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.
Este curso forma parte de Programa especializado: Investment Management with Python and Machine Learning
Acerca de este Curso
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
Programa - Qué aprenderás en este curso
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 %
Principales reseñas sobre ADVANCED PORTFOLIO CONSTRUCTION AND ANALYSIS WITH PYTHON
This course gives a good understanding of Fama-French, GARCH, Black-Litterman and risk parity models among many others, not only theoretically, but also through hands-on Lab sessions.
Another excellent course. One thing I would have liked to have is longer lab session videos like in MOOC 1 to ensure we can re-create the notebooks as we go along.
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.
Fantastic portfolio construction techniques, although black letterman model could have been explained better . Overall great course with real world financial applications
Acerca de Programa especializado: Investment Management with Python and Machine Learning
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