Quant

Liyao Wei

Los Angeles, CA ***** 213-***-**** ********@*.****.*** www.linkedin.com/in/liyaowei/ EDUCATION

UCLA Anderson School of Management Los Angeles, CA Master of Financial Engineering (GPA: 3.8/4.0) December 2019 x Relevant Coursework: Data Analytics & Machine Learning, Quantitative Asset Management, Stochastic Calculus Occidental College Los Angeles, CA

B.A. (Cum Laude), Mathematics with Distinction (GPA: 3.9/4.0) & B.A., Economics (GPA: 3.6/4.0) May 2017 x Relevant Coursework: Applied Econometrics, Operations Research, Partial Differential Equations, Statistics SKILLS AND CERTIFICATES

x Analytics Tools: R, Python, SQL, Java, LaTeX, Stata, MS Excel, LINDO x Certificates: BMC Terminal, DataCamp R Programming Track, DataCamp Python Programming Track x Languages: Mandarin Chinese (native)

EXPERIENCE

UCLA Anderson School of Management Los Angeles, CA Research Assistant April 2020 Present

x Calculated the volatility of idiosyncratic returns of 26,821 U.S. firms relative to the CAPM and Fama French models within each calendar month from 1926 to 2019 (1127 months) x Replicated common factors of idiosyncratic risk in Professor Bernard Herskovic V paper by calculating common idiosyncratic variance (CIV) as the cross-sectional average of all model residual variance and the market variance (MV) x Sorted firms into 5 quintiles based on replicated CIV-beta (firms exposure to the CIV shock) each month and validated that stocks in the lowest quintile earned average annual returns 5.4% higher than those in the highest quintile Mizuho Bank, Ltd. Los Angeles, CA

Summer Quantitative Researcher June September 2019 x Collected 212 minXWeV of meeWingV of Whe Federal ReVerYe V Open MarkeW CommiWWee (FOMC) from 1993 to 2019 and their corresponding release dates using web crawler technique based on the BeautifulSoup Library in Python x Converted each minute into a numeric vector by NLP models including tf-idf model, word2vec model, and doc2vec model x Implemented machine learning algorithms to predict the U.S. interest rate fluctuations using information from minutes data, and developed the doc2vec model with XGBoost algorithm to have the best out-of-sample performance CITIC Trust Co., Ltd. Beijing, China

Financial Analyst August 2017 June 2018

x Assisted in raising funds of approximately 850 million yuan (128 million dollars) for Kaisagroup (SEHK:1638) by offering commercial banks and individual investors an APR around 6.8% x Forecasted future cash flows of multiple real estate enterprises by analyzing their financial statements, ownership structures, operation models, and credit ratings, thus to evaluate their profitability and default probability x Assisted team make better loan issuing decisions by extracting information and data on comparable land sales, financing package, and market position of construction projects from China Index Academy to forecast their future sales Xinhu Futures, Xinhu Zhongbao Co., Ltd. Beijing, China Summer Market Analyst June August 2016

x Anal\]ed ZarehoXVe clXb indXVWr\ V deYelopmenW in the U.S and examined its future potentials in China by analyzing its business model, post-harvest supply chain, and location choosing strategy x Collected market and macroeconomic information by using Wind (China V Bloomberg ) on the retail industry and adopted DCF and Residual Income analysis methods Wo eVWimaWe a fair range of CoVWco V VWock price x Calculated implied volatility of Costco stock by bisection and the Black Scholes model, and used it to validate stock price PROJECTS

TECTA Invest Los Angeles, CA

Quantitative Manager Fund Selection Model (Python) February December 2019 x Used 276-month (1994/01 2016/12) data of 8099 hedge funds from BarclayHedge database, macroeconomic information, and market sensitivity features from CBOE to construct 21 factors for machine learning models x Applied linear regression (OLS) and machine learning methods (random forest and gradient boost) to cross-sectionally predict hedge fund future returns, and grouped funds into ten equal-sized deciles (D1 to D10) based on the predictions x Found gradient boost with a rebalancing period of 3 months the best model in predicting D10 - D1 portfolio returns, with returns in the top decile 8.65% per year higher than those in the bottom decile

Contact this candidate