Course - Portfolio Management and Indexes Investing (MSc Level)

CCPI > Course – Portfolio Management and Indexes Investing (MSc Level)

Course - Portfolio Management and Indexes Investing (MSc Level)

Where Portfolio Theory Meets Real‑World Precision

Course Description

This course provides advanced theoretical and practical knowledge in portfolio management and index investing. Students will learn to construct, manage, and evaluate investment portfolios, understand the role of indexes, and apply quantitative and technological tools to optimize investment outcomes in global financial markets.

Learning Objectives

By the end of the course, students will be able to:

Analyze and construct diversified investment portfolios.
Evaluate and apply index-based investment strategies.
Assess and manage portfolio risk using quantitative methods.
Critically analyze market trends, investor sentiment, and behavioral finance concepts.
Utilize financial technology and big data in portfolio management.
Communicate investment strategies and recommendations effectively.

Duration

Option 1

Lecture/Seminar: 1 day per week, 3 hours per week × 15 weeks = 45 hours
Self-study, assignments, and projects: 3 times the contact hours

Option 2

Lecture/Seminar: 2 days per week, 6 hours per week × 7.5 weeks = 45 hours
Self-study, assignments, and projects: 3 times the contact hours

Course Outline & Weekly Topics

Week 1: Introduction to Portfolio Management

Overview of investment objectives and constraints
Types of investors and investment vehicles

Week 2: Modern Portfolio Theory

Risk and return concepts
Mean-variance optimization
Efficient frontier

Week 3: Asset Allocation Strategies

Strategic vs. tactical asset allocation
Life-cycle investing

Week 4: Indexes and Index Investing

Construction and types of indexes (price-weighted, value-weighted, equal-weighted)
Role of indexes in investment management
Passive vs. active investing

Week 5: Exchange-Traded Funds (ETFs) and Index Funds

Structure and mechanics
Advantages and limitations
Tracking error and replication methods

Week 6: Asset Pricing Models

Capital Asset Pricing Model (CAPM)
Arbitrage Pricing Theory (APT)
Multifactor models

Week 7: Risk Management Techniques

Value at Risk (VaR)
Tail risk and stress testing
Portfolio insurance

Week 8: Performance Measurement and Attribution

Sharpe ratio, Treynor ratio, Jensen’s alpha
Benchmarking and peer comparison

Week 9: Behavioral Finance and Investor Sentiment

Market anomalies and behavioral biases
Sentiment analysis and its impact on portfolio decisions

Week 10: Application of AI and Big Data in Portfolio Management

Machine learning for asset selection and risk prediction
Sentiment analytics platforms (e.g., SenFin)

Week 11: ESG Investing and Sustainable Portfolios

Principles of ESG (Environmental, Social, Governance)
Integration of ESG factors in portfolio construction

Week 12: International Portfolio Management

Currency risk and hedging
Global diversification

Week 13: Portfolio Simulation and Case Studies

Real-world portfolio construction exercises
Use of Bloomberg/Excel/Portfolio simulation tools

Week 14: Advanced Topics and Innovations

Smart beta, alternative indexing
Robo-advisors and fintech trends

Week 15: Final Project Presentations & Review

Student presentations of portfolio management projects
Course review and exam preparation

Assessment Methods

Homework Assignments (problem sets, data analysis): 20%
Midterm Exam: 20%
Group Project (portfolio simulation and presentation): 25%
Final Exam: 25%
Class Participation: 10%

Recommended Textbooks & Resources

Bodie, Kane, Marcus, Investments (latest edition)
Elton, Gruber, Brown, Goetzmann, Modern Portfolio Theory and Investment Analysis
Academic articles and case studies (provided via course platform)
Bloomberg Terminal, Excel, Python/R for quantitative analysis

Prerequisites

Undergraduate-level statistics and finance
Basic proficiency in Excel and/or programming (Python/R recommended)

Additional Notes

Guest lectures from industry professionals
Access to financial databases and simulation platforms (CCPR/CCPI)
Actual projects using BeQ Big Data and Platforms (Challenge)
Opportunities for CFA exam preparation