Information-Theoretic Approach to Financial Market Modelling
🤖 AI Summary
This work proposes a parsimonious information-theoretic framework for modeling financial markets by conceptualizing the market as a communication system. Building upon four information-theoretic assumptions, the authors construct a single-parameter idealized model that characterizes asset dynamics through minimization of the Kullback–Leibler divergence between market surprise and a reference measure. The core innovation lies in the first systematic application of information-theoretic principles to financial modeling, yielding an analytically tractable structure wherein the state variable is a scalar stationary diffusion process. The model reveals that the state variable, its cumulative sum, and the growth-optimal portfolio each follow a squared radial Ornstein–Uhlenbeck process under their respective activity times, thereby achieving both interpretability and mathematical elegance.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
financial market modelling
information theory
state variables
Kullback-Leibler divergence
squared radial Ornstein-Uhlenbeck process
Innovation
Methods, ideas, or system contributions that make the work stand out.
information-theoretic modeling
Kullback-Leibler divergence
squared radial Ornstein-Uhlenbeck process
growth optimal portfolio
market surprisal
🔎 Similar Papers
No similar papers found.
