Modeling the Stock Market prior to large crashes

TL;DR

This paper introduces a set of minimal criteria for modeling stock market fluctuations, compares two models of pre-crash behavior, and finds that the model meeting these criteria significantly outperforms the other.

Contribution

It establishes minimal modeling requirements and demonstrates that a model adhering to these outperforms previous models in predicting large market crashes.

Findings

The model with minimal requirements outperforms the alternative with high significance.

Time asymmetry and robustness are crucial for accurate crash modeling.

Probabilistic approaches improve the understanding of pre-crash market behavior.

Abstract

We propose that the minimal requirements for a model of stock market price fluctuations should comprise time asymmetry, robustness with respect to connectivity between agents, ``bounded rationality'' and a probabilistic description. We also compare extensively two previously proposed models of log-periodic behavior of the stock market index prior to a large crash. We find that the model which follows the above requirements outperforms the other with a high statistical significance.