About this Course
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Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 13 hours to complete

Suggested: 4 weeks, from 3 to 4 hours per week...

English

Subtitles: English

What you will learn

  • Check

    Analyze style and factor exposures of portfolios

  • Check

    Implement robust estimates for the covariance matrix

  • Check

    Implement Black-Litterman portfolio construction analysis

  • Check

    Implement a variety of robust portfolio construction models

Learners taking this Course are
  • Data Scientists
  • Financial Analysts
  • Analysts (General)
  • Data Engineers
  • Engineers

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 13 hours to complete

Suggested: 4 weeks, from 3 to 4 hours per week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
3 hours to complete

Style & Factors

9 videos (Total 114 min), 3 readings, 1 quiz
9 videos
Introduction to factor investing12m
Factor models and the CAPM9m
Multi-Factor models and Fama-French7m
Factor benchmarks and Style analysis8m
Shortcomings of cap-weighted indices11m
From cap-weighted benchmarks to smart-weighted benchmarks12m
Introduction to Lab sessions6m
Module 1 Lab Session - Foundations42m
3 readings
Requirements2m
Material at your disposal5m
Module 1- Key points2m
1 practice exercise
Module 1- Graded Quiz1h
Week
2
2 hours to complete

Robust estimates for the covariance matrix

7 videos (Total 70 min), 1 reading, 1 quiz
7 videos
Estimating the Covariance Matrix with a Factor Model9m
Honey I Shrunk the Covariance Matrix!7m
Portfolio Construction with Time-Varying Risk Parameters8m
Exponentially weighted average8m
ARCH and GARCH Models9m
Module 2 Lab Session - Covariance Estimation13m
1 reading
Module 2-Key points2m
1 practice exercise
Module 2 - Graded quiz1h
Week
3
3 hours to complete

Robust estimates for expected returns

7 videos (Total 77 min), 2 readings, 1 quiz
7 videos
Agnostic Priors on Expected Return Estimates6m
Using Factor Models to Estimate Expected Returns11m
Extracting Implied Expected Returns8m
Introducing Active Views6m
Black-Litterman Analysis10m
Module 3 Lab Session- Black Litterman23m
2 readings
Module 3-Key points2m
The Intuition Behind Black-Litterman Model Portfolios10m
1 practice exercise
Module 3 - Graded Quiz1h
Week
4
3 hours to complete

Portfolio Optimization in Practice

7 videos (Total 67 min), 3 readings, 1 quiz
7 videos
Scientific Diversification11m
Measuring risk contributions6m
Simplified risk parity portfolios7m
Risk Parity Portfolios7m
Comparing Diversification Options8m
Module 4 Lab Session - Risk Contribution and Risk Parity15m
3 readings
Module 4-Key points2m
Survey: Alternative Equity Beta Investing10m
Dive into heuristic diversification10m
1 practice exercise
Module 4 - Graded quiz1h

Instructors

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Lionel Martellini, PhD

EDHEC-Risk Institute, Director
Finance
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Vijay Vaidyanathan, PhD

Optimal Asset Management Inc.
CEO

About EDHEC Business School

Founded in 1906, EDHEC is now one of Europe’s top 15 business schools . Based in Lille, Nice, Paris, London and Singapore, and counting over 90 nationalities on its campuses, EDHEC is a fully international school directly connected to the business world. With over 40,000 graduates in 120 countries, it trains committed managers capable of dealing with the challenges of a fast-evolving world. Harnessing its core values of excellence, innovation and entrepreneurial spirit, EDHEC has developed a strategic model founded on research of true practical use to society, businesses and students, and which is particularly evident in the work of EDHEC-Risk Institute and Scientific Beta. The School functions as a genuine laboratory of ideas and plays a pioneering role in the field of digital education via EDHEC Online, the first fully online degree-level training platform. These various components make EDHEC a centre of knowledge, experience and diversity, geared to preparing new generations of managers to excel in a world subject to transformational change. EDHEC in figures: 8,600 students in academic education, 19 degree programmes ranging from bachelor to PhD level, 184 professors and researchers, 11 specialist research centres. ...

About the Investment Management with Python and Machine Learning Specialization

The Data Science and Machine Learning for Asset Management Specialization has been designed to deliver a broad and comprehensive introduction to modern methods in Investment Management, with a particular emphasis on the use of data science and machine learning techniques to improve investment decisions.By the end of this specialization, you will have acquired the tools required for making sound investment decisions, with an emphasis not only on the foundational theory and underlying concepts, but also on practical applications and implementation. 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 through a series of dedicated lab sessions....
Investment Management with Python and Machine Learning

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.