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Artul.ai Research LibraryStudy No. 79Call SignalsUpdated 2026-08-28

Big Claims, Thin Results: Scale-Dependent Advantage Talk on Earnings Calls

By Artul.ai Research Group · n = 18,297 earnings calls · First published 2026-08-28
Abstract

This study examines 18,297 earnings calls (11.1% of a 165,182-call corpus spanning 1990 to 2026) where a model flagged claims that a company's advantages depend on operating at scale. These calls differ sharply in tone: promotion runs 6.07 versus a 5.05 baseline, while specificity sits at 6.83 against 7.56 and candor at 6.21 against 6.86. Guidance behavior skews negative: 8.8% raised guidance versus 21.1% in the base group, and 13.0% lowered it versus 11.6%. Post-call price reactions were modestly worse, with a median return of -0.32% versus -0.07% baseline and only 26.0% beating expectations against 39.5% baseline. The theme's prevalence peaked at 13.5% of calls in 2022 before falling to 9.8% in 2025.

Key findings
  • Calls with scale-dependent advantage claims account for 11.1% of the corpus (18,297 of 165,182 calls).
  • Promotion tone is elevated on these calls (6.07 versus 5.05 baseline) while specificity (6.83 versus 7.56) and candor (6.21 versus 6.86) are depressed.
  • Only 26.0% of these calls beat expectations versus 39.5% of the 22,449-call base sample, with a median post-call return of -0.32% versus -0.07%.
  • The share of such calls rose from 9.1% in 2020 to 13.5% in 2022, then declined to 9.8% in 2025.

1Introduction

Executives often argue that scale itself is the moat: more volume lowers unit costs, entrenches the leader, and starves rivals. That framing is seductive because it converts a promise into a physics lesson. But talk of scale-dependent advantage is also a convenient place to hide vagueness, and a careful reader of earnings calls should know what tends to accompany it. Using Artul.ai's library of 165,182 transcripts from 1990 through 2026, this study profiles the 18,297 calls where the model answered YES to the battery item 'Scale-Dependent Advantage Claims,' comparing their tone, guidance behavior, thematic profile, and post-call price reactions against the rest of the corpus.

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 where the model answered YES to the battery item "Scale-Dependent Advantage Claims" (n = 18,297; 11.1% of the reference set, 95% Wilson interval 10.9%–11.2%). 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 tonal profile is the most striking pattern: promotion is up 1.02 points (6.07 versus 5.05) while specificity is down 0.73 (6.83 versus 7.56) and candor down 0.65 (6.21 versus 6.86), and stress runs 0.93 points higher. Guidance activity skews cautious: 8.8% raised versus 21.1% baseline, while 13.0% lowered versus 11.6%. Thematically, these calls over-index on 'A Tiny Fraction of the Market' (2.48x), 'The Question Left Hanging' (2.02x), and 'Underused Fixed Costs' (1.97x), and strongly under-index on 'Skeptic Reassured' (0.09x) and 'Guidance Worth Underwriting' (0.2x). Post-call returns are weaker: a median of -0.32% versus -0.07%, with 26.0% beating versus 39.5%.

Table 1. Mean behavioral scores (0–9 scale), study group versus baseline
MeterStudy groupBaselineΔ
Candor6.216.86-0.65
Evasion3.262.70+0.57
Specificity6.837.56-0.73
Stress3.352.43+0.93
Promotion6.075.05+1.02
Confidence6.897.21-0.32
Table 2. Guidance actions, study group versus baseline
ActionStudy groupBaseline
Raised8.8%21.1%
Maintained35.5%48.8%
Lowered13.0%11.6%
Withdrawn3.2%2.7%
Table 3. Co-occurring battery signals ranked by lift (group prevalence ÷ baseline prevalence)
SignalLiftIn groupBaseline
A Tiny Fraction of the Market2.48×74.5%30.0%
The Question Left Hanging2.02×96.7%48.0%
Underused Fixed Costs1.97×82.0%41.6%
Founder-Led Companies1.76×35.2%20.0%
Results Worse Than Direction1.70×87.2%51.1%
Skeptic Reassured0.09×6.0%66.4%
Guidance Worth Underwriting0.20×14.2%71.5%
Calls That Resolve Doubts0.33×26.2%79.5%
Confidence Proportionate to Evidence0.38×33.5%87.6%
Pricing Recovering0.59×12.7%21.5%
201511.05%
201610.42%
201710.49%
201810.17%
201910.04%
20209.14%
202111.74%
202213.54%
202312.57%
202411.53%
20259.76%
Figure 1. Share of all analyzed calls matching the study definition, by year.
Table 4. Twelve-month excess total returns versus SPY (descriptive history, not a signal)
StatisticStudy groupReturns sample
Median excess return-31.9%-7.2%
Interquartile range-58.5% to +1.3%
Share beating SPY26.0% (95% CI 23%–30%)39.5%
Observations56522,449
Table 5. Most recent calls matching the study definition
TickerQuarterCall dateCall grade
VRTSQ2 20252025-07-25C+
ASPSQ2 20252025-07-25D
INTCQ2 20252025-07-24D
DAIOQ2 20252025-07-24D
FPHQ2 20252025-07-24D
RPTQ2 20252025-07-24D
MBLYQ2 20252025-07-24B
IRDMQ2 20252025-07-24F

4Discussion

A careful reader should conclude that calls emphasizing scale-dependent advantage are, in this dataset, associated with more promotional, less specific language, more guarded guidance, and weaker subsequent price reactions. These are co-occurrences, not causes: the scale talk does not make outcomes worse, and worse outcomes do not necessarily produce scale talk. The pattern is consistent with companies leaning on structural narratives during harder periods, but the data here cannot confirm intent. No conclusion in this study should be read as a signal for predicting any individual call or trade.

5Limitations

The battery items are AI-read and inherently noisy; the same transcript could plausibly be labeled differently on another pass. The returns sample covers 22,449 calls skewed toward liquid names, so the 565-call flagged subset may not generalize. Our own forward tests falsified directional prediction, and LLMs partially remember famous stocks' histories, contaminating any backtest. The trend figures mix regimes and sample sizes, including a partial 2025. Treat every number here as descriptive of this corpus only. 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). “Big Claims, Thin Results: Scale-Dependent Advantage Talk on Earnings Calls.” Artul.ai Earnings-Call Research Library, Study No. 79. https://artul.ai/research/scale-dependent-advantage-claims-earnings-calls

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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.