The investment management industry is undergoing significant shifts as passive managers have grown substantially and are now making independent decisions, putting pressure on active managers to deliver performance. Data science can help address these changes by using descriptive analytics and visualizations to better inform clients, and predictive analytics to develop new tools that marginally improve complicated tasks. The document argues that banks should leverage big data and technology to enhance resource efficiency, create differentiated content, and empower bankers to focus on generating insights and actionable ideas for clients.

1. Driving Change in a Relationship-Driven
Business
Dan Costanza
Chief Data Scientist
November 2019
Strictly Private and Confidential
Institutional Clients Group | Mergers and Acquisitions - Shareholder Advisory Group

2. 1. Motivating Example

3. The Investment Management Industry Is Undergoing Tectonic Shifts
Dynamics Shifting Among Active
Managers
Passive Managers Becoming More
“Active”
Passive Managers Are Making Independent
Decisions In Proxy Contests
• Fee pressure
• Influx of big data and algorithmic risk
/ analytic tools
• Rise of ETFs
• Need to find alternative means of
differentiation, besides performance
• Fee vs. “Free”
• Scale brings scope (and appetite) for
increased influence
• Greater resources devoted to
Stewardship and engagement
• Funds expanding scope of influence
in ESG related matters through
voting and engagement
• Increased reliance on “active” teams
and sub-advisors
More Funds Are Willing To Agitate In
Search For Returns
Number of Investors
Intense Pressure On Active Investors To
Deliver Performance
(U.S. Equities Fund Flows, $ in billions)
38%38%36%33%34%32%30%28%27%27%26%
22%22%
56%56%58%61%62%63%65%68%68%68%70%
73%74%
5%6%6%5%4%5%5%4%5%6%4%5%4%
2019
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
Passive Active Hedge Fund
Institutional Assets Have Shifted Towards
Passive
SPX Investor Type Over Time (1)
Source: ISS, Activist Insight, Factset, Wall Street Research.
(1) Percentages of total passive, active, and hedge fund ownership within the S&P. Does not take into account other investor types.
$(250)
$(150)
$(50)
$50
$150
$250
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Actively Managed Passively Managed
30%
40% 39%
7%
21%
37%
Blackrock SSGA Vanguard
Voted dissident card in 2017
Voted dissident card in 2018
0
50
100
150
2012 2013 2014 2015 2016 2017 2018
First Time Activists Repeat Activists
“Traditional” Active Managers Now
Willing To Wage Fights

4. R² = 71%
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
0.0 2.0 4.0 6.0 8.0
%ofFundAllocatedtoPosition
Weight in Benchmark
R² = 39%
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
0.0 2.0 4.0 6.0
%ofFundAllocatedtoPosition
Weight in Benchmark
Active Investors Are Becoming More Passive
Source: FactSet, IBES, Thomson Reuters, Python Software foundation as of 5/22/2019.
Implied Benchmark Accurately Reflects Disclosed Benchmark
CREF Growth Account - 2004
(vs. Russell 1k Growth)
CREF Growth Account - 2019
(vs. Russell 1k Growth)
In 2004, 40% of CREF Growth Account’s portfolio was Russel 1k Growth “beta” - today it is nearly 75%, driven by a
decreased willingness to take large “views”
7 companies
3 companies

5. 2. The Real Stuff

6. • Focus on descriptive tools, analytics and
visualizations
• Apply existing tools and analytics in creative
ways to address new business problems
What Actually Is a Data Scientist?
Data Scientists use data
to solve real-world problems
Genre 1: Describe & Inform
!𝒚 = $𝜷 𝟎 + $𝜷 𝟏 𝑿 𝟏
Genre 2: Predict
!𝒚 = 𝟔. 𝟏𝟒𝟕𝟐
• Focus on predictive analytics
• Develop new tools and methods to do
complicated tasks marginally better

7. Why Do We Actually Need Data Scientists?!
Traditional Analytical
Approaches Are Insufficient
Data Science Without Domain
Expertise & Judgement
Is Useless
Data Scientist and Business
Analyst Roles Will Continue
To Merge

8. • Recognize that the current way is not
wrong
• Follow demand, don’t force supply
• Identify & empower early adopters
• Build on initial successes
• Be nimble, not perfect
• Think modularly
• Find the low hanging fruit
So How Do We Lead Them to Water?

9. Quantity
$ Value
Current:
– Bankers bogged down with volume of data resources
§ As a result, either don’t use or too much human
capital spent manually (re)churning data
– Under-utilization of both data and intellectual resources
Focus:
– Enhance data efficiency and analytical capabilities
– Leverage available resources to enhance client content,
idea generation and banker impact
• Efficiency & Process
– Use automation and analytics to reduce cost of
accessing and processing data
– Enhance client targeting and identification of deal
opportunities
– Enhance junior banker experience
• Analytics
– Differentiated & applied analytics
– Support insights and actionable client ideas
• Empowerment and Impact
– Increase amount of banker time focused on the
right hand side of the “value-add curve”
– Empower bankers with insights and content to
support advice and judgements
– Give clients confidence to transact
Scalable Data Science & Technology – Strategy
Leverage big data and technology to enhance resource efficiency, create differentiated content, and empower
bankers to focus further on insights and actionable ideas.
Value-Add Curve
Data
Information
Insight
Action
Resource Allocation Today
A
B
C
A
B
C

10. 3. What’s Next?