The Theory of Intrinsic Time: A Primer

ArXiv ID: 2406.07354 “View on arXiv”

Authors: Unknown

Abstract

The concept of time mostly plays a subordinate role in finance and economics. The assumption is that time flows continuously and that time series data should be analyzed at regular, equidistant intervals. Nonetheless, already nearly 60 years ago, the concept of an event-based measure of time was first introduced. This paper expands on this theme by discussing the paradigm of intrinsic time, its origins, history, and modern applications. Departing from traditional, continuous measures of time, intrinsic time proposes an event-based, algorithmic framework that captures the dynamic and fluctuating nature of real-world phenomena more accurately. Unsuspected implications arise in general for complex systems and specifically for financial markets. For instance, novel structures and regularities are revealed, otherwise obscured by any analysis utilizing equidistant time intervals. Of particular interest is the emergence of a multiplicity of scaling laws, a hallmark signature of an underlying organizational principle in complex systems. Moreover, a central insight from this novel paradigm is the realization that universal time does not exist; instead, time is observer-dependent, shaped by the intrinsic activity unfolding within complex systems. This research opens up new avenues for economic modeling and forecasting, paving the way for a deeper understanding of the invisible forces that guide the evolution and emergence of market dynamics and financial systems. An exciting and rich landscape of possibilities emerges within the paradigm of intrinsic time.

Keywords: Intrinsic Time, Event-Based Measure, Scaling Laws, Complex Systems, Time Series Analysis, General Financial Markets

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 2.0/10
• Quadrant: Philosophers
• Why: The paper presents a conceptual framework and historical overview of intrinsic time with minimal mathematical formalism (a simple algorithmic definition), but lacks empirical backtesting, statistical metrics, or implementation details, making it theoretical rather than data-driven.

  flowchart TD
    A["Research Goal<br>Explore Intrinsic Time Paradigm"] --> B["Methodology<br>Comparative Analysis vs. Equidistant Time"]
    B --> C["Computational Process<br>Event-Based Algorithm & Scaling Law Identification"]
    C --> D{"Key Findings / Outcomes"}
    D --> E["Revealed Hidden<br>Market Regularities"]
    D --> F["Multiple Scaling Laws<br>as System Signature"]
    D --> G["Time is Observer-Dependent<br>No Universal Time"]