Course Syllabus
MFIT5009 – Optimization in FinTech
MSc in FinTech
Spring 2025-26, HKUST
Description
This course introduces the basic theory of convex optimization and illustrates its practical employment in a wide range of FinTech applications. Techniques and applications of nonconvex optimization are also considered. Examples of the problems considered include Markowitz portfolio optimization and its many variations (e.g., maximum Sharpe ratio portfolio, risk-parity portfolio, robust portfolio, sparse portfolio, index tracking), the practical problem of data cleaning (imputation of missing values and outlier detection), machine learning, data clustering, and graph learning. Half of the course will focus on the mathematical foundation, while the other half will consider the practical implementation using Python or R programming languages.
Textbooks
- Daniel P. Palomar (2025). Portfolio Optimization: Theory and Application. Cambridge University Press. portfoliooptimizationbook.com Links to an external site.
- Yiyong Feng and Daniel P. Palomar, A Signal Processing Perspective on Financial Engineering. Foundations and Trends® in Signal Processing, Now Publishers, 2016. [pdf]
- Konstantinos Benidis, Yiyong Feng, and Daniel P. Palomar, Optimization Methods for Financial Index Tracking: From Theory to Practice. Foundations and Trends® in Optimization, Now Publishers, 2018. [pdf]
- Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004. [pdf Links to an external site.]
- G. James, D. Witten, T. Hastie, and R. Tibshirani. An Introduction to Statistical Learning with Applications in R. Springer, 2013. [pdf Links to an external site.]
- G. Cornuejols and R. Tutuncu, Optimization Methods in Finance. Cambridge Univ. Press, 2007.
Prerequisites
Good knowledge of linear algebra and some programming knowledge. Willingness to spend countless of hours programming in Python or R.
Grading
| Homeworks & Quizzes: | 35% |
| Midterm: | 15% |
| Final project: | 35% |
| Final lightening presentation: | 15% |
Course Schedule
| Date | Week | Lect | Topic |
|---|---|---|---|
| 4-Feb | 1 | 1 | Theory: Introduction to convex optimization |
| 2 | Practice: Primer on Python/R for finance | ||
| 11-Feb | 2 | 3 | Theory: Convex optimization problems |
| 4 | Practice: Solvers in Python/R [quiz #1, homework #1] | ||
| 25-Feb | 3 | 5 | Financial Data: Stylized Facts |
| 6 | Financial Data: IID Modeling | ||
| 4-Mar | 4 | 7 | Portfolio optimization |
| 8 | (cont’d) [quiz #2, homework #2] | ||
| 11-Mar | 5 | 9 | Theory: Algorithms primer |
| 10 | Practice: Optimization algorithms in Deep Learning | ||
| 18-Mar | 6 | 11 | Index tracking of financial markets |
| 12 | Backtesting [quiz #3] | ||
| 25-Mar | 7 | – Midterm – | |
| – Midterm – | |||
| 1-Apr | 8 | 13 | Risk parity portfolios via Newton, BCD, and SCA |
| 14 | (cont’d) | ||
| 15-Apr | 9 | 15 | Portfolio optimization with alternative risk measures |
| 16 | (cont’d) [quiz #4, homework #3] | ||
| 22-Apr | 10 | 17 | Financial graphs |
| 18 | (cont’d) | ||
| 29-Apr | 11 | 19 | Intro to High-Frequency Trading (HFT) and the Limit Order Book (LOB) |
| 20 | (cont’d) | ||
| 6-May | 12 | – Final project presentations– | |
| – Final project presentations– |
Lecture Information
Lecture Time: Wednesday 7:30pm – 10:20pm
Lecture Venue: Rm 5583 (lifts 27-28)
Teaching Team
Instructor: Prof. Daniel P. PALOMAR (https://www.danielppalomar.com Links to an external site.)
Email: palomar@ust.hk
Office: 2398 (lifts 17/18)
Office hours: By email appointment
TA: Zhewei LI <zlilj@connect.ust.hk> and Amirhossein Javaheri <sajavaheri@connect.ust.hk>
Course Summary:
| Date | Details | Due |
|---|---|---|