Class of 2020 Resume Book - New York UniversityClass of 2020 Resume Book Mathematics in Finance M.S....

Class of 2020 Resume Book

Mathematics in Finance M.S. Program

Courant Institute of Mathematical Sciences New York University

February 13, 2020

For the latest version, please go to http://math.nyu.edu/financial_mathematics

Job placement contact: [email protected]

http://math.nyu.edu/financial_mathematics

mailto:[email protected]

Dear Colleague,

We are pleased to provide you with the resumes of the first semester students in the Courant Institute's Mathematics in Finance Master's Program. They just started the program this semester and will graduate from our Master’s program in December 2020. We hope you will consider them for possible summer internship positions at your firm.

We believe our students are the most elite, most capable, and best trained group of students of any program. This year, we admitted less than 8% of those who applied. The resumes you find in the resume book describe their distinguished backgrounds. For the past years we have a placement record close to 100% for both the summer internships and full-time positions. Our students enter into front office roles such as trading or risk management, on the buy and the sell side. Their computing and hands-on practical experience makes them productive from day one.

Our curriculum is dynamic and challenging. For example, the first semester investment class does not end with CAPM and APT, but is a serious data driven class that, examines the statistical principles and practical pitfalls of covariance matrix estimation. During the second semester electives include a class on modern algorithmic trading strategies and portfolio management. Instructors are high-level industry professionals and faculty from the Courant Institute, the top ranked department worldwide in applied mathematics. You can find more information about the curriculum and faculty at the end of this document, or at http://math.nyu.edu/financial_mathematics/.

Sincerely yours, Leif Andersen, Industry Adviser Deane Yang, Chair Petter Kolm, Director

New York University UA private university in the public service

Courant Institute of Mathematical Sciences Mathematics in Finance MS Program 251 Mercer Street New York, NY 10012-1185 Phone: (212) 998-3104; Fax: (212) 995-4195

http://math.nyu.edu/financial_mathematics/

Yongnan Che (347) 205-7711 ■ [email protected]

EDUCATION NEW YORK UNIVERSITY New York, NY The Courant Institute of Mathematical Sciences MS in Mathematics in Finance (Expected – December 2020)

• Coursework in progress: Derivative Securities, Data Structure & Algorithms, Object OrientedProgramming, Black-Scholes, Derivatives Pricing, Ito calculus, Time Series Analysis, MonteCarlo Simulation

University of Wisconsin-Madison Madison, WI B.A. in Mathematics, B.A. in Computer Science and B.A. in Economics (2015-2019)

• Coursework: Data Structures with JAVA, Artificial Intelligence, Intro to Algorithms, ODE,Linear Algebra, Stochastic Process, Probability Theory, Discrete Math

• Award: Veldor Kopitzke Scholarship and Meek Bishop Scholarship in EconomicsEXPERIENCE

Guangzheng Hang Seng Securities Beijing, China Summer Researcher (June 2019 – Aug 2019)

• Conducted research on classifying stocks with their performance using basic machine learningknowledge like SVM, k-Nearest Neighbor, and decision trees, etc. with the help of Pythonpackages

• Performed data analysis and visualization to help draft weekly report using Python packages likeMatplotlib and Plotly

• Implemented a neural network with the help of Python to predict the movement of stocks andexplored its profitability to present to the trading team

Morgan Stanley New York, NY Equity Strategist Assistant (Jun 2018 – July 2018)

• Applied knowledge of Python to perform statistical analysis on different factor datasets such asSMB, HML, and Rm-Rf to visualize the data

• Built factor-based investment strategies and researched trends in risk premium investment• Analyzed factor attributions of unknown equity strategies based on Fama-French-factor model

using rolling regressions to generate potential strategies for our clientsHongta Securitites Shanghai, China Quantitative Analyst Intern (May 2017 – Jul 2017)

• Performed multi-factor regression on the CSI 500 stocks to select best stocks according to theregression model and make a list to present to traders for further analysis

• Cooperated with colleagues to gather market information on stocks for traders• Maintained and improved previous programs that are used to analyze stocks with good

performance and to predict their future movementsPROJECTS

University of Wisconsin-Madison Madison, WI In-class Kaggle competition on entity matching of items between Walmart and Amazon

• Conducted EDA and data cleaning in Python• Utilized useful packages like Regex and fuzzy matching to assign numerical score to pairs of

instances• Applied ensemble method (voting on logistic regression, KNN, and random forest) to predict if

a pair of items in these two stores are the sameCOMPUTER SKILLS/OTHER

Programming Languages: C, C++, Java, Python, R, STATA Languages: Mandarin (native), English (fluent), Spanish (intermediate)

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DAYNE J. FERNANDEZ (702) 321-5887 ■ [email protected]

EDUCATION

NEW YORK UNIVERSITY New York, NY

The Courant Institute of Mathematical Sciences

MS in Mathematics in Finance (expected – December 2020)

• Coursework: Software development for applications in finance, factor and principal-component

models, Black-Litterman, arbitrage, Black-Scholes, Brownian motion, martingales

UNIVERSITY OF CALIFORNIA – LOS ANGELES Los Angeles, CA

BS in Mathematics (September 2014 – May 2018)

• Coursework: Multivariable calculus, differential equations, real and complex analysis, probability

and statistics, computational statistics, advanced programming

EXPERIENCE

STARWORKS ARTISTS Los Angeles, CA

Accounting Assistant (2018-2019)

• Created and billed 15-25 invoices daily for client jobs, accounting for overtime and expenses

• Accommodated requests from clients for invoice revisions efficiently and accurately

• Collected receivables by reaching out to clients for overdue bills, assisting with payment as needed


Undergraduate Researcher (June 2017 – August 2017)

• Developed general mathematical model to analyze crime and patrol strategies in national parks

• Assisted NGA refining patrol strategies to protect most densely forested 5%-10% of national parks

• Collaborated as part of a team of undergraduate students, graduate students, and professors

• Implemented model in MATLAB to create ~ 30 example cases for use in further research

SOTEIRA CAPITAL Las Vegas, NV

Hedge Fund Internship (2016)

• Assisted co-manager with portfolio building to generate 12% - 13% yearly returns, ~ $300M

AUM by executing orders for long and short positions in the 1K-10K volume range

• Researched positions by tracking securities’ price differentials and monitoring financial news

• Implemented algorithm to compute maximum profit and loss on option positions

PROJECTS


Modeling Environmental Crime in Protected Areas Using the Level Set Method (June 2017 – August

2017)

• Developed general mathematical model to analyze environmental crime and patrol strategies

• Applied game theoretic concepts and PDE solving methods to optimize crime prevention

• Implemented model in MATLAB to create ~30 example cases for use in academic paper

• Published in SIAM Journal on Applied Mathematics

SOTEIRA CAPITAL Las Vegas, NV

Python Program to Track Trades and Profit/Loss (2016)

• Compiled a spreadsheet to list trades and track P/L for specific securities for a trading account

• Wrote a Python script to automatically update spreadsheet daily by reading and writing trade data

• Implemented “security” and “trade” as classes in Python, saved each trade and security as an

object to format the data and calculate new P/L

• Generalized code to store different security types (stocks, options, futures) simultaneously

COMPUTER SKILLS/OTHER

Programming Languages: C++, Java, Python, R, SQL

Other Software: MATLAB, Microsoft Office

Certifications: Machine Learning, Stanford University – Coursera; SQL for Data Science, UC Davis –

Coursera

ZHEN GUO [email protected] ■ linkedin.com/in/zhenguo1997 ■ (336) 782-0813

EDUCATION NEW YORK UNIVERSITY New York, NY The Courant Institute of Mathematical Sciences MS in Mathematics in Finance (Expected – Dec 2020)

• Coursework: Stochastic Calculus, Portfolio and Risk Management with Econometrics,Computing in Finance, Scientific Computing, Time Series Analysis & Statistical Arbitrage

WAKE FOREST UNIVERSITY Winston-Salem, NC BS in Mathematics, BS in Finance (Aug 2015 – May 2019)

• Coursework: Multivariable Calculus, Linear Algebra, Abstract Algebra, Real Analysis,Differential Equation, Probability and Statistics, Financial Derivatives, Fixed Income

• Awards: Dean’s List, all semesters; Mathematical Honor SocietyEXPERIENCE

MORGAN STANLEY CAPITAL INTERNATIONAL Beijing, China Intern, Risk Management Analyst (Jun 2018 - Aug 2018)

• Wrote a program for valuation of American styled call option based on European call option,simulated delta hedging of American options with underlying equities using historical prices

• Analyzed risks of equities traded on NYSE and NASDAQ, performed a comparison of historical,parametric and Monte-Carlo methods of calculating VaR and Expected Shortfall

• Performed Markowitz Mean Variance Analysis on a portfolio of bonds and equities in theAmerican, European, and Japanese markets, optimized the portfolio by investing in risk-free asset

• Wrote a program for swap rates and swap positions valuation of interest rate swap, developed ahedging strategy against exchange-rate risk using a currency swap, predicted its opportunity costusing a stochastic differential equation for EUR/USD

• Built an option-based convertible bond pricing model, analyzed VaR and payoff profile toevaluate investment opportunities on convertible bond, wrote a report and presented to manager

CHINA MERCHANT SECURITIES Beijing, China Intern, Industry Analyst (Dec 2017 - Jan 2018)

• Tracked macro trends of green industries in China, maintained communication with leadingcompanies, analyzed prospectuses and industry’s historical data, and wrote a research report

• Evaluate investment opportunities of a target firm by analyzing year-on-year financial ratios,innovations and marketing strategies, accelerated data processing using Excel and Java

GOLDMAN SACHS Shanghai, China Intern, Securities Department (Jul 2017 - Aug 2017)

• Wrote a program that performed a scalping trading strategy and output transactions and payoffs• Developed a cross-market arbitrage model, calculated the optimal margin of safety, and tested the

model using historical data from Shanghai Futures Exchange• Developed a linear model for prediction of trading volumes based on periodicity using R

PROJECTS Local Depth Based Clustering (C++) Winston-Salem, NC

• Formed a parameter-free solution to clustering, which is robust to outliers, entirely deterministicand run three time faster than K-Means, applied the algorithm on classifying S&P 500 componentsfor diversifying portfolio, resulting in portfolios with Sharpe ratios of nearly 1.9

Valuation Modeling (R) Winston-Salem, NC • Developed and validated a real estate valuation model based on property type, age, location and

floors with application to Bootstrap Sampling and Kernel Regression

COMPUTER SKILLS/OTHER Programming Languages/Other Software: Java, C++, MATLAB, R, Python, SQL and LaTeX Languages: English (fluent), Mandarin (native)

FURONG (NATE) HUANG ■ [email protected] ■ linkedin.com/in/nrhuang

EDUCATION




• Coursework: Java object pool, open addressing map, Ito lemma, finite difference method,

Monte Carlo methods, portfolio and risk management, simulation and back-testing strategies,

hidden Markov model

UNIVERSITY OF CALIFORNIA BERKELEY Berkeley, CA

BA in Applied Mathematics & BA in Statistics (2016 – 2018)

• Coursework: Data cleaning, exploratory data analysis, MLE, ridge regression, bias-variance

trade-off, cross-validation, k-nearest neighbor, PCA, regular expression, European options

• Awards: Berkeley Undergraduate Scholarship

EXPERIENCE

OMNIRISKS New York, NY

Quantitative Analyst Intern (January 2020 – Present)

• Build and integrate auto-collection pipelines to retrieve real-time and historical transaction from

swap data repository using python, Azure DevOps and cloud storage

• Clean and analyze the swap transaction data for zero curve construction

• Research on object relational mapping framework and design data management architecture


Recitation Leader (Mathematics of Finance) (September 2019 – December 2019)

• Planned and led weekly sessions to recite key concepts of the course materials for 34 students

• Hosted office hours to answer and break down complex concepts in quizzes and homework

• Worked with the class instructor to develop teaching plan

PROJECTS

UNIVERSITY OF CALIFORNIA BERKELEY Berkeley, CA

NYC Taxi Rides Duration Prediction (Python)

• Leveraged SQL and Python to select and clean related data from a 15 million taxi rides dataset

• Performed Exploratory Data Analysis to gain insights and feature engineered a regression model

with Scikit-Learn pipelines

• Validated model using unselected data with MAE under 4 minutes on average 12 minutes trips

Trump’s Tweets Analysis (Python)

• Structured and cleaned over 10000 raw tweets data from twitter’s API into data frame

• Transformed the test of the tweets into tidy format and performed VADER sentimental analysis

• Identified the most influential words in the tweets by aggregating their average retweet count

Handwritten Digit Recognition (R)

• Structured 5000 handwritten digits’ pixel information for visualization and computation purpose

• Applied K-Nearest Neighbor algorithm and K-Fold Cross-Validation to build a predictive model

• Predicted 5000 unknown handwritten digits with 95% accuracy


Programming Languages: Python (packages: NumPy, Pandas, Scikit-Learn), R, Java, SQL

Languages: Cantonese, Mandarin, English

Certification: CFA Level I

Citizenship: U.S. permanent resident

JINGSHENG (DAVID) HUANG [email protected] ■ linkedin.com/in/jingsheng-david

EDUCATION NEW YORK UNIVERSITY New York, NY The Courant Institute of Mathematical Sciences MS in Mathematics in Finance (expected – Dec. 2020)

• Coursework: Black-Scholes model, portfolio theory, derivatives pricing, stochastics calculus,VaR, Monte Carlo & Financial Programming in Java

• Future Coursework: Algorithm in Trading & Quantitative Strategies, Credits Analytics, Time-Series & Arbitrage

UNIVERSITY OF MASSACHUSETTS AMHERST Amherst, MA B.S in Mathematics, Statistics & Actuarial Science track (Sept. 2015 – May 2019)

• Coursework: ODE, linear algebra, actuarial probability, mathematical statistics, linear regression,numerical analysis, time-series modeling, micro & macroeconomics, Java

• Honors: Dean’s List (4 years), Outstanding Academic Achievement in Actuarial Science (2019)EXPERIENCE

GUOTAI JUNAN SECURITIES Shanghai, China OTC Derivatives Analyst Intern (May 2018 – Aug. 2018)

• Utilized Python to collect market data such as bid, ask prices, and volatility, subsequently reducinginitial 30-minute operations to 5-minute operations

• Applied Black-Scholes Model to construct and modify database of OTC derivatives and computeprices of over 40 kinds of European options of OTC derivates on daily basis

• Analyzed prices of OTC derivatives of barrier options in terms of Monte Carlo simulation, andoptimized prices using different market indexes

• Researched availability of OTC derivatives trading in rural areas independently and effectivelypresented the report to managers and shareholders

HETU EDUCATIONAL AND TECHNOLOGICAL INCORPORATED Guangzhou, China Marketing Analyst Intern (May 2017 – Aug. 2017)

• Assisted brand promotion and seasonal promotion of products and services accounting for 32%of the annual corporate income

• Implemented the linear regression model to predict trend based on over 500 datapoints fromquarterly report, then effectively presented the result to managers

PROJECTS UNIVERSITY OF MASSACHUSETTS AMHERST Amherst, MA Actuaries Student Research Case Study, Society of Actuaries (SOA)

• Utilized Python to coordinate and merge over 2000 data points and built multiple linear regressionmodels with visualizations

• Researched market trend of automatic vehicles including sales, insurance coverage, and futureavailability, then presented recommendations to faculties

Markov Chains & Applications in Pricing Stock • Utilized Python to collect, coordinate and merge two-year financial data from Yahoo Finance and

visualized stock prices distribution• Implemented Markov process on return and volatility of stock and improved with machine

learning algorithm of kernel density approximation on PDE of returnCOMPUTER SKILLS/OTHER

Programming Languages: Python, R, Java Other Software: Microsoft office (Excel VBA), Matlab, Bloomberg Languages: Mandarin (native), English (fluent) Certificate: CFA level I, Exam P, Exam FM


http://www.linkedin.com/in/jingsheng-david

WEI-HAN HUANG (917) 945 9677 | [email protected]

EDUCATION



MS in Mathematics in Finance (expected – January 2019)

Future Coursework: Mean-variance optimization, Black-Scholes formula and applications to

stochastic processes, Quantitative portfolio theory, Monte Carlo and finite difference methods,

dynamic asset pricing model

UNIVVERSITY OF DENVER Denver, CO

BS in Physics, BS in Mathematics with Analysis Distinction (2015-2018)

Coursework: Macroeconomics, topology, analytical mechanics, quantum physics, thermal physics

Early graduation with Honor, Math Department Award, Physics Outstanding Senior Award,

Dean’s Scholarship

LEYSIN AMERICAN SCHOOL INSWITZERLAND Leysin, Switzerland

International Baccalaureate Diploma (2012-2015)

Boarding schools with students from 60+ nationalities, developed independency and worldview

EXPERIENCE

LEYSIN AMERICAN SCHOOL IN SWITZERLAND Leysin, Switzerland

Summer Teacher & Counselor (2019)

Designed and taught science courses, enhanced organization, leadership, and communication skills

MASON INVESTMENTS CO. LTD. Taipei, Taiwan

Assistant Risk Analyst (2019)

A Hong Kong based investment company, the team managed a USD1.3 billion fund

Computed Greeks in Black-scholes model, Monte Carlo and fixed income pricing with Excel VBA

to develop hedging strategy for HIS and S&P 500 options trading

REPUBLIC OF CHINA ARMY Matsu Island, Taiwan

Matsu Defense Command, Private (2018-2019)

Volunteered Deployment: Cadre Training Class, enforced leadership, discipline, time

management, teamwork, and dependability

86 Privates in the whole country were selected, received Combat Readiness Training on a

Taiwanese island territory that is the closest to Mainland China

SOTRACOM AIR TRANSIT PARIS Paris, France

Assistant of Import/Export Analyst (2016)

Logistics cost profit analysis, arranged airlines and forwarders, executed airport customs operation

PROJECTS

UNIVERSITY OF DENVER Denver, CO

Chaotic Circuits

Implemented Fast Fourier Transform as digital processing to convert between analog/digital signal

Read voltages in and out of the computer for fast-time-critical waveform data acquisition

Combine Chua’s diode and the gyrator into Chua’s circuit and observe its chaotic behavior,

modeled using three non-linear ODEs


Programming Languages: Java, Python, R, Visual Basic

Other Software: Mathematica, LabVIEW, PASCO Capstone, LaTex, Microsoft Office

Languages: Mandarin (Native), English(fluent)

YUGE JIANG [email protected] ■ linkedin.com/in/yugejiang

EDUCATION

NEW YORK UNIVERSITY New York, NY The Courant Institute of Mathematical Sciences M.S. in Mathematics in Finance (expected – Dec. 2020)

• Future Coursework: quantitative portfolio theory, stochastic calculus, fixed income & derivatives

RENMIN UNIVERSITY OF CHINA Beijing, China B.A. in Economics (September 2014 – July 2018)

EXPERIENCE

TAIKANG ASSET MANAGEMENT Beijing, China Risk Management Intern (April 2018 – June 2018)

• Constructed performance attribution program based on Barra China Equity Model (CNE5) andCarino (1999) multi-period model, improved accuracy and efficiency

• Created VBA script to retrieve data from Oracle that improved monitoring efficiency• Computed performance and risk indicators, reconciled and reported results for 10+ portfolios• Drafted and published daily reports of risk attribution and performance analyses

QUANTUM FINANCIAL SERVICES Beijing, China Quantitative Analyst Intern (November 2017 – March 2018)

• Initiated automatic factor mining program to develop new factors and filtrate the ineffective ones• Researched 29 industries, enlarged factor pool by devising 300+ financial factors based on different

industry characters• Programmed and tested style factors defined in MSCI 2012 CNE5 report to attribute performance• Studied multi-factor model including factors combination, scoring, effective test and backtest

CDH INVESTMENT Beijing, China Fund Operation Intern (July 2017 – October 2017)

• Devised Excel Macros to capture and align inconsistent data from 10 + custodian banks• Created SSIS packages to formalize and import custodian data into SQL Server, and reconciled

data• Analyzed and reported yield rates of 20+ portfolios on weekly basis

PROJECTS

RENMIN UNIVERSITY OF CHINA Beijing, China Python Realization of Risk Parity Strategy

• Selected stocks from Shanghai Stock Exchange 50 Index by scoring with French-Fama threefactors, estimated weights using risk-parity strategy, and constructed a portfolio

• Achieved a 20% higher accumulative rate of yield compared to SSE 50 Index as benchmark whenbacktesting from January 2010 to October 2017 (NOT dates but months)

Python Realization of Binomial Trees • Adopted payoff function to compare current strike price and European option price by object-

oriented American call option Tree• Compared simulated prices and B-S solutions and observed prices of American options computed

converges and are higher than those of European options


Computer Skills: Python, Java, C++, VBA, SQL, MATLAB Languages: Chinese Mandarin (native), English (fluent)

BOYI LI [email protected] ■ www.linkedin.com/in/boyi-li


Current Coursework: Quantitative portfolio theory, interest rate derivatives, Monte Carlo and finitedifference methods, fixed income and currency derivatives, stochastic calculus, OOP, option pricing

Future Coursework: Portfolio management, continuous time finance, algorithmic trading, marketmicrostructure, scientific computing, advanced econometrics, data science in quantitative finance

UNIVERSITY OF TORONTO Toronto, Canada BASc in Engineering Science major in Mathematics, Statistics and Finance with Honor (2014 – 2019)

Coursework: Financial optimization, stochastic processes, linear regression, stochastic methods infinance, statistical learning, deep learning, Monte Carlo methods, data structures and algorithms

EXPERIENCE UNIVERSAL PORTFOLIO Toronto, Canada Quantitative Research Intern (June 2018 – Nov. 2018)

Set up and managed database and web-scrapped financial data of 100+ cryptocurrencies per minute Compared financial data from Huobi and Gate.io per second to search for arbitrage opportunities Conducted research on price movements of cryptocurrencies with respect to financial factors Created Volatility-Timing portfolio using cryptocurrencies and evaluated the performance in Python

KING MONGKUT’S UNIVERSITY OF TECHNOLOGY THONBURI Bangkok, Thailand Research Intern (May 2017 – August 2017)

Proposed and implemented disturbed labels in Python to add regularization to loss layer of deepneural network to alleviate the overfitting problem when small size facial expression dataset is used

Recommended a tool to visualize stock price by parallel coordinates and data envelopment analysis Published two papers at IES 2017 and VINCI 2017 of above topics at the end of the internship

PROJECTS UNIVERSITY OF TORONTO Toronto, Canada Statistical Arbitrage Strategy

Formulated a mathematical model to derive high-frequency statistical arbitrage strategy when short-term drift of asset is observed and used stochastic control approach to address the problem

Applied finite difference method to numerically solve nonlinear PDEs to obtain the optimal strategy Simulated 1000+ paths of trading strategy and evaluated the right-skewed distribution of the profit

Robo-Advisor for Personal Financial Management Designed a decision support system in the form of web application by creating an optimal portfolio

that consisted of 8 assets under uncertainty that meets the liabilities and financial goals of users Implemented stochastic programming, goal programming model to generate optimal investment

portfolio in Python. The portfolio overperforms S&P 500 when market shock happensExploration of Markov Chain Monte Carlo Algorithms – Undergraduate Thesis

Systematically reviewed and comprehended theories of MCMC and adaptive MCMC Explored applications of MCMC, including Metropolis-Hastings algorithm, Gibbs sampler,

Adaptive MCMC, Tempered MCMC, Transdimensional MCMC and simulated annealingReaching Bequest Goal under Ambiguity Aversion – Undergraduate Thesis

Determined robust optimal investment strategy for an individual, who invested in a Black-Scholesmarket, to reach a bequest goal under hazard rate ambiguity and asset drift ambiguity

Used stochastic control approach to solve the model and numerically solved non-linear ODEs inMATLAB, analyzed investment behaviors based on strength of ambiguity aversion

COMPUTER SKILLS/OTHER Programming Languages: Java, Python, R, MATLAB, SQL Other Software: Mathematica, Latex Languages: Mandarin (native), English (fluent)

ZHENGXU (ANDREW) LI [email protected] ■ linkedin.com/in/zhengxu-li

EDUCATION



MS in Mathematics in Finance (expected – Dec. 2020)

• Current Coursework: derivative pricing, Black-Scholes formula and applications to stochastic

processes, Greeks, CAPM, mean-variance optimization, Fama-French 3-factor model, VaR, Black -

Litterman, stress testing, Monte Carlo simulation, OOP in Java, test-driven development (TDD)

• Future Coursework: algorithmic trading, machine learning, data cleaning, applications of big data

to finance, time series analysis, advanced econometrics


BA in Mathematics and Computer Science (Sept. 2014 – May 2018)

• Coursework: probability, statistics, calculus, data structures and algorithms (Python, Java), dynamic

programming, linear algebra, scientific computing, ODEs, discounted cash flows

• Honors: Phi Beta Kappa, Magna Cum Laude

EXPERIENCE

PLUSPLUS CAPITAL MANAGEMENT Jersey City, NJ

Quantitative Research Intern (June 2018 – July 2018)

• Conducted statistical analysis in R & Excel to investigate effectiveness of various metrics (Sharpe

ratio, Calmar ratio, max drawdown) as predictors of funds’ future performance; analysis showed that

the (worst-month return / best-month return) ratio best predicts future performance

• Built a model for Fund of Funds to predict fund performance and identify potential graveyard funds

• Cleaned and merged large sets of market raw data from 1991 to 2017


Researcher, Advisor: Prof. Robert V. Kohn (May 2017 – Sept. 2017)

• Investigated calibration of Ross Recovery Theorem to market data and its practical value, then

published a 20-page paper in SIURO and assisted in presenting research at SIAM CSE conference

• Key contribution: reduced noise by reformulating the optimization problems in the existing

mathematical model, then implemented the new model in MATLAB and conducted robustness test

• Examined effectiveness of the theorem by analyzing expectations, skewness, and correlations of the

SPX index distributions, and by back testing theorem-based trading strategy optimizing log-return

• Processed market data from Bloomberg, such as S&P 500 futures, options, and Treasury yields

CISDI ENGINEERING CO., LTD. Chongqing, China

Technology Summer Intern (June 2016 – Aug. 2016)

• Contributed to model-view-controller structure by adding data query-and-summary function in Java

• Offered advice for service enhancement by conducting statistical analysis of user data in Excel

• Managed code version control using Github; debugged applications with other developers

PROJECTS

Quantitative Futures Trading Strategy (Jan. 2019 – Apr. 2019)

• Codesigned futures trading strategy based on Bollinger bands and MACD; investigated its

profitability on products, such as palm oil and iron ore (36% return and 14% maximum drawdown)

The Mathematical Contest in Modeling (MCM), Meritorious Winner (Jan. 2017)

• Built a probability-based model involving car speed and reaction time to simulate the traffic flow

• Analyzed traffic throughput and cost efficiency in MATLAB, then offered advice on highway design


Programming Languages: Python (2 years), Java (4 years), MATLAB, R, C

Other Software: Bloomberg Terminal, Github, LaTeX

Languages: Mandarin (native), English (fluent)


http://www.linkedin.com/in/zhengxu-li

YIFAN (EVAN) LI ■ [email protected] ■ linkedin.com/in/yifan-li951128

EDUCATION



MS in Mathematics in Finance (expected - Dec.2020)

• Current Coursework: OOP and Data Structure in Java, Monte Carlo simulation, Brownian motion

& diffusion process, B-S Model, derivative pricing & hedging, VaR, cVaR, factor model, APT

• Future Coursework: Interest Rate & FX Model, portfolio theory, algorithmic trading, data science

SUN YAT-SEN UNIVERSITY Guangzhou, China

BMngmt in Financial Mngmt & BS in Mathematics (Sep.2014 - Jun.2019)

• Coursework: C++, Data Structure and Algorithms, calculus, Taylor's Expansion, matrix analysis,

eigenvalue, regression, probability, ODE, Basic PDE, derivatives, FX and interest rate, Investment

• Awards: Second Class Scholarship, Individual Scholarship on Social Activities

EXPERIENCE

City University of Hong Kong Hong Kong, China

Research Assistant (Jul.2018 - Sep.2018)

• Applied Python to implement a new back-testing platform, according to the characteristics of

cryptocurrency data, to support research on efficient factor signals in the cryptocurrency market

• Devised a 1000+ line code of computation formulas with Python for over 100 technical factors,

then calculated the factor value of each stock under new dynamic weight method

• Implemented back test on factor data calculated by different stock price adjustment methods and

made comparison on the results for testing the impact of adjustment method on back-testing result

Hongshu Technology, Quantitative Research Division Shenzhen, China

Quantitative Analyst Intern (Jan.2018 - Mar.2018)

• Utilized Python and Myquant terminal to create index-calculating functions of a variety of futures

• Implemented factor strategies with methods: Elastic Net, Random Forest and Stepwise Regression

Huarong Securities, Fund Management Division Beijing, China

Research Intern (Jul.2017 - Sep.2017)

• Utilized Excel and Python to process and analyze financial data including important accounting

subjects and market information, estimated increasing rate of the Lithium Battery industry.

• Used WIND and Excel to estimate the growth rate and future stock price based on my market

prospect and accounting analysis; implemented sensitivity analysis on estimated stock price

PROJECTS

Thesis: Time-varying Characteristics of the Factor Efficiency (Python) Guangzhou, China

• Analyzed the time-array of spearman correlation coefficient for each factor to preliminarily

determine whether the factor is sustainably efficient

• Employed one-sample T-test and two-sample T-test in the paper to obtain more objective results

about the time-varying efficiency of different factors

• Leveraged the out-sample data to build a simple investment strategy and checked the result

Urban Charging Stations Planning (2018 ICM/MCM Contest) (Python&Matlab) Guangzhou, China

• Applied graph theory to decipher the most efficient charging station deployment

• Created an optimization model to minimize the construction cost and the time cost of consumer

• Introduced logistic function to decide the timeline of how to build enough charging station in a city


Programming Languages: Python, Java, C++, SQL

Other Software/Tools: Jupyter Notebook, MATLAB, Excel, WIND (finance terminal)



https://www.linkedin.com/in/yifan-li951128

SEBASTIAN LINDSKOG [email protected] ■ linkedin.com/in/sebastianlindskog/


● Current coursework: Scientific computing with Java, stochastic calculus, portfolio management● Future coursework: Fixed income derivatives, algorithmic trading, scientific computing,

simulation techniques, time series analysis, data science in quantitative financeCOLLEGE OF CHARLESTON Charleston, SC BS in Finance (2010-2014)

● Coursework: Computer programming, data mining, differential & multivariate calculus, datavisualization, financial institutions & bank management, probability theory, linear algebra

EXPERIENCE CLOVER LIGHT, LLC Mt. Pleasant, SC Senior Trader (2014-2019)

● Trading strategy operator at a proprietary trading company that provides substantial liquidity onthe world’s major derivative exchanges, market making in a wide variety of asset classes

● Operated all company trading strategies by adjusting strategies in relation to liquidity, volatility,and order flow. Carefully analyzed the risk to reward for each trade

● Developed trading tools in Python to help myself and other traders enhance performance● Performed PnL analysis to optimize trading strategy configurations and identify new opportunities● Communicated with multiple exchanges to reconcile open positions, orders, and connectivity● Proficient with electronic trading platforms. Occasionally, manually traded out of positions● Trained three traders and wrote two in-depth guides on the firm’s trading strategies

PROJECTS CLOVER LIGHT, LLC Mt. Pleasant, SC Custom Depth-of-Market

● Developed a command line application in Python using the curses library, which displays a custom order book tailored to the firm’s trading strategy

● Assisted traders minimize risk and identify more trading opportunitiesTrading Simulations

● Developed event driven simulations of trading strategies in Python using tick market data● Contributed to adjusting current strategies to improve profitability● Tested new trading strategy ideas

Historical Market Data Analysis ● Developed a historical tick market data analysis tool to thoroughly breakdown dangerous trading

situations in order to help reduce risk and increase trader situational awarenessBARUCH COLLEGE / QUANTNET New York, NY Options Pricing Tool

● Developed an options pricing tool in C++ using object oriented programming, STL, and boost● Calculated option prices using Black-Scholes, Monte Carlo simulation, and FDM

COMPUTER SKILLS/OTHER Programming Languages: C++, Java, Python, R Other Software: Atlassian Suite, Git, Linux, Mathematica, Microsoft Suite, Wekka Certificates: Baruch College / QuantNet C++ for Financial Engineering - with Distinction Sports: Competed in Charleston Ocean Racing Association on sailing vessel Direction (2014-2019)

SHIJIA (SCARLETT) LIU [email protected] ■ linkedin.com/in/shijialiu/

EDUCATION NEW YORK UNIVERSITY New York, NY

The Courant Institute of Mathematical Sciences MS in Mathematics in Finance (expected – Dec. 2020)

• Current Coursework: Risk and Portfolio Management (VaR, CAPM, PCA), OOP in Java, Monte Carlo Simulation, Stochastic Calculus, Swaps, Interest Rate Models, Option Pricing and Greeks

UNIVERSITY OF MINNESOTA – TWIN CITIES Minneapolis, MN BA in Mathematics (May 2019)

• Coursework: Markov Chain, Merton’s Risk Neutral Pricing Method and Black-Scholes Formula, Optimization, Dynamic and Probability Models, Numerical Analysis, Heat and Wave Equation

• Awards: High Honors in Mathematics, High Distinction in Degree, Dean’s List EXPERIENCE BANK OF CHINA Shanghai, China

Corporate Finance Summer Analyst (Jun. 2018 – Aug. 2018) • Conducted equity research in R.Y.B Education Company and wrote research reports for initiations

of coverage, proprietary databases and tailored industry news • Analyzed data by using R and built financial models to evaluate existing and potential projects of

the company and then developed unique insights by leveraging this data • Integrated income statement, balance sheet and cash flow statements as well as performed Ratio

Analysis to evaluate the company’s financial performance HSBC Hong Kong, China

Financial Advisor Assistant (Jun. 2017 – Aug. 2017) • Illustrated currency-linked deposits to retail investors and provided suggestions based on their

financial situations, investment experience and investment objectives • Performed scenario analyses based on exchange rate movements and various risk disclosure

PROJECTS Portfolio Optimization (Python)

• Created the portfolio object by using Mean-Variance method based on data of stocks in Quandl • Estimated covariance and correlation matrix on stock returns. Found the portfolio that maximizes

Sharpe ratio and analyzed its risk and return • Visualized the efficient frontier for portfolio objects with different constraints to compare and

analyzed the portfolios’ performances by Treynor Measure • Strengthened the portfolio by applying Black-Litterman approach and compared their sensitivity

Option Strategy Trading (Python) • Constructed strangle option strategy for Alibaba based on speculation of large price fluctuation and

facilitated the trade at Investopedia with desired strike price • Simulated in Python with stock price movement following random walk with large volatility • Analyzed resulting profits/losses and performed sensitivity analyses with varying variables

Applications of SVD in PCA, Latent Semantic Indexing and Collaborative Filtering (MATLAB) • Performed PCA by using SVD on the covariance matrix to reduce the dimensionality of a dataset. • Implemented SVD on Collaborative Filtering used in the recommender systems to make automatic

predictions, and Latent Semantic Indexing to overcome synonymy and polysemy in the text • Realized the procedure of SVD in MATLAB and plotted stairstep graph of the cumulative sum of

singular values to visualize the number of principal components which explain the majority of data COMPUTER SKILLS/OTHER Programming Languages: Java, Python, MATLAB, R

Other Software: Bloomberg, Microsoft Office Languages: Mandarin (native), English (fluent)

ZHILIN LIU [email protected] ■ www.linkedin.com/in/zhilinliu1


The Courant Institute of Mathematical Sciences MS in Mathematics in Finance (expected - Dec. 2020)

Current Coursework: Stochastic Calculus, OOP in Java, Time Series, Statistical Arbitrage, Risk and Portfolio Management with Econometrics

Future Coursework: HMMs, Markov Chain Monte Carlo methods, EM algorithm, alternative data, unsupervised/supervised machine learning, FX models, interest rate models

UNIVERSITY OF CALIFORNIA, IRVINE GPA: 3.894/4.0 Irvine, CA BS in Mathematics with concentration on Finance (June 2018) BA in Quantitative Economics (June 2018) Minor in Statistics

Coursework: Logistics Regression, GLMs, Econometrics, Ito’s Lemma, Brownian Motion, Derivatives pricing, Hedging, Numerical Analysis, Probability, Linear Algebra, ODEs, PDEs

EXPERIENCE BANK OF CHINA INTERNATIONAL CO., LIMITED Shanghai, China

Investment Banking Analyst Internship, Investment Banking Division (Nov. 2018 - Jan. 2019) Assisted IPO team with due diligence, by reading financial and accounting statements Conducted research by using WIND and client’s annual reports, and assisted with the client

company’s capital operations plan MORGAN STANLEY CAPITAL INTERNATIONAL Beijing, China

Part-time Assistance Internship, Risk Management Division (Sep. 2017 - Oct. 2017) Provided support for sample data generating, variance minimization and linear transformation

using Python and R, and solved problems on PDEs and SDEs Processed BS model, Monte Carlo simulation and 10-day 99% VaR estimation using Python/R Helped to implement algorithms and to reproduce the results in Statistical Arbitrage in The US

Equity Market by M. Avellaneda and J.-H. Lee.

PROJECTS UNIVERSITY OF CALIFORNIA, IRVINE Irvine, CA

Volatility in Stock Market - Econometrics project with R (Winter 2018) Researched Time-varying mean process, AR/ARDL, Dickey-Fuller test, heteroskedastic errors Explained the various perspectives of the leptokurtic fat-tailed nature of real stock returns Identified ARCH effect in monthly returns of the US S&P 500 by Lagrange multiplier test, then

compared estimated ARCH, GARCH, T-GARCH, GARCH-in-mean models using R Proposed investment plans with five risk indicators, forecasted conditional volatility, and return

Numerical Analysis and Algorithm - MATLAB project (Fall 2017) Implemented Power Method, built algorithms for Jacobi/GS/SOR methods with MATLAB Discovered the relation among spectral radius, matrix size and speed of convergence

Anteater Bed and Breakfast - Python project (Spring 2017) Programmed a hotel room reservation system with strong user interface using Python

Calculus on Manifolds - Advanced Mathematics Research (Fall 2017 - Spring 2018) Led a team to discover omissions and presented improved proofs from Calculus on Manifolds:

A modern approach to classical theorems of advanced calculus by Michael Spivak


Programming: Java, R, Python, MATLAB, Stata Languages: English, Mandarin (native)

ZIHAN (PETE) LIU Ph: (646)-321-6854 | Email: [email protected] | www.linkedin.com/in/pete-zihan-liu

EDUCATION



MS in Mathematics in Finance (expected - Dec. 2020)

• Current Coursework: Financial computing in Java, dynamic asset pricing modelling, volatility

modelling, Black-Litterman modelling, interest-based derivatives, Black-Scholes PDEs, Feynman-

Kac and Cameron-Martin Formulas

• Future Coursework: Data science in quantitative finance (various optimization methods and high-

dimensional supervised-learning problems in finance), interest rate related derivatives in bonds,

swaps, flow options and other structured products

AUSTRALIAN NATIONAL UNIVERSITY Canberra, Australia

BS in Mathematics & BS in Finance (Feb. 2015 - Dec. 2018)

• Coursework: Advanced Derivatives Pricing Theories and Models, Continuous Time Finance,

Corporate Finance, Investment, Stochastic Processes, Probability Theory and Modelling, Real

Analysis, Topology and Hilbert Spaces, Statistical Learning, Numerical Analysis

UNIVERSITY OF SOUTHERN CALIFORNIA Los Angeles, CA

Global Exchange Program (Aug. 2017 - Dec. 2017)

• Coursework: Financial Valuation and Analysis, Applied Finance in Fixed Income Securities

EXPERIENCE

EARNEST EDUCATION Canberra, Australia

Academic Tutor (Dec. 2017 - Dec. 2018)

• Prepared lecture style review sessions for first-year finance and mathematics classes and provided

1-1 academic tutoring catered to needs of 5-10 first-year students

• Designed mathematical and finance problem sets and revision materials for investment, derivative,

and calculus lectures

CHINA MERCHANTS BANK Guangzhou, China

Winter Internship at Personal Retail Service Department (Dec. 2015 - Feb. 2016)

• Promoted sales of credit card to clients and streamlined transaction process for clients

• Maintained client relationship between banks and high net-worth individuals through working with

senior financial advisor to develop customized investment plans and close monitor of the investment

portfolio to achieve yields maximization to clients as well as risk protection

PROJECTS

AUSTRALIAN NATIONAL UNIVERSITY Canberra, Australia

High Dimensional Density Estimation with Sparse Grids (Feb. 2018 - Jul. 2018)

• Studied and researched on the sparse grids algorithm to explore the possibilities of its application in

high dimensional density estimation calculation (speed and accuracy trade-off)

• Implemented image recognition to newly augmented model and gained a better (7%) accuracy while

maintaining the same magnitude of time consumption

Gaming Bot in Kalaha (Apr. 2016 - Jun. 2016)

• Researched and implemented alpha-beta pruning algorithm with a creative heuristic function

(gaming strategies that depend on the phases of the game) in Haskell to construct a gaming bot to

compete among 500 gaming bots with potentially different strategies

• Ranked 37th in the final try-out, which includes 10 lecturer-created bots and 10 tutor-created bots

and gained deeper understanding of data structures and algorithmic optimization


Programming Languages: Python (Proficient), R (Intermediate), Haskell (Intermediate), Java (Basic)

Other Software: Microsoft Office, MATLAB, Bloomberg, CapIQ

Languages: Mandarin Chinese (Native), English (Fluent) and Cantonese (Fluent)

YI (BRIAN) SHAN [email protected] ■ www.linkedin.com/in/yishan-brian/



• Current coursework: Stochastic Calculus, OOP in Java, interest rate models, GARCH family UNIVERSITY OF WATERLOO Waterloo, ON

BMATH in Computer Science, Mathematical Finance, Computational Math. (Jan. 2015 – Apr. 2019) • Coursework: option pricing (Black-Scholes, finite difference), stochastic process (DTMC, Poisson

Process, CTMC), quantitative ERM (VaR, CVaR, EVT, copulas), fixed income security, corporate finance theory (MPT, CAPM, APT), statistical learning (Bayes, regression, SVM, K-nearest neighbors, DNN, CNN, boosting), data structure (heap, AVL, tries, hashing, range-searching, string matching), algorithm (divide & conquer, DP, greedy, BFS, DFS, NP), operating system

EXPERIENCE MENGXI INVESTMENT MANAGEMENT CO. LTD Shanghai, Shanghai

Quantitative Analyst Summer Intern, Stock Alpha Group | Python, SQL, VBA (May 2019 – Aug. 2019) • Led two interns to reimplement data cleaning and loading framework; optimized performance by

85% through modifying searching algorithms & data structures and applying concurrent running • Script programmed to calculate performance metrics (annual return, variance, max drawdown,

downside deviation, Sharpe & Sortino Ratios), and to automatically generate monthly reports • Researched on stock trading volume and prices; identified two short strategies by pattern analysis

PROJECTS UNIVERSITY OF WATERLOO Waterloo, ON

VIX FUTURE TIME SERIES TRADING STRATEGY | Python (Dec. 2019 – Dec. 2019) • Applied maximum likelihood estimation approach to fit 1990 - 2013 VIX daily training data on

ARIMA, ARIMA-GARCH, ARIMA-GJRGARCH, Heston model and O-U process; selected O-U process as the best model with lowest AIC result by using 2014 -2016 VIX daily validation data

• Built a weekly trading strategy of long or short VIX futures based on the mean and standard deviation of 5-day’s close-to-maturity predicted distribution using Monte Carlo method

• Tested strategy on 2017-2019 VIX futures; generated 3-year return: 361.2% and sharp ratio: 20.40 HIGH-DIMENSIONAL ANALYSIS OF STOCK RETURNS | R (Mar. 2018 – Apr. 2018)

• Implemented & calibrated GARCH-Gaussian Copula model with different marginal distributions and Regime Switch Lognormal Model to predict future returns of 45 stocks from S&P 500 index

• Formulated portfolio by applying Modern Portfolio Theory, investigated prediction ability of two models by comparing p-value errors in their forecast distributions using Monte-Carlo simulation

IN-CLASS KAGGLE COMPETITIONS | Python (Jan. 2018 – Apr. 2018) • Compared logistic regression, random forest in predicting annual residents’ income (Acc: 87.4%) • Conducted digital recognition from 100,000 street-scene pictures using CNN (Acc: 92.9%) • Collaborated in a NLP project: classifying online toxic comments by applying sentence cleaning,

TF-IDF with various ML models (MultinomialNB, XGBoost, SVM, DNN). (Acc: 97.7%) OPTION PRICING | MATLAB (Jan. 2017 – Apr. 2017)

• Applied binomial lattice, Monte Carlo, finite difference in pricing European & American options • Implemented several functions for option pricing by assuming underlying assets follow Black-

Scholes, local volatility, Heston stochastic volatility and jump diffusion model CHAMBERCRAWLER 3000 – A Rogue-Like Game | C++, GIT, Shell (Mar. 2016 – Apr. 2016)

• Collaborated in a group of two people on implementing classes and methods for various combat supplies, characters and enemies with distinct abilities by applying OOP and observer design pattern

• Designed storylines, player’s operations, combat mechanisms and map printing from command line COMPUTER SKILLS/OTHER Programming Languages: C & C++ (4 years), Python (3 years), R (3 years), SQL (3 years), LaTeX (5

years), MATLAB (2 years), GIT (4 years). C#, Java, VBA, Shell script (less than 1 year)

KEXIN (COCO) SHAO [email protected] ■ linkedin.com/in/kexin-coco-shao

EDUCATION NEW YORK UNIVERSITY New York, NY The Courant Institute of Mathematical Sciences MS in Mathematics in Finance (expected - Dec. 2020)

• Current Coursework: OOP in Java, Black-Scholes model, Greeks, one-factor interest rate models,VaR, linear regression, stress testing, covariance and correlation matrices, CAPM and factormodels, Brownian motion, diffusion processes

MARIETTA COLLEGE Marietta, OH BS in Math and BA in Economics & Music (2015 - 2019) GPA 3.83/4.0

• Awards: Magna Cum Laude, Math & Music Capstone Award, Theodore Bennett Prize in Math,Lewis-Riggs Business & Economics Scholarship, First Chair Percussionist

EXPERIENCE BBVA Hong Kong, China Global Finance Summer Analyst (Summer 2019)

• Analyzed clients’ leverage and balance sheets, determined their profit model and business risks• Recalculated VaR to update clients’ credit limits and investigated industrial credit risk• 6 clients’ annual financial programs were analyzed, 2 corporate loan contracts completed

LENOVO Beijing, China Operational Summer Analyst, Enterprise Cloud Services Department (Summer 2018)

• Constructed Lenovo Cloud internal data platform, organized product usage and sales data with SQL • Devised business plan, examined market structure, and analyzed competitors’ business model• Records since 2015 were allocated into data platform, business plan resulted in 5 contracts

MASSMUTUAL FINANCIAL GROUP New York, NY Financial Advisory Intern (Summer 2017)

• Analyzed clients’ financial situations and attitudes towards risk• Conducted marketing research, maintained Salesforce database, and coordinated client acquisition• Organized over 7000 new prospect records and built relationships with over 200 new prospects &

clients, then created 24 insurance & investment proposals, and closed 11 casesPROJECTS

New York University New York, NY Machine Learning: K-Means Clustering

• Compared two different K-means clustering algorithms with random initialization• Implemented metrics to evaluate and compare performance of the 2 algorithms

MARIETTA COLLEGE Marietta, OH Pricing the European Call Option with the Black-Scholes Formula

• Verified the properties of normal and lognormal distributions• Priced the European call option by applying the Black-Scholes formula

Why Do Some People Live Longer Than Others? A Cross-National Regression Analysis • Investigated impact of life expectancy at birth by OLS using EViews• Constructed regression model by identifying dependent variables, organized cross-sectional data of

50 different countries, and tested for multicollinearity and heteroskedasticityQuantitative Analysis of EconFantasy Football League

• Operated a virtual professional football team, applied the dynamic pricing model to set the ticketand concession price, analyzed market demand of team’s city and generated sponsorship

• Ran regressions on players’ sports performance to manage trades deals, analyzed balance sheet tomaximize profit and wins, ranked the 4th out of 12 team with highest profit in the league

COMPUTER SKILLS/OTHER Programming: Python (3 years), Java(1 year), SQL(2 year), C++ (1 year) Other Software: Bloomberg (BMC), EViews (2 years) Languages: Mandarin (native), English (fluent)


http://www.linkedin.com/in/kexin-coco-shao

CHENHAO (AUSTIN) SU ■ [email protected] ■ https://www.linkedin.com/chenhao-su ■ New York, NY


The Courant Institute of Mathematical Sciences MS in Mathematics in Finance (Sep.2019 – Dec. 2020)

• Current Coursework: High frequency trading data processing in Java, data structure and algorithms in Java, Stochastic Calculus, Risk Management, common derivatives

• Future Coursework: Algorithm trading, Black-Scholes formula and applications, simulation techniques in finance, Scientific Computing

EAST CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY Shanghai, China Bachelor of Science in Math and Applied Math; Minor in Finance (Sep. 2015 – Jul. 2019)

• Coursework: Probability Theory, solutions of common ODEs, Statistics, C++ programming skills, Application of Database(SQL), Numeric Analysis(MATLAB), classic questions in Operational Research, Mathematical Modeling

EXPERIENCE CHINA MERCHANTS SECURITIES Shanghai, China

Quantitative Analyst Summer Intern (Jul. 2018 – Aug. 2018) • Calculated dividends and added it into the company’s factor model, then rewrote the company’s

alpha-strategy in Python, increasing the strategy’s rate of return by approximately 10% • Modified an event-driven trading strategy by adding “path skewness” variable into the

mathematical model to control its risk, making successful rate reach 90% WANLIAN SECURITIES Chongqing, China

Researcher Summer Intern (Aug. 2017 – Sep. 2017) • Sorted annual reports of Chongqing Landai Powertrain Corp.,ltd. to perform due diligence • Abstracted all important data such as profit percentage and debt ratio from balance sheet, cash

flow statement and income statement to write a risk evaluation report PROJECTS EAST CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY Shanghai, China

Using Gaussian Mixture Model to cluster cell data(MATLAB) (Sep. 2018 – Jul. 2019) • Improved original Gaussian Mixture Model in the context of truncated and censored data, and

increased the accuracy of parameter estimation by at least 15% • Applied Gaussian Mixture Model to clustering cellular flow data in flow cytometry, getting

reliable results whose error were controlled under 5% Fama-French five-factor model in China Mainland Stock Market (Sep. 2018– Feb. 2019)

• Tested whether the Fama-French five-factor model can describe stock prices in the context of China Mainland Stock Market or not

• Developed a five-factor model by replacing the profitability factor with the volume factor, which is applicable to the China Mainland Stock Market

Team Leader in China Undergraduate Mathematical Contest in Modeling (Sep. 2017) • Led a team of three to build a pricing model for a deliver service based on k-means clustering

algorithm and greedy algorithm • Implemented the model in C++ and increase the users of this service by approximately 20% • Got the third prize among hundreds of teams

COMPUTER SKILLS/OTHER Programming Languages: C++, Java, Python, SQL

Other Software: Bloomberg, MATLAB Languages: Mandarin (fluent), English (fluent)

https://www.linkedin.com/in/chenhao-su/

SONG TIAN 25 River Drive South, Jersey City, NJ | [email protected] | (917) 392-4140

EDUCATION New York University – Courant Institute of Mathematical Sciences New York, NY Master of Science in Mathematics of Finance September 2019 – January 2020

• Relevant Coursework: Quantitative Portfolio Theory, Interest Rate Derivatives and One-factor Models,Monte Carlo and Finite Difference Methods, Fixed Income and Currency Derivatives, Black-ScholesFormula and Applications to Stochastic Processes

Sun Yat-Sen University Guangzhou, China Bachelor of Science in Mathematics & Applied Mathematics September 2015 – July 2019

• Cumulative GPA: 4.0/4.0• Honors: University Scholarship for Outstanding Students (3 years), KPMG Elite Program• Relevant Coursework: Mathematical Analysis (Calculus), Functional Analysis, Complex Analysis, Linear

Algebra, Matrix Analysis, Ordinary & Partial Differential Equations, Numerical Analysis, Abstract Algebra,Theory of Probability, Mathematical Statistics, Numerical Statistics, Data Structure & Algorithms, ArtificialIntelligence & Neural Network

EXPERIENCE Sun Yat-Sen University Guangzhou, China Research Assistant – Department of Finance June 2018 – June 2019

• Built the Systematic Risk Contribution Index by using Lagrange Multiplier and KKT conditions andcollected the data of counterparty debt of 12 European countries using Python

• Completed the index computation and contagious path simulation of European Debt Crisis using MATLAB

Nine Courser Asset Management Guangzhou, China Quantitative Summer Analyst July 2018 – September 2018

• Built a Post-Earnings Announcement Drift Factor to increase the back-tested annual return rate andSHARPE ratio and decrease maximum drawdown of the company’s stock selection strategy

• Optimized the factor selection strategy with feature selection through adopting the false discovery rate, falsepositive rate, k-best and through training Random Forest, xgBoost, LASSO, SVM, KNN, and RNN

• Implemented and tested the risk parity, equal weight, mean variance, maximum diversification, and industryrotation strategies on the China A-Shares market

KPMG Guangzhou, China Audit Intern January 2018 – March 2018

• Prepared cash flow statements of 3 companies across industries by verifying the company’s balance sheetsand source documents and calculating depreciation and amortization of operating facilities and vehicles

• Verified the clients’ inventory and bank conciliation statements through phone calls and on-site inspections

Wanlian Securities Guangzhou, China Debt Capital Markets Summer Analyst July 2016 – September 2016

• Prepared pitchbook materials by researching into the pros and cons of the subordinate corporate bond andanalyzing the previously issued bond types and sizes as well as competitors’ pitchbook materials

• Performed due diligence on the three financial statements of a chemical company based in Hunan Province• Verified the accuracy of 30+ project drafts based on regulation files of corporate bond issuance

LANGUAGES, SKILLS & INTERESTS Languages: Mandarin (Native), Cantonese (Professional Proficiency) Technical Skills: C++, Python, R, MATLAB, Microsoft Office Suite, SPSS, Wind (Chinese Bloomberg) Personal Interests: Travel, Basketball, Swimming, Reading (Walden)

ZETIAN (SIMON) SUN (346) 232-1726 ■ [email protected] ■ www.linkedin.com/in/ttss

EDUCATION

New York University, The Courant Institute of Mathematical Sciences New York, NY

MS in Mathematics in Finance sSep 2019 – Dec 2020

• Accomplished Coursework: Brownian Motion, Ito Lemma, Markov Chain, Martingale, Monte Carlo, PCA,

ARIMA, Garch, State-Space, Penalty Regression, Cross-validation, Gradient Descent, Bootstrap, Bagging

• Ongoing Coursework: Heston, SABR, Local Volatility Model, Structured Product and Exotic Option Pricing

BA in Liberal Arts Study; Concentrations: Mathematics and Computer Science sSep 2015 – May 2019

• GPA/Honors: 3.9/4.0, Deans List (2015-2019)

• Coursework: Real Analysis, Newton Iteration, VaR, Black-Scholes Model and Greeks, Bi/Tri-nomial Pricing,

Implied Volatility, Black-Litterman Portfolio Optimization, Basic Algorithm and Data Structures, OOP in Java

EXPERIENCE

ZheShang Securities Asset Management Shanghai, CN

Quant Research Intern on Volatility Index and Arbitrage (Python) sJun 2019 – Jul 2019

• Cleaned the last 3 years’ ETF50 option data collected through API, reproduced the Chinese volatility index

iVIX within an average error of 2.23%, and identified a way to reduce the error by including dividend yield

• Compared (i)VIX with ETF50’s historical volatility data to test the volatility prediction accuracy of (i)VIX

and conducted numerical analysis (Riemann integral with Euler method) to calculate the error bound of them

• Simulated the put-call parity arbitrage strategy on Chinese ETF50 options and determined that such strategy

was not applicable in the Chinese option market due to liquidity inefficiency and ephemeral arbitrage signals

Bibox Exchange New York, NY

Fall Part-time Intern Sep 2018 – Dec 2018

• Facilitated the acquisition of DEx.top, a cryptocurrency exchange under Bitmain’s incubator, by performing

due diligence on the company’s business model, technical white paper, financial report, and management team

• Assisted the quantitative research team in developing a cryptocurrency evaluation model by cleaning input

data and adjusting coefficients of the logistic regression model in Python, and compared results to real world

• Recommended the team supervisor 3 crypto projects out of 20 applications to be listed on Bibox each week

PROJECT

New York University New York, NY

VIX Future Trading Strategy (Python)

• Fitted daily VIX index into 2 categories of time series (ARIMA-Garch and OU models) with MLE, validated

models by comparing AIC of both training (data from 1990 to 2013) and validation (from 2014 to 2016) sets

• Applied Monte Carlo method to price close-to-maturity (5 days to maturity) VIX future by simulating VIX

with trained OU model; based on the pricing model, long VIX future if underpriced or short if overpriced

• Tested the trading strategy on historical data of VIX futures from 2017 to 2019, and concluded an annualized

PnL of 53.4% without considering transaction cost and market impact; analyzed deficiencies of the strategy

Time Series Analysis: S&P500 and Gold Investment (Python)

• Cleaned 7 categories of data from 1880 to 2010, detrended the time series through 2 methods (multivariate

linear regression and first difference), and analyzed their ACF and PACF for model and feature selections

• Exploited different ARMA models on 2 detrended stationary processes correspondingly and compared their

accuracies on predicting SPX and gold price (R-squared of the time series detrended by MLR is 5.75% higher)

• Simulated 2 investment strategies (investing in SPX only or investing in SPX and gold interchangeably) based

on the ARMA model; the second method doubled the profit due to the negative correlation of SPX and gold

Implied Volatility Parameterization (Python)

• Cleaned Chinese ETF50 option data and calculated the Black-Scholes implied volatility with Newton iteration

• Parameterized the implied volatility to strike price curve with Stochastic Volatility Inspired (SVI) model with

MLE approach, and concluded that SVI works well only around ATM options but not deep ITM/OTM options

COMPUTER SKILL/OTHER

Programming Languages: Java (5 years), Python (3 years), MATLAB (5 years), R (1 year), C (1 year)

Languages: Chinese (native), English (bilingual)

Others: Hosted the 2018 Zongwei (Aska) Yang NYC Solo Concert and the 2018 Jony J NYC Live Concert

mailto:s

KEREN WANG (217)979-1678 ■ [email protected]■www.linkedin.com/in/keren-wang1113


The Courant Institute of Mathematical Sciences MS in Mathematics in Finance (expected – January 2021)

• Coursework: Pricing of derivative securities, stochastic calculus, portfolio management, Blake-Scholes formula and applications, simulation and strategy realization based on Java, statistical methods (MCMC, GLM, PCA, SVM, etc.) on financial data

UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Urban, IL BS in Mathematics and Statistics (August 2016 – December 2018)

• Coursework: Calculus, differential equations, linear algebra, numerical analysis. probability and statistics, applied regression and design, machine learning, and data analysis

• Honors: High Distinction of Department., Cum Laude of the Institute, and Dean’s List EXPERIENCE CICC FINTECH FUNDS Shanghai, China

PE Investment Internship (2019) • Launched due diligence including market research, review companies’ BPs and profiles,

and reports in technology industry (Semiconductors, AI, IoT, etc.) • Used Wind to find comparable companies’ information (P/E, EPS, EBITDA, Market

Capitalization, etc.) and build models with Excel to value firms that met our interest • Wrote competitive product analysis, financial forecast, valuation of investment memo • Updated portfolio companies and pipelines about their features and recent activities

DELOITTE TOUCHE TOHMATSU LIMITED Shanghai, China Risk Advisory Internship (2018)

• Communicated with customers and read profiles and policies to learn their business pattern and collect operation and contract data from various departments

• Created rules and developed risk evaluation models with Python according to the situation • Leveraged analytic methods and machine learning algorithms (PCA, Clustering, Random

Forest, etc.) to find significant features that classify risky instances, and avoid further loss PROJECTS UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Urban, IL

Deep Learning Application on Futures Price Direction Prediction (2018) • Preprocessed data for over 100 thousand of lines and use Keras on AWS to design models • Used ggplot to explore the shape and features of data, applied clustering to create features • Developed a pre-trained CNN model and a LSTM model to predict the price direction

(increase, decrease, stable), achieved accuracy around 37%, ranked 3rd among all groups NBA Player Ranking Model (2018)

• Use Jackknife and bootstrap to calculate mean of players’ performance indices from play-by- play data, and set up a model based on Gaussian distribution assumption �

• Apply Bayesian approach and Gibbs Sampler on the parameters’ uncertainties estimation COMPUTER SKILLS/OTHER Programming Languages: Python, R, C/C++, Java

Other Software: Microsoft Offices; LaTex, SAS Languages: Mandarin(native), English(fluent), Japanese(fluent)

ZHENGQING (CODY) WAN [email protected] ■ linkedin.com/in/CodyWan/



● Coursework: Continuous-Time Derivative Pricing, Scientific Computing, Algorithmic Trading Framework, Market Microstructure

Wellesley College Wellesley, MA Completed Major Coursework in Mathematics (2016 - 2019)

● Coursework: Data Structures, Algorithms, Digital Logic and Memory Management, Probability Distributions and Markov Chain, Vector Space and Matrix Decompositions, ODE, Real Analysis, Lebesgue Integral and Theory

● Awards: Camellia Student Leadership Award - for automating LGBTQ Office’s big-little matching program; automated I/O via text-based UI and implemented Gale-Shapley Algorithm

Babson College Wellesley, MA BS in Business Administration (2015 - 2019) Magna Cum Laude

● Awards: Honors Scholar - Highest Academic Distinction EXPERIENCE Hwabao WP Fund Management Shanghai, China

Quantitative Analyst Intern (Jun. 2018 – Aug. 2018) ● Built backtesting framework with SQL database and back-tested momentum and financial-ratios

based quantamental strategies on CSI 300 and SSE index with data from inception to June 2018 in Python (numpy, pandas etc.)

● Researched literature and validated an algorithm that computed overfitting probability in hyperparameter selection by Dr. Marco Lopez de Prado etc., and presented it senior management

● Drafted regulatory documents for issuing a new ESG ETF PROJECTS Team Captain - IAQF Academic Competition (on-going) New York, NY

● Computing statistical differences and expected performances of equity portfolios that bet on presidential election results and devising corresponding trading strategies in Python

● Managing a six-person team from hypothesis generating, code review and report write-up Derivative Pricing (Python) - NYU Courant New York, NY

● Derived closed-form formula for option on future; computed with trinomial-tree the price of an European option on future and implied volatilities of options on ETFs

Honors Thesis: Can Financial Engineering Really Cure Cancer? Wellesley, MA ● Tested factors such as size, valuation and default correlation, and portfolio composition on the

feasibility/credit rating of an research-backed security ● Built from scratch the simulation and pricing framework in Python with multiprocessing

Option Pricing Model Implementation (C++) - CUNY-Baruch/DataSim New York, NY ● Built Black-Scholes, binomial-tree and simulations for European, American and Asian options ● Used Generics, OOP, STL; implemented random number generators and matrix decompositions ● Completed the C++ course with Distinction (90+/100)

COMPUTER SKILLS/OTHER Technical Skills: C/C++, Java, Python, R, SQL, Linux (novice), MS Excel

Extracurricular: Stage-singer (4 years of training), Babson College Diversity Ambassador Languages: Mandarin (native), English (bilingual)

http://linkedin.com/in/CodyWan/

LINGLAN WANG [email protected] ■ www.linkedin.com/in/linglan-wang


The Courant Institute of Mathematical Sciences MS in Mathematics in Finance (expected – December 2020)

• Current Coursework: Derivative Securities, risk management, CAPM, stochastic calculus • Future Coursework: Quantitative portfolio theory, Monte Carlo and finite difference methods,

fixed income and currency derivatives, derivative pricing and trading UNIVERSITY OF CALIFORNIA, IRVINE Irvine, CA

BS in Mathematics & BA in Business Economics (2014 – 2018) • Coursework: Stochastic process, linear algebra, numerical analysis, partial differential equation,

statistical modelling, object-oriented programming, data structure and algorithm • Honors: Dean’s List, Phi Beta Kappa, Magna Cum Laude, Pi Mu Epsilon

EXPERIENCE HENGYI CAPITAL Hangzhou, China

Investment Management Intern (April 2019 – August 2019) • Analyzed data from semiannual financial statements of public companies listed on SSE • Conducted stock selection, developed trading strategy, and constructed various financial models

for equity valuation and return analysis • Performed quantitative and qualitative analysis, and explored fixed-income strategies

GLOBAL AI CORPORATION New York, NY Quantitative Strategy Intern (June 2018 – February 2019)

• Implemented constrained regression and rolling window regression models for hedge funds’ performance replication with tradable ETFs on the market

• Researched 15 different hedge fund strategies, replicated its returns and trends using liquid, transparent ETFs, and explored the efficacy of different linear models for hedge fund replication

• Backtested replication strategy and implemented linear clones of different-strategy hedge fund performance using Python, then applied data visualization with Tableau

• Used NumPy and pandas to extract and clean large-scale data, which made by 30% better than previous methods in terms of mean squared error

PROJECTS UNIVERSITY OF CALIFORNIA, IRVINE Irvine, CA

Machine learning project • Performed presentation of machine learning-based research that predominantly focused on

gradient descent and neural network, primarily using various modules of Python • Tuned and optimized factor model using cross validation and LASSO to predict expected returns

for more than 30 portfolios Financial project

• Used Geometric Brownian motion to simulate stock price paths after exploring the fluctuation of stock market under efficient market hypothesis

• Estimated the volatility and correlation parameters between different stocks, and visualized the results using the matplotlib module Python

COMPUTER SKILLS/OTHER Programming Languages & Others: Python(Coursera Certification), Java, MATLAB, R

Other Software: Microsoft Office (Word, Excel, PowerPoint, Outlook), Tableau, EViews Languages: English (fluent), Mandarin (fluent)

RUI WANG

(646) 578-4125 ■ [email protected]

EDUCATION



MS in Mathematics in Finance (expected – Jan 2021)

• Coursework: SDEs, Ito calculus; econometrics with time series; derivatives hedging strategies;

Java (Lloyds’ algorithm, market impact models, Monto Carlo simulation)

ZHEJIANG UNIVERSITY Hangzhou, China

BS in Statistics and BA in English (Sept 2015- June 2019)

• Coursework: financial mathematics, probability, regression analysis, numerical analysis, real

analysis, random process, micro & macroeconomics, financial econometrics, international

finance, accounting, cooperate finance

EXPERIENCE

CITIC SECURITY Shenzhen, China

Algorithmic Trading Summer Intern (July 2019 - Aug 2019)

• Employed Logistic Regression and Neural Network to solve multi-category problem to improve

the performance of the TWAP trading algorithm

• Used Python multithreading to calculate TWAP and VWAP based on high-frequency data of

stocks on SSE and conducted data analysis

• Built API to specifically compare back-test of different strategies based on modules pandas and

XlsxWriter

INDUSTRIAL AND COMMERICAL BANK OF CHINA Hangzhou, China

Summer Intern of Software Development Center (July 2018 - Aug 2018)

• Created score system for currencies using Pearson correlation to rank levels of importance for

different indicators and used non-parametric estimation to score data

• Employed “bag of words” model and Logistic Regression to produce sentiment-based scores for

6 major developed country currencies and CNY

BANK OF JIANGSU FINANCIAL MARKET DEPARTMENT Shanghai, China

Intern of Foreign Exchange Team (Jan 2018-Mar 2018)

• Developed CTA strategy by generating trading signals based on technical indicators such as KDJ,

and cross-asset indicators

• Conducted macroeconomy research and analyzed potential risk and return of structured products

from UBS

PROJECTS

ZHEJIANG UNIVERSITY Hangzhou, China

The Comparison Between Traditional Quantitative Trading Strategies and Strategies base on ML

• Built traditional trading strategies based on KDJ, MACD and RSI indicators on different markets

• Created new trading patterns based on KDJ and RSI indicators and improved performance by 60%

based on personal investment experiences

• Constructed Hidden Markov Model (HMM) on SSE 50 with an average daily excess return of

0.28% from 2016-2019, on Dow Jones with average daily excess return of 0.08% from 2015-

2019, on EUR/USD with average daily excess return of 0.07% from 2016-2019


Programming Languages: C++ (Baruch C++ Certificate), Python, SQL, R, Java, C


Interests: Personal investment in Chinese stock market

LONGDE (BILL) WANG [email protected] ■ 858-531-4255 ■ linkedin.com/in/longde-wang


MS in Mathematics in Finance at the Courant Institute (expected – Dec 2020) • Coursework: Ito calculus, diffusion processes, mean-variance optimization, factor models, PCA,

GARCH, bagging, boosting, neural networks, binomial and trinomial trees, Black-Scholes • Future Coursework: Monte Carlo, dynamic programming, Bayesian regression, SVM, ICA, HMM

UNIVERSITY OF CALIFORNIA, SAN DIEGO La Jolla, CA BS in Applied Mathematics (High Honors) and BA in Economics, GPA: 3.85 (Sept 2015 – June 2019)

• Honors: Physical Sciences Dean’s Award and Scholarship (top 1%), Phi Beta Kappa • Coursework: linear, Ridge, Lasso, logistic, Poisson, and kernel regressions; instrumental variables;

multiple testing; bisection, Newton’s, and secant methods; ARMA; bootstrap EXPERIENCE YINHUA FUND MANAGEMENT Beijing, China

Quantitative Analyst Intern (July 2018 – Sept 2018) • Implemented and tested risk parity strategy on global stock indices in Python; achieved an annual

return of 11.75%, a max drawdown of 25.09% and a Sharpe ratio of 0.9 in back test • Applied the multiple factor model to the Chinese stock market; performed single factor tests using

information coefficients and regressions to create factor pool; filtered out factors using the Fama-MacBeth model; adjusted weights using information ratio and covariance matrix

CHINA CONSTRUCTION BANK PRINCIPAL ASSET MANAGEMENT Beijing, China Quantitative Research Intern (July 2017 – Sept 2017)

• Optimized the MACD strategy on the CSI 300 index using signals with particular DIF values in MATLAB; achieved an annual return of 16.75% and a Sharpe ratio of 1.0 in back test

• Developed a strategy in which signals of the constituent stocks determined signals of the CSI 300 index; evaluated the strategy’s risk using maximum drawdowns with different thresholds

RESEARCH/PROJECTS NEW YORK UNIVERSITY New York, NY

Risk and Portfolio Management (Python) • Applied PCA and fitted a Nelson-Siegel curve to yields; analyzed interest rates’ long-run behavior • Formed mean-variance currency portfolios; applied Ledoit-Wolf and Black-Litterman estimations • Fitted GARCH models to British pound data; Checked variance and kurtosis of GARCHed series

UNIVERSITY OF CALIFORNIA, SAN DIEGO La Jolla, CA Machine Learning Honors Thesis: Parameter Estimation of Topic Models (Oct 2018 – June 2019)

• Identified the symmetric tensor structures of cross moments of certain topic models; justified the use of symmetric tensor decomposition to estimate model parameters

• Applied the tensor power method and method of generating polynomials; achieved smaller errors and faster time for parameter estimation in numerical experiments in MATLAB

Bootstrap for High-dimensional Regression (R) • Analyzed the failure of conventional bootstrap for linear regression in high dimensions • Implemented deconvolution and residual standardization for residual bootstrap; drew weights from

a modified Poisson distribution to correct expected pairs bootstrap variance A Structural VAR Analysis on the Real Effect of South Korean Monetary Policy (R)

• Applied structural VAR to RGDP growth, inflation, interest rates, and US-Korea exchange rates • Obtained the impulse response function plots using Cholesky decomposition; concluded that

monetary effect on real side is not as significant as real effect on monetary side COMPUTER SKILLS/OTHER Programming Languages/Software: Python (3 years), R (3 years), Java, MATLAB, SQL, Tableau

Coursera Certificates: Machine Learning, Basic Deep Learning, Basic SQL, Tableau

TIANYU WANG (646)897-9553 ■ [email protected] ■ linkedin.com/in/wang-tian-yu/

EDUCATION




• Coursework: Stochastic Calculus, Derivative Securities, Risk Management, Java

SHANGHAI JIAO TONG UNIVERSITY Shanghai, China

BS in Mathematics, Minor in Computer Science (September 2015 – June 2019)

• Coursework: Probability, Stochastic Process, Mathematical Statistics, Partial Differential

Equations, Econometrics, C++, Python, Data Structure, Machine Learning, Deep Learning

University of Oxford Oxford, United Kingdom

Summer Exchange Student in Mathematics (August 2017)

EXPERIENCE

JUMP TRADING LLC Shanghai / Chicago, IL

Quantitative Research Intern (June 2019 – August 2019)

• Received 4-week training in Jump’s Chicago office including seminars on financial markets, quant

trading and Python/C++ programming. Manually mock traded future products in CME.

• Built market making strategy and optimized its execution during quantitative research interns' high

frequency trading strategy competition on EUREX futures.

• Researched market microstructure and implied rule of Chinese spread contracts.

• Built HFT trading signals in DCE and ZCE future products.

• Constructed low and medium frequency technical indicators in Chinese equity market.

TREXQUANT INVESTMENT LP Stamford, CT

Global Alpha Researcher (Remote, February 2019 – April 2019)

• Developed market-neutral, medium-frequency alphas that predict future stock returns based on

fundamental and technical indicators; submitted 99 alphas to production, with 34 trophy alphas.

• Designed operators for alpha performance improvement and alpha auto-generation.

JUNZHI ASSSET MANAGEMENT Shanghai, China

Quantitative Trader Intern (July 2018 – September 2018)

• Implemented stock order execution algorithms by momentum signals, SVM, and linear regression.

• Developed intraday stock trading strategies based on lead/lag relationships and momentum signals.

• Utilized wavelet denoising and machine learning methods to help improve strategy performance.

• Developed automatic tools for senior traders to help market observation and strategy analysis.

JQ INVEST Hangzhou, China

Quantitative Research Intern (July 2017 – September 2017)

• Developed high frequency pair trading strategy based on mean reversion and cointegration.

• Applied parallel computing to conduct and accelerate back-testing system in Python.

PROJECTS


Deep learning-based named entity recognition

• Used BiLSTM-CNN-CRF and BERT-based deep learning to recognize named entity in CoNLL-

2003 dataset, achieved 90.48% F1 score in test data.


Complex PCA-based stock lead-lag identification

• Used Piotroski Score in stock preselection. Predicted stock daily return based on signals generated

by LSTM networks, Hidden Markov Model and technical indicators.


Programming Languages: Python, C, C++, SQL, Linux, LATEX, MATLAB

Languages: Chinese (Natural), English (Fluent)



https://www.linkedin.com/in/wang-tian-yu/

https://www.linkedin.com/in/wang-tian-yu/

YUYING WANG ■217-979-6633 ■ [email protected]

EDUCATION

NEW YORK UNIVERSITY New York, NY The Courant Institute of Mathematical Sciences MS in Mathematics in Finance (expected - Dec. 2020)

• Current Coursework: Risk management (market, interest rate, credit risk, VAR and stress testing), OOP in Java, interest rate derivatives and Brownian Motion

• Future Coursework: Big data applications, and one-factor models and Black-Scholes formula, applications to stochastic processes, data cleaning, data structure and algorithm, time series analysis, market microstructure and Monte Carlo simulation

UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN Champaign, IL BS in Mathematics and BS in Economics - Minor in Statistics (2015 - 2018)

• Coursework: ODE of differential equations, LU and SVD decomposition, Cournot, Stackelberg and Cartel competition model, R, Python and Java/JavaScript programming

EXPERIENCE

Applied Technologies for Learning in the Arts & Science (ATLAS) Champaign, IL Data Analysis Intern, Parenthood Expenditure Program (2018 - 2019)

• Cleaned 2 GB 11-year raw files of cigarettes, alcohol and diapers consumption of various stores using R and STATA, deleted missing observations and decided variables included in the model finding factors that might affect people’s consumption behaviors after parenthood

• Assessed correlation and linear regression model along time, evaluated whether the chosen variables were significant, summarized updated model and data in weekly meetings

• Discovered and reported the flaws of the model and dataset with improvement suggestions

Marketing/ Social Media Analysis Intern, Student Money Management Center (2018) • Edited and created videos for SMMC’s YouTube Channel “ILStudentMoney” using iMovie

software, including the videos introducing banking system, saving and checking account and other financial tools in the United States to upcoming international students on campus

• Analyzed viewership data such as the number of subscribers, average watch time duration and traffic sources, created visual graphs in Excel and summarized the trend during summer

• Suggested to repost videos in other social media and rearrange the homepage to increase the views of videos and attract subscribers for the Youtube Channel in the future

GF SECURITY Zhuhai, China Summer Intern, Investment Department (2017)

• Used WIND Stock System to collect 7 IPO fund-raising companies’ financial statements and annual reports, organized them to an information report to the supervisor

• Assisted colleagues in searching potential mergers for a client by looking into the relative industries and annual reports, making a list of target acquiring companies’ basic information

PROJECTS

UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN Champaign, IL AdS/CFT Correspondence and Prisoner’s Dilemma (2017 - 2019)

• Self-learned theories in papers of RT formulas, AdS/CFT Correspondence and quantum entanglement, summarized theories and writing the paper using LaTeX

• Presented study and thoughts in front of the professor and PhD students, illustrating the overall ideas of the research, detailed formulas and theories that will be used to create model


Programming Languages: Python, Java, Java Script, HTML, R/ R studio, LaTeX Other Software: Microsoft Office, iMovie, STATA, Eviews Languages: Mandarin (native), English (fluent), Cantonese (fluent)

YIFAN WEI [email protected] ■ linkedin.com/in/yifan-wei-0a6065139/

EDUCATION NEW YORK UNIVERSITY New York, NY The Courant Institute of Mathematical Sciences MS in Mathematics in Finance (expected – Dec 2020)

● Future Coursework: Quantitative portfolio theory, interest rate and currency derivatives, risk management, stochastic calculus, dynamic programming, Monte Carlo and finite difference methods

PURDUE UNIVERSITY West Lafayette, IN BS in Mathematics, Actuarial Science & Statistics (Jan 2015 – May 2019)

● Coursework: Real analysis, measure theory, ODE, PDE, abstract algebra, Galois theory, probability, game theory, statistics, time series, annuity and interest theory, actuarial model and life contingency, Black-Scholes formula, derivative pricing, regression analysis, severity and frequency models

EXPERIENCE PURDUE UNIVERSITY West Lafayette, IN Undergraduate Teaching Assistant (Jan 2019 – May 2019)

● Led 30-40 students in discussion in python coding labs and projects ● Graded assignments and explained questions for students enrolled in python coding courses

REU in Mathematics Undergraduate Research Assistant (May 2018 – Aug 2018) ● Analyzed and classified generic four-circles configurations on Riemann sphere to derive qualitative

properties of real solutions of Painleve VI equations, prepared drafts for math paper using LaTex ● Applied Burnside’s Lemma to prove equivalent class property in classification ● Used exterior algebra and combinatorics such as Young tableau to study classification structure

GREENLAND FINANCIAL HOLDINGS GROUP Shanghai, China Asset Management Intern (May 2017 – Jul 2017)

● Researched and analyzed over 200 stocks in biological medicine industry in Chinese Security Market ● Investigated annual reports of target companies to determine earning abilities and forecast future

profitability, diagnosed financial conditions of target companies based on their financial ratios ● Collected data using Wind and transformed stock data using Excel

PROJECTS PURDUE UNIVERSITY West Lafayette, IN Python: Boiler Golden Game (Sep 2018 – Dec 2018)

● Used python functions, graphics, and other modules to lead development of the game ● Designed algorithm for the game

Investment Managements: Stock Trading Simulation (Feb 2018 – Apr 2018) ● Traded stocks in simulation market on Stock-Trak in a three-months period ● Constructed portfolio and tested trading strategy by trading stocks, options, and other derivatives

day by day to improve the portfolio value, analyzed losses and gains weekly Galois Theory: Transcendence of π (Mar 2018 – Apr 2018)

● Applied Galois Theory to study and prove Lindemann Theorem ● Presented findings to classmates and professors

COMPUTER SKILLS/OTHER Programming Languages: Python, Java Other Software: Microsoft Office, LaTex, SAS, R Languages: Mandarin (native), English (fluent) Certificates: Exam-FM (Financial Mathematics), Exam-P (Probability), VEE-APPSTATS (Applied Statistics), VEE-CORPFIN (Corporate Finance), VEE-ECON (Economics) offered by Society of Actuaries


https://www.linkedin.com/in/yifan-wei-0a6065139/

YUNXIAO (KLAUS) XIANG [email protected] ■ linkedin.com/in/yunxiaoxiang


The Courant Institute of Mathematical Sciences M.S. in Mathematics in Finance (expected – Dec. 2020)

• Coursework: Risk-neutral pricing, martingales, VaR, Markov chain, Brownian motion, Black-Scholes model, Black-Litterman, OOP, PCA, Monte Carlo simulation, Greeks, Itô calculus, GARCH

UNIVERSITY OF CALIFORNIA, SAN DIEGO La Jolla, CA B.S. in Applied Mathematics; B.A. in Economics (Sep. 2015 – Jun. 2019)

• Coursework: Arbitrage pricing, hedging, Markowitz model, CAPM, CLT, SVD, ODE, Bootstrap, MLE, hypothesis testing, regression, ACF, ARIMA model, back-testing, heat and wave equation

EXPERIENCE SHANGHAI BLACK WING ASSET CO., LTD. Shanghai, China

Summer Analyst (Aug. 2018 – Sep. 2018) • Initiated testing of trading system in illiquid commodity futures market; discovered 6% potential loss • Analyzed results and attributed poor performance to low liquidity and discreteness in prices • Customized strategy for illiquid markets by attaching greater importance to bid-ask spread • Implemented Shanghai ETF50 index and prices in prior 3 months to forecast upcoming trends • Communicated with clients in non-technical language and improved their portfolio return by 5%

NEW YORK UNIVERSITY New York, NY Teaching Assistant (Sep. 2019 – present)

• Help 50 students succeed by emphasizing salient concepts and imparting effective learning strategies • Suggest how to improve based on students’ weakness; have raised average grade by 30% in 10 weeks

PROJECTS UNIVERSITY OF CALIFORNIA, SAN DIEGO La Jolla, CA

Deal Probability of Russian Commodities – NLP in Python and Regression Modeling in R • Leveraged NLP in Python to extract numerical variables from product descriptions and images • Visualized data in R and constructed multivariate regression model after subset selection • Tested model and found its limitation on modeling skewed data with many zeros in response variable • Fixed logistic regression model by converting numerical deal probability to logistic values • Suggested variables that significantly influence deal probability of goods based on dataset - price,

presence of image, and capital letter count in description; created report to explain investigation MMORPG Price Forecasting – Data Collection in Excel and Factor Modeling in Python

• Generated over 10% profit in each new patch release by identifying underpriced items before update • Collected 3 years’ daily prices from auction hall in DFO; visualized and analyzed price movements • Quantified factors including players’ needs, related event update, market volume, and rarity of item;

subsequently constructed factor model to predict price movements after patch update and over time • Validated model predictions with microeconomics market analysis; 95% CI achieved 90% accuracy

Time Series Forecasting – SARIMA Modeling in R • Visualized data and plotted ACF to justify covariance stationarity; leveraged Ljung-Box test to check

time dependency in quarterly data of interest-spread from 1960.Q1 to 2008.Q1 • Evaluated SARIMA models based on AIC, BIC, and test on residuals to fit given time series • Simulated ARMA11 to predict upcoming trend; backtested to reveal limitation in long-run forecast

Patterns of Video Gaming – Bootstrapping in R • Applied Bootstrapping, Pearson's chi-squared test, CLT and CART to survey data collected in 1994 • Suggested relaxing environment would improve popularity of UCB statistical lab by 8%

COMPUTER SKILLS/OTHER Programming Languages / Other Software: Java, Python, R (proficient); Matlab, Excel, SQL (intermediate)

Languages: Mandarin (native), English (fluent), Japanese (elementary)

ZEPENG (SHAWN) XIAO [email protected] ■ https://www.linkedin.com/in/zepeng-xiao/

EDUCATION




• Current Coursework: Mean-variance analysis, equilibrium asset pricing models, arbitrage pricing

theory, derivatives evaluation, Black-Scholes theory, Markov Chains, Brownian motion,

Stochastic differential equations, forward and backward Kolmogorov equations

UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Champaign, IL

BS in Mathematics and Statistics (August 2016 – May 2019)

• Honors: Summa Cum Laude with Highest Distinction in Mathematics and Statistics

EXPERIENCE

INVESTMENT BANK OF CHINA MERCHANTS SECURITIES CO. LTD Shenzhen, China

Investment Banking Intern in ABS (June 2018 – August 2018) (Python and Microsoft Excel)

• Processed 50 MB historical data about past payment of loans provided by bond sponsors

• Applied statistical methods to predict key parameters like future prepayment rate and default rate

• Simulated future payment of related loans and cash flow for entire ABS project

• Constructed model to predicted future bonds dividends yield at specific time based on simulations

• Formulated trading structure to balance profits for each side, like bond sponsors, and investors

ACTUARIAL DEPARTMENT OF PICC REINSURANCE CO. LTD Beijing, China

Actuarial Science Intern (May 2017 – August 2017) (Matlab and Microsoft Excel)

• Processed historical claim records, analyzed different factors that may affect claims

• Applied actuarial methods to calculate self-estimation of company’s solvency in 2017 and

estimation of company’s reserves in second quarter of 2017

• Investigated and translated English research paper to improve team’s insurance pricing method

PROJECTS

DURHAM UNIVERSITY Online

Quantitative Research: Advanced Portfolio Theories and Models (Matlab)

• Investigated and collected different portfolio management processes and portfolio models

• Implemented portfolio models like Black-Litterman and Kan-and-Zhou model by Matlab

• Applied different models to determine different sets of portfolios

• Monitored performance for each set of portfolios by practical market data between 2001 and 2010

• Analyzed advantage and limitation of each model by evaluating performance


Regression Analysis: Simulating NBA Match Results and Predicting NBA Playoff Teams (R)

• Processed NBA historical data and determined significant factors that may affect match result

• Applied ridge regression to factors, and built models to simulate results for future games in R

• Predicted playoff teams based on results simulated by Monte-Carlo, which has 87.5% accuracy

Time-Series Analysis: Analyzing and predicting sneaker’s price (R)

• Web-scraped and processed historical data for sneaker deals on StockX, like price and time

• Analyzed relationship between price and time and overall trend for price

• Predicted future prices fluctuation based on time series analysis and predicted time when price

reach its minimum in the next year in R

• Constructed shiny app including diagrams to visualize analyzing and predicting result


Programming Languages: Java (2 Years), Python (4 Years), C++ (2 Years)

Other Software: Microsoft Office, MATLAB, R, Latex


ZHIHAO XIE [email protected] ■ linkedin.com/in/zhihaoxie ■ (+1)858-539-6558


M.S in Mathematics in Finance at Courant Institute (expected December 2020) • Current Coursework: VaR, risk & portfolio management, CAPM, Ito’s calculus

University of California, San Diego (GPA: 3.83/4.0) La Jolla, CA B.S. in Mathematics(Applied) & B.A. in Economics (2015-2019)

• Coursework: Probability, Brownian Motion, Bootstrap & Monte Carlo simulation, statistics EXPERIENCE Shenzhen Yuanzhi Fuhai Investment Management Corp. Shenzhen, China

China Merger & Acquisition Fund - Government Capital Operation platform Summer Analyst – 5G Industry Group (2018.07 – 2018.09)

• Assisted senior manager with investment reports of two SZSE-listed companies buy-back deals with focus on their growth, industry competitors, and market shares

• Prepared investment reports for companies to be listed in Shanghai Sci-tech innovation board China Construction Bank CCB Shenzhen, China

Summer Intern (2017.07 – 2017.09) • Created and curated a mortgage data loan base in Excel to evaluate customers’ credit rating • Collected loan documents across various real estate projects and analyzed the risk exposures

PROJECTS Statistical analysis on stock prices and factors dataset (R)

• Bootstrapped Hoeffding statistics, implemented Hoeffding test and Spearman test to check the independence of prices of stocks from different industries in CSI

• Implemented multiple testing using Holm procedure and Bnejamini-Yekutieli procedure to compare the pairwise P/E ratios of companies from the same industry at a certain period

Order Book Simulation (Java) • Designed book and exchange mechanisms with functions to sweep, fill, rest and cancel limit orders

Risk & Portfolio Management (Python) • Applied Levene’s test and Box M test respectively to check if sample currency covariance matrix

and the covariance matrix deriving from Monte Carlo simulation are equal • Fitted a GARCH model using the log return data of British Pounds, obtained the parameters and

formed the time series of test data to see how well the model fits (p, q started at 1) Statistical relationship between crime rates and gun control law (Stata)

• Ran OLS regression on different categories, added potential omitted variables to improve accuracy of estimators, picked proper instrumental variables to avoid omitted variables bias

• Ran panel data regression to consider time-fixed effects or state-fixed effects, checked statistical significance, concluded the negative relationship between stricter law and lower criminal rates

Markov Language Model – Machine Learning (Java) • Trained a text/speech predictor using the idea of Markov decision process in Java • Tokenized input text, created a database that stored different patterns of words with different

degrees and recorded the frequency accordingly, fitted them into the prediction map COMPUTER SKILLS/OTHER Programming Languages: Java (4 years), R (3 years), Stata (2 years), Python (2 years), Excel (2 years)

Languages: Chinese (Native), English (Fluent)

JEREMY YANG +1 (917) 536-1998 ■ [email protected]

EDUCATION


Courant Institute of Mathematical Sciences GPA: N/A/4.00

Master of Mathematics in Finance (September 2019 – May 2021)

• Future Coursework: Quantitative portfolio theory, derivative models, Black-Scholes & stochastic processes,

PDEs & dynamic programming, risk management (VAR & stress testing), big data, and continuous time finance

DUKE UNIVERSITY Durham, NC

Fuqua School of Business GPA: 3.81/4.00

Master of Management Studies: Foundations of Business (July 2018 – May 2019)

• Selected Coursework: Financial analysis, statistical analysis using STATA (PCA), Monte Carlo simulation


Courant Institute of Mathematical Sciences GPA: 3.77/4.00

Bachelor of Mathematics and Economics Joint, Music Minor (September 2015 – May 2019)

• Selected Coursework: Econometrics with coding in R, theory of probability, linear algebra, and ODEs

EXPERIENCE

NEW YORK BAY CAPITAL New York, NY

Investment Banking Analyst (June 2019 – August 2019)

• Developed processes to analyze & pitch company’s first debt capital market client, $5B LatAm government bonds;

modeled bond valuation and quantified credit risks through comparables and fundamental cash-flow analysis

• Constructed DCF models for $300M EMEA agribusiness merger, focusing on pre/post-merger valuations

• Guided marketing strategies, sourced potential investors, and presented pitching materials for deals

GRANDLY INTERNATIONAL FINANCIAL GROUP Hong Kong, China

Investments Analyst (May 2018 – July 2018)

• Implemented cross-asset, cross-region quantitative trading strategies in Python with over 40 investment assets,

conducting back-tests and parameter optimizations over a 20-year period

• Performed and published market research on cryptocurrency, detailing market views and trade ideas

• Provided ad hoc support to portfolio managers in investment research, client requests, and risk analysis

LOUIS DREYFUS COMPANY Beijing, China

Commodities Desk Analyst (July 2017 – August 2017)

• Designed and tested systematic futures trading strategies using fundamental valuation models

• Increased company-wide database efficiency by programming automated data updates and cross-checks

GUANTONG FUTURES BROKERAGE Beijing, China

Futures Desk Analyst (June 2016 – August 2016)

• Created new portfolio construction process by shifting priority from maximizing return to risk-based framework

• Serviced individual and corporate client accounts, and identified potential new clients

PROJECTS & EXTRACURRICULAR

DUKE UNIVERSITY Durham, NC

Automobile Market Segmentation (March 2019)

• Investigated in STATA demand in automobile markets by employing factor and cluster analysis on survey data

through PCA and dendrograms methods, before segmenting clusters using crosstabs

COLLEGIATE STARLEAGUE New York, NY

Team Captain & Manager (October 2017 – Present)

• Captained NYU and Duke varsity Dota 2 esports teams over 3 seasons with a division I top 16 finish

• Recruited and managed rosters, scheduled team events, developed strategies, and led in-game calls


Macroeconomic Econometrics Analysis (April 2018)

• Performed multivariate regressions in R to identify key factors influencing US wage and employment trends,

with consideration for multicollinearity, omitted variable bias, and instrumental variables •


Programming Languages & Software: Python, R, STATA, VBA, Bloomberg, Microsoft Office Suite

Languages: Mandarin Chinese (native) | Interests: Tabletop (D&D/poker), skiing, theater, composing/piano, singing


GUANYU (MARTIN) YAO [email protected] ■ linkedin.com/in/guanyu-martin-yao/

EDUCATION



MS in Mathematics in Finance (expected – Dec. 2020)

Current Coursework: Quantitative portfolio theory, interest rate and credit derivatives, Monte

Carlo and finite difference methods, factor and principal-component models, Black Scholes model,

SDE and Ito calculus, risk management, and object-oriented programming

Future Coursework: Interest rate and FX models (CIR, Hull-White), time series analysis,

optimization methods, risk management (market and credit risk, VaR and stress testing)


BA in Mathematics (2015 – 2019)

Coursework: Linear algebra, multivariate calculus, math modeling, differential equations,

probability and statistics, micro/macro-economics, and data structures

Honors: Summa Cum Laude, Phi Beta Kappa, Steffi Berne Scholarship, Dean’s Honors List

EXPERIENCE

HAITONG SECURITIES Shanghai, China

Quantitative Analyst Intern, Equity Investment Department (Jun. 2019 – Aug. 2019)

Constructed multi-factor model to select high-quality stocks in CSI 500 and CSI 300

Analyzed factors in profitability, growth, capital structure, and financial ratios, then explored their

correlation, synthesized factors, and back-tested long-short trading strategies

Performed data analysis using Python packages, such as NumPy, Pandas, and Matplotlib

GUOTAI JUNAN SECURITIES Shanghai, China

Quantitative Analyst Intern, Derivative Pricing Department (Dec. 2018 – Jan. 2019)

Researched Vanilla/OTC option pricing models for the derivatives investment division

Implemented Black-Scholes Merton model, approximated and visualized numerical Greeks

Analyzed American option pricing methods (Bjerksund and Stensland) in Excel VBA

Programmed, tested, and debugged pricing model of single-asset exotic options

Simulated the dynamic delta hedging process, and improved its hedging error

PROJECTS


Combinatorics and Simulations of Self-avoiding Random Walks

Proved sub-additive property of connective constant and resulting corollaries

Verified Hammersley-Welsh method through combinatorial arguments

Performed static Monte-Carlo simulations in Python, reduced attrition constant for long walks

Mean and Variance Portfolio Optimization

Investigated efficient asset allocations of stocks in S&P 500 through mean-variance analysis

Conducted principal component analysis on empirical correlation/covariance matrices by

extracting, cleaning, and transforming historical data in R


Programming Languages: Java, Python, R, Visual Basic for Applications

Other Software: Microsoft OS and Office, MATLAB, LaTex

Languages: English (fluent), Mandarin (native)


https://www.linkedin.com/in/guanyu-martin-yao/

SIHAN ZHA (217) 418-0232 ■ [email protected] ■ www.linkedin.com/in/sihan-zha

EDUCATION



MS in Mathematics in Finance (Dec. 2020)

• Future Coursework: Black-Scholes model, portfolio theory, arbitrage-based pricing of derivative

securities, market microstructure, risk management, Monte Carlo, dynamic programming in Java

UNIVERSITY OF CHICAGO Chicago, IL

MA in the Social Sciences, Economics Concentration (Aug. 2019)

• Coursework: PhD courses: machine learning & large-scale data, game theory, mechanism design,

econometrics, microeconomics. Financial econometrics (Booth), statistical modeling on Python

• Awards: Social Sciences Scholarship ($38,664, granted 3/50)


BS in Mathematics with Highest Distinction, BA in Economics, Magna Cum Laude (Dec. 2017)

• Coursework: Calculus, real analysis, linear & abstract algebra, ODE, probability & stats, complex

variables, linear programming, numerical methods, monetary economics, macroeconomics

• Awards: Highest University Honor “Bronze Tablet” (top 3% in the college);

EXPERIENCE

GF SECURITIES Hefei, China

Summer Internship (Summer 2017)

• Web scraped financial data using Python and formulated stock report based on fundamentals

• Estimated various GARCH models with Eviews to fit daily stock prices and predict volatility

• Cooperated with bank branches to improve operating in-branch agencies by gathering user data

PRICEWATERHOUSECOOPERS Shanghai, China

Summer Internship: Financial Services-Assurance (Summer 2016)

• Assisted in auditing half-year financial report of a large commercial bank

• Used Microsoft Excel and Word to calculate departmental revenue and compile draft report

PROJECTS/RESEARCHES

UNIVERSITY OF CHICAGO Chicago, IL

Individual MA Degree Thesis: Matching with Noisy Signals, Faculty Advisor: Prof. Philip J. Reny

• Established many-to-one matching model abstracted from college admission and job market

• Used economic methods involving mathematical analysis and probability theory to measure

inefficiency caused by the noisy ability signals sent from applicants to employers

• Proved Nash Equilibrium strategies for each college with noisy signals in different scenarios

Econometrics/Machine Learning Project: Application of Generalized Random Forests Method

• Implemented machine learning application based on Generalized Random Forests (GRF) method

combining GMM and random forests proposed by Athey, Tibshirani, & Wager in 2019

• Discussed extensions on estimating treatment effects and open problems in the paper with authors

• Extended and applied GRF method to million-scale dataset with multiple IVs using R and cloud

computing server, together with multiple LASSO methods in the first stage of IV regression

• Performed KNN and kernel methods to the same dataset and compared results with GRF method


Illinois Geometry Lab, Advisors: Dr. Ruth Davidson and Dr. Rosemary Guzman

• Review papers on phylogenetic trees and researched on trees in non-Euclidean space

• Proposed combinatorial algorithm to measure tree distance between different numbers of taxa

• Published a paper and used Python to implement and visualize the algorithm proposed


Programming Languages: Python, R, Java

Other Software: LaTeX, Microsoft Office, Eviews, Mathematica

Languages: Mandarin (Native), English (fluent)

Certificate: Passed CFA Exam Level I (scored above the 90th percentile)

YOUYUAN ZHANG [email protected] ■ linkedin.com/in/youyuan-zhang



• Current Coursework: Quantitative portfolio theory, risk management (market and credit risk, VaR and stress testing), stochastic calculus (Brownian motion and diffusion process), Monte Carlo, fixed income derivatives, Black-Scholes, PDE (heat equation), Java for quantitative finance

• Future Coursework: machine learning in C++, continuous time pricing theory, algorithmic trading PEKING UNIVERSITY Beijing, China

BS in Statistics & BA in Economics (Sep. 2015 – Jul. 2019) • Coursework: Calculus, ordinary differential equations, probability, statistics, stochastic process,

measure theory, data structures in Python and C; equilibrium and arbitrage asset pricing, portfolio management, CAPM, risk neutral pricing, econometrics (linear regression, heteroscedasticity)

EXPERIENCE HSBC Beijing, China

Factoring Analyst of GTRF Intern (Sep. 2018 – Mar. 2019) • Assisted client managers in accounts receivable financing for institutional clients; created and

updated 300+ call reports in ClientVision system, recording meetings with 20+ institutional clients • Promoted increase of accounts receivable financing by over CNY2 billion for 11 enterprises by

checking invoices and financing availability, applying for quota and pushing drawdown process • Assessed potential risks of 20+ institutional clients by analyzing accounts receivable transfer

records, funds flow and 500+ buyers’ information; analysis adopted by Risk Control Department CHINA MERCHANT BANK Changchun, China

Database Developer Intern (Jul. 2017 – Sep. 2017) • Maintained database with data generated in new transactions and created 70+ asset allocation data

charts for institutional clients with SQL to support relationship managers • Analyzed consumer distributions and sales of credit card business from 2015 to 2017 to improve

design of credit cards to match target population; analysis adopted by Product Development Team • Promoted development of new software by debugging in Python and writing notes and instructions

PEKING UNIVERSITY Beijing, China Peer Mentor (Mar. 2016 – Dec. 2017)

• Met with students to answer any inquiries on advanced mathematics related topics • Provided advice on Mathematic courses and specific methods to better grasp concepts

PROJECTS PEKING UNIVERSITY Beijing, China

Deviation and Test Upon Finite Student’s T-Mixture Model Using Two Different Methods • Conducted Gibbs Sampling, Bayesian Inference and EM Algorithm in R • Implemented inverse Chi-square distribution index to convert Normal distribution to T-distribution

Pair Trading Strategy Based on Co-integration Test • Selected stock pairs with minimum Dynamic Time Warp and simulated transaction on Joint Quant • Conducted the Engle-Granger two-step residual-based Co-integration test with Python

Evaluation and Optimization for Trash Distribution in Campus • Simulated campus population flow and trash generation with Stochastic Process and Markov Chain • Applied PageRank Algorithm, evaluation function and Monte Carlo Simulation in MATLAB

COMPUTER SKILLS/OTHER Programming Languages: Python, C, Java, SQL, R

Other Software: Stata, MATLAB, LaTeX, Microsoft Office Languages: Mandarin (native), English (fluent)


https://www.linkedin.com/in/youyuan-zhang/

MINGYUE ZHANG

(347)563-8683 ◼ [email protected] ◼ linkedin.com/in/mingyue-zhang-710159174

EDUCATION




• Coursework: asset pricing, interest rate & FX models, PCA, SVM, random forests, Ito calculus

• Future Coursework: linear and quadratic methods in regression, classification and unsupervised

learning, Bayesian approach to modeling, nonlinear PDEs

WUHAN UNIVERSITY Hubei, China

BA in Finance and BS in Mathematics, Major in Mathematical Finance (2015 – 2019)

• Coursework: Greek letters, ridge regression, k-nearest neighbors, mathematical analysis

EXPERIENCE

SHENWAN HONGYUAN SECURITIES Beijing, China

Quantitative Analyst Intern (Jan. 2019 – Apr. 2019)

• Analyzed at-the-money commodity options’ Theta and implied volatility data to make profit from

time value through VBA program which can download and manipulate data automatically over

changeable periods and commodities’ combinations

• Built commodity indices as predictor of future prices using Dow Jones Commodity Index’s

methodology on weight adjustment and programed to download, save over 100 GB tick-level data

in HDF5 format and tested strategy’s performance in Python (13% total return rate, 9% max

drawdown)

• Programed to automatically match, classify, write and save debts’ information (about 2000 lines)

into certain types and formats from TXT file to EXCEL using Python

• Maintained and ameliorated private quantitative factors library by Python

Quantitative Analyst Intern (Jul. 2018 – Dec. 2018)

• Researched and implemented Choppy Market Index and R-Breaker to construct CTA strategy,

back-tested its performance in Python and ameliorated model (12% total return rate)

• Tested CTP trading system regarding risk management and stability and wrote up detailed report

• Dug up over 300 companies’ research reports to identify their potential needs for commodity

options and rate them for sales team

• Participated in constructing local database for minute-level data of commodity futures through

Python which can automatically download and refresh data from cloud to local database

• Added time-weighted average price algorithm to CTP trading system through Python and

achieved spreading out one trading request evenly over given time

PROJECTS

Monte Carlo Simulation in Java

• Priced European and Asian options by generating paths of stock prices and evaluated the stopping

criteria based on payout's standard deviations

• Applied importance sampling to significantly reduce the variance

Stock Selection Using Machine Learning in Python

• Implemented random forest regression-based algorithm and Python framework to identify stocks

that will beat market (outperformed CSI 300 index on the 5-year period in the back-testing)

Black-Litterman Model Implementation in Python

• Applied Bayesian framework to combine investors’ views with Markowitz parameters


Programming Languages: Python, C/C++, Java, SQL, VBA, MATLAB, R


https://www.linkedin.com/in/mingyue-zhang-710159174/

SHUFAN ZHANG [email protected] ■ linkedin.com/in/sfzhang/



• Coursework: Black-Scholes formula and applications, CAPM, dynamic asset pricing models, stochastic differential equations, object-oriented programming in Java

• Future Coursework: Machine learning in quantitative finance, fixed income derivatives, portfolio construction and optimization, trading strategies

FUDAN UNIVERSITY Shanghai, China BS in Mathematics (2015 – 2019)

• Coursework: Differential equations, probability, mathematical modeling, mathematical finance, stochastic calculus for finance, international economics, nonlinear programming, C programming

• Honors: Honors Student in Mathematics in National Top Talent Undergraduate Training Program EXPERIENCE FUYUAN INVESTMENT Shanghai, China

Quantitative Analyst Intern (Apr. 2019 – Jun. 2019) • Investigated distribution, expectation, variance and covariance of stock price of 100 companies

from February to May in 2019 • Constructed a mean reversion model to forecast future stock price using Python

ZHONGTAI SECURITIES Shanghai, China Research Analyst Intern (Oct. 2018 – Jan. 2019)

• Applied linear regression model to estimate impact of Chinese monetary policy adjustments on repurchase rates using Stata

• Analyzed stock prices on day of each major event of US-China trade war • Identified top 20 most affected stocks in Chinese financial market by using Python

FUDAN UNIVERSITY Shanghai, China Research Assistant: The Divestment of Foreign Capital from China (Mar. 2018 – Sept. 2018)

• Collected financial indicators (marginal profit, factory size, etc.) for more than 2 million Chinese corporations from 1998 to 2013

• Deleted erroneous data and generated time series by using Stata • Performed Complementary log-log regression, Logistic regression and propensity score matching

method to investigate impact of financial indicators on divestment in Chinese corporations • Applied Multinomial logistic regression to investigate impact of indicators on two types of

divestment (selling and dissolving) PROJECTS FUDAN UNIVERSITY Shanghai, China

The Distribution of World Languages in Future • Used Python to construct discrete time model to investigate determinants of language distribution,

then used results to forecast the prevailing languages in different countries • Tested sensitivity of GDP and population by separately changing the value of each parameter

Pricing Photographing Tasks • Investigated impact of distance between tasks and members on tasks’ price • Obtained optimal prices by linear regression model using MATLAB

COMPUTER SKILLS/OTHER Programming Languages: Java, Python, C++, Stata

Other Software: Microsoft Office, LaTeX, MATLAB, Wind Languages: Mandarin (native), English (fluent)

ZIBIN ZHEN [email protected] ■ linkedin.com/in/zibin-zhen



• Coursework: Risk-neutral valuation, factor models, Black-Litterman, Brownian motion, Black-Scholes and application to stochastic processes, OOP in Java, Monte Carlo simulation

NEW YORK UNIVERSITY New York, NY BA in Economics and Mathematics (2015 – 2019)

• Coursework: Linear algebra, probability, differential equations, econometrics, data structures • Honors: Phi Beta Kappa, Magna Cum Laude, Gopal Varadhan Scholarship at Courant

EXPERIENCE GLOBAL AI CORPORATION New York, NY

Quantitative Strategy Intern (Sept. 2019 – present) • Transform raw stress indicators into 5 subindices based on empirical CDF and computation of

order statistics; then aggregate them into the Composite Indicator of Systemic Stress (CISS) • Analyze statistical significance of 11 ETFs’ contemporaneous returns on CISS’s exponentially

weighted moving average with window sizes 4 and 40 HUATAI SECURITIES CO., LTD. Nanjing, China

Quantitative Analyst Intern (Jul. 2018 – Aug. 2018) • Retrieved real-time market data from Wind; then conducted data cleansing using STATA • Performed validity test to risk factors using multiple linear regression model that predicted stocks’

return with risk factors and industry factors • Employed event study method to examine market reactions to M&A of A-share listed companies • Estimated each stock’s normal return with market model; then calculated its cumulative abnormal

return and validated effect of M&A through hypothesis testing NINE COURSERS ASSET MANAGEMENT CO., LTD. Guangzhou, China Quantitative Analyst Intern (Jun. 2017 – Aug. 2017)

• Built single-factor test framework to sift high-quality stocks in CSI 300 using Python • Calculated each stock’s daily value associated with factors in financial ratios; then for each factor,

categorized the stock pool into 5 groups based on their values for backtesting • Determined validity and stability of factors by visualizing their monotonicity

PROJECTS NEW YORK UNIVERISTY New York, NY

Interest Rate Parity Analysis • Applied autoregressive distributed lag model to explore correlation between China-US monthly

average interest rate differentials and their exchange rates from Sept. 2012 to Sept. 2018 • Checked unit root via Dickey Fuller test; sought optimal lag periods based on AIC and determined

the predictive power of interest rate differentials on exchange rates via Granger causality test Course Registration System (Java)

• Designed system for administrator to manage and students to select courses with OOP paradigm • Implemented serialization/deserialization mechanism to ensure consistent state of system

Consulting Queue Management (Java) • Modeled customers and service-counter on a typical day from 9 am to 5 pm using queue • Calculated consultant’s idle time, each customer’s waiting time, and number of customers served

COMPUTER SKILLS/OTHER Programming Languages: Python, Java, MATLAB, STATA

Languages: Mandarin (native), Cantonese (native), English (fluent) Citizenship: US Permanent Resident

YUEYAN (JACQUELINE) ZHUANG (312) 806-3829 ■ [email protected]



• Coursework: Derivative securities, OOP in Java, algorithmic trading & quantitative strategies, stochastic calculus, time series analysis & statistical arbitrage, fixed income derivatives

UNIVERSITY OF CHICAGO Chicago, IL BS in Mathematics & BA in Economics (September 2014 – June 2018)

• Coursework: Applied regression analysis, econometrics, investments, calculus-based probability, statistical theory and method, game theory, intermediate micro/macroeconomics, ODE

• Awards: Graduated with General Honors; Academic Dean’s List for Year 2015-2018 EXPERIENCE HAITONG SECRUITIES CO., LTD Shanghai, China

Summer Fixed Income Trading Analyst (June 2019 – July 2019) • Executed and monitored company trades with daily trading volume of more than RMB800 million • Analyzed company’s position and wrote report by the end of each trading day to portfolio manager • Researched topics such as MBS; performed quantitative industry analysis of debt and bond markets

UNIVERSITY OF CHICAGO Chicago, IL Research Assistant, Booth School of Business (October 2018 – June 2019)

• Completed pattern recognition on datasets to determine the value and influence of non-compete contracts in labor market

• Designed algorithm in Python to automate the manual identification and classification process • Initiated a project regarding Asset Pricing Theory and copy-edited related papers and documents

Teaching Assistant/ Grader, Mathematics & Statistics Department (September 2015 – June 2018) • Facilitated discussion sessions for 30-40 students in various math and statistics classes • Graded homework for 60-70 students after figuring out solutions and hosted weekly tutorial sessions • Evaluated students’ performance to provide assistance and feedback

ASPEN CAPITAL Beaverton, OR Summer Quantitative Analyst, Portfolio Analytics (June 2017 – August 2017)

• Conducted investment analysis on various assets including mortgage-backed securities and bonds • Implemented project to price mortgage servicing rights (MSR) as well as asset-backed securities • Evaluated and modeled new acquisitions and current assets and tracked portfolio performance

DELOITTE TOUCHE TOHMATSU LIMITED Shanghai, China Tax Department Intern, Business Process Solutions Team (June 2016 – August 2016)

• Analyzed financial statements and researched tax related issues for Fortune 500 companies in diverse fields including industrial maintenance, wholesale supermarket, media and technology

• Assisted with accounting vouchers, general ledgers, and tax forms for client companies KPMG Shanghai, China

Audit Department Intern (July 2015 – August 2015) • Analyzed capitalization and leverage for Standard Chartered Bank and produced year-end audit • Devised weekly reports on risk management procedures for client companies respectively

COMPUTER SKILLS AND OTHER Programming Languages: Java, Python, R, SQL

Other Software: Microsoft Office (Word, Excel, Powerpoint), MATLAB, STATA, LaTeX Languages: Mandarin (Native), Shanghainese (Native), English (Fluent), Cantonese (Fluent) Certificate: CFA Level II Candidate (Passed Level I in June 2019) Interests: Ballet (CSTD Level 6); Traveling; Wine Tasting

SIYUAN ZOU [email protected] ■ linkedin.com/in/siyuan-zou



• Coursework: Black-Scholes formula and applications, interest rate derivatives and one-factor models, Brownian Motion, portfolio management models, Object Oriented Programming

• Future Coursework: Continuous time finance, time series analysis, statistical arbitrage STONY BROOK UNIVERSITY Stony Brook, NY

BS in Applied Math and Statistics, and Mathematics (2015 – 2018) • Coursework: Linear algebra, linear regression, linear programming, probability distributions • Honors/Awards: Dean’s List all semesters, Summa Cum Laude

EXPERIENCE BANK OF CHINA NEW YORK BRANCH New York, NY

Financial Institutions Department Intern (February 2019 – June 2019) • Investigated corporate clients’ financial statements and wrote Credit Recommendation Reports

for credit facility management • Applied risk rating model to complete risk assessments including Risk & Control Self-

Assessment, Enterprise Risk Management, Key Risk Indicator, and other assessments • Conducted quality control to ensure that KYC processes follow procedures • Extracted data from internal database to check transaction activities by VBA for examining the

suspicious accounts Financial Institutions Department Summer Intern (May 2018 – August 2018)

• Monitored department’s financial planning and analysis; performed reconciliation to ensure accuracy, compared historical data against budgets, and made improvements going forward

• Arranged or monitored internal and external training for different business functions • Assigned and coordinated special projects and other duties such as overall administrative

activities for department STONY BROOK UNIVERSITY Stony Brook, NY

Teaching Assistant (August 2016 – December 2016) • Coached and tutored undergraduate students in Differential Equations course • Assisted students to understand concepts better and build up strong foundation • Evaluated students by scoring tests and examinations • Communicated results to students and reported any issues to professor • Acted as liaison between professor and students; coordinated to resolve any problems

PROJECTS STONY BROOK UNIVERSITY Stony Brook, NY

Data Analysis: find a fitted linear model – R project • Cleaned and wrangled data to build and feed into linear regression model • Generated linear model by least-square fitting and Box-Cox transformation • Utilized ANOVA table, correlations, and plots to check precision between each variable

COMPUTER SKILLS/OTHER Programming Languages: Python, R, SAS, Java

Certificates: Data Analysis with Python by IBM on Coursera (2018), Know Your Customer Due Diligence on ACAMS (2019) Languages: Mandarin (fluent), Cantonese (fluent)

The Mathematics in Finance Masters Program

Courant Institute, New York University

Academic Year 2019-2020

The curriculum has four main components:

1. Financial Theory and Econometrics. These courses form the theoretical core of the

program, covering topics ranging from equilibrium theory to Black-Scholes to Heath-Jarrow-

Morton.

2. Practical Financial Applications. These classes are taught by industry specialists from

prominent New York financial firms. They emphasize the practical aspects of financial

mathematics, drawing on the instructor’s experience and expertise.

3. Mathematical Tools. This component provides appropriate mathematical background in

areas like stochastic calculus and partial differential equations.

4. Computational Skills. These classes provide students with a broad range of software skills,

and facility with computational methods such as optimization, Monte Carlo simulation, and the

numerical solution of partial differential equations.

First Semester Second Semester Third Semester

Practical Financial

Applications

Advanced Risk

Management

___

Interest Rate and FX

Models

___

Securitized Products

& Structured

Finance (1/2 Credit)

___

Energy Market &

Derivatives (1/2

Credit)

___

Advanced Topics in

Equity Derivatives

(1/2 Credit)

___

Market

Microstructure (1/2

Credit)

Fin. Eng. Models

for Corp. Finance

___

Credit Analytics:

Bonds, Loans &

Derivatives (1/2

Credit)

___

Counter Party

Credit: Valuation

Adjustments,

Capital, and

Funding

___

Fixed Income

Derivatives:

Models & Strategies

in Practice (1/2

Credit)

Practical Training. In addition to coursework, the program emphasizes practical experience. All

students do Masters Projects, mentored by finance professionals. Most full-time students do internships

during the summer between their second and third semesters.

See the program web page http://math.nyu.edu/financial_mathematics for additional information.

MATHEMATICS IN FINANCE MS COURSES, 2014-2015

PRACTICAL FINANCIAL APPLICATIONS:

MATH-GA 2752.001 ACTIVE PORTFOLIO MANAGEMENT

Spring term: J. Benveniste

Prerequisites: Risk & Portfolio Management with Econometrics, Computing in Finance.

The first part of the course will cover the theoretical aspects of portfolio construction and optimization.

The focus will be on advanced techniques in portfolio construction, addressing the extensions to

traditional mean-variance optimization including robust optimization, dynamical programming and

Bayesian choice. The second part of the course will focus on the econometric issues associated with

portfolio optimization. Issues such as estimation of returns, covariance structure, predictability, and the

necessary econometric techniques to succeed in portfolio management will be covered. Readings will be

drawn from the literature and extensive class notes.

MATH-GA 2753.001 ADVANCED RISK MANAGEMENT

Spring term: K. Abbott

Prerequisites: Derivative Securities, Computing in Finance or equivalent programming.

The importance of financial risk management has been increasingly recognized over the last several

years. This course gives a broad overview of the field, from the perspective of both a risk management

department and of a trading desk manager, with an emphasis on the role of financial mathematics and

Financial Theory

and Econometrics

Derivative Securities

___

Risk & Portfolio

Mgmt. with

Econometrics

Active Portfolio

Management

___

Algorithmic Trading

& Quant. Strategies

___

Continuous Time

Finance

Project and

Presentation

___

Time Series

Analysis & Stat.

Arbitrage

___

Adv. Econometrics

Models & Big Data

Mathematical Tools

Stochastic Calculus

Computational Skills

Computing in

Finance

Scientific

Computing in

Finance

Computational

Methods for

Finance

Data Science in

Quantitative

Finance

http://math.nyu.edu/financial_mathematics

modeling in quantifying risk. The course will discuss how key players such as regulators, risk managers,

and senior managers interact with trading. Specific techniques for measuring and managing the risk of

trading and investment positions will be discussed for positions in equities, credit, interest rates, foreign

exchange, commodities, vanilla options, and exotic options. Students will be trained in developing risk

sensitivity reports and using them to explain income, design static and dynamic hedges, and measure

value-at-risk and stress tests. Students will create Monte Carlo simulations to determine hedge

effectiveness. Extensive use will be made of examples drawn from real trading experience, with a

particular emphasis on lessons to be learned from trading disasters.

MATH-GA 2798.001 INTEREST RATE AND FX MODELS

Spring term: F. Mercurio & T. Fisher

Prerequisites: Derivative Securities, Stochastic Calculus, and Computing in Finance (or equivalent

familiarity with financial models, stochastic methods, and computing skills).

The course is divided into two parts. The first addresses the fixed-income models most frequently used

in the finance industry, and their applications to the pricing and hedging of interest-based derivatives.

The second part covers the foreign exchange derivatives markets, with a focus on vanilla options and

first-generation (flow) exotics. Throughout both parts, the emphasis is on practical aspects of modeling,

and the significance of the models for the valuation and risk management of widely-used derivative

instruments.

MATH-GA.2799-001 SECURITIZED PRODUCTS & STRUCTURED FINANCE

Spring term: R. Sunada-Wong

Prerequisites: Basic bond mathematics and bond risk measures (duration and convexity); Derivative

Securities and Stochastic Calculus.

This half-semester course will cover the fundamentals of Securitized Products, emphasizing Residential

Mortgages and Mortgage-Backed Securities (MBS). We will build pricing models that generate cash

flows taking into account interest rates and prepayments. The course will also review subprime

mortgages, CDO’s, Commercial Mortgage Backed Securities (CMBS), Auto Asset Backed Securities

(ABS), Credit Card ABS, CLO’s, Peer-to-peer / MarketPlace Lending, and will discuss drivers of the

financial crisis and model risk.

MATH-GA.2800-001 ENERGY MARKETS AND DERIVATIVES Spring term: D. Eliezer

Prerequisites: Derivative Securities and Stochastic Calculus.

This half-semester course focuses on energy commodities and derivatives, from their basic

fundamentals and valuation, to practical issues in managing structured energy portfolios. We develop a

risk neutral valuation framework starting from basic GBM and extend this to more sophisticated multi-

factor models. These approaches are then used for the valuation of common, yet challenging, structures.

Particular emphasis is placed on the potential pitfalls of modeling methods and the practical aspects of

implementation in production trading platforms. We survey market mechanics and valuation of

inventory options and delivery risk in the emissions markets.

MATH-GA.2801-001 ADVANCED TOPICS IN EQUITY DERIVATIVES

Spring term: S. Bossu

Prerequisites: Derivative Securities, Stochastic Calculus, and Computing in Finance or equivalent

programming experience.

This half-semester course will give a practitioner’s perspective on a variety of advanced topics with a

particular focus on equity derivatives instruments, including volatility and correlation modeling and

trading, and exotic options and structured products. Some meta-mathematical topics such as the

practical and regulatory aspects of setting up a hedge fund will also be covered.

MATH-GA.2802-001 MARKET MICROSTRUCTURE

Spring term: G. Ritter

Prerequisites: Derivative Securities, Risk & Portfolio Management with Econometrics, and Computing

in Finance or equivalent programming experience.

This is a half-semester course covering topics of interest to both buy-side traders and sell-side execution

quants. The course will provide a detailed look at how the trading process actually occurs and how to

optimally interact with a continuous limit-order book market.

We begin with a review of early models, which assume competitive suppliers of liquidity whose

revenues, corresponding to the spread, reflect the costs they incur. We discuss the structure of modern

electronic limit order book markets and exchanges, including queue priority mechanisms, order types

and hidden liquidity. We examine technological solutions that facilitate trading such as matching

engines, ECNs, dark pools, multiple venue problems and smart order routers.

The second part of the course is dedicated pre-trade market impact estimation, post-trade slippage

analysis, optimal execution strategies and dynamic no-arbitrage models. We cover Almgren-Chriss

model for optimal execution, Gatheral’s no-dynamic-arbitrage principle and the fundamental

relationship between the average response of the market price to traded quantity, and properties of the

decay of market impact.

Homework assignments will supplement the topics discussed in lecture. Some coding in Java will be

required and students will learn to write their own simple limit-order-book simulator and analyze real

NYSE TAQ data.

MATH-GA.2803-001 FIXED INCOME DERIVATIVES: MODELS & STRATEGIES IN

PRACTICE Fall term: L. Tatevossian and A. Sadr

Prerequisites: Computing in Finance (or equivalent programming skills) and Derivative Securities

(familiarity with Black-Scholes interest rate models)

This half-semester class focuses on the practical workings of the fixed-income and rates-derivatives

markets. The course content is motivated by a representative set of real-world trading, investment, and

hedging objectives. Each situation will be examined from the ground level and its risk and reward

attributes will be identified. This will enable the students to understand the link from the underlying

market views to the applicable product set and the tools for managing the position once it is

implemented. Common threads among products – structural or model-based – will be emphasized. We

plan on covering bonds, swaps, flow options, semi-exotics, and some structured products.

A problem-oriented holistic view of the rate-derivatives market is a natural way to understand the line

from product creation to modeling, marketing, trading, and hedging. The instructors hope to convey

their intuition about both the power and limitations of models and show how sell-side practitioners

manage these constraints in the context of changes in market backdrop, customer demands, and trading

parameters.

MATH-GA.2804-001 CREDIT ANALYTICS: BONDS, LOANS AND DERIVATIVES

Fall term: B. Fleasker

Prerequisites: Derivate Securities and Computing in Finance (or equivalent familiarity with financial

models and computing skills)

This half-semester course introduces the institutional market for bonds and loans subject to default risk

and develops concepts and quantitative frameworks useful for modeling the valuation and risk

management of such fixed income instruments and their associated derivatives. Emphasis will be put on

theoretical arbitrage restrictions on the relative value between related instruments and practical

applications in hedging, especially with credit derivatives. Some attention will be paid to market

convention and related terminology, both to ensure proper interpretation of market data and to prepare

students for careers in the field.

We will draw on the fundamental theory of derivatives valuation in complete markets and the

probabilistic representation of the associated valuation operator. As required, this will be extended to

incomplete markets in the context of doubly stochastic jump-diffusion processes. Specific models will

be introduced, both as examples of the underlying theory and as tools that can be (and are) used to make

trading and portfolio management decisions in real world markets.

MATH-GA.2805-001 COUNTER PARTY CREDIT: VALUATION ADJUSTMENTS,

CAPITAL, AND FUNDING

Fall term: L. Andersen

Prerequisites: Advanced Risk Management, Derivative Securities (or equivalent familiarity with market

and credit risk models), and Computing in Finance (or equivalent programming experience)

This class explores technical and regulatory aspects of counterparty credit risk, with an emphasis on

model building and computational methods. The first part of the class will provide technical foundation,

including the mathematical tools needed to define and compute valuation adjustments such as CVA and

DVA. The second part of the class will move from pricing to regulation, with an emphasis on the

computational aspects of regulatory credit risk capital under Basel 3. A variety of highly topical subjects

will be discussed during the course, including: funding costs, XVA metrics, initial margin, credit risk

mitigation, central clearing, and balance sheet management. Students will get to build a realistic

computer system for counterparty risk management of collateralized fixed income portfolios, and will

be exposed to modern frameworks for interest rate simulation and capital management.

FINANCIAL THEORY AND ECONOMETRICS:

MATH-GA 2707.001 TIME SERIES ANALYSIS AND STATISTICAL ARBITRAGE Fall term: F. Asl and R. Reider

Prerequisites: Derivative Securities, Scientific Computing, and familiarity with basic probability.

The term "statistical arbitrage" covers any trading strategy that uses statistical tools and time series

analysis to identify approximate arbitrage opportunities while evaluating the risks inherent in the trades

(considering the transaction costs and other practical aspects). This course starts with a review of Time

Series models and addresses econometric aspects of financial markets such as volatility and correlation

models. We will review several stochastic volatility models and their estimation and calibration

techniques as well as their applications in volatility based trading strategies. We will then focus on

statistical arbitrage trading strategies based on cointegration, and review pairs trading strategies. We

will present several key concepts of market microstructure, including models of market impact, which

will be discussed in the context of developing strategies for optimal execution. We will also present

practical constraints in trading strategies and further practical issues in simulation techniques. Finally,

we will review several algorithmic trading strategies frequently used by practitioners.

MATH-GA 2708.001 ALGORITHMIC TRADING AND QUANTITATIVE STRATEGIES

Spring term: P. Kolm and L. Maclin

Prerequisites: Computing in Finance, and Capital Markets and Portfolio Theory, or equivalent.

In this course we develop a quantitative investment and trading framework. In the first part of the

course, we study the mechanics of trading in the financial markets, some typical trading strategies, and

how to work with and model high frequency data. Then we turn to transaction costs and market impact

models, portfolio construction and robust optimization, and optimal betting and execution strategies. In

the last part of the course, we focus on simulation techniques, back-testing strategies, and performance

measurement. We use advanced econometric tools and model risk mitigation techniques throughout the

course. Handouts and/or references will be provided on each topic.

MATH-GA 2751.001 RISK AND PORTFOLIO MANAGEMENT WITH ECONOMETRICS

Fall term: P. Kolm. Spring term: M. Avellaneda

Prerequisites: univariate statistics, multivariate calculus, linear algebra, and basic computing (e.g.

familiarity with Matlab or co-registration in Computing in Finance).

A comprehensive introduction to the theory and practice of portfolio management, the central

component of which is risk management. Econometric techniques are surveyed and applied to these

disciplines. Topics covered include: factor and principal-component models, CAPM, dynamic asset

pricing models, Black-Litterman, forecasting techniques and pitfalls, volatility modeling, regime-

switching models, and many facets of risk management, both theory and practice.

MATH-GA 2755.001 PROJECT AND PRESENTATION

Fall term and spring term: P. Kolm

Students in the Mathematics in Finance program conduct research projects individually or in small

groups under the supervision of finance professionals. The course culminates in oral and written

presentations of the research results.

MATH-GA 2791.001 DERIVATIVE SECURITIES

Fall term: M. Avellanda. Spring term: B. Flesaker

An introduction to arbitrage-based pricing of derivative securities. Topics include: arbitrage; risk-neutral

valuation; the log-normal hypothesis; binomial trees; the Black-Scholes formula and applications; the

Black-Scholes partial differential equation; American options; one-factor interest rate models; swaps,

caps, floors, swaptions, and other interest-based derivatives; credit risk and credit derivatives.

MATH-GA 2792.001 CONTINUOUS TIME FINANCE

Fall term: A. Javaheri & S. Ghamami. Spring term: B. Dupire and F. Mercurio

Prerequisites: Derivative Securities and Stochastic Calculus, or equivalent.

A second course in arbitrage-based pricing of derivative securities. The Black-Scholes model and its

generalizations: equivalent martingale measures; the martingale representation theorem; the market

price of risk; applications including change of numeraire and the analysis of quantos. Interest rate

models: the Heath-Jarrow-Morton approach and its relation to shortrate models; applications including

mortgage-backed securities. The volatility smile/skew and approaches to accounting for it: underlyings

with jumps, local volatility models, and stochastic volatility models.

MATHEMATICAL TOOLS:

MATH-GA 2706.001 PARTIAL DIFFERENTIAL EQUATIONS FOR FINANCE

Spring term: R. Kohn

Prerequisite: Stochastic Calculus or equivalent.

An introduction to those aspects of partial differential equations and optimal control most relevant to

finance. Linear parabolic PDE and their relations with stochastic differential equations: the forward and

backward Kolmogorov equation, exit times, fundamental solutions, boundary value problems,

maximum principle. Deterministic and stochastic optimal control: dynamic programming, Hamilton-

Jacobi-Bellman equation, verification arguments, optimal stopping. Applications to finance, including

portfolio optimization and option pricing -- are distributed throughout the course.

MATH-GA 2902.001 STOCHASTIC CALCULUS

Fall term: P. Bourgade. Spring term: A. Kuptsov

Prerequisite: Basic Probability or equivalent.

Discrete dynamical models: Markov chains, one-dimensional and multidimensional trees, forward and

backward difference equations, transition probabilities and conditional expectations. Continuous

processes in continuous time: Brownian motion, Ito integral and Ito’s lemma, forward and backward

partial differential equations for transition probabilities and conditional expectations, meaning and

solution of Ito differential equations. Changes of measure on paths: Feynman-Kac formula, Cameron-

Martin formula and Girsanov’s theorem. The relation between continuous and discrete models:

convergence theorems and discrete approximations.

COMPUTATIONAL SKILLS:

MATH-GA 2041.001 COMPUTING IN FINANCE

Fall term: E. Fishler and L. Maclin

This course will introduce students to the software development process, including applications in

financial asset trading, research, hedging, portfolio management, and risk management. Students will

use the Java programming language to develop object-oriented software, and will focus on the most

broadly important elements of programming - superior design, effective problem solving, and the proper

use of data structures and algorithms. Students will work with market and historical data to run

simulations and test strategies. The course is designed to give students a feel for the practical

considerations of software development and deployment. Several key technologies and recent

innovations in financial computing will be presented and discussed.

MATH-GA 2043.001 COMPUTATIONAL METHODS FOR FINANCE

Fall term: J. Guyon & B. Liang

Prerequisites: Scientific Computing or Numerical Methods II, Continuous Time Finance, or permission

of instructor.

Computational techniques for solving mathematical problems arising in finance. Dynamic programming

for decision problems involving Markov chains and stochastic games. Numerical solution of parabolic

partial differential equations for option valuation and their relation to tree methods. Stochastic

simulation, Monte Carlo, and path generation for stochastic differential equations, including variance

reduction techniques, low discrepancy sequences, and sensitivity analysis.

MATH-GA 2046.001 ADVANCED ECONOMETRICS AND BIG DATA

Fall term: G. Ritter

Prerequisites: Derivative Securities, Risk & Portfolio Management with Econometrics, and Computing

in Finance (or equivalent programming experience).

A rigorous background in Bayesian statistics geared towards applications in finance, including decision

theory and the Bayesian approach to modeling, inference, point estimation, and forecasting, sufficient

statistics, exponential families and conjugate priors, and the posterior predictive density. A detailed

treatment of multivariate regression including Bayesian regression, variable selection techniques,

multilevel/hierarchical regression models, and generalized linear models (GLMs). Inference for classical

time-series models, state estimation and parameter learning in Hidden Markov Models (HMMs)

including the Kalman filter, the Baum-Welch algorithm and more generally, Bayesian networks and

belief propagation. Solution techniques including Markov Chain Monte Carlo methods, Gibbs

Sampling, the EM algorithm, and variational mean field. Real world examples drawn from finance to

include stochastic volatility models, portfolio optimization with transaction costs, risk models, and

multivariate forecasting.

MATH-GA.2047-001 DATA SCIENCE IN QUANTITATIVE FINANCE

Fall term: P. Kolm and I. Dimov

Prerequisites: Risk & Portfolio Management with Econometrics, Scientific Computing in Finance (or

Scientific Computing) and Computing in Finance (or equivalent programming experience.

This is a full semester course focusing on practical aspects of alternative data, machine learning and

data science in quantitative finance. Homework and hands-on projects form an integral part of the

course, where students get to explore real-world datasets and software.

The course begins with an overview of the field, its technological and mathematical foundations, paying

special attention to differences between data science in finance and other industries. We review the

software that will be used throughout the course.

We examine the basic problems of supervised and unsupervised machine learning, and learn the link

between regression and conditioning. Then we deepen our understanding of the main challenge in data

science – the curse of dimensionality – as well as the basic trade-off of variance (model parsimony) vs.

bias (model flexibility).

Demonstrations are given for real world data sets and basic data acquisition techniques such as web

scraping and the merging of data sets. As homework each student is assigned to take part in

downloading, cleaning, and testing data in a common repository, to be used at later stages in the class.

We examine linear and quadratic methods in regression, classification and unsupervised learning. We

build a BARRA-style implicit risk-factor model and examine predictive models for county-level real

estate, economic and demographic data, and macro economic data. We then take a dive into PCA, ICA

and clustering methods to develop global macro indicators and estimate stable correlation matrices for

equities.

In many real-life problems, one needs to do SVD on a matrix with missing values. Common

applications include noisy image-recognition and recommendation systems. We discuss the Expectation

Maximization algorithm, the L1-regularized Compressed Sensing algorithm, and a naïve gradient search

algorithm.

The rest of the course focuses on non-linear or high-dimensional supervised learning problems. First,

kernel smoothing and kernel regression methods are introduced as a way to tackle non-linear problems

in low dimensions in a nearly model-free way. Then we proceed to generalize the kernel regression

method in the Bayesian Regression framework of Gaussian Fields, and for classification as we introduce

Support Vector Machines, Random Forest regression, Neural Nets and Universal Function

Approximators.

MATH-GA 2048.001 SCIENTIFIC COMPUTING IN FINANCE

Spring term: Y. Li and

Prerequisites: multivariable calculus, linear algebra; programming experience strongly recommended

but not required.

A practical introduction to scientific computing covering theory and basic algorithms together with use

of visualization tools and principles behind reliable, efficient, and accurate software. Students will

program in C/C++ and use Matlab for visualizing and quick prototyping. Specific topics include IEEE

arithmetic, conditioning and error analysis, classical numerical analysis (finite difference and integration

formulas, etc.), numerical linear algebra, optimization and nonlinear equations, ordinary differential

equations, and (very) basic Monte Carlo.

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