Leveraging RNNs and LSTMs for Synchronization Analysis in the Indian Stock Market: A Threshold-Based Classification Approach

TL;DR

This paper introduces a novel machine learning approach combining recurrence analysis and RNNs/LSTMs to forecast stock price synchronization in the Indian market, achieving high accuracy and F1 scores.

Contribution

It develops a new methodology integrating non-linear time-series analysis with deep learning for predicting stock synchronization, a novel application in financial analysis.

Findings

Achieved 0.98 prediction accuracy.

F1 score of 0.83 for synchronization classification.

Demonstrated effectiveness on 20 Indian stocks over 21 years.

Abstract

Our research presents a new approach for forecasting the synchronization of stock prices using machine learning and non-linear time-series analysis. To capture the complex non-linear relationships between stock prices, we utilize recurrence plots (RP) and cross-recurrence quantification analysis (CRQA). By transforming Cross Recurrence Plot (CRP) data into a time-series format, we enable the use of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks for predicting stock price synchronization through both regression and classification. We apply this methodology to a dataset of 20 highly capitalized stocks from the Indian market over a 21-year period. The findings reveal that our approach can predict stock price synchronization, with an accuracy of 0.98 and F1 score of 0.83 offering valuable insights for developing effective trading strategies and risk management tools.