Diagnosis and Prediction of Market Rebounds in Financial Markets

TL;DR

This paper introduces the concept of negative bubbles in financial markets, models them using an adapted JLS model, and demonstrates their predictive power for market rebounds through pattern recognition and statistical validation.

Contribution

It extends the JLS model to negative bubbles and shows their predictability for market rebounds using a novel pattern recognition approach.

Findings

Negative bubbles can be modeled with an adapted JLS framework.

The model predicts market rebounds with significant accuracy.

Pattern recognition methods validate the predictive power of negative bubbles.

Abstract

We introduce the concept of "negative bubbles" as the mirror image of standard financial bubbles, in which positive feedback mechanisms may lead to transient accelerating price falls. To model these negative bubbles, we adapt the Johansen-Ledoit-Sornette (JLS) model of rational expectation bubbles with a hazard rate describing the collective buying pressure of noise traders. The price fall occurring during a transient negative bubble can be interpreted as an effective random downpayment that rational agents accept to pay in the hope of profiting from the expected occurrence of a possible rally. We validate the model by showing that it has significant predictive power in identifying the times of major market rebounds. This result is obtained by using a general pattern recognition method which combines the information obtained at multiple times from a dynamical calibration of the JLS model. Error diagrams, Bayesian inference and trading strategies suggest that one can extract genuine information and obtain real skill from the calibration of negative bubbles with the JLS model. We conclude that negative bubbles are in general predictably associated with large rebounds or rallies, which are the mirror images of the crashes terminating standard bubbles.