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Advanced Portfolio Construction and Analysis with Python

Approx. 11 hours to complete

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Course Summary

Learn advanced portfolio construction techniques using Python. This course covers topics such as risk management, factor investing, and optimization.

Key Learning Points

Understand how to manage risk in investment portfolios
Learn how to create factor-based investment strategies
Optimize investment portfolios using Python

Job Positions & Salaries of people who have taken this course might have

- USA: $75,000 - $125,000
- India: ₹6,00,000 - ₹15,00,000
- Spain: €30,000 - €50,000
- USA: $75,000 - $125,000
- India: ₹6,00,000 - ₹15,00,000
- Spain: €30,000 - €50,000
- USA: $100,000 - $200,000
- India: ₹10,00,000 - ₹25,00,000
- Spain: €40,000 - €80,000
- USA: $75,000 - $125,000
- India: ₹6,00,000 - ₹15,00,000
- Spain: €30,000 - €50,000
- USA: $100,000 - $200,000
- India: ₹10,00,000 - ₹25,00,000
- Spain: €40,000 - €80,000
- USA: $150,000 - $300,000
- India: ₹20,00,000 - ₹50,00,000
- Spain: €70,000 - €120,000

Learning Outcomes

Ability to manage risk in investment portfolios
Ability to create factor-based investment strategies
Ability to optimize investment portfolios using Python

Prerequisites or good to have knowledge before taking this course

Basic programming knowledge in Python
Basic knowledge of finance and investment concepts

Course Difficulty Level

Advanced

Course Format

Online Course
Self-paced
Video Lectures
Programming Assignments

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Description

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.

Knowledge

Analyze style and factor exposures of portfolios
Implement robust estimates for the covariance matrix
Implement Black-Litterman portfolio construction analysis
Implement a variety of robust portfolio construction models

Outline

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

Summary of User Reviews

Learn advanced portfolio construction techniques using Python with this course on Coursera. Users have rated this course highly for its comprehensive and practical approach to portfolio management.

Key Aspect Users Liked About This Course

Many users appreciated the practical approach of the course, which allowed them to apply what they learned in real-world scenarios.

Pros from User Reviews

Course provides a deep understanding of portfolio construction techniques
Covers a wide range of topics related to portfolio management
Well-organized and easy to follow
Great practical exercises that help reinforce learning
Instructors are knowledgeable and responsive

Cons from User Reviews

Some users found the course to be too technical and difficult to follow
Not suitable for beginners in Python programming
Some users felt that the course did not cover enough advanced topics
The course could benefit from more interactive elements
The price of the course is relatively high compared to other online courses

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