earnings-attribution

Earnings surprise analysis identifies the exact reasons for market reaction by calculating the difference between actual financial results and expectations. It ranks the impact of EPS, revenue, and guidance surprises to pinpoint fundamental stock price drivers.

Evidence-driven financial attribution identifies the primary driver and contributing factors behind a price move using cited evidence. It assesses confidence levels as High, Medium, or Insufficient to clarify the impact of specific metric surprises.

Assess confidence levels by synthesizing earnings data from graph databases and search APIs to evaluate the strength of evidence. The analysis assigns a High, Medium, or Insufficient confidence rating based on the available financial attribution data.

Yes, earnings attribution handles complex scenarios involving conflicting data sources, historical trends, and guidance cuts. It synthesizes conflicting data points from graph databases and search APIs to accurately attribute market reactions to fundamental factors.

A stock price can drop after a positive EPS surprise if other fundamental factors like a guidance cut or weak revenue surprise outweighed the positive earnings. Earnings attribution calculates and ranks these conflicting metric surprises to identify the actual primary driver.