1. BASED ON WORK WITH GRANT FLEMING (CONTINUITY CAPITAL PARTNERS) AND FRANK LI (UNIVERSITY OF WESTERN AUSTRALIA) PRIVATE DEBT & Methodological Issues on Indexing 1

2. Learning Objectives 2 1. Topics: a) What is private debt? b) How often is private debt issued? c) What are the returns to private debt?  Is it better to invest in a newly originated issue? Or a secondary issue?  How to benchmark returns VIX Index TED Spread 2. Methods: a) How do you construct a private debt index?

3. What is Private Debt? Large body of research on private equity, especially in the last 10 years Scant work on private debt, despite strong institutional interest in the topic and investment in the asset class  Investment opportunity set is global – but little knowledge of what drives returns outside the U.S. Private Debt is debt issued by private firms to institutional investors, often private debt funds 3

4. 4 Institutional Investors Private Equity Fund Entrepreneurial Firm ReturnsCapital Equity, Warrants, etc. Capital Private Equity Financial Intermediation

5. Questions How is private debt structured? What are the returns to private debt? Are the returns to private debt affected by….  Legal conditions across countries?  Changes in market conditions over time?  Fund manager trading strategies - buy and hold versus secondary trading? Can private debt by modeled in a time series index?  Does private debt exhibit alpha over public debt? What affects excess returns to private debt?  Volatility (as measured by ΔVIX),  Credit risk (TED spread)  Market liquidity. 5

6. VIX Index 6 The CBOE Volatility Index ® (VIX ® ) is a key measure of market expectations of near-term volatility conveyed by S&P 500 stock index option prices.

7. TED Spread 7 This is the difference between the interest rate at which the US Government is able to borrow on a three month period (T- bill) and the rate at which banks lend to each other money on a three month period (measured by the Libor). Since, arguably, the risk of a bank defaulting is slightly higher than that of the US government defaulting, the Ted spread measures the estimated risks that banks pose on each other.  The higher the perceived risk that one or several banks may have liquidity or solvency problems, the higher the rate you will ask from your loans to other banks compared to your loans to the government. Consequently, the Ted spread is a great indicator of interbank credit risk and the perceived health of the banking system.

8. TED Spread 8

9. How is Private Debt Structured? 9 Senior Secured Senior Debt Subordinated Mezzanine Convertible Bonds Preference Shares Equity Warrants Payment in Kind (PIK) loan (where a borrower is not required to make interest payments until repayment or refinancing) Second Lien Portfolio of Non Performing Loans Secondary  More on these structures later

10. Why Care about a Private Debt Index? Private debt funds  Trading strategies  Which is better: buy and hold primary issuance, versus secondary market trading? A private debt return index is important for these types of decisions Need to examine factors that affect excess private debt returns Important for institutional investor asset allocation decisions 10

11. Data Private debt investments made by thirteen specialist credit investment funds in 321 private companies in 13 Asian countries from 2001 to 2014. The median fund manager had been investing in Asian 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). These data were obtained from confidential sources – Continuity Capital Partners – value of networks! 11

12. Details in the Data Issuance and realisation date of the private debt investment; Location (country) of company issuing the private debt; A company description and industry in which the issuing company operates; The type of debt instrument – senior secured loan or subordinated loan; Private debt investment metrics for the credit fund manager  the amount of capital invested in the debt instrument;  the realised component of the investment and total return; Private debt investment returns:  an internal rate of return for the investment (based in audited cashflows), and  the return on investment (or return multiple)(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). 12

13. Table 1 - Summary Statistics InvestmentRealisedUnrealised Total (Realised and Unrealised) IRRROI Average28,564,428 24,675,000 16,438,43134,507,13932%1.33 Median20,000,00011,452,528- 20,274,41620%1.23 Stdev 32,448,382 34,002,898 36,566,424 42,097,23984%0.52 Max 300,000,000 203,879,478 332,438,356 332,385,7371310%3.97 Min 200,000 (747,916) (3,977,002) --100%0.00 N 319299 Total 8,197,990,737 4,811,624,908 2,646,587,451 9,731,013,118 13

14. Table 2 Summary Stats By Country And Sector 14

15. Table 3 Primary versus Secondary Returns 15

16. Table 4 Regression Evidence on Return 16

17. Constructing a Credit Index Goal: To construct a capitalization-weighted Asia private credit return (APCR) index Challenges:  Lack of private loan revaluations information between the start and maturity dates  Lack of deal-specific information on scheduled coupons, prepayment options etc.  Not enough firm-specific information to use Moody’s KMV model to estimate the expected default frequency (EDF) 17

18. Constructing a Credit Index (Cont.) 18 First Step: We discretise the time interval between the maturity or valuation date and the start date into T days. At each time period t, there are a finite number of credit states, N, where the investment can be. In a classic lattice model analysis, when modeling a T-day loan investment as having N credit states, there are NT possible paths for this investment. These credit states include default, non-default and prepaid. It is a general practice to consider prepayment options when evaluating a loan (for example, see Agrawal, Korablez and Dwyer 2008). However, we do not directly observe sufficient information to infer whether a loan was prepaid in our data set. This reduces the possible paths in the lattice analysis down to 2T.

19. Constructing a Credit Index (Cont.) 19 For each credit state in each time period, being default or non-default, there is a risk-neutral probability of moving from this state to the next. This probability could be approximated by using the expected default frequency (EDF), which is a firm specific and forward-looking measure of actual default probability (Kealhofer 2003). A common practice is to use the Moody’s KMV model to estimate EDF, which requires inputs of the value of equity and other items from the borrower’s balance sheet (Dwyer, Kocagil and Stein 2004; Agrawal, Korablez and Dwyer 2008). Without access to such variables, we are not able to estimate the firm specific EDF and instead we use the cumulative default rates among speculative-grade ratings in Asia-Pacific region (as provided by S&P 2013) to approximate the individual default probability.

20. Constructing a Credit Index (Cont.) 20

21. Constructing a Credit Index (Cont.) 21

22. Constructing a Credit Index (Cont.) 22

23. Constructing a Credit Index (Cont.) 23

24. Constructing a Credit Index (Cont.) 24

25. Constructing a Credit Index (Cont.) 25

26. Recap: Constructing a Credit Index (Cont.) 26

27. Recap: Constructing a Credit Index (Cont.) 27

28. Coupon Frequency (days) Half CouponFull Coupon 90a. 2.50%b. 5% 180c. 5%d. 10% 360e. 10%f. 20% TABLE 5 Private Credit Return Index Coupon Frequency and Coupon Rate Assumptions Do not have details of underlying debt instrument Assume 3 coupon frequencies – quarterly, semi-annually or annually – with 6 coupon rates Half coupon means that 50% of the assumed return (a return of 20% per annum) is paid as cash coupon; full coupon means that 100% of the return is paid as a cash coupon For example, Model a assumes that the investments in the private credit index pay coupons to investors every 90 days at a rate of 2.5% per quarter (or 10% per annum, half the total return of the investment). 28

29. Do Private and Public Debt Returns Differ? 29 Our private credit return index provides a monthly return series for Asian private credit investments between 2005 and 2014. We calculate an excess return series as the difference between our APCR index and the J.P. Morgan Asia Credit Index (JACI). The JACI is a broad public credit markets index comprising 705 U.S. dollar denominated bonds issued by 312 sovereign, quasi-sovereign and corporates in 15 Asian countries, excluding Japan and Australia/New Zealand. The index is market capitalisation-weighted and is 76% investment grade debt and 24% non-investment grade debt. We take the moving average monthly return for each of our six estimations of the Asian private credit index and subtract the monthly JACI. Table 6.

30. TABLE 6 Asia Private Credit Excess Return Series Summary Statistics Excess is measured as the difference between the various APCR models and the J.P. Morgan Asia Credit Index (JACI). 30

31. Distribution of APRI Excess Returns 31 Table 6 shows that monthly average excess returns have a mean between 1.3% and 1.6% per month, with a median between 1.2% and 1.5% per month. However, we can also note periods of private credit underperformance, with minimum monthly returns ranging between -4.1% and -4.8% per month. Skewness and kurtosis statistics indicate that the distribution of excess monthly returns contains a higher proportion of positive excess returns. We test for the stability of the return series using an Augmented Dickey- Fuller test. All test statistics indicate that we cannot reject the null hypothesis that the series is stationary. Figure 1 shows the time series variation in the excess return series.

32. FIGURE 1 Asia Private Credit Excess Return Series, Moving Average Monthly Excess Returns 2005 - 2014 Figure 1 shows excess return time series graphs under six different assumptions (Models a – f) with regards to the frequency of coupon payments and coupon rates 32

33. Regression Analysis of Excess Returns 33 EXCESS = a + b(TED) + c(ΔVIX) + d(Liquidity) + e TED Global credit risk is defined as the TED spread, the daily percentage spread between 3-Month LIBOR rate (based on U.S. dollars) and the 3-Month Treasury bill rate, as calculated by the Federal Reserve Bank of St. Louis. VIX Financial market volatility is measured as the change in the volatility index (ΔVIX) as calculated by the Chicago Board Options Exchange. Liquidity We adopt an Asia-specific measure of market liquidity using the quarterly year-on- year percentage change in cross-border and domestic credit, using data from the Bank of International Settlements. An increase in the liquidity measure indicates that there is greater amount of credit available in the Asian region as compared with the previous year, due to domestic and/or cross-border capital inflows.

34. Predictions 34 TED An increase in global credit risk indicates higher levels of investor risk aversion which require higher excess returns as compensation. VIX Times of higher volatility in finance markets will be associated with higher excess returns Liquidity Increases in market liquidity in the Asian region will result in excess supply of credit for private firms, and lower excess returns

35. TABLE 7 Asia Private Credit Excess Return Series Correlation Probability Matrix TED is measured as the daily percentage spread between 3-Month LIBOR rate (based on U.S. dollars) and the 3-Month Treasury bill rate, as calculated by the Federal Reserve Bank of St. Louis VIX is measured as the change in the volatility index (VIX) as calculated by the Chicago Board Options Exchange Liquidity is measured as the quarterly year-on-year percentage change in cross- border and domestic credit, using data from the Bank of International Settlements. 35

36. TABLE 8 Regressions Results of Asia Private Credit Excess Return Series, Credit Risk, Volatility and Liquidity 36 A 1-standard deviation increase in ΔVIX causes an increase in excess returns by approximately 0.11%, which is 8.7% higher relative to the average excess return of 1.3%

37. The Returns to Private Debt in China Our study covers a period of rapid change in private debt markets in Asia, especially in Mainland China We stratified our data to examine whether there is a systematic difference in returns to Chinese versus non-Chinese (Rest of Asia) debt investments (150 China versus 170 Rest of Asia) 37

38. 38 FIGURE A - 1 China versus Rest of Asia Difference in Moving Average Monthly Excess Returns 2005 - 2014

39. 39 TABLE A - 1 China versus Rest of Asia Tests for Difference in Moving Average Monthly Excess Returns 2005 – 2014 We find that China returns were statistically different to Rest of Asia prior to 2008, but have converged since that time period

40. 40 TABLE A - 2 Regressions Results of Asia Private Credit Excess Return Series, Credit Risk, Volatility and Liquidity We use a China-specific VIX index pioneered by O’Neill, Wang and Liu (2015); others variables as in Table 8

41. Conclusions (1 of 3) Private debt is the predominant source of debt financing for companies around the world. Our paper provides the first analysis of the cross-sectional and time series returns to private debt investments in Asian companies, using a sample of credit fund manager investments across the region. We show that the returns to private debt investments are relatively uniform across size, country and industry despite country diversity. We find no evidence that “laws matter” for private debt returns; rather if laws do matter we suggest that borrowers and lenders negotiate terms and conditions in loan agreements which mitigate specific country/jurisdictional risk. 41

42. Conclusions (2 of 3) We find that strategies which involve buying/selling private debt on the secondary market deliver higher returns than a strategy of buying-and-holding a primary issuance. We find that there is no difference between LBO and non-LBO private debt issuances. Further research is required on how private credit manager trade on the secondary market through the credit cycle and on whether the success or regularity of secondary trading strategies varies due to macroeconomic and credit market factors. 42

43. Conclusions (3 of 3) Our private credit return index is the first index to show excess portfolio returns to Asian private credit investments. We used discretisation techniques and lattice models pioneered by Moody’s KMV to estimate private company credit risk and backwards induce credit returns during the holding period of the investment. We find that excess returns are on average between 1.2% and 1.5% per month, and that positive excess returns are stationary over time.  Excess returns across Asia are positively related to volatility (ΔVIX), but are not influenced by credit risk (TED spread) or market liquidity.  Excess returns in China are positively related to volatility (ΔVIX) and by credit risk (TED spread), but are not influenced market liquidity. 43

44. Extensions More details on contractual terms in private loan contracts Default probabilities on private loans Consideration of other factors on private loans  Firm specific  Macro  Country / institutional 44

45. DOUGLAS CUMMING YORK UNIVERSITY SCHULICH SCHOOL OF BUSINESS GRANT FLEMING AUSTRALIAN NATIONAL UNIVERSITY AND CONTINUITY CAPITAL PARTERS DEBT INVESTMENTS IN PRIVATE FIRMS: LEGAL INSTITUTIONS AND INVESTMENT PERFORMANCE IN 25 COUNTRIES

46. Related Prior Work Notable studies in the late 1980s (and again in the early 2000s) focused on the size of high yield and distressed markets, and the attractive relative returns generated by such investments when default rates rose and economies entered recession (see, for example, Altman, 1989, 1993; Altman & Jha, 2003). There has been a comparative dearth of attention on private debt markets and the investment into private debt securities by fund managers, and how performing debt investments (buy and hold investments) compare with non-performing (default and distressed) investments (secondary investments made by buying and selling debt).

47. Research Questions How exactly are private debt securities packaged and sold to specialist fund managers in the alternative asset industry? What are the most important determinants for investment returns (as opposed to yields) to different private debt securities: market conditions, legal conditions, investee firm-specific risk, or investor- specific risk in terms of the quality of managers undertaking due diligence and monitoring of such investments?

48. This Paper Documents the types of private debt investments made by fund managers into private firms across 25 countries over 2001-2010 Shows returns to private debt investments depend on:  Lender (fund manager) characteristics,  particularly portfolio size per manager highlights the role of time allocation for due diligence and monitoring.  Borrower (firm-specific) risk. Shows returns to private debt investments do not depend on:  Market conditions such as TED spreads  Country level legal factors such as creditor rights  Market and legal conditions are nevertheless significantly related to private debt investment volumes and location.

49. Data: 311 Investments, 25 countries

50. Investment years: 2001-2010 Exit years: 2004-2010

51. Size of US and European High Yield Bonds and Leveraged Loans Market, 2001-2010

52. Default and Recovery Rates on US and European High Yield Bonds and Leveraged Loans, 2001-2010

53. Summary Statistics (1 of 2) Number of ObservationsAverageMedianSt Dev.MinimumMaximum Performance Measures Realized and Unrealized IRR2350.1440.140.355-14.46 Winsorized Realized &Unrealized IRR2350.1280.140.208-0.8760.63 Realized IRR1190.2450.25 0.433-14.46 Winsorized Realized IRR1190.2110.250.178-0.8760.63 Gross Multiple3111.2061.1980.53203.8 Investment Duration11932.19130.516.200577 Realized3110.40200.49101 Investment and Exit Amounts Real 2010 $US'000 Capital Invested 28827396.7721040.3425459.05013.57975148593.5 Real 2010 $US'000 Realized Value28816817.604937.9130659.8700312137 Real 2010 $US'000 Unrealized Value28814688.391379.0626299.5500175410.7 Real 2010 $US'000 Total Value28831503.2622992.6835750.9600312137 Types of Debt Contracts Senior Secured3110.09900.30001 Senior Debt3110.00600.08001 Subordinated3110.10300.30401 Mezzanine3110.74310.43801 Convertible Bonds3110.00600.08001 Preference Shares3110.00300.05701 Equity3110.25700.43801 Warrants3110.01300.11301 PIK loan3110.00300.05701 Second Lien3110.01000.09801 Portfolio of Non Performing Loans3110.02900.16801 Secondary 3110.05500.22801

54. Summary Statistics (2 of 2) Number of ObservationsAverageMedianSt Dev.MinimumMaximum Fund Manager Characteristics Number of Professionals31118.4211012.766334 Real Fund Size (2010 $US millions)3111922.3691395.6481261.47161.562964289.843 Real Fund Size / Manager311109.501126.18244.33620.52099189.8975 Portfolio Size / Manager3114.4132.92.8180.3758 Vintage Year Dummy Variables Office in Investee Company Location3110.80710.39501 Country Variables Country Dummy Variables Spanmann Antidirector Rights3113.77241.32205 Creditor Rights Djankov et al Time3111.7361.90.8390.55.7 Creditor Rights Djankov et al Cost3110.0930.070.0600.010.38 Creditor Rights Djankov et al Efficiency31174.48385.818.20117.096.1 Creditor Rights LLSV 19983111.65711.26504 GDP Per Capita31140008.49045394.113947.0901066.287067.5 Industry Intangible / Tangible+Intangible Assets31115.04016.436.7111.229.57 Industry Dummy Variables

55. Comparison Tests IRR Above MedianIRR Below MedianComparison Comparison AverageMedianAverageMedianof Meansof Medians Types of Debt Contracts Senior Secured0.05300.22602.908***8.004*** Subordinated0.09600.16100.9991.007 Mezzanine0.78710.5481-2.643***15.011*** Equity and Bonds0.24500.1610-0.9200.729 PIK Loan0.00000.00901.2981.687* Portfolio NPL0.04300.0320-0.2520.800 Secondary0.12800.0320-1.5112.277** Fund Manager Characteristics Real Fund Size / Manager84.71182.233127.895139.5654.930***15.252*** Portfolio Size / Manager3.5732.3535.856.4004.446***18.485*** Same Company Location0.75510.90311.767*2.141** Country Variables Spanmann Antidirector Rights3.6783.74.02051.2270.216 Creditor Rights Time1.7961.91.85420.2576.980*** Creditor Rights Cost0.1070.070.0920.07-0.9350.330 Creditor Rights Efficiency71.21185.877.54685.81.5061.662* Creditor Rights LLSV 19981.85112.1071.880.8740.004 GDP Per Capita37481.8545123.6536474.4845394.10-0.3100.820 Market Conditions TED Spread Exit Date60.7553236.25825-1.993**4.093** US High Yield Recovery 37.1612029.85620-1.667*2.764* Rate at Exit

56. Figure 3. Realized Winsored IRRs Plotted against TED Spread Standard Deviation Over Investment Horizon and Portfolio Size Per Manager

57. Table 4: Models 3-5 Treatment Regressions Model 3:Model 4: Model 5: Coefficientt-statisticCoefficientt-statisticCoefficientt-statistic Constant0.0821.4000.2072.130**0.0570.830 Investment Amounts Real 2010 $US'000 Capital 7.050E-071.230-3.480E-08-0.0307.550E-071.610 Types of Debt Contracts Subordinated0.0653.440***0.0892.810***0.0612.960*** Mezzanine0.0502.340**0.0642.540**0.0663.080*** Equity and Bonds-0.076-1.100-0.134-1.360-0.070-0.880 PIK Loans0.1173.469***0.1572.430**0.0662.100** Portfolio of NPLs0.1413.440***0.1593.690***0.0912.440** Fund Manager Characteristics Portfolio Size / Manager-0.027-5.270***-0.031-2.920***-0.029-7.010*** Same Location-0.062-2.950***-0.181-1.420-0.047-1.540 Country Variables Creditor Rights Time0.0251.680*0.0261.0700.0211.560 GDP Per Capita2.530E-070.500-4.300E-06-1.960**1.940E-070.300 Industry Intangible / Intangible 3.625E-032.550**-9.270E-05-0.0203.938E-033.120*** Debt Market Conditions Total US Leverage Loans 2010 $billions at time of Inv 8.46E-051.920*2.80E-041.920* Realized versus Unrealized Investment and Time Dummies Realized Investment0.1112.620***0.2371.4400.0921.950* Investment Year Dummies?NoNoYes Exit Year Dummies?NoNoYes

58. Economic Significance Diseconomies of Scale  a one standard deviation increase in portfolio size per manager is associated with a reduction in returns by 8.1% on average, controlling for other things being equal. Seniority is inversely related to returns  A significant positive difference between loans lower in the capital structure (subordinated, mezzanine and PIK loans) and secured loans.  The riskiest category of loans - portfolios of non-performing loans - have higher returns on average (9.1% higher in Model 5, controlling for other things being equal, and this effect is 14.1% in Model 3 and 15.9% in Model 4 in Table 4, with similar statistical and economic significance in Table 5).  Investments in subordinated and mezzanine loans show roughly 5-6% higher returns on average Industry  A one standard deviation increase in intangible assets/tangible assets is associated with an increase in returns by roughly 2.6%

59. Amounts Invested and Location Model 1: OLS Real Cap Model 2: OLS Log CapModel 3: Same Location Coefficientt-statisticCoefficientt-statisticCoefficientt-statisticMarginal Eff. Constant-8908.300-0.7607.75411.32***1.4051.060 Types of Debt Contracts Senior Secured34857.7105.31***1.0482.72*** Mezzanine8348.6911.5300.5541.73*-0.915-1.1800.047 Equity and Bonds27.7430.010-0.350-1.500-1.175-2.61***0.063 PIK Loans20417.2000.8501.5041.060 Portfolio of NPLs-4944.080-0.3400.2500.290 Secondary24512.1700.9702.3471.580 Fund Manager Characteristics Portfolio Size / Manager-1671.136-2.32**-0.039-0.9300.0770.7900.010 Same Location15181.4903.28***0.9713.58*** Country Variables Creditor Rights Efficiency326.5753.02***0.0274.27***-0.032-2.53**0.001 GDP Per Capita-6.893E-02-0.430-2.420E-05-2.56**9.620E-055.20***0.000 Industry Intangible / Intangible -2.958E+02-1.320-9.266E-03-0.700-4.265E-02-1.420.003 Debt Market Conditions TED Spread at Investment 5.23E+012.15**3.41E-032.39**-2.18E-03-0.730.000 Total US Leverage Loans 2010 $billions at Inv-1.29E+00-0.150-5.71E-04-1.119.66E-040.9000.000 Adjusted R2 0.1390.1180.218

60. Summary Private debt investment returns depend on fund manager (lender) characteristics as much as issuing firm (borrower) characteristics.  Portfolio size per manager is inversely related to returns.  In addition, the returns to private debt are related to private firm specific risk, including asset intangibility, priority structure and past non-performance at time of investment. Legal and economic conditions affect amounts invested and location, but not returns

61. Implications Institutional investors are one of the largest suppliers of finance to sovereign, investment grade, and listed high yield debt markets.  High returns: a financial for an institutional investor to also establish and maintain a global private debt program investing in private firms Successful implementation:  Diseconomies of scale in private debt investment.  Higher returns in risker loans (e.g., NPLs, intangible assets)  Mitigate country and economic risks associated with investment location and investment amounts

62. Summary / Takeaways from Part II 62 1. Topics: a) What is private debt? b) How often is private debt issued? c) What are the returns to private debt?  Is it better to invest in a newly originated issue? Or a secondary issue?  How to benchmark returns VIX Index TED Spread 2. Methods: a) How do you construct a private debt index?