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TRUVACE RECORD VERSION
record: TRV-2026-2496
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:42:50.997790Z
status: published
lens: g_space
sector: business
headline: Generative AI, Managerial Expectations, and Economic Activity
dek: Using generative AI, we extract managers’ economic expectations from over 120,000 corporate conference call transcripts. The resulting AI Economy Score predicts GDP growth, production, employment, and wages five or more quarters ahead, beyond survey forecasts and other existing measures. Industry- and firm-level scores capture sector- and firm-specific activity, and a composite of firm, industry, and macroeconomic expectations further improves forecasting power and horizon. Predictability concentrates among hist…
gain_title: In a working paper analysing 125,830 earnings calls, an AI-derived score of manager optimism raised the share of next-quarter GDP growth explained from 43.8% to 48.6% and kept predicting GDP for five quarters.
problem_title: (none)
trace_subject: (none)
gain_reading: In a working paper analysing 125,830 earnings calls, an AI-derived score of manager optimism raised the share of next-quarter GDP growth explained from 43.8% to 48.6% and kept predicting GDP for five quarters.
gain_evidence: the AI Economy Score retains its predictive power for real GDP growth for up to five quarters
problem_reading: (none)
problem_evidence: (none)
quick_read: Economists at Georgia State, Rochester Institute of Technology and Purdue used OpenAI's GPT-5 mini to read 125,830 corporate earnings-call transcripts from 2006 to 2025 and score how optimistic managers were about the US economy on a seven-point scale. The resulting AI Economy Score predicted real GDP growth, industrial production, employment and wages beyond standard predictors.

Adding the score to a model that already included the GZ credit spread raised the share of next-quarter GDP growth explained (R2) from 0.438 to 0.486. For employment, the score alone gave an R2 of 31.1%. It predicted GDP for five quarters, employment for up to five and wages for roughly ten.

The paper says the forecasting power sits mainly with managers whose past forecasts were accurate and unbiased, and with firms that are less diversified. That pattern fits managers holding real information rather than noise. Industry-level and firm-level versions of the score also forecast sector GDP and firm sales, employment and wages, for up to four years in some cases.
limitation: The authors say that telling the model the call date does not eliminate look-ahead bias, since its training data extend past that point, and the score failed to anticipate the early-2020 pandemic collapse.
tag: Evidence-backed gain
key_points: The data covers 125,830 earnings calls from 5,513 companies, 2006 to 2025, scored in two passes: first extracting statements on the US economy, then rating optimism from 1 to 7. | A one-standard-deviation rise in the score is associated with 2.67%, 1.23% and 0.21% higher next-quarter growth in industrial production, employment and wages. | In a head-to-head test against the Survey of Professional Forecasters, the score kept its predictive power and was comparable in strength to the survey forecast, and both stayed significant together. | Rerunning the analysis on anonymised transcripts with firm and date information removed still produced a significant predictor, addressing the risk that the model had seen the outcomes in training. | An out-of-sample test using only calls from 2024Q2 to 2025Q4, after GPT-5 mini's training cutoff, still showed the firm-level score predicting firm sales and the industry score predicting industry GDP. | Replicating the method with Google's open-source Gemma model gave similar results, so the finding does not depend on one AI model. | The average score over 80 quarters was 4.252, just above the neutral midpoint of 4. The score did not anticipate the early-2020 pandemic collapse but did track the recovery.
rundown: The authors built the score in two steps because earnings calls run about 7,500 words and mostly concern the firm. GPT-5 mini first pulled out sentences where management spoke about US economic conditions, then a second request scored the whole call's economic content. Manual review of about one thousand calls found the outputs similar to human assessments, according to the authors.

On timing, the score turned down ahead of the 2008 financial crisis, before the trough in realized GDP. It fell alongside, not ahead of, output in the first quarter of 2020. A vector autoregression found that a positive shock to the score predicted higher consumption, investment and output for 8 or more quarters, followed by tighter monetary policy.

At the industry level, a one-standard-deviation rise in the adjusted industry score corresponded to 1.16 percentage points of annual employment growth and 1.65 points of annual wage growth. At the firm level, a one-standard-deviation rise in the firm component corresponded to 3.41 points of annual employment growth and 2.82 points of annual wage growth.

The authors say the score is built from public transcripts and gives researchers and policymakers a cheaper alternative to costly manager surveys. Earlier versions of the paper used GPT-3.5 Turbo and obtained comparable results.
sources:
- peer_reviewed | NBER Working Paper | https://doi.org/10.3386/w35949 | 2026-10-01
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