Engineering Mathematics, Statistics and Finance

stock market chart

 

The EngSci Mathematics, Statistics and Finance major teaches students the theory behind the financial instruments and markets that impact our global economy.

Financial markets affect everyone from companies raising capital, to pensions generating income for millions of retirees, insurance companies managing risks, and individuals investing their savings. In recent years, advanced financial technologies, such as "robo-advisors", have significantly changed the landscape merging data science and financial mathematics.

Launched in 2010, the Major was the first undergraduate program of its kind in Canada.

Modern financial theory is highly mathematical and statistical in nature. Students gain a strong background in these disciplines and learn how to apply them in quantitative finance through engineering tools such as optimization. Courses explore central finance issues like pricing of options and construction of financial portfolios, and teach students how to use engineering mathematics to tackle financial problems in any domain.

Courses are taught by professors in U of T's Departments of Mechanical & Industrial Engineering and Chemical Engineering & Applied Chemistry, in cooperation with the Department of Statistics, the Department of Mathematics and the Rotman School of Management. The program's location in downtown Toronto, close to Canada's financial heart, gives students access to professionals and companies in this vibrant sector.

Course themes include mathematics and statistics (includes probability, stochastic processes, statistical computation, econometrics), finance and financial engineering (includes economics, option pricing, portfolio optimization, real options), and computation (includes numerical methods, optimization, Monte Carol methods, and partial differential equations).

Students gain the background in mathematical sciences needed for industries such as consulting engineering, the public sector, energy, mining, insurance, banking, aerospace, and manufacturing. They are also well-equipped for graduate studies at top graduate programs in finance, operations research, business management, data science, machine learning, applied mathematics, or statistics.

FAQs

Is this a financial engineering major? Will it limit me to a career in finance?

It is much more than a financial engineering major. You will be exposed to broad areas of applied mathematics, operations research and statistics. Exposure to these topics is excellent preparation for further study in the mathematical and statistical sciences, or employment in many areas including finance, data science, supply chain management, and logistics.

This major is also good preparation for business school after a few years of work experience.

How many students from this major do a PEY Co-op after third year?

Over half of students in the Engineering Mathematics, Statistics and Finance major have participated in the PEY Co-op Program in the past few years at companies like RBC, RIM, Unilever, Hatch, Deloitte Consulting, TD Asset Management, and more.

If I want to work in the financial industry, why would I take this major instead of a business or finance degree?

This major enables access to the more quantitatively-oriented finance jobs like quantitative asset management and risk management.

Did you know...?

Students can further their knowledge in student clubs
like UTEFA and UTDST.

Find more student clubs here.

Sample Courses

MIE375 - Financial Engineering

Learn the fundamentals of financial engineering, including concepts from financial economics. Topics include interest rate theory, fixed income securities, bond portfolio construction, term structure of interest rates, mean-variance optimization theory, the capital asset pricing model (CAPM), arbitrage pricing theory (APT), forwards and futures, and introduction to option pricing and structured finance.

MIE376 - Mathematical Programming (Optimization)

This course deals with the formulation of optimization models for the design and operation of systems that produce goods and services, and the solution of such problems with mathematical programming methods. These include linear programming with the simplex method, sensitivity analysis, duality, the revised simplex, column generation, Dantzig-Wolfe decomposition and linear programming with recourse; minimum cost network flows; dynamic programming; integer programming; and non-linear programming models.

MIE377 - Financial Optimization Models

Students learn how to create optimization models for the design and selection of an optimal investment portfolio. Topics include risk management, mean variance analysis, models for fixed income, scenario optimization, dynamic portfolio optimization with stochastic programming, index funds, designing financial products, and scenario generation. These concepts are also applied to international asset allocation, corporate bond portfolios and insurance policies with guarantees.

ACT370 - Financial Principles for Actuarial Science II

This course covers mathematical theory of financial derivatives, discrete and continuous option pricing models, hedging strategies and exotic option valuation.

CHE374 - Economic Analysis and Decision Making

This course discusses economic evaluation and justification of engineering projects and investment proposals. Cost estimation; financial and cost accounting; depreciation; inflation; equity, bond and loan financing; after tax cash flow; measures of economic merit in the private and public sectors; sensitivity and risk analysis; single and multi-attribute decisions are covered. It also introduces micro-economics.

CHE375 - Engineering Finance and Economics

This course has three modules: 1) managerial accounting, 2) corporate finance and 3) macro economics. The first module introduces financial statements and double entry record keeping, and deeper aspects of revenue, expenses, assets, debt and equity. The second module introduces the concept of risk and return, the Capital Asset Pricing Model, capital budgeting, corporate financing, financial statement analysis and financial valuation. The third module covers global aspects of business, including economic, political, societal and technological, and discusses factors such as GDP, inflation, unemployment, interest rates, foreign exchange rates, fiscal debt/surplus and balance of payments, and their impact on the financials of a given country.

MIE424 - Optimization in Machine Learning

Students gain a deeper understanding of standard machine learning methods through development of machine learning from an optimization perspective.  They apply these methods to problems in finance and marketing, such as stock return forecasting, credit risk scoring, portfolio management, fraud detection, and customer segmentation.

Where this major can take you

Our graduates work in a wide range of industry and research.  Meet some of our alumni.

Employers for our recent graduates include BCG, BMO Investment Banking, Goldman Sachs, IBM Consulting, JP Morgan, McKinsey & Co., Total Portfolio Management division of CPP, Yahoo.com, and others.

Recent graduates have attended graduate school at U of T, Columbia, Cornell, MIT, Stanford, University of Michigan, and more.

Roy Kwon

Chair of the Engineering Mathematics, Statistics and Finance major

Associate Professor Roy Kwon (MIE)

Professor Kwon works on mathematical optimization for supply-chain management, financial engineering, and smart material design.

rkwon@mie.utoronto.ca

© 2020 Faculty of Applied Science & Engineering