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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Investment Management with Python and Machine Learning
von
Spezialisierung Investment Management with Python and Machine LearningEDHEC Business SchoolÜber diesen Kurs
38,624 kürzliche Aufrufe
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Stufe „Mittel“
Ca. 15 Stunden zum Abschließen
Englisch
Untertitel: Englisch
Was Sie lernen werden
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
Kompetenzen, die Sie erwerben
Programming skillsManaging your own personal invetsmentsInvestment management knowledgeComputer ScienceExpertise in data science
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Stufe „Mittel“
Ca. 15 Stunden zum Abschließen
Englisch
Untertitel: Englisch
von
EDHEC Business School
Founded in 1906, EDHEC is now one of Europe’s top 15 business schools .
Lehrplan - Was Sie in diesem Kurs lernen werden
2 Stunden zum Abschließen
Introducing the fundamentals of machine learning
2 Stunden zum Abschließen
8 Videos (Gesamt 59 min), 4 Lektüren, 1 Quiz
8 Videos
Introduction to machine-learning7m
Financial applications7m
Supervised learning7m
First algorithms7m
Highlights of best practice6m
Unsupervised learning7m
Challenges ahead10m
4 Lektüren
Requirements2m
Material at your disposal2m
Machine Learning for Investment Decisions: A Brief Guided Tour10m
References for module 1"Introducing the fundamentals of machine learning"10m
1 praktische Übung
Module 1Graded Quiz30m
4 Stunden zum Abschließen
Machine learning techniques for robust estimation of factor models
4 Stunden zum Abschließen
8 Videos (Gesamt 80 min), 2 Lektüren, 1 Quiz
8 Videos
Introducing Factor Models7m
Typology of factor models9m
Using factor models in portfolio construction and analysis10m
Penalty methods9m
Setting factor loadings and examples7m
Shrinkage concepts7m
Lab session - Jupiter notebook on Factor Models20m
2 Lektüren
References for module 2"Machine learning techniques for robust estimation of factor models"10m
Information on Jupyter notebook - Factor models10m
1 praktische Übung
Module 2 Graded Quiz1h
2 Stunden zum Abschließen
Machine learning techniques for efficient portfolio diversification
2 Stunden zum Abschließen
7 Videos (Gesamt 59 min), 2 Lektüren, 1 Quiz
7 Videos
Benefits of portfolio diversification8m
Portfolio diversification measures12m
Principle component analysis8m
Role of clustering6m
Graphical analysis8m
Selecting a portfolio of assets7m
2 Lektüren
References for the module "Machine learning techniques for efficient portfolio diversification"10m
Reference for the module "Selecting a portfolio of assets"10m
1 praktische Übung
Module 3 Graded Quiz45m
3 Stunden zum Abschließen
Machine learning techniques for regime analysis
3 Stunden zum Abschließen
7 Videos (Gesamt 65 min), 4 Lektüren, 1 Quiz
7 Videos
Portfolio Decisions with Time-Varying Market Conditions10m
Trend filtering6m
A scenario based portfolio model8m
A two regime portfolio example7m
A multi regime model for a University Endowment9m
Lab session- Jupyter notebook on regime-based investment model15m
4 Lektüren
Information on the "trend filtering" video2m
Information on "scenario based portfolio model" video2m
References for the module "Machine learning techniques for regime analysis"10m
Information on Jupyter notebookon regime-based investment model10m
1 praktische Übung
Module 4 Graded Quiz1h
Bewertungen
Top-Bewertungen von PYTHON AND MACHINE LEARNING FOR ASSET MANAGEMENT
von AT1. März 2020
would be good to focus more on the jupyter notebooks and less on multiple choice. Really interesting notebooks and quite advanced / technical material which deserves more time and coverage.
von KA5. Feb. 2020
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.
von ST9. Apr. 2020
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!
von AJ30. Mai 2020
Please consider adding additional videos for the lab sessions, as one can not gain the Machine Learning python coding skills from PPT slides!
Über den Spezialisierung Investment Management with Python and Machine Learning
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.
Häufig gestellte Fragen
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.
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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