Paper: arXiv 2409.06728

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.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced mathematics like cross-recurrence quantification analysis (CRQA) and recurrence plots (RPs), involving matrix theory and nonlinear dynamics. It demonstrates high empirical rigor by applying the methodology to a large, specific dataset (20 stocks over 21 years) with clear train-test splits, reporting concrete performance metrics (accuracy 0.98, F1 0.83) suitable for backtesting.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Predict Stock Synchronization"] --> B["Data Input<br>20 Stocks x 21 Years"]
  B --> C["Methodology<br>CRQA & Recurrence Plots"]
  C --> D["Data Transformation<br>CRP to Time-Series"]
  D --> E["Computational Process<br>RNN/LSTM Models"]
  E --> F{"Classification"}
  F --> G["Outcomes<br>Accuracy: 0.98, F1: 0.83<br>Trading Strategies & Risk Management"]

Cited References (30)

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  2. Predicting the state of synchronization of financial time series using cross recurrence plots (2022) — link
  3. Neural Networks for Financial Time Series Forecasting (2022) — link
  4. Predicting LQ45 financial sector indices using RNN-LSTM (2021) — link
  5. Study on Accuracy Metrics for Evaluating the Predictions of Damage Locations in Deep Piles Using Artificial Neural Networks with Acoustic Emission Data (2021) — link
  6. Recurrence Plot and Machine Learning for Signal Quality Assessment of Photoplethysmogram in Mobile Environment (2021) — link
  7. Deep Learning-Based Weather Prediction: A Survey (2021) — link
  8. Financial Distress Prediction Using Hybrid Machine Learning Techniques (2020) — link
and 22 more
  1. Time-driven feature-aware jointly deep reinforcement learning for financial signal representation and algorithmic trading (2020) — link
  2. Deep Learning with Long Short-Term Memory Networks for Air Temperature Predictions (2019) — link
  3. Mutual information based stock networks and portfolio selection for intraday traders using high frequency data: An Indian market case study (2019) — link
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  5. Classification of Recurrence Plots’ Distance Matrices with a Convolutional Neural Network for Activity Recognition (2018) — link
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  20. Predicting Weather Patterns Using LSTMs and Multiple Spatiotemporal Datasets (2020)
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    1. A Review of Evaluation Metrics in Machine Learning Algorithms

0 of 30 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.