Modelling financial time series with $\phi^{4}$ quantum field theory

TL;DR

This paper introduces a $\,\phi^{4}$ quantum field theory model for financial time series that captures higher-order statistics like kurtosis, outperforming Ising models, and explores its scaling properties and potential for stock price forecasting.

Contribution

It presents a novel application of $\,\phi^{4}$ quantum field theory to model financial data, addressing limitations of previous Ising-based models and demonstrating its effectiveness in capturing market statistics.

Findings

Successfully models higher-order statistics like kurtosis in financial data.

Outperforms Ising models in reproducing empirical market features.

Provides insights into scaling behavior of the model's parameters.

Abstract

We use a $\phi^{4}$ quantum field theory with inhomogeneous couplings and explicit symmetry-breaking to model an ensemble of financial time series from the S$\&$P 500 index. The continuum nature of the $\phi^4$ theory avoids the inaccuracies that occur in Ising-based models which require a discretization of the time series. We demonstrate this using the example of the 2008 global financial crisis. The $\phi^{4}$ quantum field theory is expressive enough to reproduce the higher-order statistics such as the market kurtosis, which can serve as an indicator of possible market shocks. Accurate reproduction of high kurtosis is absent in binarized models. Therefore Ising models, despite being widely employed in econophysics, are incapable of fully representing empirical financial data, a limitation not present in the generalization of the $\phi^{4}$ scalar field theory. We then investigate the scaling properties of the $\phi^{4}$ machine learning algorithm and extract exponents which govern the behavior of the learned couplings (or weights and biases in ML language) in relation to the number of stocks in the model. Finally, we use our model to forecast the price changes of the AAPL, MSFT, and NVDA stocks. We conclude by discussing how the $\phi^{4}$ scalar field theory could be used to build investment strategies and the possible intuitions that the QFT operations of dimensional compactification and renormalization can provide for financial modelling.