QF JULY FORUM 2022
QF July Forum is an annual event orgarnized by QF student society, Q.E.D. and Quantitative Finance Unit. The speakers involve QF faculties, financial industry practitioners, QF alumni, and current QF students. QF July Forum is a platform where prospective students and current students receive information on industry trend, facts about QF major, tips on taking courses, and answers to your personal queries etc.
QF JULY FORUM 2022 (Hybrid) was successfully held on 15 July 2022.
CFE Prize for Best Graduating Student Majoring in Quantitative Finance
Funded from the proceeds of an endowed gift by Professor Lim Kian Guan, the CFE Prize for Best Graduating Student Majoring in Quantitative Finance is given annually to provide recognition for an outstanding final year student with a major in Quantitative Finance at the University. The purpose of the Award is to inspire and motivate the University's talent majoring in Quantitative Finance, regardless of financial ability, and spurring students to achieve greater heights of excellence.
Eligibility Criteria
- The Award will be given annually to one outstanding final year student with a major in Quantitative Finance at the University.
- The Award will only be given if there is a candidate of sufficient merit.
Benefits of the Award
Certificate and $1,500 Cash Prize
Formed in conjunction with the launch of the undergraduate Quantitative Finance programme in August 2006, QED has been established with the objective of inculcating cohesiveness and a sense of belonging among the Quantitative Finance majors, as well as to promote the knowledge of financial engineering and computational finance to interested members of the University.
Q.E.D is an abbreviation of the Latin Phrase “Quod Erat Demonstrandum” which literally means “which was to be demonstrated”. It is commonly used to indicate that something has been definitely proven.
With this is mind, Q.E.D is responsible for organising and planning for activities that will display and highlight the usefulness and practicality of Quantitative Finance, such as seminars conducted by industry experts and intra and inter school competitions. QED also undertakes the responsibility of exposing members of the SMU community to Quantitative Finance by encouraging participation in its activities.
The QED 18th Executive Committee comprises:
President : | Htoo Myat Naing |
Vice-President : | Chen Jiaqi, Xavier |
Vice-President : | Goh Issac |
Honorary General Secretary : | Nguyen Hai Van |
Marketing Director : | Nicole Gabrielle Lee Tan |
Finance & Events Director : | Katyayni Joshi |
For further queries or to contact QED Executive Committee, please email qed@sa.smu.edu.sg
Fast-Track BBM QF-MQF Programme
Lee Kong Chian School of Business (LKCSB) has launched a Fast-Track BBM-MQF Programme. This integrated programme allows students in the Bachelor of Business Management (BBM) programme with the Quantitative Finance (QF) major to enrol in the MSc in Quantitative Finance (MQF) programme and graduate with both the BBM and MSc degrees in four years. QF Major students at Lee Kong Chian School of Business (LKCSB) with excellent academic performance can apply to be selected for this fast-track programme.
The objective of the programme is to provide an avenue for high-potential undergraduate students at LKCSB to accelerate their studies towards a master's degree. By integrating the UG and PG programmes, students can benefit from a holistic education by combining a broad-based undergraduate education with a highly specialised and technical postgraduate education.
What is Integrated BBM in QF and MQF?
Students can boost their career prospects through a holistic undergraduate business school education, coupled with a specialized quantitative finance master's degree, attaining both breadth and depth in their field of study. Graduates with quantitative finance, programming, and modelling skills are especially sought after now in the job market. This programme offers students a streamlined learning experience with a well-desgined curriculum.
A main objective of the programme is to meet Singapore's growing human resource demand for talented graduates with both financial markets and digital technology skills-the quantitative finance major is ideally suited to address this. High potential undergraduate students at LKCSB who meet the programme's entry requirements can now complete both a bachelor and master's degrees in four years, providing them with an attractive alternative to a double major. The programme also serves to provide top students the opportunity to enhance their technical skills, developing a pipeline of high-quality local graduates.
To learn more about how the programme benefits our students, click on SMU Blog for our exclusive interviews.
How is the "Fast Track" achieved?
The Integrated BBM in QF and MQF is designed to provide a seamless integration, while ensuring an excellent learning experience for our students. Academic progression is achieved by mapping related courses offered at both the BBM (introductory) and the MQF (advanced) levels. Students eligible for the integrated programme will take mapped courses at the MQF level, which will simultaneously count towards the BBM's degree requirements as well as the MQF degree's graduation requirements.
Students are expected to have cleared all 3 QF major core modules by the end of year 3. In year 4, students will spend both terms taking MQF courses :
- Year 4, Terms 1 & 2 Þ take MQF courses to fulfil QF electives and free electives (BBM), and to meet MQF's degree requirement.
- Year 4 upon completion of BBM Þ return for a special term from April to July to complete 4 remaining electives to fulfil MQF requirements
*Please refer to the Proposed course structure below
Who can apply?
LKCSB students from intake AY2020 onwards intending to major in quantitative finance will be eligible to apply for the Fast-Track BBM-MQF Programme.
Eligibility
LKCSB students in Year 2 intending to declare their major in quantitative finance may apply to the integrated programme. If shortlisted, they will receive a conditional offer subjected to a minimum cGPA of 3.4.
The actual offer of a seat in the programme will only be confirmed in Year 3 if students achieved a minimum of A- for all three QF core modules (QF101 Quantitative Finance, QF102 Investment Statistics, and QF205 Computer Technology for Finance), while maintaining a minimum cGPA of 3.4.
How to apply?
Students may apply to the integrated programme at the end of their second year when they declare QF as their major.
At the point of application students will have to submit a detailed course plan to the UG Programme Office, with the following details:
- To list 8 CUs in year 4 which will be mapped to 8 MQF courses, in which,
- 3 CUs must be reserved for mapping to QF major electives in T1 and T2
- 5 CUs must be reserved for mapping to Free Electives in T1 and T2
To apply now, click here.
Course Fees
Students will pay full BBM fees for the completion of their undergraduate (UG) degree.
Students admitted to the integrated programme will pay the postgraduate (PG) fee with a discount of more than 20% for the MQF degree. Due to annual adjustment for both the BBM and MQF course fees, the exact fee discount amount might be subjected to minimal variation over different cohorts. Interested students are advised to seek clarification with the programme office on the fee discount applicable to their cohort based on enrollment date.
Who should i contact for further details?
BBM-related queries:
- LKCSB Quantitative Finance Unit, quantfin@smu.edu.sg
- Prof Zhao Yibao, ybzhao@smu.edu.sg
MQF-related queries:
- MQF Programme Office, mqf.office@smu.edu.sg
ANNEX 1: MQF Programme
The MQF Curriculum consists of 1 university core + 1 open module + 9 core + 4 electives, making a total of 15 courses.
ANNEX 1: MQF Programme
The MQF Curriculum consists of 1 university core + 1 open module + 9 core + 4 electives, making a total of 15 courses.
Total 15 CU | Financial Data Science | Algorithmic Trading | Risk Analytics |
PGP Core (1CU) | 4 workshop topics during period of candidature | ||
Programme Core (9 CU) | Asset Pricing Stochastic Modelling in Finance Quantitative Analysis of Financial Markets Programming & Computational Finance Derivatives Fixed Income Securities Risk Analysis Numerical Methods Econometrics of Financial Markets | ||
Track Electives (4CU) | QF621 Quantitative Trading Strategies QF633 C++ for Financial Engineering QF632 Financial Data Science QF624 Machine Learning & Financial Application | QF621Quantitative Trading Strategies QF623 Portfolio Management QF633 C++ for Financial Engineering QF635 Market Microstructure & Algorithmic Trading | QF622 Credit Risk Models QF635 Market Microstructure & Algorithmic Trading QF632 Financial Data Science QF624 Machine Learning & Financial Application |
Open (1CU) | 1 from other approved PGP courses. |
ANNEX 2: BBM-MQF Curriculum Mapping
This section outlines the academic progression of a BBM student who takes the intergrated BBM-MQF programme. BBM students will need 36 CUs to complete the BBM degree. To be eligible for the Fast-Track BBM-MQF programme, they must declare the QF major by the end of Year 2. They should also follow this course sequencing.
- Complete the following degree requirements by end of Year 4 Term 1.
- Core Curriculum: 12 CUs
- Business Core: 11 CUs
- 2 out of 7 of the Free Elective CUs
(note that the 5 other CUs of free electives required under the BBM programme will map to MQF courses in Year 4, see points 2b and 3a below)
- Complete the following BBM QF major courses and MQF masters-level courses.
- BBM courses to be taken in Year 2 or 3:
- QF101 Quantitative Finance + QF102 Investment Statistics ➔ fulfil QF600 Asset Pricing^
- QF205 Computing Technology for Finance ➔ fulfils QF627 Programming and Computational Finance^
- BBM University Core ➔ fulfils IDIS625 Postgraduate Professional Development (PGPD) Module^
^ denotes the MQF courses that BBM QF majors will get exemptions for by taking the listed BBM course(s).
- MQF courses to be taken in Year 4 Term 1 (Aug-Nov):
- QF603 Quantitative Analysis of Financial Markets* ➔ fulfils BBM QF major elective
- QF620 Stochastic Modelling in Finance* ➔ fulfils BBM QF major elective
- MQF Open Module* ➔ fulfils BBM free elective
* denotes the MQF courses that will be recognised for credit in both the BBM and MQF programmes.
- BBM courses to be taken in Year 2 or 3:
- Students will follow the MQF programme schedule from BBM Year 4 Term 2 after they complete all the above-listed courses.
- MQF courses to be taken in Year 4 Term 2 (corresponds to PG Term of Jan-Apr):
- QF602 Derivatives* ➔ fulfils BBM QF major elective
- QF605 Fixed Income Securities* ➔ fulfils BBM free elective
- QF604 Econometrics of Financial Markets* ➔ fulfils BBM free elective
- QF607 Numerical Methods* ➔ fulfils BBM free elective
- QF609 Risk Analysis* ➔ fulfils BBM free elective
Note that students would have completed all of the BBM requirements after completing the above.
The below set of courses will then be taken to complete the MQF degree requirements.
- To be taken in BBM Year 4 (PG term of Apr-Jul). Choose 4 electives from the following:
- QF621 Quantitative Trading Strategies
- QF622 Credit Risk Models
- QF623 Portfolio Management
- QF624 Machine Learning and Financial Applications
- QF633 C++ for Financial Engineering
- QF6003 Hedge Funds
- MQF courses to be taken in Year 4 Term 2 (corresponds to PG Term of Jan-Apr):
Timeline: LKCSB undergraduate students who major in QF typically take 3 years and 8 months to complete their BBM degree, with their last term completing at the end of April. Upon completion, students enrolled in the integrated programme will proceed to take an extra term in the PG calendar from April to July to complete the additional MQF elective courses. As they will finish the entire programme (BBM+MQF) by the end of July, the fast-track programme will be completed in 4 years.
There are 6 courses or modules related to the Quantitative Finance major that an SMU student 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 compulsory core courses are:
- QF 101: Quantitative Finance Pre-Req: COR1201 Calculus
- QF 102: Investment Statistics Pre-Req: COR1201 Calculus or COR-STAT1202 Introductory Statistics or COR-STAT1203 Introduction to Statistical Theory
- QF 205: Computing Technology for Finance
Students need to take 3 electives out of the following offerings:
- QF 206: Quantitative Trading Strategies
- QF 208: Linear Algebra and Numerical Methods Pre-Req: COR1201 Calculus
- QF 209: Machine Learning in Quantitative Finance Pre-Req: COR1201 Calculus
- QF 210: Reinforcement Learning in Portfolio Optimization Pre-Req: COR1201 Calculus
- QF 305: Global Financial Risk Management
- QF 307: Stochastic Finance Pre-Req: COR1201 Calculus
- FNCE 204: Analysis of Fixed-Income Securities Pre-Req: FNCE101 Finance or FNCE103 Finance for Law
- FNCE 305: Analysis of Derivative Securities
- FNCE 307: Advanced Portfolio Management Pre-Req: FNCE101 Finance or FNCE103 Finance for Law
Note: Please check OASIS for the most up to date pre-requisites.
Quantitative Finance Major’s Compulsory Modules
QF101: QUANTITATIVE FINANCE
What is Quantitative Finance? Why quantitative? Increasingly, mathematical and statistical methods have been applied to analyse financial markets. Quantitatvie finance models are derived to extract critical information from the data collected in investment and trading activities. These models are used in pricing assets, managing risks, developing trading strategies, and making investment decisions. Strong quantitative skills have become an essential competence for the modern finance industry.
This 101 course introduces you to the fundamentals of quantitative finance models. It covers the foundational mathematical concepts and techniques that are used in quantitative modelling. Attention is given to topics such as rules for functions, solving systems of linear equations, solutions of differential equations,option pricing application, and optimization. This course provides the mathematical tools that can be used to solve problems encountered in financial markets.
QF102: INVESTMENT STATISTICS
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 an 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 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.
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.
LIM Kian Guan | |
DESIGNATION | Professor Emeritus of Quantitative Finance |
LIU Peng | |
DESIGNATION | Assistant Professor of Quantitative Finance (Practice) |
QUALIFICATION | PhD , National University of Singapore, 2021 |
liupeng@smu.edu.sg | |
PHONE | 6826 4917 |
TEE Chyng Wen | |
DESIGNATION | Associate Professor of Quantitative Finance (Practice) |
QUALIFICATION | PhD, University of Cambridge, 2006 |
cwtee@smu.edu.sg | |
PHONE | 6828 0819 |
ZHAO Yibao | |
DESIGNATION | Senior Lecturer of Quantitative Finance |
QUALIFICATION | PhD, National University of Singapore, 2004 |
ybzhao@smu.edu.sg | |
PHONE | 6828 0925 |
What is Quantitative Finance? Why quantitative? In the fast-paced world of finance, technological advances and regulatory requirements drive the banking industry to the point where mathematical and statistical modeling had already become a necessity. A talented person with strong quantitative skills is highly sought after by hedge funds and banks.
With a QF major, you are in a good position to wow job interviewers and headhunters seeking to employ risk analysts, junior quant research strategists, and in time to come, specialist leaders. SMU's QF courses will let you see for yourselves how cool it can be that math can help an investment/trading firm in generating revenues while limiting its risk exposures. You will also learn to write computer codes to analyze data for risk management and for research in creating new quantitative trading strategies.
Moreover, you get hands-on trading experience with live-feed market data in a Simulated Trading Room that is unique to SMU. QF faculty members are practice-oriented with strong industry experience. You may get involved in real-life research projects with our industry partners.
Students from all other Schools as well as students from the Business School, who have a different first major, are able to take QF as a second major. If you graduate with excellent results, you stand a good chance to be admitted into SMU’s one-year Master of Science in Quantitative Finance programme, a specialized master programme designed to further enhance your skill and knowledge in this area.
For LKCSB students, the school has also launched the Fast-Track BBM-MQF Programme. This integrated programme allows students in the Bachelor of Business Management (BBM) programme with the Quantitative Finance (QF) major to enrol in the MSc in Quantitative Finance (MQF) programme and graduate with both the BBM and MSc degrees in four years. Follow our SMU blog for more updates!
Hestitate no longer, act now to benefit from these programmes SMU has to offer!