Curriculum

Quantitative Finance Major’s Electives Modules

​​​​​​​QF206: QUANTITATIVE TRADING STRATEGIES
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.

​​​​​​​QF208: LINEAR ALGEBRA AND NUMERICAL METHODS
Linear Algebra is the foundation of many quantitative methods. There are a lot of applications in the fields of finance, data science, econometrics, operation management, medical science, engineering, machine learning, and physics which use tools of linear algebra to solve real-world problems. 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 process. eigenvalues, eigenvectors, eigenspaces, eigenbases and their applications. The second part of the course introduces students to a variety of classical numerical methods, such as numerically solving equations and equation systems (linear and nonlinear), numerical interpolation and integration, and Monte Carlo simulation, etc. We also apply these methods to solve problems raised from many areas of quantitative finance, data science, and econometrics.

QF209: MACHINE LEARNING IN QUANTITATIVE FINANCE 
Machine learning represent different modeling principles and techniques and underpin many successful financial applications. This course covers common machine learning models, including deep neural networks and reinforcement learning, with financial application such as stock price prediction and portfolio management. This course is designed to combine both theory and practice/implementation of model development, focusing on developing the core data analytics skills of the students and presenting specialized exposure to quantitative finance.

QF210: REINFORCEMENT LEARNING IN PORTFOLIO OPTIMIZATION
This course helps student explore the intersection of advanced machine learning techniques and strategic financial management practices. It starts by building a good foundation of both reinforcement learning and portfolio optimization, followed by an interplay between these two exciting fields. Aimed at navigating and mastering the complexities of investment strategies, the course offers practical applications in Python to construct and adapt dynamic portfolios, driving decision-making in the evolving landscape of finance.

QF305: GLOBAL FINANCIAL RISK MANAGEMENT (counted as a Global and Regional Studies module for all SMU students)
This course covers different financial institutions and various types of risks that financial institution face in their day-to-day operations, such as interest rate risk, credit risk, market risk, liquidity risk, country risk, and operational risk. A review of some of the fundamental concepts in risk management for Financial Institutions will be provided. We will also introduce risk measurement tools, such as repricing gap, duration model and Value at Risk (VaR) system, etc. The pros and cons of each model will be discussed. Regulations have a significant impact on the FI's risk management system, the course will proceed to cover the Basel principles and standards for the management of the key types of risks faced by financial institutions, including Market Risk, Credit Risk, and Operation Risk. The current Basel framework of the three pillars, namely the determination of minimum capital requirements, the supervisory review process, and market discipline will be covered.

QF307: STOCHASTIC FINANCE
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.

FNCE204: ANALYSIS OF FIXED-INCOME SECURITIES
Fixed-income securities deliver fixed cash flows, where value and risk are strongly influenced by interest rates. This course will cover the pricing, valuation, and management of fixed-income securities, portfolios, and derivatives. This course aims to provide students with a solid understanding of fixed-income securities, as well as the ability to apply this knowledge to investment decisions in the real world.

FNCE305: ANALYSIS OF DERIVATIVE SECURITIES
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 the valuation of forwards, 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 on. In addition, students will be given the opportunity to explore a comprehensive online financial markets simulation system to obtain hands-on experience (of a fund manager) in trading in the market. For instance, students can trade futures and options on commodities such as gold, silver, corn, oil, etc. at market prices.  

FNCE307: ADVANCED PORTFOLIO MANAGEMENT 
Modern theory and practice of investment have extended beyond the traditional mean-variance analysis proposed by Harry Markowitz's Modern Portfolio Theory in 1952. This course provides a conceptual framework for the implementation and analysis of strategies for various investment vehicles. 
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 some traditional and advanced concepts of investments, including risk and return, portfolio diversification, factor models, behavioral finance and empirical tests of asset pricing models.
The coverage will include applications, implementation, and evaluation of strategies of institutional investors like mutual funds, exchange-traded funds, pensions funds beside alternative investments like hedge funds, private equity funds, real estate funds (REITs), cryptocurrencies, etc. This course will also delve into long term asset management, international diversification and will include extended and interactive discussions and analyses of the contemporary investing scene and global capital markets.