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Machine Learning for Smart Beta
Master machine learning techniques for smart beta investing. Learn to create and enhance smart beta portfolios using R programming.
Course Cost
₹ 2,699
Intermediate
Skill Level
7 Hours
Self-paced lessons
This 4-week course focuses on applying machine learning techniques to smart beta investing. Students will learn about smart beta products, which combine characteristics of passive and active investments. The course covers the creation of smart beta products, data processing, overfitting prevention, and advanced machine learning methods like CART, bagging, boosting, and ensemble techniques. Participants will gain hands-on experience in recreating smart beta indices and developing improved multi-factor models using R programming. The course builds on concepts from previous courses in data-driven investment and regression analysis.
English
What you'll learn
Understand the concept and mechanics of smart beta products
Recreate smart beta indices using R programming
Apply machine learning methods to enhance smart beta portfolios
Master data processing techniques for investment analysis
Learn overfitting prevention strategies in machine learning models
Develop proficiency in CART, bagging, boosting, and ensemble methods
Create improved multi-factor models using machine learning
Explore the application of factors in bond investments and adaptive multi-factor models
Skills you'll gain
This course includes:
4.47 Hours PreRecorded video
1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
CREATED BY
PROVIDED BY
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There are 4 modules in this course
This course delves into the application of machine learning techniques for smart beta investing. Students will learn to create and enhance smart beta portfolios using R programming. The curriculum covers the mechanics of smart beta products, data processing for machine learning models, overfitting prevention, and advanced techniques such as CART, bagging, boosting, and ensemble methods. Participants will gain practical experience in recreating smart beta indices like the MSCI Enhanced Value Index and developing improved multi-factor models. The course also explores the use of machine learning in bond investments and adaptive multi-factor models.
Week 1
Module 1 · 4 Hours to complete
Week 2
Module 2 · 1 Hours to complete
Week 3
Module 3 · 55 Minutes to complete
Week 4
Module 4 · 58 Minutes to complete
Fee Structure
Payment options
Financial Aid
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Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
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Frequently asked Questions
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