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PRIVATE DEBT & Methodological Issues on Indexing 1 BASED ON WORK WITH GRANT FLEMING (CONTINUITY CAPITAL PARTNERS) AND FRANK LI (UNIVERSITY OF WESTERN AUSTRALIA) Learning Objectives 2 Topics: 1. 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 Methods: 2. a) How do you construct a private debt index? What is Private Debt? 3 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 Private Equity Financial Intermediation Institutional Investors Returns Capital Private Equity Fund Equity, Warrants, etc. Capital Entrepreneurial Firm 4 Questions 5 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. 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. 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 (Tbill) 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. TED Spread 8 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 Why Care about a Private Debt Index? 10 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 Data 11 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! Details in the Data 12 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). Table 1 - Summary Statistics 13 Investment Realised Unrealised Total (Realised and Unrealised) IRR ROI Average 28,564,428 24,675,000 16,438,431 34,507,139 32% 1.33 Median 20,000,000 11,452,528 - 20,274,416 20% 1.23 32,448,382 34,002,898 36,566,424 42,097,239 84% 0.52 Max 300,000,000 203,879,478 332,438,356 332,385,737 1310% 3.97 Min 200,000 (747,916) (3,977,002) - -100% 0.00 319 299 Stdev N Total 8,197,990,737 4,811,624,908 2,646,587,451 9,731,013,118 Table 2 Summary Stats By Country And Sector Panel A IRR Country Mainland China Australia Indonesia Greater China (Hong Kong/Mainland China) India South East Asia Korea New Zealand Thailand Hong Kong Global Singapore Asia Pacific Miscellaneous Philippines Taiwan Japan Malaysia Panel B GICS Code/Sector 10 Energy 15 Materials 20 Industrials 25 Consumer Discretionary 30 Consumer Staples 35 Health Care 40 Financials 45 Information Technology 50 Telecommunication Services 55 Utilities Miscellaneous 14 Frequency 122 47 45 28 20 12 8 6 6 5 4 4 3 3 3 3 1 1 Percent 38.0% 14.6% 14.0% 8.7% 6.2% 3.7% 2.5% 1.9% 1.9% 1.6% 1.2% 1.2% 0.9% 0.9% 0.9% 0.9% 0.3% 0.3% Mean 42.1% 22.2% 25.7% 20.9% 29.6% 35.9% 30.2% 12.7% 30.4% 20.9% 18.0% 25.1% 78.3% 19.3% 34.0% 21.7% 18.0% 29.0% Median 19.5% 16.5% 16.5% 23.9% 21.0% 26.0% 30.0% 26.5% 24.7% 21.8% 17.5% 22.5% 22.0% 6.0% 32.0% 16.0% 18.0% 29.0% Mean 1.33 1.26 1.39 1.22 1.31 1.38 1.31 1.43 1.25 1.73 1.30 1.53 1.30 1.54 2.23 1.35 NA 2.30 24 28 51 45 22 4 122 6 3 13 3 7.5% 8.7% 15.9% 14.0% 6.9% 1.2% 38.0% 1.9% 0.9% 4.0% 0.9% 21.7% 21.8% 36.8% 32.3% 31.4% 34.0% 33.9% 19.1% 41.6% 38.5% 19.3% 14.9% 20.0% 20.2% 19.1% 23.6% 30.5% 19.6% 17.9% 41.0% 28.0% 6.0% 1.19 1.30 1.22 1.38 1.26 1.91 1.34 1.29 1.21 1.58 1.54 ROI Median 1.23 1.23 1.24 1.13 1.34 1.16 1.21 1.43 1.25 1.35 1.30 1.45 1.30 1.12 2.20 1.34 NA 2.30 1.13 1.23 1.16 1.39 1.22 1.60 1.25 1.20 1.29 1.50 1.12 Table 3 Primary versus Secondary Returns Panel A Primary (0) Secondary (1) Senior (0) IRR (%) N 31.2% 218 46.3% 30 28.4% 58 27.4% 11 Subordinated (1) IRR (%) N Panel B Primary (0) Secondary (1) Senior (0) ROI N 1.26 206 1.75 30 1.26 50 1.70 11 Subordinated (1) ROI N 15 Table 4 Regression Evidence on Return IRR Model Intercept (1) 0.236*** (0.012) Secondary 0.093*** (0.031) Subordinated -0.030 (0.025) LBO Adj R 2 2.52% ROI (2) (3) (4) 0.236*** (0.012) 0.093*** (0.031) -0.036 (0.030) 0.014 (0.042) 1.250*** (0.024) 0.244*** (0.063) 0.163*** (0.056) 1.250*** (0.024) 0.246*** (0.063) 0.146** (0.065) 0.047 (0.092) 2.23% 8.15% 7.90% *p < 0.10, **p < 0.05, ***p < 0.01. Standard errors are reported in parentheses. 16 Constructing a Credit Index 17 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) 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. 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. Constructing a Credit Index (Cont.) 20 Second step: Determine the value of the investment for each credit state. Let us use Si,t to represent the value of an investment i at time t in [0, Ti], where Ti is 𝑁 𝐷 the maturity day; and 𝑆𝑖,𝑡 and 𝑆𝑖,𝑡 to represent the value at time t if it is non-default and default from previous time t-1, respectively. In this setting, only Si,0 and Si,T are known. We start at the maturity date or the last report date. In the credit state where the investment has not gone into default from the previous period Ti-1, the value of the investment at Ti is: 𝑁 𝑆𝑖,𝑇 = 𝑆𝑖,𝑇 In the alternate credit state where the investment has been defaulted from the previous period, the value of the investment at Ti is: 𝐷 𝑆𝑖,𝑇 = (1 − 𝐿𝐺𝐷)𝑆𝑖,0 Constructing a Credit Index (Cont.) 21 It is important to note that here we assume a fixed proportion of the original investment can be recovered from the original investment in the event of default. An alternative assumption could be to assume a recovery of the investment value from the previous period i.e. Si,T-1 in this case. 𝑁 However, we do not directly observe the true value Si,T-1. If we used 𝑆𝑖,𝑇−1 to proxy for the true 𝑁 value, it would have induced a loop such that 𝑆𝑖,𝑇−1 is determined by itself. We then step back one day to Ti – 1. For each credit state, we compute the expected value of the next period’s cash flows under the risk-neutral measure. In the credit state that the investment has not gone default from the previous period Ti – 2, the value of the investment at Ti-1 is: 𝑁 𝐷 𝑁 𝑆𝑖,𝑇−1 = 𝑝𝑖,𝑡 𝑆𝑖,𝑇 + 1 − 𝑝𝑖,𝑡 𝑆𝑖,𝑇 = 𝑝𝑖,𝑡 𝑆𝑖,0 1 − 𝐿𝐺𝐷 + 1 − 𝑝𝑖,𝑡 𝑆𝑖,𝑇 where pi,t is the probability of default for investment i, LGD is the loss given default rate and Si,0 is the value of investment at the start, which is known in this setting. Given the lack of firm specific information we set LGD as 20%. Our approach is consistent with Kealhofer (2003) and Gupton and Stein (2005) who argue that LGD values should be set with reference to historical averages to avoid endogeneity issues in estimating the probability of default. Constructing a Credit Index (Cont.) 22 In the alternate credit state where the investment has been defaulted from the previous period, the value of the investment at Ti-1 is: 𝐷 𝑆𝑖,𝑇−1 = 1 − 𝐿𝐺𝐷 𝑆𝑖,0 That is, the investment would terminate at Ti-1 and no further movement in valuation will be observed. We continue to work backward and track the value of the investment at each credit state until time 1, assuming that the loan investment would stop following a default. It follows some basic algebra to show that at any time t in [1, …, T-1], 𝑇−𝑡 𝑁 𝑆𝑖,𝑡 = (1 − 𝑝𝑖,𝑡 )𝑇−𝑡 𝑆𝑖,𝑇 + 𝑝𝑖,𝑡 𝑆𝑖,0 (1 − 𝐿𝐺𝐷) (1 − 𝑝𝑖,𝑡 )𝑇−𝑡−𝑗 𝑗=1 𝐷 𝑆𝑖,𝑡 = 1 − 𝐿𝐺𝐷 𝑆𝑖,0 Constructing a Credit Index (Cont.) 23 Third step: We incorporate coupon payments during the life of an investment. Most private credit investments provide an investor with the combination of cash and non-cash interest (paymentin-kind), with the proportion negotiated as part of the terms of the loan agreement between the borrower and the private credit lender at the start of the loan period. Our data includes the coupon rate on the private debt investment for approximately 20% of investments. The median coupon rate is 13.8%, payable on a quarterly basis. We assume this coupon rate for all transactions with missing data. In the Appendix, we investigate the robustness of this assumption by examining the APCR index against indices constructed using different assumptions of coupon frequencies and coupon rates (six alternative model specifications). Let us use ci to represent the coupon payment rate for investment i and Ii,t as an indicator function that equals to 1 if t is a coupon paying day. The value of the investment at any time t in [1, …, T-1] is a sum of the investment value in the non-default credit state and an accrued amount of coupons received up until t, 𝑡 𝑁 𝑆𝑖,𝑡 = 𝑆𝑖,𝑡 + 𝑐𝑖 𝑆𝑖,0 𝐼𝑖,𝑗 𝑗=1 Constructing a Credit Index (Cont.) 24 After the third step, we are able to recover one particular trajectory of investment i: 𝑆𝑖,0 , 𝑆𝑖,1 , 𝑆𝑖,2 … , 𝑆𝑖,𝑇−1 , 𝑆𝑖,𝑇 Constructing a Credit Index (Cont.) 25 Last step Construct the Asia private credit return index, which is capitalization-weighted and requires a minimum of two active investments. More specifically, the individual’s weight, wi,t, is determined by the size of the total investment at its inception Si,0. If there are N investments underlying the index on time t, the weight for investment i is, 𝑤𝑖,𝑡 = 𝑆𝑖,0 𝑁 𝑗=1 𝑆𝑗,0 For any t in the sample period from 2006 to 2015, the return index can be calculated as 𝑁 𝐴𝑃𝐶𝑅𝑡 = 𝑤𝑖,𝑡 ( 𝑖=1 𝑆𝑖,𝑡 𝑆𝑖,𝑡−1 − 1) In this model, the loan investment values are quite stable such that a daily change can be close to zero. Such observations are not uncommon in private debt valuations. Agrawal, Korablez and Dwyer (2008) find that monthly changes in loans quotes are equal to zero around 47% of the time in their sample of LPC loans quotes from 2002 to 2006. Recap: Constructing a Credit Index (Cont.) 26 Our approach 1. Discretize the time interval between maturity/valuation dates and start dates into T days; 2. Employ a lattice model and assume that there are two credit states: default and non-default. Not allowing for prepayment. 3. At each credit state in each time period, the transition probability is estimated from the cumulative default rates among speculative-grade ratings in AsiaPacific region from S&P. 4. We derive that = (1 − 𝑝𝑖,𝑡 )𝑇−𝑡 𝑆𝑖,𝑇 + 𝑝𝑖,𝑡 𝑆𝑖,0 (1 − 𝐿𝐺𝐷) (1 − 𝑝𝑖,𝑡 )𝑇−𝑡−1 +(1 − 𝑝𝑖,𝑡 )𝑇−𝑡−2 + ⋯ + 1 𝐷 𝑆𝑖,𝑡 = 1 − 𝐿𝐺𝐷 𝑆𝑖,0 𝑁 𝑆𝑖,𝑡 𝑁 𝐷 Where pi,t is the probability of default for investment i, 𝑆𝑖,𝑡 and 𝑆𝑖,𝑡 represent the value at time t if it is non-default and default from previous time t-1. We assume the loss given default rate to be 20%. Recap: Constructing a Credit Index (Cont.) 27 Our approach 5. To allow for coupon payment: 𝑡 𝑁 𝑆𝑖,𝑡 = 𝑆𝑖,𝑡 + 𝑐𝑖 𝑆𝑖,0 𝐼𝑖,𝑗 𝑗=1 6. So we can simulate one particular trajectory of investment i: 𝑆𝑖,0 , 𝑆𝑖,1 , 𝑆𝑖,2 … , 𝑆𝑖,𝑇−1 , 𝑆𝑖,𝑇 7. The capitalization-weighted return index APCR can be calculated as: 𝑁 𝐴𝑃𝐶𝑅𝑡 = 𝑖=1 8. 𝑆𝑖,0 𝑆𝑖,𝑡 ( − 1) 𝑁 𝑆 𝑆 𝑖,𝑡−1 𝑗=1 𝑗,0 Excess return index = APCR –J.P. Morgan Asia Credit Index (JACI) 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). Coupon Frequency (days) Half Coupon Full Coupon 90 a. 2.50% b. 5% 180 c. 5% d. 10% 360 e. 10% f. 20% 28 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. 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). Summary Stats Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis ADF t-stats Excess_a Excess_b Excess_c Excess_d Excess_e Excess_f 0.014 0.016 0.014 0.016 0.013 0.016 0.014 0.015 0.012 0.013 0.012 0.013 0.167 0.178 0.166 0.177 0.168 0.183 -0.046 -0.044 -0.048 -0.041 -0.046 -0.044 0.028 0.028 0.028 0.028 0.029 0.029 1.890 2.150 1.868 2.090 1.881 2.152 12.005 14.305 11.663 13.487 11.442 13.053 -7.089 -8.041 -7.033 -8.014 -7.267 -8.706 30 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. 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 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-onyear 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. 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 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 crossborder and domestic credit, using data from the Bank of International Settlements. 35 TABLE 8 Regressions Results of Asia Private Credit Excess Return Series, Credit Risk, Volatility and Liquidity Model Excess_a Excess_b Excess_c Excess_d Excess_e Excess_f Intercept 0.008** (0.004) 0.010** (0.004) 0.008** (0.004) 0.010*** (0.004) 0.009** (0.004) 0.012*** (0.004) Ted 0.004 (0.005) 0.007 (0.005) 0.003 (0.005) 0.005 (0.005) 0.002 (0.005) 0.003 (0.005) ΔVIX 0.060*** (0.014) 0.060*** (0.014) 0.061*** (0.014) 0.063*** (0.015) 0.063*** (0.015) 0.067*** (0.016) Liquidity -0.023* -0.021 (0.014) -0.020 (0.014) (0.014) -0.016 (0.014) -0.018 (0.014) -0.008 (0.015) 21.3% 22.5% 21.2% 21.9% 19.8% 18.6% Adj R 2 *p < 0.10, **p < 0.05, ***p < 0.01. Standard errors are reported in parentheses. 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% 36 The Returns to Private Debt in China 37 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) -0.02 Model a Model b Model c 38 Model d Model e -0.04 -0.06 -0.08 Model f 2013M12 2013M09 2013M06 2013M03 2012M12 2012M09 2012M06 2012M03 2011M12 2011M09 2011M06 2011M03 2010M12 2010M09 2010M06 2010M03 2009M12 2009M09 2009M06 2009M03 2008M12 2008M09 2008M06 2008M03 2007M12 2007M09 2007M06 2007M03 2006M12 2006M09 2006M06 2006M03 2005M12 FIGURE A - 1 China versus Rest of Asia Difference in Moving Average Monthly Excess Returns 2005 - 2014 0.06 0.04 0.02 0 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 a b c d e f Obs. China-Rest of Asia Return Mean China-Rest of Asia Return Mean (Std dev) 2006-08 (Std dev) 2008-2013 -0.64% (0.69%) -0.73% (1.05%) -0.61% (0.83%) -0.67% (1.38%) -0.57% (0.79%) -0.59% (1.34%) 36 0.22% (0.93%) 0.31% (0.90%) 0.21% (0.96%) 0.29% (0.94%) 0.19% (0.99%) 0.25% (0.99%) 60 Difference in Means Tests t-test SatterthwaiteWelch t-test* Anova F-test Welch F-test* -4.83*** -5.21*** 23.35*** 27.10*** -5.13*** -4.93*** 26.35*** 24.35*** -4.27*** -4.43*** 18.22*** 19.59*** -4.05*** -3.69*** 16.40*** 13.65*** -3.88*** -4.11*** 15.09*** 16.85*** -3.50*** -3.24*** 12.22*** 10.52*** *p < 0.10, **p < 0.05, ***p < 0.01. 39 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 Model Intercept Ted ΔC-VIX Liquidity Adj R2 Excess a Excess b Excess c Excess d Excess e Excess f -0.010** (0.004) 0.012** (0.005) 0.030*** (0.011) -0.024* (0.014) -0.009** (0.004) 0.014*** (0.005) 0.030*** (0.011) -0.023 (0.014) -0.010** (0.004) 0.012** (0.005) 0.030*** (0.011) -0.022 (0.014) -0.009* (0.005) 0.013*** (0.005) 0.030*** (0.011) -0.020 (0.014) -0.010** (0.005) 0.012** (0.005) 0.029*** (0.011) -0.021 (0.014) -0.009* (0.004) 0.013*** (0.005) 0.028** (0.011) -0.017 (0.014) 19.8% 20.9% 18.9% 19.2% 18.1% 17.5% *p < 0.10, **p < 0.05, ***p < 0.01. Standard errors are reported in parentheses. 40 Conclusions (1 of 3) 41 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. Conclusions (2 of 3) 42 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. Conclusions (3 of 3) 43 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. Extensions 44 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 DEBT INVESTMENTS IN PRIVATE FIRMS: LEGAL INSTITUTIONS AND INVESTMENT PERFORMANCE IN 25 COUNTRIES DOUGLAS CUMMING YORK UNIVERSITY SCHULICH SCHOOL OF BUSINESS GRANT FLEMING AUSTRALIAN NATIONAL UNIVERSITY AND CONTINUITY CAPITAL PARTERS 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). 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 investorspecific risk in terms of the quality of managers undertaking due diligence and monitoring of such investments? 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. Data: 311 Investments, 25 countries Investment years: 2001-2010 Exit years: 2004-2010 35.00% 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% 2001 2002 2003 2004 2005 2006 Exits 2007 2008 Investments 2009 2010 Size of US and European High Yield Bonds and Leveraged Loans Market, 2001-2010 Default and Recovery Rates on US and European High Yield Bonds and Leveraged Loans, 2001-2010 Summary Statistics (1 of 2) Number of Observations Performance Measures Realized and Unrealized IRR Winsorized Realized &Unrealized IRR Realized IRR Winsorized Realized IRR Gross Multiple Investment Duration Realized Investment and Exit Amounts Real 2010 $US'000 Capital Invested Real 2010 $US'000 Realized Value Real 2010 $US'000 Unrealized Value Real 2010 $US'000 Total Value Types of Debt Contracts Senior Secured Senior Debt Subordinated Mezzanine Convertible Bonds Preference Shares Equity Warrants PIK loan Second Lien Portfolio of Non Performing Loans Secondary Average Median St Dev. Minimum Maximum 235 235 119 119 311 119 311 0.144 0.128 0.245 0.211 1.206 32.191 0.402 0.14 0.14 0.25 0.25 1.198 30.5 0 0.355 0.208 0.433 0.178 0.532 16.200 0.491 -1 -0.876 -1 -0.876 0 5 0 4.46 0.63 4.46 0.63 3.8 77 1 288 288 288 288 27396.77 16817.60 14688.39 31503.26 21040.34 4937.91 1379.06 22992.68 25459.050 30659.870 26299.550 35750.960 13.57975 0 0 0 148593.5 312137 175410.7 312137 311 311 311 311 311 311 311 311 311 311 311 311 0.099 0.006 0.103 0.743 0.006 0.003 0.257 0.013 0.003 0.010 0.029 0.055 0 0 0 1 0 0 0 0 0 0 0 0 0.300 0.080 0.304 0.438 0.080 0.057 0.438 0.113 0.057 0.098 0.168 0.228 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 Summary Statistics (2 of 2) Number of Observations Fund Manager Characteristics Number of Professionals 311 Real Fund Size (2010 $US millions) 311 Real Fund Size / Manager 311 Portfolio Size / Manager 311 Vintage Year Dummy Variables Office in Investee Company Location 311 Country Variables Country Dummy Variables Spanmann Antidirector Rights 311 Creditor Rights Djankov et al Time 311 Creditor Rights Djankov et al Cost 311 Creditor Rights Djankov et al Efficiency 311 Creditor Rights LLSV 1998 311 GDP Per Capita 311 Industry Intangible / Tangible+Intangible Assets 311 Industry Dummy Variables Average Median St Dev. Minimum Maximum 18.421 1922.369 109.501 4.413 10 1395.648 126.182 2.9 12.766 1261.471 44.336 2.818 3 61.56296 20.52099 0.375 34 4289.843 189.8975 8 0.807 1 0.395 0 1 3.772 1.736 0.093 74.483 1.657 40008.490 4 1.9 0.07 85.8 1 45394.1 1.322 0.839 0.060 18.201 1.265 13947.090 0 0.5 0.01 17.0 0 1066.2 5 5.7 0.38 96.1 4 87067.5 15.040 16.43 6.711 1.2 29.57 Comparison Tests Types of Debt Contracts Senior Secured Subordinated Mezzanine Equity and Bonds PIK Loan Portfolio NPL Secondary Fund Manager Characteristics Real Fund Size / Manager Portfolio Size / Manager Same Company Location Country Variables Spanmann Antidirector Rights Creditor Rights Time Creditor Rights Cost Creditor Rights Efficiency Creditor Rights LLSV 1998 GDP Per Capita 37481.85 Market Conditions TED Spread Exit Date US High Yield Recovery Rate at Exit IRR Above Median Average Median IRR Below Median Average Median Comparison of Means Comparison of Medians 0.053 0.096 0.787 0.245 0.000 0.043 0.128 0 0 1 0 0 0 0 0.226 0.161 0.548 0.161 0.009 0.032 0.032 0 0 1 0 0 0 0 2.908*** 0.999 -2.643*** -0.920 1.298 -0.252 -1.511 8.004*** 1.007 15.011*** 0.729 1.687* 0.800 2.277** 84.711 3.573 0.755 82.233 2.353 1 127.895 5.85 0.903 139.565 6.400 1 4.930*** 4.446*** 1.767* 15.252*** 18.485*** 2.141** 3.678 1.796 0.107 71.211 1.851 45123.65 3.7 1.9 0.07 85.8 1 36474.48 4.020 1.854 0.092 77.546 2.107 45394.10 5 2 0.07 85.8 1.88 -0.310 1.227 0.257 -0.935 1.506 0.874 0.820 0.216 6.980*** 0.330 1.662* 0.004 60.755 37.161 32 20 36.258 29.856 25 20 -1.993** -1.667* 4.093** 2.764* Figure 3. Realized Winsored IRRs Plotted against TED Spread Standard Deviation Over Investment Horizon and Portfolio Size Per Manager Table 4: Models 3-5 Treatment Regressions Constant Investment Amounts Real 2010 $US'000 Capital Types of Debt Contracts Subordinated Mezzanine Equity and Bonds PIK Loans Portfolio of NPLs Fund Manager Characteristics Portfolio Size / Manager Same Location Country Variables Creditor Rights Time GDP Per Capita Industry Intangible / Intangible Debt Market Conditions Total US Leverage Loans 2010 $billions at time of Inv Realized versus Unrealized Investment and Time Dummies Realized Investment Investment Year Dummies? Exit Year Dummies? Model 3: Coefficient 0.082 t-statistic 1.400 Model 4: Coefficient 0.207 t-statistic 2.130** Model 5: Coefficient 0.057 t-statistic 0.830 7.050E-07 1.230 -3.480E-08 -0.030 7.550E-07 1.610 0.065 0.050 -0.076 0.117 0.141 3.440*** 2.340** -1.100 3.469*** 3.440*** 0.089 0.064 -0.134 0.157 0.159 2.810*** 2.540** -1.360 2.430** 3.690*** 0.061 0.066 -0.070 0.066 0.091 2.960*** 3.080*** -0.880 2.100** 2.440** -0.027 -0.062 -5.270*** -2.950*** -0.031 -0.181 -2.920*** -1.420 -0.029 -0.047 -7.010*** -1.540 0.025 2.530E-07 1.680* 0.500 0.026 -4.300E-06 1.070 -1.960** 0.021 1.940E-07 1.560 0.300 3.625E-03 2.550** -9.270E-05 -0.020 3.938E-03 3.120*** 8.46E-05 1.920* 2.80E-04 1.920* 0.111 No No 2.620*** 0.237 No No 1.440 0.092 Yes Yes 1.950* 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% Amounts Invested and Location Model 1: OLS Real Cap Coefficient t-statistic -8908.300 -0.760 Constant Types of Debt Contracts Senior Secured 34857.710 Mezzanine 8348.691 Equity and Bonds 27.743 PIK Loans 20417.200 Portfolio of NPLs -4944.080 Secondary 24512.170 Fund Manager Characteristics Portfolio Size / Manager -1671.136 Same Location Country Variables Creditor Rights Efficiency 326.575 GDP Per Capita -6.893E-02 Industry Intangible / Intangible -2.958E+02 Debt Market Conditions TED Spread at Investment 5.23E+01 Total US Leverage Loans 2010 $billions at Inv -1.29E+00 Adjusted R2 0.139 Model 2: OLS Log Cap Coefficient t-statistic 7.754 11.32*** Model 3: Same Location Coefficient t-statistic 1.405 1.060 Marginal Eff. -0.915 -1.175 -1.180 -2.61*** 0.047 0.063 0.077 3.58*** 0.790 0.010 -2.53** 5.20*** 0.001 0.000 5.31*** 1.530 0.010 0.850 -0.340 0.970 1.048 0.554 -0.350 1.504 0.250 2.347 2.72*** 1.73* -1.500 1.060 0.290 1.580 -2.32** 15181.490 -0.039 3.28*** -0.930 0.971 3.02*** -0.430 0.027 4.27*** -2.420E-05 -2.56** -0.032 9.620E-05 -1.320 -9.266E-03 -0.700 -4.265E-02 -1.42 0.003 2.15** 3.41E-03 2.39** -2.18E-03 -0.73 0.000 -0.150 -5.71E-04 0.118 -1.11 9.66E-04 0.218 0.900 0.000 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 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 Summary / Takeaways from Part II 62 Topics: 1. 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 Methods: 2. a) How do you construct a private debt index?