Benzinga has stringent processes to collect and curate the highest quality earnings data, which is consistently considered the cleanest in the market. Benzinga follows a three-step method to ensure quality, accuracy, and timeliness.

Process

  1. BZ Analysts start with GAAP earnings and identify non-comparable items which make the results unusable.
  2. Call sell side analysts when there are discrepancies on the comparable earnings figures (1,200 calls per quarter).
  3. Update API with Wall Street’s most accurate figures.

Earnings suite covers the Wilshire 5000 + 1000 additional US equities

Data field descriptions

ID Unique ID of the output
date Datestamp
date_confirmed If predicted date has been confirmed by company. 0 = No, 1 = Yes
time Timestamp of the announcement HH:MM:SS format
isin ISIN identifier
ticker Ticker symbol
exchange Exchange the security is traded on
name Company name of the security
period Fiscal period being reported
period_year Fiscal year being reported
eps Earnings per share for current quarter
eps_est Analyst prediction for the upcoming announcement
eps_prior EPS reported for the same quarter a year prior
revenue Revenue for current quarter
revenue_est Analyst prediction for the upcoming announcement
revenue_prior Revenue reporter for the same quarter a year prior
revenue_surprise Difference between the prediction and actual figure reported
revenue_surprise_percent Difference in percentage between prediction and actual figure
importance Importance of the action (scale of 0-5) 0 = least important
updated Last updated timestamp (UNIX)

Benzinga’s data samples are intended to provide a data sample large enough for testing data quality and application for the financial markets. These sample files demonstrate a sample of the formats and content that can be delivered.
Please refer to docs.benzinga.io for all your API integration needs, including parameter queries and a look at our entire offerings.