Course Syllabus

Jump to Today

ELEC3180 – Data-Driven Portfolio Optimization

Spring 2025-26, HKUST

Description

Modern portfolio theory started with Harry Markowitz’s 1952 seminal paper “Portfolio Selection,” for which he would later receive the Nobel prize in 1990. He put forth the idea that risk-adverse investors should optimize their portfolio based on a combination of two objectives: expected return and risk. Until today, that idea has remained central in portfolio optimization. However, the vanilla Markowitz portfolio formulation does not seem to behave as expected in practice and most practitioners tend to avoid it.

During the past half century, researchers and practitioners have reconsidered the Markowitz portfolio formulation and have proposed countless of improvements and alternatives such as robust optimization methods, alternative measures of risk, regularization via sparsity, improved estimators of the covariance matrix, robust estimators for heavy tails, factor models, volatility clustering models, risk-parity formulations, index tracking, etc.

This course will explore the Markowitz portfolio optimization in its many variations and extensions, with special emphasis on Python programming. All the course material will be complemented with Python code that will be studied in class. The homework and project will be in Python.

Textbooks

  • Daniel P. Palomar (2025). Portfolio Optimization: Theory and Application. Cambridge University Press. portfoliooptimizationbook.com
  • 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. Cornuejols and R. Tutuncu, Optimization Methods in Finance. Cambridge Univ. Press, 2007.
  • F. J. Fabozzi, P. N. Kolm, D. A. Pachamanova, and S. M. Focardi, Robust Portfolio Optimization and Management. Wiley, 2007.

Prerequisites

Good knowledge of linear algebra (MATH2111 or MATH2121 or MATH2131 or MATH2350), probability (IEDA2510 or IEDA2520 or IEDA2540 or ELEC2600), and some programming knowledge in Python or R.

Grading

Homeworks & Quizzes: 25% (auditors too)
Midterm:   25% (auditors too)
Final project: 35%
Final lightening presentation: 15% 

Course Schedule

Date Lect Topic
Wed 4-Feb     1 Theory: Introduction to convex optimization
Fri 6-Feb     2 Practice: Primer on Python for finance
Wed 11-Feb     3 Theory: Convex optimization problems
Fri 13-Feb     4 Practice: Solvers in Python [quiz #1, homework #1]
Fri 20-Feb     5 Financial data modeling: Stylized facts
Wed 25-Feb     6 Financial data modeling: i.i.d. case
Fri 27-Feb     7       (cont’d)
Wed 4-Mar     8       (cont’d) [quiz #2, homework #2]
Fri 6-Mar     9 Portfolio optimization
Wed 11-Mar   10       (cont’d)
Fri 13-Mar   11       (cont’d) [quiz #3]
Wed 18-Mar   12       (cont’d)
Fri 20-Mar   13 Backtesting
Wed 25-Mar    – Midterm –
Fri 27-Mar   14 Data cleaning: data munging, missing values, and outliers
Wed 1-Apr   15 Financial data modeling: time series
Fri 10-Apr   16       (cont’d) [quiz #4]
Wed 15-Apr  17 Algorithms: Primer [homework #3]
Fri 17-Apr  18 Index tracking of financial markets via MM
Wed 22-Apr  19 Risk parity portfolio via Newton, BCD, and SCA
Fri 24-Apr  20 Portfolio optimization with alternative risk measures
Wed 29-Apr 21       (cont’d)  [quiz #5]
Wed 6-May  – Project presentations by students –
Fri 8-May  – Project presentations by students –

Lecture Information

Lecture Time: Wed & Fri, 1:30pm – 2:50pm

Lecture Venue: Rm 5402 (lifts 17-18)   [Old classron: Rm 5560 (lifts 27-28)]

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

TAs: Arnau Vilella (avp@connect.ust.hk) and Vinayak Khurana (vkhuranaaa@connect.ust.hk)

Course Summary:

Course Summary
Date Details Due