Transcript Finbert Qa Dispersion
In plain terms
Listens to how steady management's tone is during analyst Q&A. If answers swing between confident and defensive, it usually signals trouble ahead.
How it works
Huang-Wang-Yang (2023 CAR) "FinBERT: A Large Language Model for Extracting Information from Financial Text." FinBERT-tone outperforms Loughran-McDonald wordcount on every post-call drift measure tested (3-day CAR R² roughly doubles). Follow-up Chen et al. (2024 JFE) reports ~5% annualised L/S on the Q&A-dispersion sub-signal. Own-history z of per-call Q&A sentiment dispersion (stdev of per-sentence pos-neg score): short when z ≥ +1 (uncertainty), long when z ≤ −1 (confident answers).
Live results
3 times picked on its own · 7 times inside a blend (4 beat the stock) · updated 2026-06-06Data dependencies
- Daily prices
Adjusted-close OHLCV for every US-listed ticker; primary price feed.
- Transcript finbert scores
A data feed this strategy reads, refreshed on its normal schedule.
Expected edge
- Reported return
- ~5% ann. L/S Q&A-dispersion (Huang-Wang-Yang 2023)
- Tested over
- 2003-2022
(2024 *JFE*) shows a ~5% annualised long-short on the Q&A-dispersion sub-signal.
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