2021 Curriculum CFA Program Level II Quantitative Methods
Online Learning Module
Dive deeper into machine learning concepts with a member-only course, eligible for up to 8.75 PL credits.
Investment firms are increasingly using technology at every step of the investment management value chain—from improving their understanding of clients to uncovering new sources of alpha and executing trades more efficiently. Machine learning techniques, a central part of that technology, are the subject of this Refresher Reading (available as a PDF and ePub and eligible for 2.5 PL credits) and our new, interactive online learning module (eligible for up to 8.75 PL credits), in which you explore the principles of machine learning and use Python to apply the techniques. These techniques first appeared in finance in the 1990s and have since flourished with the explosion of data and cheap computing power.
This reading provides a high-level view of machine learning (ML). It covers a selection of key ML algorithms and their investment applications. Investment practitioners should be equipped with a basic understanding of the types of investment problems that machine learning can address, an idea of how the algorithms work, and the vocabulary to interact with machine learning and data science experts. While investment practitioners need not master the details and mathematics of machine learning, as domain experts in investments they can play an important role in the implementation of these techniques by being able to source appropriate model inputs, interpret model outputs, and translate outputs into appropriate investment actions.
Section 2 gives an overview of machine learning in investment management. Section 3 defines machine learning and the types of problems that can be addressed by supervised and unsupervised learning. Section 4 describes evaluating machine learning algorithm performance. Key supervised machine learning algorithms are covered in Section 5, and Section 6 describes key unsupervised machine learning algorithms. Neural networks, deep learning nets, and reinforcement learning are covered in Section 7. Section 8 provides a decision flowchart for selecting the appropriate ML algorithm. The reading concludes with a summary.
The member should be able to:
distinguish between supervised machine learning, unsupervised machine learning, and deep learning;
describe overfitting and identify methods of addressing it;
describe supervised machine learning algorithms—including penalized regression, support vector machine, k-nearest neighbor, classification and regression tree, ensemble learning, and random forest—and determine the problems for which they are best suited;
describe unsupervised machine learning algorithms—including principal components analysis, k-means clustering, and hierarchical clustering—and determine the problems for which they are best suited;
describe neural networks, deep learning nets, and reinforcement learning.