Machine Learning for Smart Beta

Sungkyunkwan University via Coursera

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Overview

The “Machine Learning for Smart Beta” course explores how machine learning techniques can enhance smart beta investment strategies, which seek to outperform traditional market-cap-weighted indexes by using alternative weighting schemes based on factors like value, momentum, and volatility. Participants will learn the fundamentals of smart beta strategies and how machine learning algorithms can analyze large financial datasets to identify patterns, optimize factor selection, and improve portfolio construction. The course covers supervised and unsupervised learning methods, including regression, classification, clustering, and...

Syllabus

Week 1

Building on the concepts learned in previous courses 'The Fundamental of Data-Driven Investment' and 'Using R for Regression and Machine Learning in Investment', this course will cover 'Smart beta'. Smart betas products have the characteristics of both passive investment(having predetermined rules) and active investments(allows for factor investment). Smart beta products' investment mechanisms are open to the public, so we will recreate a MSCI smart beta product in R. Follow along the step-by-step reconstruction of the MSCI Enhanced Value Index and create your own smart beta portfolio.

Week 2

In order to effectively utilize machine learning in investment, it is important to understand the various characteristics of data. This module covers how to check the prediction accuracy of a machine learning model and prevent overfitting. Get hands on experience in R to manipulate data into a form suitable for machine learning models from regression models to classification trees.

Week 3

The asset selection method based on a score derived from a benchmark index has the problem that the selected assets do not reliably capture underlying information. To solve this problem, a non-traditional method, namely machine learning is used to create an improved multi-factor approach. Familiarize yourself with CART(Classification and Regression Tree), bagging, boosting and ensemble methods to enhance your smart beta portfolio in R.

Week 4

In this final module, we wrap up the discussion by creating a multifactor model applying all the knowledge we have learned so far. Investors have taken a steady interest in multifactor models that take into account the cyclicality of factors. Further, we expand the discussion into the use of factors in bond investment and a new method of active factor allocation.