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Machine Learning for Smart Beta
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.
Duration
4 Months
Institution
Sungkyunkwan University
Format
Online
Eligibility Criteria
school
Academic Foundation
A recognized Bachelor’s degree or high school equivalent required for admission into Sungkyunkwan University.
language
Language Proficiency
English proficiency required. IELTS, TOEFL, or standard medium-of-instruction certificates accepted.
Detailed Fees Breakdown
Base Tuition Fee
$204
Total Est. Investment
$204
Scholarships and early-bird waivers may apply. Contact admissions for exact institutional fees.
Academic Trajectory
Program Outcome
Graduates of the Machine Learning for Smart Beta program at Sungkyunkwan University are equipped with global perspectives, ready to excel in international markets and top-tier career opportunities.