The core of most financial practice, whether drawn from equilibrium economics, behavioural psychology, or agency models, is traditionally formed through the marriage of elegant theory and a kind of ‘dirty’ empirical proof. As I learnt from my years on the PhD programme at the London School of Economics, elegant theory is the hallmark of a beautiful intellect, one that could discern the subtle tradeoffs in agent‐based models, form complex equilibrium structures and point to the sometimes conflicting paradoxes at the heart of conventional truths. Yet ‘dirty’ empirical work is often scoffed at with suspicion, but reluctantly acknowledged as necessary to give substance and real‐world application. I recall many conversations in the windy courtyards and narrow passageways, with brilliant PhD students wrangling over questions of ‘but how can I find a test for my hypothesis?’.
Many pseudo‐mathematical frameworks have come and gone in quantitative finance, usually borrowed from nearby sciences: thermodynamics from physics, Eto's Lemma, information theory, network theory, assorted parts from number theory, and occasionally from less high‐tech but reluctantly acknowledged social sciences like psychology. They have come, and they have gone, absorbed (not defeated) by the markets.
Machine learning, and extreme pattern recognition, offer a strong focus on large‐scale empirical data, transformed and analyzed ...