collageThe Baruch MFE Program is a STEM-designated program that fosters the study of mathematical and algorithmic models in finance in order to generate competitive, successful, and highly qualified practitioners.
The Financial Engineering Hub at Baruch College was introduced by Baruch College President S. David Wu.
The Quantnet 2021 Ranking of Best Financial Engineering Programs placed the Baruch MFE Program at the top.
Enrollment is currently open for the February-March 2021 Pre-MFE Online seminars. There is a 40-person maximum for each seminar.
Advanced Calculus with Financial Engineering Applications (syllabus)
Probability Theory for Financial Applications (syllabus)
Numerical Linear Algebra for Financial Engineering (syllabus)
Registration information, dates, syllabi and more information can be found here.
Student Feedback: AC-FE, Probability, NLA.
The 2018-2020 Baruch MFE Employment Report contains full-time and internship numbers aggregated over the last two years, and including details on employers, job functions, job industry.
Fall 2020 Admission Statistics:
490 Applicants; 40 Admitted; 30 Enrolled (27 Full Time, 3 Part Time)
Admitted Applicants: Average GPA 3.76, Average GRE Scores: 169.8 Quantitative, 158 Verbal
Baruch MFE won the 2020 9th IAQF Student Competition; news article from Baruch MFE.
Baruch MFE won the 2020 Rotman International Trading Competition; news article from Baruch College.
The 2019 Baruch MFE 5th Year Career Development Report can be found here. Featured in The Wall Street Journal in December 2019.
Employment Statistics (December 2019 – May 2020)
Placement Rate: 30 of 30
Starting Salary: High 160K; Low 95K; Median 120K; Average 122K
First Year Guaranteed Compensation: High 235K; Low 95K; Median 140K; Average 147K
Employers (by type): Hedge Funds/Prop Trading/Asset Management: 50%, Investment Banks: 40%, FinTech/Tech: 10%
Employers (some with multiple hires; selected): AQR, Bank of America, Barclays, Beacon Platform, BlackRock, Citadel, Credit Suisse, Cubist, Goldman Sachs, IMC Trading, Millennium, Morgan Stanley, Point72, Quantitative Brokers, Societe Generale, Squarepoint Capital, TD Securities, UBS
Location: New York: 90%, US (Other) 10%
Internship Statistics (Summer 2020)
Placement Rate: 29 of 29
Monthly Compensation: High $13,500; Low $4,700; Median $10,000; Average $9,000
Employers (by type): Hedge Funds/Prop Trading/Asset Management: 45%, Investment Banks: 48%, FinTech/Tech: 7%
Employers (some with multiple hires; selected): Aigen Investment Management, Alphadyne, Arrowstreet Capital, Bank of America, Barclays, BNP Paribas, BNY Mellon, Citigroup, Credit Suisse, Goldman Sachs, IMC Trading, JPMorgan, Quantitative Brokers, RBC Capital Markets, Schonfeld, Simon Markets, Squarepoint Capital, Tibra Capital, VanEck
Location: New York: 90%, US (Other) 10%
The Baruch MFE Program was ranked Number 3 worldwide in the 2020 Risk.net Quant Finance Master’s Guide
Baruch MFE student Xiangtian (Forest) Deng was on the winning team (out of 30 teams) at the 2018 Columbia Data Science Hackathon.
Enrollment is open for the Baruch Pre-MFE Online Seminars:
C++ Programming for Financial Engineering Certificate
Advanced C++11/C++14 and Multidisciplinary Applications
VBA/Python/SQL with Applications
baruch mfe curriculum
After an extensive review which included feedback from industry practitioners, alumni, and faculty, the Baruch MFE Curriculum was reshaped to best fit the current financial employment environment.
The new Baruch MFE Curriculum is cutting edge (24 new courses taught by top practitioners introduced since Fall 2010), flexible (the number of required credits was reduced from 15 to 12, and the number of elective credits increased from 21 to 24 in Fall 2018), and streamlined (a core set of redesigned courses).
Cutting Edge Curriculum
Twenty-four new elective courses introduced since 2010:
Fall 2018:
MTH 9878 Interest Rate and Credit Models
Fall 2017:
MTH 9796 Natural Language Processing
MTH 9797 Advanced Data Analysis
MTH 9887 Blockchain Technologies in Finance
MTH 9897 Systematic Trading
Fall 2016:
MTH 9866 Modeling and Market Making in Foreign Exchange
Fall 2015:
MTH 9816 Fundamentals of Trading
MTH 9855 Asset Allocation and Portfolio Management
MTH 9886 Emerging Markets and Inflation Modeling
Spring 2015:
MTH 9878 Interest Rate Models
MTH 9898 Data Science I: Big Data in Finance
MTH 9899 Data Science II: Machine Learning
Fall 2014:
MTH 9876 Credit Risk Models
Spring 2013:
MTH 9863 Volatility Filtering and Estimation
Winter 2012:
MTH 9891 Introduction to Financial Econometrics
Fall 2012:
MTH 9883 Structured Security Valuation in the Primary Market
MTH 9896 Behavioral Finance
Spring 2011:
MTH 9879 Market Microstructure Models
MTH 9893 Time Series Analysis
MTH 9894 Algorithmic Trading
Summer 2011:
MTH 9868 Advanced Risk and Portfolio Management
Fall 2011:
MTH 9882 Fixed Income Risk Management
Fall 2010:
MTH 9865 Commodities and Futures Trading
MTH 9875 The Volatility Surface
Degree Requirements
To complete the degree, students must complete 36 credits: 12 credits by taking required courses and 24 credits by taking elective courses.
Required Courses
| Course | Name | Credits | Semester |
|---|---|---|---|
| MTH 9814 | Financial Markets and Securities | 1.5 | Fall |
| MTH 9815 | Software Engineering for Finance | 1.5 | Fall |
| MTH 9821 | Numerical Methods for Finance | 3 | Fall |
| MTH 9831 | Probability and Stochastic Processes for Finance I | 3 | Fall |
| MTH 9903 | Capstone Project and Presentation | 3 | Fall or Spring |
Elective Courses
Students take elective courses in the Mathematics Department or in the Zicklin School of Business.
Mathematics Department Elective Courses:
| Course | Name | Credits |
|---|---|---|
| MTH 9816 | Fundamentals of Trading | 1.5 |
| MTH 9841 | Statistics for Finance | 1.5 |
| MTH 9842 | Linear and Quadratic Optimization Techniques | 1.5 |
| MTH 9845 | Market and Credit Risk Management | 3 |
| MTH 9848 | Elements of Structured Finance | 3 |
| MTH 9852 | Numerical Methods for Finance II | 3 |
| MTH 9855 | Asset Allocation and Portfolio Management | 3 |
| MTH 9862 | Probability and Stochastic Processes for Finance II | 3 |
| MTH 9863 | Volatility Filtering and Estimation | 1.5 |
| MTH 9864 | Model Review for Quantitative Models in Finance | 1.5 |
| MTH 9865 | Commodities and Futures Trading | 1.5 |
| MTH 9866 | Modeling and Market Making in Foreign Exchange | 1.5 |
| MTH 9867 | Time Series Analysis and Algorithmic Trading | 3 |
| MTH 9868 | Advanced Risk and Portfolio Management | 3 |
| MTH 9871 | Advanced Computational Methods in Finance | 3 |
| MTH 9873 | Interest Rate Models and Interest Rate Derivatives | 3 |
| MTH 9875 | The Volatility Surface | 3 |
| MTH 9876 | Credit Risk Models | 3 |
| MTH 9878 | Interest Rate Models | 3 |
| MTH 9879 | Market Microstructure Models | 3 |
| MTH 9881 | Current Topics in Mathematical Finance | 3 |
| MTH 9882 | Fixed Income Risk Management | 1.5 |
| MTH 9883 | Structured Security Valuation in the Primary Market | 1.5 |
| MTH 9886 | Emerging Markets and Inflation Modeling | 1.5 |
| MTH 9887 | Blockchain Technologies in Finance | 1.5 |
| MTH 9891 | Introduction to Applied Financial Econometrics | 1.5 |
| MTH 9893 | Time Series Analysis | 1.5 |
| MTH 9894 | Algorithmic Trading | 1.5 |
| MTH 9896 | Behavioral Finance | 1.5 |
| MTH 9897 | Systematic Trading | 1.5 |
| MTH 9898 | Data Science in Finance I: Big Data in Finance | 1.5 |
| MTH 9899 | Data Science in Finance II: Machine Learning | 1.5 |
| MTH 9901 | Independent Study – Internship | 1.5 |
| MTH 9796 | Statistical Data Analysis | 1.5 |
| MTH 9797 | Advanced Data Analysis | 1.5 |
| MTH 9760 | Big Data Technologies | 3 |
Zicklin School of Business Elective Courses:
| Course | Name | Credits |
|---|---|---|
| ECO 82100 | (Term I) Econometrics I | 3 |
| ECO 82100 | (Term II) Financial Econometrics | 3 |
| FIN 9770 | Financial Markets and Institutions | 3 |
| FIN 9782 | Futures and Forward Markets | 3 |
| FIN 9783 | Investment Analysis | 3 |
| FIN 9786 | International Financial Markets | 3 |
| FIN 9790 | Seminar in Finance | 3 |
| FIN 9793 | Advanced Investment Analysis | 3 |
| FIN 9797 | Options Markets | 3 |
| STA 9700 | Modern Regression Analysis | 3 |
| STA 9701 | Time Series: Forecasting and Statistical Modeling | 3 |
Schedule of Classes
Academic Year 2018-2019
| Fall 2018 | Name | Instructor | Type | Credits |
| MTH 9814 | Quantitative Introduction Financial Instruments | Robert Spruill, State Street | Required | 1.5 |
| MTH 9815 | Software Engineering for Finance | Breman Thuraisingham, Morgan Stanley | Required | 1.5 |
| MTH 9816 | Fundamentals of Trading | Jarrod Pickens, Baruch MFE | Elective | 1.5 |
| MTH 9821 | Numerical Methods for Finance | Dan Stefanica, Baruch MFE | Required | 3 |
| MTH 9831 | Probability & Stochastic Processes for Finance | Elena Kosygina, Baruch MFE | Required | 3 |
| MTH 9842 | Optimization Techniques in Finance | Andrew Lesniewski, Baruch MFE | Elective | 1.5 |
| MTH 9893 | Time Series Analysis | Andrew Lesniewski, Baruch MFE | Elective | 1.5 |
| Spring 2019 | Name | Instructor | Type | Credits |
| MTH 9845 | Market and Credit Risk Management | Ken Abbott, Barclays | Elective | 3 |
| MTH 9855 | Asset Allocation and Portfolio Management | Gordon Ritter, GSA Capital | Elective | 3 |
| MTH 9878 | Interest Rate and Credit Models | Andrew Lesniewski, Baruch MFE | Elective | 3 |
| MTH 9879 | Market Microstructure Models | Jim Gatheral, Baruch MFE | Elective | 3 |
| MTH 9898 | Data Science I: Big Data in Finance | Giulio Trigila, Baruch MFE | Elective | 1.5 |
| MTH 9899 | Data Science II: Machine Learning | Adrian Sisser, Seven Eight Capital | Elective | 1.5 |
| Fall 2019 | Name | Instructor | Type | Credits |
| MTH 9866 | Modeling and Market Making in Foreign Exchange | Mark Higgins, Beacon | Elective | 1.5 |
| MTH 9875 | The Volatility Surface | Jim Gatheral, Baruch MFE | Elective | 3 |
| MTH 9887 | Blockchain Technologies in Finance | Andrew Lesniewski, Baruch MFE | Elective | 1.5 |
| MTH 9897 | Systematic Trading | Dmitry Rakhlin, Goldman Sachs | Elective | 1.5 |
| MTH 9903 | Capstone Project and Presentation | Core Course | 3 |
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