The Advanced Portfolio Construction and Analysis with Python course from EDHEC Business School offers a comprehensive exploration of computational methods in investment management. Through hands-on implementation in Python, you will master the estimation of risk and return parameters, and learn state-of-the-art portfolio construction techniques. The course provides a practical understanding of factor exposures, robust covariance matrix estimation, Black-Litterman portfolio construction analysis, and various robust portfolio construction models.
Through a series of modules, you will delve into topics such as style and factor exposures, robust estimates for the covariance matrix, robust estimates for expected returns, and portfolio optimization in practice.
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This course comprises modules on style and factor exposures, robust covariance matrix estimation, robust estimates for expected returns, and portfolio optimization in practice. Through practical implementation in Python, gain mastery in modern computational methods for investment management.
Welcome to the exploration of style and factor exposures, where you will learn about factor investing, factor models, multi-factor models, and the shortcomings of cap-weighted indices. The lab sessions provide practical exercises to reinforce your understanding of the concepts.
Delve into the intricacies of robust covariance matrix estimation, understanding the curse of dimensionality and implementing methods such as factor model-based covariance estimation and portfolio construction with time-varying risk parameters. The lab session provides hands-on practice for effective learning.
Explore robust estimates for expected returns, including agnostic priors, factor model-based estimation, and Black-Litterman Analysis. The lab session enhances your proficiency in implementing these techniques, ensuring practical mastery in expected return estimation.
Engage in portfolio optimization in practice, covering topics such as naive diversification, scientific diversification, risk parity portfolios, and measuring risk contributions. The lab session provides valuable hands-on experience to solidify your understanding of portfolio optimization methodologies.
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