How to Use AI for Options Research Before Earnings

Narrow the research universe

ATLAS provides controls for the scan period, minimum stock price, and session. A researcher can review a week of reporting companies or investigate an individual ticker. The interface also displays modules for earnings, catalysts, options value, and tail-event research. The earnings workflow is the example demonstrated here; the presence of a module label alone is not a validation of every underlying function.

Read past earnings reactions together

For an individual company, the dashboard displays historical earnings reactions as a chart and a table of signed percentage moves. Summary fields include average, median, maximum, and counts of positive and negative observations. Reading the chart beside the table makes it easier to inspect the individual events behind a summary.

That context matters. Two companies can have similar averages and very different histories. One unusually large observation may affect the summary substantially. The point of showing individual events is to let the researcher examine those differences instead of relying on a single headline number.

Make data quality visible

The screenshots include source labels, clean-history counts, and an insufficient-history warning. In one example, the dashboard shows a much smaller usable sample than the number of displayed historical events. A reader should be able to understand which observations contribute to each calculation before using it.

These flags are an important part of the interface, but their presence does not independently prove that the source data or filtering logic is correct. Validation still requires checking the underlying events, prices, dates, and calculations. We describe them as data-quality indicators rather than a certification of accuracy.

Distinguish past movement from future probability

ATLAS explicitly notes that its recent earnings-reaction average describes historical direction, not the probability of the next move. The screenshots also distinguish post-earnings research from a pre-earnings comparison. Keeping that context visible helps prevent a historical statistic from being interpreted as a forecast.

This article does not recommend a ticker, contract, or strategy. Nor do the screenshots demonstrate profitable execution, live brokerage orders, or an independently validated advantage. ATLAS is presented here as a custom research project.

What this means for a business research tool

The transferable idea is straightforward: define a research question, organize the relevant records, expose the comparisons, and show when the evidence is incomplete. The same approach can help structure other analytical workflows, provided the data access and validation requirements are agreed first.

Before building a similar tool, we would ask what sources are available, what an analyst compares manually, how fresh the information needs to be, and which decisions require a person’s review. Those answers define the scope more usefully than a promise that an agent can do everything.

Talk to Work AI Crew about a custom research dashboard. You can also explore our NFL Game Intelligence project to see a different application of comparative research.

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