Causality-driven multivariate stock movement forecasting
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View abstract on PubMed
Summary
This summary is machine-generated.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.
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.
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