Python and Machine Learning for Asset Management
This course will enable you mastering machine-learning approaches in the area of investment management. It has been designed by two thought leaders in their field, Lionel Martellini from EDHEC-Risk Institute and John Mulvey from Princeton University. Starting from the basics, they will help you build practical skills to understand data science so you can make the best portfolio decisions.The course will start with an introduction to the fundamentals of machine learning, followed by an in-depth discussion of the application of these techniques to portfolio management decisions, including the design of more robust factor models, the construction of portfolios with improved diversification benefits, and the implementation of more efficient risk management models.
We have designed a 3-step learning process: first, we will introduce a meaningful investment problem and see how this problem can be addressed using statistical techniques. Then, we will see how this new insight from Machine learning can complete and improve the relevance of the analysis.
You will have the opportunity to capitalize on videos and recommended readings to level up your financial expertise, and to use the quizzes and Jupiter notebooks to ensure grasp of concept.
At the end of this course, you will master the various machine learning techniques in investment management.
Learn the principles of supervised and unsupervised machine learning techniques to financial data sets
Understand the basis of logistical regression and ML algorithms for classifying variables into one of two outcomes
Utilize powerful Python libraries to implement machine learning algorithms in case studies
Learn about factor models and regime switching models and their use in investment management
Syllabus
Syllabus - What you will learn from this course
Week 1
Introducing the fundamentals of machine learning
Week 2
Machine learning techniques for robust estimation of factor models
Week 3
Machine learning techniques for efficient portfolio diversification
Week 4
Machine learning techniques for regime analysis
Week 5
Identifying recessions, crash regimes and feature selection
FAQ
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:
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. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Reviews
The topics covered in this course are really interesting. I learned a great deal by studying various papers covered in this course - Thank you to both instructors!
The ideas did not explain clearly and explicitly. They want to cover many topics but all in general so that you do not understand deeply what is going on.
Please consider adding additional videos for the lab sessions, as one can not gain the Machine Learning python coding skills from PPT slides!
Good concepts to touch but lack on coding in granulality example. But overall, I'm get a good example how to implement machine learning technique to finance perspective.
