Financial Analysts Journal ISSN: 0015-198X (Print) 1938-3312 (Online) Journal homepage: https://tandfonline.com/loi/ufaj20 The Returns to Private Debt: Primary Issuances vs. Secondary Acquisitions Douglas Cumming, Grant Fleming & Zhangxin (Frank) Liu To cite this article: Douglas Cumming, Grant Fleming & Zhangxin (Frank) Liu (2019) The Returns to Private Debt: Primary Issuances vs. Secondary Acquisitions, Financial Analysts Journal, 75:1, 48-62, DOI: 10.1080/0015198X.2018.1547049 To link to this article: https://doi.org/10.1080/0015198X.2018.1547049 View supplementary material Published online: 24 Jan 2019. Submit your article to this journal Article views: 545 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://tandfonline.com/action/journalInformation?journalCode=ufaj20 Research Financial Analysts Journal | A Publication of CFA Institute https://doi.org/10.1080/0015198X.2018.1547049 The Returns to Private Debt: Primary Issuances vs. Secondary Acquisitions Douglas Cumming , Grant Fleming, and Zhangxin (Frank) Liu Douglas Cumming is DeSantis Distinguished Professor of Finance and Entrepreneurship at the College of Business, Florida Atlantic University, Boca Raton, Florida, and visiting professor, Birmingham Business School, University of Birmingham. Grant Fleming is a partner at Continuity Capital Partners, Canberra, Australia. Zhangxin (Frank) Liu is assistant professor of accounting and finance at University of Western Australia Business School, Crawley, Australia. Private debt fund managers invest in debt positions of private companies through (1) new issuances or (2) secondary acquisition of loans. In the study reported here, we used data from more than 400 investments in private companies in 13 Asia-Pacific markets between 2001 and 2015 to examine which strategy performs best. Conditional on market and industry factors, trading private debt delivers higher returns than buying and holding a primary issuance. So, institutional investors should permit fund managers the flexibility to trade. Furthermore, a portfolio of private debt investments delivers excess returns to public markets over time, with excess returns affected by volatility, funding liquidity, and the global financial crisis. An investment in Asia-Pacific private debt should improve risk-adjusted returns for a global or emerging market fixedincome portfolio. Disclosure: The authors report no conflicts of interest. CE Credits: 1 48 T ruly private debt (i.e., debt traded only OTC at the request of the seller) is the predominant source of financing for private companies around the world and is attracting attention from asset consultants and institutional investors for inclusion in investment portfolios.1 Despite the importance of private debt, however, relatively few studies have been made of the types of private debt investment strategies available to investors and whether returns outperform public debt indexes over time. In this study, we examined two questions often encountered by institutional investors in conducting due diligence on private debt opportunities. First, should an investor invest with a private debt fund manager who solely invests in buy-and-hold strategies or permit the private debt fund manager the investment flexibility to also acquire debt on the secondary market? Second, does a diversified portfolio of private debt investments—whether buy-and-hold or secondary trading—consistently outperform public credit over time, conditional on market factors such as liquidity, volatility, and macro (systemic) credit risk? To answer these questions, we collected by hand data on 443 private direct loans made to private companies by specialist credit investment funds in 13 Asia-Pacific markets between 2001 and 2015. Eighty-six percent of the loans in the dataset were to companies in mainland China, India, Australia, and Indonesia, which resulted in diversity by legal and economic system and by size and age of the credit market. We calculated the cross-sectional variation in the performance of private debt, as measured by the internal rate of return (IRR) and the return multiple (or return on investment, ROI)—two measures commonly used by alternative fund managers and institutional investors in evaluating performance. We estimated multivariate regression models We owe thanks to the editors and referees for helpful comments and to conference participants at the Moody’s Corporation/Shanghai Advanced Institute of Finance Credit Conference, Shanghai; University of Sussex Business School; York University Schulich School of Business; Asian Finance Association Annual Conference; and Australasian Finance & Banking Conference. © 2019 CFA Institute. All rights reserved. For Personal Use Only. Not for Distribution. First Quarter 2019 The Returns to Private Debt to find out which investment strategy performs best; in these tests, we controlled for seniority of debt, size, industry, country/territory, time, and fund manager characteristics and used fund fixed effects. In addition, we measured the extent to which buy-andhold and secondary trading returns vary by a particular ownership type—leveraged buyout (LBO) debt issuer. Although we had no prior view as to whether debt issued by LBO-backed companies performs differently from non-LBO-backed private debt, we realized that LBO firms, to build their reputations in debt markets, may have incentives to ensure that private issuers do not renege on debt contracts. Thus, LBO-backed companies may have higher leverage levels and higher default risk than non-LBO-backed companies during credit booms. Next, we investigated whether a diversified portfolio of private direct loans outperforms public debt over time. No commercial return index on private debt investments is available that allows differentiation of returns by investment strategy or country/ territory. So, we built a private credit return index from the underlying loan data by using discretization techniques and lattice models pioneered by Moody’s KMV in the estimation of private debt credit risk. We present monthly returns and correlations between our Asia-Pacific private credit index, public credit indexes, and equity indexes to investigate the potential asset allocation implications of an investment in Asia-Pacific private credit. We then calculated excess returns to private debt investment as the difference between the private credit return series and a comprehensive public market return series (the J.P. Morgan Asia Credit Index). We also decomposed our credit index into two separate series—investments in primary issuances and investments in secondary acquisitions. Literature on the Performance of Private Loans The performance of private loans and returns to private lenders has received little attention in the academic and fund management literature. We highlight here four related areas of research that can provide background to our study. Default and Recovery Rates. Lenders to private companies receive financial return on their loans from the cash coupon (typically a fixed rate, paid regularly); from payment in kind (PIK, or interest accrued and paid at maturity); from upfront fees associated with providing the loan; and from early repayment penalties (penalties stipulated in loan agreements should the company repay the loan prior to maturity). Banks and nonbank lenders take all features of the loan into account when evaluating a new loan and when calculating a fair value of the loan during its holding period (Tschirhart, O’Brien, Moise, and Yang 2007). Carey (1998) showed that a portfolio of private loans has lower default and higher recovery rates than a risk-equivalent portfolio of public bonds and that the difference increases with credit risk. That is, evidence is good that the highly structured nature of private direct loans (with their collateral requirements, covenants, etc.) and close monitoring and scrutiny by private lenders have value that lowers the ex ante riskiness for the borrower.2 The Quality of Legal Systems and Institutions. The “law matters” finance literature has established a positive relationship between the strength of a country/territory’s legal system, credit rights, and structure of covenants, on the one hand, and the size of its corporate bond markets, on the other hand (Djankov, McLiesh, and Schleifer 2007). Drawing on these studies, Qian and Strahan (2007) and Bae and Goyal (2009) showed that bank loan yields are negatively related to the quality of a country/territory’s legal institutions. Cumming and Fleming (2013) extended the law and finance literature by examining the returns to private debt investments in 25 markets. They found no relationship between returns to private debt investments and a country/ territory’s legal system, which suggests that borrowers and lenders negotiate terms and conditions in loan agreements that mitigate specific jurisdictional risks. Concentrated Ownership. Private companies acquired by LBO firms have been regular issuers of private debt. LBO-backed companies have large shareholders (typically, an LBO firm will own in excess of 90% of the equity of a private company) that are motivated to maximize equity value and use debt to discipline managers by limiting free cash flow (Cao 2011). Also, LBO firms may have incentives to ensure that private issuers do not renege on debt contracts in order to build reputation in debt markets. In cases where LBO companies default on debt, Cressy and Farag (2012) found that LBO-backed companies have higher recovery rates than non-LBO-backed companies during periods of high credit availability. LBO companies also tend to use debt, however, to bolster returns during periods of high credit availability. Thus, LBO-backed companies may have higher leverage levels and higher default risk than non-LBO-backed companies during credit booms. Volume 75 Number 1 49 For Personal Use Only. Not for Distribution. Financial Analysts Journal | A Publication of CFA Institute Macroeconomic and Credit Market Conditions. Macroeconomic factors and the credit markets have been identified as important determinants of the variation in credit spreads and public bond performance over time. Greenwood and Hanson (2013) showed that the quantity of credit (market liquidity) is negatively related to credit quality and negatively related to excess returns to public bondholders. The average quality of issuances in public bond markets deteriorates during a credit boom, resulting in significant underperformance of public corporate bonds when measured against US T-bonds of similar maturity. Similarly, Collin-Dufresne, Goldstein, and Martin (2001) found that monthly changes in credit spreads are largely driven by local demand/supply shocks rather than by idiosyncratic default risk. Brunnermeier and Pedersen (2009) modeled the relationship between a security’s market liquidity and the traders’ funding liquidity, which measures the availability of capital for traders. Their model (and subsequent empirical studies) suggests that in times of market illiquidity, funding liquidity also dries up, increasing discounts in secondary markets and returns to secondary market trades (see also Brunnermeier 2009). Tang and Yan (2010) documented a positive association between credit spreads and volatility in the growth (or change) in GDP. They also showed that credit spreads widen when investors become more risk-averse (as measured by investor sentiment). Cumming and Fleming (2013) provided, to our knowledge, the only analysis of private debt returns and macroeconomic and credit market factors. Using panel data for 10 years, they found no crosssectional relationship between private debt returns and GDP per capita at the borrower’s location or between private debt returns and market liquidity as measured by the TED spread (levels or changes).3 Data and Summary Statistics Our dataset comprises private direct loans made by 15 specialist credit investment funds to 443 private companies in 13 Asia-Pacific markets from 2001 to 2015. We collected the data from confidential private placement memorandums (PPMs) issued by Asia Pacific–based credit fund managers who raised capital from “sophisticated” (or wholesale) institutional investors. The institutional investors from whom the data came invested in only a minority of the fund managers in our sample. The data represent the total investment track record for each credit fund manager (not a selected or biased history), audited by a reputable accounting firm. Representativeness is difficult to measure in young and growing asset management markets because many funds try but do not raise funds. We observe only the funds that were successful in raising some capital, which is typically the right tail of the population of fund managers. Each of the 15 PPMs provides prospective investors with the historical track of the credit fund manager at the individual private debt investment level. The sample includes PPMs received from unsolicited offerings by fund managers as well as through proactive approaches from the fund-offunds manager and, in all cases, PPMs before analysis of the track record was undertaken. It also includes fund managers that had not raised funds since their first fund because of poor performance and at least one manager that has closed. A key feature of the dataset is that the private debt fund managers have flexibility in their investment mandate (as shown in their fund documentation) to invest in a primary issuance or acquire a loan on the secondary market. Private debt funds are typically organized as limited partnerships or firms (or similar tax-transparent entities—e.g., trusts) with either perpetual life (with a “lock-up” or no-redemption period) or a fixed term (e.g., five to ten years). The legal structures, terms and conditions, and investor base are similar to those found in the private equity and hedge fund industries. Fund manager compensation is determined by management fees charged by the fund manager and based on the total capital committed to the fund or on invested capital and on the performance of the fund (performance fees or carried interest). Notably, management fees are not based on net asset values, thus removing any possibility that the performance of a private debt investment is inflated (or not written down) to increase or maintain management fee income. In addition, given that both primary and secondary investments can be undertaken in the same fund, the funds have no difference in compensation (i.e., “return to effort”) based on whether the debt manager invested in a primary or secondary investment. The data typically include the following information: •• issuance and realization data of the private debt investment; •• location (country/territory) of the company issuing the private debt; 50 First Quarter 2019 For Personal Use Only. Not for Distribution. The Returns to Private Debt •• a company description and a description of the industry in which the issuing company operates; •• the type of debt instrument—senior secured loan or subordinated loan; •• the cash coupon, payment periodicity, and overall yield on the debt instrument; •• private debt investment metrics for the credit fund manager—the amount of capital invested in the debt instrument, the realized component of the investment, and total return; and •• private debt investment returns: an IRR for the investment (based on audited cash flows) and the ROI, defined as the total amount of capital returned—principal, coupon, and additional payments (e.g., upfront arrangement fees, early prepayment fees) divided by the initial investment outlay, including transaction costs. All returns were calculated in US dollars for a US-dollar investor (limited partner). Any localcurrency returns were converted to US dollars by the fund managers from prevailing exchange rates to match cash flows of the loan. Our process for data collection and verification involved double-checking the entry of all data, cross-checking investment returns with each credit manager and against audited financial statements (where possible), and recalculating IRRs. In terms of fund manager characteristics, the median fund manager had been investing in Asia-Pacific credit markets for 13 years (average = 11.9 years), had invested US$1.7 billion (average = US$2.2 billion), and had 10 investment professionals (average = 32 investment professionals). The summary statistics for the dataset are provided in Table 1. Note that the median size of investment, US$16.6 million, delivered an investor a median IRR of 22% and ROI of 1.27. Private debt investment returns range widely—with an IRR from 1,310% to –100% and an ROI from 3.97× investment to zero (that is, a total loss of the loan). Note the difference in IRRs for primary and secondary investments. The median primary investment in a direct loan delivered an IRR of 22% compared with an IRR of a secondary acquisition of 26%. These returns may seem high for a debt investment, but the IRR of the investment comprises a yield on the loan (e.g., cash coupon plus PIK or accrued interest) as well as upfront fees and (if relevant) early prepayment fees. Overall, the relatively higher IRRs in the right-hand side of the Table 1. Asia-Pacific Private Debt Investment Returns Average Median Std. Dev. Max. Min. Investment 24.4 16.6 29.4 300.0 0.2 443 9,860 Realized return 19.8 8.0 30.6 204.0 –0.8 267 5,290 Unrealized return 16.1 2.7 32.8 332.0 –4.0 221 3,560 Total return 31.0 18.3 39.1 332.0 0.0 358 11,100 Full sample 32% 22% 76% 1,310% –100% 409 Primary deals 31% 22% 78% 1,310% –100% 368 Secondary deals 40% 26% 58% 369% –33% 41 N Sum US$ (millions) IRR ROI Full sample 1.33 1.27 0.47 3.97 0.00 393 Primary deals 1.29 1.25 0.40 3.14 0.00 352 Secondary deals 1.71 1.50 0.72 3.97 0.50 41 Notes: “Investment” is the amount of money invested in the private debt investment. “Realized return” is the return to the private debt investment comprising principal, coupon, and additional payments (e.g., upfront arrangement fees, early prepayment fees). “Unrealized return” is the fair market value of the remaining loan assessed by the credit manager. “Total return” is the sum of the investment’s realized proceeds and unrealized (fair market) value at the valuation date. Volume 75 Number 1 51 For Personal Use Only. Not for Distribution. Financial Analysts Journal | A Publication of CFA Institute percent of the loans in the dataset were issued by companies headquartered in four markets—mainland China (36.0%), India (23.4%), Australia (14.4%), and Indonesia (12.4%). The data provide diversity by legal and economic system, size, and age of credit markets. Seventy-four percent of the loans in the dataset were located in three industries—real estate (GICS code 40 [Financials], 46.6%), industrials (13.7%), and consumer discretionary (13.5%). We controlled for industry effects (where appropriate) in subsequent empirical analyses. distribution are probably explained by the fact that the dataset includes investments that are secondary trades of private debt. Secondary trading strategies involve a credit fund manager acquiring private debt OTC at discount to par at times when liquidity is at a premium or a specific holder of the debt needs to sell the debt instrument. Short holding periods and low acquisition prices can result in high IRRs as compared with buy-and-hold investment strategies (Duffie, Gârleanu, and Pedersen 2007). Similar crosssectional variation in returns has been observed in hedge fund studies on dynamic trading strategies across various styles (Fung and Hsieh 1997; Sadka 2010). Given such a large range, we winsorized the dataset to account for outliers/influential points later in our analysis.4 To expect private debt investment returns to vary by size, geography, and industry of the issuer is natural. For example, a negative relationship often exists between the size of an investment (a proxy for issuer size) and investment returns because small companies have lower credit quality and higher degrees of information opaqueness than large companies. We Figure 1 reports the country/territory (location) in which each private debt issuer operates. Eighty-six Figure 1. Asia-Pacific Private Debt Investment Returns by Geography A. Internal Rate of Return (%) China (36%) India (23.4%) 0.39 0.20 Australia (14.4%) Indonesia (12.4%) 0.27 0.16 Singapore (2.5%) Korea (2.3%) New Zealand (1.6%) Thailand (1.6%) Philippines (0.7%) 0.48 1.42 1.40 1.27 1.12 1.23 1.13 0.25 0.46 1.25 1.15 0.20 1.28 1.16 0.30 0.27 1.40 1.35 0.13 0.25 1.24 1.20 0.29 0.25 1.21 0.34 0.32 Taiwan (0.7%) 0.22 0.16 Japan (0.2%) 0.18 0.18 Malaysia (0.2%) 1.29 1.23 0.27 0.24 0.19 0.15 Hong Kong SAR (3.8%) B. Return on Investment (%) 2.23 2.20 1.35 1.34 0.29 0.29 IRR Mean 1.64 2.30 2.30 IRR Median ROI Mean ROI Median Notes: Mean and median investment returns are given according to the country/territory of the companies issuing the private debt (country/territory is defined by the credit fund manager). The investments total 443. The percentage of investments made in a country/ territory is included in parentheses. 52 First Quarter 2019 For Personal Use Only. Not for Distribution. The Returns to Private Debt found no differences in returns by size of investment in terms of the IRR of the investment, but we did find a weak significant negative association between size of investment and ROI.5 We conclude that the statistical evidence is insufficient to suggest that private debt investment returns vary by size of investment. We investigated the variation in returns by geography and industry. The law and finance literature indicates that bank loan yields are negatively related to the quality of a country/territory’s legal institutions (Qian and Strahan 2007; Bae and Goyal 2009). Cumming and Fleming (2013), however, found no relationship between private loans and location of private debt issuers. Also, one might expect to find differences in private debt returns for different industries because tangible assets, revenue, and earnings volatility vary by industry (Soriano and Climent 2006; Garcia-Feijóo, Madura, and Ngo 2012). Therefore, we investigated the variation in returns by geography and industry at the univariate level through tests for equality of market/industry means and medians by using analysis of variance tests for means and by using the Kruskal–Wallis chi-square test for medians. We found no statistically significant differences in means or medians across the dataset for either country/territory or industry. Trading Strategies: Primary Issuances vs. Secondary Acquisitions Private credit managers have several ways in which they can invest in private company debt. The credit manager can participate in primary debt issuance (solely as a bilateral loan or as part of a private syndicate) and hold the investment to maturity (or early repayment). In this scenario, the private credit manager is party to the negotiation of price and nonprice terms of the loan agreement (collateral, covenants, information rights, control rights), which mitigates credit risk (Strahan 1999; Ackert, Huang, and Ramirez 2007). An alternative investment strategy is for the private credit manager to acquire the debt instrument in the secondary market. This “dynamic” trading strategy involves the fund manager acquiring the private loan OTC, usually in a deal brokered by an investment bank. Our a priori view was that credit fund managers are rational and that their compensation structures encourage value-enhancing behavior, which drives overall fund performance and raises performance fees. On this basis, we expected the investment returns to trading in the secondary market to be at least as high as those available from primary (buyand-hold) investments. Some reasons suggest, however, that secondary private debt transaction returns would be higher than primary investment returns. Secondary debt transactions may be expected to have significant adverse-selection problems (Stiglitz and Weiss 1981), and investors, in turn, may discount the price they will pay for any secondary transaction for a debt investment in a private company (Anthony, Docherty, Lee, and Shamsuddin 2017). A secondary seller could be selling as a result of the low quality of the underlying company or because of problems of illiquidity. Risk-averse investors would discount the value of the transaction more than other investors to compensate for illiquidity risks. Furthermore, investors who suspected that the sale was motivated by the seller’s liquidity problems would also expect a discount. The net effect is that we might expect secondary debt transactions to be severely discounted and, therefore, the returns to secondary debt to be higher than returns to a primary buy-and-hold strategy. We estimated ordinary least-squares (OLS) regressions on a winsorized dataset to examine whether returns to these two investment strategies produced statistical differences. As noted previously, we observed large variation in the returns to private debt investments, resulting in several influential points that biased estimates in the OLS regressions. Thus, we adopted a 95% winsorized approach for our regressions that excluded the upper and lower 2.5% of data points (20 data points). We ran four estimates of the following generalized regression model: Returns = f (Secondary, Subordinated, LBO, Experience, Economies of scale, Size, Cost of contract enforcement, Fully realized, GFC invest, GFC realized), where the base return is a buy-and-hold investment at primary issuance and indicator variables equal 1.0 for the type of investment and zero otherwise. In addition to variables for a credit bought in the “Secondary” market and for “Subordinated” debt, we included the control variable “LBO” for whether the loan was to a company that was owned by a private equity fund. We also included a number of control variables in our regression models. First, we controlled for fund manager characteristics that might influence returns: the “Experience” of the fund manager, “Economies of scale” in the fund manager Volume 75 Number 1 53 For Personal Use Only. Not for Distribution. Financial Analysts Journal | A Publication of CFA Institute firm, and manager fund “Size.” Experience was measured as the relative position of the investment in the fund life. Economies of scale were measured as the cumulated number of investments divided by the number of investment professionals at the time of investment. Size is the total US dollar amount of assets under management the credit fund manager had raised at the time of the investment. Second, we controlled for the quality of a country/ territory’s legal system, which has the potential to influence returns (through expected cost of recovery in default); this factor is the “Cost of contract enforcement.” We used the World Bank’s Enforcing Contracts score for each jurisdiction matched to each loan in the dataset. The cost of contract enforcement is an overall score measuring the time, cost, and procedural complexity to resolve a standardized commercial dispute. A high score indicates that the country/territory has a highly efficient dispute resolution system and thus lower costs of contract enforcement.6 Third, we controlled for whether the private debt investment was realized or unrealized—“Fully realized” is an indicator variable showing whether the investment was fully realized at the time of data collection; 1 = fully realized. And we controlled for whether the investment was made during the global financial crisis (GFC) of 2008–2010 (“GFC invest” = 1 if the investment was made during 2008–2010). “GFC realized” is an indicator variable showing whether the investment was fully realized during the GFC. Table 2 reports results. Results without fund fixed effects are reported in Models 1 and 2, and results with fund fixed effects in Models 3 and 4.7 The regression results indicate that secondary trading generates additional returns above returns to a buy-and-hold strategy. The secondary coefficient is positive and statistically significant in all model estimations.8 We found no difference between LBO and non-LBO private debt investments and no significant relationship between returns and fund manager economies of scale or size or between returns and the cost of contract enforcement. Manager experience is positively associated with returns, suggesting learning effects from investing in private debt markets. To some extent, the findings for private debt returns and the legal system were not expected. Qian and Strahan (2007) and Bae and Goyal (2009) found that legal systems can influence credit spreads. Private debt, however, appears to be distinct from those findings in this respect, possibly because sophisticated private debt fund managers can write loan contracts in tailored ways to meet their needs in a wide variety of institutional contexts. The Returns to Private Credit Investing over Time Does a diversified portfolio of private debt investments—whether buy-and-hold or secondary trading— consistently outperform public credit over time when such market factors as volatility, funding liquidity, macro (systemic) credit risk, and market liquidity are considered? To answer this question, we constructed a private credit return index from underlying private debt information to chart whether and how private debt returns vary over time. We drew inspiration from the alternative assets literature, which has found excess returns (alpha) in private equity and private real estate. We postulated that we would find positive excess returns to private debt.9 In this section, we report the monthly returns of the Asia-Pacific private credit index and compare those returns with commonly invested public credit and equity indexes. We then report our calculated excess-return index and decompose the index by trading strategy to examine (1) whether excess returns accrue to primary and secondary investments and (2) whether (and how) the time series varies in accordance with credit risk, volatility, and market liquidity. Private Credit Return Index—Methodology. Private loans can be valued in several ways, and each valuation model attempts to estimate credit risk at a point in time and the net present value of expected cash flows in no-default and default scenarios (Kealhofer 2003). The challenge of building a return index with our dataset is the lack of sufficient information on loan revaluations between the start and maturity dates. For the majority of investments, only the amount of the investment on the start date and the final realized and unrealized ROIs are recorded, although for a subset of the loans, we did have information on coupon rates, payment periodicity, and overall yield. This lack of data means that the exact performance trajectory of each investment (the loan’s valuation and return since inception at discrete points in time) is unknown. To conduct the time-series analysis of the relationship between Asia-Pacific private credit returns and market volatility, we used discretization techniques and applied the Moody’s KMV lattice model to construct the credit return index (Dwyer, Kocagil, and Stein 2004; Agrawal, Korablez, and Dwyer 54 First Quarter 2019 For Personal Use Only. Not for Distribution. The Returns to Private Debt Table 2. Regressions Results for Primary Issuances and Secondary Trading Strategies (standard errors in parenthesis) Model 1 IRR Variable Secondary Subordinated LBO Experience Economies of scale Cost of contract enforcement Fully realized GFC invest 0.14** 0.120* 0.120* (0.037) (0.055) (0.055) 0.017 0.012 –0.072 –0.072 (0.035) (0.036) (0.046) (0.046) 0.017 0.034 –0.022 –0.022 (0.047) (0.047) (0.086) (0.086) 0.046** 0.035** 0.045** 0.045** (0.007) (0.006) (0.008) –0.008 –0.002 0.006 0.006 (0.007) (0.006) (0.009) (0.009) 3.2E-05* –1.6E-05 2.6E-04 2.7E-04 (9.5E-06) (9.2E-06) (3.8E-04) (3.8E-04) 1.4E-03* 8.3E-04 0.001 0.001 (5.5E-04) (5.8E-04) (7.9E-04) (7.9E-04) 0.026 0.031 0.012 0.012 (0.023) (0.023) (0.021) (0.021) 0.150** GFC realized 5.0E-04 Adjusted R2 –0.027 0.110* –0.003 (0.037) (0.032) 0.054 0.079 –0.007 –0.007 (0.038) (0.038) (0.016) (0.016) NO NO YES YES Fund fixed effects No. of observations Model 4 IRR 0.150** (0.026) Constant Model 3 IRR (0.037) –0.010 Size Model 2 IRR 400 0.192 400 400 0.166 0.215 400 0.215 Notes: In these regressions, the base return (0,0) is a buy-and-hold investment at primary issuance. Indicator variables equal 1.0 for the type of investment and zero otherwise. *p < 0.05. **p < 0.01. 2008). Although use of the KMV model has limitations, we focused on constructing a model and index that require only a base level of information on the investment and that “benchmark” private loan valuation over time against public debt default rates. Thus, we focused on possible changes in probability of default over time. The construction process is briefly described in the following paragraphs, and a full description is available upon request. 1. We discretized the time interval between a loan’s inception and maturity dates. For each day, we assumed that a loan was in one of two credit states, default or not default. We approximated Volume 75 Number 1 55 For Personal Use Only. Not for Distribution. Financial Analysts Journal | A Publication of CFA Institute the loan default probability by using the cumulative default rates among speculative-grade ratings in the Asia-Pacific region (as provided by Standard & Poor’s in 2013). 2. We determined the value of the investment for each credit state by going backward from maturity to inception. We assumed a fixed loanrecovery rate for given default throughout the life of a loan. 3. We incorporated coupon payments during the life of the loan. 4. Once we worked out the loan value backward from maturity, we constructed a value-weighted Asia-Pacific private credit return index. We required a minimum of two active investments on any given day for a loan to be in the index. Private Credit Returns, Public Credit Returns, and Stationarity. Our Asia-Pacific Private Credit Return (APCR) Index provides a monthly return series for Asia-Pacific private credit investments between January 2006 and June 2015. To investigate whether private debt returns differ from public debt returns, we calculated an excessreturn series as the difference between our APCR Index and the J.P. Morgan Asia Credit Index (JACI). The JACI is a broad index of the public credit markets comprising US$-denominated bonds designated as sovereign, quasi-sovereign, and corporate bonds in 15 Asian markets, excluding Japan and Australia/ New Zealand. The JACI is market capitalization weighted (market cap of US$544 billion on 31 October 2014). It is 76% invested in investmentgrade debt and 24% invested in non-investmentgrade debt. We chose a broad index of the public credit market with largely investment-grade credits because institutional investors (including, but not limited to, those that provided data for this study) usually compare private market returns with an “opportunity cost” public market benchmark plus a premium for illiquidity.10 The monthly JACI return was subtracted from the monthly APCR Index return to obtain the excessreturn index. The summary statistics for the APCR Index, global and emerging market public credit and public equity indexes, and the excess-return indexes are shown in Table 3. Also provided in Table 3 is a breakdown of excess returns. Table 3 shows that the APCR Index outperformed the JACI and global public credit indexes. The average monthly APCR Index also outperformed the MSCI Emerging Markets Index (EMI), the Russell 3000 Index, and the S&P 500 Index. Note also that the average monthly APCR Index return is approximately double the returns available to investors in US private direct lending as measured by the Cliffwater Direct Lending Index, although with a slightly higher standard deviation (2.1% vs. 1.6%). Table 3 also shows correlation statistics between the APCR Index and the public credit and public equity indexes. We found no statistically significant correlations with these indexes. The primary investment monthly average excess return has a mean of 1.66%, and the secondary acquisition monthly average excess return has a mean of 1.67%, but note also periods of private credit underperformance against public markets with minimum monthly returns ranging between –5.1% and –11.6%. Skewness and kurtosis statistics indicate that the distribution of excess monthly returns contains a higher proportion of positive excess returns. A key question is whether the positive alpha shown in Table 3 is persistent over time. We tested for persistence in alpha by using an augmented Dickey–Fuller test to examine the stability of the excess-return series. All test statistics indicate that we cannot reject the null hypothesis that the series is stationary, indicating that the excess return of the APCR Index is different from zero (in this case, positive) for the period. In summary, our absolute and excess-return series show that Asia-Pacific private credit delivers better performance than public market credit and equity; that the excess returns, on average, range between 1.67% per month (secondary) and 1.66% per month (primary); and that positive excess returns are stationary over time (that is, there is positive alpha). Private debt is a much newer asset class than traditional asset classes with fewer funds, particularly in the AsiaPacific region. No a priori reason suggests that returns to private debt have any resemblance to returns to private equity in the United States (see, e.g., Harris, Jenkinson, and Kaplan 2014) or other regions in the world as a result of the differences between private debt and private equity and the fact that private debt markets in Asia Pacific are less developed than those in the West. Indeed, we note consistency in returns over time, and the IRR of investment in private direct loans is high because of the multiple cash features of the loan and the short hold periods. We next examine in detail the variations in the excess-return series. 56 First Quarter 2019 For Personal Use Only. Not for Distribution. The Returns to Private Debt Table 3. Summary Statistics and Correlations Mean Median Std. Dev. Min. Max. Skewness Kurtosis 0.35 5.32 6.28 51.88 Correlation A. Indexes APCR 1.53% 1.29% 2.06% –5.37% APCR: Primary 2.24 1.20 6.77 –5.74 60.58 APCR: Secondary 2.26 0.00 5.66 –11.13 33.21 2.73 15.46 0.25* JACI 0.58 0.66 2.20 –15.22 5.92 –3.31 25.87 0.10 S&P/LSTA Leveraged Loan 0.40 0.49 2.46 –14.18 8.34 –2.01 15.88 0.07 Credit Suisse Leveraged Loan 0.37 0.51 2.30 –13.97 7.71 –2.38 17.79 0.08 Bloomberg Barclays US Aggregate Bond 0.39 0.35 0.95 –2.39 3.66 0.12 4.1 0.03 MSCI Emerging Markets 0.19 0.50 7.00 –32.16 15.41 –1.01 6.43 0.06 –0.59 0.22 9.71 –30.29 24.99 –0.35 4.18 –0.02 Cliffwater Direct Lending 0.79 0.00 1.62 –6.92 4.28 –0.1 6.37 0.05 Bank of America Merrill Lynch Global High Yield 0.63 1.01 3.14 –17.79 10.85 –1.81 14.08 0.12 Russell 3000 0.45 1.19 4.61 –19.69 10.76 –1.06 5.66 0.14 S&P 500 0.43 1.16 4.45 –18.56 10.23 –1.04 5.44 0.15 0.95% 0.89% 2.85% 1.33 9.33 1.00 US Government 10Year Yield 8.96% 1.00 0.52** B. Excess returns Excess: All –5.10% 16.28% Excess: Primary 1.66 0.64 6.97 –6.59 58.55 5.45 42.05 0.53** Excess: Secondary 1.67 0.08 5.94 –11.61 32.91 2.45 12.7 0.38** ΔVIX 0.05 –0.41 5.01 –15.28 20.50 0.73 6.68 0.41** ΔTED –0.04 –0.25 29.28 –86.00 203.00 3.41 25.87 0.42** 116.75 98.50 56.74 54.00 343.00 2.44 8.92 Liq1 –8.79 –10.41 16.76 –33.55 35.14 0.84 3.62 –0.02 Liq2 0.04 0.04 0.05 –0.14 0.15 –1.14 6.56 –0.06 CreditSpread N 0.14 112 Notes: Publicly available indexes were downloaded from Bloomberg. In Panel B, excess returns are measured as the difference between the overall APCR Index, the APCR primary index, the APCR secondary index, and the JACI. VIX (measuring volatility) is the Chicago Board Options Exchange volatility index; in Panel B, the variable ΔVIX is measured as the change in the VIX estimated as, for example, ΔVIXt = VIXt – VIXt–1. Funding liquidity is measured as the change in the TED, the daily percentage spread between the three-month LIBOR rate (based on US dollars) and the three-month T-bill rate, as calculated by the Federal Reserve Bank of St. Louis. CreditSpread is measured as the yield spread between the BAA and AAA corporate bond rate indexes as supplied by the Federal Reserve Bank of St. Louis. Liq1 is the quarterly year-on-year percentage change in cross-border and domestic credit from data from the Bank of International Settlements. Liq2 is the quarterly percentage change in cross-border credit. *p < 0.05. **p < 0.01. Volume 75 Number 1 57 For Personal Use Only. Not for Distribution. Financial Analysts Journal | A Publication of CFA Institute Time-Series Variation in Returns. To what extent do excess returns to private debt vary with global volatility, trader funding liquidity (i.e., the availability to funds), global credit risk, and fund flows into/out of the region (i.e., market liquidity)? Also, do the returns to different investment strategies—buy-and-hold versus secondary trading—vary differently because of market factors? To answer these questions, we estimated various models by regressing the excess-return private credit index against four variables measuring change in volatility (ΔVIX), change in funding liquidity (ΔTED), global credit risk (CreditSpread), and Asia-Pacific market liquidity (Liquidity); GFC is an indicator variable with a value of 1 if the issuing date fell during the crisis. And we controlled for the largest industry (real estate) and the largest market (mainland China): EXCESSt = a + b(ΔVIXt) + b(ΔTEDt–1) + b(CreditSpreadt) + b(Liquidityt) + b(GFC) + b(Real Estate) + b(China) + e. Financial market volatility was measured as the change in the VIX, and following Brunnermeier, Nagel, and Pedersen (2008) and Brunnermeier (2009), we measured funding liquidity as the change in the TED spread, the daily percentage spread between the three-month LIBOR (based on US dollars) and the three-month T-bill rate as calculated by the Federal Reserve Bank of St. Louis. We used the immediate historical change in the TED spread (ΔTEDt–1– ΔTEDt–2) as lagged variables, which in our view better approximates the change in funding liquidity in the market. Global risk (CreditSpread) is the yield spread between BAA and AAA corporate bond indexes. Liquidity is Liq1 and Liq2 from Table 3. An increase in the liquidity measure indicates that a greater amount of credit was available in the Asia-Pacific region in one year compared with the previous year because of domestic and/or cross-border capital inflows. In addition, because 46.6% of investments in the region are in the real estate industry and 36.0% of investments are in China (see Figure 1), we included variables based on estimates of the proportion of active investments made in real estate and China each month. Summary statistics for ΔVIX, ΔTEDt–1, CreditSpread, and Liquidity are provided in Panel B of Table 3. We hypothesized that excess returns are positively related to credit risk and volatility but negatively related to funding liquidity and market liquidity. All else being equal, we expected that an increase in global credit risk would indicate higher levels of investor risk aversion, which require higher excess returns as compensation. Similarly, times of higher volatility in the financial markets will be associated with higher excess returns (Tang and Yan 2010; Greenwood and Hanson 2013). In terms of funding liquidity, if liquidity shrinks (the ΔTED spread rises), secondary market price discounts increase, thereby increasing the returns to secondary investments (Brunnermeier 2009; Brunnermeier et al. 2008). We did not expect a relationship between primary investment returns and funding liquidity. Finally, we hypothesized that increases in market liquidity in the Asia-Pacific region would result in an excess supply of credit for private companies and lower excess returns (Collin-Dufresne et al. 2001). The correlation probabilities for the excess-return series and main explanatory variables used in our regressions are shown in Table 3. The correlation probabilities show some statistically significant correlations between several of the explanatory variables and the excess return to the private credit index. For example, ΔVIX (higher volatility) and ΔTED (funding illiquidity) are significantly positively correlated with each of the excess-return series (APCR, primary investment, and secondary investment), in most cases at the 1% or 5% significance level. Liquidity is not significantly correlated with the excess-return indexes. The results of the regression analysis are reported in Table 4. The data show a significant positive association between the excess-return indexes and ΔVIX, although this association has greater economic and statistical significance for primary investments than for secondary investments. The coefficient estimate of 0.21 in Model 4 is significant at the 5% level for primary investments. For secondary investments, however, the coefficient estimates in Models 5 and 6 are insignificant. In contrast, for secondary investments, we found a significant positive association between the excess-return indexes and ΔTED. In Model 5, the coefficient is 0.049, and in Model 6, the coefficient is 0.048; both are significant at the 1% level. For Models 3 and 4, for primary investments, the coefficients are insignificant. Overall, the data highlight a stronger impact of volatility on primary investments and a stronger impact of funding liquidity on secondary investments. The data indicate no association between the two Asia-Pacific liquidity measures and excess returns. These results are consistent for the APCR excessreturn index, the primary investment excess-return index, and the secondary investment excess-return index. The liquidity variable was not found to be 58 First Quarter 2019 For Personal Use Only. Not for Distribution. The Returns to Private Debt Table 4. Regression Results for APCR Excess-Return Series and Primary and Secondary Investment Strategies (standard errors in parentheses) Variable ΔVIXt ΔTEDt–1 CreditSpreadt Liq1t Real estate Mainland China Constant No. of observations Adjusted R2 (4) Excess Primary (5) Excess Secondary (6) Excess Secondary (2) Excess All 0.20** 0.20** 0.19 0.21* 0.11 0.098 (0.057) (0.058) (0.086) (0.070) (0.064) (0.067) 0.036** 0.036** 0.033 0.035 0.049** 0.048** (0.0089) (0.0095) (0.013) (0.014) (0.0063) (0.0075) –0.0091 –0.0079 –0.021 –0.015 –0.019 –0.030 (0.019) (0.015) (0.019) (0.019) (0.0071) (0.0090) 0.00037 (0.017) Liq2t GFC (3) Excess Primary (1) Excess All –0.021 –0.025 (0.026) (0.057) 1.75 15.5 –9.31 (7.42) (16.2) (17.4) 0.011 0.011 0.015 0.015 0.051* 0.048 (0.0072) (0.0066) (0.018) (0.018) (0.019) (0.020) 0.048** 0.049** 0.090 0.097 –0.032 –0.047 (0.013) (0.014) (0.043) (0.048) (0.025) (0.026) 0.031 0.030 0.058 0.055 0.010 0.012 (0.013) (0.013) (0.036) (0.038) (0.020) (0.021) –0.017 –0.020 –0.029 –0.042 0.035 0.061 (0.014) (0.018) (0.022) (0.018) (0.033) (0.035) 112 0.37 112 0.37 112 112 0.07 112 0.07 0.11 112 0.11 Notes: See Table 3 for explanations of how the variables are defined and calculated. The standard errors were computed by using max [3, 2*horizon] Newey–West lags. *p < 0.05. **p < 0.01. significant. Alternative estimations (not reported) combining liquidity measures are also not significant. The data show that secondary investments, unlike primary investments, are positively and significantly related to the GFC. These data are consistent with the view that the crisis gave rise to secondary sales at a higher discount than in noncrisis periods, enabling purchasers to achieve abnormal profits. The coefficient is 0.051 in Model 5 (significant at the 5% level). The GFC is not statistically significant in Models 3 and 4 for primary investments or for the full sample in Models 1 and 2. Finally, the data show that the real estate sector outperformed other industries in the sample period. A 1.0% increase in the proportion of investments made in the real estate industry implies respective increases of 4.8% and 9.0% in the excess return and excess primary return (Models 1 and 3). Also evident is that private credit investments made in mainland China do not tend to outperform those in other markets. Conclusion Our empirical findings show that the inclusion of Asia-Pacific private credit in an investor’s portfolio has the potential to increase return with relatively low correlation with public leveraged loan indexes, emerging market equity indexes, and US direct lending indexes. We found that the returns to Volume 75 Number 1 59 For Personal Use Only. Not for Distribution. Financial Analysts Journal | A Publication of CFA Institute Asia-Pacific private credit in the period were 1.53% per month, on average, with a standard deviation of 2.1%. Our APCR Index exhibited low (and statistically insignificant) correlations with emerging market public credit (JACI, 0.103), emerging market equity (MSCI EMI, 0.059), global leveraged loan indexes (e.g., Credit Suisse Leverage Loan Index, 0.078), and direct lending in the United States (Cliffwater Direct Lending Index, 0.053). We also note differences in return, however, between primary and secondary investments and differences in return over time. Investors face several due diligence questions when seeking to include Asia-Pacific credit in their portfolios. We addressed two in this article. First, should an investor invest with a fund manager who solely invests in buy-and-hold strategies (primary investments), or should the investor permit the fund manager greater investment flexibility to also acquire debt in the secondary market (secondary investments)? Second, does a diversified portfolio of Asia-Pacific private debt investments consistently outperform public credit over time? And if so, to what extent do returns to Asia-Pacific private credit vary with such market factors as volatility, funding liquidity, macro (systemic) credit risk, and market liquidity? We found that strategies involving buying/selling private debt in the secondary market in Asia Pacific deliver higher returns than a strategy of buying and holding a primary issuance. In the regressions, secondary trading returns were positive and statistically significant at the 5% level in all model estimations using IRR and ROI. We found no difference between LBO and non-LBO Asia-Pacific private debt investments. Our results suggest that credit fund manager trading skills, over and above the skills involved in the evaluation of private debt opportunities at issuance, are important in assessing excess returns, no matter whether debt is senior secured or subordinated or in LBO-backed or non-LBO-backed private companies. Our private credit return index, the APCR Index, is the first index to show excess investment returns to private credit investments in Asia Pacific (or, as far as we are aware, anywhere in the world). We used discretization techniques and lattice models pioneered by Moody’s KMV to estimate private company credit risk and backward induced credit returns during the holding period of the investment. We found that excess returns are, on average, 0.95% per month and that positive excess returns are stationary over time. Excess returns to primary investments are positively related to volatility (as measured by the change in the VIX). Excess returns to secondary investments are positively related to funding illiquidity (change in the TED spread), indicating that when funding liquidity shrinks (i.e., the TED spread rises), secondary market price discounts increase, resulting in an increase in the returns to secondary investments. Excess returns to secondary investments were also positively and significantly affected by the GFC. Neither primary nor secondary returns were influenced by Asia-Pacific market liquidity. Our findings are robust to various model specifications. Asia-Pacific direct lending is a relatively new market for institutional investors. Although asset consultants have championed the role of private loans in an institutional investor’s portfolio as a way of delivering excess returns and diversification to public debt and emerging market equity, the evidence to date is that few institutional investors have ventured into Asia-Pacific markets.11 A cross-market study provides insights for practitioners, whether focused on Asia Pacific or not, as such studies provide results that are, potentially, a better approximation of the expected returns to a diversified global private debt portfolio (incorporating developed and emerging markets) than those focusing solely on a single market (e.g., the United States) or a set of developed markets (e.g., Western Europe). Editor’s Note Submitted 15 September 2017 Accepted 9 August 2018 by Stephen J. Brown Notes 1. Aon Hewitt Investment Consulting (2018); Cambridge Associates (2017); Preqin (2016); Roddick (2016); Kidd (2015); Cliffwater (2016); Towers Watson (2015a, 2015b). 2. Carey’s (1998) sample is based on 13 major life insurance companies in the United States from 1986 to 1992, but not all companies contributed data for all years, so the sampling raises issues. In our article, we cover a range of countries before and after the global financial crisis, thus providing diversity of legal and creditor systems and time periods. 3. TED stands for Treasury–Eurodollar rate. The TED spread is the difference between the interest rate on short-term US government debt and the interest rate on interbank loans. 60 First Quarter 2019 For Personal Use Only. Not for Distribution. The Returns to Private Debt 4. Winsorizing limits extreme values in statistical data to reduce the effect of possibly spurious outliers. 5. To test for differences in investment returns by size, we used ordinary least-squares regressions on size (investment cost) and log of size. The full sample and winsorized samples produced similar results, although with extremely low model-adjusted R 2s (approximately 1%–2%) and F-statistics (significant at the 10% level only). 6. For details on the methodology involved in calculating the World Bank’s Enforcing Contracts score, see www. doingbusiness.org/Methodology/enforcing-contracts. 7. We also estimated models with a dummy variable for realized investments and country dummies for China and India to address the fixed country effects. Our results did not change qualitatively. 8. We also estimated models using ROI as the dependent variable. Our results were qualitatively similar to the results reported here. 9. On private equity, see Nielsen (2008); Dittmar, Li, and Nain (2012); Fan, Fleming, and Warren (2013); Fidrmuc, Palandri, Roosenboom, and Van Dijk (2013); and Tykvová (2017). On private real estate, see Kaiser (2005) and Alcock, Baum, Colley, and Steiner (2013). 10. With the information provided in the online supplemental material (available at https://www.tandfonline.com/doi/sup pl/10.1080/0015198X.2018.1547049), one can replicate the results with alternative benchmarks; of course, alternative benchmarks with greater volatility give rise to findings that are more sensitive to outliers and the period selected. 11. In a recent survey, Preqin (2017) noted that four out of five institutional investors (78%) were not targeting AsiaPacific private debt in the next 12 months. References Ackert, Lucy, Rongbing Huang, and Gabriel Ramírez. 2007. “Information Opacity, Credit Risk, and the Design of Loan Contracts for Private Firms.” Financial Markets, Institutions and Instruments 16 (5): 221–42. Agrawal, Deepak, Irina Korablez, and Douglas Dwyer. 2008. “Valuation of Corporate Loans: A Credit Migration Approach.” Moody’s KMV (25 January). 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