文献类型：期刊文献

英文题名：Financial Relativity: An Information-Geometric Interpretation of Asset Pricing

作者：Li, Lin[1]

机构：[1] Department of Finance, Business School, East China University of Science and Technology, China

年份：2026

外文期刊名：arXiv

收录：EI(收录号：20260191395)

语种：英文

外文关键词：Costs - Dynamics - Economics - Financial markets - Geometry

摘要：Classical asset pricing relies on the risk-neutral measure Q for valuation, yet its economic meaning is typically grounded in a physical measure P. This creates an implicit asymmetry: pricing is governed by Q, while interpretation is anchored in P. As a result, the same pricing relation may admit multiple, and sometimes conflicting, explanations depending on how the link between P and Q is specified, making it difficult to provide a unified account of asset pricing within a single conceptual framework. This paper proposes an alternative perspective based on information geometry, termed financial relativity. The central principle is a relativity of probabilistic reference frames: P and Q possess no intrinsic hierarchy, but instead correspond to geometric structures induced by different informational constraints. Terminal structural information determines how probability geometry is shaped, and this geometry in turn governs how information is expressed in prices. Within this framework, the risk-neutral measure is reinterpreted as a posterior probability geometry shaped by structural constraints. Asset prices are characterized as geometric projections of terminal payoffs onto information subspaces, and price dynamics emerge as the progressive manifestation of structural information under evolving geometry. We develop discrete and continuous financial field equations to describe the formation of probability geometry, and derive geodesic price dynamics in which volatility is endogenously determined by posterior uncertainty. This framework provides a unified explanation for price fluctuations, event-driven dynamics, and risk premia, and yields testable implications, including structural links between volatility and posterior variance, as well as measures of the informational efficiency of prices. By integrating structural information, probability measures, and price dynamics within a unified geometric framework, the paper offers a coherent, extensible, and empirically tractable reinterpretation of asset pricing theory. ? 2026, CC BY-SA.