How AI Earnings Analysis Works: Behind BigEarnings' Insights

Every ticker page on BigEarnings includes an AI-generated earnings analysis. Before the report, it gives you context. After the report, it interprets the results. We've generated these across 29,000+ earnings reports to date, and they're one of the most-used features on the platform.

This post explains exactly what goes into them, what comes out, and where AI analysis works well versus where it doesn't.

What Goes In

Each analysis ingests 40+ data points per company before generating a single sentence. The inputs fall into five categories:

• Fundamentals — Revenue, EPS, margins, free cash flow, debt-to-equity, return on equity, and year-over-year changes for each metric
• Beat history — Last 8 quarters of EPS surprise percentages, revenue surprises, and whether the stock actually went up or down after each report
• Price action — Trailing 1-month, 3-month, and 6-month returns heading into the report, plus the stock's average post-ER move across all four time windows
• Sector context — How the sector is performing this earnings season. Beat rate, average price reaction, and whether peers have already reported
• Guidance — Prior quarter's forward guidance compared to current consensus estimates. Did management guide above, below, or inline?

Pre-Earnings Context

Before a company reports, the AI generates a pre-earnings brief. This answers three questions:

1. What does the data say? — The company's fundamental trajectory, beat consistency, and how the stock has historically reacted to earnings
2. What is the market expecting? — Consensus estimates, implied move from options pricing, and whether the stock has run up or pulled back into the report
3. What should you watch for? — The specific metrics that will determine whether this is a beat-and-rally or a beat-and-sell situation, based on historical patterns

This is not a prediction. It's context that would take you 30+ minutes to assemble manually.

Post-Earnings Interpretation

After results drop, the AI updates the analysis within hours. It compares actuals to estimates, flags guidance changes, and explains the price reaction in context of the company's history.

A stock that beats EPS by 12% but drops 4% the next day gets a very different analysis than one that beats by 3% and rallies 8%. The AI has pattern data from thousands of similar scenarios to draw on. It can tell you: "This company has beaten estimates 7 of the last 8 quarters, but the stock has dropped after 4 of those 7 beats. Guidance tends to be the determining factor."

Actionable Signals

The analysis surfaces signals you can act on:

• Growth Trajectory Score updates — The score recalculates after each report. A jump from 62 to 78 is meaningful.
• Drift probability — Based on the surprise magnitude and historical drift patterns, is post-earnings drift likely?
• Sector comparison — How this company's results compare to peers that already reported this season
• Guidance trajectory — Whether management is getting more or less optimistic quarter over quarter

Where AI Works Well

Pattern recognition at scale. We tracked 29,000+ reports. No human analyst can hold that much context simultaneously. The AI is good at identifying companies where the stock consistently moves opposite to the beat/miss label, sectors where beats are being discounted, and guidance trends that span multiple quarters.

It also saves time. Reading an earnings report, the press release, checking estimates, pulling up historical reactions, and comparing to sector peers takes 30-45 minutes per stock. The AI does it in seconds.

Where AI Falls Short

Predicting individual outcomes. We're honest about this. AI can tell you that a company's setup looks similar to past scenarios where the stock rallied 70% of the time. It cannot tell you what will happen this specific quarter. One-off events (CEO departure, product recall, surprise acquisition) aren't in the pattern data.

The AI also doesn't replace reading the actual earnings report. It summarizes and contextualizes, but if you're making a large position bet, read the full release yourself.

How to Use AI Insights

1. Pre-earnings — Read the AI brief on any ticker page before the report. Use it to set expectations and identify the metrics that matter most for that specific stock
2. Post-earnings — Check the updated analysis to understand why the stock moved the way it did. Compare to the report itself
3. AI Top Picks — These are the stocks where the AI's pattern recognition identifies the strongest setups heading into earnings
4. Calendar — Every stock on the calendar has AI context available before its report date

Start free to see AI earnings analysis for any stock in our 6,200+ company coverage.