Columbia CS - Roles in Quant Finance


Published on

Talk given to Columbia University Computer Science students on careers in quantitative finance

Published in: Economy & Finance, Business
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Columbia CS - Roles in Quant Finance

  1. 1. Careers in Quantitative Finance Ashwin Rao Columbia University, CS Lounge April 2013
  2. 2. My Background● B.Tech. Computer Science. IIT-Bombay.● Ph.D. Algorithmic Algebra. USC, Los Angeles.● VP. Quant Strategist. Goldman Sachs, NY.● Managing Director. Modeling. Morgan Stanley.● Founder. (A Tech Startup).
  3. 3. Define Quantitative Finance?● For this talk, we limit the scope of definition to:● Roles at Large Banks & Hedge Funds● Trading Businesses involving Quant Analysis● Requires advanced skills in Math/Stats/CompSci● Requires sound understanding of trading markets
  4. 4. Some Examples● Derivatives Trader● Trading Desk Quant Strategist● Derivatives Modeler, Econometric Modeler● Algorithmic Trading Quant● Analytics Developer
  5. 5. Trading Desk Strategist● Focused on a specific business or product● Deep knowledge of the specific market● Blend of Math, Stats and programming skills● Trading Strategies & Risk Management● Work closely with Traders, Sales, Risk, IT, Ops
  6. 6. Derivatives Modeler● Modeling stochastic dynamics of markets● Solving derivatives pricing and hedging problems● Expertise in Arbitrage-Free Pricing Theory● Stochastic Calculus, PDEs, Numerical Methods● Requires programming skills too, typically C++
  7. 7. Analytics Developer● Requires strong Computer Science background● Understanding of products and pricing models● Tools for pricing, risk metrics, scenario analysis● Data models, algorithms, functional programming● Development of Domain Specific Languages
  8. 8. Algorithmic Trading Quant● Markets are going increasingly electronic● Systematic exploitation of market inefficiencies● Analysis of historical market behavior & patterns● Fleeting inefficiencies - Speed of execution key● Systems programming & Statistics backgrounds
  9. 9. Preparation while at School● Algorithms, Machine Learning, Functional Prog.● Probability, Linear Algebra, Stats Modeling● Basics of Derivatives Pricing (book by Shreve)● Avoid studying advanced quant finance● Much of your learning will happen on the job
  10. 10. What to expect during interviews● Represent your abilities clearly and accurately● Typically, a large and diverse set of interviewers● Flood of puzzles, programming & math problems● Questions in your claimed areas of expertise● Evaluation of your communication and attitude
  11. 11. Current Wall Street Scenario● Regulations have hurt the industry● Compensation levels down from 5 years ago● Still good for people with STEM backgrounds● The tide is turning● More emphasis on vanilla trading businesses
  12. 12. ZLemma - Algorithmic Career Guidance● evaluates your profile in detail● ZLemma Quotient (ZQ) - your suitability for a job● ZQ is your score out of 100 for a specific job● Apply for high-ZQ jobs of interest to you● Jobs ranging from Wall Street to Silicon Valley
  13. 13. Addendum● Tune in to:● Write to:● Our app is your friend: