債券運用における ESG 定量評価モデルとパフォーマンスの検証〔Reference Translation〕

Fixed Income ESG Model and Performance Analysis
Nomura Asset Management (NAM)
Jason Mortimer, Taro Nagao▮ Abstract ▮1.

The concept of investing with consideration to social responsibility and sustainability has
developed considerably since the 2000s, largely centered on the equity market, and led
particularly by European investors. In recent years, the increasing commitment by large
Japanese public pension funds to ESG investment has triggered a surge in investor
awareness and interest in ESG investing among Japanese institutional investors as well.

2.

Still, on the topic of ESG in Fixed Income, there is widespread skepticism regarding theactual economic impact from considering such factors, and in some cases outrightconcern for potential negative impact on investment returns. Furthermore, certain ESG
factors are normative in nature, and carry implicit value judgements that are notnecessarily universally recognized or acceptable to clients in the Japanese investor
community.

3.

With these issues in mind, at Nomura Asset Management (NAM) we developed our own
quantitative fixed income ESG model, specifically designed for systematic andtransparent assessment of the down-side risk factors material to corporate credit
investment. We have applied the model output in our investment processes from late
2018, and continue to actively upgrade the model.

4.

To determine what effect the ESG risks assessed by our quantitative Fixed Income ESG
model have on risk-adjusted returns in corporate credit markets, and whether it is
economically rational to integrate these factors in the actual investment process, we backtested the model against historical returns under certain preconditions.

5.

We found that over the long term, issuers with higher (lower) NAM Credit ESG quality
delivered higher (lower) risk-adjusted returns, in both US and European corporate credit
markets. These results suggest that by constructing corporate bond portfolios withconsideration for ESG factors based on an asset-class appropriate framework, it ispossible to achieve superior investment performance through ESG integration.

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野村サステナビリティクォータリー 2020 Autumn

Rising investor demand for comprehensive, high-quality, and potentially risk-return enhancing ESG
integration in Fixed Income strategies in led Nomura Asset Management (NAM) to develop a fully
quantitative Credit ESG Model based on risk-and-return ESG factors. Historical back-testing based on
decades of index data indicates a robust linear relationship between issuer NAM Credit ESG Scores andlong-term risk-adjusted returns in both European and US corporate credit markets. In this paper we
describe our motivations and methods for developing a systematic approach to ESG integration and present
proprietary quantitative analysis of the relationship between Credit ESG quality and investment performancewith discussion.

I

Background and Recognition of Challenges

While the rise of Sustainable finance has been ongoing for well over a decade, the investor focus on the
ESG-integration aspect in investment processes has been arguably centered on the equity asset class, andparticularly with a European perspective. Japanese investor awareness and interest developed later on but
has now firmly taken hold among the country’s institutional investors.
Still, despite now widespread interest in Japan on the topic of ESG, we encountered widespread skepticism
that many common ESG factors and practices are either unsuitable, or irrelevant (or worse negative) for
fixed income investment returns. Certain ESG factors are normative in nature, and carry implicit value
judgements that are not necessarily universally recognized or acceptable to clients in the Japanese investor
community. We believe that convincingly addressing these legitimate concerns, both to our clients andourselves, is a necessary factor for developing a truly sustainable fixed income investment practice inJapan.

II

Guiding principles for NAM’s Fixed Income ESG Model

With this in mind, we decided to develop a comprehensive and practical fixed income ESG investment
system specifically designed for corporate credit strategies that would be universally applicable,transparent, internally-consistent, resource-efficient, and – most importantly – potentially alpha-generating.
In considering traditional off-the-shelf solutions from ESG ratings providers, three issues became apparent:
1) ESG ratings from third-party sources tend to be a “black-box” system that is difficult to comprehensively
understand and explain, 2) ESG ratings primarily reflect the concerns of equity investors (in particular“upside opportunity” ESG factors) that in some cases have limited relevancy to down-side risk focused fixedincome investors, and 3) ESG ratings often mix normative screening factors with informative risk factorsthat limit the resulting assessment’s utility as an investment signal.
To address these issues, we exclusively focus the NAM Credit ESG Model on downside ESG risk factors

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債券運用における ESG 定量評価モデルとパフォーマンスの検証

material to credit quality and financial risk, based on a transparent and internally consistent data-based
process. As a result, our quantitative Credit ESG model generates scores by extracting and processing ESGrelated data for each issuer based on a fully systematic framework, distinct and separate from thejudgmental ESG analysis undertaken by credit analysts.

III Framework Overview
The NAM model framework is constructed in three steps: (1) Data Selection, (2) Data Mapping, and (3)Score Calculation.
In the first step (1) Data Selection, we select a subset of model inputs for assessing ESG factors from theuniverse of available data, based on guidelines defining the specific categories of ESG risk that the model isintended to reflect. Model inputs are chosen only for those ESG factors with the objective potential tomanifest as down-side market risk based on issuer-specific aspects, e.g. contingent liabilities fromnegative externalities, loss of access to refinancing from reputational problems, long-term strategy andbusiness model sustainability, etc. Data for inclusion in the model must also be consistent and continuously
available. These data inputs measure the issuer’s relative performance in material ESG factors such as GHG
reduction strategy, human capital development, and accounting quality, etc.
We consciously exclude ESG factors that describe “upside opportunities” such as “green technologies”
and “access to finance”, as well as certain normative ESG factors that are not universally applicable as
downside market risk. We also avoid “high-frequency” type ESG data that is reactive to news flow or
recent controversies, in order to focus on long-term structural signals and preserve the forward looking-nessof the model output.
In the second step (2) Data Mapping, we classify these ESG data series into NAM “Sustainability Issues”
that we consider fundamental to evaluating ESG credit risk, such as “GHG Emissions”, “Product Safety”
and “Governance Quality” etc. NAM Sustainability Issue scores are calculated on an issuer basis from theavailable input data from step (1). Sustainability Issues are further grouped into Environmental, Social, andGovernance categories, according to our own concept for translating ESG into practical market risk
considerations. This categorization can and sometimes does differ from the consensus formulation of ESG.
The E, S, and G scores are calculated as the average of each category’s underlying Sustainability Issues.
Industry-specific weights for E, S, and G scores are determined by formula from NAM’s proprietary
Materiality Map, as the relative importance of these categories is different for each industry. We apply aunique formula-based approach to derive the E, S, and G weights based on the relative number of
Sustainability Issues deemed material to credit investors for each industry. I.e. the higher the number of29野村サステナビリティクォータリー 2020 Autumn

material sustainability issues present, the higher the relative E, S, or G weights.
In the third step (3) Score Calculation, we calculate the NAM Credit ESG Score for each issuer as the sumof the product of issuer ESG scores and industry ESG weights. Data is updated each month for over 6000
global debt issuers, for updated and comprehensive coverage.
The resulting NAM Credit ESG scores are integrated in the investment process for domestic and global
corporate credit strategies, in a customized manner to suit each team’s investment approach, mandate, andasset class.

IV Back-testing the NAM ESG Score Model
As the NAM ESG model inputs are selected to be “values-neutral” and downside-risk focused, the resulting
output scores can approximate a relative degree of “ESG market discount” to be applied to issuers andsectors as a complement for traditional fundamental financial analysis. In fact, because the model isdeterministic with fully quantitative output, it is possible to back-test the NAM Credit ESG Model for
performance against historical market data, in ways that are not possible with subjective ESG assessments.
That said, the NAM Credit ESG model is not intended to be used as a “trading signal”, and we never
optimize the framework for back-tested Sharpe Ratios etc. Rather, we believe that ESG factors – whenproperly selected and considered from a forward-looking pricing perspective – can be a source of alpha forinvestors facing long-dated and in some cases unprecedented sources of risk. So we approach back testingof the NAM Credit ESG scores not for its value as a trading system component, but rather as a check ofwhether our particular formulation of ESG risk has informative value, and how these ESG signals can beharnessed for our investments.

V

Back-test Analysis Methodology

We back-tested NAM Credit ESG scores for the universe of US and European corporate credit issuers to test
the relationship between ESG quality (ESG Credit scores) and historical risk and return based on daily
ICE BofA credit index data.
The investment universe is defined as a company with NAM Credit ESG Score among ICE BofA's U.S.
and European investment-grade corporate bond indices.



For each issuer, we assumed that NAM Credit ESG score at the end of the backtesting was unchanged
from the beginning. (See Box 1)30債券運用における ESG 定量評価モデルとパフォーマンスの検証At the end of each month, the issuer is divided into quartiles by NAM Credit ESG Score and a model
portfolio is formed for each quartile. (We assume equal investment in each issuer; if one issuer havemultiple bonds outstanding, we assume equal investment in all bonds.)



The rebalancing of the portfolio is performed at the end of each month. Transaction costs are notconsidered.

The ESG data input used for the back-test was the single NAM Credit ESG score for each issuer, as thislevel of the score hierarchy incorporates the full spectrum of the model parameters for data selection, data
weighting, and relative ESG factor materiality. These scores were then bucketed into quartiles with theresulting number 1-4 assigned to the issuer and used as the actual back-test variable. As a result, the targetof the analysis was on the relative level of the issuer scores, more than the absolute level or relative position
within the quartile.

Box 1: Explanation of methodological choices and limitations to the analysis
Because the necessary long-term historical ESG data inputs for the NAM Credit ESG model are not available
and thus the historical database of scores is limited, we applied the most recent issuer score from a single
point at the end of the back-test. This choice has several implications: first, we recognize that the back-test
results are not entirely consistent since historical returns are being compared to current ESG scores. However,by empirical observation we note that the NAM Credit ESG scores do not actually change very much overtime, and this is the expected outcome due to the data input choices that focus on long-term structural factors.
Thus we assume that the issuers score is stable over time, at least relative to other issuer scores, and that thecurrent score quartile is a valid proxy for historical levels.
Another issue is survivorship bias. Companies that have gone bankrupt or otherwise ceased to exist as
independent entities due to merger and acquisition activity cannot not have ESG scores today, and thus are notincluded in the back-test results. This biases the back-test results depending on what the issuer’s ESG score
would have been at the time of bankruptcy (and presumably poor risk-adjusted-return). However we believethat the net effect will be to under-estimate the positive relationship between ESG quality and risk-adjustedreturn, based on experience from recent actual high-profile bankruptcies where issuer scores were in the bottom
quartile well before the change in price.

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野村サステナビリティクォータリー 2020 Autumn

VI Results and Discussion
We found that corporate issuers with higher credit ESG quality (ie higher NAM Credit ESG score quartiles)delivered higher risk-adjusted returns during the observation period, in both the US and European
markets. In almost every case, not only were risk-adjusted returns higher for higher ESG quality, but thelevel of returns was higher, and the level of risk (volatility) was lower, ie both aspects showed independent
improvement with the ESG quality variable.
This indicates that corporate bond portfolios with higher overall credit ESG quality may outperform overthe long run. These results corroborate the findings of other studies on ESG and market performance.
Figure 1: Long-term Return in the US/European Markets(Unit: %)US Corp Risk-Return Simulation by ESG Score Quartile(Apr 2004 - Apr 2020)

Euro Corp Risk-Return Simulation by ESG Score Quartile(Jan 2003 - Apr 2020)

Return (annualized)）

Return (annualized)
5

7

Top
3rd
2nd

6

4

Top
Bottom

Bottom

2nd
3rd

5

3

2

4
4

5

6

7

2

Risk (annualized)

3

4

5
Risk (annualized)

Source: ICE BofA indices, Nomura Asset Management calculations

We also individually analyzed select periods of credit market stress to test for a relationship between issuer
index price level drawdown and ESG quality. The periods analyzed for both US and European markets
were: Global Financial Crisis (February-October 2008), Emerging Market Selloff (March-September 2015),
and Covid-19 Selloff (March 2020). Here again we found that higher ESG quality was negatively related to
drawdown.

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債券運用における ESG 定量評価モデルとパフォーマンスの検証

Figure 2: Long-term Return at the Market Turmoil(Unit: %)US: Global Financial Crisis (Feb-Oct 2008)

Bottom3rd

2nd

Europe: Global Financial Crisis (Feb-Oct 2008)

Top

Bottom

0
-5
-10.9

-15
-15.5

-16.2

-14.5

-10

-7.8

-6.8

Top

Bottom

3rd

2nd

3
0.5

1

Europe: European Debt Crisis (Jul-Oct 2011)
Bottom

Top
1.7

2

-1.2

US: Covid-19 (Mar 2020)

3rd

2nd

Top

Top

-1.7

-2.9
-3.6
Europe: Covid-19 (Mar 2020)

-4

-0.4

2nd

-1
-3

0

Bottom

3rd

0
-2

0.2

-4.9

-12.4

-15

US: European Debt Crisis (Jul-Oct 2011)

-1

2nd

-5

-10
-20

3rd

0

Bottom

3rd

2nd

-7.0

-6.7

-6.6

Top

0

0

-2
-4

-5
-10

-9.3

-8.4

-6.8

-6.5

-6
-8

-5.6

Source: ICE BofA indices, Nomura Asset Management calculations

These results give credence to the hypothesis that ESG quality is positively related to investment
performance, and that integrating ESG may have utility for investors seeking to maximize risk-adjusted–returns in fixed income. In considering the results, we note that the lowest levels of ESG quality did appear
correlated with both higher levels of risk (volatility) as well as lower total returns, consistent with our ESG
score framework’s explicit focus on downside factors and the avoidance of such risks. What was more
surprising was that the opposite was also true – risk and return results were both maximized at the highest
levels of ESG quality, possibly indicating a degree of credit ESG quality “upside”.
While we did not make specific adjustments for issuer size and credit rating in the back test, no clear
differences in the issuer size or credit rating were identified among portfolios at the end of the back test.

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野村サステナビリティクォータリー 2020 Autumn

VII Additional comments
We caveat these results with the following: First, given the data limitations we were in fact comparing past
performance with current ESG quality (see Box 1 discussion), when contemporaneous or leading measuresof ESG quality would have been more appropriate. Thus the results may be interpreted as “better risk
adjusted performance leads to better ESG quality” which would be an interesting observation but less usefulas a forward-looking indicator. Also, the results are based on the proprietary formulation of ESG used by
NAM Fixed Income – other market participants may define and analyze ESG risks differently, with different
results. Overall however, we do believe there is enough evidence to convincingly reject the null hypothesisthat “ESG is unrelated or harmful to credit investment returns.”
Second, ESG analysis is typically thought of as being focused on longer-term risks such as climate change,
but certain ESG factors appear to back-cast from previous experience (particularly governance factors
introduced specifically to explain certain Global Financial Crisis risk factors). These factors may fit withhistorical market outcomes, while actually becoming less relevant to Credit ESG quality and market
outcomes over time. As a result, we believe it is important to periodically review the assumptions and data
validity inherent in models of ESG quality, based on informed judgmental and objective assessment of what
ESG factors can drive price and spreads, universally.

VIII Conclusion
These back-test results show that ESG Credit quality - as determined by NAM Credit ESG scores – appears
to correlate positively with long term market risk-and-return for corporate credit issuers in the US and Europe.
At Nomura Asset Management we research and systematically incorporate ESG performance analysis in thedesign and investment process across corporate credit and fixed income portfolios. These insights areapplied to investment portfolios, contributing to the pursuit of superior risk-adjusted returns while building
a more sustainable financial market in Japan one asset allocation decision at a time.

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