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Quantitative Risk modeling

Location:
United States
Salary:
$60000/Year
Posted:
February 28, 2020

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Resume:

Baozhou Cao

Boston, MA 612-***-**** adb1ti@r.postjobfree.com LinkedIn.com/in/Baozhou-Cao

SKILLS AND CREDENTIALS

Programming: Python, SQL, C++, Machine Learning, R, MATLAB, SAS, VBA, Tableau Certification: CFA level 2 candidate, SAS Certified Advanced Programmer EDUCATION

Boston University, Questrom School of Business Boston, MA M.S. Mathematical Finance [GPA 3.62/4.0] September 2018 - January 2020

• Coursework: Statistics, Fixed Income, Monte Carlo Simulation, Stochastic Methods of Asset Pricing, Computational Method, Portfolio Optimization, Data Analysis and Financial Econometrics, Corporate and Credit Risk Management, Accounting

University of Minnesota Minneapolis, MN

B.A. Mathematics & B.S. Economics [GPA 3.92/4.0] September 2015 - May 2018

• Merit award: Graduate with High Distinction (awarded to top 1% students)

• Coursework: Econometrics, Stochastic Process, Financial Economics, Probability-Statistics Theory INTERNSHIP

GF Securities Co., Ltd Beijing, China

Quantitative Analyst Intern May 2019 – August 2019

• Collected daily global monetary news, tracked economic indices and financial market data; Analyzed effects of data variation and summarized daily reports to clients

• Analyzed historical performance of different assets (US market) under multiple economic cycle theories, investigated implied characteristics, and reported potential trend to senior analysts

• Gathered, cleaned and organized financial market data, and detected the correlation among different assets by using Time Series, drafted part or all of investment strategy reports to senior analysts

• Researched more than 10 quantitative asset allocation models and back-tested in historical markets PROJECT

Boston University, Questrom School of Business Boston, MA Stock Price Forecasting by Machine Learning Project November 2019 – December 2019

• Processed data for thousands of publicly traded companies in 4 years, constructed forecasts of the monthly return of stocks, compared results of 3 different machine learning methods (XGBoost, GBM, LASSO); achieved 55% accuracy in the optimal forecasting Delta Hedging Strategy Project February 2019 – April 2019

• Utilized different stochastic pricing models to calculate delta; Used delta from different models to do delta hedging strategy and chose optimal delta

• Performed delta hedging strategy on historical market with daily update, achieved 1.46 Sharpe Ratio Option Pricing Project October 2018 – November 2018

• Collected and cleaned millions of call and put option data to train and calibrate parameters in stochastic option pricing models

• Developed a Python program to price multiple exotic options by using Monte Carlo simulation, different stochastic models, and Fourier Transform

University of Minnesota Minneapolis, MN

Black Litterman Portfolio Optimization Project February 2018 - May 2018

• Gathered, cleaned and organized thousands of stock market data over 10 years, employed Python to analyze statistical risks

• Chose equities by using momentum strategy and applied Black Litterman Portfolio theory to obtain an optimal equity portfolio (Sharpe Ratio 1.57), summarized process to generate an insight report



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