AI-Powered Earnings Surprise Tahmin: How Machine Learning Forecasts Beats and Misses

Earnings surprises are among the most powerful short-term catalysts in equities.

Why Earnings Surprises Matter

A single earnings beat or miss can move a stock 10-20% in a session. Traditional analysts rely on management guidance and industry models, but AI can ingest orders of magnitude more data to find patterns invisible to humans.

Feature Engineering for Earnings Models

Key inputs include web-traffic trends, app-download velocity, credit-card transaction data, satellite imagery of parking lots and shipping ports, and natural-language signals extracted from earnings-call transcripts of peers.

Model Architecture

An ensemble layer merges both to output a probability distribution of EPS relative to consensus.