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Master of Science in Finance
Position Yourself for Success
Understand the role of finance in business. This full-time program is based in downtown Vancouver and teaches you the fundamentals of finance, asset pricing, data analytics, investment banking, and private equity. But it鈥檚 not all theory. You鈥檒l also be able to manage the Student Investment Advisory Service Fund, an investment portfolio with a market value of over $20 million CAD.
Academic streams
As part of the MSc Finance program, you can choose between two academic streams.
Investment Management
- Investment Banking & Private Equity
This course mixes financial theory with concrete applications in investment banking and private equity settings. The main areas of study are around Corporate Finance, Equity Valuation and Investments that are of importance to investment bankers and private equity professionals. - Portfolio Theory and Asset Pricing
This course covers the theory and empirical evidence relevant for investing, particularly in the context of portfolio management. (i.e., Markowitz optimal allocation, CAPM, Index and multi-factor models). It stresses issues of portfolio strategy, asset allocation and performance evaluation. The course is geared towards the understanding and implementation of 鈥渕odern portfolio theory,鈥� which is a general approach for maximizing the expected return of a portfolio given a certain amount of risk. This approach is the basis of virtually all quant investing strategies and is widely used by traditional portfolio managers. - Strategic Asset Allocation
Aiming to equip students with a solid understanding of both theoretical and practical aspects related to investment policy establishment, this course delves into factors such as return objectives, risk tolerance, investment horizon, tax considerations, and liquidity needs. The course starts by examining the standard mean-variance framework and the impact of capital market expectations on asset allocation. Subsequently, the framework is expanded to address non-standard preferences, alternative investments, and the effects of taxes and investment horizons. Behavioral models from current literature provide a foundation for investor risk assessment and profiling while readings from practitioner-oriented sources, demonstrate the real-world applicability of theoretical models. - Special Topic: FinTech
Disintermediation, Blockchains, Bitcoin, Ethereum, Smart Contracts 鈥� what do all these terms mean and what is the momentous impact they have on finance and the economy? These questions are addressed in this cutting-edge course on the latest developments in fintech. - Special Topic: Machine Learning
This course provides an in-depth exploration of the application of machine learning techniques in finance and is designed to provide hands-on experience in using machine learning models for algorithmic trading. The course covers topics such as data preprocessing, feature engineering, supervised and unsupervised learning, deep learning, natural language processing, and reinforcement learning. By the end of the course, students will be able to understand the basic concepts and techniques of machine learning and their application in finance, design and implement machine learning models, and critically evaluate their performance. The course will be highly interactive, involving a combination of lectures and hands-on projects. - Special Topic: CSR & Impact Investing
This course offers an interdisciplinary view of sustainable finance, encompassing historical, political, scientific, and economic perspectives. It explores the potential contradictions between maximizing firm value and sustainability while examining the role of externalities. Key topics include the Coase Theorem, implicit and explicit contracts, and the influence of human behavior on corporate decision-making and sustainability. The course also investigates human capital mismanagement, income inequality, and climate change, delving into the challenges of finding solutions based on scientific knowledge and economic principles.
Research
- Business Econometrics I
Covers the fundamentals of identification, estimation and inference methods used in modern research. We first cover ordinary least squares and then introduce the very important instrumental variable estimators. Finally, we examine the bootstrap as the most general application of simulation-based econometric methods. You will get a lot of hands-on experience with using these methods on real Finance datasets. Python will be used extensively. - Theory of Financial Markets
This course will be an introduction to advanced topics in financial economics. We will cover both discrete time models and continuous time models in finance. Topics include: Utility Theory, Risk Aversion, Portfolio Selection Problem, Discrete Time Intertemporal Portfolio Selection, Math of Continuous Time Finance, Continuous Time Diffusion Process, Continuous Time Portfolio Selection and Asset Pricing, Continuous Time Option Pricing, and Stochastic Models of the Term Structure of Interest Rates. - PhD Course: Business Econometrics II
In Business Econometrics II, the skills needed to do state-of-the art empirical research in Finance are developed. You will study the techniques for estimation and empirical testing of static and dynamic models including maximum likelihood, generalized method of moments (GMM), bootstrapping and simulation methods. The methods are illustrated with Economic and Finance applications, often with the help of Python. - PhD Course: Corporate Finance Theories and Methods
Advanced analysis of decision making at the corporate level. Topics include investment decisions, the issuance of corporate securities, corporate governance, capital structure, and dividend policy. Special attention will be paid to the importance of asymmetric information, taxes, and contract enforcement costs in corporate decisions. - PhD Course: Asset Pricing
Students will gain an in-depth understanding of the theoretical foundations and empirical methods used in modern asset pricing. The course will study the recent developments in equilibrium asset pricing modeling, the assumptions, the pricing puzzles and the advanced techniques required for their empirical tests. The primary objective of this course is to equip students with the necessary knowledge and analytical skills to conduct original research in theoretical and/or empirical asset pricing.
Please note the exact selection of all courses is subject to change and stream offerings are subject to minimum enrollment numbers in each stream.
Commitment to Truth and Reconciliation
As a business school, SFU Beedie School of Business is committed to supporting student learning and being in right relations with Indigenous peoples. Part of the core curriculum is learning about the history and aspirations of Indigenous communities to understand better how to build respectful partnerships with communities and Nations in alignment with their economic development goals and enact economic reconciliation.