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  • Description
  • Data Access
  • Data Dictionary
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  1. REALTIME DATASETS
  2. Equity Datasets

Earnings Surprise

Earnings announcements are obtained from external sources as well as estimate information leading up to the actual announcement.

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Last updated 6 months ago

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Data arrives late Friday night 11 pm - 12; the model also retrains weekly.

Tutorials are the best documentation —

Description

The Earnings Surprise dataset provides detailed insights into the financial performance of publicly traded companies by capturing the discrepancies between reported earnings and analysts' estimates. This dataset includes metrics such as the probability of an earnings surprise, the magnitude of earnings per share (EPS) surprises, actual earnings results, estimated earnings, and the publication dates of earnings reports.

By offering a granular view of earnings performance, this data serves as a vital tool for investors to assess company performance, predict stock price movements, and make informed investment decisions based on the reliability and accuracy of earnings forecasts.

Data Access

import sovai as sov
df_earn_surp = sov.data("earnings/surprise", tickers=["AAPL", "MSFT"])

Data Dictionary

Name

Description

Type

Example

ticker

Stock ticker symbol of the company

object

AAPL

date

Date of the earnings report

object

2016-12-30

surprise_probability

Probability of an earnings surprise occurring

float64

-0.496

eps_surprise

Earnings per share surprise value

float64

0.040

actual_earning_result

Actual reported earnings per share

float64

0.840

estimated_earning

Analysts' estimated earnings per share

float64

0.800

date_pub

Publication date of the earnings report

object

2017-01-31T00:00:00

market_cap

Market capitalization of the company (in USD)

float64

1.5e+12


Use Cases

  • Investment Signal Generation: Utilize earnings surprise metrics to identify potential investment opportunities by spotting companies that consistently outperform or underperform earnings expectations.

  • Risk Management: Assess the risk associated with investments by monitoring the frequency and magnitude of earnings surprises, identifying companies with unstable earnings.

  • Event-Driven Investment Strategies: Develop strategies around earnings report dates, capitalizing on anticipated surprises to execute buy or sell orders based on expected market reactions.


Input Datasets

Public filings, news, analyst reports

Models Used

Parsing, Regex

Model Outputs

Standardized Rows

Earnings Surprise Tutorial