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ESG Safeguard - Transcripts Sentiment Dataset - Trial Product

Provided By: Amenity Analytics

ESG Safeguard - Transcripts Sentiment Dataset - Trial Product

Provided By: Amenity Analytics

This trial dataset is industrial-scale NLP applied to earnings call transcripts to develop in-depth, real-time scoring at the company level on ESG issues. It provides company-specific scoring on 12,000 companies globally to track portfolio and company exposures. Scoring derives from net sentiment divided by total neg. and pos. extractions in the transcript, and per ESG topic. Also includes counts making up those scores. *The trial dataset does not update. Paid subscriber datasets update daily.*

Product offers

The following offers are available for this product. Choose an offer to view the pricing and access duration options for the offer. Select an offer and continue to subscribe. Your subscription begins on the date that your request is approved by the provider. Additional taxes or fees might apply.

Public offer

Payment schedule: Upfront payment | Offer auto-renewal: Supported
$0 for 1 month

Overview

This dataset can be treated as a stand alone data product that could be integrated into any big data analytics tool at the security level. Alternatively, for customers wishing to procure Amenity Analytics' custom dashboard can do so via Marketplace.


ESG Safeguard - Transcripts Sentiment - Trial

ESG has never been more important, but amongst all the disconnected ESG stories, how do you monitor the information that matters? We believe that a comprehensive natural language processing solution that is detailed, accurate, and transparent will empower you to monitor thousands of the most important sources to gain an ESG edge.  

The Amenity Safeguard ESG dataset applies NLP to track portfolio or single security ESG exposures with ease, leveraging our comprehensive approach to ESG data. Protect your portfolio from downgrades and defend your decisions to your investors with confidence. Amenity's industry leading approach to AI delivers accurate, domain-specific NLP at scale. This dataset provides company-specific scoring to track portfolio and company exposures. The score is derived from the net sentiment divided by the total negative and positive extractions in the transcript, and per each ESG topic. Also includes counts that make up those scores.  

The entities covered in this dataset are a majority of public companies worldwide that do earnings calls ( 12,000 companies). The underlying source of the data is Factset earnings call transcripts.  

The Values in this dataset are represented with scores between -1 and 1. A score of -1 is the most negative and +1 is the most positive. Neutral is 0.  

The dataset includes total score + counts and score + counts per key driver (Governance, General, Social and Environmental).


Key Benefits

  • Systematically evaluate and quantify the materiality of Environmental, Social and Governance (ESG) factors in earnings call transcripts of companies in your investment universe.
  • Unbiased and transparent data. Our ESG model employs contextual analysis and language patterns to capture and analyze all critical events across a rich dataset of financial topics providing investors broadly aggregated and unbiased E, S, and G-related evidence and scores
  • Accuracy at scale. Analyze and monitor developments related to your ESG themes across your investment universe of watchlists, portfolios, and individual equities
  • Via Marketplace: Dashboard interface. Features an intuitive user interface that applies an ESG lens to companies within your investment universe. Create sentiment summaries, or segment baskets of equities based on distinct ESG profiles

Use Cases

  • Screening and Idea Generation
  • Stock Selection and Relative Stock Selection
  • Transcript Surveillance and Monitoring
  • Factor Attribution
  • Baskets and Trading Structure Creation
  • Alpha Generation via Analysis

Verticals

  • Asset Managers
  • Banks
  • Capital Markets
  • Insurance

DescriptionValue
Update FrequencyDaily
Data Source(s)FactSet
Original Publisher of dataFactSet
Time period coverage2018-01-01 till present
Is historical data “point-in-time”Yes
Data Set(s) Format(s)CSV
Raw or scraped dataRaw
Number of companies/brands coveredMost public companies worldwide that do transcripts
Standard entity identifiersticker + region, cik, isin, sedol

Data Description and Data Dictionary

The file contains the the following fields:

Column NameDescriptionData TypeExample
documentTypeType of Documentstring"EARNINGS_CALL"
companyIdFactSet's unique identifier for a company. Helps in tracking a company in instances where the company’s ticker has changedstring"000D63-E"
companyNameThe company namestring"Apple"
ciksThe CIK or multiple CIKS of the companylist['1052054']
mainIdentifier tickerThe company's ticker symbolstring"AAPL"
mainIdentifier exchangeThe exchange the company is traded onstring"NASDAQ"
mainIdentifier regionThe region of the company’s tickerstring“US”
mainIdentifier sedolThe SEDOL of the companystring“2245575”
mainIdentifier isinThe ISIN of the companystring“US30049R2094”
titleThe title of the documentstring"RAW TRANSCRIPT: Applied Digital Corp.(APLD-US), Q2 2023 Earnings Call"
documentEventIdThe id of the transcript eventint“2434816”
documentPublicationIdThe id of the transcript versionint“5046058”
publicationTimeA timestamp for which the earnings call was published by FactSetdate"2020-03-01T12:30:00"
eventTimeA timestamp for which the earnings call took placedate"2020-03-01T09:30:00"
environmentalCountPositiveTotal count of positive extractions related to Environmental key driver, for a dateinteger"5"
Total Environmental keyDriver negativeEventCountTotal count of negative extractions related to Environmental key driver, for a dateinteger"5"
Total Environmental keyDriver negativeScoreTotal weighted count of negative extractions related to Environmental key driver, for a dateinteger"5"
Total Environmental keyDriver positiveEventCountTotal count of positive extractions related to Environmental key driver, for a dateinteger"5"
Total Environmental keyDriver positiveScoreTotal weighted count of positive extractions related to Environmental key driver, for a dateinteger"5"
Total Environmental keyDriver scoreA weighted sentiment score for a company for extraction related to Environmental key driver. Sentiment score defined as (total weighted positive extraction - total weighted negative extractions) / (total weighted positive extractions - total weighted negative extractions + 1)float"-1.0"
Total General keyDriver negativeEventCountTotal count of negative extractions related to General key driver, for a dateinteger"5"
Total General keyDriver negativeScoreTotal weighted count of negative extractions related to General key driver, for a dateinteger"5"
Total General keyDriver positiveEventCountTotal count of positive extractions related to General key driver, for a dateinteger"5"
Total General keyDriver positiveScoreTotal weighted count of positive extractions related to General key driver, for a dateinteger"5"
Total General keyDriver scoreA weighted sentiment score for a company for extraction related to General key driver. Sentiment score defined as (total weighted Positive extraction - total weighted negative extractions) / (total weighted positive extractions - total weighted negative extractions + 1)float"-1.0"
Total Governance keyDriver negativeEventCountTotal count of negative extractions related to Governance key driver, for a dateinteger"5"
Total Governance keyDriver negativeScoreTotal weighted count of negative extractions related to Governance key driver, for a dateinteger"5"
Total Governance keyDriver positiveEventCountTotal count of positive extractions related to Governance key driver, for a dateinteger"5"
Total Governance keyDriver positiveScoreTotal weighted count of positive extractions related to Governance key driver, for a dateinteger"5"
Total Governance keyDriver scoreA weighted sentiment score for a company for extraction related to Governance key driver. Sentiment score defined as (total weighted positive extractions - total weighted negative extractions) / (total weighted positive extractions - total weighted negative extractions + 1)float"-1.0"
Total Social keyDriver negativeEventCountTotal count of negative extractions related to Social key driver, for a dateinteger"5"
Total Social keyDriver negativeScoreTotal weighted count of negative extractions related to Social key driver, for a dateinteger"5"
Total Social keyDriver positiveEventCountTotal count of positive extractions related to Social key driver, for a dateinteger"5"
Total Social keyDriver positiveScoreTotal weighted count of positive extractions related to Social key driver, for a dateinteger"5"
Total Social keyDriver scoreA weighted sentiment score for a company for extraction related to Social key driver. Sentiment score defined as (total weighted Positive extraction - total weighted negative extractions) / (total weighted positive extractions - total weighted negative extractions + 1)float"-1.0"
Total negativeEventCountTotal count of negative extractions for a dateinteger"5"
Total negativeScoreTotal weighted count of negative extractions for a dateinteger"10"
totalPositiveCountTotal count of positive extractions for a dateinteger"3"
Total positiveScoreTotal weighted count of positive extractions for a dateinteger"12"
Total scoreA weighted sentiment score for a company. Sentiment score defined as (total weighted positive extractions - total weighted negative extractions) / (total weighted positive extractions - total negative extractions + 1)float"-0.676754"
Total wordCountTotal count of words in a documentinteger"2500"

Update Frequency

  • For trials the data does not update
  • Paid subscriber data updates daily

Applications

  • What you can do: Develop time series, use for backtesting or fundamental analysis or for company rankings
  • What you cannot do: Dive into specific event/extractions counts or see what the text extractions were; in addition this cannot be resold in part or in full as a commercial dataset

Additional Information


Regulatory and Compliance Information

Portions of the Services (including the Content) may be provided through third-party providers, such as FactSet, LexisNexis, and/or EDGAR, which may impose certain restrictions or additional terms and conditions.


Required Information to Start a Subscription

  • EIN number
  • Number of applications,
  • Number of users
  • Number of regions
  • Number companies in coverage
  • User(s) email addresses

Need Help?

  • If you have questions about our products, contact us using the support information: vijay@amenityanalytics.com

About Amenity

We develop cloud-based analytics solutions to help businesses draw actionable insights from text on a massive scale. Fortune 100 companies, hedge funds, financial exchanges, and insurance companies rely on our proprietary NLP technology for use on sources ranging from regulatory filings and earnings call transcripts to news coverage, social media activity, and research reports.

Fulfillment Method
AWS Data Exchange

Data sets (1)

You will receive access to the following data sets

Revision access rules
All historical revisions | All future revisions
Name
Type
Data dictionary
AWS Region
ESG Safeguard - Transcripts Sentiment Dataset - Trial
Not included
US East (N. Virginia)

Usage information

By subscribing to this product, you agree that your use of this product is subject to the provider's offer terms including pricing information and Data Subscription Agreement . Your use of AWS services remains subject to the AWS Customer Agreement  or other agreement with AWS governing your use of such services.

Support information

Support contact email address
Refund policy
Not applicable for trials.
General AWS Data Exchange support