Quantitative Theory of Meaning. Application to Financial Markets. EUR/USD case study

TL;DR

This paper introduces a quantitative model linking information, meaning, and investor expectations to market price dynamics, demonstrated through EUR/USD currency market analysis using wavelet transformation.

Contribution

It develops a novel quantitative theory of meaning as a complement to information theory, applied to financial markets to better understand and forecast asset price movements.

Findings

Latent EUR/USD trend structure identified

Model predictions align with observed market patterns

Method demonstrates potential for improved market forecasting

Abstract

The paper focuses on the link between information, investors' expectations and market price movement. EUR/USD market is examined from communication-theoretical perspective on the dynamics of information and meaning. We build upon the quantitative theory of meaning as a complement to the quantitative theory of information. Different groups of investors entertain different criteria to process information, so that the same information can be supplied with different meanings. Meanings shape investors' expectations which are revealed in market asset price movement. This dynamics can be captured by non-linear evolutionary equation. We use a computationally efficient technique of logistic Continuous Wavelet Transformation (CWT) to analyze EUR/USD market. The results reveal the latent EUR/USD trend structure which coincides with the model predicted time series indicating that proposed model can adequately describe some patterns of investors' behavior. Proposed methodology can be used to better understand and forecast future market assets' price movement.