When working with financial texts, embedding models that understand domain-specific terminology, numerical data, and contextual nuances tend to perform best. General-purpose language models like BERT or RoBERTa can be effective but often require fine-tuning on financial datasets to capture the unique vocabulary and concepts in this domain. For example, models pre-trained on financial reports, earnings calls, or regulatory filings typically outperform generic embeddings because they’ve learned patterns specific to financial language, such as terms like "EBITDA," "derivatives," or "liquidity ratios." Additionally, models designed to handle numerical data—common in financial documents—are advantageous, as they can better interpret metrics, percentages, and monetary values.
One practical approach is to use domain-specific variants of popular transformer models. For instance, FinBERT, a version of BERT fine-tuned on financial news and SEC filings, has shown strong performance in tasks like sentiment analysis for stock market predictions. Similarly, Sentence-BERT (SBERT) can be adapted for financial texts by training on pairs of financial statements or earnings call transcripts to create sentence embeddings optimized for similarity comparisons. Another example is BloombergGPT, a model trained on a vast corpus of financial data, which excels at tasks like entity recognition and document classification in banking or investment contexts. These models are available via libraries like Hugging Face Transformers, making them accessible for developers to integrate into pipelines.
For developers needing lightweight solutions, models like FastText or GloVe can still be useful if combined with domain-specific preprocessing. For example, augmenting these embeddings with custom tokenization rules for financial abbreviations (e.g., "FY23" for fiscal year 2023) or training them on a corpus of financial news (e.g., Reuters or Bloomberg articles) can improve relevance. Hybrid approaches, such as using a general-purpose model for initial embeddings and layering a domain-specific fine-tuning step, also work well. Tools like spaCy’s Tok2Vec component allow for training custom embeddings on financial texts, which can capture nuances like the difference between "leverage" in physics versus finance. Ultimately, the best choice depends on balancing computational resources, task complexity, and the need for precision in financial contexts.