Manager Data

Hanzhang Fang

**** ********** ****, ********, ** 475-***-**** ********.****@*****.*** https://www.linkedin.com/in/Fanghz EDUCATION

UNIVERSITY OF CONNECTICUT STAMFORD, CT

M. S. in Financial Mathematics (Risk Management) (GPA: 3.96/4.3, Top 10%, Scholarship) May 2020

• Financial Engineering, CVA/DVA, Corporate Finance, Market/Credit Risk, Bank Regulations, Stress Testing ZHEJIANG UNIVERSITY OF FINANCE AND ECONOMICS HANGZHOU, CHINA B. S. in Mathematics and Applied Mathematics 2014-2018

• Calculus, Linear Algebra, Probability and Statistics, Econometrics, ODE, Multivariate Statistical Analysis TECHNICAL SKILLS

Programming: Python, SQL, C#, VBA, Data Structure and Algorithm Analysis, OOP, Tableau, JavaScript, HTML Machine Learning Models: Logistic Regression, Decision Tree, Random Forest, SVM, NLP, KNN SELECTED PROJECTS

PORTFOLIO MARKET RISK ANALYSIS Oct 2018 – Dec 2018

• Applied Taylor Series to approximate the change of bond’s price; Calculated duration/convexity/key rate duration (in Excel)

• Estimated portfolio volatility using Filtered Historical methods in collaboration with GARCH models (in R)

• Conducted historical simulation/variance covariance/Monte Carlo simulation methods to calculate VaR & ES (in Python) FINANCIAL TIME SERIES MODELING Jan 2019 – Apr 2019

• Adopted ADF test; utilized differentiation methods to ensure stationarity; Computed coefficients for ARIMA

• Conducted back-testing on historical data establishing Chi-square statistical distribution to validate risk models CREDIT RISK MODELING May 2019 – Oct 2019

• Conducted data cleaning through Winsorization/Logarithmic Transformation/Function relationship selection

• Operated MLE to estimate coefficients of logit regression model and calculate default probability

• Established credit rating transition matrices through cohort approach & hazard rate approach

• Optimized linear regression model through clustered regression & beta distribution transmission to predict loss given default

• Measured credit portfolio risk with the asset value approach & Monte Carlo simulation to produce loss distribution

• Built and fit logistic regression model in Python, to detect possible credit card frauds from transaction dataset. Recall rate= 89.12%, accuracy rate = 87%;

FINANCIAL ENGINEERING Dec 2019 – Feb 2020

• Conducted Option valuation using binomial tree and BS model; Computed spread for Interest Rate Swap and CDS

• Synchronized derivatives from basic product and conducted pricing analysis WORK EXPERIENCE

BH ASSET MANAGEMENT, LLC Greenwich, CT

Quantitative Analyst Aug 2019 – Dec 2019

• Model Development: Built a stock scoring model framework in VBA for factor analysis; Provided portfolio manager with model results as benchmark of existing strategies

• Data Engineering: Responsible for interfacing with Bloomberg terminal and other data vendors to perform the data collection, data cleaning, data exploration and data loading

• Data Analysis: Performed time series analysis on individual stock price by consolidating fundamental data and pricing data. Adapted SQL to perform aggregation analysis and batched weekly/monthly analytical report to support the portfolio manager.

• Model Validation: Applied historical Monte Carlo Simulation to perform P/L analysis to design and optimize existing strategy; Compared and validated existing index-tracking strategies by analyzing model’s tracking error. DISCIPLINED ALPHA LLC Boston, MA

Capstone Project May 2019 – Jul 2019

• Infrastructure: Facilitated the research by designing and implementing the architecture of a back-testing engine in Python that accepts data input from csv.

• Alpha Research: Applied Logistic Regression and feature engineering to build a robust scoring system. Developed a long/short equity strategy based on the scoring system and won first prize among 6 teams.

• Model Validation: Adapted cross-validation and rolling validation to validate model performance.

Contact this candidate