Machine Learning for Smart Beta
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.
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What you'll learn
- understanding the mechanics behind Smart Beta products
- applying machine learning methods to financial models
- recreating Smart Beta products using R programming
- implementing overfitting prevention techniques
- creating a multi-factor model using CART, bagging, and boosting
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