Machine Learning for Smart Beta
Master machine learning techniques for smart beta investing. Learn to create and enhance smart beta portfolios using R programming.
Master machine learning techniques for smart beta investing. Learn to create and enhance smart beta portfolios using R programming.
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.
Instructors:
English
Powered by
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
Skills you'll gain
This course includes:
4.47 Hours PreRecorded video
1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
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
Instructors
Instructor at Sungkyunkwan University Specializing in Machine Learning and Finance
Haeram Joo is an instructor at Sungkyunkwan University, where he teaches the course "Machine Learning for Smart Beta." This course focuses on applying machine learning techniques to enhance investment strategies, particularly in the context of smart beta investing. Students learn to leverage data analysis and machine learning algorithms to optimize portfolio performance and risk management. Through practical examples and case studies, Haeram aims to equip learners with the skills needed to implement machine learning solutions in financial contexts, making it suitable for those interested in finance, data science, and quantitative analysis.
Associate Professor at Sungkyunkwan University Specializing in Machine Learning and Investment Strategies
Youngju Nielsen is an Associate Professor at Sungkyunkwan University, where she teaches courses such as "Machine Learning for Smart Beta," "The Fundamentals of Data-Driven Investment," and "Using R for Regression and Machine Learning in Investment." With a Ph.D. from the University of Pittsburgh and extensive experience in systematic trading and portfolio management on Wall Street, she brings a wealth of practical knowledge to her teaching. Youngju has managed substantial investment portfolios and has held significant roles in quantitative hedge funds, further enriching her academic contributions. Her research interests include fixed income, tactical allocation, and hedge fund portfolio strategies, making her a valuable resource for students interested in finance and data-driven investment methodologies.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.