2024.02.08

Visualizing sustainable growth and quantifying the impacts reflectedin share prices–Creating a common set of outcome labels using generative AI–
Contents
I.

Introduction
1. The work of lifting P/B multiples and continuously creating value
2. Intrinsic value and impact
3. What our research attempts to do

II.

Existing approaches to impact visualization
1. A framework and logic model for the value creation process
2. Examples of impact visualization by means of outcome indicators
3. Existing outcome indicator classifications and the problems with themIII.

Visualizing intrinsic value
1. Creating a catalog of standardized outcome indicators
2. Extracting outcome labels from the standardized catalog and generating a value
creation process
3. Remaining challenges and the way forward

IV.

Using outcome labels to quantify the impacts reflected in share prices
1. Estimating the P/B premium or discount
2. Analyzing the P/B premium or discount using outcome labels

V.

The practical significance of visualizing and quantifying impacts
1. Significance in furthering constructive dialogue between investors and listedcompanies
2. Significance with respect to as-yet-unlisted venture firms
3. Significance for equity valuations
4. Applications in M&A valuations and other areas

VI.

In conclusion: What needs doing and what lies ahead

Yoko Ohta, Toshinao Akuzawa, Junta Kuramochi
Quantitative Solution Research Dept., Financial Engineering & Technology Research Center,
Nomura Securities Co., Ltd.
Tomomichi Nakano
Frontier Research Dept., Financial & Economic Research Center, Nomura Securities Co., Ltd.
Minoru Hayashida
Investment Banking Business Development Dept., Nomura Securities Co., Ltd.

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Summary and conclusions
1. In the research we present here, we use the term "intrinsic value" to describe the companyspecific portion of a given firm's market capitalization that cannot be explained withreference only to financial information and macro-level variables. We interpret this intrinsic
value to be the wellspring of a company's sustainable growth. We learn through analysis
that while longer-term growth expectations are factored into the intrinsic value portion ofcompanies' share prices in North America and Europe, the share prices of Japanesecompanies do not—on average—reflect such growth expectations. However, we also learn
through measurement that companies mentioned in the impact reports issued by asset
management companies generally have positive intrinsic value, which suggests to us that
impact investors, who typically have long investment horizons, assess the substance ofcompanies' intrinsic value and make investments in companies that they expect will see
sustainable growth.
2. Companies often explain the impacts they realize in terms of outcomes, but investors haveoften observed that making lateral comparisons across companies is complicated by thehighly company-specific character of the outcomes looked at. In this paper, we use
generative AI to help us create a standardized catalog of outcome indicators. We then feed
the text of company websites into the generative AI and ask the AI to extract appropriate
outcome indicators from the catalog. Using the outcome indicators thus selected, we
produce a model of the value creation process for the company in question and makevisible the substance of its intrinsic value. On top of that, we develop an equity valuation
model that uses the outcome labels extracted from the standardized catalog as explanatory
variables. Using this, we break down the intrinsic value component and measure the value
associated with each of the various impacts reflected in companies' share prices.
3. It is also our belief that being able to use standardized outcome labels as a means by whichto discuss individual companies' value creation processes in the context of dialogue
between investors and companies ought to be a highly effective means by which to
showcase the intrinsic value that many Japanese companies already quietly possess. We
think that making that intrinsic value plain and visible should help lift expectations forcontinuous growth in a way that becomes reflected in companies' share prices and makes
Japanese companies more competitive. Investors, meanwhile, can make their investment
portfolios more sustainable by focusing on impacts and investing in companies that seek
to achieve sustainability across society as a whole.
4. The catalog we have created, and the outcome labels within it, are still at the prototype
stage, but by putting some earnest work into refining it, our hope is that, in the near future,
we can turn this approach into a genuinely useful tool by which to evaluate impacts. Being
able to clearly delineate how highly the market values each of a company's businessstrategies and innovations with social or environmental impacts could help broaden thebase of investors interested in impacts and could lead to an increase in the amount of riskmoney allocated to impact projects, which in turn should help advance the work ofaddressing societal challenges.

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I. Introduction
1. The work of lifting P/B multiples and continuously creating value
Japanese companies in general have been trading at chronically low P/B multiples, and this has
recently come to be viewed as a problem in need of addressing. About half of all companies on the
TSE Prime Market and about 60% of companies on the TSE Standard Market are currently saddledwith ROE below 8% and a P/B below 1x. In an attempt to bring about a change in this situation, theTokyo Stock Exchange (TSE) issued a document in March 2023 titled "Action to Implement
Management that is Conscious of Cost of Capital and Stock Price". The document takes the formof a list of requests presented to listed companies, urging them to draw up plans to remedy the
problem and to disclose those plans. It may well be that some companies trading below their bookvalue are simply not getting adequate recognition from investors for their growth prospects. On
the other hand, it has been argued that another reason for the low valuations is that many Japanesecompanies are failing to adequately disclose the actual value of their non-financial information onsustainability.
P/B can be expressed as the product of ROE multiplied by P/E. From the perspective of a given
company whose stock is trading at a low P/B, it is important to have a clear understanding ofwhether the low valuation is traceable to poor capital efficiency or to low growth expectations—something that can be done with reference to P/B multiples for industry peers. An ROE level thatmight be reasonable in one industry may not be attainable in another, but ROE is something thatcompanies can lift to some extent through their own efforts. P/E, in contrast, depends on themarket's perceptions, which makes it important for companies to also be adept in disclosing
information and in engaging in dialogue with investors. Japanese companies' fundamentals have
improved remarkably over the past decade, making Japanese equities an asset worth holding forthe long term. On their own, however, strong fundamentals do not in any way guarantee higher
market valuations. To boost the P/B multiples at which their shares trade, companies need to do
more than simply bring about improvement in their earnings performance; it is incumbent upon
management teams to also clearly communicate with investors regarding their growth prospects
and the risks to their businesses.
P/E can be approximated with the formula 1 ÷ (cost of shareholders' equity – sustainable growthrate), and can thus be calculated from the difference between the market-implied cost ofshareholders' equity (as reflected in market share prices) and the expected rate of profit growth. A
company hoping to lift its P/E would do well to take the two-pronged approach of driving the costof shareholders' equity down by curbing business risks through ESG risk management while also
working towards higher sustainable growth by pursuing innovations that can have a positive impact
in resolving environmental and societal issues.
Over the past few years, Japanese firms have enthusiastically engaged in ESG risk management withthe aim of boosting their ESG scores. But efforts on the ESG front are not something that can be
expected to immediately lead to more rapid growth in earnings. Simply put, risk management byitself seems unlikely to yield higher growth expectations in the market.

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2. Intrinsic value and impact
1) Quantifying intrinsic value
In the research we present here, we use the term "intrinsic value" to describe the portion of acompany's market capitalization that cannot be explained by means of financial information or
macro-level variables. We view this intrinsic value—representing the value specific to a particularcompany—as the wellspring of sustainable growth. We start by estimating intrinsic value by meansof a quantitative model. We choose three metrics to represent value already made manifest in
financial data: the debt ratio, the dividend on equity (DOE) ratio, and the short-term growth rate.
We then construct a multiple regression model (formula (1) below) that adds an industry dummy
and a country dummy to these three metrics, and we use this to estimate price-to-sales (P/S) ratios.
The universe analyzed is the set of MSCI ACWI Investable Market Index (IMI) constituents, narrowed
down to companies for which the combination of country, exchange, and currency is shared by atleast 20 companies (including the company in question), excluding companies listed in China andHong Kong as well as companies with negative shareholders' equity. The coefficient ofdetermination for this model is 0.4487.
log⁡(𝑃𝑆𝑅𝑖,𝑡 ) = 𝐴1 × 𝑑𝑒𝑏𝑡⁡ 𝑟𝑎𝑡𝑖𝑜𝑖,𝑡 + 𝐴2 × 𝐷𝑂𝐸𝑖,𝑡 + 𝐴3 × 𝑠ℎ𝑜𝑟𝑡⁡ 𝑡𝑒𝑟𝑚⁡ 𝑔𝑟𝑜𝑤𝑡ℎ⁡ 𝑟𝑎𝑡𝑒𝑖,𝑡 + 𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦⁡ 𝑑𝑢𝑚𝑚𝑦𝑖 + 𝑐𝑜𝑢𝑛𝑡𝑟𝑦⁡ 𝑑𝑢𝑚𝑚𝑦𝑖 (1)Individual companies' actual P/S ratios and market capitalizations will be either higher or lower thanthe estimates output by the model. The difference between the actual P/S and the estimated P/S
represents a company-specific something that is not captured by the available financial informationor macro-level variables (in the form of the company's industry or country). We suspect that nonfinancial information explains most of this company-specific difference. Multiplying this difference
by forecast sales yields an estimate of intrinsic value, which can be read as an expression of the
expectations the market has placed on a given company.
2) The relationship between intrinsic value and longer-term growth expectations
Next, we take our estimates of intrinsic value and investigate whether they are indeed reflective of
longer-term growth expectations.
Our starting point here is an analysis of the relationship between the estimates of intrinsic value
output by our model (for dates in the past) and the rates of growth actually achieved thereafter, fora group of global companies heavy on companies listed in Europe and North America. In Figure 1,
estimates of intrinsic value as of 2010 are plotted along the horizontal axis, and rates of corporate
growth achieved over the subsequent 12 years are plotted along the vertical axis. The resulting
scatter plot shows that companies measured to have higher intrinsic value did in fact tend to
subsequently have higher long-term rates of growth in sales. What we take from this is that
estimates of intrinsic value derived from the share prices of global companies (with an emphasis oncompanies listed in Europe and North America) are indeed reflective of the market's longer-term
growth expectations.

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Figure 1. Relationship between intrinsic value and subsequently realized sales growth rates among
global companies

Note: The universe analyzed is the set of MSCI ACWI Investable Market Index (IMI) constituents asof 30 June 2010, narrowed down to companies for which the combination of country, exchange,
and currency is shared by at least 20 companies (including the company in question), excluding
companies listed in China and Hong Kong as well as companies with negative shareholders' equity.
Chart includes 2,720 companies for which FactSet consensus forecasts are available. Sales growth
calculated from the latest sales figures available as of 30 June 2010 and the latest sales figuresavailable as of 30 June 2022.
Source: Nomura Securities, based on data from FactSet and Capital IQ

We then look at whether longer term growth expectations have historically been reflected in
intrinsic value for a variety of time spans subsequent to the point of measurement. Figure 2 is aplot of regression coefficients for follow-up periods ranging from one year to 12 years. The
regression coefficient from Figure 1 (0.1734) is included in Figure 2, as the value for the 12-year
follow-up period. What we see in this chart is that for each 1ppt increase in intrinsic value as a shareof market capitalization, a company's sales at the end of a five-year follow-up period will on average
be approximately 0.10% higher than average sales among the constituents of the benchmark (MSCIACWI IMI). When the follow-up period is extended to 10 years, each 1ppt increase in intrinsic value
tends to correspond to a 0.15% increase in sales over the benchmark average.

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Figure 2. Relationship between global companies' intrinsic value and subsequent sales growth forfollow-up periods of varying durations

Note: Coefficients in a regression of intrinsic value as of 2010 against rates of sales growthmeasured at X years after 2010 using the latest available sales results at each point in time, where
the X values of 1–12 correspond to the years 2011–2022.
Source: Nomura Securities, based on data from FactSet and Capital IQ

Figure 3 shows the t-values for each of the follow-up periods. This further shows that therelationship between intrinsic value and subsequent growth obtains regardless of the follow-up
period chosen. Also, although all of the analysis presented thus far has used 2010 as the base year,
we have reliably arrived at similar results when using other base years as well.

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Figure 3. T-values for the relationship between global companies' intrinsic value and subsequentsales growth for follow-up periods of varying durations

Note: T-values for the coefficients in a regression of intrinsic value as of 2010 against rates of sales
growth measured at X years after 2010 using the latest available sales results at each point in time,
where the X values of 1–12 correspond to the years 2011–2022.
An absolute t-value of 2 or higher indicates that the mean difference between the two samples
analyzed is statistically significant.
Source: Nomura Securities, based on data from FactSet and Capital IQ

These results show that globally—and for whatever chosen span of time—there is a statisticallysignificant relationship between intrinsic value and longer-term growth potential, and that thelonger the span of time examined, the stronger the relationship becomes.
We have chosen to use P/S ratios in our analysis because this measure, unlike the price-to-earnings(P/E) ratio, can be used in analyses of loss-making companies, thus allowing for a broader look atmarket assessments of companies' growth potential. We did develop a similar model built around
P/E, however, which yielded a coefficient of determination of 0.3844. A P/E-based analysis thus
uncovers a relationship between intrinsic value and growth potential that is similar to what our P/Sbased model finds.

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We also ran the same analysis on Japanese companies. As with our model for global companies,
we constructed a multiple regression model (formula (2) below) that estimates P/S ratios using
three financial metrics (the debt ratio, the DOE ratio, and the short-term growth rate) and an
industry dummy (while leaving out the country dummy for obvious reasons). The explanatory powerof this model is even higher than that of our global model, with a coefficient of determination of
0.5259.
log⁡(𝑃𝑆𝑅𝑖,𝑡 ) = 𝐴1 × 𝑑𝑒𝑏𝑡⁡ 𝑟𝑎𝑡𝑖𝑜𝑖,𝑡 + 𝐴2 × 𝐷𝑂𝐸𝑖,𝑡 + 𝐴3 × 𝑠ℎ𝑜𝑟𝑡⁡ 𝑡𝑒𝑟𝑚⁡ 𝑔𝑟𝑜𝑤𝑡ℎ⁡ 𝑟𝑎𝑡𝑒𝑖,𝑡 + 𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦⁡ 𝑑𝑢𝑚𝑚𝑦𝑖(2)

Using this model, we then sought the relationship between Japanese companies' intrinsic value asof 2010 and their rates of growth over the following 12 years. We found no statistically significant
relationship here (Figure 4). We re-ran the analysis for other follow-up periods but were unable tofind a statistically significant correlation in any of them (Figure 5).
We also developed a model for Japanese companies based on P/E. The model's findings are similar
to what we found with the P/S model, with a coefficient of determination of 0.3910 and nostatistically significant correlation between intrinsic value and subsequent growth.
What this suggests to us is that, on average, forward growth expectations do not factor much into
Japanese companies' share prices, leaving Japanese companies to trade at lower P/B multiples thantheir peers in Europe and North America.

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Figure 4. Relationship between intrinsic value and subsequently realized sales growth rates among
Japanese companies

Note: The universe analyzed is the set of companies listed on the TSE as of 30 June 2010, narrowed
down to the 569 companies for which FactSet consensus forecasts are available and which had
positive shareholders' equity and a market capitalization of at least ¥10bn. Sales growth calculated
from the latest sales figures available as of 30 June 2010 and the latest sales figures available as of
30 June 2022.
Source: Nomura Securities, based on data from FactSet and Capital IQ

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Figure 5. Relationship between Japanese companies' intrinsic value and subsequent sales growthfor follow-up periods of varying durations

Note: Coefficients in a regression of intrinsic value as of 2010 against rates of sales growthmeasured at X years after 2010 using the latest available sales results at each point in time, where
the X values of 1–12 correspond to the years 2011–2022.
Source: Nomura Securities, based on data from FactSet and Capital IQ
3) Intrinsic value and impact
The above analysis shows that, on average, longer-term growth expectations have not beenreflected in Japanese companies' share prices. However, we find it hard to imagine that this isactually true of all Japanese companies. The question is one of determining what sorts of Japanesecompanies have share prices that are reflective of the market's longer-term growth expectations
for them. In looking for an answer to this question, we turn to the impact reports issued by asset
management companies engaged in impact investing, looking specifically at the included
calculations of intrinsic value for Japanese companies.
The purpose of impact investing is to make investments that have a positive impact on theenvironment or society while also generating economic investment returns. The companies
targeted for investment by impact investors tend to be firms that possess innovations that make itpossible to address societal and environmental challenges while also generating profits, by10

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lowering the cost of creating those beneficial societal and environmental outcomes. These
innovations can take many forms, including groundbreaking ideas or technologies, transformative
business models, or creative ways of achieving differentiation from the existing market. Whatever
the specifics, the innovation is something that is viewed as the engine of sustainable growth for thecompany in question, and our thinking is that this corresponds to the intrinsic value we have been
discussing here. Japanese companies mentioned in impact reports are presumably being
recognized as targets for investment precisely because they possess such innovations that facilitatesustained growth. To investigate this idea, we look into whether the Japanese companies
mentioned in asset managers' impact reports have positive intrinsic value by our chosen measure.
In Figure 6 below, the P/S ratio estimates output by our model (formula (2)) are plotted along thehorizontal axis, and forward P/S multiples as actually measured are plotted along the vertical axis,
with the dots in all cases representing the Japanese companies included in our analysis. On top of
that, we add dots for the companies discussed in the impact reports issued by two major asset
management companies.

Figure 6. Intrinsic value of companies included in asset managers' impact reports

Note: As of 30 June 2022. Excludes financial companies. Values for P/S, the debt ratio, the DOE ratio,
and the short-term growth rate that differ from the mean by more than three standard deviations
in either direction are replaced with the value at three standard deviations. Companies included
here are the 24 companies mentioned in Nomura Asset Management's report and the 25

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companies mentioned in Resona Asset Management's report, narrowed down to the 22 companies
for which FactSet consensus forecasts are available.
Source: Nomura Securities, based on data from FactSet

The companies represented by circles above the diagonal line are those trading in the market at
P/S ratios higher than the P/S ratio estimates output by our model, which is to say that companies
above the diagonal line have positive intrinsic value, while those below the line have negative
intrinsic value. The companies represented by red circles appear in the impact reports of both of
the asset management firms included in this analysis; all of these have positive intrinsic value. It isalso apparent from the chart that companies included in one or the other of the two impact reports
are valued by the market as having positive intrinsic value in more instances than not. What we takefrom this is that "impact companies" are those that are viewed by the market as having a positive
impact, and for which that impact is linked to corporate value. Investors' belief that these companies
can achieve sustainable growth may explain why impact companies' shares are priced in a way thatreflects a positive rate of expected growth.
In conventional equity investing, a potential target for investment need only be assessed along two
axes: risk and return. Impact investing adds impact as a third axis along which one must weigh aninvestment. Whereas risk and return are mostly assessed in terms of economic value based on
financial information, it seems reasonable to conclude that impact investors also have to look at
the non-financial side of things in making their decisions, specifically by delving into the nonfinancial information that accounts for a company's intrinsic value and then investing in companies
for which that intrinsic value points to the potential for sustainable growth. In Section II of thispaper, we take a conceptual look at how impact can be measured, drawing on perspectives fromthe world of impact investing.

3. What our research attempts to do
In our research, we attempt to isolate the company-specific slice of corporate value left over after
subtracting the value explainable by means of financial information and macro-level variables. We
then apply the label of "intrinsic value" to this residual value and attempt to visualize and quantify
it.
In the impact investing world, visualization often involves the use of outcome indicators. However,investors have pointed out that outcomes and impacts are often highly company-specific in a waythat complicates lateral comparisons. As a way of coping with this problem, we focus on startup
firms as subjects of analysis, as their businesses are often simpler in structure than those of publicly
listed companies. This simplicity makes for more readily expressible impacts. In studying thesestartups, we attempt to arrive at a set of standardized outcome measures that facilitate side-byside comparisons. Then, using this common set of standardized outcome indicators, we generatemodels of the value creation process and attempt to visualize the content of the intrinsic value thatis the wellspring of a company's sustainable growth. We deploy generative AI as a tool throughout
this analytical process.

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We then use this common set of outcome indicators to break down the intrinsic value componentof listed companies' share prices, estimate the value associated with each outcome indicator, andattempt to elucidate the mechanism of share price formation from both financial and non-financial
angles.
After that, we consider the practical significance of impact visualization, both in dialogue between
investors and listed companies and with regard to as-yet-unlisted venture firms. We also look into
how valuation approaches can be refined by quantifying the impacts reflected in share prices.
Finally, we explore the unaddressed challenges in the approach we spell out here, and consider
some possible practical applications. We also comment on the societal significance of being able
to measure the value of non-financial information while using commonly employed valuation
models.

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II. Existing approaches to impact visualization
In this section, we go over the existing approaches to impact visualization used in the impact
investing world, and give some thought to what sorts of common indicators would be best used
for the purpose of visualizing impacts.

1. A framework and logic model for the value creation process
Starting with their annual securities reports for the fiscal year 2022, all listed Japanese companies—
regardless of the market on which they are listed—are now obligated to make disclosures detailingtheir thinking on the topic of sustainability and the sustainability-related initiatives they are
undertaking. Most notably, all listed companies are being asked to make disclosures regarding their
human capital that include a human resource development policy, an internal environment
development policy, a list of measurable non-financial indicators (inputs/outcomes), targets for
those non-financial indicators, and progress towards those targets. The key point for our purposes
here is the push for disclosures with respect to outcomes.
In their integrated reports, many companies broadly explain how their businesses create value,
using the value creation process framework. In many such examples, however, outputs andoutcomes are commingled, and the disclosures are slanted towards presenting information thatleaves a positive impression.
To address these problems, the updated International Integrated Reporting Framework specificallydefines outputs as "an organization's key products and services" and outcomes as "the internal andexternal consequences (positive and negative) for the capitals as a result of an organization's
business activities and outputs." Figure 7 is an example of what the difference might look like foran automobile manufacturer. In this example, the output is simply automobiles, while on theoutcome side, positive outcomes include contributions to the community through the payment oftaxes and improved customer satisfaction, and negative outcomes include the depletion of fossil
fuels and degraded air quality.

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Figure 7. International Integrated Reporting Framework diagram of the value creation process withspecific examples for an automaker added

Source: Nomura Securities, using a graphic from the International Integrated Reporting Council(IIRC)

This value creation process framework is based on a logic model, and it is one tool used by impact
investors to assess investments along the third axis (which is impact, the other two axes again being
risk and return). This tool is in widespread use around the world to help investors sketch out
companies' business models as the first step in visualizing impacts.
In Figure 8, we lay out the definitions used in a typical logic model. Here, outcomes are defined
simply as "effects on stakeholders", while impacts are defined as "effects on society, theenvironment, and the economy in the short, medium, and long term". Long spans of time are
required to measure how far society has progressed as a result of some impact, and external factors
interact with the impact in complex ways. There are thus a great many problems one encounters intrying to measure an impact and manage it as a KPI. This makes impacts poor choices for KPIs. In
actual practice, the recommendation is to use outcomes or outputs for one's KPIs instead. Indeed,
the SDG Compass, developed by the Global Reporting Initiative (GRI), the United Nations Global
Compact (UNGC), and the World Business Council for Sustainable Development (WBCSD),
recommends that companies manage their businesses against outcome or output indicators.
Assessments of outcomes or outputs can then be used as feedback to be put to work in improving
the business so that the company can realize sustainability by means of its business activities.

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Figure 8. Definitions in a typical logic model

Source: Nomura Securities

2. Examples of impact visualization by means of outcome indicators
The unfortunate truth about the outcome-based disclosures related to human capital thatcompanies have submitted in their securities reports to date (up through November 2023) is thatmost of them in fact do not go beyond the output level. The entire point of measuring outcomesin the human capital domain is to incorporate employee capital (skills and capabilities) into thecompany's organizational capital and thereby drive organizational growth and create value(outcomes). To execute its business strategies, a company needs to define its personnel
requirements, and then based on those requirements draw up and execute concrete strategies forsecuring, retaining, and developing the needed human resources. Outcome indicators measure theresults of these strategies.
European companies are at the vanguard in this area, and one of the leading companies among
those is the German firm SAP, which has gone through three steps in its visualization of nonfinancial information: the disclosure of non-financial information, the quantification of non-financial
information, and the assignment of monetary values to non-financial information. The first step was
to begin disclosures of information at the output level. In the second step, the company formulated
its own outcome indicator called the Business Health Culture Index (BHCI), calculated from suchoutput indicators as employee leadership and stress levels. This outcome indicator was created asa means of explaining the results of the output indicators to shareholders. Finally, in the third step,
the company analyzed the relationship between its BHCI and its operating profit and revealed thateach 1pt improvement in the BHCI adds about ¥11.7bn–¥13.0bn to its operating profit.
In Japan as well, some companies are now beginning to examine the financial impact of their nonfinancial initiatives. Ajinomoto, for example, has linked up output indicators related to its
investment in human capital to proprietary outcome indicators called ASV indicators (abbreviatedfrom "Ajinomoto Group Creating Shared Value") that measure employee engagement. It thenmonitors and discloses the improvement it observes, expressed as a value of consolidated sales perhour (consolidated sales divided by the number of employees multiplied by the total number ofhours actually worked). Nissin Foods Holdings, meanwhile, has employed something called Value
Tree Analytics (VTA)—similar to a logic model—to ascertain whether the outputs of its human162024.02.08

capital initiatives lead to improvement in its business KPIs (which are comparable to outcome
indicators), or whether there is a correlation at all. The company had previously conducted a value
relevance analysis that traced the pathways by which the outputs and outcomes generated byhuman capital feed through into improved corporate value, and the deployment of VTA allowed
the company to validate this story with reference to actual initiatives it has in place.
Many companies are trying out a variety of approaches to these questions of how to express nonfinancial information in monetary terms and how to identify links between non-financial
information and corporate value, but the reality is that an effective approach has yet to beestablished. What the various approaches being tried have in common, however, is the attempt to
express non-financial information in the form of outcome indicators. Institutional investors focusedon longer-term performance make their investment decisions after performing comparisons withother companies from both financial and non-financial angles. Proprietary outcome indicators canbe a useful and important way for companies to show off their individuality, much like figure skaters
in the free skating segment of a competition, but investors also need the equivalent of the shortprogram—a system under which companies make disclosures under a common set of rules for thesake of like-to-like comparisons.

3. Existing outcome indicator classifications and the problems with themTaking all of the above on board, we argue that outcome indicators make sense as the components
of a common set of indicators to be used in visualizing impacts. However, because outcome
indicators as they exist now are expressions of corporate individuality, the choices of indicators canvary considerably from company to company. In addition to the open-ended sorts of outcome
indicators in use now, we think that a catalog of common outcome indicators that companies could
choose from among would make it possible to make apples-to-apples comparisons among
different companies' social and environmental impacts. If investors find such a set of common
outcome indicators easy enough to use that the system eventually gains widespread adoption, we
might expect to see the market develop a deeper understanding of—and heightened interest in—
impact investing.
In the next section of this paper, we attempt to create just such a standardized catalog of outcome
indicators, but before we proceed to that, below we look at some of the indicator inventories thatare already available.
1) IRIS+The most well-known of all the global catalogs of impact metrics at present is IRIS+, which professes
to be "the generally accepted system for measuring, managing, and optimizing impact". The themebased classification system used by IRIS+ starts with 17 major impact categories (agriculture, air,etc), which are then subdivided into impact themes (like "smallholder agriculture") and further
broken down into lists of strategic goals for each theme. Strategic goals have already been
determined for many of the selected themes, but some themes are still under development.
Although the system offers an inventory of measurables called the IRIS Catalog of Metrics, thenumber of metrics included is overwhelming (more than 700), and outcome indicators and output
indicators are commingled. This makes these metrics difficult to use as labels that convey a given
company's individuality.

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2) United Nations Impact Radar
The United Nations Environment Programme Finance Initiative has developed something it calls
the Impact Radar (Figure 9). This is a system based on the UN's Sustainable Development Goals(SDGs), and in that respect it seems somewhat ill-suited as a classification scheme for companies inmore economically developed countries.

Figure 9. The United Nations Impact Radar

Source: Nomura Securities, from the United Nations Environment Programme Finance Initiative3) Keidanren impact metrics
On 14 June 2022, the Japan Business Federation (Keidanren) announced a set of 84 impact metrics
intended as a tool for furthering dialogue between companies and investors, with the idea of
purpose as the starting point. The Keidanren's materials define impact metrics as "metrics showing
the social and environmental changes and effects produced by business operations and activities".
And the list includes both cross-sectional metrics (which address impacts across various social
issues and can be used across industries) and issue-based metrics (that examine impacts on
individual issues). There are 16 cross-sectional metrics including not just financial indicators but
also measures such as the number of jobs created and improvement in energy efficiency. Amongthe issued-based metrics are 34 resilience impact indicators (which relate to community
development and the like) as well as a set of healthcare impact indicators. All have been designed
so as to be easily adopted by Japanese companies, as the Keidanren's aim is to encourage18

2024.02.08

companies to make disclosures in accordance with these metrics so that companies and investors
can have a common language to use in their dialogues about sustainability. If there is sufficient
uptake, we think the Keidanren's framework may help further the cause of constructive dialogue,
but we see room for improvement as well, as the assessments are subjective, and no concrete
numerical targets have been established. Also, because the companies would be making thedisclosures of their own accord, the trustworthiness of those disclosures could be called into
question.

192024.02.08

III. Visualizing intrinsic value
Starting with the existing frameworks that we outlined in Section II, in this section we make an
attempt at visualizing intrinsic value by means of a set of common outcome indicators. We use
generative AI to help us create a new catalog of indicators to take the place of existing indicatorclassifications. Our first step in doing this is to prime the generative AI with some conceptual
background knowledge about outcome indicators, and then have it generate a list of outcome
indicator candidates with reference to companies' explanatory materials. We then have the AI go
through the lists of outcome indicators it has produced to construct a standardized catalog ofoutcome indicators. Then we prompt the AI to select outcome indicators from among those in the
catalog that align with the characteristic features of individual companies. Finally, we take theextracted list of outcome indicators and use it as the starting point for generating models of thevalue creation processes for specific companies. We employ generative AI at each step in thisprocess, and perhaps the most striking aspect of what we discover is that it is now possible, in somecases, to create a purpose-built catalog of indicators at a cost so low that it could be justified even
if the results were to be treated as disposable.

1. Creating a catalog of standardized outcome indicators
1) Using generative AI
Generative AI is an applied type of machine learning in which the AI, after being trained on a corpus
of digital content, generates creative, novel outputs in response to prompts. One subset ofgenerative AI systems are large language models (LLMs), which are trained on large volumes of text
data and which can perform a variety of language processing tasks.
Generative AI systems differ from earlier forms of AI in that they require much less preparatory
work. In a conventional AI project, one had to gather training data with the desired correct
responses, train the AI system with that data, and then evaluate the system for its usefulness in realworld business situations. Gathering adequate quantities of training data in advance was costly,
and many systems thus developed ultimately made no sense from a cost-benefit standpoint. In
contrast, generative AI systems are widely expected to find use in a range of business applications,as they can generate outputs to a reasonable standard of quality even without any training specific
to one's business.
That said, it is fairly common for generative AI systems to generate outputs that are not as expected.
Getting appropriate outputs from a generative AI system requires care on the user's part in feeding
prompts to the system. The process of researching and developing prompts that draw out thedesired outputs has come to be referred to as prompt engineering.
In our analysis here, we use the GPT-4 version of OpenAI's ChatGPT tool.
2) Generating a list of outcome indicator candidates
Given what we know about how generative AI works, our starting expectation is that GPT-4 can be
induced to generate a highly convincing list of outcome indicator candidates after being taught
what is meant by an "outcome" in this context and after being fed some documentation on thecompany in question.

20

2024.02.08

The flow we established for generating lists of outcome indicator candidates for individualcompanies is as follows. First, we instruct GPT-4 to adopt the role of a social impact investor. Then
we provide the system with an idea of what we mean by "outcome indicators" and "output
indicators". After that, we feed the system some textual information that describes the company inquestion, and from there instruct the system to generate a list of suitable outcome indicators. For
our purposes here, we used the descriptions of companies' businesses given in their annual
securities reports as the company-specific textual information for the AI to consider.
Below, we outline the actual prompts we used to induce the AI to generate a list of outcome
indicator candidates. At the final step in which we input text data drawn from companies' securities
reports, one could choose to input text from some other source instead.

(Note: The generative AI prompts and outputs used in this research were written in Japanese, but
are presented here in English translation for the reader's benefit.)#request
You are {#role}. Please suggest output indicators and outcome indicators in {#format} based on{#business description} while strictly abiding by the following {#rules}.
#roleSocial impact specialist#rules

Suggest 10 output indicators and 10 outcome indicators in descending order of importance.
No explanations of the indicators are required.
Outputs are the tangible products that result from actions or work. Outcomes refer to theeffects ultimately generated by the products, and the amount of value created. Please makeclear distinctions between outputs and outcomes.
Please suggest outcome indicators that correspond to social goals.

#format

JSON
The JSON object must have two keys: outputs and outcomes
Values must be in a list format, with 10 output indicators and 10 outcome indicators#business description[Business description given in the company's securities report]

Below, we show that the above process yielded in terms of outcome indicator candidates forautomotive companies (Figure 10) and software services companies (Figure 11). The results do give
one a sense of each company's particular characteristics, but there are also places where similar21

2024.02.08

ideas are merely being expressed in different terms. The results do not rise to the level of a catalog
of common indicators. More work needs to be done to create a standardized catalog.

Figure 10. Outcome indicator candidates for automotive companies

Source: Nomura Securities

Figure 11. Outcome indicator candidates for software services companies

Source: Nomura Securities
3) Generating a standardized catalog from among the outcome indicator candidates
To create a standardized catalog, we then fed the outcome indicator candidates for a total of 69
companies (a mix of automotive companies and software services companies) back into GPT-4 andasked it to organize them by concept. In this particular instance, we gave GPT-4 a list of 690 items,
made up of the 10 outcome indicator candidates generated for each of the 69 companies included.
Below is an example of the prompt used to instruct GPT-4 to arrange the outcome indicator
candidates into a standardized catalog organized by concept.

#request
I would like to organize the {#outcome indicator list}. Please output your response in {#format}
while obeying the {#rules}.

222024.02.08#rulesPlease sort through the indicators in the {#outcome indicator list} and combine those with the same
basic meaning into one. For example, please treat "reduction in CO2 emissions" and "reduction ingreenhouse gas emissions" as the same thing.
The indicators in the {#outcome indicator list} do not all occupy the same conceptual layer. Pleaseorganize them so that superordinate concepts and subordinate concepts are clearly differentiated.
#formatBulleted list. However, please output the list in a form that makes the relationships between theconceptual layers clear.
#outcome indicator list
[Outcome indicators for all of the companies included]

Figure 12 presents the standardized catalog of outcome indicators for automotive companies andsoftware services companies output by GPT-4 after this conceptual organizing step. If companies
were to select outcome indicators from this sort of catalog and then make disclosures of
information on value creation in accordance with them, investors might find it easier to make crosscompany comparisons. This might also spur more understanding and interest in impacts, andultimately help make it so that a company's intrinsic value can become more fully reflected in its
share price.

Figure 12. List of outcome indicators after standardization

Source: Nomura Securities23

2024.02.08

2. Extracting outcome labels from the standardized catalog and generating avalue creation process
1) About the venture companies analyzed
Next, we prompted GPT-4 to select outcome indicators suited to individual companies from among
the items in the catalog of outcome indicators we just created. We will refer to the outcome
indicators selected by GPT-4 as "outcome labels".
In this instance, we ran this process for two venture companies in the self-driving car space, which
would thematically appear to be an area of high social impact. Both companies examined are in thebusiness of expanding the market penetration of self-driving cars, but they differ in their approachto self-driving vehicles, in their targeted customer bases, in their earnings models, and in the driversof their earnings growth.
Venture A is a vertically integrated company that produces self-driving cars from beginning to end,
from design through manufacturing (Figure 13). As a company that aspires to be an actualcommercial automaker, it has to be engaged in the design process from the basic vehicle design
down to the finer details, and it needs to acquire the production capacity for vehicle manufacturing.
Its customers are the individuals and businesses that use automobiles, and its earnings come fromthe sale of vehicles to these customers.

Figure 13. Venture A's business model

Source: Nomura Securities

Venture B is a platform company that provides the software needed for self-driving cars to work
(Figure 14). Its customer base is made up of existing automakers, which make use of Venture B's
software to enable autonomous driving functionality in the vehicles they manufacture. Its earnings

24

2024.02.08

come from software licensing fees, and its earnings grow in tandem with growth in the number ofcustomer firms and the number of vehicles in which the software is used.

Figure 14. Venture B's business model

Source: Nomura Securities
2) Extracting outcome labels
Now we turn to an examination of whether GPT-4 can help us to visualize the difference between
the two venture companies by means of labels in the form of outcome indicators. After feeding
GPT-4 the text of each company's website, we asked it to select suitable outcome indicators foreach company from among the options in the catalog. Below is the prompt we used to do this.

#request
You are {#role}. Please use the {#outcome indicator list} and make suggestions in {#format} while
strictly abiding by the following {#rules}.
#role
Social impact specialist#rulesPlease refer to the {#business description} and suggest outcome indicators that would appear to
be important.
#formatBulleted list.
#outcome indicator list25

2024.02.08[Standardized list of outcome indicators]#business description[Text of venture company's website (main page)]

Figure 15 shows the outcome indicators that GPT-4 selected from the catalog; these are theoutcome labels for each company. The outcome indicators "improvement in automobile safety","reduction in traffic accidents", "improvements in social infrastructure", and "improvement intransportation efficiency" all appear to be about contributions to the popularization of self-driving
cars, and all are common to both companies. Beyond those, however, other outcome indicators
selected by GPT-4 differ between the two companies in ways that get at the precise differences
between the two companies' businesses.
The outcome indicators for Venture A include "reduction in greenhouse gas emissions","improvement in product durability", and other indicators suited to a company that manufacturesand sells actual vehicles. In contrast, one of the outcome indicators selected for Venture B is"improvement in digital literacy", which captures something of the company's identity as a seller ofsoftware.

Figure 15. Outcome labels for Venture A and Venture B as selected by a generative AI

Note: Outcome labels in red are those that were selected for only one of the two companies
Source: Nomura Securities
3) Generating a value creation process
Finally, we have GPT-4 generate a value creation process using the outcome labels we just extracted.
Much as with the procedure we used above, we asked GPT-4 to play the part of a social impact
specialist, and gave it background information on what a value creation process is. We thenpresented it with the list of selected outcome labels and instructed it to generate a model of thevalue creation process. The prompt we used to do this looks like this:26

2024.02.08#request
You are {#role}. Please generate a value creation process for an enterprise in accordance with thefollowing {#rules}. Please output the value creation process in {#format}.
#roleSocial impact specialist#rules

A value creation process is something that expresses what sort of value an enterprisecreates for the environment, for society, and for its stakeholders as a result of conducting
its business activities after inputting capital. It has four elements: (1) inputs, (2) businessactivities, (3) outputs, and (4) outcomes.
(4) outcomes are defined as {#outcome indicator list}.
Please refer to the {#business description} and {#outcome indicator list}, and based on thosegive suggestions for the (1) inputs, (2) business activities, and (3) outputs. Present youranswers not in sentence form but as a bulleted list.

#formatBulleted list.
Please include entries for each of these four headings: (1) inputs, (2) business activities, (3)outputs, (4) outcomes.

#outcome indicator list
[List of outcome indicators that the generative AI selected for the company in question]#business description[Text from the company's website]

Figure 16 is the value creation process that GPT-4 generated for Venture A. Under "businessactivities", GPT-4 has suggested "development, mass production, and sale of EVs", reflecting thebusiness model of Venture A, which aspires to be a full-fledged automaker that manufactures selfdriving cars in-house. Similarly, the list of outputs includes "supply of fully self-driving EVs".

27

2024.02.08

Figure 16. Value creation process that a generative AI produced for Venture A

Source: Nomura Securities

Figure 17 is the value creation process created for Venture B. For this company, GPT-4 hassuggested "sale of autonomous driving software" under "business activities" in a nod to thecompany's business model, which involves the creation of a platform for the provision of software
needed for self-driving cars. Also, the suggested outputs include "advanced autonomous driving
software". The generative AI seems to have appropriately described how Venture B's businessactivities ("development of autonomous driving technologies" and "sale of autonomous driving
software") lead to the creation of "advanced autonomous driving software" as an output.

28

2024.02.08

Figure 17. Value creation process that a generative AI produced for Venture B

Source: Nomura Securities

If one were to decide that the results produced by the AI were not suitable, the problem could be
addressed in a number of ways: a human could manually edit the output, or could prompt the AI
to revise the outputs through further dialogue, or could re-run the process from scratch bychanging the content of the original prompt. All of these can be done easily. The value creation
process models generated through the AI-based process spelled out here seem to be wholly
adequate as starting points for internal discussions within a company.

3. Remaining challenges and the way forward
Because the attempt we have made here to produce a standardized catalog of outcome indicatorsdraws on examples from two fairly broad categories (automotive companies and software services
companies), the catalog we created does not seem to work especially well to capture the fineparticulars of individual businesses. It may be that by producing a catalog using the examples ofcompanies in more narrowly defined sectors, one could get a clearer picture of the sector-specificimpacts that are the wellsprings of sustainable growth. It would also be helpful, we think, to use
this approach to extract outcome labels for a larger number and variety of industries. Identifying
extracted outcome labels that are shared across multiple industries is arguably one way to identify
some of the key social issues running through Japanese society.
The approach we have taken here does away with the arbitrariness introduced by humans in that it
relies on a generative AI tool to produce a standardized catalog with reference to information fromcompany disclosures. The process is also notable for requiring very little in the way of manual laboror time, as the AI is also used to extract outcome labels suited to particular companies from among
those in the catalog, and to generate the value creation process models. Combining the catalog ofstandardized outcome indicators made by means of generative AI with the existing catalogs

292024.02.08

discussed in Section II opens up some further practical applications. For example, one way to pairthis AI-based approach with Keidanren's impact metrics would be to use this tool to generate abroad value creation process model and then select the individual Keidanren impact metrics thatare the best fit for each of the outcome labels in it. We propose that one effective way for companies
to engage in constructive dialogue with investors might be to gauge how much value each outcome
label contributes to the company's stock price (using the method detailed in the next section), andbased on that develop a company-specific value creation story using the Keidanren impact metrics
that best correspond to each of those labels.

30

2024.02.08

IV. Using outcome labels to quantify the impacts reflected in sharepricesIn this section, we attempt to quantify the value of impacts reflected in companies' share prices,
analyzing intrinsic value from an equity valuation standpoint using the outcome labels extracted
from the standardized catalog we created in Section III. As discussed above, we picture thisapproach as eventually being used in analyses that cut across industries, but as an initial foray wechoose here to focus on companies in the software services sector. Our hope is that the reader willfind the results of our analysis to be a useful reference, with the caveat that the analysis we perform
here is essentially a prototype, run on a narrowly defined universe.

1. Estimating the P/B premium or discountWe have put together a quantitative model that explains P/B multiples for 29 Japanese software
services companies (a group that includes system integrators, software-as-a-service (SaaS)
companies, and other software services companies) in terms of ROE and short-term growth rates(formula (3) below). We will refer to this as the "ROE and short-term growth model". The coefficientof determination for this model is quite high, at 0.68, but the P/B multiples output by the model dodiffer somewhat from the companies' actual P/B multiples. We will refer to this error as the "P/B
premium or discount", and analyze it by means of the abovementioned outcome labels.
𝑃𝐵𝑅𝑖,𝑡 = 𝐴1 × 𝑅𝑂𝐸𝑖,𝑡 + 𝐴2 × 𝑠ℎ𝑜𝑟𝑡⁡ 𝑡𝑒𝑟𝑚⁡ 𝑔𝑟𝑜𝑤𝑡ℎ⁡ 𝑟𝑎𝑡𝑒𝑖,𝑡(3)

ROE = one⁡ year⁡ forward⁡ net⁡ income⁡ forecast⁡ /⁡ shareholders′⁡ equity
One year forward net income calculated as the weighted average of the current-year net income forecast and thenext-year net income forecast
Short⁡ term⁡ growth⁡ rate = next⁡ year⁡ sales⁡ forecst⁡ /⁡ current⁡ year⁡ sales⁡ forecast − 1

The predicted P/B multiples for each of the companies studied, as output by our ROE and shortterm growth model, are plotted along the horizontal axis in Figure 18. The actual P/B multiples areplotted along the vertical axis. Points that land above the diagonal line represent companies forwhich the actual P/B multiple is higher than the multiple predicted by the model. Company X, forexample, trades at a P/B that is 2.0x higher than the predicted value. One way to read this is to saythat the market values the stock at a 2.0x P/B premium to what can be explained by ROE and theshort-term growth rate alone. Points that land below the diagonal line, in contrast, represent
companies that trade at a P/B discount.

312024.02.08

Figure 18. P/B multiples predicted by the model vs actual P/Bs

Source: Nomura Securities

2. Analyzing the P/B premium or discount using outcome labels
1) Extracting outcome labels that explain the P/B premium or discount
Next, we attempt to identify what sorts of outcomes are responsible for these observed P/Bpremiums and discounts. Outcomes are intimately tied in with the impacts that companies have onsociety and the environment, so in attempting to isolate the relevant outcomes, we are hoping todiscover which sorts of corporate initiatives the market values the most in terms of the specificimpacts that those initiatives relate to.
For this analysis, we created a model that uses the outcome labels as factors to explain the P/B
premium or discount. We will refer to this as the "outcome label model". To be specific, we use amachine learning algorithm called DART (for Dropouts meet Multiple Additive Regression Trees),
which is one type of gradient boosted regression tree algorithm. Using GPT-4, we refer to thebusiness descriptions given in companies' securities reports, and assign a 1 to each outcome forwhich the company appears to be engaged in relevant initiatives, and a 0 to each outcome forwhich there appear to be no such relevant initiatives. Because we have opted for a limited sample
population, we whittle down the overall standardized catalog of outcome indicators to the 1032

2024.02.08

outcome labels that GPT-4 determines to be especially relevant to the sample population. We thenanalyze what contribution each of the outcome labels makes to the P/B premium or discount.
Figure 19 presents the 10 outcome labels that GPT-4 has selected on the vertical axis. In our findings,
the outcomes that make positive contributions to the P/B premium are, in descending order ofcontribution, "improvement in the quality & efficiency of public services", "advancement of work
style reforms", "improving the reach and convenience of electronic payments", "improvements inworking conditions and employee diversity", and "employee education & training". We also findthat the stocks of companies without any initiatives corresponding to the chosen outcome labels
trade at a 1.2x P/B discount to the value predicted by the ROE and short-term growth model. In theanalysis of P/B premiums and discounts for individual companies that follows, we treat this lower
P/B level (after the 1.2x discount) as the baseline, and then look into the extent to which eachoutcome label pushes up the P/B from there.

Figure 19. List of 10 outcome labels extracted by generative AI and the contribution each makes to
the P/B premium (or discount)Source: Nomura Securities

The software services industry helps users perform more efficiently through the use of computer
technology. For this industry, the outcome labels estimated to contribute positively to P/Bpremiums include "improvement in the quality & efficiency of public services", "advancement ofwork style reforms", and "improving the reach and convenience of electronic payments". These
outcome labels can be read as items that can help improve Japan's overall business efficiency andhelp relieve labor shortages. Japan's population has begun shrinking, and it may be that the market
has decided that these outcomes merit the assignation of a valuation premium in that they canhelp boost the country's growth and sustainability over the longer term.
On the other hand, our findings indicate that the outcome labels "strengthened information
security", "narrowing of the digital divide", and "improvement in digital literacy" do not contributeto P/B premiums. While these outcome labels do seem to represent things that contribute toimprovements in business efficiency, they also appear to apply generically to software services
companies across the board. This may explain why these outcome labels were not singled out as33

2024.02.08

points by which to differentiate one software services company from another in horizontal
comparisons. Much the same can be said of the outcome label "industry reform through new
business creation".
Next, we present the results of an attempt to use these 10 outcome labels to break down the P/B
premiums or discounts assigned to individual stocks.
2) Example of a P/B premium broken down by outcome label
Figure 20 presents the results of our attempt to break down the P/B premium for Company X using
these outcome labels. Among the labels that GPT-4 picked for Company X were "improvement in
the quality & efficiency of public services", "advancement of work style reforms", "improving thereach and convenience of electronic payments", and "employee education & training". These arelabels that were determined to be making positive contributions to the P/B premium, so what ourfindings show is essentially a stack of factors all making positive contributions. GPT-4 also picked
the label "industry reform through new business creation", but because this was judged to be aslightly negative element, this label is treated as making a negative contribution to the P/B premiumfor Company X. Starting from the baseline of a company that has no disclosures of initiatives
relevant to any of the outcome labels, the accumulation of the contributions from the outcome
labels for Company X adds up to a P/B premium of 1.4x, which is to say that the outcome labels
explain about 70% of the P/B premium of 2.0x at which Company X's shares actually trade (over thevalue predicted by our ROE and short-term growth model).

Figure 20. Outcome label breakdown of the P/B premium estimated for Company X

Note: The baseline used is the example of a company with no disclosures of initiatives relevant toany of the outcome labels (a P/B discount of 1.2x)
Source: Nomura Securities34

2024.02.08

3) Example of a P/B discount broken down by outcome label
To make a contrast with the example of Company X given above, we now present an analysis for
Company Y (Figure 21).
In Company Y's case, GPT-4 assigned no labels judged to contribute to a P/B premium, so theappropriate P/B premium as estimated by the outcome label model ended up being the baseline
itself, or -1.2x. The actual P/B at which Company Y's stock trades was 1.1x lower than the P/B
predicted by our ROE and short-term growth model, and this error is a very near match for the -1.2x
output by our outcome label model. All of this may indicate that Company Y is not making adequate
disclosures related to the outcome labels that the market rates most highly, and that the shares are
trading at a discount as a result.

Figure 21. Outcome label breakdown of the P/B discount estimated for Company Y

Note: The baseline used is the example of a company with no disclosures of initiatives relevant toany of the outcome labels (a P/B discount of 1.2x)
Source: Nomura Securities35

2024.02.08

V. The practical significance of visualizing and quantifying impacts
1. Significance in furthering constructive dialogue between investors and listedcompanies
The impacts that are the wellspring of sustainable value creation are highly company-specific.
Companies have to date been fairly free-ranging in their commentary on impacts, in part out of adesire not to be forced into a one-size-fits-all scheme. Investors, on the other hand, noting that theavailable information on impacts is primarily qualitative, have been complaining that it can be quite
hard to make lateral comparisons between companies, and that it is therefore difficult to fold theinformation on impacts into assessments of corporate value. Given that reality, our aim in this paper
has been to use a logic model framework to come up with a set of standardized outcome-levelindicators that capture these highly company-specific impacts.
We think that making the intrinsic value of companies more visible by means of these outcome
indicators can help advance the cause of fruitful dialogue between investors and companies. A
company that can present a tangible sense of its impact on society can expect to draw in people
with whom the company's objectives resonate, thereby adding to its base of shareholders and fans.
Moreover, we think that the recent attention being given to ethical consumption can benefit
companies whose businesses involve addressing societal issues, as these companies may attract
more support from customers and consumers, which in turn can boost their sales and the power of
their brands. On top of that, companies that also look likely to achieve sustainable growth in sales
and profits should be able to attract loyal shareholders in the form of ESG investors and impact
investors with long horizons. Companies with a base of stable shareholders should find themselvesbetter able to cope with market fluctuations and uncertainty, and that stability should help to lower
their share price volatility.
Investors, meanwhile, can make their investment portfolios more sustainable by focusing on
impacts and investing in companies that seek to achieve sustainability in their own operations andacross society as a whole. In speaking with impact investors outside Japan, one opinion we haveheard is that pre-specified indicators can serve as a clear objective for dialogue, and that this helps
drive companies to improve their operations in a way that enhances corporate value. The result isthe emergence of a positive correlation between impacts and corporate value.
The standardized outcome labels that we have produced here, while still in need of improvement,
are something that we think could function as a tool to stimulate fresh dialogue regarding the
intrinsic value of companies. Sustainable growth is made possible by robust corporate disclosuresand the presence of high-quality dialogue between companies and investors. If more and more
institutional investors take an interest in outcome KPIs and impact KPIs in addition to the usual
financial KPIs, and if we start to see more engagement that rests on the assumption that these other
KPIs will be referenced in business decision-making, we would then expect to also see more
Japanese companies becoming the subject of higher expectations for sustainable growth.

362024.02.08

2. Significance with respect to as-yet-unlisted venture firms
We think that venture companies may find a common set of outcome labels to be a highly effective
framework within which to discuss their impacts, starting well before they go public. Venture firms
are often founded through a willingness to take risks for the sake of resolving some societal issue,
but it often happens that these companies find it difficult to explain the actual social impact that
resolving the chosen issue would have. These companies often lack the time and human resources
needed for thorough explanations. The use of standardized outcome labels would give thesecompanies an effective means by which to help investors understand what problem the company
is trying to solve, and what impacts a solution to that problem might have on society. This would
make it easier for investors to discern the intrinsic value of venture companies, and could lead tomore activity in the venture investment space.
For the sake of sustainable growth in Japan's economy, it is important not just to increase thenumber of "unicorn" companies valued highly at the time of their initial public offerings, but also
to encourage sustainable gains in the corporate value of companies after they have gone public.
As discussed in Section II of this paper, publicly listed companies are being encouraged to makedisclosures at the level of outcomes. Highly individualized startups that want to earn fair valuations
after they go public might find it helpful to take steps well before their initial offerings to tell agrowth story that references concrete numbers and data and presents a picture of their efforts togenerate impacts.
Also, an oft-cited problem with capital markets in Japan is that there is a divide running throughthe market, with pre-IPO investors on one side of the line and post-IPO investors on the other. Oneof the functions of securities brokerages is to manage the smooth handoff from one set of investors
to the other. We think that the tool we have developed here can help companies to realize theenhancement of corporate value seamlessly as they make the transition from private to public.

3. Significance for equity valuations
Relative valuations have an important role to play in a variety of situations. However, attempts at
relative valuations that mechanistically determine the universe of companies to compare using
standard industry classifications often yield results that are intuitively unsatisfying. Such approaches
also fail to make clear distinctions between companies with significant longer-term exposure tosome particular impact and those without. In practice, then, it is quite common for analysts andinvestors to define their own comparison universes as they see fit. For example, one can consume
textual information about business risks and opportunities and then define a universe of companies
that look comparable based on one's reading of that information, but any analysis of that universe
will be compromised by the arbitrariness that creeps into the universe selection process. Even if
one were to set that problem aside, it is also the case that it would be impossible to assemble a listof companies that are comparable in all respects—now more than ever, given the degree to which
Japanese companies have diversified. The obvious solution to this problem would be to use a multilabel model that assigns multiple labels to individual companies, and then use the set of allcompanies as the universe for analysis. But it is unrealistic to expect humans, with their inbuilt
limitations, to assign multiple labels to each and every listed company.

37

2024.02.08

In this paper we have proposed what is essentially a multi-labeling framework that systematicallyassigns multiple outcome labels to individual companies with the help of a generative AI. We think
this approach may be the solution needed for the problem spelled out above. By isolating thecontributions made by each variable, a valuation model that employs multiple outcome labels as
explanatory variables can make it clear how highly the market values each of a company's businessstrategies and innovations with social or environmental impacts. This model would also let investors
perform individual company valuations that take into account both financial and non-financial
information. This would have significant implications for valuations of individual companies, first inthat stock valuations would capture something of a company's specific character in terms of its
impacts, and second in that one would have an objective means by which to draw out information
about how the market as a collective values companies' specific initiatives with respect to importantsocial and environmental impacts. In the section that follows we look at how this approach might
apply to mergers & acquisitions.

4. Applications in M&A valuations and other areas
Relative valuations are commonplace in the M&A realm as well, where one often hears valuation
multiples for same-industry peers referenced in discussions of whether a particular company isovervalued or undervalued. However, a company whose business is ambitiously designed toachieve substantial social or environmental impacts over the longer term could easily end up
looking highly overvalued relative to industry peers when using some off-the-shelf valuation
measure like one-year forward P/E. By combining the conventional use of valuation multiples that
capture overvaluation or undervaluation relative to sector peers with the framework outlined in thispaper, investors should have a means by which to look at companies making forays into similarpotential markets and judge whether they are overvalued or undervalued. The approach we havepresented here allows one to access the market's collective intelligence, for instance in seeking todetermine how highly the market rates a particular approach to tapping a specific potential revenue
pool, and in sorting out whether a particular company is rated more highly than others in its pursuitof that approach. We think that the availability of access to that collective intelligence cancontribute greatly to furthering the understanding of future-oriented efforts to address societal
challenges.
Looking ahead, we may see more M&A actions geared specifically towards realizing impactsynergies is it becomes easier to assess positive impacts in a logical way based on an accumulationof knowledge about impacts from a variety of perspectives. Many startups exist specifically toaddress a societal issue of some kind, but getting the business off the ground is often hugely
expensive and time-consuming. Also, because of the time needed to deal with the societal issue,
the timeline for the realization of impacts can be quite long. There is a significant risk, then, thatfunding could dry up before the impacts are realized, but putting the startup under the umbrellaof a large company can increase the likelihood of the impacts being realized eventually. Japan's
Cabinet Secretariat has issued a five-year plan for startup development that that includes theobservation that sustainable growth becomes more likely if startups can license their new
technologies out to high-quality listed companies. If a diverse array of companies with different
technological proficiencies and different business models can come together and cooperate, wewould expect them to be able to generate synergies and act quickly in attempting to tackle societal
challenges. Improving the quality of impact assessments and building up a storehouse of38

2024.02.08

knowledge on the subject can, we think, help a great deal in helping to bring about that kind ofsociety.

VI. In conclusion: What needs doing and what lies ahead
In the first half of this paper, we attempted to develop a standardized set of outcome labels to use
in assessing impacts. This work is still at the prototype stage, but by putting some earnest work into
refining it, our hope is that, in the near future, we can turn this approach into a genuinely useful
tool by which to evaluate impacts. Our take on "standardization" is that it involves applying labels
to as many companies as possible using the same set of standards; we do not think that the workof assessing impacts necessarily requires a fixed labeling scheme. If anything, good labelingschemes need to be able to make distinctions among companies based on what sort of initiatives
are being undertaken to tap what revenue pools, for example, and what sort of strategies orbusiness models are being adopted in the interest of capturing market share. The task now is toestablish a flow for generating satisfying labeling schemes that meet this requirement and that
apply horizontally across all industries.
One of the biggest lessons learned in our research thus far is that the use of generative AI has
radically expanded the range of possibilities for flexible and customized labeling. Recent advancesin prompt engineering make what we are attempting to do here look achievable, and we intend toproceed quickly with development.
It is also our belief that being able to use standardized outcome labels as a means by which toqualitatively and quantitatively discuss individual companies' impacts and value creation processes
in the context of dialogue between investors and companies ought to be a highly effective meansby which to showcase the intrinsic value that many Japanese companies already quietly possess.
We think that making that intrinsic value plain and visible should help lift expectations forcontinuous growth in a way that becomes reflected in companies' share prices and makes Japanesecompanies more competitive.
In the second half of this paper, we measured the value of impacts built into share prices by applying
these standardized outcome labels to a conventional valuation method. Impact investing involves
long time scales and considerable risk, and this makes it difficult to deductively arrive at fairstandards by which to judge risks and returns. Even so, we think that impacts can be assessed withina conventional risk & return context. The critical thing is to measure the impacts rooted in nonfinancial information using capital market yardsticks that traditional investors can accept. Progresson that front could broaden the base of investors interested in impacts and could lead to anincrease in the amount of risk money allocated to impact projects. We think it would be a societallysignificant outcome if, in the future, risk money could be supplied under a fair set of conditions tobusinesses that aim to realize social and environmental impacts while also generating substantial
economic returns. By accumulating more know-how in the area of impact assessment and refining
the flow of dialogue and analysis involved, we hope to play a part in supporting the allocation ofrisk money—on fair terms—to projects that could have a substantial impact on society.

392024.02.08

Sources referenced
Financial Services Agency (Japan), Report by the Working Group on Impact Investment – Toward
Growth and Sustainability through Addressing Social and Environmental Issues –, June 30, 2023
Cabinet Office (Japan), Grand Design and Action Plan for a New Form of Capitalism (2023 Revised
Version), June 16, 2023
Tokyo Stock Exchange, Inc., Action on Cost of Capital-Conscious Management and Other Requests,
April 14, 2023 (original Japanese version published on March 31, 2023)
Keidanren, Using Impact Metrics to Promote Dialogue with Purpose as Starting Point - Action for
Sustainable Capitalism by Companies and Investors, June 14, 2022
Nomura Asset Management Co., Ltd., Impact Report 2021
Resona Asset Management Co., Ltd., Japanese Stock Impact Investment Fund Impact Report 2022
November (only available in Japanese)
WBCSD, 2013. Measuring socio-economic impact
GRI, United Nations Global Compact, WBCSD, 2017. SDG Compass
SAP, Annual Report 2020
Ajinomoto Co., Inc., ASV Report 2023
NissinFoods
Holdings
Co.,
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Analysisof

ESG

Issues,

Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu,
2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree
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K. V. Rashmi, Ran Gilad-Bachrach, 2015. DART: Dropouts meet Multiple Additive Regression Trees

40

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coupon payment is calculated by multiplying the coupon rate by the notional principal at the time of payment. The maturity value is the amountof the notional principal when the issue becomes due. For JI17 and subsequent issues, the maturity value shall not undercut the face amount.
Purchases of investment trusts (and sales of some investment trusts) are subject to a purchase or sales fee of up to 5.5% (tax included) of thetransaction amount. Also, a direct cost that may be incurred when selling investment trusts is a fee of up to 2.0% of the unit price at the time ofredemption. Indirect costs that may be incurred during the course of holding investment trusts include, for domestic investment trusts, an asset
management fee (trust fee) of up to 5.5% (tax included/annualized basis) of the net assets in trust, as well as fees based on investment
performance. Other indirect costs may also be incurred. For foreign investment trusts, indirect fees may be incurred during the course of holding
such as investment company compensation.
Investment trusts invest mainly in securities such as Japanese and foreign equities and bonds, whose prices fluctuate. Investment trust unit
prices fluctuate owing to price fluctuations in the underlying assets and to foreign exchange rate fluctuations. As such, investment trusts carry
the risk of losses. Fees and risks vary by investment trust. Maximum applicable fees are subject to change; please thoroughly read the writtenmaterials provided, such as prospectuses or documents delivered before making a contract.
In interest rate swap transactions and USD/JPY basis swap transactions (“interest rate swap transactions, etc.”), only the agreed transaction
payments shall be made on the settlement dates. Some interest rate swap transactions, etc. may require pledging of margin collateral. In someof these cases, transaction payments may exceed the amount of collateral. There shall be no advance notification of required collateral value or
collateral ratios as they vary depending on the transaction. Interest rate swap transactions, etc. carry the risk of losses owing to fluctuations inmarket prices in the interest rate, currency and other markets, as well as reference indices. Losses incurred as such may exceed the value ofmargin collateral, in which case margin calls may be triggered. In the event that both parties agree to enter a replacement (or termination)transaction, the interest rates received (paid) under the new arrangement may differ from those in the original arrangement, even if terms otherthan the interest rates are identical to those in the original transaction. Risks vary by transaction. Please thoroughly read the written materials
provided, such as documents delivered before making a contract and disclosure statements.
In OTC transactions of credit default swaps (CDS), no sales commission will be charged. When entering into CDS transactions, the protection
buyer will be required to pledge or entrust an agreed amount of margin collateral. In some of these cases, the transaction payments may exceed
the amount of margin collateral. There shall be no advance notification of required collateral value or collateral ratios as they vary depending on
the financial position of the protection buyer. CDS transactions carry the risk of losses owing to changes in the credit position of some or all of
the referenced entities, and/or fluctuations of the interest rate market. The amount the protection buyer receives in the event that the CDS is
triggered by a credit event may undercut the total amount of premiums that he/she has paid in the course of the transaction. Similarly, theamount the protection seller pays in the event of a credit event may exceed the total amount of premiums that he/she has received in thetransaction. All other conditions being equal, the amount of premiums that the protection buyer pays and that received by the protection seller
shall differ. In principle, CDS transactions will be limited to financial instruments business operators and qualified institutional investors.
Transfers of equities to another securities company via the Japan Securities Depository Center are subject to a transfer fee of up to ¥11,000(tax included) per issue transferred depending on volume. No account fee will be charged for marketable securities or monies deposited.

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Additional information regarding the methodologies or models used in the production of any investment recommendations contained
within this document is available upon request by contacting the Research Analysts of Nomura listed on the front page. Disclosuresinformation is available upon request and disclosure information is available at the Nomura Disclosure web page:
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Disclosures as of 08-Feb-2024.