SKKU - Machine Learning for Smart Beta
- Offered byCoursera
Machine Learning for Smart Beta at Coursera Overview
Machine Learning for Smart Beta
at Coursera
Duration | 7 hours |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Machine Learning for Smart Beta at Coursera Highlights
Machine Learning for Smart Beta
at Coursera
- Earn a certificate from Sungkyunkwan University
- Add to your LinkedIn profile
- September 2023
- 1 assignment
Read more
Machine Learning for Smart Beta at Coursera Course details
Machine Learning for Smart Beta
at Coursera
Skills you will learn
More about this course
- In this 4 week course, you will learn about Smart Beta products. Smart betas products have the characteristics of both passive investment(having predetermined rules) and active investments(allows for factor investment). We will walk through the creation mechanisms behind different smart beta products and recreate some of them using R programming. Then we will apply machine learning methods. Data processing, overfitting prevention techniques will be covered. Finally we will try to create an improved multi-factor model using CART, bagging, boosting and ensemble methods. Students are expected to have listened to my first and second course 'The Fundamental of Data-Driven Investment' and 'Using R for Regression and Machine Learning in Investment', or having equivalent knowledge in investment concepts and a firm grasp on R programming.
Machine Learning for Smart Beta at Coursera Curriculum
Machine Learning for Smart Beta
at Coursera
Week 1
L1 Beta Investing
L2 Creating a Smart Beta Index
L3 Weighting Schemes
L4 Smart Beta
L5 Recreating the MSCI Value Index
L5 Assignment
Week 2
L6 Data Processing
L7 Overfitting
L8 Tree Model
L9 Classification Tree in R
Week 3
L10 Classification and Regression Tree (CART)
L11 Bagging
L12 Boosting
L13 Ensemble
Week 4
L14 Multifactor Models
L15 Traditional Methods v. Machine Learning
L16 Bond Factors
L17 Adaptive Multifactor Models
L18 Final Recap
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