Information-Theoretic Approach to Financial Market Modelling

TL;DR

This paper models financial markets using information theory, deriving a simplified, parameterized model where market variables follow specific stochastic processes, linking market behavior to communication system principles.

Contribution

It introduces an information-theoretic framework for market modeling, connecting stochastic processes with communication theory concepts, and derives a minimal-parameter idealized market model.

Findings

Market variables follow squared radial Ornstein-Uhlenbeck processes.

The model minimizes market surprisal and divergence between measures.

State variables are scalar stationary diffusions.

Abstract

The paper treats the financial market as a communication system, using four information-theoretic assumptions to derive an idealized model with only one parameter. State variables are scalar stationary diffusions. The model minimizes the surprisal of the market and the Kullback-Leibler divergence between the benchmark-neutral pricing measure and the real-world probability measure. The state variables, their sums, and the growth optimal portfolio of the stocks evolve as squared radial Ornstein-Uhlenbeck processes in respective activity times.