class of 2019 resume book - math.nyu.edu · class of 2019 resume book mathematics in finance m.s....
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Class of 2019 Resume Book
Mathematics in Finance M.S. Program
Courant Institute of Mathematical Sciences
New York University
March 13, 2019
For the latest version, please go to
http://math.nyu.edu/financial_mathematics
Job placement contact: Michelle Shin, (212) 998-3009
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Dear Colleague,
We are pleased to provide you with the resumes of second semester students in the Courant Institute's Mathematics in
Finance Master's Program. They are in their first semester and will graduate from our Master’s program in December
2019. 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
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YITONG CAI (720) 755-0024 ■ [email protected] ■ www.linkedin.com/in/yitongcai ■
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
MS in Mathematics in Finance (expected – January 2020)
Coursework: OOP in Java, martingales, Brownian motion, econometrics application to portfolio and
risk management, pricing of derivative securities
Future Coursework: interest rate & FX models, algorithm trading and strategies, equity statistical
arbitrage strategy development in Python
UNIVERSITY OF COLORADO DENVER Denver, CO
BS in Mathematics; BA in Economics (2015 – 2018)
Coursework: hypothesis testing, linear regression, probability, linear algebra, ODE, markov chains,
renewal process, unit root test, VAR, cointegration, time series analysis, instrumental variables and two
stage least squares
EXPERIENCE
BOC INTERNATIONAL HOLDINGS LIMITED Beijing, China
Quantitative Research Intern (June 2018 – August 2018)
Multi-Factor Model in Machine Learning: Imported equities data using API and analyzed correlation
effects between financial factors and monthly return rate of individual stocks in single-factor and multi-
factor models with linear regression, logistic regression and LASSO regression, XGBoost Model
Factors Selection: Implemented backward selection, forward selection and stepwise selection to
choose factors, checked for stationary by fixing heteroskedasticity with WLS regression and fixing
collinearity
CHINESE ACADEMY OF SCIENCES Beijing, China
Research Assistant (August 2017)
Random Walk Algorithm: Developed random walk with restart, network-based random walk with
restart on the heterogeneous network, and interactions inference based on random walk with restart
algorithms in Matlab, all of which got higher AUC under leave-one-out cross validation
Developed random walk algorithm whose random walker stops at the original start point
PROJECTS
NEW YORK UNIVERSITY New York, NY
Monte Carlo Option Pricing Approach in Java
Monte Carlo Simulation: Priced European and Asian options with antithetic decorator to reduce
variance and determined stopping criteria dynamically
Parallel Computing: Developed client/server model via Java Message Service and ActiveMQ
Order Book Simulation& Data Processing in Java
Execution Management System: Simulated an exchange with optimizing data structures that allow
quick adding, changing and cancelling orders, sending corresponding messages to clients
Back-Test Data Processing: Converted and merged high frequency trading records with developed
reader framework
K-Means Clustering in Java
Lloyd’s algorithm: Performed generic multidimensional point clustering and fixed-point-size
clustering, and measured efficiency with inner-cluster distance sum and outer-cluster centroids distance
UNIVERSITY OF COLORADO DENVER Denver, CO
VAR and VECM for U.S. and China Exchange Rate
Time Series Analysis: Fitted vector autoregression model on U.S. and China exchange rate and
economic indicators to capture linear interdependencies, tested for stationary and conducted ADF test,
and estimated a vector error correction model inR
COMPUTER SKILLS/OTHER
Programming Languages: Java, Python, C++
Other Software: R, Matlab, Stata, Eviews, SAS
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YUAN (ALEX) CHENG
(917) 443-2806 ■ [email protected]
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
M.S. in Mathematics in Finance (Sept. 2018 – Dec. 2019)
Coursework: Portfolio theory, Monte Carlo simulation, stochastic calculus, Black-Scholes models
UNIVERSITY OF CHINESE ACADEMY OF SCIENCES Beijing, China
B.S. in Mathematics and Applied Mathematics (2014 - 2018)
Awards: National Scholarship (for top 1), Honorable Mention in Probability and Statistics of S.-T.
Yau College Students Mathematics Contest, 2nd
prize in Chinese Mathematics Competitions
COLUMBIA UNIVERSITY New York, NY
Fu Foundation School of Engineering and Applied Science (Sept. 2017 – Dec. 2017)
EXPERIENCE
HYPERPLANE ASSET MANAGEMENT CORPORATION LTD Beijing, China
Alpha Researcher (May 2018 – June 2018)
Constructed over 50 price-volume trading factors in Chinese equity market with Python
Back-tested trading factors and analyzed annualized return, Sharpe ratio and maximum drawdown
Quantitative Trading Strategy Intern (June 2018 – July 2018)
Predicted stocks’ trading volume by regression, and stock price movement by decision tree model
Employed Time Series and Principal Component Analysis to enhance VWAP strategy in Python
Implemented trading strategies by translating the ideas in academic papers into quantified signals
NEW YORK UNIVERSITY New York, NY
Teaching Assistant: Mathematics of Finance (September 2018 – December 2018)
Graded homework, provided solutions and answered questions from students
Communicated with the professor about students' performance and course materials
UNIVERSITY OF CHINESE ACADEMY OF SCIENCES Beijing, China
Teaching Assistant: Real Analysis, Functional Analysis (September 2016 – May 2017)
Assigned homework, held recitation sections, tracked attendance of students for these two courses
PROJECTS
NEW YORK UNIVERSITY New York, NY
Course Projects (September 2018 – October 2018)
Option Pricing with Monte Carlo in Java: Priced European, Asian and Look-Back options
using antithetic variates, control variates and importance sampling to reduce variance
K-Means Clustering in Java: Performed Lloyd’s algorithm based on re-definable factors,
modified it by clustering objects into fixed size and compared efficiencies using several metrics
Portfolio Optimization in Python: Implemented minimum variance portfolio (return constrained
and unconstrained) and visualized the efficient frontier
VaR Estimation in Excel: Applied infill techniques for missing data (Brownian Bridge,
regression) and estimated VaR by V/CV, historical simulation and Monte Carlo simulation
UNIVERSITY OF CHINESE ACADEMY OF SCIENCES Beijing, China
Wasserstein Distance over Probability Measure Space and Its Applications (August 2017 – May 2018)
Studied properties of Wasserstein space under Optimal Transportation theory
Compared different metrics (TV, Prokhorov, Levy, Kolmogorov) and divergences (K-L, J-S) over
probability measure space, summarized equivalences between and obtained inequalities of them
Discussed the mechanism of applying Wasserstein distance in GAN and implemented GAN using
Python with MNIST data to generate pictures of handwritten digits COMPUTER SKILLS/OTHER
Programming Languages and Software: Python, Java, Microsoft Office, R
Languages: Mandarin (native), English (fluent)
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JINGRAN CUI (917) 975-1706
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
MS in Mathematics in Finance (expected – January 2020)
Coursework: Stochastic Calculus, risk management, option pricing and hedging, OOP in Java, portfolio
management, Black-Scholes formula
UNIVERSITY OF ROCHESTER Rochester, NY
BS in Mathematics and BA in Statistics (2014 - 2018)
Coursework: Calculus, differential equations, probability and statistics, linear algebra, computer science,
financial mathematics, number theory, linear regression
Awards: Dean’s List, Phi Beta Kappa
EXPERIENCE
UNIVERSITY OF ROCHESTER Rochester, NY
Teaching Assistant (September 2016 – September 2018)
Held office hours to review and explain problems for various math and statistics courses
Prepared answer sheets for each homework and exams
Conducted review sessions before exams for 30-40 students
Graded homework, quizzes, and exams
CREDITEASE CORP. Beijing, China
Department Assistant (June 2017 – August 2017)
Utilized excel to calculate expected returns & risk tolerance to assist team members in providing best
investment plan for clients
Used R to calculate insurance plan for clients.
Collected colleagues’ sales performance and implemented system of rewards and penalties
Collected and designed employee standing posts for company’s reward program
PROJECTS
New York University New York, NY
K-Means Clustering in Java
Implemented Lloyd’s algorithm to perform generic multi-dimensional points clustering efficiently
Improved algorithm to fixed-size clustering and measure
Measured efficiency with within-cluster distance variance and compared efficiencies with several metrics
Mean Variance Portfolio Optimization in R
Performed Mean Variance Portfolio Theory for a portfolio with six different types of funds
Calculated the maximum Sharp ratio portfolio
Computed the weight allocation for a given set of subjective views
UNIVERSITY OF ROCHESTER Rochester, NY
The exploration of the relationship between the keywords in social networks & stock prices
Collected frequencies of appearance of keywords (positive/negative words) in social networks (Twitter,
Facebook, Instagram) in latest two years by Python
Used R to find relationship between frequencies of keywords and stock prices in technology field
The relationship between the faculty salary and state, college type, number of faculty, & faculty position
Used existed dataset collected from website
Utilized R to find statistical significance between faculty salary and state, college type, number of faculties,
faculty’s position respectively
Used R to draw plot and built models to represent relationship between faculty salary and state, college
type, number of faculty, faculty’s position
COMPUTER SKILLS/OTHER
Programming Languages: C/C++, Python, JAVA, R
Certificate: Actuarial Studies Certificate, Passed CFA Level I Exam
Languages: Mandarin (native), English (fluent)
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HA DU (415) 539-5859 ■ [email protected]
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
MS in Mathematics in Finance (expected – January 2020)
Coursework: Econometrics techniques in risk & portfolio management, arbitrage-based pricing
of derivative securities, simulations & test strategies in trading
LAWRENCE UNIVERSITY Appleton, WI
BA in Mathematics & Economics (September 2010 – June 2013)
EXPERIENCE
J.P. MORGAN ASSET MANAGEMENT New York, NY
Finance & Business Management Associate (May 2017 – August 2018)
Prepared strategic and interactive presentations for the CFO to present at quarterly Asset
Management Global Town Halls and monthly Operating Committee meetings
Analyzed and stress-tested the profitability of approximately 60 investment groups and 60 sales
channels in order to determine highly scalable businesses versus unsuccessful ones
Examined the productivity metrics of approximately 160 investment specialists across 6 main
asset classes to identify underperforming groups and restructure the client service model
Designed and implemented 6 major series of quarterly, monthly, and bi-weekly management
meetings, actively communicating with leadership teams and cross-functional groups
Institutional Advisory & Sales Analyst (June 2014 – April 2017)
Researched current asset allocation strategies, external asset managers and consultant relationships
of approximately 100 pension plans, using public filings and third-party databases
Advised pension plans on adding suitable J.P. Morgan strategies to their portfolios in order to
achieve higher returns and better diversification
Drafted cover letters and comprehensive responses to approximately 25 RFPs from pension plans
for both public and private investment strategies
Performed portfolio analysis and peer comparison to retain several important fixed income clients
during periods of unfavorable market conditions and portfolio manager turnovers
Responded to questions from clients on markets, investment trends, and strategy performance,
using both J.P. Morgan proprietary insights and industry reports
THOMSON REUTERS CORPORATION San Francisco, CA
Sales & Account Management Associate (June 2013 – June 2014)
Evaluated investment expertise, client bases, technology platforms, and key contacts of over 100
asset management firms and registered investment advisors
Collaborated with product specialists to introduce Thomson Reuters technology solutions to
prospective clients through emails, phone calls, and in-person demos
Prepared impactful presentations that highlighted the benefits of Thomson Reuters Eikon, leading
to a large investment management firm subscribing to over 100 Eikon desktops
DELOITTE TOUCHE TOHMATSU Hanoi, Vietnam
Mergers & Acquisitions Summer Intern (June 2012 – August 2012)
Researched prospective clients for M&A advisory services in oil & gas, automobile, and food
industries in Vietnam, using various industry reports and news sources
Prepared financial statements and built valuation models to assess the viability of M&A
transactions for both sell-side and buy-side clients
COMPUTER SKILLS/OTHER
Programming Languages & Other Software: Tableau, Python, SQL, Javascript, Excel, PowerPoint
Certification: Chartered Financial Analyst (CFA), Series 7, Series 63
Languages: English (fluent), Vietnamese (native)
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JINGYUAN FENG (917) 402-5994 ■ [email protected]
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
MS in Mathematics in Finance (expected – January 2020)
Coursework: Backward and forward PDEs, Ito calculus, linear Gaussian processes; robust
portfolio theory, econometrics with time series forecasting; implied volatility surfaces, futures
trading strategies; Java
IMEPERIAL COLLEGE LONDON London, UK
MSci in Mathematics (Sep 2013 – Jul 2017)
Coursework: Real analysis, functional analysis, number theory, linear algebra, random processes
EXPERIENCE
TSINGHUA UNIVERSITY, PBC SCHOOL OF FINANCE Beijing, China
Summer Researcher (Jul 2017 – Sep 2017)
Built & optimized architecture of deep feedforward networks, convolutional neural networks, and
recursive neural networks to forecast cross-sections of expected stock returns
Compared portfolio performances based on Fama French five-factor model alpha and Sharpe ratio
CHINA FINANCIAL FUTURES EXCHANGE Shanghai, China
Analyst Intern, Interest Rate Derivatives (Jul 2016 – Aug 2016)
Analyzed three special forms of securities (CMB/TIPS/FRN)
Produced report on US treasury securities issuance structure & interest rate derivative pricing
Led a team of two on project on pricing models of stock indices/swaps/stock option
SHANGHAI STOCK EXCHANGE Shanghai, China
Analyst Intern, Derivative (Jul 2015 – Sep 2015)
Compiled comprehensive table presenting equity options including specifications on options
trading volume, open interest (OI) and calculated put/call ratio in R/VBA
Back tested the volatility of S&P 500 based on 40-trading day period in R
BLOOMBERG CAPITAL London, UK
Team Leader (Jun 2015 – Jul 2015)
Proposed purchase of Petrobras ADR following its share price collapse in wake of the crude oil
oversupply and its impending debt obligations
Received a 30% return by hedging against oil price uncertainty through long put options at strike
price of $5; limited the overall downside to -33% loss against potential upside of +100% return
PROJECTS
ZHEJIANG UNIVERSITY, ACADEMY OF FINANCIAL RESEARCH Hangzhou, China
Researched on pricing risk management of credit default swaps
Tested ISDA CDS standard model on 2Y CDS & set of standard CDs
Queueing Theory and Stochastic Approximations in a Markovian Limit Order Book
Described orders in Limit Order Book as a M/M/1+M queue
Identified diffusion approximation of M/M/1+M LOBs as reflected Ornstein-Unlenbeck process
Simulated heavy-traffic diffusion approximation in C
High Performance Computing
Parallelized a recursive network model with OpenMP
Developed Fortan program parallelized with MPI using explicit-Euler time marching to simulate
weakly-coupled system
COMPUTER SKILLS/OTHER
Programming Languages: Python (proficient), C, Fortan (intermediate), C++, Java, SQL (basic)
Other Software: Microsoft Office, Matlab, Mathematica, Maple, R
Languages: Mandarin (native), English (fluent), Italian (conversational), Arabic, Cantonese (basic)
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YINAN (DAVID) HU (646) 217-8726 ■ [email protected]
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
MS in Mathematics in Finance (expected – January 2020)
Coursework: Portfolio theory, risk management, volatility modeling, Black-Scholes PDE, interest
rate derivatives, computing applications in trading and hedging, Ito calculus
Future Coursework: FX derivatives, algorithmic trading, optimal strategies, simulation techniques
LONDON SCHOOL OF ECONOMICS AND POLITICAL SCIENCE London, UK
BSc in Mathematics with Economics (2015 – 2018)
Coursework: Linear algebra, calculus, analysis, probability, econometrics, asset pricing
EXPERIENCE
FOUNDER SECURITIES CO., LTD. ASSET MANAGEMENT COMPANY Beijing, China
Investment Management Department Summer Analyst (2018)
Priced corporate bonds and computed yields using bond pricing model
Carried out credit analysis for corporations through Altman Z-score calculations
Researched policy effects on financing platforms; collected and processed industry data from Wind
RENAISSANCE ERA INVESTMENT CO., LTD. Beijing, China
Quantitative Department Summer Analyst (2017)
Analyzed over 10 million data on target snack food brands using Python and Mongo DB
Wrote programs of various functions such as market share computation and word cloud generation
Studied trend following strategies; implemented Dual Thrust CTA strategy and backtested strategy
SINOLINK SECURITIES CO., LTD. Beijing, China
M&A Department Summer Analyst (2016)
Conducted financial modeling including DCF and comparable valuation in CNY 3.5 billion deal
Facilitated meetings with acquirer and target firm, tailored integration plan based on negotiations
CAPITAL SECURITIES CO., LTD. Shijiazhuang, China
Brokerage Department Summer Intern (2014)
Compiled financial news for team’s daily update; assisted manager in contract signing with clients
PROJECTS
LONDON SCHOOL OF ECONOMICS AND POLITICAL SCIENCE London, UK
Testing the Small World Phenomenon
Computed maximum shortest-path distance between any two nodes in Facebook network subgraph
Designed the algorithm in Python using breadth-first search, conducted running time analysis
Backtesting of EWMA
Backtested performance of VaR under EWMA using three stocks from Chinese and HK markets
Investigated violation ratios, VaR volatility; carried out independence test and sub-period analysis
MBP Capital, the LSE Student Investment Fund
Composed weekly reports; proposed investments for FX portfolio in multi-asset consideration
Personal Virtual FX Trading
Generated 24% profits in 2 months; used fundamental approach utilizing macroeconomic analysis
#Hashtags and Bullets: Mapping Citizen Journalism and Unarmed U.S. Police Shootings
Collected data from Twitter; implemented linear regression and ANOVA using SPSS
COMPUTER SKILLS/OTHER
Programming Languages & Other Software: Python, Java, R; Stata, Wind, SPSS, Microsoft Office
Languages: Mandarin (native), English (fluent)
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YUNDAI (DAISY) HU
(631) 291-2651 ▪ [email protected] ▪ https://www.linkedin.com/in/yundai-daisy-hu/
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
MS in Mathematics in Finance (Expected – December 2019) (GPA: 3.75/4.0)
• Current Coursework: Data structures and algorithm, Black-Scholes formula, Option Greeks,
Derivatives Pricing, Monte Carlo simulation, Black-Litterman model, Market Microstructure,
Ridge & Lasso Regression, Markov Process, Tension Spline
• Future Coursework: Interest Rate & FX Models, Statistical Arbitrage, Time Series Analysis,
Counterparty Credit, Machine Learning
STONY BROOK UNIVERSITY Stony Brook, NY
BS in Applied Mathematics and Statistics & BA in Economics (August 2015 – May 2018) (3.95/4.0)
• Coursework: Common Distributions, Linear Algebra, OLS, ODE, Probability
EXPERIENCE
FULLGOAL FUND MANAGEMENT CO., LTD Shanghai, CHINA
Quantitative Summer Intern, Equity Investment Dept. (June 2018 - August 2018)
• Application of Models: Conducted research on relationship between idiosyncratic volatility (IV)
and excess return of Chinese A-share stocks, using CAPM, Fama-French, and Carhart models,
and found that there exist negative correlation; tested further to see how IV would predict excess
return, based on historical A-share data from 2005 to 2015 • Analysis of Speculation Cycle: Conducted speculation analysis of A-shares stocks by examining
stocks’ idiosyncratic volatility, specificity, turnover rate and price delay to identify under/over
speculated stocks PROJECTS
NEW YORK UNIVERSITY New York, NY
Implementations of Roll Model and Tick Test in Python (Spring 2019)
• Sequentially read .xz file which contains all trades of a day to calculate the Roll-implied spread
for each ticker by implementing Roll model
• Implemented tick test to classify each trade as buyer-initiated, seller-initiated, or undetermined
Merging Binary Trades’ Files in Java (Fall 2018)
• Worked with high frequency data and raw binary databases for the purpose of backtesting
• Implemented DBReader to sequentially read one-day trades data of GE, IBM, MSFT and PFE
stocks, and used DBProcessor to merge the records from DBReader objects; iteratively merge
two files at a time until there is only one file
Monte Carlo Simulation for Option Pricing in Java (Fall 2018)
• Priced European and Asian options using Monte Carlo simulation
• Impletemented Box-Muller algorithm to generate normal random variables
• Later upgraded this project by using Active MQ Broker to send and receive messages
K-Means Clustering in Java (Fall 2018)
• Implemented Lloyd’s algorithm to perform flexible-size clustering and fixed-size clustering in
two-dimensional space
• Built a metrics to measure the goodness of the implementation
NATIONAL SUPERCOMPUTER CETNER Jinan, CHINA
Taishan Supercomputer (Summer, Fall 2017)
• Performance testing : Installed, compiled, and tested benchmarks, FFTW and Graph 500, on 64-
processors Taishan supercomputer in order to rate and improve its performance; inputted
experiments’ results into R to plot and analyze data
COMPUTER SKILLS/OTHER
Programming Language: Python, Java, MATLAB, R
Other Software: MS Excel, Word, PowerPoint
Languages: Mandarin (native), English (fluent)
Certifications: FRM Part I
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NAOTAKA IKEDA (317) 903-2722 ■ [email protected]
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
MS in Mathematics in Finance (expected – January 2020)
Coursework: Stochastic calculus, OOP in Java, time series analysis, data mining, machine
learning, Bayesian statistics, portfolio optimization & risk management
PURDUE UNIVERSITY West Lafayette, IN
Master of Business Administration (June 2016 – May 2017)
KYOTO UNIVERSITY Kyoto, Japan
MS in Mathematics (Apr 2007 – Mar 2009)
SAITAMA UNIVERSITY Saitama, Japan
BS in Mathematics (Apr 2003 – Mar 2007)
EXPERIENCE
THE NORINCHUKIN BANK Tokyo, Japan
Risk Manager, Risk Monitoring Division (May 2017 – June 2018)
Responsibility: Credit risk management by Monte Carlo Simulation; Calibrated default
probability of CDS transition rate matrix based on customized Merton model by company-wide
risk monitoring system
Reduced calibration time by over 20% in 2 months by reviewing existing MATLAB codes
Negotiate with IT department for a $2 million budget to renovate the system
System Planner/IT Developer, Operations Planning & Solutions Division (July 2013 – May 2016)
Implemented thin-client system to update portfolio management system with IT subsidiary staff
Led data migration of largest client data sets worth $16B for client’s merger
Built Excel VBA to automate the conversion of client data into new database format
IT Liaison, London Branch (London, England) (July 2012 – June 2013)
Earned competitive assignment as liaison between System Division in Head Office and 7 member
IT team at London Branch
Resolved 4 years longstanding discrepancies within 6 months by establishing new London Branch
IT security procedure to harmonize with Head Office IT policies
Eased London’s staff use of Head Office accounting system by generating migration data SQL for
utilization in London’s more user-friendly interface
IT Planner, System Division (Apr. 2009 – June 2012)
Converted operational risk management system used by over 3,000 employees from application-
based to web-based system to centralize business flow to risk management division
Created data migration tool to develop credit risk management system that recovered delays
resulting from 2011 Tohoku earthquake and tsunami
Reduced annual hardware cost for in-house shared core system by 10%; assessed 1,000+ contracts
with PwC
PROJECTS
PURDUE UNIVERSITY West Lafayette, IN
Experimental Learning Initiative (IT Consultation)
Designed seamless testing and deployment virtualized environments and agile process flow for
reducing US Coast Guard application development time with 4 other students (3 months project)
Time Series Data Analysis (SAS)
Estimated deviations of historical jet fuel prices by ARIMA model in SAS
COMPUTER SKILLS/OTHER
Programming Languages: MATLAB, SQL, C++, Java, R, Python, Bloomberg Terminal, LaTeX, VBA
Certificate: GARP Financial Risk Manager (FRM) Part I (November 2017), Part II (May 2018)
Languages: English (fluent), Japanese (native)
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TIANHAO LU (347) 446-2986 ■ [email protected] ■ https://www.linkedin.com/in/tianhao-henry-lu/
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
MS in Mathematics in Finance (expected – January 2020)
• Coursework: Arbitrage-based pricing, risk management, Brownian motion and martingales,
object-oriented programming, Black-Scholes formula and applications • Future Coursework: Interest rate & FX models, stochastic volatility models, statistical arbitrage
IMPERIAL COLLEGE London, United Kingdom
BSc and ARCS in Mathematics with Statistics for Finance (October 2015 – June 2018)
• Coursework: Real analysis, differential equations, option pricing, probability and statistics,
statistical modelling, time series, applied probability, survival modelling
• Distinctions: Graduated with Ken Allen Prize (5/200), three years dean’s list (top 10%)
EXPERIENCE
HUA AN FUND MANAGEMENT Shanghai, China
Quantitative Research Intern (June 2018 – August 2018)
• Developed event-driven strategy and used machine learning methods to construct portfolios with
13% annual return and low leverage by using Python and R
• Extended and implemented Fama-French model into stock market, getting portfolios with neutral
beta
• Implemented GARCH model and its family to model volatility of index of market using R,
summarizing applicable scenarios for each model
IMPERIAL COLLEGE London, United Kingdom
Research Assistant: Modeling pseudo periodic time series (CO2 data) (July 2017 – August 2017)
• Used an Autoregressive model to fit CO2 data by considering its spectrum structure
• Utilized Bayesian methods to determine coefficient of Autoregressive model and compared to
built-in function in R, result of these simulations gave a better QQ norm plot
XINGTAI CAPITAL Shanghai, China
Equity Research Intern (June 2016 – August 2016)
• Researched consumer product companies by performing channel checking and analyzing third-
party data
• Utilized Bloomberg Markets to write research reports about peer company comparable analysis
and assemble relevant data for consumption by investment committee and senior staffers
PROJECTS
IMPERIAL COLLEGE London, United Kingdom
Credit scorecard building (Nov 2017 – Dec 2017)
• Prepared and fitted real market data using machine learning techniques such as discretization,
regression imputation and Lasso
• Evaluated the model using AUC and statistical inference, performed some quantitative insights
about the data
Kalman filter and its variant (May 2017 – June 2017)
• Determined the performance of different variants of Kalman filter applied to non-linear mapping
• Wrote a backtesting trading algorithm for European financial data by trading proportional to
negative Z-score at high frequency and got 0.1% daily return
COMPUTER SKILLS/OTHER
Programming Languages: Java, Python, R, MATLAB
Other Software: Microsoft Word, Excel, LaTex
Languages: Mandarin (native), English (fluent)
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JUNQI QIAN (413) 888-8446
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
MS in Mathematics in Finance (expected – January 2020)
Coursework: Quantitative portfolio theory, interest rate derivatives and one-factor models, Monte
Carlo and finite difference methods, fixed income and currency derivatives, Black-Scholes
formula and applications to stochastic processes, PDEs and dynamic programming, risk
management
Future Coursework: Risk management (market and credit risk, VAR and stress testing),
advanced econometrics, big data applications, and continuous time finance
UNIVERSITY OF CALIFRONIA, BERKELEY Berkeley, CA
BA in Mathematics (2016 - 2018)
Coursework: Differential equations, computer science, real, complex, and numerical analysis
SMITH COLLEGE Northampton, MA
Mathematics (2014 - 2016)
EXPERIENCE
PRINCETECHS Beijing, China
Data Analyst (2017)
Developed machine learning program PLKJ_toolkit in data cleaning and sampling in Python
Researched and compared machine learning software functions (model training and parameter
optimizing) to improve PLKJ_toolkit’s capacity for big data analysis and its competence
Communicated with four teams to optimize PLKJ_toolkit’s workflow by testing transaction data
Developed workflow’s visualization interface for demo presentation to potential customers by
designing the interface
DRAGON TELEVISION, CHINA BUSINESS NETWORK Shanghai, China
Assistant Director (2016)
Designed and produced content for one season of Chinese top 25 business TV program about
funds, including ETF
Organized investment workshops monthly for potential professional investors and communicated
with audiences to obtain feedback
PROJECTS
UNIVERSITY OF CALIFRONIA, BERKELEY Berkeley, CA
Reconstructing 3D Protein Structures with Simulated Cryo-electron Microscopy Images Projects
Researched mathematical model of TEM and transformation of the image
Analyzed data given electron density information in different resolutions
Simulated microscopy 2D images and reconstructed 3D protein structures of Zika virus by Python
Algorithm Development of Cholesky Factorization’s Rank One Update
Developed algorithm by fortran for decomposing matrix with new data to increase efficiency
Applied rank-one update theory to increase numerical stability and decrease storage space
COMPUTER SKILLS/OTHER
Programming Languages: Python, Fortran, Rstudio, STATA
Other Software: Microsoft Office, iWork; MATLAB, Mathematica
Languages: Chinese (Native), English (Fluent), Japanese (intermediary), French (basic)
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JIAHAO (NICK) REN (858) 766-8329 ■ [email protected]
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
MS in Mathematics in Finance (expected – December 2019)
Coursework: Dynamic asset pricing models, volatility modeling, regime-switching models, Black-
Scholes formula and applications, interest rate derivatives, one-factors interest rate models, credit
derivatives
Future Coursework: Continues time finance, IEEE arithmetic, Monte Carlo methods, optimization
methods, local volatility models, stochastic volatility models, Black-Scholes model
UNIVERSITY OF CALIFORNIA SAN DIEGO San Diego, CA
BS in Joint Major Mathematics & Economics (2014-2018)
Coursework: Probability, statistics, linear algebra, numerical methods, programming in Java,
regression analysis in econometrics
EXPERIENCE
UNIVERSITY OF CALIFORNIA SAN DIEGO San Diego, CA
Research Assistant, Instructor: Prof. Steven Benjamin Levkoff (August 2017 – December 2017)
Used R programming to classify 500 students according to their prerequisites
Analyzed correlation between students’ categories and their test scores in game theory
Planed three First-Price Auctions activities to test conclusions in “Rationalizable Bidding in First-
Price Auctions” written by Pierpaolo Battigalli & Marciano Siniscalchi
CHINA GALAXY SECURITIES COMPANY LIMITED Beijing, China
Summer Intern (July 2016 – September 2016)
Researched on abnormal returns of stocks, and summarized 16 types of events that may lead to
abnormal returns
Categorized types into 5 groups by evaluating abnormal returns of 20 trading days before and after
each event using T-test in R
Built Event-driven strategy that beats CSI 500 SSE Stocks Index during 2011 to 2016
HENAN FUTURES AND ITS DERIVATIVES RESEARCH INSTITUTE Zhengzhou, China
Summer Intern (August 2015 – September 2015)
Researched on CME, ICE, and other exchange groups’ expansion strategies and wrote a report
Analyzed impact of “HKEx Group Strategic plan 2013-2015” on Mainland China Fixed income
and commodity market
PROJECTS
UNIVERSITY OF CALIFORNIA SAN DIEGO San Diego, CA
Mathematical Modeling
Modified Blackjack’s basic strategy through counting number of cards with 10 points based on
probability theory, logical reasoning and MATLAB programming; improved player edge
Research on the New Counting Strategy of Blackjack Self-studied “Beat the Dealer” and other counting strategies Analyzed strategies through using them and collecting game data in Excel Developed new counting strategy with 0.993 betting correlation (BC), 0.567 playing efficiency
(PE) and 0.745 insurance correlation (IC) based on logical reasoning
COMPUTER SKILLS/OTHER
Programming & Software: Java (2 years), MATLAB (1 year), R (1 year), LaTeX, Stata
Languages: Mandarin (native), English (fluent)
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XIAOCEN (SHELLY) SHANG (858) 766-1877 ■ [email protected]
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
MS in Mathematics in Finance (expected – January 2020)
• Coursework: Derivative securities pricing, Black-Scholes model and applications, risk
management, Black-Litterman model, dynamic asset pricing model, Brownian motion, time series
analysis
UNIVERISTY OF CALIFORNIA, SAN DIEGO La Jolla, CA
BS in Mathematics & BA in Economics (2014 - 2018)
• Coursework: Differential equations, operations research, probability, stochastic processes,
numerical analysis, statistics, microeconomics, macroeconomics, econometrics
• Honors: Summa Cum Laude, Provost Honors (4 years)
EXPERIENCE
PICC THE PEOPLE’S INSURANCE COMPANY (GROUP) OF CHINA LIMITED Beijing, China
Intern (July 2018 – August 2018)
• Developed Copula based pricing strategy in R and applied model to calculate YP, RP, and RPHPE
loss ratios and premiums of insurance under different deductibles
• Adjusted prices according to CPI and government’s pricing adjustments and met risk management
requirements
• Constructed and modified database of formal and informal names of over 46000 locations using
Excel and R, to help insurance company capture keywords and locate the place
MOTORSKILL VENTURE GROUP Sugar Land, TX
Quant Analyst Intern (Aug 2017 – Sept 2017)
• Analyzed financial reports and translated information into Chinese
• Applied economic model to forecast impacts of investment such as placement opportunities
produced, expected investment returns, and risks
• Back-tested and optimized model in R, by data cleaning, data visualization, and regression
analysis
CHINA SECURITIES CO., LTD Beijing, China
Bond Underwriting Intern (Jul 2015 – Aug 2015)
• Coordinated in assembling finance team by working with accounting firms, credit consulting
firms
• Examined industry analysis and financial statement analysis to evaluate target company
• Coordinated with accounting firm to prepare prospectus and investment presentations
• Utilized relative valuation method, in term of Price/Earnings ratio, to price the security
PROJECTS
UNIVERISTY OF CALIFORNIA, SAN DIEGO La Jolla, CA
Multifractal Detrended Fluctuation Analysis (MF-DFA) Case Study
• Applied MF-DFA model into CSI 300 stock index on its realized volatility
• Constructed a trading model in R using results of obtained Hurst index based on 2015
performances
• Achieved 18% expected returns with setting trading strategies, beating the 11% market return
COMPUTER SKILLS/OTHER
Programming Languages: Java, R, Matlab, Stata
Other Software: Microsoft Office, MATLAB
Languages: Mandarin (native), English (fluent), Korean (1 year)
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YIWEI SHI (914) 839-2354
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
MS in Mathematics in Finance (expected – January 2020)
Coursework: Financial computation programming skills, risk management, quantitative portfolio
theory, Black-Scholes theory, equilibrium asset pricing models, arbitrage pricing theory,
stochastic differential equations, stochastic calculus
Future Coursework: Continuous time finance, time series analysis, statistical arbitrage, partial
differential equation, scientific computing in finance
NEW YORK UNIVERSITY SHANGHAI Shanghai, China
BS in Honors Mathematics (2014 - 2018)
Coursework: Calculus, linear algebra, differential equation, probability and statistics, stochastic
process, real analysis, and computer science
Honors: Founders’ Day Award, Magna cum laude, 3-year Dean’s List
EXPERIENCE
GUOTAI JUNAN SECURITIES Shanghai, China
Analyst Intern, Department of Derivative Investments (June 2017 – Aug 2017)
Investigated and programmed different methods of American option pricing: binary tree, finite
difference method and Barone-Adesi-Whaley model
Compared American option pricing models using control variable method, reported on how to
make use of their own advantages to price options efficiently
Researched on Multivariate Regime Switching (MRS) model in strategic asset allocation
Wrote MATLAB code and implemented MRS model with real data (2005-2013) downloaded
from Wind
Trained model with data and tested model on test data, analyzed the performance
NEW YORK UNIVERSITY SHANGHAI Shanghai, China
Vice President of Math Society (September 2017 – May 2018)
Communicated with prestigious mathematical professors in order to set up student meetings
Organized the marketed four student events by creating advertisements
PROJECTS
NEW YORK UNIVERSITY SHANGHAI Shanghai, China
Independent Study on Brownian Motion and Stochastic Calculus
Researched Brownian motion under the guidance of Prof. Vladas Sidoravicius
Read papers and books, summarized key ideas and reported weekly in form of whiteboard
presentation
Toxic Comments Classification
Downloaded a 150,000 lines csv data file from Kaggle
Utilized Excel to clean data to ensure blank spaces were deleted and format was corrected
Wrote Python code using bag-of-words algorithm to transport sentences into feature vectors
Applied two machine learning algorithms, support vector machine and logistic regression, to
classify online toxic comments from non-toxic ones
COMPUTER SKILLS/OTHER
Programming Languages: Java, Python
Other Software: Microsoft office, MATLAB, Wolfram alpha
Languages: Chinese (native), English (fluent)
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MEIXI (MAYSIE) SUN (734) 881-0460 ■ [email protected]
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
MS in Mathematics in Finance (expected – January 2020)
Coursework: Derivative securities, stochastic calculus, risk management, continuous time
finance, time series analysis and statistical arbitrage
UNIVERSITY OF MICHIGAN Ann Arbor, MI
BS in Mathematics & Statistics with Distinction (2016 - 2018)
Coursework: Probability, advanced calculus, math of finance, stochastic process, numerical
methods, mathematical statistics, applied regression, and data mining
Honors: University Honors (2016 – 2018)
EXPERIENCE
CHINA SECURITIES Beijing, China
Investment Banking Summer Analyst (Jun 2018 – Jul 2017)
Participated in very first Chinese credit rating company IPO program
Evaluated company’s performance by examining financial statements from WIND
Researched on investor relations of the company by reading their board and shareholder’s
meetings notes
Performed market, management, and risk due diligence on the target company
THE BANK OF EAST ASIA (CHINA) LIMITED Beijing, China
Summer Analyst (Jun 2017 – Aug 2017)
Analyzed recent credit proposals and clients’ financial statements to find out potential credit risks
Input the data into internal database and discussed solutions with colleagues
Wrote the credit proposal for a real estate company regarding their construction project and
researched on relative China Banking Regulatory Commission (CBRC) policies
Made revisions and updates to previous credit proposals for quarterly audit and for credit limit
adjustments
PROJECTS
UNIVERSITY OF MICHIGAN Ann Arbor, MI
Piazza Posts Classification
Wrote a C++ program to identify and classify subjects of Piazza posts of a certain EECS course
Applied natural langrage processing and machine learning techniques, especially binary search
trees and the map data structure
Designed multiple tests to exam the functionalities of the project
ILLINOIS INSTITTUE OF TECHNOLOGY Chicago, IL
Tollbooth Optimization
Built mathematical optimization model regarding number of tollbooths with respect to number of
incoming lanes of a toll plaza
Applied concepts of Queuing Theory, Markov Chains, and other probability techniques to
simulate the waiting and serving process of a certain toll plaza and to build simulation algorithm
Utilized Java to realize the simulation algorithm and to compute numerical results for conclusion
COMPUTER SKILLS/OTHER
Programming Language: C++, Java, R, SQL
Other Software: Microsoft Office, MATLAB, Mathematica
Languages: Mandarin (native), English (fluent)
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YINONG TANG (917) 815-3877 ■ [email protected]
EDUCATION NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences MS in Mathematics in Finance (expected – January 2020)
• Coursework: One-factor interest ate models, Black-Scholes formula and applications, credit risk and credit derivatives, dynamic asset pricing models, forecasting techniques and pitfalls
• Future Coursework: One-factor short-rate models (Vasicek, CIR, Hull-White), two-factor Hull-White, forward rate models, LIBOR market model, numerical linear algebra, Monte Carlo
UNIVERSITY COLLEGE LONDON London, UK BSc in Mathematics (2015 - 2018)
EXPERIENCE MORGAN STANLEY CAPITAL INTERNATIONAL Beijing, China
Risk Management Analytics Intern (Aug 2017 – Oct 2017) • Computed value of convertible bond in Mathlab and priced bonds with Monte Carlo simulation • Generated data sample that follows T distribution in Matlab and designed corresponding code • Calculated Monte Carlo Simulation based VaR for a portfolio using Matlab • Analyzed PDE/SDE problems related to Black-Scholes and stochastic process
HENTAICHANGCAI SECURITIES CO. LTD Beijing, China Financial Market Intern (Jul 2016 – Aug 2016)
• Assisted manager with daily, weekly, quarterly and annual reports • Analyzed and audited financial statements by collating and sorting out financial data • Followed-up supervision of Fixed-income products which Hentaichangcai serves as the Bond
Trustee and compiled trustee report PROJECTS UNIVERSITY COLLEGE LONDON London, UK
Algebra/Number Theory/Combinatroics • Described and implemented Quadratic Sieve algorithm and solved some computational problems
in finding generators and discrete logarithms in cyclic group Z*p • Synthesized best algorithms for factoring product of two primes by comparing Quadratic Sieve
algorithm with the Continued Fraction and Number Field Sieve Molecular BioSystems Research
• Predicted ACTH-Secreting Pituitary Adenoma potential miRNA-disease associations in Matlab • Performed Drug–target interaction prediction by random walk on the heterogeneous network to
predicting prioritization of candidate targets for given drug in Matlab • Conducted KATZ measure for lncRNA association prediction through algorithm-based analytics
NEW YORK UNIVERSITY New York, NY Computing in Finance(Java)
• Designed Monte Carlo based simulation code to price European/Asian options with Mockito for unit testing and used importance sampling to perform variance reduction during simulations
• Implemented and improved K-means algorithm to demonstrate multi-dimensional point/fixed size clustering with followed up unit testing.
Risk & Portfolio Management • Computed excess returns for different funds by using money market fund as the risk free rate and
construct the Black-Litterman asset allocation model COMPUTER SKILLS/OTHER Programming Languages: Java, python
Other Software: Microsoft office, MATLAB, Wolfram alpha Languages: Chinese (native), English (fluent)
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TONG (JASPER) WU (310) 500-0949 ■ [email protected]
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
MS in Mathematics in Finance (Sept. 2018 – Jan. 2020)
Coursework: Stochastic Calculus, Derivative Pricing and Hedging, Volatility Modeling, OOP in
Java, Order Execution/Algorithmic Trading, Active Portfolio Management, Statistical Arbitrage
UNIVERSITY OF CALIFORNIA, LOS ANGELES Los Angeles, CA
BS in Applied Mathematics, minor in CS and Statistics, Honors (Sept. 2014 – Jun. 2018)
Coursework: Algorithms and Data Structures, PCA, Machine Learning, Probability, Optimization,
Numerical Methods, Linear Regression, Monte Carlo Simulations, Black-Scholes & Greeks, VaR
EXPERIENCE
BANK OF CHINA GROUP INVESTMENT LIMITED Beijing, China
Investment Analyst (Jun. 2018 – Aug. 2018)
Optimized raw investment estimation models and forecasted IRR by testing multiple variables
such as loan rate and leverage ratio; improved structures and functions and rebuilt models
Evaluated macro risk of market quantitatively and composed industry research reports of long-term
rental apartment sector to support potential investment decisions
LERUI ASSET MANAGEMENT Beijing, China
Quantitative Analyst (Dec. 2016 – Jan. 2017)
Performed regression, found correlations and did back-testing on different factors such as P/E,
ROE, BP, etc. to test their efficiency in large stocks and delivered performance analysis
Implemented automatic web scraping (HTML parsing) tools, such as Beautifulsoup, selenium,
with functionalities of dealing with dynamic web pages and automatic local storage updating in
Python
CHINA INVESTMENT SECURITIES Beijing, China
Summer Research Intern (Jun. 2015 – Aug. 2015)
Built database for bond issuance and implemented automatic reconciliation using VBA
PROJECTS
UNIVERSITY OF CALIFORNIA, LOS ANGELES Los Angeles, CA
Machine Learning: FOREX Price Prediction
Predicted CADUSD FOREX price using SVMs (with cross validation) in Python and Matlab
Built a prediction model after comparing its performances with different machine learning models
Added 10 features such as moving average, moving standard deviation, and value differences, to
raw dataset, and back tested carry strategies with 15% annualized return rate in five years
Portfolio Optimization and Risk Management
Selected 25-30 stocks from 5 industries according to their Sharpe ratio, excess return, and P/E
Constructed portfolio based on different models (Single Index, Multi Index, Multi Group, etc.)
Analyzed performances (such as Sharpe ratio, Treynor Measure, VaR) of different portfolios
Options Pricing Using Different Pricing Methods
Priced European and American options by using Black-Scholes model, binomial, trinomial models
and Monte Carlo simulation with Python
Data Mining & Machine Learning: Expedia Hotel Bookings Prediction
Performed data extraction and visualization on a 10-million-row dataset using Python and Tableau
Created a costumer ranking and classification system based on KNN model with 82% accuracy
Crafted a hotel bookings prediction system by using our designed weighted filter
COMPUTER SKILLS/OTHER
Programming Languages: C++, Java, R, Python (numPy, Pandas, etc.), MATLAB, VBA, SQL
Other Software: Microsoft Office, Qt, Tableau, Wind (Bloomberg)
Languages: Mandarin (native), English (proficient), Japanese (elementary)
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GENG (ALEX) YAN (646) 243-0002 ■ [email protected]
EDUCATION NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences MS in Mathematics in Finance (expected – January 2020)
• Coursework:, Black-Scholes, Greeks, arbitrage-based pricing of derivative securities, Brownian motion, Itô’s calculus, Monte Carlo, PCA, clustering methods, curve building, risk management
• Future Coursework: Time series analysis, interest rate & FX models, algorithmic trading NANJING UNIVERSITY Nanjing, Jiangsu
BS in Mathematics (2014-2018) • Coursework: Analysis (complex, real and functional), algebra and representation theory, ODE,
PDE, topology, differential geometry, statistics, numerical analysis, data structures and algorithms • Awards: First Prize in Chinese Mathematical Olympiad in Senior
EXPERIENCE CHINA ORIENT ASSET MANAGEMENT CO. Nanjing, Jiangsu
Financial Analyst Intern (July 2017 – Aug. 2017) • Manipulated company’s data to simulate optimization process of capital allocation • Applied empirical statistic models to give confidence intervals of market valuation using R • Allocated 780 million liabilities by acquisition of big collateral debts, and obtained about 1 billion
net profits after revaluing those collaterals
NANJING UNIVERSITY Nanjing, Jiangsu Research Assistant (Mar. 2018 – June 2018)
• Improved quantile regression models and algorithms to accommodate with clustered data • Conducted 2 Monte Carlo simulations in R to show better inference accuracy compared with other
general methods • Generated a report about case study on how some factors contribute to the different levels of high-
school student’s score distribution PROJECTS NEW YORK UNIVERSITY New York, NY
Derivatives Pricing with Monte Carlo Simulation (Java & Python) • Built generic Monte Carlo pricing framework for European and Asian options, and improved this
framework by using Middleware, OpenCL and multi-threading • Applied importance sampling to significantly reduce the variance • Priced options with a barrier using trinomial tree and finite-difference scheme
NANJING UNIVERSITY Nanjing, Jiangsu Machinery Fault Detection for Imbalanced Datasets based on Deep Learning (Python)
• Initiated idea of using Convolution Neural Networks to extract features and EasyEnsemble.M to train imbalanced datasets
• Trained data to classify them into three different classes and compared with several algorithms • Designed experiment to show its superiority for imbalanced datasets, and the accuracy reached
99.85% and 97.14% in bi-classification and multi-classification COMPUTER SKILLS/OTHER Programming Skills: C++, Java, SQL, R, Python, MATLAB, LaTeX
Programming Awards: National First Prize in National Olympiad in Informatics in Provinces (NOIP) Languages: Mandarin (native), English (proficient)
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JIACHENG YUE (201) 885-0562 ■ [email protected]
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
MS in Mathematics in Finance (expected – January 2020)
Coursework: Continuous time finance, stochastic calculus, time series analysis, market
microstructure, portfolio management, risk management, OO programming in Java
PEKING UNIVERSITY Beijing, China
BS in Environmental Science & Economics (Sept 2013 – Jul 2018)
Coursework: Stochastic process, derivative pricing, econometrics, differential equations (wave,
heat, harmonic equations, etc.), data structure & algorithm, financial economics, probability
theory
EXPERIENCE
GUOTAI JUN’AN SECURITIES Shanghai, China
Fixed Income Intern Analyst (Jan 2017 – Apr 2017)
Calibrated parameters in SABR model to get implied volatility of SSE 50 ETF options; provided
the implied volatility results for traders to construct delta neutral portfolios
Created automated interface to retrieve and organize the data flow from Wind database for
traders’ use in Python, which increased time efficiency by 90% for trading desks
ABC INTERNATIONAL Beijing, China
Direct Investment Intern Analyst (Jun 2016 – May 2016)
Modified existing momentum trading strategies by implementing Kaufman adaptive moving
average model (KAMA); raised annual return by over 10% on treasure future (TF) contracts
Implemented DCF, P/E ratio and fundamental analysis to evaluate a subsidiary of Petro China
GUOTAI JUN’AN SECURITIES Shanghai, China
Investment Banking Division Intern (Jan 2016 – May 2016)
Performed generalized linear regression and applied CAPM model to estimate beta for M&A
Analyzed sensitivity of delusive impact of issuance of perpetual bond for a commercial bank to
reinforce Tier 1 capital instrument
MORGAN STANLEY HUAXIN SECURITIES Beijing, China
Fixed Income Division Summer Intern (Jun 2015 – Aug 2015)
Conducted duration and convexity analysis to match loan terms for commercial banks in issuance
of first green financial bond in China
Analyzed credit risk and capital tools to strip bad loans for commercial banks
PROJECTS
PEKING UNIVERSITY Beijing, China
Variance of Variance Model
Modeled variance of variance for GARCH family models based on Robert Engle’s (2002) paper
Performed maximum likelihood estimation to estimate CEV GARCH diffusion power with 30-
year S&P 500 daily data in Matlab
Option Pricing in C++
Priced plain vanilla, American options in C++; solved closed form of Black- Scholes model,
simulated the solution with Monte Carlo Method and reduced variance using antithetic variates
Approximated CIR PDE on a continuous space by finite differences on discrete space; methods
included explicit Euler, implicit Euler and Crank Nicolson
COMPUTER SKILLS/OTHER
Programming Languages & Software: C++, Python, Matlab, R, VBA, Stata, SQL, Microsoft Office
Certification: Passed CFA Level 1
Languages: English (fluent), Mandarin (native),
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TIANYU (WILLIAM) ZHANG (201) 238-9665 ■ rebrand.ly/ty_zhang ■ [email protected]
EDUCATION NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences M.S. in Mathematics in Finance (Aug. 2018 – Dec. 2019)
Coursework: Portfolio management, derivative pricing and hedging, Java and Python for quantitative finance, algorithmic trading, machine learning, interest rate model, fixed income
WUHAN UNIVERSITY Wuhan, China B.S. in Mathematics & B.A. in Finance, Ranked 1st, First-class Honor (Sept. 2014 – Jun. 2018)
Coursework: Stochastic calculus, regression analysis (LASSO, Ridge), algorithms, data structure, advanced econometrics, optimization, numerical analysis, statistical inference, time series, database
EXPERIENCE GUANGYUNQIANFAN ASSET MANAGEMENT Shenzhen, China
Quantitative Analyst & Financial Data Scientist (Part-time job) (Aug. 2017 – Jul. 2018)
Trading signal construction: trained pre-pruning decision tree classifiers to classify trading intervals from the trade-quote data of equities and constructed signals based on actions of traders in large funds
Reinforcement learning trading: adopted trading signals as state vectors in Q-learning on portfolios to get Q-functions under actions by policy iteration (PnL reached 14%, the max drawdown is 0.13)
Classification of clients: pre-processed clients’ unlabeled data by kernel PCA and grouped clients into diverse risk-aversion levels by K-means clustering
Noise reduction of price data: adopted B-spline for reducing the noise of forex option mid-price Dimensionality reduction for multi-factors: performed LASSO to avoid multicollinearity, PCA for
dimensionality reduction in an interest rate econometrics model Data cleansing: performed Box-Cox transformation for regressions, judged outliers with the local
outlier factor and handled missing data with decision tree regressors KPMG ADVISORY Shenzhen, China
Summer Intern (Jul. 2016 – Aug. 2016) Risk-warning system: updated the Markov transition matrix in the risk-warning system and
constructed a KMV-based model to determine the threshold of EDF (14.9bp) Predicted default risk: adopted logistic regression to predict default risk of companies and adopted
AUC to judge the classifying power of the model (Excellent when AUC>0.8; failure when AUC<0.5) PROJECTS
NEW YORK UNIVERSITY New York, NY
Computing in Finance: Order Book Simulation and Options Pricing
Simulated exchange order mechanisms with sweeping, resting, canceling Adopted HashMap as a data structure that contains all resting orders indexed by client orders Adopted TreeMap as a data structure of order books to operate the book ordered by time and price Implemented Monte Carlo simulation to price European, Asian options Adopted the decorator pattern of antithetic variates as a variance reduction technique in MC Adopted GPU computing to enhance the performance under the OpenCL by the JOCL API
WUHAN UNIVERSITY Wuhan, China Alpha-seeking Strategy on Equity Trading on GARCH, Sentiment Analysis and Multi-factor Strategy
Built an ARMA-GARCH and filtered efficient factors for regressions on the equity returns Performed a mean-reverting strategy to seek alpha in equities which have negative excess returns in
the past Computed weights by mean-variance optimization and modified weights from the news event
assessments and comments alternative data by natural language processing (LSTM) Result: Reached annualized return of 11% and won an outstanding national innovation project
COMPUTER SKILLS/OTHER Programming Language: C/C++, Java, Python, SQL
GRE Mathematics Subject Test: 95 Percentile; GRE General Test: Quant:170/170, Verbal:160/170
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YUENING ZHANG (848) 218-2864 ■ [email protected] ■ https://www.linkedin.com/in/yuening-zhang/
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
M.S. in Mathematics in Finance (expected – Dec 2019)
Coursework: Black-Scholes formula and applications, arbitrage-based pricing of derivative
securities, portfolio management, Brownian motion properties, Martingales
RUTGERS UNIVERSITY—NEW BRUNSWICK New Brunswick, NJ
BA in Mathematics and Economics (Sept 2014 – May 2018)
Coursework: Differential equation, statistics, mathematical finance, math analysis, econometrics,
economics forecasting including economics models for time series
EXPERIENCE
CDH Investments Beijing, China
Venture and Growth Capital Intern (July 2018-August 2018)
Wrote market reports weekly and assisted in identifying investment opportunities for social media
market
Interviewed executives of target companies during preliminary due diligence and composed
interview summaries and investment analysis for 3 new deals
Structured project briefs for 3 new deals and prepared investment decks for 2 investment targets
Conducted market research for 2 industries in TMT sector
Rutgers University—New Brunswick New Brunswick, NJ
Tutor (Jan 2017-Dec 2017)
Directed students to learn in various courses including calculus, linear algebra, real analysis,
macroeconomics, microeconomics, and probability theory
Explained course materials to students and helped them better understand materials
Peer Mentor (Sept 2015-Dec 2015)
Led group of students up to 25 in discussion in calculus during recitations
Conducted online office hours for students enrolled in various sections of calculus class
PROJECTS
Rutgers University—New Brunswick New Brunswick, NJ
Honors Thesis: “Whether the Retail Purchase of Gold is an Indicator of Economic Activities”
Applied Diffusion Index model and created four principal components for measures of retail
purchase of gold
Investigated correlation between macroeconomics variables and first principal components at
various time periods
Utilized R program to forecast future macroeconomics variables and calculated mean square errors
Structural Shocks in Fed Funds, M1 Growth, Output Growth, Inflation
Utilized EViews program to create a Vector Autoregression (VAR) of given four variables and
estimated the VAR
Computed structural impulse response function for each variable and identified structural shocks
COMPUTER SKILLS/OTHER
Programming Languages: Java, R
Other Software: Microsoft Office, SAS, EViews (for econometrics)
Languages: Chinese (native), English (fluent)
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YUKE (SALLY) ZHAO (917) 496-2546 ■ [email protected]
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
M.S. in Mathematics in Finance (expected – January 2020)
Coursework: Quantitative portfolio theory, risk management, Monte Carlo simulation, Black-
Scholes formula and applications to stochastic processes, and dynamic programming, etc.
Future Coursework: Advanced econometrics, big data applications, and continuous time finance
RENMIN UNIVERSITY OF CHINA Beijing, China
BS in Economics, BS in Mathematics (September 2014 – June 2018)
Coursework: Probability, statistics, ODEs, numerical analysis, econometrics, financial economics
UNIVERSITY OF CALIFORNIA, SAN DIEGO San Diego, CA
Exchange Student (September 2015 – June 2016)
EXPERIENCE
DONGXING SECURITIES Beijing, China
Quantitative Analyst (February 2018 – June 2018)
Set up automatically updated A-share and H-share stock databases in MATLAB (about 2,000
lines), diagnosed deficiencies, prescribed fixes, and conducted maintenance on previous data
Constructed generalized A-share and H-share multi-factor backtesting platforms (about 1,500
lines), and backtested 8 multi-factor models on platforms
Built regression models to estimate daily fund holdings in Chinese market; implemented a market-
timing strategy based on stock held heavily by funds with an annual return of 21.2%
Wrote VBA scripts to monitor net capital inflow and event-driven strategies’ latest performances
in A-share market, and published weekly reports on market liquidity based on the first model
RENMIN UNIVERSITY OF CHINA Beijing, China
Teaching Assistant (March 2017 – June 2017)
Led 60 students in discussion sessions and evaluated student performance in advanced algebra
Graded assignments and exams, and helped students with their questions on course material
JINHUWULIANG TECHNOLOGY CO. LTD Beijing, China
Quantitative Research Assistant (July 2017 – October 2017)
Researched alpha under Fama-Macbeth framework with 1.19 Sharpe ratio in out-of-sample tests
Collaborated to develop a futures pair-trading strategy based on stochastic oscillator
PROJECTS
RENMIN UNIVERSITY OF CHINA Beijing, China
Banking System Competition, Factor Reallocation, and Productivity Loss (February 2018 – April 2018)
Applied triple difference method and monopolistic competition model to investigate the impact of
increasing bank competition in China on factor reallocation and economic output using STATA
Calculated provincial bank competition index with license numbers; processed raw data from
China Industry Business Performance Database and calculated deflated TFP by LP method
RENMIN UNIVERSITY OF CHINA Beijing, China / Kyoto, Japan
The Dynamics of Lead Investor Mode in Equity Crowdfunding (September 2016 – November 2016)
Justified existence of lead investor mode in equity crowdfunding based on principal-agent models
Devised a mathematical collusion-proof contract between invested enterprises and lead investors
Presented on the 19th Joint Seminar on Studies of Economics in Doshisha University
COMPUTER SKILLS/OTHER
Programming Languages: Java, C++, R, Python
Other Software: MATLAB, STATA, Microsoft Office
Languages: Mandarin (native), English (fluent)
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ZIMO (STUART) ZHAO (347) 722-0229 ■ [email protected]
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
MS in Mathematics in Finance (Expected – January 2020)
Coursework: Pricing w/ Black-Scholes model, probability, OOP in Java, VaR, & Monte Carlo
Future Coursework: Algorithmic trading, Continues time finance, Market microstructures and
Interest rate & FX models
IMPERIAL COLLEGE LONDON London, United Kingdom
MSc in Statistics (2017 - 2018)
BSc in Mathematics with Statistics for Finance (2014 - 2017)
Coursework: Bayesian data analysis with RStan, Markov Chain Monte Carlo, Machine
Learning, HDFS (Hadoop & Spark), statistical inference, Stochastic processes, and Time Series
EXPERIENCE
EVERBRIGHT SECURITIES Beijing, China
Bond Underwriting Assistant Intern (July 2015 – August 2015)
Researched and investigated annual reports and internal publications for company history and
background for clients’ investment thesis
Performed sensitivity analysis on companies to help investors understand potential risk of their
investments PROJECTS
IMPERIAL COLLEGE LONDON London, United Kingdom
New Ideas for Implementing Particle Filters on GPU (June 2018 – August 2018)
Investigated algorithm parallelization in sequential Monte Carlo (SMC)
Implemented Metropolis, and Rejection resampling methods for SMC on CPU and GPU with
C++ & CUDA
Performed simulations and algorithmic comparison on GPUs in contrast to CPU
Machine Learning (Jan 2018 – Feb 2018)
Processed graphic recognition on human cell clusters through K-Means and Gaussian Mixture
methods and other clustering methods
Applied principle component analysis on face imagining followed by KNN and cross-validation
Predicted one hour ahead cryptocurrency prices using neural networks and Gaussian processes
Diagnostic Rate Modelling in England (Feb 2018 – Mar 2018)
Cleaned and arranged data set in R for further analysis
Estimated diagnostic rate of a disease over 2012 to 2016 with emphasis on heterogeneity
Inferenced model from a Bayesian perspective, reviewed posterior checks, and confidence level
associating with results
Big Data: Data Cluster Application (Feb 2018 – Mar 2018)
Ran Hadoop (Python) on cluster to perform massive map-reduce technique
Utilized Spark with Scala shell to analyze customers’ location and activities
Group Project: Optimal Investment Strategy (May 2016 – Jun 2016)
Solve constraint mean-variance portfolio (no short-selling) using quadratic programming in R
Utilised financial time series including EWMA and GARCH on constructed portfolios with
historical data against the S&P 500 on return and on market volatility
Delivered a presentation on this topic to fellow students
COMPUTER SKILLS/OTHER
Programming Language: Python, Java, R, MATLAB
Languages: Mandarin (native), English (fluent)
Certifications: FRM Part I
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YIXUAN (JESSIE) ZHOU (702)337-7500 ■ [email protected]
EDUCATION NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences MS in Mathematics in Finance (expected – Jan. 2020)
• Coursework: Stochastic Calculus, Monte Carlo method, OOP in Java • Future Coursework: Interest rate models, machine learning, securitized products
WUHAN UNIVERSITY Wuhan, China B.S. in Mathematics & B.A. in Economics, GPA:3.92/4.00 (Sept. 2014 – Jun. 2018)
• Coursework: Time series models, statistics, econometrics, data structure, derivatives pricing, numerical methods, micro & macro economics, database
EXPERIENCE CHAINEXT FINTECH LTD Shenzhen, China
Quantitative Analyst Intern (May 2018 – Aug. 2018) • Index rotation strategy research: implemented and back tested index rotation strategies with
historical data of cryptocurrency market and analyzed the returns and P&L attribution (Python) • Technical indicator generation: designed fear index of crypto market with volatility, market
momentum, Bitcoin market cap share, sentiment analysis result and other factors REDCAT ASSET MANAGEMENT CO. LTD (hedge fund) Shanghai, China
Quantitative Analyst Intern (Aug. 2017 – Apr. 2018) • High-frequency arbitrage strategy research (half-second level): constructed calender spreads with
Dickey Fuller test, designed and back tested Bollinger Band based pair trading strategy (C++) • Signal selection and high-frequency limit order book dynamics modeling: utilized machine
learning techniques, Random Forrest and SVM, to predict mid-price movement with technical indicators; performed cross validation and grid search for feature selection; applied SMOTE to resample the imbalanced data set; classification performances were evaluated with accuracy (0.66) and F-1 score (0.73) (Python)
• Tick data pre-processing: aligned time tickers and filled missing values, automated daily descriptive statistical reports generation according to traders’ demand (Python)
PROJECTS Option Pricing with Monte Carlo Simulation (Java)
• Built an extendable Monte Carlo option pricing framework, reduced MC errors and achieved faster convergence rate by antithetic variate and importance sampling
• Accelerated by GPU parallel computing (OpenCL) and distributed computing (Middleware) Technical Alpha Research in MATLAB as PTA for GFund (MATLAB)
• Constructed over 30 price-volume factors in Chinese A-share market and tested their significance • Back-tested trading factors and analyzed annualized return, sharp ratio and maximum drawdown
K-Means Clustering (Java) • Implemented and improved the Lloyd’s algorithm to perform generic multi-dimensional point
clustering and fixed-size clustering; measured the efficiency with Davies Bouldin Index Orderbook Management System construction (Java)
• Designed orderbooks with functions in sweeping, resting, filling and cancelling orders Detection of the Volatility Jumps with ICSS Algorithm (R, MATLAB)
• Built ARMA-GARCH model to capture volatility of Shanghai Composite Index • Determined ICSS Algorithm threshold by Monte Carlo Simulation to identify volatility jumps
COMPUTER SKILLS/OTHER Programming Languages and other software: Python, JAVA, C++, R, SQL
Other Software: MATLAB, LaTex, Excel VBA
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ZERUI (JERRY) ZHOU (201) 401-7441 ■ [email protected] ■ www.linkedin.com/in/jerry-z/
EDUCATION
NEW YORK UNIVERSITY New York, NY The Courant Institute of Mathematical Sciences MS in Mathematics in Finance (expected – January 2020)
• Coursework: Option pricing, Mean-variance optimization, CAPM, factor model, OOP and data structure in Java, Stochastic Calculus, Black-Scholes
• Future Coursework: Algorithmic trading and quantitative strategies, continuous time finance, time series analysis
PEKING UNIVERSITY Beijing, China BS in Physics and BA in Economics (September 2014 – June 2018)
• Coursework: Linear algebra, Calculus, computation (C++), data structure and algorithm, intermediate macro- & micro-economics, probability and statistics
• Awards: Guanghua Scholarship, 3rd Prize Scholarship for Outstanding Freshmen
EXPERIENCE
ALL-WEATHER QUANTITATIVE TECHNOLOGY Beijing, China Quantitative Analyst (July 2017 – October 2017)
• Researched on alpha trading strategies in Chinese A-share market • Back-tested and compared performance of strategies on basis of annualized returns, Sharpe ratios,
maximum drawdowns, and etc. • Researched on intra-day trading strategies by dividing stocks into groups and observing their
trendency in one trading day • Gathered and processed data from Wind terminal to assist in research and trading
PEKING UNIVERSITY Beijing, China Research Assistant (January 2018 – June 2018)
• Created a Fully Convolutional Network to achieve semantic segmentation of cancer cell images in C++
• Used GoogLeNet as background and Fully Connected Crfs to reach high efficiency; neural network resulted in an accuracy of 84.8%
PEKING UNIVERSITY STUDENT UNION Beijing, China Leader, Public Relations Dept. School of Physics & Art Dept. (September 2015 – June 2016)
• Organized and coordinated variety of campus fairs and competitions • Delegated tasks such as marketing, set-up, and etc. for various events
PROJECTS
PEKING UNIVERSITY Beijing, China Solving the eigenvalue of one-dimensional non-harmonic oscillator
• Computed all the roots of Hermite polynomials numerically in python • Implemented QR decomposition to Hamiltonian operator matrix to solve the eigenvalues
Navigation Simulator on Campus
• Tested the optimal route between two random places on campus in C++ • Utilized method of Dijkstra’s algorithm
NEW YORK UNIVERSITY New York, NY Computing in Finance (Java)
• Partitioned 10000 points on the plane into 500 clusters using Lloyd’s algorithm; enhanced performance by clustering points into fixed size and compared efficiencies using several metrics
• Priced European and Asian options using Monte Carlo Simulation and applied decorator pattern to accelerate convergence
COMPUTER SKILLS/OTHER
Programming Languages: C++, Java, Python Other Software: Microsoft Office; MATLAB, STATA, LaTeX Languages: Chinese Mandarin (native), English (fluent)
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JIAHAO (JASON) ZHU
(332) 201-3724 ▪ [email protected]
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
MS in Mathematics in Finance (expected – January 2020)
Coursework: Quantitative portfolio theory, fixed income and currency derivatives, and Black-
Scholes formula and applications to stochastic processes
Future Coursework: Interest rate and Fx models, Monte Carlo and finite difference methods,
PDEs and dynamic programming, risk management, continuous time finance, and advanced
econometrics
UNIVERSITY OF ILLINOIS Urbana, IL
BS in Statistics, Minor in Mathematics (2015 - 2018)
Coursework: Probability and statistics, time series analysis, calculus, linear algebra, ODE, real
analysis, computer science
EXPERIENCE
UNIVERSITY OF ILLINOIS Urbana, IL
Math Teaching Assistant (2017 - 2018)
Led 20-30 students in reviewing linear algebra materials before monthly and final exams
Graded weekly quizzes and exams for approximately 100 students enrolled in linear algebra
Java Teaching Assistant (2016 - 2017)
Led 35 students in weekly discussions in JAVA programming
Held office hours twice a week providing assistance with JAVA programming to students
PROJECTS
UNIVERSITY OF ILLINOIS Urbana, IL
Data Mining in Graduate School Admissions (May 2017 - Dec 2017)
Utilized python to implement web spiders to scrape web data from user postings
Used methods such as Rating Percentage Index to rank math PhD programs across US, with
accuracy around 90% close to US News official ranking
Fitted predictive models to forecast application results, using SVM and logistic regression, with
accuracy up to 76% under cross-validation
Implemented interactive visualization using R-Shiny and presented at JMM 2018
Big Data and Traffic Patterns (Aug 2017 - Dec 2017)
Utilized Python to clean a data set of 1,500,000 and converted data set into matrix format
Visualized parking densities across Seattle and San Francisco in dynamic heatmaps
Precipitation Probabilistic Forecast (May 2017 - Aug 2017)
Measured accuracy of precipitation forecast made by WU (Weather Underground) using Brier
Score and Threat Score
Identified a wet bias made by WU and improved the probabilistic forecast accuracy by using
scaling factors, moving average, and log-transformation
Sunrise Futures Financial Modeling Competition (April 2017)
Led team of 3 working on a trading data set of 3 instruments over a 3-month trading period
Identified correlations between instruments and investigated the relationship between desired
variable and trading spreads of these instruments
Fitted regression models towards the desired variable with a goal of obtaining highest R-square
COMPUTER SKILLS/OTHER
Programming Language: Python, Java, R, C++
Other Software: SAS, R-shiny
Python Packages: Talib, Scrapy, Beautiful Soup, NumPy, Pandas
Languages: Mandarin (native), English (fluent)
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XUEWEI (KATHY) ZHU (216) 334-3503 ■ [email protected] ■ https://www.linkedin.com/in/xuewei-kathy-zhu-43453416b/
EDUCATION
NEW YORK UNIVERSITY New York, NY
The Courant Institute of Mathematical Sciences
M.S. in Mathematics in Finance (expected - January 2020)
Coursework: Black-Scholes model, Monte Carlo and finite difference methods, object-oriented
programing, portfolio management
CASE WESTERN RESERVE UNIVERSITY Cleveland, OH
B.S. in Applied Mathematics and B.A. in Economics (2014 - 2018)
Coursework: Calculus, differential equations, probability and statistics, linear algebra, modeling
and scientific computing
EXPERIENCE
NOAH HOLDINGS LTD ChangZhou, China
Summer Intern (Jul 2018 - Aug 2018)
Collected and summarized domestic and international financial news from China Securities
Journal, People Daily, Securities Times, and CNBC
Summarized about 5 financial news to 150-250 words to submit to manager and have it posted on
company WeChat
Ensured daily webinar on breaking financial news, analyst opinions, branch performance, and
financial products (fixed-income securities, VC/PE, and REIT ETFs) was up and running and
wrote up minutes
CASE WESTERN RESERVE UNIVERSITY Cleveland, OH
Research Assistant (Mar 2017 - May 2018)
Generated demographically structured human-snail model of schistosomiasis transmission for
Kenya SCORE study with Mathematica
Implemented model calibration to estimate human transmission parameters like worm fecundity
from egg-count test data
Simulated dynamic mass drug administration (MDA) responses and explored control strategies
Teaching Assistant/Peer Tutor (Sept 2015 - May 2018)
Organized probability and behavioral economics study sessions
Graded homework and exams for students enrolled in probability and behavioral economics
courses
Gave one-to-one tutor sessions on various undergraduate courses
PROJECTS
CASE WESTERN RESERVE UNIVERSITY Cleveland, OH
Hardcoat Acrylic Film Degradation: Data Assembly, Cleaning, EDA and Summarizing
Manipulated, cleaned, summarized and visualized massive degradation data on two different
substrate films with 5 steps and 6 exposure conditions by using R studio
Determined structural equation model (SEM) network models of the degradation pathways
Tree Based Methods: Classification and Regression Tree
Built a classification tree that identifies the source of the glass samples with MATLAB
Calculated the misclassification error and Gini index to assess the quality of the tree
Pruned the tree by seeking a balance between the misclassification cost and complexity
COMPUTER SKILLS/OTHER
Programming Languages: Java
Other Software: Microsoft Office; MATLAB, Mathematica, R, Stata
Certificates: Society of Actuaries exams P (Probability)
Languages: Mandarin (native), English (fluent), Japanese (intermediary)
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LIANG (ADAM) ZOU (812) 349-8508 ■ [email protected] ■ https://www.linkedin.com/in/liazou0508/
EDUCATION NEW YORK UNIVERSITY New York, NY The Courant Institute of Mathematical Sciences MS in Mathematics in Finance (expected - January 2020)
• Coursework: Stochastic Calculus, Portfolio Optimization, OOP in Java, Data Structure, RiskManagement, Derivative Pricing and Hedging, Black-Scholes
INDIANA UNIVERSITY Bloomington, IN BS in Applied Mathematics & Statistics - High Distinction (May 2018)
• Coursework: Statistical Learning, Bayesian Statistics, Time Series, Multivariate Linear Models,Statistical Consulting, Probability Theory, Stochastic Processes, ODE, Complex Analysis
EXPERIENCE MEGAPUTER INTELLIGENCE INC Bloomington, IN Data Analyst Intern (May 2018 - August 2018)
• Utilized non-linear machine learning techniques including random forest, neural network, andCHAID to predict regulatory risk given taxonomy data of bank customer complaints (252k rows)
• Performed model selection based on error rates and cross validation to control overfitting, raisedaccuracy of model, achieved an accuracy of 99.48%
• Designed taxonomy of complaints data and applied entity extraction to explore drivers of non-sufficient funds and overdraft complaints to improve feature quality
GUANGZHENG HANG SENG RESEARCH CO., LTD. Guangzhou, China Research Intern (July 2017 - August 2017)
• Researched portfolio optimization of insurance fund based on RAROC model under value at risk• Generated buy/sell signals based on technical indicators such as moving averages in Excel after
refining calculation of SSE Index fluctuation, along with 50% decrease in operation time• Produced a proposal about financial development of foreign banks in Guangdong-Hong Kong-
Macao Greater Bay Area; pitched strategies to Hang Seng Bank in banking, insurance, securitiesGUANGZHOU RURAL COMMERCIAL BANK Guangzhou, China Finance Intern (June 2014 - July 2014)
• Researched interest rate strategies of 10 commercial banks after benchmark interest rate increased• Built Excel spreadsheets to automatically collect, clean, and analyze bank loan data
PROJECTS NEW YORK UNIVERSITY New York, NY Computing in Finance Projects (Java)
• Order Book Design: Simulated sweeping, resting and cancellation of orders in stock exchange• Monte Carlo Simulation: Priced Asian and vanilla options by generating paths of stock prices and
evaluated the stopping criteria based on payout's standard deviationsINDIANA UNIVERSITY Bloomington, IN Time Series Forecasting on FX and Interest Rate (R)
• Verified stationarity via Augmented Dickey-Fuller test and causality via Granger-Causality test• Constructed seasonal ARIMA models and implemented model selection based on AIC and BIC
Thesis: Classifier Stacking for Native Language Identification (Python) • Extracted lexical and syntactic features from TOEFL essays and utilized models including linear
SVM and multilayer perceptron to predict authors’ native languages• Improved predictability by chi-square feature selection and model ensemble based on stacking• Achieved an out-of-sample accuracy of 86.55% among one of the best performing systems
COMPUTER SKILLS/OTHER Programming & Software: R, Python, SQL, Java, Microsoft Excel & Access, SPSS, SAS Languages: Cantonese (native), Mandarin (native), English (fluent)
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The Mathematics in Finance Masters Program
Courant Institute, New York University
Academic Year 2018-2019
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)
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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
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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
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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
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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
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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.
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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.
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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.