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.
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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.
Course 2 of 4 in the Investment Management with Python and Machine Learning Specialization.
What You Will Learn
- 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
Syllabus
WEEK 1: Style & Factors
WEEK 2: Robust estimates for the covariance matrix
WEEK 3: Robust estimates for expected returns
WEEK 4: Portfolio Optimization in Practice
