Smart Earnings

Upcoming reports with AI-powered beat probability analysis

Data Sources

We aggregate institutional-grade data from official publicly available sources to ensure maximum accuracy and broad coverage across a universe of 5,000+ global tickers.

Surprise History

Weight: 50%Does the company systematically sandbag estimates? We analyze the last 4 quarters of actual vs. estimated EPS to detect consistent "beaters".

Price Momentum

Weight: 30%Stocks rallying into earnings often signal insider confidence. We measure Relative Strength vs the S&P 500 over the last 14 days.

Risk Profile

Weight: 20%We factor in analyst revision trends and historical volatility. Disclaimer: This is a probabilistic model, not financial advice.

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Earnings Intelligence Methodology

The Probabilistic Model

Our Smart Earnings engine utilizes a proprietary multi-factor model to calculate the probability of a company beating consensus EPS estimates. Unlike standard calendars, we analyze the structural behavior of corporate reporting and analyst sentiment cycles.

The model weights historical "Surprise Alpha" heavily—identifying firms that systematically under-promise and over-deliver. We further cross-reference this with 14-day relative strength momentum to detect institutional "buying into the print," which often precedes positive surprises.

Key Intelligence Signals

01.Analyst Revisions: Tracking the second-derivative of consensus changes in the 7 days leading up to the report.
02.Implied Volatility (IV): Analyzing the options-implied move vs. historical actual moves to identify mispriced risk.
03.Sector Correlations: Factor-based adjustments based on peer-group performance during the same earnings cycle.