Questromworld.bu.edu
DEZHENG XUBrookline, MA | (617) 909 – 1098 | zh686437@bu.edu | in/xudezhengSKILLS AND CERTIFICATIONProgramming: Python, R, Solidity, SQL, Excel, Stata 15, LatexMathematics: Computational methods for solving differential equations, Statistics, Analysis of complex variable, Applied Linear Algebra, Measure-theoretic Probabilities, Time Series Analysis, Forecasting Regression Analysis Model: Monte Carlo Simulation, Merton Model, Multiple-factor Regression, Fast Fourier Transform, PCA Based Trading Model, CAPMCertification: Certification in FinTech (May 2021), CFA Exam Level 1 CandidateEDUCATIONBoston University, Questrom School of Business Boston, MAM.S. Mathematical Finance [GPA 3.2/4.0] January 2021Coursework: Computational Methods of Mathematical Finance, Stochastic Methods of Asset Pricing, Options and Financial Risk Management, Economics of FinTechDalhousie University Halifax, CanadaEconomics and Mathematics [GPA 3.93/4.3] April 2018Coursework: Statistics I, Mathematics for Economics, Financial Economics, Intermediate Calculus I, Intermediate Calculus II, Financial Mathematics, Programming, Multivariable Calculus, Probability & Statistics, Ordinary Differential Equations, Linear AlgebraEXPERIENCE ZhongDe Securities Beijing, China SDD term Intern June 2019 – August 2019Managed 3 interns to collect information disclosures of 35 union companies to generate investment report Completed an investment data project three months in advance by using Multiple-factor RegressionConducted data assortment planning by using Bloomberg to increase team efficiencyPROJECTSBoston University Boston, MACME Trading Challenge May 2020 - CurrentAnalyzing 6 different trading assets and decided investment weight by Risk Parity modelComparing the expected return under CAPM model by the different indexesEvaluating the portfolio 1-day 99% VaR by Monte Carlo Simulation to complete Venture capital report Boston University Boston, MABlack-Litterman Model for Portfolio Optimization February 2020 – April 2020Estimated the expected return under CAPM model by reviewing the historical data from 10 securitiesConstructed investor view matrix P and adjusted factor by machine learning methodAdjusted investor view by adding dynamic factors to improve the portfolio weights with Black-Litterman ModelADDITIONAL INFORMATIONLanguages: Mandarin, EnglishInterests: Fitness, play basketball, reading book ................
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