Probabilistic Machine Learning for Finance and Investing

Chapter 1. The Need for Probabilistic Machine Learning

Essentially, all models are wrong, but some are useful. However, the approximate nature of the model must always be borne in mind.

—George Box, eminent statistician

A map will enable you to go from one geographic location to another. It is a very useful mathematical model for navigating the physical world. It becomes even more useful if you automate it into a GPS system using artificial intelligence (AI) technologies. However, neither the mathematical model nor the AI-powered GPS system will ever be able to capture the human experience and richness of the terrain it represents. That’s because all models have to simplify the complexities of the real world, thus enabling us to focus on some of the features of a phenomenon that interest us.

George Box, an eminent statistician, famously said, “all models are wrong, but some are useful.” This deeply insightful quip is our mantra. We accept that all models are wrong because they are inadequate and incomplete representations of reality. Our goal is to build financial systems based on models and supporting technologies that enable useful inferences and predictions for decision making and risk management in the face of endemic uncertainty, incomplete information, and inexact measurements.

All financial models, whether derived theoretically or discovered empirically by humans and machines, are not only wrong but are also at the mercy of three types of errors. In this chapter, we explain ...