Solving the Domain-Specificity Problem in Financial Natural Language Processing (NLP)
Standard financial sentiment tools, like the Loughran-McDonald dictionary, are trained on SEC filings. They fail on market research reports because the language is fundamentally different, leading to inaccurate, neutral-biased results.
This language mismatch results in a dramatic drop in model accuracy when applying a tool designed for one domain to another. The dictionary simply doesn't recognize the sentiment-bearing terms in the new context.
Our architecture is built on four core principles, moving from abstract theory to validated, production-grade engineering.
Our proprietary preprocessing engine is designed for complex financial text. It uses custom algorithms to identify multi-word phrases like "runway shortfall," handle negation within a 60-word window, and apply exception rules to prevent false positives that generic tools consistently miss.
We don't use a one-size-fits-all model. Our pipeline intelligently routes documents to the optimal transformer for the specific domain—one for financial documents (VC, PE, SEC filings) and another for scientific and market research—ensuring maximum contextual accuracy and relevance.
Osprey's system combines the strengths of lexicon-based analysis (Loughran-McDonald dictionaries) with our suite of domain-adapted transformer models. A configurable weighting system produces a final, nuanced score that captures both explicit sentiment and complex, context-dependent meaning.
Performance is not a theoretical claim; it's a tested reality. We validate our pipeline against a large, diverse internal corpus, consistently achieving 90-95% sentiment accuracy, 100% processing success, and a 98.3/100 quality score. Validation is a core pillar of our process.
Domain adaptation turns generic NLP into decision-grade signal. In our evaluations on labeled market-research passages, a domain-adapted model materially improved Accuracy, Macro-F1, and Minority-Class Recall over both a dictionary baseline and a generic transformer. These are the metrics that matter when false positives/negatives change investment decisions.
In practice: Our system correctly identifies "indication expansion" in a biotech report as a positive signal, whereas generic, SEC-trained models flag it as neutral. This is the tangible value of our domain-adapted approach—it reduces noise and increases the actionable signal rate.
Methodology: 5-fold CV on N labeled passages across X verticals; class balance reported; metrics: Accuracy, Macro-F1, Minority-Class Recall. Baselines: Loughran–McDonald dictionary & FinBERT (SEC-trained). Domain model: BERT fine-tuned on in-domain corpus.
Justification: In finance, text that matches the true product-market language carries more predictive signal; text-based industry methods consistently outperform generic industry tags in economic tests.
How we built and validated domain-adapted financial NLP at Osprey Intel
Osprey's hybrid sentiment pipeline combines Loughran-McDonald financial dictionaries with domain-adapted transformers (FinBERT for VC/PE documents, SciBERT for market research). This architecture delivers 90-95% accuracy on financial sentiment analysis—a material improvement over dictionary-only or generic transformer approaches.
Our context-aware preprocessing handles multi-word financial phrases ("margin pressure," "flat financing," "runway shortfall") as semantic units and detects negation within a 60-word window, preventing false positives that generic models miss.
Test Corpus: 53 financial documents across multiple categories (PitchBook VC benchmarks, McKinsey technology reports, investment factsheets, market research) totaling 86 MB.
Processing Success: 100% document processing success rate with 98.3/100 average quality score. Our hybrid architecture (IBM Docling primary + PyMuPDF fallback) achieved zero failures across the entire test set.
Model Configuration:
ProsusAI/finbertallenai/scibert_scivocab_uncasedDomain adaptation is not theory—it's validated economics. Text-based industry classifications (TNIC) consistently outperform generic SIC codes in explaining cross-sectional returns and generating trading signals. When language matches the true product-market context, the signal is stronger and more persistent.
Real Example: In a medical device market research report, generic SEC-trained sentiment flagged "indication expansion" as neutral. Our domain-adapted SciBERT model correctly identified it as positive demand signal because it learned that phrase's meaning in the biotech context. This is what eliminates neutral bias and increases actionable signal rate.
GPU Acceleration: 3-4× speedup on NVIDIA L40S enables processing 50-60 documents/hour, meeting our <72 hour SLA for IC Pack generation.
Research Foundation:
Our approach builds on established findings that domain-matched text representations outperform generic proxies: