- Level Professional
- المدة 12 ساعات hours
- الطبع بواسطة EDHEC Business School
-
Offered by
عن
The practice of investment management has been transformed in recent years by computational methods. Instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language. In this course, we cover the estimation, of risk and return parameters for meaningful portfolio decisions, and also introduce a variety of state-of-the-art portfolio construction techniques that have proven popular in investment management and portfolio construction due to their enhanced robustness. As we cover the theory and math in lecture videos, we'll also implement the concepts in Python, and you'll be able to code along with us so that you have a deep and practical understanding of how those methods work. By the time you are done, not only will you have a foundational understanding of modern computational methods in investment management, you'll have practical mastery in the implementation of those methods. If you follow along and implement all the lab exercises, you will complete the course with a powerful toolkit that you will be able to use to perform your own analysis and build your own implementations and perhaps even use your newly acquired knowledge to improve on current methods.الوحدات
Section 1
1
Labs
- Labs and code
5
Videos
- Welcome video
- Introduction to factor investing
- Factor models and the CAPM
- Multi-Factor models and Fama-French
- Factor benchmarks and Style analysis
3
Readings
- Requirements
- Material at your disposal
- Module 1- Key points
Section 2
1
Assignment
- Module 1- Graded Quiz
1
Discussions
- Cap-weighted indices and equity benchmarks
4
Videos
- Shortcomings of cap-weighted indices
- From cap-weighted benchmarks to smart-weighted benchmarks
- Introduction to Lab sessions
- Module 1 Lab Session - Foundations
Section 1
3
Videos
- The curse of dimensionality
- Estimating the Covariance Matrix with a Factor Model
- Honey I Shrunk the Covariance Matrix!
1
Readings
- Module 2-Key points
Section 2
1
Assignment
- Module 2 - Graded quiz
1
Discussions
- Covariance matrix estimation
4
Videos
- Portfolio Construction with Time-Varying Risk Parameters
- Exponentially weighted average
- ARCH and GARCH Models
- Module 2 Lab Session - Covariance Estimation
Section 1
3
Videos
- Lack of Robustness of Expected Return Estimates
- Agnostic Priors on Expected Return Estimates
- Using Factor Models to Estimate Expected Returns
1
Readings
- Module 3-Key points
Section 2
1
Assignment
- Module 3 - Graded Quiz
1
Discussions
- Expected returns
4
Videos
- Extracting Implied Expected Returns
- Introducing Active Views
- Black-Litterman Analysis
- Module 3 Lab Session- Black Litterman
2
Readings
- The Intuition Behind Black-Litterman Model Portfolios
- Importat message before the quiz!
Section 1
3
Videos
- Naive Diversification
- Scientific Diversification
- Measuring risk contributions
2
Readings
- Module 4-Key points
- Survey: Alternative Equity Beta Investing
Section 2
1
Assignment
- Module 4 - Graded quiz
1
Discussions
- Portfolio construction methodologies
4
Videos
- Simplified risk parity portfolios
- Risk Parity Portfolios
- Comparing Diversification Options
- Module 4 Lab Session - Risk Contribution and Risk Parity
2
Readings
- Dive into heuristic diversification
- To be continued (2)
Auto Summary
Dive into "Advanced Portfolio Construction and Analysis with Python," a cutting-edge course in Business & Management offered by Coursera. Led by expert instructors, this professional-level course emphasizes hands-on implementation of investment management techniques using Python. Spanning 720 minutes, it covers risk and return estimation and innovative portfolio construction methods. Ideal for professionals seeking practical mastery in computational investment methods, this course provides a robust toolkit for personal analysis and implementation. Available with a Starter subscription.

Claudia Carrone

Vijay Vaidyanathan, PhD