Paper: arXiv 2603.00361

Authors: Bernhard K Meister

Abstract

Financial markets convert the incremental arrival of information into asset price changes. In a sandpile model grains of sand represent bits of data, and the size of an avalanche, governed by a scaling law, is linked to price volatility. While this model of self-organized criticality reproduces stylized facts, it also identifies a structural tension between the non-arbitrage condition and price adjustments consistent with a constant Sharpe ratio.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 2.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper presents an intriguing theoretical synthesis of self-organized criticality (sandpile models) and information geometry (Fisher-Rao metrics) to model market dynamics and dynamic arbitrage. However, it is entirely conceptual and lacks empirical validation, real-world data analysis, or backtesting to substantiate the proposed trading strategies.

Research Flowchart

  flowchart TD
    A[Research Goal: Explain market dynamics & volatility using Self-Organized Criticality] --> B[Input Data: Bits of information modeled as grains of sand]
    B --> C[Method: Apply Sandpile Model to simulate information avalanches]
    C --> D[Compute: Evaluate avalanche sizes against price adjustments]
    D --> E[Outcome 1: Scaling laws reproduce market price volatility]
    D --> F[Outcome 2: Reveals tension between Non-Arbitrage & Constant Sharpe Ratio]