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Artul.ai Research LibraryStudy No. 41Hypotheses TestedUpdated 2026-08-28

Show Us the Runway: A Profile of Calls That Explained the Engine

By Artul.ai Research Group · n = 103 earnings calls · First published 2026-08-28
Abstract

This study examines 103 earnings calls (out of a 391-call corpus, 26.3%) that answered YES to the research hypothesis 'Engine explained, runway named' — calls where management explained the growth engine and named the runway ahead. Compared with the rest of the corpus, these calls score higher on confidence (7.76 vs 7.27), specificity (7.85 vs 7.63), and promotion (5.30 vs 5.07), while showing lower stress (1.96 vs 2.42) and evasion (2.52 vs 2.71). They raise guidance more often (32.0% vs 21.0%) and lower it less (7.8% vs 15.1%). A signature phrase, 'Skeptic Reassured,' appears on 91.3% of these calls versus 68.3% of others. Post-call returns for a 41-call subsample had a median of -6.8% versus -11.0% for the base.

Key findings
  • Calls answering YES to 'Engine explained, runway named' score higher on confidence (7.76 vs 7.27) and lower on stress (1.96 vs 2.42) than the rest of the corpus.
  • These calls raised guidance on 32.0% of occasions versus 21.0% for the base, and lowered it on 7.8% versus 15.1%.
  • The phrase 'Skeptic Reassured' appears on 91.3% of YES calls versus 68.3% of others, a 1.34x lift.
  • Among 41 YES calls with returns, the median post-call return was -6.8% versus -11.0% for the 152-call base, with nearly identical beat rates (36.6% vs 36.2%).

1Introduction

Earnings calls are famously a game of selective disclosure: executives narrate momentum while quietly dodging the durability question. A subset of management teams does the opposite — they explain, in concrete terms, what drives growth (the engine) and how long it can run (the runway). That combination of candor and specificity is rare and worth isolating. If it clusters with measurable traits like confidence, low evasion, and raised guidance, it gives call-watchers a concrete pattern to study. This study profiles the 103 calls in a 391-call corpus that answered YES to 'Engine explained, runway named,' describing their language profile, guidance behavior, phrase lifts, prevalence over time, and post-call returns.

2Data & methodology

The corpus comprises 391 earnings-call transcripts published between 2015 and 2024, 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 that answered YES to the research hypothesis "Engine explained, runway named" (n = 103; 26.3% of the reference set, 95% Wilson interval 22.2%–30.9%). Baseline figures use the set of calls on which this question was tested. 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 behavioral profile is coherent: YES calls run higher on confidence (7.76 vs 7.27), specificity (7.85 vs 7.63), and promotion (5.30 vs 5.07), and lower on stress (1.96 vs 2.42) and evasion (2.52 vs 2.71). Guidance skews favorable: 32.0% raised versus 21.0% in the base, and only 7.8% lowered versus 15.1%. Phrase lifts reinforce the picture — 'Volume About to Step Up' appears 1.47x more often, and 'Skeptic Reassured' shows up on 91.3% of YES calls versus 68.3% otherwise. Under-indexed phrases include 'Results Worse Than Direction' (0.45x) and 'When the CFO Dominates' (0.66x). Prevalence peaked at 0.12 in 2023, fell to 0.0 in 2020, and stood at 0.07 in 2024. Returns tell a modest story: median -6.8% vs -11.0% base, but beat rates are nearly identical (36.6% vs 36.2%).

Table 1. Mean behavioral scores (0–9 scale), study group versus baseline
MeterStudy groupBaselineΔ
Candor6.926.91+0.01
Evasion2.522.71-0.19
Specificity7.857.63+0.23
Stress1.962.42-0.46
Promotion5.305.07+0.23
Confidence7.767.27+0.49
Table 2. Guidance actions, study group versus baseline
ActionStudy groupBaseline
Raised32.0%21.0%
Maintained55.3%51.2%
Lowered7.8%15.1%
Withdrawn0.0%1.3%
Table 3. Co-occurring battery signals ranked by lift (group prevalence ÷ baseline prevalence)
SignalLiftIn groupBaseline
Volume About to Step Up1.47×37.9%25.8%
Early Products Growing Fast1.38×49.5%35.8%
A Tiny Fraction of the Market1.34×40.8%30.4%
Skeptic Reassured1.34×91.3%68.3%
Results Worse Than Direction0.45×21.4%47.8%
The Question Left Hanging0.59×26.2%44.2%
When the CFO Dominates0.66×9.7%14.6%
20150.11%
20160.06%
20170.05%
20180.09%
20190.01%
20200.00%
20210.05%
20220.11%
20230.12%
20240.07%
20250.00%
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-6.8%-11.0%
Interquartile range-28.2% to +6.4%
Share beating SPY36.6% (95% CI 24%–52%)36.2%
Observations41152
Table 5. Most recent calls matching the study definition
TickerQuarterCall dateCall grade
CRGOQ1 20242024-05-20C+
WRBYQ1 20242024-05-09A
ECPGQ1 20242024-05-08B
AZEKQ2 20242024-05-08B+
LINCQ1 20242024-05-06B+
AESQ1 20242024-05-03C+
TTIQ1 20242024-05-01A
LTRXQ3 20242024-04-29C

4Discussion

A careful reader should conclude that calls pairing a clear growth engine with a named runway also carry a distinctive tone: more confident, more specific, less evasive, and accompanied by stronger guidance actions. That is a descriptive association within this corpus, not a signal. The returns gap — median -6.8% versus -11.0% — looks favorable, but the beat rates are essentially tied (36.6% vs 36.2%), and the returns sample is small (41 calls). Prevalence also swings widely year to year, from 0.0 in 2020 to 0.12 in 2023, so the pattern is not a stable feature of the market. Nothing here implies that naming a runway causes anything, good or bad.

5Limitations

The call annotations are AI-read, so tone scores and phrase tags inherit model noise and cannot be treated as ground truth. The returns subsample covers 22,449 calls and is skewed toward liquid, well-covered names, limiting generalizability. Our own forward tests falsified directional prediction, so no reading of the returns comparison should be taken as an edge. Finally, LLMs partially remember famous stocks' histories, which can contaminate any backtest-style analysis of earnings-call language. See the full methodology, including the C1 pattern’s forward-test failure and the LLM-memorization finding.

Companion page: every company matching this hypothesis is listed at the question’s own page.

Cite this study Artul.ai Research Group (2026). “Show Us the Runway: A Profile of Calls That Explained the Engine.” Artul.ai Earnings-Call Research Library, Study No. 41. https://artul.ai/research/hypothesis-engine-explained-runway-named

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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.