Model Card for Finetuned FinBERT on Market-Based Facts
This LLM is fine-tuned on market reactions to events. By utilizing market-based data, it avoids human biases present in traditional annotation methods.
Our FinBERT model, finetuned on impactful news headlines about global equity markets, has shown significant performance improvements over standard models. Its training on real-world market impact rather than subjective financial expert opinions sets a new standard for unbiased financial sentiment analysis. 📈 The dataset is uploaded on HuggingFace here.
Outperforms FinBERT
- 🎯 +25% precision
- 🚀 +18% recall
Outperforms DistilRoBERTa finetuned for finance
- 🎯 +22% precision
- 🚀 +15% recall
Outperforms GPT-4 zero-shot learning
- 🎯 +15% precision
- 🚀 +8.2% recall
Validation Metrics
| Metric | Value |
|---|---|
| loss | 0.9176467061042786 |
| f1_macro | 0.49749240436690023 |
| f1_micro | 0.5627105467737756 |
| f1_weighted | 0.5279720746084178 |
| precision_macro | 0.5386355574899088 |
| precision_micro | 0.5627105467737756 |
| precision_weighted | 0.5462149036191247 |
| recall_macro | 0.517542664344306 |
| recall_micro | 0.5627105467737756 |
| recall_weighted | 0.5627105467737756 |
| accuracy | 0.5627105467737756 |
This model has been developed after publishing in the Risk Forum 2024 conference a paper that can be found here (https://arxiv.org/abs/2401.05447).
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