A hybrid self attentive linearized phrase structuredtransformer based RNN for financial sentenceanalysis with sentence level explainability
This paper presents xFiTRNN, a hybrid transformer-based self-attentive RNN for financial sentence analysis and explainable predictions.
What it examines
The study addresses financial text sentiment analysis using a hybrid model that integrates transformer-based FinBERT embeddings, BiGRU, self-attention, and explainability methods. It aims to capture nuanced financial sentiment at sentence-level for improved transparency and better automated predictions, supporting real-time market monitoring.
What it concludes
The research shows that the xFiTRNN model outperforms existing methods, offering clear, explainable financial sentiment predictions. Its applications include real-time market analysis, risk management, and automated trading. Future work should address multilingual data and regional adaptations to further improve model generalizability and performance.
Evidence objects
Researchers unveil xFiTRNN, a novel hybrid self-attentive transformer-based RNN integrating FinBERT, BiGRU, and dynamic attention mechanisms, achieving outstanding accuracy of $$95.86%$$ and $$96.83%$$ AUC on benchmark financial datasets with precision.
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Enhancements include advanced embedding techniques, dynamic attention pruning, and explainability tools like LIME and Anchors, linking financial regulation and deep learning by providing transparent insights into model predictions, ensuring interpretability.
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Experiments on diverse datasets and adversarial tests reveal improved cross-market resilience and resistance to textual perturbations; however, challenges persist in scalability, overfitting, and multilingual validation for broader financial AI adoption.
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The paper introduces the innovative, robust model $xFiTRNN$, seamlessly integrating self-attentive mechanisms, linearized phrase structures, and transformer-based RNNs with explainability techniques such as LIME and Anchors. Its originality lies in marrying adaptations with existing models. The approach addresses financial sentence analysis, ensuring transparency and sentiment extraction for effective decision making.
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Raw abstract and provenance
- … highlights the potential of hybrid transformer-based RNN architectures for fostering moreaccountable and understandable Artificial Intelligence (AI) applications in finance. …
Source row: 39 · abstract type: snippet