Nomura AI Companies 70
EQUITY QUANTITATIVE RESEARCH

Equally weighted index of AI-related stocks
Stocks selected on basis of searches of TV/online
news, newspapers, magazines
Index overview and concept
The Nomura AI Companies 70 is an equally weighted index made up of 70 stocks thatengage in business related to artificial intelligence (AI).
Stocks are selected by using quantitative evaluation indicators based on article search
results, from among listed companies in Japan that are covered in the media in
connection with AI-related businesses, as determined by scanning big data comprising
articles and reports from online and TV news, newspapers, magazines, and the like.
"Artificial intelligence" and several related keywords selected with the use of AI are
employed in the article searches.

Global Markets Research
25 January 2017

Research analysts
Japan quantitative research
Satoshi Yamanaka - NSC
satoshi.yamanaka@nomura.com+81 3 6703 1728
Yuko Onishi - NSC
yuko.onishi@nomura.com+81 3 6703 3915

Sectors that actively use AI, including electric appliances & precision instruments, IT &services, others, automobiles & transportation equipment, machinery, and the like, areat the heart of AI investment themes, and include small and midcap stocks related to AI
and robots.
The third AI boom has arrived on the back of strides made in AI technology around theworld in the 2010s. In Japan, government and government agencies have beenworking together to promote R&D and commercialization of AI. AI technology is being
applied to business and the Nomura AI Companies 70 was created to make it possible
to invest intensively in companies that engage in AI-related business.

Fig. 1: Performance of Nomura AI Companies 70
350
Nomura AI Companies 70
300

TOPIX

250
200
150
100
50
0
2001/11(yyyy/mm)
2003/11

2005/11

2007/11

2009/112011/112013/112015/11

Note: Data for 30 November 2001 given a value of 100. Analysis is based on historical share prices and does notguarantee future performance.
Source: Nomura

Japanese version published on
January 18, 2017
Production Complete: 2017-01-25 21:14 UTC

See Appendix A-1 for analyst certification, important disclosures and the status of non-US analysts.

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25 January 2017

1. Growth in artificial intelligencebusiness
Arrival of third boom in AI
Rapid advances have been made in AI technology in the 2010s, which have beenreported by the media. By using AI to learn from big data and the like, decisions andtasks transcending human capacity can be automated and efforts have been made toapply this to various fields of business.
AI was proposed at a conference on the campus of Dartmouth College in the US in 1956,
and the ideas for machine learning 1 and the perceptron algorithm (used in the first neuralnetwork) were devised and established in the late 1950s. The roots of AI technology
used today were formed during the first boom in AI. The second boom in AI arrived alongwith the spread of databases in the 1980s, following the development of expert systems
that make near-expert decisions using the knowledge base of experts. At this time inJapan, a state-sponsored project was under way to research and develop a fifthgeneration computer.
The arrival of the third boom in AI came in the 2010s, revolving around deep learning.
Much progress has been made in image and audio recognition technology, andinnovative products, including self-driving cars and humanoid robots, are close to being
realized. AI and related technology traditionally used mostly by IT companies is now
being used in myriad industries spanning autos, manufacturing and construction, electric
power, pharmaceuticals, and finance. As a result AI business has expanded.

Fig. 2: History of AI

First boom in AI,
the 1950-60s(the era ofsearching)

Second boom inAI, the 1980s(the era ofknowledge)

Third boom in AI,
the 2010s(the era oflearning)

1956: AI term coinedat conference at
Dartmouth College

1977: knowledge
engineering proposed
at international AI
conference

1989: artificial market
model built

2011: bigdata-crunching
Watson becomes quizshow champion

2014: humanoid robot
Pepper unveiled

1959: machinelearning
defined/established

Late 1970s: expert
systems developed

1997: Deep Blue beats
world chess champion

2012: first license plateissued for test driving
autonomous car

2016: AlphaGo, using
deep learning, beats
world's top go player

Source: Nomura

1

Arthur Samuel, known as the father of machine learning, defined machine learning as the field of study that gives
computers the ability to learn without being explicitly programmed.

2

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2. Nomura AI Companies 70 stock
selection
Quantitatively selected based on results of searches of online
and TV news, newspapers, magazines, and other media
The Nomura AI Companies 70 is an equally weighted index made up of 70 stocks ofcompanies covered by the media in connection with business related to AI. It is a themespecific index enabling focused investment in companies associated with AI, an
investment theme that has been gathering interest.
Figure 3 shows the flow of steps involved in selecting stocks. Stocks for inclusion are
selected from the score calculation universe of all common stocks listed on exchanges inJapan that are in the top 98% in terms of market cap and in the top 90% in terms of
average market turnover. In the interest of investability, stocks with extremely low market
caps or low liquidity are not included.
Online and TV news, newspapers, magazines, and other media are searched for themention of stocks in the score calculation universe. "Artificial intelligence" and several
related keywords selected with the use of AI are employed in the search for articles.
Ultimately 70 stocks are selected with the use of a quantitative evaluation indicator
based on the results of these article searches, which we will describe in more detail later.
The 70 stocks in the index are weighted equally. By making it an equally weighted index,
a flat rate of investment is made in not only large cap but also small and midcap AIrelated stocks. Keywords related to AI are reviewed annually and published in August.
The annual review is carried out on the first business day of December.
We call this stock selection methodology the Nomura AI Thematic Stock Search (NAITS).
Thematic stock search can be used for other investment themes by searching articles
with related keywords selected with the use of AI.

Fig. 3: Flow for selecting stocks for Nomura AI Companies 70 (Nomura AI Thematic Stock Search)

Stock selection
universe

Score
calculation universe

Top 98% in termsof market cap

Top 90% in termsof average market
turnoverAI ranking score
calculation
Article search
Related keyword
ranking score
calculation

Top 10 companies
by AI ranking score+
Selection of 70
companies fromamong those withhighest averageranking score

Nomura AI
Companies 70
Equally weighted

Related keyword
selection with use ofAI

Source: Nomura

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25 January 2017

Related keyword selection with use of AI
AI-related keywords are selected on the basis of their similarity to AI upon analyzing text
in documents related to AI that have been published by government offices and researchorganizations. As shown in Figure 4, first a broad net is cast to collect documents
published by various entities that include "artificial intelligence" and the documents areconverted to a text format after removing tables, figures, illustrations, and the like. The
text is then segmented into the smallest grammatical units (morphemes) of the language.
By using AI to learn the morpheme clusters, words with high similarity to the phrase“artificial intelligence” can be extracted. Ultimately, words are selected that are suitable
as keywords in the search of articles in which AI companies have been mentioned.
Related keywords that were initially selected in 2016 are "deep learning," "machinelearning," "big data," "internet of things," "robotics," "data mining," "sensing," "cybersecurity," "robot," and "autonomous driving."
The similarity of each word is shown in the top portion of Figure 5. Similarity shows how
close a word is to "artificial intelligence." The closer the numerical representation is to"1," the higher the similarity. Aside from the selected keywords, words with high similarity
include "human," "general purpose," "large volume," and others, but these were notselected as they were not deemed to be suitable keywords for searches on articles inwhich companies are mentioned. Similarity was particularly high for "deep learning" and"machine learning," indicating that these are gathering much attention as coretechnological drivers of AI.
The lower part of Figure 5 breaks down the number of times the keywords werementioned in documents containing "artificial intelligence." "Robot" appeared mostfrequently, followed by "big data" and "internet of things." This is based on documentsfrom the five years from April 2011. If we look at documents from the past couple years,
we see that keywords other than "robot" have increased in prevalence.

Fig. 4: Extraction of keywords with high similarity to “artificial intelligence” with the use of text analysis(1) Acquisition fromvarious entities of
documents including"artificial intelligence"...systems that
contribute toautonomous driving
technology, by
analyzing big datawith AI (machinelearning)...

(2) Segmentation of
text into morphemes
after removing
tables, etc...systems that
contribute toautonomous driving
technology, by
analyzing big datawith AI machinelearning...

(3) Learning by AI(4) Extraction of
keywords with highsimilarity to "artificial
intelligence"(5) Selection ofrelated keywords- Big data
- Machine learning- Analysis- Autonomous
driving- Technology- System

Big data
Machine learning
Autonomous

Examples
Source: Nomura

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25 January 2017

Fig. 5: Quantitative indicators for related keywords
0.8

Similarity to "artificial intelligence"

0.6
0.4
0.2

Sensing

Cyber security

Robot

Autonomous
driving

Cyber security

Robot

Autonomous
driving

Data mining

Robotics

Internet of things

Big data(%)

Sensing

60

Machine learning

Deep learning

0

Frequency of appearance

50
40
30
20
10

Data mining

Robotics

Internet of things

Big data

Machine learning

Deep learning

0

Note: Sample period is April 2011 through March 2016.
Source: Nomura, based on documents from government offices and research organizations

Related keywords found in AI-related policy materials
As discussed above, we selected our related keywords based on documents related to
AI that have been published by government offices and research organizations. In thissection, we discuss how these keywords are used in actual policy materials.
The government and its various agencies are all working together to promoteaccelerated R&D into AI and to make it into a viable industry. A strategic committee for
AI technology was established on the prime minister's orders in April 2016, and METI,
the Ministry of Internal Affairs and Communications (MIC), and the Ministry of Education,
Culture, Sports, Science and Technology (MEXT) are now prepared to work together on
AI R&D and innovation policy. As part of these efforts, RIKEN in April 2016 launched the
RIKEN Center for Advanced Intelligence Project with a subsidy for the Advanced
Integrated Intelligence Platform Project (AIP), which is meant to integrate AI with big data,
IoT, and cybersecurity. The high similarity shown in Figure 5 for "big data," "internet ofthings," and "cyber security" may reflect the influence of the AIP in particular.
As shown in Figure 6, the National Institute of Advanced Industrial Science andTechnology's Artificial Intelligence Research Center, RIKEN's Center for AdvancedIntelligence Project, and the R&D institutions under the National Institute of Information
and Communications Technology are working together to promote partnerships between
universities, industry, and government for research into AI technology, and to develop
businesses in this field.

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25 January 2017

Strategic committee on artificial intelligence technology

Fig. 6: AI efforts by the national government, government agencies, and research institutions

National Institute ofAdvanced Industrial Scienceand Technology (AIST),(METI)

Artificial Intelligence Research Center (AIRC) established (May 2015);⇒ Research into brain-inspired computation, neuro-computing,machine learning, and data–knowledge integration AI

RIKEN Center for Advanced Intelligence Project (AIP) established(April 2016);

RIKEN (MEXT)⇒ Applying AI platform technologies in science and actual society

National Institute ofInformation andCommunications
Technology (NICT) (MIC)

Universal Communication Research Institute (UCRI), Center for
Information and Neural Networks (CiNet), etc;
⇒ Research to understand and create cognition based on big data
and brain function

Source: Nomura, based on documents from government offices and research organizations

The government's Council on Investments for the Future met in September 2016 todirect the government's growth strategies, with the aim of creating ¥30trn in value-addedby 2020 by promoting a fourth 2 industrial revolution in the corporate sector. This fourthindustrial revolution is envisioned as a transformation that would allow for thedevelopment of new goods and services by connecting all manner of things to theinternet and by using AI to analyze the mass of data thus generated. As can be seen in
Figure 7, the use of AI, IoT, big data, and robots gives rise to numerous examples ofinnovation in a wide range of business domains.
"Data mining" refers to techniques used to analyze big data to extract useful information,and is thus quite similar to machine learning as a technology. "Sensing" is a technology
to collect quantitative data using a variety of sensors, and is an essential technology forautonomous driving. This technology is also used for remote sensing, used in remote
operation.

Fig. 7: Actual uses of technological innovations in society
Health, medical, and nursingcare• Creation and analysis ofelectronic medical charts
and invoices• Use of ICT for preventive
medicine, independence
support, remote treatment,
and nursing care robots• Diagnosis using AI, sensors,
and robots

Mobility, transportation

Manufacturing• Autonomous driving• Smart manufacturing• Autonomous driving andconvoying for trucks on
expressways• Prediction and early
detection of abnormalities
using IoT, big data, and AI• Early implementation ofdrone deliveries• Cyber security in an IoTsociety• Logistics revolution to
overcome personnel
shortages

Energy, construction• Visibility of energy
consumption• Widespread adoption ofsmart meters and homeenergy management
systems (HEMS)• Productivity revolution atconstruction sites• Use of ICT to promotespread of "i-Construction"

Source: Nomura, based on the priority agenda in the second round of materials from the Economic Revitalization Headquarters' Council on Investments for the Future

2

The fourth industrial revolution indicates changes in industrial structure, following on from the first industrial
revolution (in the 18th century), which involved the use of the steam engine, the second industrial revolution (in thesecond half of the 19th century), in which electric power was used to enable mass production, and the third
industrial revolution (in the second half of the 20th century), which involved automation via the use of computers.

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25 January 2017

Stock selection using Nomura AI Thematic Stock Search (NAITS)
Stocks for inclusion in the Nomura AI Companies 70 are selected using quantitative
indicators based on the results of searches for articles that include the phrase “artificial
intelligence” and related keywords. Essentially, companies that are the subject of a large
number of articles related to AI are selected for the index. We set out the stock selection
process in more detail below.
As we have already explained, the universe of stocks eligible for score calculation for the
Nomura AI Companies 70 comprises the stocks that are in both the top 98% of stocks
listed on Japanese stock exchanges in terms of free-float-adjusted market cap and the
top 90% of stocks listed on Japanese stock exchanges in terms of average daily market
turnover over the past 60 days. The AI ranking score and related keyword ranking score
are then calculated for each of the stocks in the score calculation universe.
First, the AI ranking score is calculated for every stock. The number of articles in the past
two years containing both the stock's ticker code and the phrase "artificial intelligence" iscalculated for every stock once a year, and the total AI article score is obtained bydoubling the figure for the most recent year. Each stock's ranking in terms of total AI
article score is its AI ranking score, with rankings in ascending order starting with thestock with the highest number of articles.
Next, to calculate each stock’s related keyword ranking score, the number of articles
containing the stock's ticker code and a related keyword but not the phrase "artificial
intelligence" is calculated in the same way as for the AI ranking score. Each stock's
ranking in terms of total related keyword article score is its keyword ranking score, withrankings in ascending order starting with the stock with the highest number of articles.
The top 10 stocks in terms of AI ranking score and the top 60 stocks in terms of theaverage of AI ranking score and keyword ranking score, ranked in ascending order, areselected as the constituent stocks of the Nomura AI Companies 70. The above is a
simplified version of the stock selection process. Refer to the Nomura AI Companies 70
index rulebook for a more detailed description of the stock selection process.

Fig. 8: Stock selection process using NAITS (2017)

Each stock is
given a scorebased on numberof articles

Artificial
intelligence

Deeplearning

Cyber
security

Autonomous
driving

Robot

Robotics

Big data

SensingDataminingInternet ofthings(IoT)

Each stock is
given a scorebased on numberof articles

2016 article
score
×1
＋

2017
AI ranking
score

2017 article
×2
score

2016 article
×1
score
＋
2017 article ×2
score

Top 10stocks interms of AI
ranking score+
top 60 stocks
in terms of
average ofboth ranking
scores2017
Nomura AI
Companies
70

2017
Relatedkeywordranking
score

Machinelearning

Source: Nomura

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3. Nomura AI Companies 70:
performance and industry/style mix
Outperforming TOPIX
Figure 9 shows the performance of the Nomura AI Companies 70. Over the past 15
years (since 2001), the roughly 9.9% average annual return of the index has surpassed
the 3.5% average of the TOPIX. The total excess return was 52.8%.
While the strong performance likely owes primarily to industry and size factors, we thinkthat the number of news reports could have been a share price-lifting factor too, withinvestor attention stoked by a high volume of media reports. We cover this in more detaillater in this report, but we think the TOPIX-beating performance was driven byconcentrated investment at a set weight (1/70th each) in a cluster of small/midcap AIand robot-related stocks that were frequently mentioned in online and TV news reports,
newspapers, and magazines.
The average beta 3 was 1.02, showing sensitivity to the market average that was similarto (or slightly higher than) the TOPIX.

Fig. 9: Index performance: Nomura AI Companies 70
350
300

60
Nomura AI Companies 70

(%)

50

TOPIX

250

40

200

30

150

20

100

10

50

0

0

-10

Accumulated excess returns

Nomura AI
Companies 70

TOPIX

Average return (%)

9.92

6.61

Standard deviation (%)

23.48

22.20

Sharp ratio

0.38

0.25

Max draw dow n (%)

59.10

60.15

Excess returns (%)

3.51

Information ratio

0.65

−
−

Note: Sample period is November 2001 through December 2016. 30 November 2001 = 100. Annualized based on daily results. TOPIX includes dividends, excludes effects ofdividend taxation. For the Nomura AI Companies 70, calculation of excess return and information ratio is dividend inclusive to match conditions for TOPIX. Others calculated fromindices that are net of dividend tax. Transactions costs have not been taken into account. Analysis is based on historical share prices and does not guarantee future
performance.
Source: Nomura

3

Beta is average of figures for November 2003 to December 2016, with those figures calculated from two years'
weekly data for the TOPIX (dividend inclusive) and the Nomura AI Companies 70 (dividend inclusive).

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Index centered around electric appliances & precision
instruments and IT & services sectors
Figure 10 shows the average sector composition of the Nomura AI Companies 70. The
top chart shows average sector weights at the time of the periodic reconfigurations
carried out from 2001 through 2016, based on TOPIX-17 Series sectors. The bottom
chart shows the average excess weight for each sector in the index versus its weight in
the TOPIX.
It is clear from the bottom chart that the electric appliances & precision instruments, IT &services, others, automobiles & transportation equipment, and machinery sectors have
had particularly large excess weights, on average. One way of interpreting this is to saythat the sectors for which the development of AI-related technology has led directly to
improvements in operational efficiency or innovations have had high weights in the index.
At the same time, the pharmaceutical, financials (including banks), and raw materials &chemical sectors have on average been underweight versus the TOPIX. AI is currently
used in a wide range of sectors, but we think that its uptake in these sectors was
relatively low in the past.
Below, we look at the ways in which AI and related technologies are being used in thevarious sectors.

Fig. 10: Sector composition of Nomura AI Companies 70(%)
30

Average sector weight

28.2
22.2

3.3

2.9

2.5

2.1

1.2

1.6

Steel & nonferrous
metalsRaw materials &chemicals

Other

4.7

Transportation &logistics

6.3

Construction &materials

8.3

10

Retail trade

16.6

20

20

(%)
13.9

Electric power & gas

Commercial &wholesale trade

Machinery

Automobiles &transportationequipment

IT & services, others

Electric appliances &precision instruments

0

Average excess sector weight (versus TOPIX)
9.3

10

4.8

4.0

2.1

1.3

-8.5
-1.4

-2.0

-2.2

-2.8

Foods

Transportation &logistics

Real estate

Retail trade

Energy resources

Construction &materials

Steel & nonferrous
metalsElectric power & gas

Commercial &wholesale trade

Machinery

Automobiles &transportationequipment

IT & services, others

Electric appliances &precision instruments

-10

-4.5

-5.0

-5.7

Banks

-1.3

Raw materials &chemicals-1.1

Financials (ex banks)-1.0

Pharmaceutical0Note: Shows average sector weights as of the periodic reconfiguration date (the first business day of December) each year from 2001 through 2016
Source: Nomura

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Electric appliances & precision instruments and IT & services, others
More and more PCs and smartphones are now able to offer the appropriate service
automatically on the basis of voice input recognition. Dialog with humanoid robots is also
being put to practical use, and products that use AI are becoming increasingly familiar inboth the home and the workplace. All the major electronics manufacturers havelaunched plans aimed at substantially increasing their numbers of AI engineers from2017 onward, and this is likely to be the sector that accounts for the majority of AI-related
business from now on.
In the field of medical equipment, automatic diagnosis using medical image analysis
technologies such as X-rays and the automatic proposal of treatment methods based on
automatic diagnosis are now being put to practical use. Healthcare robots fall into thecategories of long-term care support robots that provide support for activities such as
moving around and bathing, independence support robots that assist in walking andrehabilitation, for example, and, most importantly, communications robots able, thanks tothe application of AI technology, to engage in conversation and communicate with theuser. Demand for all three categories of healthcare robot is growing as Japanese society
ages.
Meanwhile, as a wide range of data, including confidential and personal data, is now
being exchanged frequently, the problems of data leaks and hacking have also surfaced,and data security is regarded as a high priority. In October 2016, METI launched anational qualification system, called the IT Security Support Provider System, toencourage companies to hire experts who will be responsible for in-house cyber security.
AI and related technologies have started to be used in the field of crime prevention too.
Products such as smart doorbells that are able to detect and provide notification ofvisitors, smart doorknobs that can lock and unlock doors, and smart alerts that detectand provide notification of intruders and other abnormalities are emerging as new typesof consumer electronics for residential use. Demand is also growing for biometricauthentication technologies such as face recognition and fingerprint recognition, and forequipment that uses these technologies, and AI is increasingly being used to help
improve security.
Automobiles & transportation equipment, transportation & logistics
Autonomous driving is a keyword for these two sectors. Progress has been made in thedevelopment of image recognition and spatial recognition. Autonomous driving vehicles
that use sensing technologies and 3D mapping to recognize external data have beendeveloped, and in some countries such vehicles are already being tested on public roads.
Companies have been taking advantage of their respective areas of strength to develop
technologies ranging from quasi-autonomous driving aided both by the driver andinfrastructure installed on the roads to fully automated driving. Substantial progress has
also been made in the development of individual technologies such as automatic braking
and automatic parking.
In the transportation & logistics sector, companies are looking into new types of services
such as delivery using unmanned taxis and drones, and trucks installed with 3D printersthat can carry out manufacturing and transportation at the same time.
Machinery and construction & materials
Companies in these two sectors are making use of IoT and ICT. There have been
proposals for smart factories (fully automated factories) and smart construction, andattempts have also been made to use ICT to control all onsite operations. Measurement
and transportation using driverless dump trucks, self-driving tractors, and drones areexamples of moves toward automation and greater efficiency in these sectors.

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Other sectors
In the retail trade sector, self-checkout systems are becoming increasingly common, andcompletely unmanned stores are also being looked into. The idea is that the use ofomnichannel retailing that links together all the sales and distribution channels (bricksand-mortar stores, the internet, and mobile phones, for example) will enable retailers to
attract new customers and reduce their personnel expenses.
In the electric power & gas sector, progress is being made in the installation of smart
meters with telecommunications functionalities and EMS (energy management systems)with energy-saving functionalities, and the creation of smart grids that incorporate bothtypes of products is leading to greater efficiency in the sector and more appropriate
energy usage overall.
In new drug research and development, AI is being used in experiments (clinical trials)
that take time and effort in particular, thus making the search for potential new drugs
more efficient.
In the financial sector, including banks, fintech, which brings together finance and IT, isattracting attention, and electronic settlement in particular is becoming increasingly
common. Robo-advisors that suggest and manage portfolios automatically on the basisof investors' preferences and risk tolerance, and blockchain technology, exemplified bybitcoin, are also being used more and more.
In food-related industries, progress is being made in standardizing the technologies used
in touch, taste, and smell sensors, for example, and it now appears that Japanesetechnologies might become the global standard in this area. Progress is also being madein improving the efficiency of food manufacturing processes via the use of robot hands
and other types of industrial robot.
In the retailing, pharmaceutical, and financial sectors, AI is beginning to be used inhumanoid robots for customer service use, and for the purpose of answering questions,for example in call centers. These sectors in the past had low weights in the Nomura AI
Companies 70, but their index weights might rise if there is an increase in news ornewspaper reports, for example, about the use of AI in these sectors.

Fig. 11: Examples of use of AI and automation, and keywords, by sector
Electric appliances & precision
instruments
IT & services, others

Automobiles &transportation equipment
Transportation & logistics

Machinery sector
Construction & materials
sector• Voice input in information devices• 5G• Humanoid robot• Healthcare robot• Medical image analysis• Automatic diagnosis• Cyber security• Autonomous driving• Quasi-autonomous driving• Fully autonomous driving• Automatic braking• Automatic parking• Smart factory (fully
automated factory)• Smart construction(use of ICT)
• Project risk management• Sensing technology• 3D mapping• Unmanned dump truck• Self-driving tractor• Measurement/
transportation by drone• Smart refrigerator• Smart cleaning robot• Smart doorbell (visitor detection)• Smart doorknob (locking andunlocking door)• Smart alert (detection ofabnormalities)• Unmanned taxi
• Truck installed with 3D
printer• Delivery by drone

Other sectors• Omnichannel• Smart grid
• Smart meter• EMS (energy management
system)• New drug development• FinTech• Robo-advisor• Blockchain• Touch sensor• Taste sensor• Smell sensor• Robot hand

Source: Nomura

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Areas in which AI technology is being used, based on number of patentapplications
The electric appliances & precision instruments, IT & services, other, and automobiles &transportation equipment sectors have had high weights in the Nomura AI Companies 70,
and these sectors also appear to be important as far as we can tell from a breakdown of
the areas in which patent applications involving the use of AI have been filed.
Figure 12 shows the breakdown of Japanese, US, European, Chinese, and South
Korean patent applications involving the use of AI technology, by area in which thetechnology is to be applied, based on Japan Patent Office data. The categories are
public sector (traffic/transportation, telecommunications, utilities, climate/environment,
and disaster/crime prevention), industrial (finance, manufacturing, agriculture, forestry,
and fishery, legal, commerce, management/oversight), lifestyle (amusement/games,
healthcare/medical, education), general, and other. Areas of application with aparticularly high number of patent applications are manufacturing, telecommunications,healthcare/medical, management/oversight, and commerce, and while there are somedifferences in terms of categorization these are largely the same as the sectors with highweights in the Nomura AI Companies 70. These can thus be said to be the areas thatare likely to see a direct boost from the development of AI technologies.
In terms of the technology on which the patent applications are based, Japan Patent
Office data indicate that machine learning and neural networks account for 30-40% of
patent applications in the areas of manufacturing, telecommunications,healthcare/medical, management/oversight, and commerce, and more than 60% in thearea of robots (based on another categorization composed of robots, cars andhealthcare). The importance of "machine learning" and "deep learning," which have been
selected as related keywords for the Nomura AI Companies 70 stock selection process,is also clear from the number of patent applications.

Fig. 12: Sector breakdown based on number of global AI-related patent applications
(%)
15

10

5

Legal

Agriculture, Forestry,
and Fishery

Education

Financials

Climate/environment

Disaster/crime
prevention

Amusement/games

Utilities

Traffic/transportation

Commerce

Management/oversight

Healthcare/medical

Telecommunications

Manufacturing0Note: Based on number of patent applications in Japan, US, Europe, China, and South Korea that claimed priority in 2008-12. Sector breakdown in terms of area of application
excludes "general" and "other.”
Source: Nomura, based on Japan Patent Office data

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25 January 2017

Small caps, midcaps, and growth stocks tend to account for alarge share of the index
We looked at the average breakdown of the Nomura AI Companies 70 from 2001 to2016 by investment style and company size.
Figure 13 shows that the index had a higher weighting of small and midcap stocks than
the TOPIX, with the average weighting about 10ppt higher than for the TOPIX during thisperiod. We think the index had excess returns attributable to size factors as it invested at
a higher rate than the TOPIX in AI-related small and midcaps.
The index had a higher weighting of growth stocks than the TOPIX, with the average
weighting about 5ppt higher than for the TOPIX during this period. This shows to us thatthe index tended to include AI-related companies expected to see high growth.

Fig. 13: Investment style and company size composition of Nomura AI Companies 70(%)

Size composition breakdown

100
17.5

13.4

41.5

35.3

80
60

Small CapMid Cap

40

Top Cap
51.3

41.0

20
0
Nomura AI Companies 70
(%)

TOPIX

Value/growth composition breakdown

100
80

45.3

50.4
Value

60

Growth
40
54.7

20

49.6

0
Nomura AI Companies 70

TOPIX

Note: Sample period is 2001-16. We took the average of monthly composition breakdowns that we calculated by multiplying the number of stocks in style indexes of theRussell/Nomura Japan Equity Indexes by composition weightings.
Source: Nomura

13Nomura | Nomura AI Companies 70

25 January 2017

Returns tend to be higher the larger the number of articles
with "artificial intelligence" and related keywords
Finally, we look at the relationship between the number of articles and excess returns.
As of the date of periodic reconfigurations from 2001 to 2016, we divided constituent
stocks into tertiles according to the number of articles related to each stock. We thencalculated excess returns over the next year for each tertile. The averages for the
sample period are shown in Figure 14.
Companies referenced in a large number of articles containing "artificial intelligence" andrelated keywords belong to the tertile with the highest number of articles, and companies
included in the Nomura AI Companies 70 generally belong to this tertile. The tertile withthe largest number of articles had the highest average excess returns and the tertile withthe smallest number of articles tended to have the lowest average excess returns.
The larger the number of articles, the more the company's reputation and visibility
improved via mass media coverage and the more interested investors became in thecompany and bought shares, and the more, on average, we think this could lead tohigher share prices, even for relatively long investments of a year.

Fig. 14: Excess return averages by tertile based on number of article rankings
(%)
1.00
0.67

0.75
0.50
0.25
0.00
-0.00
-0.25
-0.50
-0.75

-0.66

-1.00
Lowest ranking

Middle ranking

Highest ranking

Ranking by number of articles
Note: Sample period is December 2001 through December 2016. Once a year the stock universe is divided into tertiles according to the number of articles for the past two years
as of December (the number of articles for the most recent year is doubled) and after calculating excess returns for one year, the average of all years is taken. The universe is
the top 98% of listed stocks in terms of market cap and the benchmark assumes all stocks in the universe are owned with an equal weighting. We did not take transaction costs
into account. Analysis is based on historical share prices and does not guarantee future performance.
Source: Nomura, based on Nikkei Media Marketing data

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Index disclaimer
The Nomura AI Companies 70 is released by Nomura Securities Co., Ltd. ("Nomura")
and its intellectual and all other rights belong to Nomura. Nomura does not guarantee theaccuracy, completeness, reliability, usefulness, marketability, merchantability or fitnessof the index, and takes no responsibility for business activities or services by index users
and/or their affiliates involving the use of the index.
In the case of an unavoidable event such as computer breakdown or natural disaster,
calculation of the indices may be delayed or suspended.
While every effort is made to ensure that the information used in this report and all
published information is based on reliable data, it should be noted that data may bechanged or amended when necessary.
The Nomura AI Companies 70 index is not related to Nikkei Inc. and is not endorsed or
guaranteed by Nikkei Inc.

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Appendix A-1
Analyst Certification
We, Satoshi Yamanaka and Yuko Onishi, hereby certify (1) that the views expressed in this Research report accurately reflect
our personal views about any or all of the subject securities or issuers referred to in this Research report, (2) no part of ourcompensation was, is or will be directly or indirectly related to the specific recommendations or views expressed in thisResearch report and (3) no part of our compensation is tied to any specific investment banking transactions performed by
Nomura Securities International, Inc., Nomura International plc or any other Nomura Group company.

Important Disclosures
The lists of issuers that are affiliates or subsidiaries of Nomura Holdings Inc., the parent company of Nomura Securities Co., Ltd., issuers thathave officers who concurrently serve as officers of Nomura Securities Co., Ltd., issuers in which the Nomura Group holds 1% or more of any
class of common equity securities and issuers for which Nomura Securities Co., Ltd. has lead managed a public offering of equity or equity
linked securities in the past 12 months are available at http://www.nomuraholdings.com/report/. Please contact the Research ProductManagement Dept. of Nomura Securities Co., Ltd. for additional information.

Online availability of research and conflict-of-interest disclosures
Nomura Group research is available on www.nomuranow.com/research, Bloomberg, Capital IQ, Factset, MarkitHub, Reuters and ThomsonOne.
Important disclosures may be read at http://go.nomuranow.com/research/globalresearchportal/pages/disclosures/disclosures.aspx or requested
from Nomura Securities International, Inc., or Instinet, LLC on 1-877-865-5752. If you have any difficulties with the website, pleaseemail grpsupport@nomura.com for help.
The analysts responsible for preparing this report have received compensation based upon various factors including the firm's total revenues, aportion of which is generated by Investment Banking activities. Unless otherwise noted, the non-US analysts listed at the front of this report are
not registered/qualified as research analysts under FINRA rules, may not be associated persons of NSI or ILLC, and may not be subject to
FINRA Rule 2241 restrictions on communications with covered companies, public appearances, and trading securities held by a researchanalyst account.
Nomura Global Financial Products Inc. (“NGFP”) Nomura Derivative Products Inc. (“NDPI”) and Nomura International plc. (“NIplc”) areregistered with the Commodities Futures Trading Commission and the National Futures Association (NFA) as swap dealers. NGFP, NDPI, andNIplc are generally engaged in the trading of swaps and other derivative products, any of which may be the subject of this report.

Distribution of ratings (Nomura Group)
The distribution of all ratings published by Nomura Group Global Equity Research is as follows:
50% have been assigned a Buy rating which, for purposes of mandatory disclosures, are classified as a Buy rating; 39% of companies with thisrating are investment banking clients of the Nomura Group*. 0% of companies (which are admitted to trading on a regulated market in the EEA)
with this rating were supplied material services** by the Nomura Group.
42% have been assigned a Neutral rating which, for purposes of mandatory disclosures, is classified as a Hold rating; 52% of companies withthis rating are investment banking clients of the Nomura Group*. 0% of companies (which are admitted to trading on a regulated market in the
EEA) with this rating were supplied material services by the Nomura Group
8% have been assigned a Reduce rating which, for purposes of mandatory disclosures, are classified as a Sell rating; 7% of companies with thisrating are investment banking clients of the Nomura Group*. 0% of companies (which are admitted to trading on a regulated market in the EEA)
with this rating were supplied material services by the Nomura Group.
As at 31 December 2016.
*The Nomura Group as defined in the Disclaimer section at the end of this report.
** As defined by the EU Market Abuse Regulation
Distribution of ratings (Instinet, LLC)
The distribution of all ratings published by Instinet, LLC Equity Research is as follows:
53% have been assigned a Buy rating which, for purposes of mandatory disclosures, are classified as a Buy rating; Instinet LLC has provided
investment banking services to 0% of companies with this rating within the previous 12 months.
42% have been assigned a Neutral rating which, for purposes of mandatory disclosures, is classified as a Hold rating; Instinet LLC has provided
investment banking services to 0% of companies with this rating within the previous 12 months.
5% have been assigned a Reduce rating which, for purposes of mandatory disclosures, are classified as a Sell rating; Instinet LLC has provided
investment banking services to 0% of companies with this rating within the previous 12 months.

Definition of Nomura Group's equity research rating system and sectors
The rating system is a relative system, indicating expected performance against a specific benchmark identified for each individual stock,subject to limited management discretion. An analyst’s target price is an assessment of the current intrinsic fair value of the stock based on an
appropriate valuation methodology determined by the analyst. Valuation methodologies include, but are not limited to, discounted cash flow
analysis, expected return on equity and multiple analysis. Analysts may also indicate expected absolute upside/downside relative to the statedtarget price, defined as (target price - current price)/current price.

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STOCKSA rating of 'Buy', indicates that the analyst expects the stock to outperform the Benchmark over the next 12 months. A rating of 'Neutral',indicates that the analyst expects the stock to perform in line with the Benchmark over the next 12 months. A rating of 'Reduce', indicates thatthe analyst expects the stock to underperform the Benchmark over the next 12 months. A rating of 'Suspended', indicates that the rating, target
price and estimates have been suspended temporarily to comply with applicable regulations and/or firm policies. Securities and/or companies
that are labelled as 'Not rated' or shown as 'No rating' are not in regular research coverage. Investors should not expect continuing or
additional information from Nomura relating to such securities and/or companies. Benchmarks are as follows: United States/Europe/Asia exJapan: please see valuation methodologies for explanations of relevant benchmarks for stocks, which can be accessed
at: http://go.nomuranow.com/research/globalresearchportal/pages/disclosures/disclosures.aspx; Global Emerging Markets (ex-Asia): MSCIEmerging Markets ex-Asia, unless otherwise stated in the valuation methodology; Japan: Russell/Nomura Large Cap.

SECTORSA 'Bullish' stance, indicates that the analyst expects the sector to outperform the Benchmark during the next 12 months. A 'Neutral' stance,
indicates that the analyst expects the sector to perform in line with the Benchmark during the next 12 months. A 'Bearish' stance, indicates thatthe analyst expects the sector to underperform the Benchmark during the next 12 months. Sectors that are labelled as 'Not rated' or shown as'N/A' are not assigned ratings. Benchmarks are as follows: United States: S&P 500; Europe: Dow Jones STOXX 600; Global Emerging
Markets (ex-Asia): MSCI Emerging Markets ex-Asia. Japan/Asia ex-Japan: Sector ratings are not assigned.

Target Price
A Target Price, if discussed, indicates the analyst’s forecast for the share price with a 12-month time horizon, reflecting in part the analyst's
estimates for the company's earnings. The achievement of any target price may be impeded by general market and macroeconomic trends, andby other risks related to the company or the market, and may not occur if the company's earnings differ from estimates.

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Nomura | Nomura AI Companies 70

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------

Disclaimers required in Japan

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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 the

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25 January 2017transaction. 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.
No account fee will be charged for marketable securities or monies deposited. Transfers of equities to another securities company via the Japan
Securities Depository Center are subject to a transfer fee of up to ¥10,800 per issue transferred depending on volume.

Nomura Securities Co., Ltd.
Financial instruments firm registered with the Kanto Local Finance Bureau (registration No. 142)
Member associations: Japan Securities Dealers Association; Japan Investment Advisers Association; The Financial Futures Association ofJapan; and Type II Financial Instruments Firms Association.
The Nomura Group manages conflicts with respect to the production of research through its compliance policies and procedures (including, but
not limited to, Conflicts of Interest, Chinese Wall and Confidentiality policies) as well as through the maintenance of Chinese walls andemployee training.
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 listed on the front page. Disclosures information
is available upon request and disclosure information is available at the Nomura Disclosure web
page: http://go.nomuranow.com/research/globalresearchportal/pages/disclosures/disclosures.aspx
Copyright © 2017 Nomura Securities Co., Ltd. All rights reserved.

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