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

Vijay Vaidyanathan, PhD +1 more instructor

Offered By

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. This course provides an introduction to the underlying science, with the aim of giving you a thorough understanding of that scientific basis. However, 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. 
This course is the first in a four course specialization in Data Science and Machine Learning in Asset Management but can be taken independently. In this course, we cover the basics of Investment Science, and we'll build practical implementations of each of the concepts along the way. We'll start with the very basics of risk and return and quickly progress to cover a range of topics including several Nobel Prize winning concepts. We'll cover some of the most popular practical techniques in modern, state of the art investment management and portfolio construction. 

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.

Shareable Certificate

Earn a Certificate upon completion

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Start instantly and learn at your own schedule.

Course 1 of 4 in the

Investment Management with Python and Machine Learning Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 24 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish

What you will learn

Gain an intuitive understanding for the underlying theory behind Modern Portfolio Construction Techniques

Write custom Python code to estimate risk and return parameters

Utilize powerful Python optimization libraries to build scientifically and systematically diversified portfolios

Build custom utilities in Python to test and compare portfolio strategies

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 1 of 4 in the

Investment Management with Python and Machine Learning Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 24 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish

Instructors

Instructor rating

4.9/5 (235 Ratings)

Vijay Vaidyanathan, PhD

Optimal Asset Management Inc.

CEO

31,937 Learners

2 Courses

Lionel Martellini, PhD

EDHEC-Risk Institute, Director

Finance

34,087 Learners

3 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

Content Rating94%(2,630 ratings)

Week

Week 1

6 hours to complete

Analysing returns

6 hours to complete

14 videos (Total 225 min), 5 readings, 1 quiz

14 videos

Welcome video3m

Installing Anaconda3m

Fundamentals of Returns10m

Lab Session-Basics of returns29m

Measures of Risk and Reward9m

Lab Session-Risk Adjusted returns28m

Measuring Max Drawdown10m

Lab Session-Drawdown30m

Deviations from Normality9m

Lab Session-Building your own modules12m

Downside risk measures8m

Lab Session-Deviations from Normality30m

Estimating VaR10m

Lab Session-Semi Deviation, VAR and CVAR27m

5 readings

Material at your disposal5m

Material for the Lab Sessions10m

Module 1- Key points2m

INCORRECT STATEMENT IN “DEVIATION FROM NORMALITY” VIDEO10m

Before the Quiz10m

1 practice exercise

Module 1 Graded Quiz1h

Week

Week 2

4 hours to complete

An Introduction to Portfolio Optimization

4 hours to complete

10 videos (Total 172 min), 1 reading, 1 quiz

10 videos

The only free lunch in Finance11m

Lab Session-Efficient frontier-Part 123m

Markowitz Optimization and the Efficient Frontier9m

Applying quadprog to draw the efficient Frontier11m

Lab Session-Asset Efficient Frontier-Part 220m

Lab Session-Applying Quadprog to Draw the Efficient Frontier38m

Fund Separation Theorem and the Capital Market Line7m

Lab Session-Locating the Max Sharpe Ratio Portfolio25m

Lack of robustness of Markowitz analysis5m

Lab Session-Plotting EW and GMV on the Efficient Frontier20m

1 reading

Module 2 - Key points2m

1 practice exercise

Module 2 Graded Quiz1h

Week

Week 3

5 hours to complete

Beyond Diversification

5 hours to complete

15 videos (Total 236 min), 4 readings, 1 quiz

15 videos

Limits of diversification8m

Lab session- Limits of Diversification-Part119m

Lab session-Limits of diversification-Part 222m

An introduction to CPPI - Part 17m

An introduction to CPPI - Part 210m

Lab session-CPPI and Drawdown Constraints-Part129m

Lab session-CPPI and Drawdown Constraints-Part228m

Simulating asset returns with random walks10m

Monte Carlo Simulation6m

Lab Session-Random Walks and Monte Carlo22m

Analyzing CPPI strategies11m

Lab Session-Installing IPYWIDGETS5m

Designing and calibrating CPPI strategies12m

Lab session - interactive plots of monte Carlo Simulations of CPPI and GBM-Part119m

Lab session - interactive plots of monte Carlo Simulations of CPPI and GBM-Part221m

4 readings

Module 3 - Key points2m

ipywidgets installation - info5m

gbm function10m

Instruction prior to begin the module 3 graded quizz10m

1 practice exercise

Module 3 Graded Quiz45m

Week

Week 4

9 hours to complete

Introduction to Asset-Liability Management

9 hours to complete

12 videos (Total 327 min), 5 readings, 1 quiz

12 videos

From Asset Management to Asset-Liability Management7m

Lab Session-Present Values,liabilities and funding ratio22m

Liability hedging portfolios12m

Lab Session-CIR Model and cash vs ZC bonds1h 8m

Liability-driven investing (LDI)10m

Lab Session-Liability driven investing51m

Choosing the policy portfolio14m

Lab Session-Monte Carlo simulation of coupon-bearing bonds using CIR33m

Beyond LDI11m

Lab Session-Naive risk budgeting between the PSP & GHP44m

Liability-friendly equity portfolios10m

Lab Session-Dynamic risk budgeting between PSP & LHP40m

5 readings

Module 4 - Key points2m

Dynamic Liability-Driven Investing Strategies: The Emergence Of A New Investment Paradigm For Pension Funds?1h 30m

Liability-Driven-Investing1h

Instruction prior to begin module 4 graded quizz2m

To be continued (1)5m

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?
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.

Introduction to Portfolio Construction and Analysis with Python