Skip to content Skip to navigation

Curriculum After AY15/16

Quantitative Finance

There are 6 courses or modules related to the Quantitative Finance major that an SMU student (matriculated from AY15/16 onwards) must complete before successfully graduating with a Bachelor of Business Management, majoring in Quantitative Finance, 3 of which are compulsory. Students will also need to satisfy all the other related University degree requirements. Whether taken as a first major for the BBM requiring the standard total of 36 course units or as a second major in all SMU degree programs, the Quantitative Finance Major's courses are as follows:

The 3 core courses are:

»         QF 101: Quantitative Finance 
»         QF 102: Investment Statistics - Pre-Req: STAT 101/151: Introductory Statistics/Introduction to Statistical Theory
»         QF 205: Computer Technology for Finance

Students only need to take 3 electives out of the following 9 offerings:

»         QF 206: Quantitative Trading Strategies - Pre-Req: FNCE 101/103: Finance/Finance for Law
»         QF 207: Structured Products Sales and Trading - Pre-Req: MATH001: Calculus
»         QF 208: Linear Algebra and Numerical Methods (NEW)
»         QF 305: Global Financial Risk Management
»         QF 307: Stochastic Finance - Pre-Req: MATH001: Calculus (NEW)
»         FNCE 204: Analysis of Fixed-Income Investments Pre-Req: FNCE 101/103: Finance/Finance for Law
»         FNCE 305: Analysis of Derivative Securities Pre-Req: FNCE 101/103: Finance/Finance for Law
»         FNCE 307: Portfolio Management Pre-Req: FNCE 101/103: Finance/Finance for Law 
»         STAT 313: Quantitative Risk Analysis - Pre-Req: STAT201: Probability Theory and Applications

Note: Please double check OASIS for the most up to date pre-requisites.


Quantitative Finance Major’s Compulsory Modules

What is Quantitative Finance? Why quantitative? Increasingly, mathematical and statistical methods are being applied by hedge funds and asset managers to generate superior returns while minimizing their risk exposures. The famous examples are the Renaissance Technologies’ Medallion Fund in the U.S, and Quantedge Capital in Singapore. Strong quantitative skills are the foundation for these hedge funds. They are extremely good at applying Quantitative Finance models to extract critical investment and trading signals from big data. For day-to-day risk management in any bank these days, quantitative skills are also indispensable to quantify market risks, credit risks, liquidity risks, interest rate fluctuations, funding costs, capital adequacy, and the list goes on.

This 101 course introduces you to the essentials of Quantitative Finance models, starting with three basic principles to look at risk and return. It’s going to be cool and fun to see how your pre-university math can be applied to solve problems faced by quantitative strategists and risk analysts. 

Have you ever wondered how do outstanding asset managers consistently outperform the market and generate alpha? How can we predict important financial time series like earning, volatility, volume, and return? The answer lies in clever use of investment statistical techniques. This course teaches you how to extract patterns from historical data, create investment strategies, and test profitability and hypothesis.

The application of statistical methods to investment and trading is one of the areas experiencing the fastest pace of growth and development in the world of investment banks, hedge funds and asset managers. Mathematical models for trading and investment management are rapidly growing both in terms of sophistication and scope. On the buy side, hedge funds and asset managers make constant use of empirical statistical models to analyse financial time series for optimal investment decision. On the sell side, front office trading teams in investment banks employ risk-neutral probability models to price and risk manage their portfolio to hedge their exposure. Students aspiring to careers in the financial market ought to be proficient in investment statistics to fully comprehend the dynamics behind the financial market.

QF205: COMPUTING TECHNOLOGY IN FINANCE (counted as a Technology & Entrepreneurship module for all SMU students)
This course aims to expose students to the use and usefulness of computing technology in the realm of finance. From the collation of data, analysis of data in order to tease out relevant information, to the presentation and visualization of information, computing technology plays an important role that is increasingly essential as one faces the need to assimilate an astronomical amount of information in today’s world. The course is structured in such a way as to employ topics in finance to motivate the discourse on computing technology. Equipped with the computing skills, in turn, students are motivated to handle more challenging problems in finance.


Quantitative Finance Major’s Elective Modules

Like any financial investment, trading in stocks, currencies, commodities, and fixed income instruments may lead to substantial profits but they can also lead to substantial losses. It goes without saying that a suite of trading strategies is needed to keep winning the game of probability while limiting the downside risk. In this course, practicable trading strategies coupled with risk management will be covered in detail. Algorithmic trading, high-frequency trading, and the likes will be demystified along with quantitative trading. Using the MSCI Singapore Free Index futures as a case study, students will get to see concretely what a limit-order book and its dynamics look like throughout the trading session. This practical course also provides students with a rare opportunity to learn and practise trading on a software platform used by professional traders.


The global financial market has experienced a tremendous growth since its inception in the early 1970s. As of the end of 2013, the notional amount of outstanding over-the-counter (OTC) derivatives totalled $710 trillion. This phenomenal growth can be attributed to the fact that there is a genuine need for financial risk management. Structured products are financial instruments created based on options and derivatives, tailored to the specific needs of the clients. Global businesses are exposed to movements in exchange rates, commodity prices, interest rates and equity prices. Structured products designed to effectively transfer these market sensitivities for effective risk management serve a real purpose and are consequently in high demand. Clearly, students interested in a sales and trading career must develop a deep insight in the market mechanics for them to price, trade and hedge these products efficiently.

This course is designed to equip you with a strong foundation on the theoretical and practical aspects of sales, structuring, and trading. You will be brought up to speed with all the major current subjects, including the necessary knowledge and insights to appreciate the latest developments in the financial market. Throughout the course, we provide practical examples for every concept introduced, so that students can get a good grasp on the practical aspects of derivative trading and how they can be applied to the financial market.



Linear algebra is the foundation of modern mathematics. There are a lot of applications in the fields of finance, data science, econometrics, operation management, medical science, engineering and physics which use tools of linear algebra to solve real-world problem. This course consists of two parts. The first part covers matrices (including matrix operations, inversion) and systems of linear equations (including their solutions by Gauss elimination and matrix operations). Determinants, Euclidean space, general vector spaces, sub-spaces idea, linear independence, dimension, row, column, and null spaces concepts will be introduced.  We will also discuss norms, distance ideas, operations such as inner product, concepts of orthogonal bases, and Gram-Schmidt orthogonalization. Eigenvalues, eigenvectors, eigenspaces, eigenbases and their applications. The second part of the course introduces students to a variety of classical numerical methods and then applies these methods to solve problems raised from many areas of quantitative finance, data science and econometrics. 


QF305: GLOBAL FINANCIAL RISK MANAGEMENT (counted as a Global and Regional Studies module for all SMU students)
This course will begin with characterizing global financial risks and discussing different quantitative methods to evaluate risk, such as VAR. A review of some of the fundamental concepts in risk management for commercial banks will be provided. The course will proceed to cover in some detail the Basel principles and standards for the management of the key types of risks faced by commercial banks, such as Market Risk, Credit Risk, and Operational Risk. The Basel II framework of the three pillars, namely the determination of minimum capital requirements, the supervisory review process, and market discipline will be covered.  



The objective of this course is to introduce students to stochastic modelling of financial assets and the valuation of derivatives.

The concept that created the subject and led to the development of the field of financial derivatives is the work of Fisher Black and Myron Scholes (1973). Stochastic models based on the principle of no-arbitrage, dynamic hedging, martingale valuation, and risk-neutrality can be formulated to price derivatives traded in the financial market. The same framework has subsequently been applied to the pricing and hedging of other more exotic financial products.

The course is an interesting mix of finance and mathematics. Students will see that fundamental financial concepts like the no-arbitrage principle, coupled with careful mathematical reasoning, lead to a sophisticated framework of valuation and hedging.

The mathematical tools employed are calculus, stochastic calculus, probability theory and numerical methods. A good background in calculus and probability is assumed. The other mathematical requisites will be furnished during the course.


Fixed Income Securities are securities whose income is literally fixed, and more generally any claims whose value or risk is related to interest rates and interest rate uncertainty. The course will cover the evaluation of these securities, the market operations and the risk involved. It then discusses how you can manage fixed income security portfolios and manage the resulting risk.


Financial derivatives have applications across many areas of finance, such as hedging, swaps, convertible claims, and corporate decision making. The course objective is for students to understand profoundly the valuation of forward, futures, options and other derivative securities, and their use in hedging risk exposures, such as commodity price risk, currency risk, interest rate risk, stock portfolio risk, and so forth.

Furthermore, students will be given the opportunity to explore a comprehensive online financial markets simulation system (Stock-Trak) to obtain hands-on experience (of a fund manager :) in trading in the "real" market. For instance, students can trade futures and options on commodities such as gold, silver, corn, oil etc at the 'market prices' through Stock-Trak.

Given the advance nature of this module, strong math and statistics backgrounds and good achievements in other finance courses (e.g., Portfolio Management, Analysis of Equity Investments, and Analysis of Fixed-Income Investments) may provide some advantages in handling this challenging module.

This course would cover the current theories of portfolio management and provide a conceptual framework for the evaluation of investment strategies. It aims to provide students with exposure to the process of investment management including identification of investment objectives and constraints, determining asset allocations, and measuring portfolio performance. It will also cover the fundamental concepts of investments, including risk and return, market efficiency, portfolio diversification, CAPM and risk management using derivatives. The coverage will include applications and implementation, in particular performance evaluation and international diversification. Lessons will also include extended and interactive discussions and analyses of the contemporary investing scene and global capital markets.


This course covers measures that quantify major risk exposures arising from financial and non-financial risks, such as market risk, interest rate risk, liquidity risk, operational risk and model risk. The topics include variance-covariance method, historical simulation, Monte Carlo, backtesting, statistical calibration strategies, GARCH models, copulas and extreme value theory.


The curriculum and the list of courses provided are not exhaustive and will be updated from time to time. Please refer to the Course Catalogue on OASIS for the most updated list of electives available and their course attributes.


Last updated on 25 Jan 2018 .