The Shape of Markets: Machine learning modeling and Prediction Using 2-Manifold Geometries

TL;DR

This paper presents a novel financial forecasting model that embeds market data onto 2-manifolds with different geometries, revealing the torus as the most effective shape for capturing cyclical economic dynamics.

Contribution

It introduces a geometry-informed approach using differential geometry and manifold learning to improve financial market modeling and prediction.

Findings

The torus geometry outperforms spherical and hyperbolic models in forecasting accuracy.

Latent curvature inference reveals the torus as the best fit for cyclical market behaviors.

The model aligns geometric structures with macroeconomic cycles like interest rates and inflation.

Abstract

We introduce a Geometry Informed Model for financial forecasting by embedding high dimensional market data onto constant curvature 2manifolds. Guided by the uniformization theorem, we model market dynamics as Brownian motion on spherical S2, Euclidean R2, and hyperbolic H2 geometries. We further include the torus T, a compact, flat manifold admissible as a quotient space of the Euclidean plane anticipating its relevance for capturing cyclical dynamics. Manifold learning techniques infer the latent curvature from financial data, revealing the torus as the best performing geometry. We interpret this result through a macroeconomic lens, the torus circular dimensions align with endogenous cycles in output, interest rates, and inflation described by IS LM theory. Our findings demonstrate the value of integrating differential geometry with data-driven inference for financial modeling.