factor · NLP
NLP scores the tone of management's narrative in the 10-K MD&A section using a finance-specific dictionary. Negative-leaning language predicts negative forward returns; obscure or hedge-laden language predicts uncertainty. The factor reads what management is signalling, not what the spreadsheet says.
When a CEO writes 'we faced significant headwinds in our consumer segment, characterised by inventory recalibration and competitive repositioning' instead of 'sales fell because customers switched to a cheaper competitor', the obfuscation is the signal. Loughran-McDonald counts how many such hedge words, negative-finance words, and uncertain qualifiers appear relative to the section length. Li adds the readability dimension — sentences over 25 words, paragraphs over 100 words, Latin-derived rare vocabulary. Both compress to one truth: when management writes opaquely, the next four quarters tend to disappoint.
inputs · 10-K Management Discussion & Analysis section text
· Loughran-McDonald financial-domain sentiment dictionary
· Gunning Fog readability index
ideas · word-fraction tallies for negative, uncertain, litigious tone
· readability score for sentence-density complexity
· weighted blend, sign-flipped so positive tone reads bullish
output · cross-sectional standardised scoreFour anchors so a single category can't dominate. The four blend weights, the readability normalisation strategy, and any modern-phrase extensions are calibrated and proprietary. Public: the anchors (negative, uncertain, litigious tone fractions, plus readability), and the academic citations (Loughran-McDonald 2011, Li 2008).
The NLP cron runs Sunday 11:30 UTC. It pulls the latest 10-K from SEC EDGAR XBRL, extracts the Item 7 (MD&A) section via a regex over the embedded HTML, tokenises with the LM dictionaries (negative, positive, uncertain, litigious, modal, constraining), and scores. Coverage is currently about two-thirds of the universe (~65%) — foreign filers file no 10-K at all, and the regex fails on a long tail of non-standard formats, mostly older filings or REITs with irregular item-numbering. News sentiment fills much of the remaining gap. We're tuning the regex pass each month.
NLP is the strongest companion factor for PEAD — together they decompose earnings news into the number (PEAD) and the narrative around the number (NLP; Tetlock 2007's mechanism — when management hedges around a beat, the beat is suspect). In the live confluence library NLP works as an amplifier rather than a standalone trigger: pessimistic filing tone strengthens the VALUE TRAP and QUALITY CRACK patterns when the accounting factors fire (Li 2008). Finally, NLP and Quality interact — high quality with deteriorating tone is an early warning the moat is cracking.
Three known failure modes. (1) Boilerplate inflation. Compliance counsel adds risk-factor language each year; the same company's 10-K has more LM-negative words in 2025 than in 2015 even if the business is unchanged. We partly mitigate via z-scoring across the universe, but absolute trend in negative tone has slowly drifted up. (2) Foreign filers. 20-F filings (used by ADRs) follow a different structure than 10-K, and our extractor's coverage is weaker there — pending fix. (3) Partial intra-year coverage. Earnings press releases (8-K Item 2.02) are scored quarterly and blended in, but that pass is scheduled over roughly the first 400 tickers of the universe, so about six stocks in ten carry annual-filing tone alone. Conference-call audio and 10-Q MD&A are not incorporated anywhere — nor is the Q&A half of an earnings call, which is where the unscripted tone lives.
Every ticker page shows the per-factor decomposition. The NLP score is one of thirteen composing the 0–100 the composite score.