Driving Change in Relationship-Driven Businesses | How Citi Uses Data Science to Inform Decision-Making - Dan Costanza
- 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
- 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?