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ESG Safeguard - Transcripts Sentiment Dataset - Trial Product
Provided By: Amenity Analytics
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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
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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
Description | Value |
---|---|
Update Frequency | Daily |
Data Source(s) | FactSet |
Original Publisher of data | FactSet |
Time period coverage | 2018-01-01 till present |
Is historical data “point-in-time” | Yes |
Data Set(s) Format(s) | CSV |
Raw or scraped data | Raw |
Number of companies/brands covered | Most public companies worldwide that do transcripts |
Standard entity identifiers | ticker + region, cik, isin, sedol |
Data Description and Data Dictionary
The file contains the the following fields:
Column Name | Description | Data Type | Example |
---|---|---|---|
documentType | Type of Document | string | "EARNINGS_CALL" |
companyId | FactSet's unique identifier for a company. Helps in tracking a company in instances where the company’s ticker has changed | string | "000D63-E" |
companyName | The company name | string | "Apple" |
ciks | The CIK or multiple CIKS of the company | list | “['1052054']” |
mainIdentifier ticker | The company's ticker symbol | string | "AAPL" |
mainIdentifier exchange | The exchange the company is traded on | string | "NASDAQ" |
mainIdentifier region | The region of the company’s ticker | string | “US” |
mainIdentifier sedol | The SEDOL of the company | string | “2245575” |
mainIdentifier isin | The ISIN of the company | string | “US30049R2094” |
title | The title of the document | string | "RAW TRANSCRIPT: Applied Digital Corp.(APLD-US), Q2 2023 Earnings Call" |
documentEventId | The id of the transcript event | int | “2434816” |
documentPublicationId | The id of the transcript version | int | “5046058” |
publicationTime | A timestamp for which the earnings call was published by FactSet | date | "2020-03-01T12:30:00" |
eventTime | A timestamp for which the earnings call took place | date | "2020-03-01T09:30:00" |
environmentalCountPositive | Total count of positive extractions related to Environmental key driver, for a date | integer | "5" |
Total Environmental keyDriver negativeEventCount | Total count of negative extractions related to Environmental key driver, for a date | integer | "5" |
Total Environmental keyDriver negativeScore | Total weighted count of negative extractions related to Environmental key driver, for a date | integer | "5" |
Total Environmental keyDriver positiveEventCount | Total count of positive extractions related to Environmental key driver, for a date | integer | "5" |
Total Environmental keyDriver positiveScore | Total weighted count of positive extractions related to Environmental key driver, for a date | integer | "5" |
Total Environmental keyDriver score | A 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 negativeEventCount | Total count of negative extractions related to General key driver, for a date | integer | "5" |
Total General keyDriver negativeScore | Total weighted count of negative extractions related to General key driver, for a date | integer | "5" |
Total General keyDriver positiveEventCount | Total count of positive extractions related to General key driver, for a date | integer | "5" |
Total General keyDriver positiveScore | Total weighted count of positive extractions related to General key driver, for a date | integer | "5" |
Total General keyDriver score | A 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 negativeEventCount | Total count of negative extractions related to Governance key driver, for a date | integer | "5" |
Total Governance keyDriver negativeScore | Total weighted count of negative extractions related to Governance key driver, for a date | integer | "5" |
Total Governance keyDriver positiveEventCount | Total count of positive extractions related to Governance key driver, for a date | integer | "5" |
Total Governance keyDriver positiveScore | Total weighted count of positive extractions related to Governance key driver, for a date | integer | "5" |
Total Governance keyDriver score | A 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 negativeEventCount | Total count of negative extractions related to Social key driver, for a date | integer | "5" |
Total Social keyDriver negativeScore | Total weighted count of negative extractions related to Social key driver, for a date | integer | "5" |
Total Social keyDriver positiveEventCount | Total count of positive extractions related to Social key driver, for a date | integer | "5" |
Total Social keyDriver positiveScore | Total weighted count of positive extractions related to Social key driver, for a date | integer | "5" |
Total Social keyDriver score | A 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 negativeEventCount | Total count of negative extractions for a date | integer | "5" |
Total negativeScore | Total weighted count of negative extractions for a date | integer | "10" |
totalPositiveCount | Total count of positive extractions for a date | integer | "3" |
Total positiveScore | Total weighted count of positive extractions for a date | integer | "12" |
Total score | A 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 wordCount | Total count of words in a document | integer | "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
- API Documentation Available to subscribed users only
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.
Provided By
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
Support contact URL
Refund policy
Not applicable for trials.
General AWS Data Exchange support