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Berkeley Financial Engineering - Guidance on Careers in Quantitative Finance

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Talk given to Berkeley Financial Engineering Masters students. Overview of careers in quantitative finance.

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Berkeley Financial Engineering - Guidance on Careers in Quantitative Finance

  1. 1. Careers in Quantitative FinanceA Students PerspectiveBerkeley MFE, April 2013Ashwin Rao
  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. ZLemma.com (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● Develop coding skills, eg: Python, Java, C++● Algorithms, Databases, Numerical Methods● Data Analysis, Econometric Modeling skills● Avoid studying advanced quant finance● Much of your learning will happen on the job
  10. 10. Theory versus Practice - Case Study● Mortgage products are complex and messy● Blend of risk-neutral and econometric modeling● Understanding the Price of Model Risk● Capturing liquidity risk and transaction costs● Need for advanced data/software enginering
  11. 11. Current Wall Street Scenario● Regulations have hurt the industry● Compensation levels down from 5 years ago● People with STEM backgrounds are thriving● Markets getting increasingly electronic● More emphasis on vanilla trading businesses
  12. 12. 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
  13. 13. ZLemma - Algorithmic Career Guidance● ZLemma.com 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
  14. 14. Addendum● Tune in to: blog.zlemma.com● Write to: ashwin@zlemma.com● Our app is your friend: zlemma.com

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