About this Course

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Approx. 24 hours to complete
English
Subtitles: English

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.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 24 hours to complete
English
Subtitles: English

Offered by

EDHEC Business School logo

EDHEC Business School

Syllabus - What you will learn from this course

Content RatingThumbs Up92%(1,369 ratings)Info
Week
1

Week 1

5 hours to complete

Analysing returns

5 hours to complete
14 videos (Total 225 min), 5 readings, 1 quiz
14 videos
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
2

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
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
3

Week 3

5 hours to complete

Beyond Diversification

5 hours to complete
15 videos (Total 236 min), 4 readings, 1 quiz
15 videos
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
4

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
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

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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

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