Data Project

YOUHENG (JAMIE) ZHANG

862-***-**** • *******.*******@*****.***

EDUCATION

RUTGERS BUSINESS SCHOOL, NEWARK (GPA: 3.8/4.0) 08/17 - Present Master of Quantitative Finance expected December 2018 Courses: Quantitative Equity Trading Strategies, Statistics & Machine Learning, Financial Modeling, Derivatives NANJING UNIVERSITY (GPA: 4.1/5.0) 09/13 - 06/17

Bachelor of Science in Statistics

Courses: Mathematical Analysis, Advanced Linear Algebra, Stochastic Processes, Mathematical Statistics FINANCIAL MODELING SKILLS

• Option Pricing: Black-Scholes framework, Binomial tree model, Monte-Carlo simulation, pricing of exotic options

• Statistics: Linear/logistic regression, Maximum likelihood estimation, ARIMA model, GARCH model

• Machine learning: neural network, Decision tree, SVM

• Stochastic Calculus: Wiener process, Itô integral, Martingale

• Optimization: Linear/nonlinear optimization, Dynamic programming

• Portfolio Optimization: Markowitz mean-variance model, Consumption based asset pricing model PROGRAMMING AND COMPUTING SKILLS

• Python, C++, R, MATLAB, Microsoft Excel, Word

FINANCIAL EXPERIENCE

ZHENGYUANXINYI ASSET MANAGEMENT Ltd., Suzhou, China 07/18 - 08/18 Intern, Quantitative Researcher

• Developed statistical arbitrage trading strategy based on stock price convergence applied to Chinese stocks

• Executed back test to examine performance which yielded 40% annualized return

• Provided trading signals in minute level by grabbing real-time data using Wind API

• Interpreted model assumptions and methodology to managers RESEARCH EXPERIENCE

NUMERICAL ANALYSIS COURSE PROJECT 11/18 - 12/18

Exotic Option Pricing via Two Methods using Monte Carlo Simulation

• Priced Bermudian-style options by determining an optimal exercise policy

• Calculated the option price by computing a hedging policy and finding a minimizing martingale QUANTITATIVE EQUITY TRADING STRATEGY COURSE PROJECT 03/18 - 04/18 Equity Trading Algorithm Based on Reinforcement Learning

• Built Q-learning neural network and used historical data of stocks to train the network

• Collected trading signals from this network using testing dataset

• Created a portfolio by applying those trading actions on the same testing data and calculated portfolio returns

• Compared the returns to Dow Jones Index and concluded that our portfolio had a higher return and lower volatility OBJECTED ORIENTED PROGRAMMING COURSE PROJECT 12/17 Applied Artificial Neural Network to Predict Futures Prices

• Captured daily commodity futures prices data from dynamic web pages using beautifulsoup and selenium package in Python

• Built an artificial neural network model in C++ to forecast futures prices with input historical data CERTIFICATIONS

• CFA Level 1

06/18

AWARDS

Top 5 in Bloomberg Trading Challenge, 2018

EXTRACURRICULAR

Poker Games (Texas hold ‘em)

Sector Chief of Volunteers Association, Kuangyaming Honor School, Nanjing University 09/14 - 06/15 Nunchaku Coach in Kung Fu Association, Nanjing University 09/14 - 06/15

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