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This study reveals that financial news sentiment significantly impacts international stock markets. Dynamic Transfer Entropy (DTE) accurately captures this information flow, improving stock forecasting accuracy.

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Area of Science:

  • Quantitative Finance
  • Computational Linguistics
  • Time Series Analysis

Background:

  • Understanding the interdependence between financial news sentiment and international stock market dynamics is crucial for accurate forecasting.
  • Existing methods often fail to capture the complex causality and information flow between news sentiment and market behavior.

Purpose of the Study:

  • To investigate the interdependence between international stock markets and financial news sentiment for improved stock forecasting.
  • To introduce Dynamic Transfer Entropy (DTE) as a novel method for analyzing information flow propagation between sentiment and market dynamics.
  • To demonstrate the superiority of a DTE-informed Temporal Fusion Transformer (TFT) model for stock price and return forecasting.

Main Methods:

  • Utilized FinBERT for textual analysis of financial news to generate sentiment time series.
  • Employed Transfer Entropy and heat maps to analyze net information flows.
  • Calculated Dynamic Transfer Entropy (DTE) time series as covariates for stock price forecasting.
  • Implemented Temporal Fusion Transformers (TFT) for stock market forecasting using DTE-derived information.

Main Results:

  • The proposed DTE-based causality method, integrated with TFT, demonstrated superior accuracy in stock price and return forecasting.
  • Dynamic Transfer Entropy effectively identified critical information propagation paths, including market spikes and sudden jumps.
  • The model successfully incorporated intra- and inter-market correlations and information flow interactions.

Conclusions:

  • Financial news sentiment exhibits a significant causal relationship with international stock market movements.
  • Dynamic Transfer Entropy is a powerful tool for uncovering complex information flow dynamics in financial time series.
  • The combined DTE and TFT approach offers a robust and accurate method for stock market forecasting.