapping (see Figure 1 and Table 1 in the appendix). The multivariate regressions reveal similar results in terms of magnitude, but the overall significance is lower. However, that lower significance is accompanied by a smaller sample size, because the balance sheet item “net income for the year before the most recent year to mini-bond issue” (𝑋9) required for the o-score calculation is generally less available in Dafne and Amadeus. In summary, the results of the multivariate analyses using the o-score are strongly similar to those using the mapping or z-score (see Table 10). 14 —Please insert Table 10 about here—
14
In unreported results, we address country differences. We derive scores and mapping using U.S. firms, which probably deviate from German SME companies with respect to characteristics such as financing structure (public listings), principal bank lending versus the use of capital markets, different sample industries, and different accounting standards. To address the issue of different country sensitivities, we use coefficients from Beinert et al. (2008) to calculate Altman’s (1968) z-score based on a German sample rated by Moody’s �𝑧𝐺𝑒𝑟𝑚𝑎𝑛𝑦 𝑀𝑜𝑜𝑑𝑦′𝑠 = – 0.354 ∙ 𝑋1– 12.222 ∙ 𝑋2– 11.456 ∙ 𝑋3 + 4.274 ∙ 𝑋4 + 8.906�, Standard & Poor’s �𝑧𝐺𝑒𝑟𝑚𝑎𝑛𝑦 𝑆&𝑃 = 0.185 ∙ 𝑋1 + 0.238 ∙ 𝑋2 − 4.472 ∙ 𝑋3 − 0.027 ∙ 𝑋4 + 0.545�, and HGB as the accounting standard. The results were again highly similar to those obtained from the other approaches, and are available from the authors upon request.
23
7.
Conclusion After the recent worldwide financial crisis, it became exceedingly difficult for many
German SMEs to obtain or increase bank financing. Since 2010, some SMEs have responded by issuing mini-bonds in specialized market segments at various German exchanges. The majority of mini-bond issuers were initially rated at BB and BBB levels, which suggested generally high borrower quality. This was not surprising, because these companies (the German “Mittelstand”) are perceived as the backbone of the economy, and are often referred to as hidden champions. However, competition in the market for issuer ratings, economic growth, investor inexperience, as well as the demand for mini-bonds, may have contributed to rating inflation. This resulted in a substantial underestimation of issuer default risk. With the lack of accuracy, investors cannot rely on ratings as their primary reference point to assess investment riskiness, which is why rating agencies ultimately fail to reduce information asymmetries to a large extent. Low-quality firms with a distinct desire to raise capital can take advantage of a “window of opportunity” by issuing overvalued mini-bonds during these times. According to information-based corporate finance theory, underpricing can serve as a credible signal that allows investors to separate low-quality black sheep from the hidden champions. Using a unique and hand-collected sample of all available mini-bonds issued by German SMEs between 2010 and 2015, we found two main conclusions from our analysis. First, credit rating agencies understate the credit risk inherent in mini-bond offerings, and therefore tend to inflate ratings. This result is consistent with the observation that more mini-bonds defaulted than would be expected using historical default probabilities in the respective rating classes. This environment, along with high investor demand, created a “window of opportunity” for low-quality firms to obtain debt financing using mini-bonds. Second, high-quality firms competing for external debt financing have an incentive to signal their quality using mini-bond underpricing. Our data supports the view that higher 24
underpricing is correlated with less riskiness and higher firm quality. On a related note, issuers with higher underpricing are less likely to default, which could reflect firm quality. Several tests for robustness do not alter our results. Our results should be significantly relevant to investors, mini-bond issuers, and policy makers. For uninformed (private) investors who are otherwise unable to distinguish between high- and low-quality issuers, it would be advisable to use mini-bond underpricing for assessing credit risk. In contrast, SMEs that intend to issue mini-bonds should recognize that underpricing can help reduce asymmetric information and signal their quality, which may place them in an advantageous position for subsequent (mini-)bond offerings. For policy makers, our findings highlight the importance of creating regulations that will foster a more transparent information flow, such as obligations to inform issuers and investors in order to reduce difficulties arising from asymmetric information. A lack of investor trust will most likely affect this young and promising corporate financing alternative negatively, not only in Germany, where segment bondm from Börse Stuttgart has already been shut down, but in other countries such as Italy, where mini-bonds were recently introduced. However, the industry is still in its infancy, and has already demonstrated a strong potential to at least reduce the existing funding gap for SMEs. This topic will provide fertile opportunities for future research, particularly with the advent of more data availability.
25
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29
Table 1: Market Overview Panel A shows the derivation of the German mini-bond sample. Panel B shows an overview of industries the issuing company operates in. For each issue year, the number of mini-bonds issued in the various industries is shown according to the first two-digit SIC code group (http://siccode.com/). Panel A #Issued before 04/2013
# Underpricing Available from Datastream
#Underpricing Available and Issued before 04/2013
118
73
98
55
Time-to-Maturity
118
73
98
55
Listed
118
73
98
55
ROA
95
58
83
47
95
58
83
47
Coupon
118
73
98
54
Relative Offering Size
102
62
88
49
Equity Ratio
101
62
87
49
Implied PD z-score
99
63
85
51
Intangible Assets
98
60
84
47
∆ Implied PD
88
59
75
48
80
52
69
42
#Bonds All Mini-Bonds Obtained from Stock Exchanges Bonds Not Initially Listed in the Mini-Bond Segment Bonds Issued in 2004 Bonds with a Prospective Volume Greater than €200 Million Frankfurt am Main (Entry Standard)
135 2 1
7
Frankfurt am Main (Prime Standard)
3
Stuttgart (mbonds)
3
Bond with No Information Available
1
All Mini-Bonds in the Full Sample Control Variables
All Remaining Explanatory Variables
All Control and Explanatory Variables Panel B Industry
2010
2011
2012
2013
Construction
1
1
1
2
Finance, Insurance, Real Estate
3
11
8
10
7
Manufacturing
3
8
8
12
3
Mining Retail Trade
2014
39 1
35
1
1
2
1
3
Services
7
5
4
3
4
2
Wholesale Trade
1
5
1
1
31
33
33
13
7
Total 5
Transportation & Public Utilities Total
2015
2
18 9 8 1
118
30
Table 2: Market Overview This table gives an overview of the German mini-bond market. For each exchange and respective segment(s), the number of bonds issued (#) and the prospective issue volumes in € million (volume) for each respective year are given. Exchanges and Segments
2010 Vol.
2011 #
Düsseldorf Mittelstandsmarkt Frankfurt am Main Entry Standard
2012
2013
2014
2015
Total:
Total:
Vol.
#
Vol.
#
Vol.
#
Vol.
#
Vol.
300
9
138
5
45
2
7,5
1
491
17
300
9
138
5
45
2
7,5
1
491
17
25
1
395
9
888
20
963
25
690
12
25
1
2,985
68
25
1
295
8
713
18
763
23
210
8
25
1
2,030
59
100
1
175
2
200
2
480
4
955
9
75
2
75
2
75
2
75
2
Prime Standard Hamburg Mittelstandsbörse München m:access
15
1
15
1
50
3
15
1
15
1
50
3
Stuttgart
298
6
690
11
363
7
177
4
1,528
28
mbonds
298
6
690
11
363
7
177
4
1,528
28
323
7
1,460
31
1,404
33
1219
33
5,129
118
698
13
25
1
31
Table 3: Summary Statistics This table gives descriptive statistics (mean, standard deviation, min, and Max) for the full sample presented in Table 2, if data items are available (see Table 1 for data availability). All variables are winsorized at a 2.5% level on both sides. All variables are considered in subsequent analyses (see the appendix for variable descriptions and calculation methods). Min
Max
0.007
Standard Deviation 0.024
-0.050
0.066
0.074
0.124
-0.266
0.410
102
1.152
2.131
0.029
11.556
99
0.136
0.160
0.001
0.706
∆ Implied PD
88
-0.122
0.162
-0.702
0.008
Coupon
118
0.073
0.009
0.046
0.093
Intangible Assets
98
0.081
0.128
0.000
0.516
Time-to-Maturity
118
5.135
0.530
4.003
7.005
Listed
118
0.339
0.475
0
1
ROA
95
3.097
5.920
-14.286
15.650
Variable Name
#Obs
Mean
Underpricing
98
Equity Ratio
101
Relative Offering Size Implied PD z-score
32
Table 4: Correlation Matrix This table gives Pearson correlation coefficients for the full sample presented in Table 2, if data items are available (see Table 1 for data availability). All variables are considered in subsequent analyses (see the appendix for variable descriptions and calculation methods). * indicates correlations are statistically significant at least at the 5% level. (1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(1)
Underpricing
1
(2)
Equity Ratio
0.1851
(3)
Relative Offering Size
0.1242
0.2202*
1
(4)
Implied PD z-score
0.1224
0.2016
0.0935
1
(5)
∆ Implied PD
0.1206
-0.2138
-0.0719
-0.9830*
1
(6)
Coupon
0.1203
0.1929
0.0555
0.0985
-0.0778
(7)
Intangible Assets
0.2120
0.112
-0.1047
0.0997
-0.107
0.2133*
1
(8)
Time-to-Maturity
0.1300
-0.1293
-0.1198
-0.1138
0.0761
-0.0948
-0.0594
1
(9)
Listed
0.0681
0.2078*
-0.0773
-0.1162
0.1989
-0.2431*
0.028
0.0253
1
(10)
ROA
0.0633
0.0632
-0.0911
-0.3538*
0.3721*
-0.0617
-0.0441
0.2094*
0.0237
(10)
1
1
1
33
Table 5: Differences in Probability of Default by Ratings and Implied by Altman’s zScore This table shows that a one-sided test for difference-in-means is smaller than 0. Mean PD by ratings is therefore equal to the mean of the probability of default within one year after the bond listing given by the ratings of the rating agencies, and mean implied PD by z-score is equal to the mean of the implied probability of default within one year using Altman’s z-score. Difference-in-mean is calculated by subtracting implied PD by z-score from mean PD by ratings and calculating the average. All calculations are performed for all rating groups separately and jointly (all groups). ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Mean Mean Difference-inImplied PD by PD by Ratings Mean z-score
Rating Group
#Obs
A
5
0.07%
31.20%
-31.13%
1.91*
BBB
36
0.33%
10.55%
-10.22%
4.45***
BB
42
1.08%
11.09%
-10.00%
5.80***
B
4
4.48%
33.91%
-29.44%
1.56
CCC
1
28.83%
52.96%
-24.13%
All Groups
88
1.19%
13.52%
-12.34%
|t-Value|
6.85***
34
Table 6: Rating Migration after Mini-Bond Issuance Panel A shows initial mini-bond ratings by the respective rating agencies, if a rating was available, and rating migration for a two-year period after mini-bond issuance. The rating considered corresponds to the rating closest to the two-year period after issuance, and is therefore not quite two years after issuance. This is because ratings are typically available only once a year, except when the agency proactively issues a new rating that is not a regular follow-up rating. Deviations also occur if the mini-bond defaults, which we classify as “D.” In panel B, we group the “BBB+, BBB, and BBB” ratings from panel A into “BBB” and “BB+, BB, and BB+” to “BB” in order to compare them with the available twoyear rating migration matrix from Standard & Poor’s, which shows less granularity. Panel C shows the rating migration matrix from Standard & Poor’s (2006). Panel D shows the difference between the respective cell entries of panel C minus panel B. Cells highlighted in red signal an inferior development of the mini-bonds compared to the benchmark Standard & Poor’s (2006) developments, and vice versa for cells highlighted in green. Panel A From/To
A
BBB+
BBB
BBB-
BB+
BB
BB-
B
C/CCC
D
#
A
33.33%
0.00%
16.67%
33.33%
0.00%
0.00%
0.00%
0.00%
0.00%
16.67%
6
BBB+
0.00%
0.00%
16.67%
0.00%
0.00%
16.67%
0.00%
0.00%
0.00%
66.67%
6
BBB
0.00%
5.88%
41.18%
5.88%
5.88%
0.00%
0.00%
5.88%
0.00%
35.29%
17
BBB-
0.00%
0.00%
10.53%
31.58%
15.79%
5.26%
10.53%
5.26%
0.00%
21.05%
19
BB+
0.00%
0.00%
0.00%
5.00%
30.00%
25.00%
5.00%
15.00%
5.00%
15.00%
20
BB
0.00%
0.00%
0.00%
5.26%
15.79%
26.32%
21.05%
21.05%
0.00%
10.53%
19
BB-
0.00%
0.00%
0.00%
0.00%
0.00%
12.50%
37.50%
37.50%
0.00%
12.50%
8
B
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
71.43%
28.57%
0.00%
7
C/CCC
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
100.00%
0.00%
2 (continued)
35
Table 6: Rating Migration after Mini-Bond Issuance—continued Panel B From/To
A
BBB
BB
B
C/CCC
D
#
A
33.33%
50.00%
0.00%
0.00%
0.00%
16.67%
6
BBB
0.00%
42.86%
19.05%
4.76%
0.00%
33.33%
42
BB
0.00%
4.26%
59.57%
21.28%
2.13%
12.77%
47
B
0.00%
0.00%
0.00%
71.43%
28.57%
0.00%
7
C/CCC
0.00%
0.00%
0.00%
0.00%
100.00%
0.00%
2
A
BBB
BB
B
C/CCC
D
A
83.99%
10.67%
1.03%
0.37%
0.07%
0.12%
BBB
7.43%
81.40%
7.97%
1.76%
0.34%
0.67%
BB
0.73%
10.10%
70.69%
13.56%
1.76%
3.00%
B
0.43%
1.00%
10.40%
69.15%
6.59%
12.31%
C/CCC
0.52%
0.84%
2.81%
17.45%
30.55%
47.81%
A
BBB
BB
A
50.66%
-39.33%
BBB
7.43%
38.54%
BB
0.73%
5.84%
B
0.43%
C/CCC
0.52%
Panel C From/To
Panel D From/To
B
C/CCC
D
1.03%
0.37%
0.07%
-16.55%
-11.08%
-3.00%
0.34%
-32.66%
11.12%
-7.72%
-0.37%
-9.77%
1.00%
10.40%
-2.28%
-21.98%
12.31%
0.84%
2.81%
17.45%
-69.45%
47.81%
36
Table 7: Initial Mini-Bond Returns This table reports the initial mini-bond returns within the first five trading days after the prospective date of issuance/listing. The initial mini-bond return is calculated as in Equation (1) for the first trading day after the mini-bond was issued, if the mini-bond was traded. The mini-bond is not considered in the sample if it was not traded within the first five trading days after the issue/listing date. Mini-bond prices are obtained from Datastream. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively, for a two-sided test. First Trading Day
#Obs
Mean
Standard Deviation
1
92
0.75%
2.54%
2.84***
2
4
0.11%
0.64%
0.35
5
2
-2.13%
0.67%
4.47
All
98
0.67%
2.50%
2.64***
|t-Value|
37
Table 8: Determinants of Mini-Bond Underpricing We run standard OLS regressions (using two-way clustered standard errors among the dimensions—time in terms of year of bond issue and issuer) to identify the factors that determine bond underpricing as calculated in Equation (1). The coefficients and respective t-statistics are in parentheses below. The independent variables are Equity Ratio, Relative Offering Size, Implied PD z-score, ∆ Implied PD, Coupon, Intangible Assets, Time-to-Maturity, Listed, and ROA (see the appendix for variable descriptions and calculation methods). To reduce the influence of outliers, we winsorize all variables except the Listed dummy variable at the 2.5% level on both sides. N represents the numbers of observations, which varies due to data availability (see Table 1 for a description of which variables are responsible for the reduction in the number of observations). Investigating the variance inflation factors (VIFs) reveals no multicollinearity. Because the mean VIF is 1.19 and the maximum value is 1.28, all values are well below the critical value of 5 (see Kutner et al., 2005). ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
Equity Ratio Relative Offering Size Implied PD z-score ∆ Implied PD Coupon Intangible Assets
Time-to-Maturity Listed ROA Constant Observations Adjusted R2 F-value
(1) 0.085*** (3.75) -0.003*** (-3.05) -0.028 (-1.65)
(2) 0.091*** (3.85) -0.003** (-2.26)
(3) 0.042*** (3.07)
(4) 0.063*** (4.02) -0.003*** (-4.26)
(5) 0.065*** (3.80)
(6) 0.080*** (3.70)
(7) 0.047*** (3.52)
(8) 0.060*** (6.36)
-0.032** (-2.38)
-0.237 (-1.42) -0.046* (-1.87)
0.037** (2.62) -0.292 (-0.85) -0.042 (-1.55)
0.007 (1.45) -0.005 (-1.47) -0.001 (-0.77) -0.004 (-0.25) 78 0.094 1.810
0.009** (2.08) -0.006 (-1.65) -0.001 (-1.18) -0.007 (-0.31) 69 0.090 1.911
0.039*** (3.86) -0.368** (-2.46) -0.046* (-1.88) 0.007** (2.38) 0.001 (0.46) 0.000 (0.18) -0.032** (-2.48) 82 0.003 1.499
Control Variables 0.007** 0.008* (2.37) (1.87) -0.000 -0.002 (-0.04) (-1.34) 0.000 -0.001 (0.02) (-0.88) -0.031** -0.035* (-2.49) (-1.81) 82 80 0.033 0.036 2.346 2.155
0.010** (2.45) -0.004 (-1.28) -0.001 (-1.03) -0.042** (-2.47) 71 0.055 2.754
0.007* (1.81) -0.001 (-0.36) 0.000 (0.05) -0.004 (-0.33) 82 0.005 1.421
0.007** (2.30) -0.002 (-0.52) -0.000 (-0.60) -0.027** (-2.50) 79 0.055 2.091
38
Table 9: Determinants of Early Insolvency Risk We run logistic regressions (using two-way clustered standard errors among the dimensions—time in terms of year of bond issue and issuer) to identify the factors that determine early bond defaults within the first two years after issuance (1 is equal to a default within the first year, and 0 otherwise). The coefficients and respective t-statistics are in parentheses below. The independent variables are Equity Ratio, Relative Offering Size, Implied PD z-score, ∆ Implied PD, Coupon, Intangible Assets, Time-to-Maturity, Listed, and ROA (see the appendix for variable descriptions and calculation methods). To reduce the influence of outliers, we winsorize all variables except the Listed dummy variable at the 95% level. N represents the numbers of observations, which varies due to data availability (see Table 1 for a description of which variables are responsible for the reduction in the number of observations). ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
Underpricing Equity Ratio Relative Offering Size Implied PD z-score ∆ Implied PD Coupon Intangible Assets
Time-to-Maturity Listed ROA Constant Observations pseudo R2
(1) -27.333** (-2.56) -0.581 (-0.17) 0.369*** (2.70) -4.463*** (-2.69)
(2) -29.158** (-1.97) 2.444*** (3.82) 1.387** (2.43)
(3) -23.219** (-2.34) 1.399 (0.31)
(4) -22.701** (-2.29) 0.983 (0.21) 0.088 (1.29)
(5) -24.859** (-2.40) 2.005 (0.38)
(6) -24.028** (-2.34) 3.254 (0.71)
(7) -22.577** (-2.08) 1.072 (0.25)
(8) -23.841*** (-2.67) 0.753 (0.21)
-2.008*** (-4.19)
-36.501 (-0.51) 3.449** (2.53)
3.770** (1.99) -21.907 (-0.24) 3.503*** (5.79)
-1.424*** (-2.83) -0.524 (-0.39) -0.172 (-1.44) 8.616 (1.12) 45 0.1759
-0.407 (-0.36) 0.150 (0.10) -0.211 (-1.11) 1.220 (0.09) 42 0.2514
1.258** (2.27) -21.888 (-0.37) 1.649 (1.14) -0.816** (-2.41) -0.055 (-0.03) -0.061* (-1.95) 2.294 (1.32) 47 0.1032
Control Variables -0.796** (-2.24) -0.077 (-0.05) -0.070** (-2.29) 2.184 (1.22) 47 0.1057
-0.952*** (-5.70) -0.187 (-0.12) -0.105*** (-2.64) 3.362*** (3.34) 47 0.1185
-0.740*** (-4.40) 0.023 (0.01) -0.086** (-2.07) 2.023** (2.17) 44 0.1139
-1.010*** (-2.59) -0.041 (-0.03) -0.056* (-1.67) 4.912 (0.81) 47 0.1056
-0.826** (-2.26) -0.144 (-0.09) -0.053 (-1.06) 2.265 (1.17) 45 0.1145
39
Table 10: Determinants of Mini-Bond Underpricing and Early Insolvency Risk Using Alternative Probability of Default Approaches This table presents the results of the multivariate analyses to identify the determinants of mini-bond underpricing (panel A) and early insolvency risk (panel B), considering the probabilities of default based on Altman’s z-score, mapping by Altman and Saunders (1998), and Ohlson’s o-score separately. Panel A is based on Table 8, and panel B is based on Table 9. For the sake of brevity, we only report the coefficients related to the probabilities of default and underpricing. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Panel A Implied PD z-score
(1) -0.028 (-1.65)
Implied PD o-score
(2)
(3)
(4) -0.032** (-2.38)
-0.119* (-1.80)
Implied PD mapping z-score
(5)
(7)
0.039*** (3.86) 0.177*** (9.44)
∆ Implied PD Mapping
Implied PD z-score
YES YES 78 (1) -27.333** (-2.56) -6.338*** (-2.86)
Implied PD o-score
YES YES 78 (2) -37.418*** (-3.74)
YES YES 71 (3) -9.179 (-0.92)
NO YES 80 (4) -24.859** (-2.40) -2.008*** (-4.19)
-17.776*** (-3.24)
Implied PD mapping z-score
NO YES 80 (5) -32.604*** (-4.31)
NO YES 71 (6) -24.859** (-2.40)
NO YES 71 (7) -29.670*** (-4.44)
NO YES 71 (8) -29.406*** (-6.02)
0.023* (1.97) NO YES 64 (9) -14.109*** (-3.41)
-10.630*** (-8.48) -2.869 (-0.82)
∆ Implied PD z-score
1.258** (2.27) 2.900** (2.45)
∆ Implied PD o-score
4.617 (1.33)
∆ Implied PD Mapping Further Explanatory Variables Control Variables Observations
(9)
-0.025* (-1.92)
∆ Implied PD o-score
Further Explanatory Variables Control Variables Observations Panel B Underpricing
(8)
-0.141** (-2.38) -0.031* (-1.97)
∆ Implied PD z-score
(6)
NO YES 45
NO YES 45
NO YES 39
NO YES 47
NO YES 47
NO YES 39
NO YES 44
NO YES 44
3.163 (0.76) NO YES 37
40
Figure 1: Probability of Default by Ratings and Implied by Scores This figure compares the probability of default within one year after bond listing, and compares with the implied probability of default within one year using Altman’s z-score in Equation (4) (panel A), Altman’s z-score and Altman and Saunders’ (1998) mapping (panel B), and Ohlson’s o-score using Equation (7) (panel C). Panel A
PD implied by Rating
PD implied by z-score*
Panel B
PD implied by Rating
PD implied by z-score mapping**
Panel C PD implied by Rating
PD implied by o-score*
41
Appendix: Variable Descriptions This appendix provides a detailed overview of our calculation methods and databases used. Variable name means the name used in all tables and figures, Database shows which database was used to obtain the information, Variable ID is the name of the data item in the respective database, and Description and Calculation Method describe how the variable was derived or calculated. Variable Name
Database
Variable ID (Dafne)
Description and Calculation Method
Book value of equity
Dafne or Amadeus
Eigenkapital
Book value of equity
Current Assets
Dafne or Amadeus
Current Assets
Current Assets
EBIT
Dafne or Amadeus
EBIT
Earnings before interest and taxes
Net Income
Dafne or Amadeus
Net Income
Net Income
Equity incl. Reserves
Dafne or Amadeus
Sonstiges Eigenkapital (inkl. Rücklagen)
Equity other including reserves
ROA
Dafne or Amadeus
Subscribed Capital
Dafne or Amadeus
Balance Sheet Items
Total Assets Total Intangible Assets Total Liabilities Working Capital
Dafne or Amadeus Dafne or Amadeus Dafne or Amadeus Dafne or Amadeus
Gesamtkapitalrentabilität
Return on Assets
Gezeichnetes Kapital
Subscribed capital
Bilanzsumme Aktiva
Total assets
Immaterielle Vermögensgegenstände
Total intangible assets
Langfristige Verbindlichkeiten + Kurzfristige Verbindlichkeiten
Sum of long-term liabilities and short-term liabilities
Working Capital
Working Capital (continued)
42
Appendix: Variable Descriptions—continued Balance Sheet Ratios z-score
Dafne or Amadeus
𝑋1
Dafne or Amadeus
𝑋2 𝑋3 𝑋4
Equity Ratio Implied PD z-score Implied PD o-score Intangible Assets
o-score
Dafne or Amadeus Dafne or Amadeus Dafne or Amadeus Dafne or Amadeus Dafne or Amadeus Dafne or Amadeus Dafne or Amadeus
Dafne or Amadeus
𝑧 = 6.56 ∙ 𝑋1 + 3.26 ∙ 𝑋2 + 6.72 ∙ 𝑋3 + 1.05 ∙ 𝑋4
Altman’s z-score
Working Capital / Bilanzsumme Aktiva
Working capital to total assets
Sonstiges Eigenkapital (inkl. Rücklagen) / Bilanzsumme Aktiva
Retained earnings to total assets
EBIT / Bilanzsumme Aktiva
EBIT to total assets
Eigenkapital / (Book value of equity to total liabilities)
Book value of equity to total liabilities
Gezeichnetes Kapital / Bilanzsumme Aktiva
Ratio of subscribed capital to total assets
1 / (1+exp(z-score))
1-year probability of default
exp(o-score) / (1+exp(o-score))
1-year probability of default
Immaterielle Vermögensgegenstände / Bilanzsumme Aktiva
Ratio of total intangible assets to total assets
𝑜 = −1.32 − 0.407 ∙ 𝑋1 + 6.03 ∙ 𝑋2 − 1.43 ∙ 𝑋3 + 0.757 ∙ 𝑋4 − 2.37 ∙ 𝑋5 − 1.83 ∙ 𝑋6 + 0.285 ∙ 𝑋7 − 1.72 ∙ 𝑋8 − 0.521 ∙ 𝑋9,
Ohlson’s o-score
(continued)
43
Appendix: Variable Descriptions—continued 𝑋1
Dafne or Amadeus
𝑋2
Dafne or Amadeus
𝑋3
Dafne or Amadeus
𝑋4
Dafne or Amadeus
𝑋5
Dafne or Amadeus
𝑋6
Dafne or Amadeus
Log(Bilanzsumme Aktiva/GNP price-level index)
log(total assets/GNP price-level index) assuming a base value of 100 in 1968
(Langfristige Verbindlichkeiten + Kurzfristige Verbindlichkeiten) / Bilanzsumme Aktiva
Total liabilities to total assets
Working Capital / Bilanzsumme Aktiva
Working capital to total assets
Kurzfristige Verbindlichkeiten / Current Assets
Current liabilities divided by current assets
𝑋5 = 1 if (Langfristige Verbindlichkeiten + Kurzfristige Verbindlichkeiten) > Bilanzsumme Aktiva, 0 otherwise
Dummy variable equal to 1if total liabilities exceed total assets, and 0 otherwise
Net Income / Bilanzsumme Aktiva
Net income to total assets
EBIT / (Langfristige Verbindlichkeiten + Kurzfristige Verbindlichkeiten)
Funds provided by operations to total liabilities
𝑋7
Dafne or Amadeus
𝑋8
Dafne or Amadeus
𝑋8 = 1 if Net Income < 0, 0 otherwise
𝑋9
Dafne or Amadeus
(𝑁𝐼𝑡 − 𝑁𝐼𝑡−1 )⁄(|𝑁𝐼𝑡 | + |𝑁𝐼𝑡−1 |)
Relative Offering Size
Dafne or Amadeus and exchanges
Issue Volume / (Langfristige Verbindlichkeiten + Kurzfristige Verbindlichkeiten)
Dummy variable equal to 1 if net income is negative, and 0 otherwise Change in net income, where 𝑁𝐼𝑡 is net income from the most recent period Ratio of issue volume according to the prospectus to total liabilities (continued)
44
Appendix: Variable Descriptions—continued Bond Characteristics Default probability from rating Implied PD z-score
1-year probability of default based on rating according to rating agency minus implied PD zscore
∆ Implied PD
Dafne or Amadeus and hand collection from mini-bond prospectus or rating agency
Coupon
Hand collection from mini-bond prospectus
Time-to-Maturity
Hand collection from mini-bond prospectus
Underpricing
Datastream (Primary Exchange data item MP) and Data from Issuing Exchange
See Equation (1)
Early Bond Default
Hand collection from exchanges
1 if mini-bond defaults within the first two years, 0 otherwise
Dummy variable indicating whether the minibond was in default within the first two years after issuance
Hand collection from exchanges
1 if the mini-bond issuing company is listed on the stock market, 0 otherwise
Dummy variable indicating whether the minibond issuing company is listed on a stock exchange
Mini-bond coupon (Bond maturity date – issue date) / 365
Bond lifetime in years 𝑈𝑛𝑑𝑒𝑟𝑝𝑟𝑖𝑐𝑖𝑛𝑔𝑖 =
𝑃𝑖,𝑛 𝑃𝑖,0
− 1, where 𝑃𝑖,𝑛 is the
price at 𝑛’th-trading day
Others Listed
45
Appendix-Table 1: Differences in Probability of Default by Ratings and Implied by Mapping z-Score and Ohlson’s Scores This table replicates Table 5, but the probabilities of default are based on Altman and Saunders’ (1998) mapping (panel A), and Ohlson’s o-score (panel B). ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively, for a two-sided test. Panel A
Rating Group
#Obs
Difference-inMean
|t-Value|
A
5
-8.07%
1.67*
BBB
36
-1.03%
1.75**
BB
42
-0.23%
0.68
B
4
-21.75%
0.93
CCC
1
10.56%
All Groups
88
-1.86%
1.65**
Panel B
Rating Group
#Obs
Difference-inMean
|t-Value|
A
3
-10.56%
2.06*
BBB
31
-18.18%
5.24***
BB
36
-16.56%
5.78***
B
4
-24.48%
1.67*
CCC
1
24.91%
All Groups
75
-16.86%
7.72***
46
