The document is a slide deck presentation given by Sri Krishnamurthy on the topic of "The 21st Century Quant". Some key points from the presentation include: - An overview of the history and evolution of quantitative roles on Wall Street from the 1980s to today. - Emerging trends in technologies like big data, cloud computing, machine learning/AI that are changing quant jobs. - Opportunities for quants in growing areas like fintech, cryptocurrencies, and addressing regulatory requirements. - Sri Krishnamurthy's background and the analytics certification programs being offered through QuantUniversity.

1. Location:
QuantUniversity Meetup
6/22/2017
Boston MA
The 21st Century Quant
2017 Copyright QuantUniversity LLC.
Presented By:
Sri Krishnamurthy, CFA, CAP
sri@quantuniversity.com
www.analyticscertificate.com

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Slides will be available at:
https://www.slideshare.net/QuantUniversity

3. • Founder of QuantUniversity LLC. and
www.analyticscertificate.com
• Advisory and Consultancy for Financial Analytics
• Prior Experience at MathWorks, Citigroup and
Endeca and 25+ financial services and energy
customers.
• Regular Columnist for the Wilmott Magazine
• Author of forthcoming book
“Financial Modeling: A case study approach”
published by Wiley
• Charted Financial Analyst and Certified Analytics
Professional
• Teaches Analytics in the Babson College MBA
program and at Northeastern University, Boston
Sri Krishnamurthy
Founder and CEO
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Quantitative Analytics and Big Data Analytics Onboarding
• Trained more than 1000 students in
Quantitative methods, Data Science
and Big Data Technologies using
MATLAB, Python and R
• Launching
▫ Analytics Certificate Program
(August 2017)
▫ Fintech Certification program
(October 2017)

6. Late Summer 2017: http://www.analyticscertificate.com

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• Boston
• New York
• Chicago
• Washington DC
• San Francisco
QuantUniversity meetups

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• Big Data : Opportunities for the financial industry
• Cloud computing : Are we there yet?
• Retooling the Quant: The Programming Language Wars
• Machine learning, AI, Deep Learning: Sifting the hype from reality
• Fintech: Bringing Silicon Valley to Wall Street
• Regulation and Risk : Accept, Optimize, Innovate
Topic for today’s talk

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The 1980s and 1990s
- Ronald Kahn, Blackrock, Barclays, Barra

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The 1980s and 1990s
- Peter Carr, Morgan Stanley, NYU

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The 1980s and 1990s
- Neil Chris, Morgan Stanley, Goldman Sachs

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The 1980s and 1990s
- Andrew Lo, MIT, Alpha Simplex

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The 1980s and 1990s

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Financial engineering and Math Finance Programs

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Financial engineering and Math Finance Programs

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As per Wikipedia.. “It focuses on, among other things, the
2007 subprime mortgage crisis and how it helped trigger a
sudden and massive unwind of complex, highly leveraged
quantitative strategies. ”

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The Demise of the Quant Role?

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• Front office quant roles
• Risk management
• Model Validation
The transitionary quant roles
http://www.investopedia.com/articles/professionals/121615/q
uantitative-analyst-job-description-average-salary.asp

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The rise of the quants?

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• Up
▫ R, Python
▫ Big Data
▫ Data Science, Machine Learning, AI
▫ Ability to work with large datasets
▫ Optimization skills, Distributed and parallel computing paradigms
▫ Understanding of stress testing, model validation etc.
▫ Ability to glue multiple, disparate systems
▫ Quantamental roles
• Down
▫ Great with ideation but little implementation experience
▫ Pure Mathematical finance grads with little programming experience
▫ Quant Operations and Quant IT roles
▫ Engineers, Economists, Applied Mathematicians, Physicists without domain
experience
▫ Fundamental analysts without Quant skills
Quant Job profiles are changing

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http://www.risk.net/risk-management/5289871/the-quant-
factory-not-muppets-but-not-perfect

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1. Big Data : Opportunities for the financial industry
2. Cloud computing : Are we there yet?
3. Retooling the Quant: The Programming Language Wars
4. Machine learning, AI, Deep Learning: Sifting the hype from reality
5. Fintech: Bringing Silicon Valley to Wall Street
6. Regulation and Risk : Accept, Optimize, Innovate
Six areas every quant should know about

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Big Data
Source: http://www.ey.com/gl/en/services/advisory/ey-big-
data-big-opportunities-big-challenges

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• Apache Spark enterprise adoption of HDFS based architectures
• Alternate data and going beyond traditional factors
• Incorporating Text and Sentiment analysis
▫ https://www.ravenpack.com/research/jp-morgan-big-data-ai-machine-
learning-alternative-data/
• More realistic use-cases
▫ Large data sets for anomaly detection
▫ Credit risk modeling
 Freddie mac dataset
http://www.freddiemac.com/research/datasets/sf_loanlevel_dataset.html
 Lending club dataset :
https://www.lendingclub.com/info/download-data.action
Big Data trends and opportunities

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Cloud computing
Source: https://www.skyhighnetworks.com/cloud-security-
blog/microsoft-azure-closes-iaas-adoption-gap-with-amazon-
aws/

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• Scaling stress and scenario tests
• Model calibration and parameter tuning
• More frequent model updates
• Scraping datasets for proprietary data sources
• Dynamic sandboxes for environment and product testing
• Shorten the Quant Research -> Quant Deployment cycle
• Microservices and Docker to enable dynamic environments
• GPUs in the Cloud makes massive computing possible!
Cloud computing trends and opportunities

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The programming language wars

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• R, Python on the rise
• Julia is getting attention
• Focus on environment management and supporting open source
languages
▫ Domino Data Labs
▫ IBM Data science Experience + Hortonworks
▫ Cloudera Data science workbench
▫ Azure Machine Learning and R programming integration
• Package support and community support increasing for R & Python
• Acceptance of open source language risk increasing and reliance on
commercial languages slowing
• Cloud vendors making it easy to “rent” their infrastructure for model
building
Programming languages trends and opportunities

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AI and Machine learning

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Some concepts are not new ; But interest in ML and AI re-emerging

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Lots of books, conferences, demos and vendor-driven seminars

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• Silicon valley leading the way
• Lots of interest but few proven use-cases
• Many organizations intrigued and are prototyping applications
• Many niche solutions that offer promise ( no generic solutions out there
yet)
• Companies like J. P. Morgan heavily investing in Machine Learning and AI
• May not replace traditional Quant Finance but could complement it in
applications like Anomaly Detection, segmentation, scoring, classification
etc.
• Lots of Snake oil vendors
• Knowledge is power : Understand before applying and use at your own
risk
Machine Learning and AI : Trends and opportunities

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• CFA Survey 2016
Fintech in the news
https://www.cfainstitute.org/Survey/fintech_survey.PDF

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• CFA Survey 2016
Fintech in the news
https://www.cfainstitute.org/Survey/fintech_survey.PDF

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Bitcoin

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Global Fintech Landscape

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Fintech education becomes serious

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http://www.analyticscertificate.com/fintech/

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http://www.analyticscertificate.com/fintech/

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http://www.analyticscertificate.com/fintech/

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http://www.analyticscertificate.com/fintech/

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Lots of interesting problems for Quants to engage in
• Equity crowdfunding platforms intermediate share placements
• Peer-to-peer lending platforms intermediate or sell loans
• Robo-advisers provide automated investment advice
• Social trading platforms offer brokerage and investing services
• Just in time insurance purchases
• Longevity risk and insurance
• Health care costs and engaging millennials through gamification
• Domain expertise, risk and regulatory insights
• Potential to model interesting scenarios and innovate!
Fintech: Opportunities and trends
Ref: https://www.iosco.org/library/pubdocs/pdf/IOSCOPD554.pdf

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• Post 2008 crisis, multiple regulations in effect
▫ CCAR, DFAST
▫ Model Validation
▫ BASEL
• SR 11-7 attachment: Supervisory Guidance on Model Risk
Management
• Most banks now approaching and addressing model risk in a
systematic way
Regulation

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Fintech being noticed by Regulators

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• https://www.americanbanker.com/opinion/banks-should-see-
stress-tests-as-an-opportunity-not-a-chore
• Blackbox models viewed with skepticism. Transparency and explain
ability as more machine learning models enter decision making
• Newer frameworks to optimize model tests and model governance
• Automation of stress and scenario tests
• Hardware to accelerate Model validation
• New opportunities in addressing risk in Fintech enterprises
Regulation: Opportunities and trends

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60. Thank you!
Checkout our programs at:
www.analyticscertificate.com
Sri Krishnamurthy, CFA, CAP
Founder and CEO
QuantUniversity LLC.
srikrishnamurthy
www.QuantUniversity.com
Information, data and drawings embodied in this presentation are strictly a property of QuantUniversity LLC. and shall not be
distributed or used in any other publication without the prior written consent of QuantUniversity LLC.
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