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Stephen Andrew McDonald
26 Prospect Avenue
Princeton University
Princeton NJ 08540-5296
E-mail: [email protected]
Education:
Princeton University: Master in Finance 2013-2015 (Anticipated) Expected Graduation: May 2015
Coursework: Stochastic Calculus, Financial Econometrics, High Dimensional Statistics, Statistical Learning,
Regression and Time Series Analysis, Artificial Intelligence, Scientific Computing, Econometric Modeling
GRE Scores: Math: 169 Reading: 168 Analytical Writing: 5.0
Georgetown University: Bachelor of Science in International Economics 2009-2013
GPA: 3.79
Honors: Magna Cum Laude and Honors in Major
Coursework: Honors Macro/Microeconomics, Stochastic Simulations, Multivariable Calculus, Time Series
Analysis, Intermediate Econometrics, Linear Algebra, Differential Equations
Technical Skills:
High Dimensional Regression/Dimension Reduction:
Experience with data cleaning, exploratory data analysis, model fitting and validation as well as prediction
for high-dimensional data. Techniques implemented include projection pursuit, kernel regression, principal
components analysis, factor models, and regularized regression techniques including Ridge, Lasso, and
elastic net. Particular experience in forecasting using financial data.
Clustering/Classification Techniques:
Experience with K-means, the EM algorithm, Self-Organizing maps (SOM) and kernel variants for
unsupervised clustering including network segmentation. For supervised classification, techniques
implemented include logistic regression, Linear and Quadratic Discriminant Analysis, Successively
Optimized Discriminant Analysis (SODA), Support Vector Machines (SVM) and ridge/kernel variants.
Time Series Analysis:
Fitted ARMA-GARCH models to asset returns to forecast volatility, implemented regime switching model
to identify changes in underlying asset’s structural regime, fit mean-reverting models including Vasicek.
Programming Language/Technical Skills:
Intermediate Knowledge:
R (various machine learning/optimization packages), Python (NumPy, SciPy, SciKit), C++
Basic Knowledge:
Java, Matlab, VBA, SQL, Unix Tools (bash, make, awk, etc), Git, emacs, OpenMP, Tableau,
SQL, Hadoop, Amazon Web Services, Pig, Hive, among others
Work Experience:
Kiski Alpha Partners Quantitative Analyst Intern: June 2014-August 2014
 Used R, SQL, Tableau, and VBA for data storage, analysis, and visualization for hedge fund data
 Developed multi-strategy product using variety of portfolio construction techniques, including
minimum variance, momentum, risk parity, and omega optimization
 Followed academic literature to implement cutting edge portfolio construction techniques
 Managed daily risk reporting process for hedge fund clients
Computational Statistics Project:
Python-based program which uses Numpy, Scipy, and SciKit libraries to automate analysis of major
regression and classification techniques. The program performs initial analysis of data, then automatically
performs diagnostics of model fit and chooses additional techniques most likely to produce improved
results. Ongoing work involves adding R interface that will choose the best implementation of an algorithm
available in either language.