BERT vs GPT for financial engineering

ArXiv ID: 2405.12990 “View on arXiv”

Authors: Unknown

Abstract

The paper benchmarks several Transformer models [“4”], to show how these models can judge sentiment from a news event. This signal can then be used for downstream modelling and signal identification for commodity trading. We find that fine-tuned BERT models outperform fine-tuned or vanilla GPT models on this task. Transformer models have revolutionized the field of natural language processing (NLP) in recent years, achieving state-of-the-art results on various tasks such as machine translation, text summarization, question answering, and natural language generation. Among the most prominent transformer models are Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT), which differ in their architectures and objectives. A CopBERT model training data and process overview is provided. The CopBERT model outperforms similar domain specific BERT trained models such as FinBERT. The below confusion matrices show the performance on CopBERT & CopGPT respectively. We see a ~10 percent increase in f1_score when compare CopBERT vs GPT4 and 16 percent increase vs CopGPT. Whilst GPT4 is dominant It highlights the importance of considering alternatives to GPT models for financial engineering tasks, given risks of hallucinations, and challenges with interpretability. We unsurprisingly see the larger LLMs outperform the BERT models, with predictive power. In summary BERT is partially the new XGboost, what it lacks in predictive power it provides with higher levels of interpretability. Concluding that BERT models might not be the next XGboost [“2”], but represent an interesting alternative for financial engineering tasks, that require a blend of interpretability and accuracy.

Keywords: Transformers, BERT, GPT, Sentiment Analysis, Natural Language Processing (NLP), Commodities

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Philosophers
• Why: The paper primarily discusses Transformer architectures and benchmark comparisons with minimal heavy mathematical derivation, focusing more on conceptual explanations and existing model comparisons. Empirical rigor is moderate, presenting F1-scores and confusion matrices for commodity sentiment tasks, but lacks detailed backtesting results or implementation specifics for live trading.

  flowchart TD
    A["Research Goal: Benchmark BERT vs GPT for Sentiment Analysis in Commodity Trading"] --> B["Data: Financial News Events"]
    B --> C["Methodology: Fine-tune Transformer Models"]
    C --> D["Process: CopBERT Model Training"]
    C --> E["Process: CopGPT / GPT-4 Baseline"]
    D --> F["Key Findings: CopBERT Outperforms Domain Models"]
    E --> G["Key Findings: GPT-4 Dominates but Lacks Interpretability"]
    F --> H["Conclusion: BERT Offers Balance of Accuracy & Interpretability"]
    G --> H