Earnings Call Statistics Database
A comprehensive reference database of earnings call statistics: coverage volume, AI sentiment analysis accuracy, processing efficiency, market impact, and technology adoption. Updated for 2026.
Ecomerate Research··10 min read
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- • 2.5M+ earnings transcripts are now AI-searchable across 15,000+ companies
- • AI beats human analysts on EPS prediction accuracy: 68% vs 61%
- • 62% of asset managers now use NLP for earnings call analysis
- • 60% of stock volatility occurs on just 10-15 earnings days per year
- • AI processes earnings calls 5-8x faster than human analysts
Earnings Call Coverage & Volume
2.5M+
Earnings call transcripts available
Across 15,000+ public companies globally. Historical data going back 10+ years.
50,000+
Quarterly earnings calls held globally
Approximately 50,000-55,000 per quarter across all public exchanges.
94%
US public companies covered by AI tools
vs 40% coverage by traditional sell-side research alone (FactSet 2025)
12,000+
Unique companies with AI-searchable transcripts
Available in modern AI investing platforms like Ecomerate.
15,000+
Words per average earnings call transcript
30-45 minutes of prepared remarks + 45-60 minutes of Q&A.
AI-Powered Sentiment Analysis Accuracy
58%
Management tone predicts stock movement
AI sentiment analysis of management language predicts 58% of post-earnings price direction (Stanford 2025)
73%
AI detection of 'red flags' in earnings calls
Language patterns that correlate with future underperformance (Harvard Business Review 2025)
64%
Sentiment accuracy on 7-day forward returns
AI sentiment models predict short-term price impact with 64% accuracy (Journal of Financial Economics 2025)
42%
Improved accuracy vs human analysis alone
AI + human combined analysis beats either alone by 42% on accuracy (CFA Institute 2025)
81%
AI accuracy on 'bullish' vs 'bearish' classification
Binary sentiment classification accuracy across the full transcript (MIT 2025)
Processing & Analysis Efficiency
45 min
Average time to transcribe and analyze with AI
vs 4-6 hours for human-only analysis of the same transcript.
31%
Increase in AI earnings call analysis since 2024
Per Bloomberg Terminal usage data. AI analysis is the fastest-growing terminal feature.
68%
EPS prediction accuracy using AI
vs 61% for human analysts. AI beats human EPS forecasts by 7 percentage points. (University of Chicago Booth 2025)
12x
More transcripts analyzed by AI vs humans per day
AI can process 12x more earnings call transcripts per day than a human analyst.
92%
Filing coverage via EDGAR RAG
SEC filings are 92% more searchable with AI-powered semantic retrieval vs keyword search.
Financial Impact & Market Reaction
3.5%
Average stock move on earnings day
The average absolute price move on earnings day is 3.5% for S&P 500 companies (FactSet)
18%
Average annualized earnings day return
Most of the equity risk premium is captured on earnings days (Research Affiliates)
60%
Of total annual stock volatility occurs on earnings days
Just 10-15 trading days per year account for majority of price movement (BlackRock)
2.1%
Average post-earnings drift
Stocks that beat estimates continue to drift upward for 30-60 days post-earnings (Duke University)
75%
Of beats see follow-through momentum
75% of earnings beats are followed by positive returns in the subsequent quarter.
Technology & Tooling
62%
Asset managers using NLP for earnings analysis
Natural language processing to analyze management tone and extract insights (McKinsey 2025)
40%
Of earnings analysis now automated in AI tools
40% of the standard earnings analysis workflow is fully automated in modern AI platforms.
85%
Of earnings datasets accessible via API
85% of earnings call data is available through modern API-connected platforms (Ecomerate)
73%
Hedge funds using AI for earnings analysis
The most common AI use case among hedge funds (Greenwich Associates 2025)
3.2M
Documents analyzed per month by AI investing tools
SEC filings, earnings transcripts, and financial reports processed by Ecomerate's AI systems.
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