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Artul.ai Research LibraryStudy No. 6Management BehaviorUpdated 2026-08-28

The Sound of One Hand Answering: Earnings Calls That Stay Vague

By Artul.ai Research Group · n = 431 earnings calls · First published 2026-08-28
Abstract

This study examines 431 earnings calls, out of a corpus of 165,182 calls spanning 1990 to 2026, that score 2 or lower on Artul.ai's 0-9 specificity meter — roughly 0.26% of all calls. These calls are strikingly quieter across the board: specificity runs 1.31 versus a 7.56 baseline (a gap of -6.25), candor 3.42 versus 6.86 (-3.44), and confidence 4.35 versus 7.21 (-2.86). Guidance behavior differs too: 21.05% of baseline calls raise guidance, while none of these low-specificity calls do, and only 0.93% maintain guidance versus 48.79% at baseline. The phenomenon is also recent: it appears in under 0.05% of calls through 2023 but reaches 4.87% of calls in 2025.

Key findings
  • Low-specificity calls show a specificity score of 1.31 against a baseline of 7.56, a gap of -6.25, the largest behavioral delta in the study.
  • Candor (3.42 vs 6.86, -3.44), promotion (2.34 vs 5.05, -2.71), and confidence (4.35 vs 7.21, -2.86) are all sharply lower on these calls.
  • None of the 431 low-specificity calls raised guidance, compared with 21.05% of baseline calls, and only 0.93% maintained guidance versus 48.79% at baseline.
  • The share of low-specificity calls jumped from 0.01% in 2023 to 0.69% in 2024 and 4.87% in 2025.

1Introduction

Anyone who follows earnings calls knows the feeling: a long Q&A, polished executives, and somehow no concrete answer to anything. Most calls leave traces — numbers, commitments, direct answers. A small slice does not. Identifying what those calls look like, and how common they are over time, matters because specificity is one of the few things a listener can judge without a model: did management actually say anything testable? This study examines 431 calls scoring 2 or lower on Artul.ai's 0-9 specificity meter, drawn from a corpus of 165,182 calls covering 1990 through 2026, comparing their language profile, guidance behavior, and prevalence over time.

2Data & methodology

The corpus comprises 165,182 earnings-call transcripts published between 1990 and 2026, each scored independently by a large language model on an identical 37-field battery: seven categorical business verdicts, eight 0–9 behavioral meters, and twenty yes/no judgments. The study group is defined as calls scoring 2 or lower on the 0–9 specificity meter (n = 431; 0.3% of the reference set, 95% Wilson interval 0.2%–0.3%). Baseline figures use all scored calls. Market outcomes join a fixed sample of 22,449 calls with twelve-month total returns in excess of SPY, measured from the first close after each call; this sample skews toward liquid U.S. names and is reported as descriptive history only.

3Results

The language deltas are the headline. Against a baseline of 7.56, these calls score 1.31 on specificity — a gap of -6.25 — and they lag on candor (3.42 vs 6.86), promotion (2.34 vs 5.05), stress (1.32 vs 2.43), and confidence (4.35 vs 7.21). Guidance behavior is similarly muted: 0% raise guidance versus 21.05% at baseline, and 0.93% maintain it versus 48.79%. Thematic over-representation is mild at best — 'Calls That Read Rehearsed' sits at 2.26% versus a 0.88% base — while themes like 'Calls That Resolve Doubts', 'Guidance Worth Underwriting', and 'Skeptic Reassured' never appear in this group. The trend is the surprise: near-zero shares through 2023, then 0.69% in 2024 and 4.87% in 2025.

Table 1. Mean behavioral scores (0–9 scale), study group versus baseline
MeterStudy groupBaselineΔ
Candor3.426.86-3.44
Evasion0.302.70-2.40
Specificity1.317.56-6.25
Stress1.322.43-1.11
Promotion2.345.05-2.71
Confidence4.357.21-2.86
Table 2. Guidance actions, study group versus baseline
ActionStudy groupBaseline
Raised0.0%21.1%
Maintained0.9%48.8%
Lowered0.2%11.6%
Withdrawn0.0%2.7%
Table 3. Co-occurring battery signals ranked by lift (group prevalence ÷ baseline prevalence)
SignalLiftIn groupBaseline
Calls That Read Rehearsed2.26×87.7%38.7%
The Question Left Hanging2.09×100.0%48.0%
Scale-Dependent Advantage Claims1.59×17.6%11.1%
Calls That Resolve Doubts0.00×0.0%79.5%
Guidance Worth Underwriting0.00×0.0%71.5%
Skeptic Reassured0.00×0.0%66.4%
Pricing Recovering0.02×0.5%21.5%
Deferred Revenue Growing0.03×0.2%8.9%
20150.03%
20160.03%
20170.03%
20180.01%
20190.00%
20200.01%
20210.00%
20220.01%
20230.01%
20240.69%
20254.87%
Figure 1. Share of all analyzed calls matching the study definition, by year.
Table 4. Most recent calls matching the study definition
TickerQuarterCall dateCall grade
CRDOFQ1 20252025-05-29F
AMWDQ4 20252025-05-29F
REXQ1 20252025-05-28F
TPTAQ1 20252025-05-22F
WBQ1 20252025-05-21F
WRDQ1 20252025-05-21F
MDWDQ1 20252025-05-21D
BIOXQ3 20252025-05-21D

4Discussion

A careful reader should conclude that calls scoring very low on specificity are rare, measurably quieter in candor, confidence, and promotion, and much more common in 2024-2025 than in any prior year. They should not conclude that low specificity causes anything — these are descriptive associations, not causal claims — or that such calls predict returns, guidance misses, or trouble ahead. The 2025 figure partly reflects a smaller recent sample of 6,012 calls. What the data shows is a distinctive, increasingly frequent communication style; what it means for outcomes is a separate, unanswered question.

5Limitations

The specificity score and all behavioral fields are AI-read and noisy; a 0-2 rating may misclassify calls that a human would score differently. Any returns comparison would rest on a sample of 22,449 calls skewed toward liquid names. Our own forward tests falsified directional prediction, so nothing here should be read as an edge. LLMs partially remember famous stocks' histories, which can contaminate any backtest of scoring rules. Finally, the 2025 sample covers only 6,012 calls, so the apparent surge in low-specificity calls carries wide uncertainty. See the full methodology, including the C1 pattern’s forward-test failure and the LLM-memorization finding.

Cite this study Artul.ai Research Group (2026). “The Sound of One Hand Answering: Earnings Calls That Stay Vague.” Artul.ai Earnings-Call Research Library, Study No. 6. https://artul.ai/research/answers-without-numbers

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Not investment advice. Artul.ai publishes AI-generated earnings-call quality grades and expected-volatility estimates — never buy or sell recommendations. We tested over 1,600 predictive hypotheses against 165,000 transcripts; the honest result, including what failed, is documented in our methodology.