Course – Portfolio Management and Indexes Investing (MSc Level)

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

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