Advanced Portfolio Construction and Analysis with Python

This course is part of the Investment Management with Python and Machine Learning Specialization

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360 ratings
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93 reviews

Lionel Martellini, PhD +1 more instructor

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Investment Management with Python and Machine Learning SpecializationEDHEC Business School

About this Course

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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.
As we cover the theory and math in lecture videos, we'll also implement the concepts in Python, and you'll be able to code along with us so that you have a deep and practical understanding of how those methods work. By the time you are done, not only will you have a foundational understanding of modern computational methods in investment management, you'll have practical mastery in the implementation of those methods. If you follow along and implement all the lab exercises, you will complete the course with a powerful toolkit that you will be able to use to perform your own analysis and build your own implementations and perhaps even use your newly acquired knowledge to improve on current methods.

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Earn a Certificate upon completion

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Course 2 of 4 in the

Investment Management with Python and Machine Learning Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 11 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

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

Investment Management with Python and Machine Learning Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 11 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

Instructors

Instructor rating

4.86/5 (74 Ratings)

Lionel Martellini, PhD

EDHEC-Risk Institute, Director

Finance

38,616 Learners

3 Courses

Vijay Vaidyanathan, PhD

Optimal Asset Management Inc.

CEO

36,323 Learners

2 Courses

Offered by

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.

Syllabus - What you will learn from this course

Week

Week 1

3 hours to complete

Style & Factors

3 hours to complete

9 videos (Total 114 min), 3 readings, 1 quiz

9 videos

Welcome video3m

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

Week 2

2 hours to complete

Robust estimates for the covariance matrix

2 hours to complete

7 videos (Total 70 min), 1 reading, 1 quiz

Week

Week 3

3 hours to complete

Robust estimates for expected returns

3 hours to complete

7 videos (Total 77 min), 2 readings, 1 quiz

7 videos

Lack of Robustness of Expected Return Estimates10m

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

Week 4

3 hours to complete

Portfolio Optimization in Practice

3 hours to complete

7 videos (Total 67 min), 4 readings, 1 quiz

7 videos

Naive Diversification11m

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

Reviews

4.8

93 reviews

5 stars
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4 stars
12.98%
3 stars
3.86%
2 stars
0.55%
1 star
0.55%

TOP REVIEWS FROM ADVANCED PORTFOLIO CONSTRUCTION AND ANALYSIS WITH PYTHON

by MMApr 13, 2020

Loved how this course was presented. It built well off of the first course and provided labs that let me explore the content. I really enjoyed how Lionel and Vijay presented the material.

by LTFeb 17, 2021

Good overview on portfolio theory with some of the latest trends (multi-factor models) and Python Lab sessions follow the same logic than the first course, with good tips and good timing.

by MMDec 2, 2019

The course is excellent, one of the best finance courses on coursera, but you should know in advance that you will not have any help from the staff, at least that was my experience.

by KAJan 4, 2020

I like the way instructors explained difficult topic and digest it to simple way. The coding side was also impressive. WIth novice background in Python, I would able to understand.

View all reviews

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?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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.
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.
Will I earn university credit for completing the Course?
This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

More questions? Visit the Learner Help Center.

Advanced Portfolio Construction and Analysis with Python