Careers in Quant Finance talk at UCLA Financial Engineering

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Careers in Quantitative Finance A Student's Perspective UCLA MFE, April 2013 Ashwin Rao

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Guest lecture given to students at the UCLA Masters in Financial Engineering program on 'Careers in Quantitative Finance'

Transcript of Careers in Quant Finance talk at UCLA Financial Engineering

Page 1: Careers in Quant Finance talk at UCLA Financial Engineering

Careers in Quantitative FinanceA Student's Perspective

UCLA MFE, April 2013

Ashwin Rao

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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).

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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

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Some Examples

● Derivatives Trader

● Trading Desk Quant Strategist

● Derivatives Modeler, Econometric Modeler

● Algorithmic Trading Quant

● Analytics Developer

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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

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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++

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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

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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

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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

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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

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An example: Mortgage Trading

● 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

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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

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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

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Addendum

● Tune in to: blog.zlemma.com

● Write to: [email protected]

● Our app is your friend: zlemma.com