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.
This course is part of the Investment Management with Python and Machine Learning Specialization
About this Course
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 - What you will learn from this course
Style & Factors
Robust estimates for the covariance matrix
Robust estimates for expected returns
Portfolio Optimization in Practice
- 5 stars81.75%
- 4 stars13.30%
- 3 stars3.64%
- 2 stars0.64%
- 1 star0.64%
TOP REVIEWS FROM ADVANCED PORTFOLIO CONSTRUCTION AND ANALYSIS WITH PYTHON
Great course, nice balance between the theory (which is well explained) and the practical (python jupyter notebooks where you need to explore to gain a good understanding)
Really a great course, instructors video then are a great resource. I'd have liked more mathematical analysis but I understand it could have gone beyond scope.
Enjoyed the part on the implementation of the Black-Litterman model and the Risk Parity portfolios. Looking forward to the third course.
Very good course and well taught. Vijay and Lionel are great communicators. I have enjoyed the course a lot and learned a great deal. Thank you both.
About the Investment Management with Python and Machine Learning Specialization
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