The Basics of Financial Econometrics: Tools, Concepts, and Asset Management Applications

CHAPTER 14

Model Selection

After reading this chapter you will understand:

• The notion of machine learning.
• The difference between an approach based on theory and an approach based on learning.
• The relationship between the size of samples and the complexity of models that can be learned.
• The concept of overfitting.
• The use of penalty functions in learning.
• The concept of data snooping.
• The concept of survivorship bias.
• The concept of model risk.
• Methods for mitigating model risk.
• Model averaging.

In the previous chapters in this book, we described the most important financial econometric tools. We have not addressed how a financial modeler deals with the critical problem of selecting or perhaps building the optimal financial econometric model to represent the phenomena they seek to study. The task calls for a combination of personal creativity, theory, and machine learning. In this chapter and the one to follow we discuss methods for model selection and analyze the many pitfalls of the model selection process.

PHYSICS AND ECONOMICS: TWO WAYS OF MAKING SCIENCE

In his book, Complexity, Mitchell Waldrop describes the 1987 Global Economy Workshop held at The Santa Fe Institute, a research center dedicated to the study of complex phenomena and related issues.¹ Attended by distinguished economists and physicists, the seminar introduced the idea that economic laws might be better understood by applying the principles of physics and, in particular, the newly developed theory of complex ...