A Step Toward a Cybernetic Whale:
An Interim Report on“A Study on the Use of Artificial Intelligence
for Learning Characteristicsof Funds’ Behavior”

Takao Tajiri, Takahiro Sasaki, and Hiroaki Kitano
Sony Computer Science Laboratories, Inc.
December 2019

This report should be cited as: Tajiri, T. , Sasaki, T. and Kitano, H. (2019). A Step Toward a
Cybernetic Whale: An Interim Report on “A Study on the Use of Artificial Intelligence for Learning
Characteristics of Funds’ Behavior”. Tokyo, Japan: Government Pension Investment Fund.

ExecutiveSummary
Highlights of this intermediate progress report
This document reports an intermediate progress of the commissioned research by Government Pension Investment Fund (GPIF) to Sony Computer Science Laboratories, Inc. (Sony CSL) on “A
Study on the Use of Artificial Intelligence for Learning Characteristics of Fund’s Behavior”.
Major progresses are:
1. Development of a new style detection mechanisms termed “Resembler” that captures an inherent style of each fund manager that may not be captured by mere combination of simplified
styles.
2. Resembler detected potential style drifts that were not detected by Style Detector Array(SDA) used in the previous report. The use of both Resembler and SDA significantly improved analysis capability of fund manager’s style and multi-dimensional style drift detection
capability. Mutual-resemblance uncovers similarities among funds and their temporal changes
assisting GPIF to maintain diversity of funds from their actual investment practices.
3. An explorative study was performed to identify characteristics of fund manager comparing
against benchmark to identify where excess return actually come from and visualized using
Self-Organized Map (SOM). This may enable us to analyze if excessive returns are due to luckor skill. An intermediate result is promising. It implies that GPIF may be able to uncoverskill vs. luck of investment results data-driven manner and much more precisely than fundmanagers.
4. Continuation of such research and development shall enable GPIF to analyze, visualize, andexplain explicit and implicit investment behaviors of each fund manager, manage overall risk
profile, and optimize GPIF portfolio. Most analysis and visualization can be made semiautomated and dramatically improve the efficiency, accuracy, and accessibility of profiling on
manager structure.
5. The system is now implemented on ABCI —a 1088 nodes GPGPU cluster at the National
Institute of Advanced Industrial Science and Technology (AIST).
As a result of these improvement, the system is now capable of detecting and analyzing behaviors
of fund managers and their relationship in the context of overall GPIF investment far more precise
than previously possible. This shall dramatically improve GPIF’s capability to understand behaviors
of fund managers and engaging in-depth dialogue with them.
These outcomes have major implications on the direction of GPIF operation beyond improvement of the current practices. Two future directions for GPIF has been suggested that are:
1. GPIF’s augmented capability to access long-tail side of the players, and
2. GPIF-metrics for evaluating fund managers that is based on AI and data analysis characteristics.
These directions may represent evolution of GPIF’s fund management structure and more fundamentally transformation of the organization.
iAI and data-driven approach as described in this report direct the future where major asset
owners can dissect and analyze behaviors of fund managers from multiple dimensions and uncover
their characteristics much accurately and precisely than fund managers themselves. It may result increation of GPIF-metrics that reflects policies and value of GPIF. Furthermore, the overall analysis
process can be semi-automated, including report generation used for due processes. This opens up
a new opportunity for both GPIF and smaller funds, especially this gives GPIF access to the “longtail” of fund scale in the investment management industry. Due to statistical feature of complex
systems including a global stock market, how to access and benefits from long-tail part of the players
and assets is critically important. The use of AI that enable GPIF and other major asset owners
to access long-tail part of the players could be a major breakthrough and may trigger qualitativetransformation in the industry.
An implication is clear. Whale will turn into Cybernetic Whale, that is empowered by AI.
Cybernetic Whale will be able to see what human may not be able to see without the assistance ofAI.

1 ɹ The context of the study and the interim report
This is a follow-on to a research study conducted in FY2017, under which a proof-of-concept prototype system called Style Detector Array (hereinafter called “SDA”) based on deep learning wasdeveloped [1]. The previous study drew interest from asset owners in Japan and globally, leading
to awards from overseas media. Building on those results, we developed a system called “Resembler” as an extension and application of SDA in the current research program. Resembler provides
quantitative metrics for a type of information about fund management firms that previously was
only qualitative: their distinct characteristics or uniqueness. This system will help GPIF address
two concerns about its qualitative evaluation of fund management firms: (1) difficulties in avoiding
arbitrariness and subjectiveness, and (2) the dependence on a small pool of staff with specialized
expertise.

Universe
size and
asset class
Indices tobe detected
Target ofthe development

FY2017
Small universe (100 Japaneseequities)

October 2018 - March 2020
Large universe (1,000
Japanese or foreign equities)

Investment style based on
widely-used traditional
factors
Proof-of-concept prototype tovalidate the possibility ofdetecting investment style

Resemblance among fundmanagers’ characteristics
New model and various
system upgrades to support
experimental
implementations

This report covers interim results as of October 2019 of a research program that began in October
2018; since research is still ongoing, many of the findings contained herein are provisional. We plan
to issue a final report covering the entire program of research after its completion in 2020.

ii

2 ɹ Methodology: Investigating funds’ characteristics based on trading data
SDA and Resembler are systems built on a deep learning based neural networks to classify anddetect fund characteristics. These two systems have the same architecture and function. SDA istrained on virtual trading data generated by simulations of virtual fund managers. On the contrary,
Resembler uses actual trading data from actually existing funds as a training data. This difference
results in SDA detecting investment style, an objective indicator defined in terms of factor exposure,while Resembler supplies a relative indicator of a fund’s “uniqueness”, a distinct trading style ofeach fund.
In FY2019, we made significant advances in our research resulted in building Resembler as an extension of SDA and develop its applications. Resembler is a system that evaluates self-resemblance,by comparing a fund to its own past characteristics and uniqueness, and mutual-resemblance, whichis the degree of inter-similarity among multiple funds. Development has proceeded through cooperation with GPIF on experimental implementations on their front-line.
SDA, developed during the previous research contract, was refined by re-evaluating what factors
it should targets for detection, feeding more data by scaling up the universe of equities, introductionof round-robin sampling for equity selection, and refining the trading logic of the VFMs that generate
training data for SDA.
Since beginning of FY2019, we have deployed our system on AI Bridging Cloud Infrastructure(ABCI) [2, 3], which is provided by the National Institute of Advanced Industrial Science andTechnology (AIST). ABCI is a GPGPU cluster computing platform, which provides an optimal
environment for the vast calculations that is required in training deep learning neural networks like
SDA and Resembler. ABCI ʟs exceptional cost-performance enabled our research to progress far
more efficiently than in FY2017. We anticipate making even greater use of it as we move forward.
In addition to SDA and Resembler, we have been applying self-organizing maps (SOMs) as ameans of visualizing fund characteristics to provide a means of deepening analytical insights. We
are exploring usage of this technique to track trends in the equity holdings of a fund over time, andto evaluate the skill with which the fund is managed.

3 ɹ Results of experimental implementation in GPIF front-line operations
An initial evaluation was carried out using SDA and Resembler on all the actively managed Japaneseand foreign equity funds in GPIF’s manager structure. In this interim report, we highlight fourcase studies of special interest. (For this summary, abbreviated findings of two of those case studies
follow). Here we would mention two particular cases that illustrate the value of the approach takenin this study.
Case Study 1: Resembler detected change while SDA unchanged
In Case Study 1, during the period of observation the fund’s self-resemblance measured by
Resembler plunged at one point and remained at a low level for some time, while mutual-resemblance
to other funds edged upwards; later the self-resemblance indicator slightly trended up again. No
corresponding changes were detected by SDA. The suggested cause is multifactor, involving equity
holding shifts and changes in buying and selling behavior such as turnover; the timing of thesecan largely be seen as reflecting efforts to improve the fund performance. GPIF received advanceiii

notification of model changes and changes in the concentration of portfolio holdings. In reality,
however, it is difficult to flag sequences of events as being a connected wave of subtle changes,
since communication between GPIF and funds is wide-ranging, with many points of discussion, andmostly each anomaly has to be discussed as an isolated event. Thus, even if GPIF dives into thedetails with the fund manager, it would be difficult to uncover the whole picture without support ofadvanced analysis system as we discussed here. Resembler makes it possible to accurately flag large
waves in order to establish new hypotheses, which support discussions about how to improve a fundʟsperformance by narrowing down the points at issue. In addition, Resembler provides a quantitativebasis for logging changes in fund behavior that up to now depended on the intuition of GPIF
personnel that something is “off”. This could yield an operationalizable basis for improvement.
Case Study 4: Fluctuating Resembler index
In Case Study 4, large decline in self-resemblance was observed concomitant with a certain
degree of rises and falls of resemblance to various other funds Resembler’s output. This implies thefund in question has a policy of opportunistically switching between different investment strategies
based on market conditions, and this can be considered an investment strategy unto itself. In thiscase the lack of a firm self-resemblance is what is unique about the fund. On the other hand, fundsof this nature can lose flexibility if they become locked into one strategy over a long period, and even
if they retain their flexibility, their switching could start to lag behind changes in market conditions,scenarios which merit more detailed examination.

(a) Output of Resembler for fund A(b) Output of SDA for fund A

Case study 1

Interim summary of experimental implementations
Concerning the deployment of AI in front-line operations at GPIF, we figured out a three-step process which seems promising: 1) Using AI system (Resembler/SDA) for initial detection of changes;
2) Performing causal analysis on detected changes; 3) Delivering output in a form that is immediately actionable in the operational environment. Step 2) in particular would require a systematic
framework for a seamlessly integrated processing flow. For example, a follow-on investigation is
now initiated on whether approaches including topic analysis and VAE would be suitable. On step3), in the course of ongoing experimental implementations, we are testing ways of integration of ourAI system within current operations of IT systems that output human-readable text descriptions of
the key features of changes, which makes communicating with fund management firms as smoothand efficient as possible.
iv

Case study 4: Output of Resembler for fund W

4 ɹ Exploratory Studies on Advanced Analysis
In parallel with the development of Resembler and SDA, we are making progress on a number ofexperimental implementations including: (1) a resembler-based investment strategy visualizationand (2) a return quality analysis using Self-Organized Map.
The first one is an application of Resembler for visualization based on mutual-resemblance,
which is the degree of similarity among the characteristic behavior patterns of multiple funds. It
plots mutual-resemblance as time-series in phase space. In an example analysis case, although thecharacteristics of each fund, represented as coordinates on the resemblance map, are initially fairly
well-scattered, they gradually converge toward two funds positioned in the upper right of the graphover time. It is considered that market condition in 2019, which performance was positive withlow risk diversified investment, induced an overall shift toward similar strategy. Unlike relying
on conventional diversification assessment by factor exposure, this is higher level diversification orconvergence assessment with more expansive characteristic of investment management. GPIF that
allocates among a set of many different fund managers is the entity which can overview this kindof trend and which is able to apply data for the purpose.
Secondly, experimental implementation was carried out to investigate a method of evaluating
the “edge” of an active manager: the degree to which its performance (alpha) is attributable toskill rather than luck. When fund managers report to GPIF on their investment activities, there
is a psychological incentive for them to attribute performance to their skill if performance has
been good, but to blame bad luck due to unfavorable market conditions or other economic factors
outside their control when performance has been poor. In that context, if there was a systematic
methodology to classify what component of a fund’s positive (or negative) returns is attributableto skill and what component is attributable to luck, it would provide a common ground on which
GPIF could evaluate fund performance without being swayed by the spin of fund managers. Basedon this idea, we used the pattern of active weights in the fund ʟs portfolio to divide between
domains of intentional active weight and non-intentional active weight, visualized these by SOMs,
and attempted to compare their respective active returns. We found that for clusters of equities witha high degree of intentionality behind the active weight, some had correspondingly good returns,
but some exhibited negative returns, meaning that the fund manager’s made a bad call. There werealso cases where equities delivered positive returns even though the fund was not holding them with
the intention of achieving active returns, but only out of necessity for risk management purposes.
vChange in mutual-resemblance for Japanese funds over time
We consider firther investigation shall enable us to identify true skill of fund manager.

5 ɹ How AI could enable GPIF to unlock value
Transformation of operations at GPIF
Needs and expectation of GPIF for the use of AI in daily operation has been carefully identified andagreed between GPIF and Sony CSL team. During experimental implementations and trials, ourteam had close, deep, and frequent discussions on the requirements for business process at GPIF.
Through such discussions, consensus has been developed on what GPIF expects to AI and how
to apply it. There are feedbacks from persons in charge at GPIF regarding specific benefit andexpectation once AI applied, these include: “Monitoring broader range of information”, “Efficientmanagers evaluation”, “Prediction based monitoring” and “Automation of selecting new manager”.
The ultimate goal of this research is to find solutions for and more rigorously define issues raised
at GPIF, referring the potential for accusations of arbitrariness and subjectivity with qualitativeevaluation and the process being is heavily dependent on a small pool of staff with specialized expertise. These include detection of any changes in manager reports, confirming how much improvement
is conducted by managers which have been put on notice to improve performance and detection of
unreported changes. These would serve as the basis for improvement of more constructive and efficient discussion with managers and for standardization and commoditization of skills by exploiting
big data at GPIF.

How “Whale” attracts the industry
If AI is fully deployed at front-line operations in the future, then there will no longer be a distinction
or information gap in evaluating pre-contract candidates versus existing managers. In addition,vi

GPIF’s onerous reporting requirements have blocked smaller fund management firms which do
not have sufficient resources, however the barrier would be lowered for them to pitch themselves
to GPIF, giving GPIF access to the “long tail” of the fund scale in the investment management
industry, if the output of AI systems was able to take over and eliminate much of this reporting
obligation and workload. Asset management industry as a whole could benefit from being able toredirect resources from reporting to core investment performance. Since asset owners, including
GPIF, are in a position to accumulate bigger data than asset managers, this could allow them todevelop the most insightful analytical results, giving asset owners the information advantage overasset managers. This paradigm shift echoes the “Money Ball” strategy that enabled successful
teams to be built in Major League Baseball by employing SABRmetrix. This new approach wemight call “GPIF-metrix” would lead to the formation of “manager teams” which do not dependon traditional and established value and attract young generation and investment professionals.
GPIF’s capability to efficiently evaluate and monitor smaller funds and potential GPIF-metrics
will transform GPIF into AI and Data-driven organization that may set a new standard in theindustry.

6 ɹ Future work
While the initial stage of our research started in October 2018 was basic and exploratory investigation, current one has been shifted to an application phase as pilot test. The application phase has
cleared numerous suggestions and issues to be resolved from actual business process, and this phaseis moving forward in parallel with the following agenda: “expansion of experimental implementation”, “deepening, improving, and operationalizing based upon trial result” and “streamlining andintegration of the technology platform”.

7 ɹ Implications• GPIF, the world’s largest public pension fund, AIST that operates ABCI, a GPGPU cluster
attaining the world-class computing performance, and Sony CSL with deep expertise and aproven track record in AI, as a trio of Japanese based organizations, have combined forces tobreak new ground in experimental implementation of AI in investment management industry.
This demonstrates managing major funds may require cross-boundary highly competent teamand resources at the highest-level.
• As these research results have implications for all asset owners collectively, we propose a“Global Data Consortium” to solve essential problems at investment management industry,
such as “Whales breach wildly because an earthquake happened, or whales breaching wildly
causes the earthquake to be happened”. We expect a series of discoveries can be made thatshould uncover reality of investment practices for large-scale asset owners, and scientific understanding should be possible. Such efforts shall open a new era of science of investment andasset management.
• There are broader subject critical to asset management where basic scientific and engineering
insights can be highly applicable. Many of them are implicated in the development of AI
applications.

vii• As AI systems move toward operational deployment, the time has come to make clear plans
for redirection of human resources. This affects the organizational composition of asset owners
as well as fund managers.

viii

Contents
ExecutiveSummaryi

1 The context of the study and the interim report
1.1 Revisiting FY2017 research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Research agenda and activities in FY2018-2019 . . . . . . . . . . . . . . . . . . . . .

1
1
2

2 Methodology: Investigating funds’ characteristics based on trading data
2.1 Deep learning systems to detect and classify the characteristics of fund managers . .
2.1.1 Style Detector Array(SDA) . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.2 Resembler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.3 Using ABCI to improve the efficiency of development for SDA and Resembler
2.2 Where the alpha comes from? – Self-Organizing Maps for visualization of fund characteristics to explain what SDA and Resembler detected . . . . . . . . . . . . . . . .

3
3
4
7
9
10

3 Results of experimental implementation in GPIF front-line operations
12
3.1 Case studies on domestic (Japanese) equity funds . . . . . . . . . . . . . . . . . . . . 12
3.2 Case studies for foreign equities funds . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.3 Interim summary of experimental implementations . . . . . . . . . . . . . . . . . . . 20
4 Exploratory Studies on Advanced Analysis
22
4.1 Assessment of portfolio diversification using Resembler . . . . . . . . . . . . . . . . . 22
4.2 Evaluating “edge” in active fund management . . . . . . . . . . . . . . . . . . . . . . 23
5 How AI could enable GPIF to unlock value
28
5.1 Transformation of operations at GPIF . . . . . . . . . . . . . . . . . . . . . . . . . . 28
5.2 How “Whale” attracts the industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
6 Future work

31

7 Implications

33

A Appendix: Full results of all funds
36
A.1 Outputs of Resembler for Japanese equity funds . . . . . . . . . . . . . . . . . . . . . 36
A.2 Outputs of SDA for Japanese equity funds . . . . . . . . . . . . . . . . . . . . . . . . 39
A.3 Outputs of Resembler for foreign equity funds . . . . . . . . . . . . . . . . . . . . . . 42
A.4 Outputs of SDA for foreign equity funds . . . . . . . . . . . . . . . . . . . . . . . . . 44

1

The context of the study and the interim report

This paper is an interim report on “A Study on the Use of Artificial Intelligence for Learning
Characteristics of Funds’ Behavior” by Sony Computer Science Laboratories, Inc. (hereinafter
called “CSL”) which was commissioned in October 2018, by the Government Pension Investment
Fund of Japan (hereinafter called “GPIF”).
The following sections describe background of research and technical details of the researchcommissioned in October 2018. Section 5 discusses implications of outcome obtained to date.

1.1

Revisiting FY2017 research

In 2017, GPIF commissioned Sony CSL to conduct a research titled “A Study on the Use of Artificial
Intelligence within Government Pension Investment Fund’s Investment Management Practices.”
The research was oriented toward harnessing the power of AI for one of GPIF’s core functions,which is maintaining the manager structure. This function means to oversee the allocation of
GPIF’s pension assets among a diversified set of fund management firms (hereinafter called “funds”
or “managers”). The aim of the research was to provide more sensitive tools for detecting whether
the investment behaviors of funds are kept consistent with their prospectus, and if not so evaluate
whether this deviation is appropriate or need to be watched carefully.
For this research program, we developed a proof-of-concept prototype of deep learning AI system
which we named as Style Detector Array (SDA). SDA takes fund manager’s trading behavior data
as input, and outputs the fund’s character which is expressed as a combination of factor-based
investment styles such as “value”, “market cap”, “momentum”, etc. For the ease of explanation
toward audiences in the financial industry, we chose these well-known investment styles as “referenceindices” for our first SDA. The system’s architecture, however, can take any other reference indices,not limited only to the factor-based styles mentioned above. The initial goal of the development was
to validate whether the fundamental principle of our idea works properly or not. Therefore, insteadof considering the full universe, we conduct experiments on small universe that consists of 100
representative Japanese equities, selected mainly according to market cap. By directly analyzing
the investment behavior of fund managers, we confirmed that style analysis could be performed inreal time with an evidence-based approach, enabling GPIF to evaluate and select its fund managers
more accurately.
The Summary Report published in 2018 [1] earned an “Academic Research Paper Of The Year
- Machine Learning & Big Data” award for GPIF and the Sony CSL project members at The
Volatility & Risk Premia Awards 2019 presented by EQ Derivatives, which is a special interest
magazine serving the financial industry that caters to asset owners and hedge funds; this was thefirst Japanese organization to receive the award, and Sony CSL was the first non-financial institution
to receive the award. Our research outputs contributed also to GPIF’s win of 2018 Institutional
Excellence Awards (its fourth consecutive win) from Asian Investor magazine, a special interest
magazine for institutional investors in the Asia-Pacific region.
The paper received various feedbacks from asset owners, fund management firms, portfolio
advisors, legal practitioners, and so on. The feedbacks included their expectation for extending our
method to other asset classes than Japanese equities, questions about the difference between what
SDA can detect and what existing tools do, questions about SDA’s output and funds’ performance
relationship, awareness and debate about the “black box” property of AI, and more.

1

1.2

Research agenda and activities in FY2018-2019

With the success of on the preliminary outcomes obtained in FY2017, an extensional research
program was initiated titled as; “A Study on the Use of Artificial Intelligence for Learning Characteristics of Funds’ Behavior”, runs from October 2018 to March 2020. In order to investigate
the applicability of our system within the actual business scenes of GPIF, we have set our research
agenda and experimental conditions as the following table, based on the discussion described in lastyear’s report and from extensive communications within GPIF.

Universe
size and
asset class
Indices tobe detected
Target ofthe development

FY2017
Small universe (100 Japaneseequities)

October 2018 - March 2020
Large universe (1,000
Japanese or foreign equities)

Investment style based on
widely-used traditional
factors
Proof-of-concept prototype tovalidate the possibility ofdetecting investment style

Resemblance among fundmanagers’ characteristics
New model and various
system upgrades to support
experimental
implementations

Table 1: Comparison between FY2017 and current research activities

As described in the section titled “Process for selecting fund management firms” within GPIF’s
annual report 2018 [4], GPIF emphasized the importance of qualitative evaluation of funds’ investment policies, operating procedures, and strength of organization/personnel. On the other hand,according to the minutes of 2017 Board of Governors, there are some concerns that qualitative evaluation of fund management firms could invite criticism for arbitrariness and subjectivity. Meanwhile,interviews with GPIF personnel revealed a process that is heavily dependent on a small pool of staff
with specialized expertise. Meaning that if GPIF had access to quantitative metrics for a fund’scharacteristics encompassing qualitative aspects such as investment policies, operating procedures,and strength of organization/personnel, changes in those aspects could be detect without any ambiguity. Thus, it could be the solution to GPIF’s concerns mentioned above. Selection and evaluationof funds could be fairer and more accurate. It could also provide a quantitative backing for the“expert’s intuition” or “sixth sense” of specialized GPIF personnel who noticed something is “off”
about any fund’s behavior.
To solve these problems, we focused out research to implement extensions on SDA so that it could
detect the fund managers’ “characteristics” embodying qualitative information, such as investment
policies and operating procedures, that is difficult to quantify with existing analytical tools. IT
was built on the basis of the core technology of SDA, which we initially implemented in FY2017
to output easy-to-understand investment style (such as “value”, “market cap”, “momentum”, etc.),
but redesigned to meet these emerging demands.
This report covers interim results as of October 2019 of a research program that began in October
2018; since research is still ongoing, many of the findings are provisional. We plan to issue a final
report covering the entire program of research after its completion in 2020.

2

2

Methodology: Investigating funds’ characteristics based on trading data

Figure 2: Overview of SDA and Resembler
Deep learning, which mimics the neural networks found in living organisms, is a technology
currently at the forefront of the AI field. Without algorithms explicitly preprogrammed by humans,
deep learning systems automatically learn rules for identifying or classifying tremendous amount ofdata from training datasets.Thanks to the abundancy of computing powers and the availability ofhuge data, it has proven to outperform human capability in some tasks especially in gaming, voice
and image recognition and natural language processing. This triggered major excitements on deeplearning as a major contributor to the current AI boom [5, 6]. Utilizing this technology, we havebeen developing Style Detector Array and its extension as Resembler that aim to derive funds ʟcharacteristics from trading behavior data.
In addition to deep leatning, we introduced self-organizing map, which is another technique inthe field of AI, as a method of creating intuitive visualization of fund characteristics and changes
that help humans to easily draw decision making from data.
In this chapter, we describe our methodology, focusing on the technical aspects.

2.1

Deep learning systems to detect and classify the characteristics of fundmanagers

Style Detector Array (hereinafter referred as “SDA”) and Resembler are systems that detect andclassify the characteristics of funds using deep learning neural network technology. Both systems
need to be trained in advance before they can function as detectors/classifiers; in fact, they bothhave the same underlying system architecture, differing only in what data is used for initial training.
They take as input data about the market environment and a fund’s trading behavior, and output an-dimensional vector whose attribute values represent the intensity of one of more (n) characteristics
of interest (Figure 2)1 . Since the input consists of time-series data, the n-dimensional attribute
vector will track changes over time. Because the input includes not only fund’s activity, but also
the exogenous context of market conditions, the pattern of how the fund’s behavior responds toparticular changes in the market can be interpreted as that fund’s characteristics.
1

The name of Style Detector “Array” comes from its form of architecture that outputs n-dimensional vector.

32.1.1

Style Detector Array(SDA)

SDA was updated to handle larger asset universe with higher precision. Given a fund manager’s
trading behavior data as input, SDA is designed to output the fund’s character which is expressed inn-dimensional vector, whose elements represent strength of (or similarity to) pre-defined referenceindices. The references are selected from factor-based investment styles such as “value”, “marketcap”, “momentum”, etc. Although prototype development was completed as of our FY2017 research
program [1], considerations of real world application were not a priority because the primary purposeat that time was to validate whether the fundamental principle of our idea works properly or not.
Therefore, one objective of the current research program has been to upgrade SDA to be practicallyuseful.
The key system component in enabling SDA to accurately classify investment behavior patterns
by similarity to pre-defined reference styles is the deep learning neural network, which must be
trained in advance. We took an approach to train SDA by using virtual trading data generated by
Virtual Fund Managers (hereinafter called “VFM”). As such, analysis of a fund using SDA consistsof the following three steps (Figure 3):
1. Generate virtual trading data through simulation of VFMs.
2. Train SDA using virtual trading data.
3. Input actual trading data from real funds into the trained SDA for analysis.
The reason for using virtual trading data in training the SDA is that there are no real-world
funds which maintain a “pure” pattern of trading as defined in the reference styles over a sufficiently
long time span to provide the quantity of data that would be needed for training.

Figure 3: Flow of analysis made by SDA

Upgrading SDA by improving the Virtual Fund Managers
A VFM is an agent program that formalizes the logic of a reference investment style and it is used tocarry out trading simulations under exogenous market conditions provided by historical data. Since
SDA receives the virtual trading data generated by VFMs as training datasets, the effectiveness of
SDA heavily depends on the quality of the VFMs implementation. Therefore, upgrading SDA must4

be accomplished by improving the VFMs. Below, we describe how our current generation of VFM
has been improved over the previous prototype.
• Scaling up the universe of equities
In developing the prototype system, we had to limit the execution time required for training
SDA within affordable length, in order to carry out iterations of trials and errors as many as
possible within a limited period of time. Therefore, we assumed a small universe consistingof the 100 largest Japanese equities by market cap to perform the VFM simulations and SDA
training. In contrast, our current VFMs and SDA are now able to run on a much larger
universe of 1,000 equities, so that the amount of data fed into the system has been increased.
It is technically feasible to go beyond 1,000 equities and work off a full-sized universe of data.
However, with progress by stages in validating SDA, we came to a judgment that by also
applying round-robin sampling (discussed below), we can obtain virtual fund data that is
sufficient for our current stage of analytical purposes. Accordingly, we have established auniverse of 1,000 equities for our current testing environment.
• Round-robin sampling for defining the universe
With the small universe used by the SDA prototype, limited to the top equities ranked bymarket cap, it was not possible to analyze funds holding a substantial number of equities
outside that universe. Namely, it was impossible to analyze funds focused on small-cap stocks.
Therefore, besides expanding the size of universe in our current SDA, we define it by picking
equities by turns (i.e., round-robin) from each fund which is entrusted with investing for GPIF,
in order of the fund’s quantity of shares in that equity (Figure 4). Using this method, evenwhen the size of the universe is capped at 1,000 equities, it provides 98% coverage (in the caseof Japanese domestic equity funds) on a market value basis of equities held by all active funds
in GPIF’s portfolio. (Figure 5). This means SDA can now analyze a wider range of funds
while making efficient use of the available data.

Figure 4: Round-robin sampling• Investment strategies adopted as reference styles
While a group of extremely simple and naive implementations of investment strategies were5

Figure 5: Coverage changes according to the size of sampled sub-universe
set as reference styles for the prototype systems, our current VFMs have been redesignedwith real-world application in mind. Specifically, we referred to theoretical frameworks whose
effectiveness has been demonstrated by academic research, including the Fama-French threefactor model [7] and the Carhart four factor model [8], in order to capture orthodox risk
factors that all bottom-up fund managers are conscious of. We defined VFMs that selectequities based on seven indicators: BP (net assets), CFP (cashflow), DP (dividend yield),
EP (earnings), MOM (long (12-month) positive momentum), Rev-MOM (short (1-month)
negative momentum), and SIZE (market cap) 2 .
Adding more and more indicators would be one possible approach to give SDA a more richand elaborated ability of detection. On the other hand, our basic stance has been to avoid
thoughtlessly expanding the parameters, since the nonlinearity of the deep learning algorithm
within SDA will capture some of the nuanced spread of investment styles. However, since wehave found in the outputs of SDA’s testing to date that there are certain discrepancies withthe understanding at GPIF, we plan to revise the mix of indicators that we use in order tocorrect this.
• VFM as aggregated clusters of sub-VFMs
Because our prototype VFMs were implemented as monolithic agents and each executing
trading on the basis of a “pure” and simple investment strategy logic, there was a tendency
for them to exhibit extreme turnover of equity holdings (“all buy” or “all sell”). We enabled
our current VFMs retain the simple logic while expressing a more natural pattern of trading
activity, by having each VFM be an aggregate of multiple sub-VFMs with slight offsets to theirtrade timing. This eliminates discontinuities of equity turnover, reduces noise, and allows us
to model the totality of market participants adopting an arbitrary spread of strategies.
2

Factors which end in a “P” indicate the named parameter is “to market price”, i.e., on a per market price basis

6

Figure 6: VFM as aggregated clusters of sub-VFMs• Smoothing by category during equity selection
Our prototype VFMs did not distinguish among categories such as industries, domicile (territory), etc., treating the universe of equities as monolithic with respect to these, and making
decisions about whether to hold or not hold each equity by comparing all equities in the universe solely on the basis of the indicators targeted by the investment strategy style. However,since these indicators have mean baseline values that systematically differ among categories ofindustry, domicile, etc., there is a concern that this simplistic selection method just described
would cause the portfolio to be skewed toward certain categories. Therefore, we introduced
into the current VFMs’ equity selection logic a weighting adjustment by category to ensurethat the portfolio is unskewed.
Applying the latest version of SDA, upgraded in line with the concepts above, we analyze
the characteristics of existing domestic and foreign active funds which GPIF has mandated asset
management. Those results are the basis of case studies on several funds discussed in Chapter
3. For a list of all the results, including those not featured in case studies, see the Appendix.
Although these results are closer to how GPIF’s personnel understand funds’ behavior than ourprevious prototype achieved, SDA is still not yet fine-tuned to a fully satisfactory capability. We
are currently planning improvements by reconsidering the mix of factors given to train SDA, andby modifying the VFM logic.
2.1.2

Resembler

In parallel with upgrading SDA, we undertook the development of Resembler as an extensional
application of SDA.
Given investment behavior pattern, SDA judges the degree of similarity to preset reference
investment styles such as “value”, “market cap”, “quality”, “momentum”, and so on. As discussed
in Chapter 1, we chose these well-known investment styles as “reference indices”, for the ease of
explanation of our technology and application toward audiences in the finance and investing sphere.
One of the key features of our system is that it is built with an array of neural networks thatcan be trained by any given data. Therefore, by choosing training data that reflects reference stylesof analytical interest, users of the system can freely customize SDA for their purposes.
Resembler is one of such extensions and applications of that principle. Resembler takes as
its training data the actual trading data of actually existing funds (instead of virtual trading data
generated by VFMs as with SDA). Thus, Resembler’s output is not a vector whose components show
its degree of similarity to objective investment styles, but is instead a time-series of vectors that7

track the resemblance of a fund to each of the actual funds used as references: i.e., its resemblance toFund A, resemblance to Fund B, etc. The system architecture and operating principle of Resembler
are exactly the same as SDA; only the data used to train it are different. Nevertheless, Resembler isable to analyze existing funds in terms of their resemblance to other funds (or to themselves), which
illuminates characteristics of fund behavior that SDA alone cannot adequately capture (Table 2).

Training
data
Target ofdetection

SDA
Virtual trading data
generated by VFMs
Objective indicators of“investment style” definedaccording to widely-usedfactor exposure

Resembler
Actual trading data fromexisting funds
Relative indicators such as“resemblance to Fund A”

Table 2: Comparison between SDA and Resembler

The following are two examples of Resembler applications we have been carrying out so far. We,
however, continue to explore other possible applications of Resembler, as well.
• Evaluating self-resemblance: comparing a fund to its own past characteristics.
By including the past investment behavior patterns of the fund itself in the training data set,
we can assess the degree to which a fund is maintaining consistency with its own characteristics
and uniqueness; alternatively, to analyze self-resemblance as an indicator of whether the fundhas been changing its style. To obtain such self-resemblance, firstly the data of all active
funds targeted for analysis are fed as training dataset into the detector array, which creates
the Resembler system, and then give to that system as input the data for each individual
fund. During periods where a fund is strongly maintaining its particular characteristic, theself-resemblance score will remain high and stable. But when the fund deviates from its own
particular characteristic, its self-resemblance score will also drop. Furthermore, in the eventthat the self-resemblance score is constantly fluctuating3 , we can conclude that the fund lacks
a particular characteristic of its own, or that it is unclassifiable.
Here it is important to note that changes in SDA and changes in Resembler are independent:
in some cases they both happen at the same time, but in some cases one changes while theother does not change. This indicates the fact that SDA and Resembler address different aspects of the characteristics of funds. While SDA assesses fund behavior from the third person
perspective in terms of commonly-used investment styles like value, momentum, etc. It is
possible that actual investment style change without affecting composition of simplified styleindex as captured in SDA. Resembler, from the internal party perspective, can be thought of as
quantifying characteristics such as the uniqueness and consistency of how the fund in question
is managed, which conventional analysis could only describe qualitatively. By combining theanalyses of SDA and Resembler, we expect that a more multifaceted understanding of fundbehavior characteristics could be delivered. In Chapter 3, we use this framework to analyze
several domestic and foreign equity funds as case studies. A table of the full results for
self-resemblance of all funds is in the Appendix.
3

In many cases, relatively high values of resemblances to other funds appear at the same time.

8• Evaluating mutual-resemblance: comparing a fund to other funds.
Firstly, Resembler is trained with investment activity data of a certain number of, say, nfunds selected as “anchors”. Next, given the data of another fund, the target of analysis, as
an input, the already-trained Resembler outputs n-dimensional vector, whose attribute values
express the degree of resemblance to the corresponding anchor funds. By measuring the
distance between these vectors for multiple funds, we can evaluate the mutual-resemblanceamong these funds; i.e., how similar they are to one another. With regard to the anchor
funds, we need to select a combination suitable to the purpose of the analysis. In principle,
it is important to select anchors whose characteristics except for the control conditions varyfrom one another. In the case where we want to evaluate the resemblance only among quantfunds, for example, then we should choose anchors from quant funds whose characteristics aredifferent as much as possible from one another.
By tracing the changes in these resemblance scores over time, it may support asset owners
to judge whether their manager structures are maintaining the desired diversification. Ourcurrently underway experiments to explore that concept are covered in detail in Chapter 4.
2.1.3

Using ABCI to improve the efficiency of development for SDA and Resembler

Traning deep learning system requires large data set and extensive computing time. Thus, we madea decision to deploy our system on a large-scale GPGPU cluster machine owned by the Japanese
Government.
Training SDA and Resembler requires massive computing resources. There are a large numberof adjustable external parameters for execution of SDA and Resembler. In order to obtain useful
results, it is necessary to seek the optimal combination of parameters. This tuning process requires
performing a large number of trial iterations while slightly tweaking the conditions of each run.
In addition, even after the combination of parameters has been set, when training a deep learningneural network, especially for complex problems, it is known that the output is sensitive to thevalues of parameters that are initially set at random at the beginning of training [9, 10, 11]. For
that reason, we use the same data set to perform multiple training runs, and the ensemble averageof the independent outputs is taken as the final output of the SDA or Resembler.
All of the procedures described above require enormous computation. In developing the prototype in 2017, a major reason that we opted for a small universe and simplified the implementation
was the limited computing resources available.
Since beginning of FY2019, we have employed AI Bridging Cloud Infrastructure (hereinafter
called “ABCI”) [2], which is provided by the National Institute of Advanced Industrial Science andTechnology (AIST). ABCI is a computing cluster constructed from 1,088 nodes with GPUs, andprovides an optimal infrastructure for parallel process execution of deep learning algorithms [3].
While it depends on the number of nodes available simultaneously, with the typical configurationthat we use, iteration cycles which took a week or more to run previously can now be completed
in about a day and a half. As a result, our research progress has become far more efficient than in
FY2017. We project that a vast number of iterations will be needed moving forward, so ABCI with
its exceptional cost-performance will be a crucial key to the success of our research.

9

2.2

Where the alpha comes from? – Self-Organizing Maps for visualization offund characteristics to explain what SDA and Resembler detected

Quality and sustainability of the fund’s performance can be evaluated by identifying where thealpha comes from. Combination of Self-Organizing Map (SOM) and a series of deep learning based
identifiers such as SDA and Resembler enable us to visualize characteristics of investment behaviors
of funds in the context of the source of excessive return. Deep learning neural networks, which
are the foundational technology for SDA and Resembler, can automatically and instantaneously
identify rules within vast data sets that would not be evident from an ordinary perusal. They makeit possible to tackle applications in areas such as pattern recognition and pattern classification
without explicitly predefining those rules in a human-written formal computer program. However,this advantage comes with the disadvantage of having to accept the operation of the neural network
itself as a black box. Since GPIF and other institutions which are fiduciaries of the public interest
have an obligation to be transparent about the basis of their decision-making, this black box natureof neural networks is undesirable. In practice, even if a system like SDA or Resembler detects somekind of changes, but the reasons for those change cannot be explained, GPIF would, in many cases,
not be able to proceed to decision making or taking action on that basis alone.
Therefore, we are seeking various ideas and use cases for existing methods in data science thatwould be suited to probing the causes of changes detected by SDA and Resembler. One promising
method that we have tried is the self-organizing map (hereinafter called “SOM”) as a technique forvisualizing fund characteristics.
A SOM is a type of neural network that maps a large number of individual items, each of which
has a certain number of attributes, onto a 2D or 3D spaces according to the resemblance among
those attributes. In essence, SOM performs unsupervised clustering on the attribute vectors ofcomplex high-dimensional data, and creates visual representations enabling humans to grasp trends
and correlations that would be difficult or impossible to spot in the raw data. The basic flow of
SOM is as follows:
1. An item is selected randomly from the dataset on which clustering is to be performed.
2. Compare the attribute vector of that item with the reference vector of each unit on the map
and select the best matching unit.
3. Place the item in the best matching unit and adjust the reference vectors of the surrounding
units to be closer to the item’s attribute data vector.
4. Return to step 1.
When this series of operations is performed over a sufficient number of iterations, the resulting map
will ultimately consist of clusters of items whose attribute vectors are most similar to each other4 .
For our application, the items are individual equities held by a certain fund, and their corresponding attribute vectors consist of each equity’s attributes, some of which (e.g., market cap,
PER, PBR) are values independent of the fund, and other attributes whose values are specific tothat fund (e.g., active weight, active return). Through this procedure, the equities held by a fundare plotted on a 2D map with a color gradation corresponding to the values of the attribute ofinterest, which makes it possible to readily discern fund characteristics and their changes over time.
4
It is called “self”-organizing map, since items are automatically classified in an unsupervised way without any
prior knowledge.

10

Although strictly speaking, we use SOM only for visualization, its usefulness is tremendous. For
example, if we place the items (equities) on the 2D space simply in the order of securities tickers,
we will just get random pattern of colors without any meaningful underlying structure, because thetickers are arbitrarily assigned to each equity without relationship to any of its attributes.
For example, in Figure 7, the equities held by a certain fund are clustered using a SOM, which
has been colored according to the active weight in the equities corresponding to each unit. Red
zones indicate equities which the fund overweight and blue zones indicate underweighted equity
holdings. Zooming in on the units, it is possible to see which specific equities are in the cluster andinvestigate the attributes corresponding to the units. By generating a time series of SOMs depicting
active weight, we can visually track changes in the fund’s portfolio. In Chapter 3, we discuss theuse of SOMs in this fashion to perform causal analysis of changes detected by Resembler.
Moreover, by fixing the positions of equities on maps colored with different attributes, we canconduct multifaceted complex analysis among various attributes of equities. For example, given two
SOMs, one of which is colored according to active weight and the other by active return, we canvisualize in a readily discernable form whether clusters of high-active-weight equities are actually
generating high active returns. In Chapter 4, we discuss the case of what we named “Distiller”, anexperimental effort to differentiate the contributions of luck from those of skill in generating active
return.

Figure 7: Visualization of holding patterns through SOM

11

3

Results of experimental implementation in GPIF front-line operations

A series of technologies described above has been implemented in our experimental system andtested using data on all the actively managed Japanese and foreign equity funds to which GPIF has
allocated assets.
In this section, we will report four cases in depth as we judged them to be of particular value
for gaining insight. The results for all funds analyzed can be found in the Appendix.
The workflow in all cases began with firstly examining the output of Resembler and SDA, andthen proceeding to causal analysis using other techniques where features of the case warranted closer
study.

3.1

Case studies on domestic (Japanese) equity funds

Case Study 1: Assessment using data from all periods
Case Study 1 considers Fund A. In this case, using data from all periods, we assessed whether
changes took place in the past or not, and the causes of those changes; we then considered how
Resembler could be deployed most effectively into GPIF’s operations.
< Detection of changes >The output of Resembler shown in Figure 8a exhibits the following three trends.
• Between July 2017 and January 2018, there was a slight uptrend in self-resemblance, followed
by a large drop.
• Between January 2018 and March 2019, self-resemblance was stable at a low level, while
another reference (resemblance to another fund) trended up.
• From March 2019 onward, self-resemblance began to slightly trend up again.
On the other hand, during the same time frames, the output of SDA (which detects investment
style) did not exhibit any notable changes (Figure 8b). This indicates that while no changes were
noticeable from the viewpoint of conventional investment style factors, Resembler did detect somekind of changes from the standpoint of the fund’s uniqueness.
< Causual analysis of changes >We attempted to analyze the cause behind changes in Resembler’s output from several perspectives,
as follows:• Active weight:
In Figure 9, each equity has been mapped into a SOM with a color gradation according toeach equities’ active weight, which visually highlights patterns in the evolution of portfolio
composition.
– Comparing the SOM for Jan. 2017 (Figure 9a) to the one for January 2019 (Figure 9b),
the red units inside solid circles shrank, while the red units inside dotted circles expanded.
This indicates that portions of the equities in which Fund A strongly overweight were12

(a) Resembler(b) SDA

Figure 8: Case study 1: Analysis of characteristics for fund A
rebalanced. At the same time, the total area of dark red on the SOM shrank, indicatingthat strong overweighting and increasingly concentrated in fewer stocks than before.
– In June 2019 (Figure 9c) the overweight areas circled in solid line expanded, indicatingthat Fund A was reverting slightly toward its previous level of self-resemblance. However,since strongly overweight clusters circled in dotted line remained, and the recovery ofoverweighting of whole map was limited, this suggests that the fund did not fully revert
to its previous self-resemblance.
– Because the time frame of all these SOMs changes align with the changes in Resembler’s
result, Resembler’s output may reflect the evolution of portfolio composition revealed by
the SOMs.
• Number of equities held
From July 2017 to January 2018, January 2018 to March 2019, and from March 2019 onward,
we can observe prominent fluctuations in the number of equities held by Fund A, which are
generally synchronous with changes in Resembler’s output, indicating the possibility thatthese changes in the number of equities held are also being noticed by Resembler (Figure 10).
• Portfolio turnover
During the timeframes when Resembler noticed changes, turnover showed less frequent changes
than in the past. Although the relationship is not as prominent as those with active weight andnumber of equities, the possibility that portfolio turnover is one cause of change in Resembler’s
output cannot be ruled out (Figure 11).

<Considerations for deployment at GPIF>
Fund A, on multiple occasions in 2016, gave GPIF advance notification of model revisions, andResembler re-validated that changes took place from July 2017 to January 2018. In addition,
Resembler detected a slight reversion to greater self-resemblance from March 2019 onward, which
we assume to be reflection of the changes in the pattern of number of equities held and portfolio
turnover during this period.
Concerning the latter changes, although GPIF consulted with the fund manager to understandthem as isolated moves, it is possible that these changes should be scrutinized as part of a large-scale13

(a) January 2017(b) January 2019(c) June 2019

Figure 9: Case study 1: Evolution of the pattern of portfolio composition (active weight)

Figure 10: Case study 1: Evolution of number of equities held

Figure 11: Case study 1: Evolution of turnover

14

qualitative shift in investment style in a bid to boost performance, rather than as a series of isolated
moves in light of the performance issues of the fund in question,
It is possible that GPIF is not able to capture slight changes in investment style that may leads
to stagnating performance because signals are subtle and often embedded in a wave of larger shift
in economic conditions and other issues. Furthermore, daily communication between GPIF andfunds is wide-ranging, with many points of discussion. Thus, even if GPIF dives into the details
with the fund manager on each occasion and probe each issue’s nature in isolation, it would be
difficult to constantly spot such signal in advance without support of such a system. Resembler
makes it possible to detect specific instances as part of sequences of large-scale shifts in order toestablish new hypotheses. This capability could support discussions about how to improve a fund’s
performance by narrowing down the points at issue to the assessment of these hypotheses.
Furthermore, Resembler could contribute to operational improvements at GPIF through enabling quantitative logging of strategy changes by fund managers. When changes in fund behavior
are detected solely through the “expert’s intuition” or “sixth sense” of GPIF oversight personnel
that tells them something is “off”, no matter how strong that intuitive conviction might be, it
nevertheless makes a weak justification for logging officially a change in fund behavior. However, if
Resembler registers a change, this can be used as a quantitative basis for logging a change in fundbehavior, and triggering the start of comprehensive monitoring. In other words, while declines infund performance, key person risk, etc. have been used up to now to justify logging fund changes,
Resembler allows new considerations to be added. By increasing the frequency and robustness ofmonitoring, this could deliver better fund selection and monitoring over the long term.

Case Study 2: Detecting change that is currently underway
Our next case study concerns Fund F. Resembler’s output did not exhibit any change in over a long
period of time in the past, but recently a change was observed. In this case study, we consider how
the start of the change would be reflected in Resembler’s output, assuming that new updated data
are continuously provided on a monthly basis.
<Detection of changes>
During June-July 2019, the fund in question exhibited a change in Resembler that had never been
observed previously (Figure 12a). In contrast, there was no comparable change detected by SDA
over the same period. (Figure 12b). As well as the previously mentioned case study, although nostyle change was evident from the standpoint of conventional factors, Resembler did detect somekind of change from the standpoint of the fund’s uniqueness.
<Causal analysis of the changes>
Next, we probed for the cause of this change from several angles.
• Active weight– In December 2018 (Figure 13b), in comparison to June 2018 (Figure 13a), the regionsthat are strongly overweight in the middle of the left and right sides (shown in dark redunits inside solid circles) have contracted slightly, and a few white dots surrounded bydotted circle have appeared in the center. At this point, a certain amount of change isalready being recognized.
15

(a) Resembler(b) SDA

Figure 12: Case study 2: Analysis of characteristics for fund F– On the other hand, in June 2019 (Figure 13c), the strongly overweight region circled
with a solid line has noticeably shrunk. And we can clearly see the scattering of white
dots in the center now, indicating that these equities in which the fund was strongly
underweighting (dark blue) are shifting to neutral weighting (white). These tell us that
there is a shift from the pattern of investment being pursued previously, and at the sametime that the fund is slightly cutting back its active risk.
Since these changes visualized in SOMs all correspond to changes in Resembler, it is possiblethat Resembler was flagging these changes.

(a) June 2018(b) December 2018(c) June 2019

Figure 13: Case study 2: Evolution of the pattern of portfolio composition (active weight)<Considerations for deployment at GPIF>
During the recent period when changes were detected by Resembler, Fund F was showing strong
performance. Conventional wisdom would suggest a hypothesis that a fund which is delivering
good performance would not have much motivation to adjust its investment policies or models. Yet
Resembler might detect the emergence of an equity selection pattern not seen before, and a change
in the degree of concentration in their investment policy.
GPIF has confirmed these changes by “separately” receiving notifications as “individual instances” occurring in “different timeframes independent from one another”. In contrast, Resembler
might detect the “onset of a sequence of systematic shift taking place from now on.” For seamless16

integration of AI detection into GPIF’s actual operating environment with respect to continuity,
there are two issues to deal with: first, “Why pay attention to changes at a fund which is performing
well?”; second, “How can the system configure output as actionable material in the GPIF operating
environment?”• “Why pay attention to changes at a fund which is performing well?”
Because GPIF’s operations are broad and multifaceted, and much of that work is handledby small pools of personnel with specialized expertise, we assume that the inevitable reality
is that relatively worse-performing funds receive the most attention. On the other hand, afund that is performing well, as in Case Study 2, will not necessarily continue to perform wellindefinitely. If there are precursor signals of the fund’s results taking a turn for the worse,and change responsible for that deterioration happens gradually, being able to detect such
patterns in advance would unquestionably be valuable.
The change that was detected in this case does not imply such changes are always bad. It
merely means that there are changes in the style. Resembler and SDA, however, can besystematically vigilantly in assessing changes currently underway. If, at the point in time atwhich a certain threshold is crossed, a fund manager exhibiting precursor signals of change
could be given priority monitoring by GPIF staff. Thus, it would allow the organization to
expand their range of scrutiny in their operations without increasing their workload.
Moreover, funds that have good performance are constantly making efforts to maintain thoseresults; the changes detected by Resembler in this case may represent the fund’s proactive
moves in anticipation of possible future market conditions. By combining indicators from
Resembler, fund performance, changes in investment behavior and market conditions, GPIF
could build up an archive of quantitative insights into the moves made by the best-performing
funds, raising the knowledge base throughout the organization, and enabling sharing of quantitative insights among relevant personnel. The better your knowledge, the better the discoveries you can make, and we accordingly recommend the development of applications that willfurther elevate GPIF’s fund selection capabilities.
• “How can the system configure output as actionable material in the GPIF operating environment?”
Using other methods to analyze the cause of the changes detected by Resembler yielded results suggesting the possibility of “the emergence of an equity selection pattern not previously
exhibited” and “a point of departure from the initial prospectus with respect to the degree ofconcentration in their investment policy.” However, a request was made by GPIF that there
should be “a human readable text materials that breaks down to an even simpler level how
to explain the nature of the change (assuming the change is not routine factor rotation)”.
The backdrop to this feedback is that although both the GPIF and the fund manager sides
are in close contact on equity selection and portfolio rebalancing plans on a daily basis, they
may not be able to detect subtle changes in the context of dynamically changing environment.
Resembler may systematically detect such changes and signal them to GPIF. We consider thisis a great example of complementation between the understanding of GPIF personnel and theAI. In order to reinforce such a complementation, the system needs to configure its output
as actionable information as described in the feedback from GPIF. We will investigate how
AI should be integrated with the appropriate processes or modules of IT systems currently
used, in order to obtain materials based on which GPIF personnel can make actual decisions.
17Regarding SOMs, we consider it can be used to visualize the cause of change in fund behavior
more intuitively understandable. Substantial training costs, however, would be incurred toenable staff without preexisting expertise to make full use of this evaluation method andinterpretation in operations. Improvements of the user interface is desired to make the output
of causal analysis more visually and intuitively accessible.

3.2

Case studies for foreign equities funds

With foreign equities funds, we selected two case studies that reflect a different perspective thanthe Japanese equities case studies; “assessing the validity of the contents of reports received fromfund managers” and “identifying a lack of self-resemblance as uniqueness”.

Case Study 3: Assessing the validity of the contents of reports received fromfund managers
Case Study 3 is about Fund N. In this case, we validated that the content of the fund manager’s
past reports was valid.
Fund N experienced a fairly important corporate event in its organization in 2018, and GPIF
received a report giving advance notification of this change. In addition, GPIF received a reportto the effect that the fund would be continuing its previous investment policies unchanged, andGPIF had used existing tools to confirm through factor analysis, etc., whether any change actually
took place. We also re-validated this in Resembler, and confirm that its output showed almost nochanges, meaning that policies remained basically the same as before (Figure 14).

Figure 14: Case study 3: Analysis of characteristics for fund N (Output of Resembler)
We also performed follow-up assessment using several supplementary analytical methods, which
confirmed that Fund N’s investment behavior had not changed to any extent worthy of special
mention.
• Number of equities
Although a slight change in the number of equities held occurred in 2018, when the event
happened, this was not flagged as a serious change because scale of that change was same in
the past (Figure 15).

18

Figure 15: Case study 3: Changes in number of equities• In and Out of equities
We confirm that the numbers of INs and OUTs of equites changed slightly, but not substantively, around 2018, when the important corporate event occurred. (Figure 16)

Figure 16: Case study 3: In and Out of equities• Change in equities making up the portfolio
Between the portfolio of any given month and the portfolio of six months previous to that,
we calculated the correlation coefficient of the holding ratio and the concordance rate of theequities held (Figure 17). Correlation of holding ratios and concordance ratio maintained asteady high value indicating that investment style was continued after the event.
Fund management firms have an obligation to make reports of material changes to GPIF, andGPIF validates the contents of those reports to the extent possible with existing tools. Resembler
is able to detect changes that existing tools cannot fully characterize, at a more high-level, holistic
level than traditional indicators such as investment styles and risk factors. It might allow a moresensitive reassessment from a different standpoint of whether fund managers have changed their
behaviors.

Case study 4: Uniqueness defined by absence of self-resemblance
Case Study 4 is about Fund W, which does not stick to a certain single investment strategy, but
continuously switches among various strategies. Resembler detects large decline in self-resemblance,
19

Figure 17: Case study 3: Changing in equities making up the portfolio
as well as a certain degree of rises and falls of resemblance to various other funds during the time.
This lack of self-resemblance is, in a sense, what constitutes the uniqueness of this fund, and isbeing consistently detected by Resembler (Figure 18).

Figure 18: Case study 4: Analysis of characteristics for fund W (Output of Resembler)
Regarding those like fund W which from the outset take an approach of opportunistically switching between different investment strategies, if those changes are consciously and proactively made,can be considered an investment philosophy unto itself. On the other hand, if such a fund become
locked into one style over a long period, or if their strategy switching starts to lag rather than leadchanges in market conditions, it could be considered a precursor signal that an in-depth consultation
should be held.

3.3

Interim summary of experimental implementations

Through the experimental implementation of AI in actual operations at GPIF, we became awarethat the following three-step process is needed:
1. Using AI system (Resembler/SDA) for initial detection of changes;
The first step for AI (Resembler/SDA) is to detect whether any change has taken place ornot. The type of change that can detected is not limited to those which relate to factor
20

exposure and style, but also qualitative aspects such as “uniqueness” of the fund behavior
that is difficult for existing tools to capture.
2. Performing causal analysis on detected changes;
The next step is to analyze the cause of the change. In case studies described above, otherdata science methods including SOM were used, but these require labor-intensive ad-hoc work.
What is needed is a systematic framework for a seamlessly integrated processing flow andsystem that takes over after change detection occurs. a follow-on investigation into ideas like
topic analysis, various clustering methods, Variable auto-encoders (VAE), and other methods
are currently underway.
3. Delivering output in a form that is immediately actionable in the operational environment;
As mentioned in Case Study 2, we received feedbacks that GPIF would want output whenchange is detected that clearly presents the salient points of the change (“what did they do
and why is it concerning”) as human-readable text. This will help GPIF’s personnel to makeconversation more fruitful with the fund manager especially who has not been aware of thechanges. Moreover, if the outputs are given in easily accessible format based on which concretedecisions can be made, this would reduce workload and enable the situations to be handled
in a way that is more likely to have results than with current systems. Through ongoingexperimental implementations, we will work on perfecting the integration of our AI systemfor data sharing with existing operational and IT systems.

21

4

Exploratory Studies on Advanced Analysis

4.1

Assessment of portfolio diversification using Resembler

GPIF allocates its overall manager structure among a diversified set of managers to optimize therobustness to market changes. An important aspect of GPIF’s operations is to monitor diversification, detect when the behavior of fund managers exhibits convergence in response to the economy,
and evaluate whether this convergence can be mitigated to maintain diversification.
Generally, diversification of the manager structure has been assessed on the basis of fund performance and factor-based analysis. We introduce an experimental application within the frameworkof Resembler, whose output is the uniqueness of fund managers, to assess diversification of manager
structure at GPIF. Mutual-resemblance among funds is calculated as discussed in section 2.1.2,
then funds are visualized together within the same phase space by dimensionality reduction method
called multi-dimensional scaling (MDS) [12].

Figure 19: Changes in mutual-resemblance for Japanese funds over time
It is required to select several “anchor” funds to serve as references to obtain a map of the mutualresemblance. We chose four funds as anchors which are markedly different from one another in termsof investment policy, performance, etc. to assess the resemblance among all funds comprehensively.
Figure 19 shows the resemblance among domestic active funds over the period from July 2015 to
September 2019. Trajectories over time from start to end (in the direction indicated by purple
arrows on the graph) indicate that at the outset they are dispersed through the phase space,22

representing the uniqueness of each fund manager, over time they gradually converge toward Fund
A and Fund E at the upper right of the graph. There are several conceivable causes for this trend
toward convergence; as one example, Figure 20 graphs estimated tracking error, a measure of theportfolio risk that fund managers have taken on that is calculated from the variance-covariancematrix of daily returns for each equity on past twelve month.
The estimated tracking error for most of the funds squeezes into the same narrow range in early2019. This indicates that the active risk of each fund’s portfolio has similarity. It is consideredthat market condition in 2019, which performance was positive with low risk diversified investment,
induced an overall shift toward similar strategy. Unlike relying on assessment of specific factor exposure, this is higher level diversification assessment with more expansive characteristic of investment
management, i.e. its uniqueness requires further assessment what kind of risk can be occurred incase of the convergence. GPIF that allocates among a set of many different fund managers is theentity which can overview this kind of trend, otherwise each fund manager does not. It follows that,even if each manager behaves to maximize their own performance, their investment behavior could
increase the risk exposure of the entire structure of GPIF with respect to some particular factor.
Even though it is not necessarily to hedge the convergence of mutual resemblance, and it can beallowed or even encouraged in some scenarios, Resembler has potentiality to apply to capture highlevel and entire information to optimize portfolio at GPIF.
As for another application, Resembler-based diversification assessment can be applied to selection process of a new fund manager; to evaluate similarity to the currently outsourced managers
and to avoid of convergence at manager structure. It could also contribute to resolve concern “arbitrariness of qualitative evaluation” with the quantitative evaluation of qualitative information byresemblance and the causal analysis at the process of similarity assessment to the currently outsourced managers. It would become possible that GPIF with limited resources fairly and efficiently
evaluates a broad range of qualitative information from numerous candidates at the selection process
by a system which streamlines from AI to causal analysis to use the data provided from candidates
to evaluate qualitative information.
Further studies are needed on how new candidate provides their data under constraint due
to the importance and confidentiality and whether AI could output properly with monthly orquarterly based data instead of daily based one. the limited number of data such as monthly orquarterly. Accordingly, it is also to be experimented to achieve an acceptable quality by data science
methodology even with such coarse data.

4.2

Evaluating “edge” in active fund management

GPIF allocates approximately 20% of its total assets to active funds. However, over the threeyear period from 2014 to 2016, only a few of these active funds reached their targets for excess
returns. GPIF has identified the primary reason for this as a problem with fund selection capability
at GPIF, along with other possibilities such as fund managers not setting their target for excess
returns appropriately, or being more focused on increasing the amount of funds under management
than on capacity management. GPIF has taken various actions to address this, including revising
its previous policies of fixed management fees or moderate performance fee in favor of performancebase fee [13].
In this context, we set out to experimentally evaluate the “edge” of active fund managers:whether an active fund’s performance comes from skill as opposed to luck. This is expressed as a“batting average” comparing how much of the return that they are achieving on equity investment
is due to the manager’s intentional active weight (skill), to how much is derived from unintentional23

Figure 20: Changes in tracking errors
active weight (luck). Figure 21a shows SOM of the active weight by actively manager of domestic
equity fund. As discussed in section 2.2 above, this method takes all the equities composing TOPIX
and clusters them on a 2D map according to a rule that results in equities with the most resemblance
to each other being closest together, without meanings both on vertical and horizontal axes.
In this case equities in which the fund in question is overweight are colored in red, those inwhich it is underweight are colored in blue, and the saturation of the color indicates the degree ofover- or under-weightiness. In Figure 21b the active returns for equities held by the same fund areshown; as in Figure 21a, positive returns are shown in red and negative returns in blue. In otherwords, we can juxtapose the two SOMs in order to assess whether equities with high active weightalso have high active return. For this fund, Figure 21a shows that high-active weight equities are
clustered midway up the right and left edges. When we juxtapose Figure 21b, in those same areas
we can see both positive and negative returns. On the other hand, while the central area of Figure
21a shows that the fund is underweight in those equities, in Figure 21b we see that returns on thoseequities are fairly good. Note that while this pair of SOMs only show the results for a predefinedtime period, a time series of SOMs can be generated to analyze trends over time.
As for next step, we defined categories of active weight as described below to classify the attributability of the respective returns (Figure 22):• Intentional active weight:
Within the distribution of active weight, this domain – defined by having the large absolutevalues of active weight – is where manager applied the strategy to make an investment decision that these are promising stocks to hold. The intentional active weight domain is divided
into two subdomains: where the values are positive and relatively large number is intentional overweight and where the values are negative and relatively large number is intentional
underweight.

24

(a) Active weight(b) Active return

Figure 21: Visualization of active weight and active return with SOM• Non-intentional active weight:
As opposed to the intentional active weight noted above, non-intentional active weight domain
is divided into two subdomains: where the values are positive and relatively small number is
non-intentional overweight and where the values are negative and relatively small number is
non-intentional underweight. This type of position is required by portfolio risk management,
thus does not always reflect managers’ market perspective. Hence performance yielded by
non-intentional active weight has no difference between overweight and underweight.

Figure 22: Separation of intentional and non-intensional positions
The procedure we used to calculate the degree of attributability to the intentionality of the fundmanager is as follows:• Calculate the active weights of all the equities, and arrange them in order from largest to
smallest.
25

• Cumulative active weight starts from zero and increases as the active weight of each equity isadded. It reaches a peak value, where underweights start to be added and cumulative active
weight starts declining, ultimately reaching zero.
• A level of 90% of the peak value of the cumulative active weight distribution is defined as thethreshold for attributing an equity holding to intentionality. We anticipate that this thresholdwould be adjusted as necessary depending on the analysis scenario, taking into account, e.g.,
investment approach (highly active fund, quant, etc.) and the active risk level.
The result of this is Figure 23, where we see on the left side of the graph the “intentional
overweight” domain as defined above, and on the bottom right of the graph the “intentional underweight” domain, while the rest of the graph (circled with a dotted blue line) is the “non-intentional
active weight” domain.

Figure 23: Order of equities by active weight
Based on this methodology, we calculated performance for each of the domains across the entire
portfolio, to obtain the daily return against each day’s active weight, to create a metric for skill
shown in Figure 24.
Looking at the results of the above calculation for the “intentional active weight” and “nonintentional active weight” domains, intentional active weight is neutral, while non-intentional active
weight exhibits slightly negative returns, with overall performance being neutral. In other words,
this fund failed to achieve positive returns in equities where it aimed to generate them, and experienced slight negative returns in equities it held without having such aims, which can be interpreted
as grading the fund’s skill as neutral to slightly negative.
The goal of this experiment was as follows:• When managers report to GPIF on their investment activities, a psychological incentive comes
into play: if performance has been good they attribute it to their skill, but when performancehas been poor, they blame bad luck due to unfavorable market conditions or other economic
factors outside their control. While such explanations may seem to constitute a plausible
story-telling from a short-term perspective, it is mandatory for GPIF to verify or falsify theseclaims scientifically and sustainable way from a long-term investment perspective. Thus, using
26

Figure 24: Decomposition of cumulative active return
a methodology that makes it possible to discriminate between returns to intentional/nonintentional active weight over the long-term based on consistent rules would provide a common
ground on which GPIF can conduct reviews of fund performance of funds, and supply fundmanagers with reference materials for further improvement.
• Recognizing that there are cases where it is difficult to judge the validity of fund managers’
own text-based reports about how the attribution of their active returns, SOM-based visualization and analysis may enable GPIF to share and discuss performance with a higher levelof awareness of the causes behind it.
In conjunction with this, we received feedback in response to last year’s research summary reportthat it would be “desirable research has correlation to performance”. Evaluation and selection ofmanagers based on a combination of a fund’s “edge”, assessing performance quality, with Resembler
and SDA are promising basis that provide new and different angle than existing practice. With thisresearch initiative, we intend to go deeper to develop a comprehensive methodology for GPIF and
its fund managers to evaluate returns. Impacts of this initial outcome is far reaching because suchapproach shall uncover true skill of fund managers as opposed to luck, and it may dramaticallychange the way active funds are managed.

27

5

How AI could enable GPIF to unlock value

Insight obtained from the trial results and experimental implementation, applies to the operationlevel and the management level: how AI could transform GPIF’s operations and how GPIF should
deploy AI.

5.1

Transformation of operations at GPIF

Feedback from GPIF:
During experimental implementations and trial with close, deep and frequent discussion about therequirements for business process at GPIF, consensus has been developed what GPIF expects to
AI and how to apply it. There are feedbacks from persons in charge of daily manager monitoring
operation at GPIF regarding benefit and expectation once AI applied.
• Monitoring broader range of information
Contribution to better monitoring is expected by detecting changes in organizational information that are not covered in reporting guidelines of GPIF.
• Efficient managers evaluation
Resembler takes over a part of the evaluation report, which currently requires a large amountof manpower.
• Prediction-based monitoring
Deploying Resembler to predict how managers will behave in response to market environment
could prepare for communication with managers and be used for stress-testing portfolio at
GPIF.
• Automation of selecting new manager
If the same type and quantity of data could be obtained from funds that are prospective
additions to GPIF’s manager structure as from those that are already in it, then monitoring ofprospective funds could begin in the pre-contract stage, which would eliminate the information
gap in evaluating new versus existing funds. This would allow GPIF to gauge the uniquenessof funds which are being contemplated for addition, in order to avoid those which exhibit ahigh degree of resemblance with current funds whose performance has been unsatisfactory.
Also, it could help identify funds with an edge that is not in current manager structure at
GPIF.
• Manager Replication
Once a large amount of data from Resembler has been accumulated, it may be possible toreplicate idealized “cloned managers.” These model funds could be used as benchmarks forgrading existing fund managers and selecting new ones.
Boosting GPIF’s front-line operational capabilities with AI
In our FY2017 research, target to deploy AI is to support GPIF’s manager structure by providing
more sensitive tools for detecting when the managers’ behavior deviates from its prospectus andwhether the change is appropriate or should trigger an alert, if it is changed from original policy. Our
research from October 2018 onward has set out to find solutions for and more rigorously define the28issues raised at GPIF. GPIF had up to that point emphasized qualitative evaluation of managers’
policies, operating procedures, and organization/human resource capability, however in 2017 theBoard of Governors at GPIF expressed concern that qualitative evaluation of managers could invite
criticism for arbitrariness and subjectivity, while persons in charge also noted that the process isheavily dependent on a staff with specialized expertise. Based on these organizational concerns andthe results of experimental implementations of AI, GPIF expects the following transformations ofoperations:• Improvement of constructive and efficient discussion with managers.
– Detection of any changes in investment behavior contained in reports from managers.
– Confirming how much improvement is conducted by managers which have been put on
notice to improve performance.
– Detection of unreported changes (whether or not those changes should have been reported
according to guidelines).
– Direct capture of investment behavior of foreign managers through data, in addition to
information from gatekeeper.
– Elimination of reporting costs by funds and reduction call time.
• Standardization and commoditization of skills by exploiting big data at GPIF.
– The output of AI running on GPIF’s in-house data will enable novel perspectives andunique hypotheses to be built.
– Integration spanning from AI to day-to-day operational systems will allow a reliable
quality level that is not dependent on a small pool of personnel with years of experienceor specific career backgrounds.

5.2

How “Whale” attracts the industry

Longer tail —Cybernetic Whale will have a longer tailIf this research is fully deployed at front-line operations in the future, then to the extent that it
is possible to obtain the same level of data from pre-contract candidates as from existing fundmanagers, there will be no longer a distinction or information gap between them as far as GPIF isconcerned. In that scenario, the long-term relationships between asset managers who are alreadypart of the manager structure and GPIF may lose significance. Up to now asset managers, once undercontract, have been able to maintain a privileged position by assigning personnel to manage therelationship with GPIF and supply various kinds information that creates a gap with competitors.
Deploying AI would diminish the value of these efforts and potentially lead to a revolution in thebusiness models of asset management firms. For new funds who want mandate from GPIF, thebarrier would be lowered for them to pitch themselves to GPIF.
Because GPIF’s reporting requirements are so onerous, smaller asset management firms which
do not have sufficient resources have been blocked from doing business with GPIF. But if the outputof AI system was able to take over and eliminate much of this reporting obligation and workload,these managers would be able to pitch themselves based on performance and practices. And wholeinvestment management industry itself could benefit from being able to redirect resources from
reporting to core investment performance.
29

This opens up a new opportunity for both GPIF and smaller funds, especially this gives GPIF
access to the “long tail” of fund scale in the investment management industry. Due to statisticalfeature of complex systems including a global stock market, how to access and benefits from longtail part of the players and assets is critically important. The use of AI that enable GPIF and othermajor asset owners to access long-tail part of the players could be a major breakthrough and maytrigger qualitative transformation in the industry.
“Moneyball” for GPIF: Toward GPIF-Metrics
Asset owners, including GPIF, can have outside vendors and consultancies provide with variety ofmarket and product information, benchmark analytics, and so on, in addition to the performanceand trading data of the funds they invest into. This means that asset owners can be in a positionof having bigger data than asset managers. Once AI becomes more heavily used in coming years,
it should be realized that asset owners have the information advantage over asset managers, since
they have access to the bigger data to develop the more insightful analytical results.
This represents a paradigm shift away from the situation up to now, where the professionaland specialized expertise of asset managers gave them an information dominance over asset owners.
In the future, the use of statistical metrics, including AI, will untangle the data that spans theinvestment world and form “manager teams” which do not depend on traditional and establishedvalue. It will be interesting to see if this quest to unravel the mysteries of GPIF-metrics may impact
young generation and investment professionals.

30

6

Future work

While the initial stage of our research started in October 2018 was basic and exploratory investigation, current one has been shifted to an application phase as pilot test. The application phase has
cleared numerous suggestions and issues to be resolved from actual business process, and this phaseis moving forward in parallel with the following agenda: “enriching report generating function of
the system”, “deepening, improving, and operationalizing based upon trial result” and “streamline
and integration of the technology platform”.

Enriching report generating function of the system
As of October 2019, the system operates in the way Sony CSL generated reports based on output
from Resembler on Japanese equity manager every month and SDA a few times with monthly
detection of changes and causal analysis of these changes. The report was provided to GPIF fortheir internal discussion with managers. The next step is Sony CSL to provide monthly report using
both outputs from Resembler and SDA on Japanese and global equity space.

Deepening, improving, and operationalizing the system• Improving and deepening causal analysis output protocols with AI detection:
In case Resembler and SDA have detected change, causal analysis of the change is required
to be explainable. One of the analysis methods produces output through SOMs, clustering,and other means. However, a certain level of specialized expertise is required to evaluate andinterpret this type of output, which would incur training costs to roll out to GPIF’s operations.
The user interface has to be improved to make the output of causal analyses more visually andintuitively accessible. SOM was applied to analyze changes of holding data so far. Sony CSL
will conduct research exploring whether this method can be extended to identify backgroundcaused these changes (e.g., change in growth forecast).
• Operationalizing and streamlining final output for actionability:
After AI detected change of managers’ bahavior, Sony CSL conducted causal analysis of thechanges and suggested how to communicate with the managers. GPIF provided feedback thata clearer presentation of the salient points is needed in order to succeed in bringing about“more constructive and efficient discussions with fund managers”. For example, it is assumed
that actionable context is at the level of specificity of, e.g., “we determined that investmentof equity ’A’ last month appears out of character with the investment behavior you have done
ever,” or “you have retained your position in equity ’B’ even though it has been in quite
negative performance, which is not consistent with your past pattern”. Multiple different
format of reporting can be assumed corresponding to each case. Priorities and categorization
will be made to implement such automated report generation functions.
• Updating the system for better handling over dynamic change of universe:
The current generation of SDA and Resembler are composed of dedicated input units in theneural network for each equity, which means that the system cannot take into account any
newly listed equities. In the event of an IPO of a large market cap equity that will play animportant part in the market, the output of SDA, especially, could veer far from the market5 .
5Since Resembler is based on comparisons among funds’ characteristics with past of their own, the influence of31

In order to solve this problem, the data structure of the input to thße neural network should
be re-engineered.
• Further improvements of SDA:
Although Resembler provides output on the characteristics of funds from a different viewpointof conventional analytical tools, in considering the validity of the output, it is essential to checkthe validity of the functioning of SDA, which shares the same core system with Resembler.
Accordingly, although the operating principle has already been confirmed, there is room for
improvement in the virtual data for VFMs to train, we are continuously upgrading that aspectwith reference to feedback from experimental implementation of the system. In addition, we
will work to polish SDA into a tool that provides a novel analytical angle in its own right.
• Granularity of data for new fund selection:
Resembler, which can measure self-resemblance and resemblance to other funds, is underconsideration as one piece of information to use in fund selection. One issue is different
granularity of data between currently contracted funds and new candidate funds. Current
Resembler outputs base on daily data from contracted funds. However, data from a candidate
fund could be less granular, e.g., only monthly or quarterly, due to the proprietary value andconfidentiality. We will investigate whether Resembler produces appropriate output, evenwhen the system is fed much less granular data, and whether the use of data science methods
could enable the results to still achieve an acceptable level of quality.

Streamline and integration of the technology platform
Since FY2017, Sony CSL has carried out research and development of the system and their modules
with numbers of training and verification processes. Each module of the system is not fully integrated. and some still at the stage of proof of concept. However, it must be integrated seamlessly
in order to configure the final output to be operational in GPIF ʟs daily use with proper output
formats, as noted above in “deepening, improving, and operationalizing based upon trial result.”
For the full scale deployment of AI and causal analysis programs, data cleansing and databasearchitecture are to be organized and integrated as a part of the daily operation process.

ignoring newly listed equities would be limited to some extent, until the number of such new comers is accumulated
to a certain level.

327Implications

Combined power of a major asset owner, AI expert, and massive computing
resource
The process of R&D of such system requires large numbers of trials and verification on how well
the idea work against large-scale real-world data. This is where AIST’s AI Bridging Cloud Infrastructure (ABCI), one of the world’s most powerful computing facilities, came along to makea huge contribution. With the trio of AIST with their ABCI, GPIF (the world’s largest public
pension fund) and Sony CSL (with deep expertise and a proven track record in AI), Japan-basedorganizations have combined forces to not just carry out basic research, but carry that forward to aground-breaking experimental implementation of AI for detecting the investment behavior of active
manager. This demonstrates managing major funds may require cross-boundary highly competent
team and resources at the highest-level.

Formation of a “Global Data Consortium” of asset owners
This research was commissioned by GPIF, and the findings must first and foremost meet the needsof GPIF. However, evaluation of active funds is a need well shared by all asset owners. One possible
collaborative project that maximizes the benefit of this study is to apply the method of assessing
diversification/convergence of funds using Resembler, as covered in section 4.1, to manager structureof multiple asset owners.
Projections show that in 2020 global active investment will be USD74 trillion, reaching 87.6
trillion in 2025 [14]. If global asset owners with a certain scale joined together in a data consortium,
Resembler could run on that database to determine convergence among the active managers, which
would capture not only the manager structure risk confined to each asset owner, but a global asset
owner-level view of the investment behavior of active funds at all market environments. Within
the manager structure of a single asset owner, investment behavior may be appeared as diversified,
yet there may be convergence when viewed through the global data consortium. This would detect
any rise in localized risk among the world’s active funds that could ultimately develop into a risk
affecting all asset owners.
There are some practices utilizing inter-aggregated data at the exchange level. For example,
NASDAQ has adopted deep learning systems to enhance its capability to detect trades that may
be attempts at market manipulation. The system “works in tandem with human analysts” and“augments surveillance system that uses statistics and rules to flag any signs of market abuse.”[15]
There is a question for extremely large asset owners that need to be answered: “Whales breachwildly because earthquake happened, or whales breaching wildly causes the earthquake to be happened?” Public institutions which share the mission of investing over the very long term havea keen interest in the mechanism behind this problem and what the precursor signals might be.
And tackling a problem on this scale requires cooperation among asset owners. GPIF, AIST andSony CSL, a trio of three highly unique Japan-based institutions, propose that asset owners with ashared interest in this issue form a global data consortium to further develop a robust solution tothis market challenge. We expect a series of discoveries can be made that should uncover reality ofinvestment practices for large-scale asset owners, and scientific understanding should be possible.
Such efforts shall open a new era of science of investment and asset management.

33

Areas where academic insights could contribute to application
Outcome of this research uncovered interesting opportunities to scientifically understand behaviors of market, fund manager’s behaviors, and impacts of large scale funds in the makret. For
example, experimental implementation of the Resembler system to detect changes in the investment behavior of fund managers has been based on the idea that a nonlinear model could provide
more encompassing and responsive detection than interpretation of conventional linear factor-basedmodels. We believe there is a need for a deeper discussion of on detection of investment behavior
in linear versus nonlinear models. Given the amount of data from the asset owners, computing
resources available today, and computational approaches including deep learning that captures highdimensional non-linear space, we may be entering the new era of computational asset management.

Will AI steal jobs, or create jobs?
Although there are still many hurdles to clear before this research (now at the experimental implementation level) reaches full operational deployment, the roadmap to achieving full-scale deploymentof AI systems becomes clearer. Once these systems are in place at GPIF in the future, it will transform the way GPIF maintains manager structure and monitor their overall portfolio. It will also
results in a major reduction in workload as part of the process of selecting and monitoring active
managers is handled by AI. It is also possible that this would also eliminate some of the reporting
obligations that fund managers have towards GPIF. In that case, what will happen to all thoseman-hours that people no longer need to perform?
According to the provocative thesis that “AI will steal jobs”, reduced workload will lead to staff
cutbacks, because employers will take it as a way to cut labor costs. On the other hand, consider
what happened in the banking industry in the past: once, a huge amount of staff and labor wereneeded to calculate manually, until the development and deployment of accounting system allowedthat function to be executed with systematically greater speed and accuracy. The human labor
saved by this was used to provide better customer service, develop new financial products, andmanage the increasingly complex risks.
Humans and AI have each own excellence, and in cases where AI becomes able to take overwork that had previously been done by people, GPIF and investment management firms will haveto carefully study how to redirect human resources into areas of human strength, and what thoseareas are.
It is critically important to recognize that the use of AI shall have higher impact when it directed
to trigger qualitative transformation of the organization, mode of operations, and quality of the workby assisting us with its capability to see what human cannot see. Reduction of workload takes place
along with the organizational change, but mostly directly to enable the organization to refocus theireffort to the issues that was not possible before. We consider there is unprecedented opportunity inthe proper introduction of AI technologies to asset management industry for quality management
and transparency which shall benefits the society as a whole.

Acknowledgement
We received a great deal of feedback in response to the report we issued about our FY2017 research.
As this is an interim report and many findings are provisional, we hope to once again enjoy a healthy

34amount of feedback. And we will take into account that feedback to help build out our research into
pioneering, highly unique, standard-setting AI systems. At the same time, by continuing to deepen
dialogue with all parties involved, we intend to elevate the standard of operations on all sides.
We extend our sincere thanks to all the people we worked with at GPIF who took time outof their extremely busy day-to-day responsibilities, to the part time researchers and assistant researchers at Sony CSL who constantly managed to complete difficult research assignments withintight timeframes, and to everyone else who supported the efforts that resulted in this paper.

35

A

Appendix: Full results of all funds

A.1

Outputs of Resembler for Japanese equity funds

Figure 25: Outputs of Resembler — Japanese equity funds (1/3)

36

Figure 25: Outputs of Resembler — Japanese equity funds (2/3)

37

Figure 25: Outputs of Resembler — Japanese equity funds (3/3)

38

A.2

Outputs of SDA for Japanese equity funds

Figure 26: Outputs of SDA — Japanese equity funds (1/3)

39

Figure 26: Outputs of SDA — Japanese equity funds (2/3)

40

Figure 26: Outputs of SDA — Japanese equity funds (3/3)

41

A.3

Outputs of Resembler for foreign equity funds

Figure 27: Outputs of Resembler — Foreign equity funds (1/2)

42

Figure 27: Outputs of Resembler — Foreign equity funds (2/2)

43

A.4

Outputs of SDA for foreign equity funds

Figure 28: Outputs of SDA — Foreign equity funds (1/2)

44

Figure 28: Outputs of SDA — Foreign equity funds (2/2)

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Copyright owner:
Government Pension Investment Fund (GPIF))
7F Toranomon Hills Mori Tower,
1-23-1 Toranomon, Minato-ku,
Tokyo 105-6377 Japan
Tel: 03-3502-2480

Reporter:
Takao Tajiri
Takahiro Sasaki
Hiroaki Kitano
Sony Computer Science Laboratories, Inc.
Takanawa Muse Bldg. 3F,
3-14-13, Higashigotanda, Shinagawa-ku,
Tokyo, Japan 141-0022 Tel: 03-5448-4380

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