Advanced Portfolio Construction and Analysis with Python
- Offered byCoursera
Advanced Portfolio Construction and Analysis with Python at Coursera Overview
Duration | 11 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Advanced Portfolio Construction and Analysis with Python at Coursera Highlights
- This Course Plus the Full Specialization.
- Shareable Certificates.
- Graded Programming Assignments.
Advanced Portfolio Construction and Analysis with Python at Coursera Course details
- 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.
Advanced Portfolio Construction and Analysis with Python at Coursera Curriculum
Style & Factors
Welcome video
Introduction to factor investing
Factor models and the CAPM
Multi-Factor models and Fama-French
Factor benchmarks and Style analysis
Shortcomings of cap-weighted indices
From cap-weighted benchmarks to smart-weighted benchmarks
Introduction to Lab sessions
Module 1 Lab Session - Foundations
Requirements
Material at your disposal
Module 1- Key points
Module 1- Graded Quiz
Robust estimates for the covariance matrix
The curse of dimensionality
Estimating the Covariance Matrix with a Factor Model
Honey I Shrunk the Covariance Matrix!
Portfolio Construction with Time-Varying Risk Parameters
Exponentially weighted average
ARCH and GARCH Models
Module 2 Lab Session - Covariance Estimation
Module 2-Key points
Module 2 - Graded quiz
Robust estimates for expected returns
Lack of Robustness of Expected Return Estimates
Agnostic Priors on Expected Return Estimates
Using Factor Models to Estimate Expected Returns
Extracting Implied Expected Returns
Introducing Active Views
Black-Litterman Analysis
Module 3 Lab Session- Black Litterman
Module 3-Key points
The Intuition Behind Black-Litterman Model Portfolios
Module 3 - Graded Quiz
Portfolio Optimization in Practice
Naive Diversification
Scientific Diversification
Measuring risk contributions
Simplified risk parity portfolios
Risk Parity Portfolios
Comparing Diversification Options
Module 4 Lab Session - Risk Contribution and Risk Parity
Module 4-Key points
Survey: Alternative Equity Beta Investing
Dive into heuristic diversification
To be continued (2)
Module 4 - Graded quiz
Other courses offered by Coursera
Student Forum
Anything you would want to ask experts?
Data ScienceData Science BasicsPython for data scienceAdvanced Portfolio Construction and Analysis with Python