Analyst Data

Jiaying Ji

571-***-**** adk9ri@r.postjobfree.com

Quantitative Analyst with 3 years’ experience, Solid quantitative background & Programming skills SKILLS

Programming: Python C++ MATLAB R C Stata SAS DBMS: MSSQL MySQL MongoDB AWS Data Analysis: Pandas NumPy SciPy Tableau Machine Learning: TensorFlow Sklearn PROFESSIONAL EXPERIENCE

CITIC Securities Company Limited Shenzhen, China 03/2020 – 01/2021

(SSE: 600030) Top 2 investment bank in China with $52 billion revenue Quantitative Analyst

• Constructed a low-risk fixed income ETF portfolio with Sharpe ratio 1.05 (10% higher than benchmark AGG) using optimization with past 10 years' data in python

• Built linear regression model using US/UK Covid-19 cases and mortality data (4/1/2020 - 12/31/2020) to predict equity markets, bond markets, FX markets and commodity markets performance Bank of Communications Hangzhou, China 06/2017 – 06/2018

(SSE: 601328) is Top 5 largest bank in China with $371 billion revenue Quantitative Analyst

• Developed a Robo-advisor algorithm using Content-based and Collaborative filtering-based recommendation systems for bank clients

• Recommended highly correlated stocks (through volume and return analysis) in different industries/countries to clients based on their previous trading behaviors

• Categorized clients into different segments according to clients’ profiles and recommended popular stocks in each segment to clients

Noah Holdings Limited Shanghai, China 06/2018 – 06/2019

(NYSE: NOAH) is a leading wealth and asset management service provider with $22.9 billion AUM Quantitative Analyst

• Analyzed relationship between Bitcoin Price and global stock market /commodities market in the past 10 years with millions of observations in python

• Selected 8 out of 47 variables using Lasso and Ridge to model Bitcoin prices within 10% error rate

• Trained ARIMA model to forecast Bitcoin prices within 5% error rate ACADEMIC EXPERIENCE

Carey Business School Washington, D.C. 01/2020 – 12/2020 Market Risk Analysis

• Built a SPY daily risk forecasting system with Moving Average (m), Risk Metrics(λ), Full Sample and Out of Sample GARCH (ω, α, β) models with 5% error rate using 20 years’ SPY daily return data

• Predicted next week’s tail risk (VaR & ES) by simulating 10,000 paths for GARCH + Monte Carlo Simulation / Filtered Historical Simulation model

Data Analysis

• Built house pricing evaluation model for real estate buyer/sellers using MLR with >80% R square

• Processed residential house data of millions of observations and selected 6 features from 179 features EDUCATION

Johns Hopkins Carey Business School Washington, D.C. 07/2019 – 06/2021 Master of Business Analytics and Risk Management (GPA: 4.0/4.0) Master of Econometrics Finance (GPA: 3.8/4.0)

• Coursework: Econometrics, Database Management, Machine Learning, Business Analytics Wuhan University Wuhan, China 09/2015 – 06/2019 Bachelor of Science in Finance (GPA:3.8/4.0)

• Coursework: Time Series Analysis, Stochastic, Derivatives, Fixed Income, Hedge Fund

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