class of 2019 resume book - math.nyu.edu · class of 2019 resume book mathematics in finance m.s....

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

[email protected]


mailto:[email protected]

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

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

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


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


http://www.linkedin.com/in/yitongcai

YUAN (ALEX) CHENG

(917) 443-2806 ■ [email protected]

EDUCATION



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


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


Teaching Assistant: Real Analysis, Functional Analysis (September 2016 – May 2017)

Assigned homework, held recitation sections, tracked attendance of students for these two courses

PROJECTS


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


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)


JINGRAN CUI (917) 975-1706

[email protected]

EDUCATION




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


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


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


Programming Languages: C/C++, Python, JAVA, R

Certificate: Actuarial Studies Certificate, Passed CFA Level I Exam



HA DU (415) 539-5859 ■ [email protected]

EDUCATION




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


Programming Languages & Other Software: Tableau, Python, SQL, Javascript, Excel, PowerPoint

Certification: Chartered Financial Analyst (CFA), Series 7, Series 63

Languages: English (fluent), Vietnamese (native)


JINGYUAN FENG (917) 402-5994 ■ [email protected]

EDUCATION




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


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)


YINAN (DAVID) HU (646) 217-8726 ■ [email protected]

EDUCATION




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


Programming Languages & Other Software: Python, Java, R; Stata, Wind, SPSS, Microsoft Office



YUNDAI (DAISY) HU

(631) 291-2651 ▪ [email protected] ▪ https://www.linkedin.com/in/yundai-daisy-hu/

EDUCATION



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


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


Programming Language: Python, Java, MATLAB, R

Other Software: MS Excel, Word, PowerPoint


Certifications: FRM Part I


https://www.linkedin.com/in/yundai-daisy-hu/

NAOTAKA IKEDA (317) 903-2722 ■ [email protected]

EDUCATION




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


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)


TIANHAO LU (347) 446-2986 ■ [email protected] ■ https://www.linkedin.com/in/tianhao-henry-lu/

EDUCATION




• 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


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


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


Programming Languages: Java, Python, R, MATLAB

Other Software: Microsoft Word, Excel, LaTex



https://www.linkedin.com/in/tianhao-henry-lu/

JUNQI QIAN (413) 888-8446

[email protected]

EDUCATION




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


Programming Languages: Python, Fortran, Rstudio, STATA

Other Software: Microsoft Office, iWork; MATLAB, Mathematica

Languages: Chinese (Native), English (Fluent), Japanese (intermediary), French (basic)


JIAHAO (NICK) REN (858) 766-8329 ■ [email protected]

EDUCATION



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


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


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


Programming & Software: Java (2 years), MATLAB (1 year), R (1 year), LaTeX, Stata



XIAOCEN (SHELLY) SHANG (858) 766-1877 ■ [email protected]

EDUCATION




• 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


Programming Languages: Java, R, Matlab, Stata

Other Software: Microsoft Office, MATLAB

Languages: Mandarin (native), English (fluent), Korean (1 year)


YIWEI SHI (914) 839-2354

[email protected]

EDUCATION




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


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


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


Programming Languages: Java, Python

Other Software: Microsoft office, MATLAB, Wolfram alpha

Languages: Chinese (native), English (fluent)


MEIXI (MAYSIE) SUN (734) 881-0460 ■ [email protected]

EDUCATION




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


Programming Language: C++, Java, R, SQL

Other Software: Microsoft Office, MATLAB, Mathematica



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)

TONG (JASPER) WU (310) 500-0949 ■ [email protected]

EDUCATION



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


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)

GENG (ALEX) YAN (646) 243-0002 ■ [email protected]


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)

JIACHENG YUE (201) 885-0562 ■ [email protected]

EDUCATION




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


Programming Languages & Software: C++, Python, Matlab, R, VBA, Stata, SQL, Microsoft Office

Certification: Passed CFA Level 1

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


TIANYU (WILLIAM) ZHANG (201) 238-9665 ■ rebrand.ly/ty_zhang ■ [email protected]


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


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

YUENING ZHANG (848) 218-2864 ■ [email protected] ■ https://www.linkedin.com/in/yuening-zhang/

EDUCATION



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


Programming Languages: Java, R

Other Software: Microsoft Office, SAS, EViews (for econometrics)

Languages: Chinese (native), English (fluent)


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

YUKE (SALLY) ZHAO (917) 496-2546 ■ [email protected]

EDUCATION



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


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


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


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

Other Software: MATLAB, STATA, Microsoft Office



ZIMO (STUART) ZHAO (347) 722-0229 ■ [email protected]

EDUCATION



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


Programming Language: Python, Java, R, MATLAB


Certifications: FRM Part I


YIXUAN (JESSIE) ZHOU (702)337-7500 ■ [email protected]


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

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


Programming Languages: C++, Java, Python Other Software: Microsoft Office; MATLAB, STATA, LaTeX Languages: Chinese Mandarin (native), English (fluent)


JIAHAO (JASON) ZHU

(332) 201-3724 ▪ [email protected]

EDUCATION




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


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


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


Programming Language: Python, Java, R, C++

Other Software: SAS, R-shiny

Python Packages: Talib, Scrapy, Beautiful Soup, NumPy, Pandas



XUEWEI (KATHY) ZHU (216) 334-3503 ■ [email protected] ■ https://www.linkedin.com/in/xuewei-kathy-zhu-43453416b/

EDUCATION



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


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


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


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)


https://www.linkedin.com/in/xuewei-kathy-zhu-43453416b/

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)

http://www.aclweb.org/anthology/W17-5044

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)

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


modeling in quantifying risk. The course will discuss how key players such as regulators, risk managers,

and senior managers interact with trading. Specific techniques for measuring and managing the risk of

trading and investment positions will be discussed for positions in equities, credit, interest rates, foreign

exchange, commodities, vanilla options, and exotic options. Students will be trained in developing risk

sensitivity reports and using them to explain income, design static and dynamic hedges, and measure

value-at-risk and stress tests. Students will create Monte Carlo simulations to determine hedge

effectiveness. Extensive use will be made of examples drawn from real trading experience, with a

particular emphasis on lessons to be learned from trading disasters.

MATH-GA 2798.001 INTEREST RATE AND FX MODELS

Spring term: F. Mercurio & T. Fisher

Prerequisites: Derivative Securities, Stochastic Calculus, and Computing in Finance (or equivalent

familiarity with financial models, stochastic methods, and computing skills).

The course is divided into two parts. The first addresses the fixed-income models most frequently used

in the finance industry, and their applications to the pricing and hedging of interest-based derivatives.

The second part covers the foreign exchange derivatives markets, with a focus on vanilla options and

first-generation (flow) exotics. Throughout both parts, the emphasis is on practical aspects of modeling,

and the significance of the models for the valuation and risk management of widely-used derivative

instruments.

MATH-GA.2799-001 SECURITIZED PRODUCTS & STRUCTURED FINANCE

Spring term: R. Sunada-Wong

Prerequisites: Basic bond mathematics and bond risk measures (duration and convexity); Derivative

Securities and Stochastic Calculus.

This half-semester course will cover the fundamentals of Securitized Products, emphasizing Residential

Mortgages and Mortgage-Backed Securities (MBS). We will build pricing models that generate cash

flows taking into account interest rates and prepayments. The course will also review subprime

mortgages, CDO’s, Commercial Mortgage Backed Securities (CMBS), Auto Asset Backed Securities

(ABS), Credit Card ABS, CLO’s, Peer-to-peer / MarketPlace Lending, and will discuss drivers of the

financial crisis and model risk.

MATH-GA.2800-001 ENERGY MARKETS AND DERIVATIVES Spring term: D. Eliezer

Prerequisites: Derivative Securities and Stochastic Calculus.

This half-semester course focuses on energy commodities and derivatives, from their basic

fundamentals and valuation, to practical issues in managing structured energy portfolios. We develop a

risk neutral valuation framework starting from basic GBM and extend this to more sophisticated multi-

factor models. These approaches are then used for the valuation of common, yet challenging, structures.

Particular emphasis is placed on the potential pitfalls of modeling methods and the practical aspects of

implementation in production trading platforms. We survey market mechanics and valuation of

inventory options and delivery risk in the emissions markets.

MATH-GA.2801-001 ADVANCED TOPICS IN EQUITY DERIVATIVES

Spring term: S. Bossu

Prerequisites: Derivative Securities, Stochastic Calculus, and Computing in Finance or equivalent

programming experience.

This half-semester course will give a practitioner’s perspective on a variety of advanced topics with a

particular focus on equity derivatives instruments, including volatility and correlation modeling and

trading, and exotic options and structured products. Some meta-mathematical topics such as the

practical and regulatory aspects of setting up a hedge fund will also be covered.

MATH-GA.2802-001 MARKET MICROSTRUCTURE

Spring term: G. Ritter

Prerequisites: Derivative Securities, Risk & Portfolio Management with Econometrics, and Computing

in Finance or equivalent programming experience.

This is a half-semester course covering topics of interest to both buy-side traders and sell-side execution

quants. The course will provide a detailed look at how the trading process actually occurs and how to

optimally interact with a continuous limit-order book market.

We begin with a review of early models, which assume competitive suppliers of liquidity whose

revenues, corresponding to the spread, reflect the costs they incur. We discuss the structure of modern

electronic limit order book markets and exchanges, including queue priority mechanisms, order types

and hidden liquidity. We examine technological solutions that facilitate trading such as matching

engines, ECNs, dark pools, multiple venue problems and smart order routers.

The second part of the course is dedicated pre-trade market impact estimation, post-trade slippage

analysis, optimal execution strategies and dynamic no-arbitrage models. We cover Almgren-Chriss

model for optimal execution, Gatheral’s no-dynamic-arbitrage principle and the fundamental

relationship between the average response of the market price to traded quantity, and properties of the

decay of market impact.

Homework assignments will supplement the topics discussed in lecture. Some coding in Java will be

required and students will learn to write their own simple limit-order-book simulator and analyze real

NYSE TAQ data.

MATH-GA.2803-001 FIXED INCOME DERIVATIVES: MODELS & STRATEGIES IN

PRACTICE Fall term: L. Tatevossian and A. Sadr

Prerequisites: Computing in Finance (or equivalent programming skills) and Derivative Securities

(familiarity with Black-Scholes interest rate models)

This half-semester class focuses on the practical workings of the fixed-income and rates-derivatives

markets. The course content is motivated by a representative set of real-world trading, investment, and

hedging objectives. Each situation will be examined from the ground level and its risk and reward

attributes will be identified. This will enable the students to understand the link from the underlying

market views to the applicable product set and the tools for managing the position once it is

implemented. Common threads among products – structural or model-based – will be emphasized. We

plan on covering bonds, swaps, flow options, semi-exotics, and some structured products.

A problem-oriented holistic view of the rate-derivatives market is a natural way to understand the line

from product creation to modeling, marketing, trading, and hedging. The instructors hope to convey

their intuition about both the power and limitations of models and show how sell-side practitioners

manage these constraints in the context of changes in market backdrop, customer demands, and trading

parameters.

MATH-GA.2804-001 CREDIT ANALYTICS: BONDS, LOANS AND DERIVATIVES

Fall term: B. Fleasker

Prerequisites: Derivate Securities and Computing in Finance (or equivalent familiarity with financial

models and computing skills)

This half-semester course introduces the institutional market for bonds and loans subject to default risk

and develops concepts and quantitative frameworks useful for modeling the valuation and risk

management of such fixed income instruments and their associated derivatives. Emphasis will be put on

theoretical arbitrage restrictions on the relative value between related instruments and practical

applications in hedging, especially with credit derivatives. Some attention will be paid to market

convention and related terminology, both to ensure proper interpretation of market data and to prepare

students for careers in the field.

We will draw on the fundamental theory of derivatives valuation in complete markets and the

probabilistic representation of the associated valuation operator. As required, this will be extended to

incomplete markets in the context of doubly stochastic jump-diffusion processes. Specific models will

be introduced, both as examples of the underlying theory and as tools that can be (and are) used to make

trading and portfolio management decisions in real world markets.

MATH-GA.2805-001 COUNTER PARTY CREDIT: VALUATION ADJUSTMENTS,

CAPITAL, AND FUNDING

Fall term: L. Andersen

Prerequisites: Advanced Risk Management, Derivative Securities (or equivalent familiarity with market

and credit risk models), and Computing in Finance (or equivalent programming experience)

This class explores technical and regulatory aspects of counterparty credit risk, with an emphasis on

model building and computational methods. The first part of the class will provide technical foundation,

including the mathematical tools needed to define and compute valuation adjustments such as CVA and

DVA. The second part of the class will move from pricing to regulation, with an emphasis on the

computational aspects of regulatory credit risk capital under Basel 3. A variety of highly topical subjects

will be discussed during the course, including: funding costs, XVA metrics, initial margin, credit risk

mitigation, central clearing, and balance sheet management. Students will get to build a realistic

computer system for counterparty risk management of collateralized fixed income portfolios, and will

be exposed to modern frameworks for interest rate simulation and capital management.

FINANCIAL THEORY AND ECONOMETRICS:

MATH-GA 2707.001 TIME SERIES ANALYSIS AND STATISTICAL ARBITRAGE Fall term: F. Asl and R. Reider

Prerequisites: Derivative Securities, Scientific Computing, and familiarity with basic probability.

The term "statistical arbitrage" covers any trading strategy that uses statistical tools and time series

analysis to identify approximate arbitrage opportunities while evaluating the risks inherent in the trades

(considering the transaction costs and other practical aspects). This course starts with a review of Time

Series models and addresses econometric aspects of financial markets such as volatility and correlation

models. We will review several stochastic volatility models and their estimation and calibration

techniques as well as their applications in volatility based trading strategies. We will then focus on

statistical arbitrage trading strategies based on cointegration, and review pairs trading strategies. We

will present several key concepts of market microstructure, including models of market impact, which

will be discussed in the context of developing strategies for optimal execution. We will also present

practical constraints in trading strategies and further practical issues in simulation techniques. Finally,

we will review several algorithmic trading strategies frequently used by practitioners.

MATH-GA 2708.001 ALGORITHMIC TRADING AND QUANTITATIVE STRATEGIES

Spring term: P. Kolm and L. Maclin

Prerequisites: Computing in Finance, and Capital Markets and Portfolio Theory, or equivalent.

In this course we develop a quantitative investment and trading framework. In the first part of the

course, we study the mechanics of trading in the financial markets, some typical trading strategies, and

how to work with and model high frequency data. Then we turn to transaction costs and market impact

models, portfolio construction and robust optimization, and optimal betting and execution strategies. In

the last part of the course, we focus on simulation techniques, back-testing strategies, and performance

measurement. We use advanced econometric tools and model risk mitigation techniques throughout the

course. Handouts and/or references will be provided on each topic.

MATH-GA 2751.001 RISK AND PORTFOLIO MANAGEMENT WITH ECONOMETRICS

Fall term: P. Kolm. Spring term: M. Avellaneda

Prerequisites: univariate statistics, multivariate calculus, linear algebra, and basic computing (e.g.

familiarity with Matlab or co-registration in Computing in Finance).

A comprehensive introduction to the theory and practice of portfolio management, the central

component of which is risk management. Econometric techniques are surveyed and applied to these

disciplines. Topics covered include: factor and principal-component models, CAPM, dynamic asset

pricing models, Black-Litterman, forecasting techniques and pitfalls, volatility modeling, regime-

switching models, and many facets of risk management, both theory and practice.

MATH-GA 2755.001 PROJECT AND PRESENTATION

Fall term and spring term: P. Kolm

Students in the Mathematics in Finance program conduct research projects individually or in small

groups under the supervision of finance professionals. The course culminates in oral and written

presentations of the research results.

MATH-GA 2791.001 DERIVATIVE SECURITIES

Fall term: M. Avellanda. Spring term: B. Flesaker

An introduction to arbitrage-based pricing of derivative securities. Topics include: arbitrage; risk-neutral

valuation; the log-normal hypothesis; binomial trees; the Black-Scholes formula and applications; the

Black-Scholes partial differential equation; American options; one-factor interest rate models; swaps,

caps, floors, swaptions, and other interest-based derivatives; credit risk and credit derivatives.

MATH-GA 2792.001 CONTINUOUS TIME FINANCE

Fall term: A. Javaheri & S. Ghamami. Spring term: B. Dupire and F. Mercurio

Prerequisites: Derivative Securities and Stochastic Calculus, or equivalent.

A second course in arbitrage-based pricing of derivative securities. The Black-Scholes model and its

generalizations: equivalent martingale measures; the martingale representation theorem; the market

price of risk; applications including change of numeraire and the analysis of quantos. Interest rate

models: the Heath-Jarrow-Morton approach and its relation to shortrate models; applications including

mortgage-backed securities. The volatility smile/skew and approaches to accounting for it: underlyings

with jumps, local volatility models, and stochastic volatility models.

MATHEMATICAL TOOLS:

MATH-GA 2706.001 PARTIAL DIFFERENTIAL EQUATIONS FOR FINANCE

Spring term: R. Kohn

Prerequisite: Stochastic Calculus or equivalent.

An introduction to those aspects of partial differential equations and optimal control most relevant to

finance. Linear parabolic PDE and their relations with stochastic differential equations: the forward and

backward Kolmogorov equation, exit times, fundamental solutions, boundary value problems,

maximum principle. Deterministic and stochastic optimal control: dynamic programming, Hamilton-

Jacobi-Bellman equation, verification arguments, optimal stopping. Applications to finance, including

portfolio optimization and option pricing -- are distributed throughout the course.

MATH-GA 2902.001 STOCHASTIC CALCULUS

Fall term: P. Bourgade. Spring term: A. Kuptsov

Prerequisite: Basic Probability or equivalent.

Discrete dynamical models: Markov chains, one-dimensional and multidimensional trees, forward and

backward difference equations, transition probabilities and conditional expectations. Continuous

processes in continuous time: Brownian motion, Ito integral and Ito’s lemma, forward and backward

partial differential equations for transition probabilities and conditional expectations, meaning and

solution of Ito differential equations. Changes of measure on paths: Feynman-Kac formula, Cameron-

Martin formula and Girsanov’s theorem. The relation between continuous and discrete models:

convergence theorems and discrete approximations.

COMPUTATIONAL SKILLS:

MATH-GA 2041.001 COMPUTING IN FINANCE

Fall term: E. Fishler and L. Maclin

This course will introduce students to the software development process, including applications in

financial asset trading, research, hedging, portfolio management, and risk management. Students will

use the Java programming language to develop object-oriented software, and will focus on the most

broadly important elements of programming - superior design, effective problem solving, and the proper

use of data structures and algorithms. Students will work with market and historical data to run

simulations and test strategies. The course is designed to give students a feel for the practical

considerations of software development and deployment. Several key technologies and recent

innovations in financial computing will be presented and discussed.

MATH-GA 2043.001 COMPUTATIONAL METHODS FOR FINANCE

Fall term: J. Guyon & B. Liang

Prerequisites: Scientific Computing or Numerical Methods II, Continuous Time Finance, or permission

of instructor.

Computational techniques for solving mathematical problems arising in finance. Dynamic programming

for decision problems involving Markov chains and stochastic games. Numerical solution of parabolic

partial differential equations for option valuation and their relation to tree methods. Stochastic

simulation, Monte Carlo, and path generation for stochastic differential equations, including variance

reduction techniques, low discrepancy sequences, and sensitivity analysis.

MATH-GA 2046.001 ADVANCED ECONOMETRICS AND BIG DATA

Fall term: G. Ritter

Prerequisites: Derivative Securities, Risk & Portfolio Management with Econometrics, and Computing

in Finance (or equivalent programming experience).

A rigorous background in Bayesian statistics geared towards applications in finance, including decision

theory and the Bayesian approach to modeling, inference, point estimation, and forecasting, sufficient

statistics, exponential families and conjugate priors, and the posterior predictive density. A detailed

treatment of multivariate regression including Bayesian regression, variable selection techniques,

multilevel/hierarchical regression models, and generalized linear models (GLMs). Inference for classical

time-series models, state estimation and parameter learning in Hidden Markov Models (HMMs)

including the Kalman filter, the Baum-Welch algorithm and more generally, Bayesian networks and

belief propagation. Solution techniques including Markov Chain Monte Carlo methods, Gibbs

Sampling, the EM algorithm, and variational mean field. Real world examples drawn from finance to

include stochastic volatility models, portfolio optimization with transaction costs, risk models, and

multivariate forecasting.

MATH-GA.2047-001 DATA SCIENCE IN QUANTITATIVE FINANCE

Fall term: P. Kolm and I. Dimov

Prerequisites: Risk & Portfolio Management with Econometrics, Scientific Computing in Finance (or

Scientific Computing) and Computing in Finance (or equivalent programming experience.

This is a full semester course focusing on practical aspects of alternative data, machine learning and

data science in quantitative finance. Homework and hands-on projects form an integral part of the

course, where students get to explore real-world datasets and software.

The course begins with an overview of the field, its technological and mathematical foundations, paying

special attention to differences between data science in finance and other industries. We review the

software that will be used throughout the course.

We examine the basic problems of supervised and unsupervised machine learning, and learn the link

between regression and conditioning. Then we deepen our understanding of the main challenge in data

science – the curse of dimensionality – as well as the basic trade-off of variance (model parsimony) vs.

bias (model flexibility).

Demonstrations are given for real world data sets and basic data acquisition techniques such as web

scraping and the merging of data sets. As homework each student is assigned to take part in

downloading, cleaning, and testing data in a common repository, to be used at later stages in the class.

We examine linear and quadratic methods in regression, classification and unsupervised learning. We

build a BARRA-style implicit risk-factor model and examine predictive models for county-level real

estate, economic and demographic data, and macro economic data. We then take a dive into PCA, ICA

and clustering methods to develop global macro indicators and estimate stable correlation matrices for

equities.

In many real-life problems, one needs to do SVD on a matrix with missing values. Common

applications include noisy image-recognition and recommendation systems. We discuss the Expectation

Maximization algorithm, the L1-regularized Compressed Sensing algorithm, and a naïve gradient search

algorithm.

The rest of the course focuses on non-linear or high-dimensional supervised learning problems. First,

kernel smoothing and kernel regression methods are introduced as a way to tackle non-linear problems

in low dimensions in a nearly model-free way. Then we proceed to generalize the kernel regression

method in the Bayesian Regression framework of Gaussian Fields, and for classification as we introduce

Support Vector Machines, Random Forest regression, Neural Nets and Universal Function

Approximators.

MATH-GA 2048.001 SCIENTIFIC COMPUTING IN FINANCE

Spring term: Y. Li and

Prerequisites: multivariable calculus, linear algebra; programming experience strongly recommended

but not required.

A practical introduction to scientific computing covering theory and basic algorithms together with use

of visualization tools and principles behind reliable, efficient, and accurate software. Students will

program in C/C++ and use Matlab for visualizing and quick prototyping. Specific topics include IEEE

arithmetic, conditioning and error analysis, classical numerical analysis (finite difference and integration

formulas, etc.), numerical linear algebra, optimization and nonlinear equations, ordinary differential

equations, and (very) basic Monte Carlo.

class of 2019 resume book - math.nyu.edu · class of 2019 resume book mathematics in finance m.s....

Documents