Course Syllabus
MFIT5009 – Optimization in FinTech
MSc in FinTech
Spring 2020-21, 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 the R programming language.
Textbooks
- 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]
- 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 in R (or similar). Willingness to spend countless of hours programming in R.
Grading
| Homework: | 35% |
| Midterm: | 15% |
| Final project: | 35% |
| Final lightening presentation: | 15% |
Course Schedule
| Date | Week | Lect | Topic |
|---|---|---|---|
| 2-Feb | 1 | 1 | Theory: Introduction to convex optimization |
| 2 | Practice: R for finance primer | ||
| 9-Feb | 2 | 3 | Theory: Convex optimization problems |
| 4 | Practice: Solvers in R | ||
| 23-Feb | 3 | 5 | Portfolio optimization |
| 6 | (cont’d) | ||
| 2-Mar | 4 | 7 | Algorithms: Primer |
| 8 | Algorithms: Majorization-Minimization (MM) and Successive Convex Approximation (SCA) | ||
| 9-Mar | 5 | 9 | Index tracking of financial markets via MM |
| 10 | Backtesting | ||
| 16-Mar | 6 | - Midterm - | |
| - Midterm - | |||
| 23-Mar | 7 | 11 | Risk parity portfolio via Newton, BCD, and SCA |
| 12 | (cont’d) | ||
| 30-Mar | 8 | 13 | Portfolio optimization with alternative risk measures |
| 14 | (cont’d) | ||
| 13-Apr | 9 | 15 | Supervised machine learning: Trees and random forests |
| 16 | (cont’d) | ||
| 20-Apr | 10 | 17 | (cont’d) |
| 18 | Unsupervised machine learning: PCA | ||
| 27-Apr | 11 | 19 | Unsupervised machine learning: Clustering |
| 20 | (cont’d) | ||
| 4-May | 12 | 21 | Unsupervised machine learning: Graphs |
| 22 | (cont’d) | ||
| 11-May | 13 | Project presentations by students | |
| Project presentations by students |
Lecture Information
Lecture Time: Tue 7pm – 9:50pm
Lecture Venue: Online via Zoom and, covid permitting, LSKG001
Teaching Team
Instructor: Prof. Daniel P. PALOMAR (https://www.danielppalomar.com)
Email: palomar@ust.hk
Office: 2398 (Lifts 17/18)
Office hours: By email appointment
TA: Xiwen WANG (xwangew@connect.ust.hk)
Course Summary:
| Date | Details | Due |
|---|---|---|