Financial Text Nlp

Barrios Skills — John Barrios's curated workflow for economists and accountants. Prioritize reproducible empirical work, clear identification language, and journal-ready output.

Financial & economic text NLP

When to use

• Tone of MD&A, risk factors, 8-K text, or ESG/boilerplate sections
• Earnings-call transcripts (prepare/Q&A separately when possible)
• Central-bank or supervisory communications (hawkish/dovish stance)
• Building panel covariates (firm–year sentiment) for regressions in pyfixest or Stata

Not for: casual product reviews or generic Twitter sentiment without a finance lexicon/model.

Tool map

Iron law: define the unit and the label

1. Unit of analysis: sentence, paragraph, section, or full document (document scores need aggregation rules)
2. Label meaning: polarity ≠ hawkishness ≠ forward-looking statements
3. Train/test hygiene: do not tune thresholds on the same sample used for causal estimates
4. Inspect: print 10 scored excerpts before merging to Compustat

Minimal FinBERT pattern

pythonCopy code
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "ProsusAI/finbert"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

def score_texts(texts: list[str], max_length: int = 256):
    inputs = tokenizer(
        texts, padding=True, truncation=True, max_length=max_length, return_tensors="pt"
    )
    with torch.no_grad():
        probs = torch.nn.functional.softmax(model(**inputs).logits, dim=-1)
    # labels: positive, negative, neutral (confirm order via model.config.id2label)
    return probs

Aggregate to firm–year with an explicit rule (mean of sentence scores; or net positivity = pos − neg). Document the rule in the paper.

Accounting / finance research notes

• Boilerplate: length and legalese dilute tone — consider section fixed effects or residuals
• Attribution: link scores to accession IDs / transcript IDs for replication
• Identification: sentiment as outcome vs covariate vs instrument — state which; NLP error is measurement error
• Robustness: report FinBERT + lexicon (FinVADER) or alternative chunking

Checklist

• Text source and cleaning steps logged
• Model id + revision pinned
• Aggregation rule written down
• Hand-checked excerpts saved
• Merge keys to firm identifiers validated (CIK / gvkey / ticker map)