Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots

TL;DR

This paper presents a novel nonlinear method using cross-recurrence plots combined with deep learning to predict the synchronization state of financial time series, validated on S&P100 stocks.

Contribution

It introduces a new approach integrating cross-recurrence analysis with deep learning for predicting financial time series synchronization.

Findings

Prediction is generally challenging but achievable for certain stock pairs.

The method achieves satisfactory performance on specific stock pairs.

Cross-recurrence features effectively capture synchronization dynamics.

Abstract

Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series. This study introduces a new method for predicting the future state of synchronization of the dynamics of two financial time series. To this end, we use the cross-recurrence plot analysis as a nonlinear method for quantifying the multidimensional coupling in the time domain of two time series and for determining their state of synchronization. We adopt a deep learning framework for methodologically addressing the prediction of the synchronization state based on features extracted from dynamically sub-sampled cross-recurrence plots. We provide extensive experiments on several stocks, major constituents of the S\&P100 index, to empirically validate our approach. We find that the task of predicting the state of synchronization of two time series is in general rather difficult, but for certain pairs of stocks attainable with very satisfactory performance.