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
Offered By
Investment Management with Python and Machine Learning SpecializationEDHEC Business SchoolAbout this Course
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Course 2 of 4 in the
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Intermediate Level
Approx. 11 hours to complete
English
Subtitles: English
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
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 4 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 11 hours to complete
English
Subtitles: English
Offered by
EDHEC Business School
Founded in 1906, EDHEC is now one of Europe’s top 15 business schools .
Syllabus - What you will learn from this course
3 hours to complete
Style & Factors
3 hours to complete
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
2 hours to complete
Robust estimates for the covariance matrix
2 hours to complete
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
3 hours to complete
Robust estimates for expected returns
3 hours to complete
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
3 hours to complete
Portfolio Optimization in Practice
3 hours to complete
7 videos (Total 67 min), 4 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
4 readings
Module 4-Key points2m
Survey: Alternative Equity Beta Investing10m
Dive into heuristic diversification10m
To be continued (2)10m
1 practice exercise
Module 4 - Graded quiz1h
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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
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.
What will I get if I subscribe to this Specialization?
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.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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