Executive Education

Building on the cutting-edge research of our faculty, we create programmes to help you level up your financial expertise on investment management, factor investing, goal-based investing, sustainable investing, data science and machine learning, so you can stay ahead of the game!

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It is time to start applying academic research to your business and benefit from a unique experience with on-the-ground relevance, which will challenge and guide you towards converting theoretical concepts into practical results. 

Our courses are run in different formats to match your needs: 100% online, on site, or a combination of the two.

Those who successfully complete the course earn the prestigious EDHEC-Risk certificate, a recognised marker of expertise.

Develop your expertise with state-of-the-art investment management concepts and techniques.

Increase your impact and take your career to the next level.

 

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Programmes

The Advances in Asset Allocation Online Course

The Investment Solutions course is tailored to be an introduction to the conceptual, technical and implementation challenges involved in the design of innovative forms of welfare-improving investment solutions, building upon the expertise developed within EDHEC-Risk Institute over the last 15 years.

Learn to improve your investment process through relevant academic insights in the areas of Diversification, Hedging and Insurance.

Format :Online course

Time commitment: 9 weeks (3 flexible hours per week)

Faculty: Professor Lionel Martellini, Professor of Finance at EDHEC Business School and Director of EDHEC-Risk Institute, PhD Berkeley

Output: An EDHEC-Risk Institute Certification

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Customised Programmes

Bespoke solutions tailored to your unique challenges

 

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Moocs

Explore the 4 MOOCs below on offer as part of the Investment Management with Python and Machine Learning Specialisation.
We remind you that each one leads to a Certificate and can be taken independently.

You will learn at your own pace and benefit from the expertise of global thought leaders from EDHEC Business School, Princeton University and the finance industry.
Make the most of your e-learning journey!

More information on the Specialisation      Overview

Introduction to Portfolio Construction and Analysis with Python

Key learning objectives

  • Gain an intuitive understanding for the underlying theory behind Modern Portfolio Construction Techniques 
  • Utilize powerful Python optimization libraries to build scientifically and systematically diversified portfolios 
  • Write custom Python code to estimate risk and return parameters 
  • Build custom utilities in Python to test and compare portfolio strategies 

 Format :Open Enrolment

 Time Commitment :4 weeks / 3 to 7 hours per week 

 Output : Certificate proving that you have mastered the key concepts  

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Advanced Portfolio Construction and Analysis with Python

Key learning objectives

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

 Format :Open Enrolment

 Time Commitment :4 weeks / 2 to 3 hours per week 

 Output : Certificate proving that you have mastered the key concepts 

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Python Machine-Learning for Investment Management

Key learning objectives

  • Learn the principles of supervised and unsupervised machine learning techniques to financial data sets 
  • Utilize powerful Python libraries to implement machine learning algorithms in case studies 
  • Understand the basis of logistical regression and ML algorithms for classifying variables into one of two outcomes 
  • Learn about factor models and regime switching models and their use in investment management 

 Format :Open Enrolment

 Time Commitment :5 weeks / 2 to 4 hours per week 

 Output : Certificate proving that you have mastered the key concepts 

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Python Machine-Learning for Investment Management with Alternative Datasets

Key learning objectives

  • Learn why alternative data could be useful in financial market applications, 
  • Utilizing various types of alternative data to identify behaviour, predict return and asses risks. 
  • Perform (alternative) data analysis using Python (and libraries), replicate relevant data analysis sections of seminal academic and practiocioner work. 
  • Gain an understanding of advanced data analytics methodologies, visualization and quantitative modeling applied to alternative data in finance 

 Format :Open Enrolment

 Time Commitment :4 weeks / 2 to 3 hours per week 

 Output : Certificate proving that you have mastered the key concepts 

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Faculty


Research Associate


Director of EDHEC-Risk Institute - Professor


Research Associate