Quantitative Analyst

Qihang (David) Yu

*****@*******.*** 646-****-***

**** ****** **, *** ****, 11101 Linkedin: https://www.linkedin.com/in/guapo0813/ GitHub: https://github.com/qihangy EDUCATION

Fordham University, Gabelli School of Business New York, NY Master of Science in Quantitative Finance, GPA:3.7 08/18 – 05/20 University of California, Irvine Irvine, CA

Bachelor of Science in Mathematics, Minor in Statistics, GPA:3.63 09/14 – 06/18 Bachelor of Art in Quantitative Economics

EXPERIENCE

NYC Department of Health and Mental Hygiene New York, NY Data Engineer 02/20 – 05/20

• Data Extraction: Developed ETL processes to standardize public health data from multiple source into one SQL table structure; designed and populated relational data warehouse (SQL databases) to maintain person-centric matched analytics data

• Data Mining: Tokenized elements to enrich matching variables in Quality Stage Software by IBM

• Matching algorithm: Accelerated matching process by designed algorithm based on the probabilistic linkages of fields in datasets Global AI New York, NY

Quantitative Researcher 01/19 – 12/19

• Time Series Analysis: Constructed time series of US-specific stress indices (CISS) using exponentially weighted returns of ETFs

• Markov Model: Identified different stress levels by using the three-stage Markov Regime Switching Model

• Statistical Modeling: Evaluated the performances and risk exposures of ETFs in different stress levels based on regression analysis

• Regression Analysis: Backtested the stock trading strategies formulated on the basis of heteroskedasticity robust regression result

• Multi-Factor Model: Built a multifactor model motivated by investment-based asset pricing; implemented utility function to derive Theta’s for all 24 macro factors, and allocated Z-score for each ticker throughout the entire time

• SDG/ESG Trading Strategies: Extracted, transformed and analyzed 17 SDG factors over 1 million documents; implemented various regression and validation methods to predict high ranking SDGs with respect to returns and fundamental ratios Ping’an Securities Shanghai, China

Investment Strategies Analyst 06/18 – 09/18

• Portfolio Optimization: Implemented Markowitz mean-variance on five funds to get minimum variance and maximum Sharpe ratio portfolio; improved robustness by estimating the expected return with Black-Litterman model

• Backtesting and Automation: Conducted daily rebalancing weight allocation automation with APIs PROJECT

Validating psychometric survey responses 06/20 – present

• Built a validation framework to identify non-valid survey takers using Machine learning algorithms on mouse movement behavior

• Transferred the mouse movement data into a string and having an LSTM and Hidden Markov model to predict the next "word" Derivatives Pricing and Implied Volatility Curve Fitting (Python) 01/20 – 05/20

• Priced American option in Monte Carlo Simulation Simple Least-Squares Approach, evaluated theoretical boundary of early exercising, used variance reduction techniques to reduced MC errors and achieved faster convergence rate

• Fitted SABR model parameters under market convention with Eurodollar futures quotes and constructed volatility smile curve Yelp context analysis using Nature Language Processing 12/19 – 02/20

• Set up a web scraping to retrieve multiple data source on Yelp, and converted text into numerical representation using Tensors

• Applied Bidirectional LSTMs instead of one LSTM on the input sequence using Keras Pretrained Glove word Embeddings HDFS Design and K-means Clustering 09/19 – 11/19

• Set up a 3-node cluster with Hadoop Distributed File System and on top of HDFS, set up the cluster with MapReduce and Spark

• Developed a MapReduce-based flamework to classify each NBA player’s records comfortable zones based on a K-means algorithm Default Modeling and Counterparty risk valuation (Python) 05/19 – 07/19

• Calculated the Default probability of each firm using both KMV Model and Geske Model

• Computed counterparty risk valuation adjustments such as CVA, DVA, variation margin EVA and initial margin MVA IR Curve Building (Python) 05/19 – 07/19

• Calculated bond yield, modified duration; Bootstrapped zero-coupon price curve for the securities

• Computed clean and dirty prices for bonds and priced different swaptions by performing interpolation

• Performed Interest Rate Model Calibration for different Interest Rate Models March Madness Data Crunch 01/19 – 05/19

• Conducted feature selection using Lasso Regression and Principal Components Analysis; predicted the outcomes of the 2019 NCAA Basketball Tournament using Adaptive Boosting with Random Search, Cross Validation, and Logistic Regression ADDITIONAL

Certificates: FRM Part I (Passed), CFA Level I (Passed), Deep Learning Specialization by Deeplearning.ai Leadership: President of Quantitative Finance Club of Fordham University Programming skills: Python (Pandas, NumPy, Pyspark, Tensorflow, Keras), C++, R, SQL, Linux, Matlab, Mathematica, Stata Interest: Saxophone (CM 10), UCI Varsity Tennis Team (Top 3 Team in California), China National-Level Athlete, Go (5 Dan)

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