FactSet GeoRev API

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  • Overview
  • API Definition
  • API Documentation
  • SDK Libraries
  • Notebooks
  • Code Snippet
  • Changelog
Overview

FactSet Revere Geographic Revenue ("GeoRev") Exposure data provides a highly structured and normalized display of companies’ revenues by geography. Using a four level taxonomy structure, understand the companies' Super-Region-->Region-->Area-->Country revenue breakdowns. Quickly understand a company’s revenue exposure in countries impacted by geopolitical, macroeconomic, and market risk. Understand the geographic footprint of a company based on sources of revenue versus country of domicile, and analyze global revenue exposures at the company, index, or portfolio level.

Geographic revenue has historically been difficult to analyze due to companies’ non-standard and incomplete reporting. Investors relying solely on this as-reported data are limited in their ability to compare, aggregate or screen exposures across a universe or portfolio of companies. To achieve normalization, FactSet GeoRev captures data through a proprietary four-level geographic classification structure. An estimation algorithm based on GDP weighting and accounting logic is then applied to solve for any non-explicit disclosures. The result is a consistent, accurate, and flexible dataset that can take a company’s revenues and break them down into any geographic country or region categories.

As markets become more integrated and companies expand operations beyond their domestic markets, GeoRev provides a new and valuable country factor to help investors discover alpha, model risk exposure, optimize portfolio weighting, and improve fund administration and reporting.

Data Frequency -  Annual; Update Frequency - Daily. 49,000+ Publicly Listed Companies. All Russell 3000 and MSCI ACWI Index Constituents. U.S. Data is available from 2003, with Non-US data from 2007.

API Definition
SDK Libraries
Notebooks
Getting Started - Jupyter Notebook
Getting Started - Jupyter Notebook SDK
Code Snippet
# Imports
import pandas as pd

# FDS packages
import fds.sdk.FactSetGeoRev # To install, please use "pip install fds.sdk.utils fds.sdk.FactSetGeoRev==1.0.7"
from fds.sdk.FactSetGeoRev.api import countries_api, regions_api
from fds.sdk.FactSetGeoRev.models import *
from fds.sdk.utils.authentication import ConfidentialClient

# Authentication using OAuthndame
configuration = fds.sdk.FactSetGeoRev.Configuration(
    fds_oauth_client=ConfidentialClient('<PATH_TO_CONFIG_FILE>') # Path to config file from OAuth registration
)

# Get Regions Data
with fds.sdk.FactSetGeoRev.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = regions_api.RegionsApi(api_client)

    # Request Object to Define Parameters
    region_request = RegionRequest(
        ids=Ids(["AAPL-USA"]),
        region_ids=RegionIds(["R101","R101","R167"]),
        start_date="2019-01-01",
        end_date="2019-12-31",
        frequency=Frequency("FY"),
        currency="USD",
    )
    
    # Send Request
    api_response = api_instance.get_regions_for_list(region_request)
    
    # Convert to Pandas Dataframe
    results = pd.DataFrame(api_response.to_dict()['data'])
    print(results)
Changelog

1.0.1

Summary

Bug fix addressing an issue with Frequency defaulting to Daily. The service will now default to "Fiscal Year" frequency.

Bug Fixes

  • `frequency` parameter added to GET and POST methods and now defaults to FY.