Download II. Private Debt - University of Sussex

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?