Earnings Surprise

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

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

Dataset contains 4330+ tickers, available from 2016-12-30 onwards.

Tutorials are the best documentation — Earnings Surprise Tutorial

Input Datasets

Public filings, news, analyst reports

Models Used

Parsing, Regex

Model Outputs

Standardized Rows

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

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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.

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