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Returns to Private Debt

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