Brain Language Metrics on Earnings Calls - 4500+ US Stocks
# | COMPOSITE_FIGI |
DATE |
LAST_TRANSCRIPT_DATE |
LAST_TRANSCRIPT_QUARTER |
LAST_TRANSCRIPT_YEAR |
MD_N_CHARACTERS |
MD_SENTIMENT |
MD_SCORE_UNCERTAINTY |
MD_SCORE_LITIGIOUS |
MD_SCORE_CONSTRAINING |
MD_READABILITY |
MD_LEXICAL_RICHNESS |
MD_LEXICAL_DENSITY |
MD_SPECIFIC_DENSITY |
AQ_N_CHARACTERS |
AQ_SENTIMENT |
AQ_SCORE_UNCERTAINTY |
AQ_SCORE_LITIGIOUS |
AQ_SCORE_CONSTRAINING |
MA_N_CHARACTERS |
MA_SENTIMENT |
MA_SCORE_UNCERTAINTY |
MA_SCORE_LITIGIOUS |
MA_SCORE_CONSTRAINING |
MA_READABILITY |
MA_LEXICAL_RICHNESS |
MA_LEXICAL_DENSITY |
MA_SPECIFIC_DENSITY |
|
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx |
2 | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx |
3 | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx |
4 | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx |
5 | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx |
6 | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx |
7 | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx |
8 | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx |
9 | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | Xxxxx | Xxxxx | Xxxxxxx | xxxxx |
10 | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx |
... | xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxx |
# | COMPOSITE_FIGI |
DATE |
LAST_TRANSCRIPT_DATE |
LAST_TRANSCRIPT_QUARTER |
LAST_TRANSCRIPT_YEAR |
PREV_TRANSCRIPT_DATE |
PREV_TRANSCRIPT_QUARTER |
PREV_TRANSCRIPT_YEAR |
MD_DELTA_PERC_N_CHARACTERS |
MD_DELTA_SENTIMENT |
MD_DELTA_SCORE_UNCERTAINTY |
MD_DELTA_SCORE_LITIGIOUS |
MD_DELTA_SCORE_CONSTRAINING |
MD_DELTA_READABILITY |
MD_DELTA_LEXICAL_RICHNESS |
MD_DELTA_LEXICAL_DENSITY |
MD_DELTA_SPECIFIC_DENSITY |
MD_SIMILARITY_ALL |
MD_SIMILARITY_POSITIVE |
MD_SIMILARITY_NEGATIVE |
MD_SIMILARITY_UNCERTAINTY |
MD_SIMILARITY_LITIGIOUS |
MD_SIMILARITY_CONSTRAINING |
AQ_DELTA_PERC_N_CHARACTERS |
AQ_DELTA_SENTIMENT |
AQ_DELTA_SCORE_UNCERTAINTY |
AQ_DELTA_SCORE_LITIGIOUS |
AQ_DELTA_SCORE_CONSTRAINING |
AQ_SIMILARITY_ALL |
AQ_SIMILARITY_POSITIVE |
AQ_SIMILARITY_NEGATIVE |
MA_DELTA_PERC_N_CHARACTERS |
MA_DELTA_SENTIMENT |
MA_DELTA_SCORE_UNCERTAINTY |
MA_DELTA_SCORE_LITIGIOUS |
MA_DELTA_SCORE_CONSTRAINING |
MA_DELTA_READABILITY |
MA_DELTA_LEXICAL_RICHNESS |
MA_DELTA_LEXICAL_DENSITY |
MA_DELTA_SPECIFIC_DENSITY |
MA_SIMILARITY_ALL |
MA_SIMILARITY_POSITIVE |
MA_SIMILARITY_NEGATIVE |
MA_SIMILARITY_UNCERTAINTY |
MA_SIMILARITY_LITIGIOUS |
MA_SIMILARITY_CONSTRAINING |
|
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx |
2 | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx |
3 | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx |
4 | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx |
5 | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx |
6 | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | Xxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxx |
7 | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxxxxxx | xxxxx |
8 | Xxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxx | Xxxxx | Xxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxx | Xxxxxxx | xxxxxxxxxx |
9 | Xxxxxxx | Xxxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | xxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxxx | xxxxx | xxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxx |
10 | xxxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxxxx | xxxxx | xxxxxxxxxx | xxxxxxx | xxxxxxxx | Xxxxx | xxxxx | xxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxx | Xxxxxx | xxxxxx | xxxxx | Xxxxxxxxx | Xxxxxxxxx | Xxxxxx |
... | Xxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxx | xxxxxx | xxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxx | xxxxxx | Xxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxx | xxxxxx | Xxxxxxxx | Xxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxx |
Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
COMPOSITE_FIGI
|
String | BBG000C2V3D6 | |
String | A | Stock Ticker | |
DATE
|
DateTime | 2021-07-22T00:00:00+00:00 | |
LAST_TRANSCRIPT_DATE
|
DateTime | 2021-05-26T00:00:00+00:00 | |
LAST_TRANSCRIPT_QUARTER
|
Integer | 2 | |
LAST_TRANSCRIPT_YEAR
|
Integer | 2021 | |
MD_N_CHARACTERS
|
Float | 17327.0 | |
MD_SENTIMENT
|
Float | 0.4907 | |
MD_SCORE_UNCERTAINTY
|
Float | 0.0992 | |
MD_SCORE_LITIGIOUS
|
Float | 0.0229 | |
MD_SCORE_CONSTRAINING
|
Float | 0.0076 | |
MD_READABILITY
|
Float | 10.3541 | |
MD_LEXICAL_RICHNESS
|
Float | 0.3656 | |
MD_LEXICAL_DENSITY
|
Float | 0.5225 | |
MD_SPECIFIC_DENSITY
|
Float | 0.0785 | |
AQ_N_CHARACTERS
|
Float | 10744.0 | |
AQ_SENTIMENT
|
Float | 0.4163 | |
AQ_SCORE_UNCERTAINTY
|
Float | 0.3333 | |
AQ_SCORE_LITIGIOUS
|
Float | 0.0312 | |
AQ_SCORE_CONSTRAINING
|
Float | 0.0104 | |
MA_N_CHARACTERS
|
Float | 24893.0 | |
MA_SENTIMENT
|
Float | 0.506 | |
MA_SCORE_UNCERTAINTY
|
Float | 0.1737 | |
MA_SCORE_LITIGIOUS
|
Float | 0.018 | |
MA_SCORE_CONSTRAINING
|
Float | 0.024 | |
MA_READABILITY
|
Float | 10.2549 | |
MA_LEXICAL_RICHNESS
|
Float | 0.3218 | |
MA_LEXICAL_DENSITY
|
Float | 0.4592 | |
MA_SPECIFIC_DENSITY
|
Float | 0.0763 |
Attribute | Type | Example | Mapping |
---|---|---|---|
COMPOSITE_FIGI
|
String | BBG000C2V3D6 | |
String | A | Stock Ticker | |
DATE
|
DateTime | 2021-07-22T00:00:00+00:00 | |
LAST_TRANSCRIPT_DATE
|
DateTime | 2021-05-26T00:00:00+00:00 | |
LAST_TRANSCRIPT_QUARTER
|
Integer | 2 | |
LAST_TRANSCRIPT_YEAR
|
Integer | 2021 | |
PREV_TRANSCRIPT_DATE
|
DateTime | 2021-02-17T00:00:00+00:00 | |
PREV_TRANSCRIPT_QUARTER
|
Integer | 1 | |
PREV_TRANSCRIPT_YEAR
|
Integer | 2021 | |
MD_DELTA_PERC_N_CHARACTERS
|
Float | 0.1015 | |
MD_DELTA_SENTIMENT
|
Float | 0.0336 | |
MD_DELTA_SCORE_UNCERTAINTY
|
Float | -0.0153 | |
MD_DELTA_SCORE_LITIGIOUS
|
Float | -0.0076 | |
MD_DELTA_SCORE_CONSTRAINING
|
Float | -0.0229 | |
MD_DELTA_READABILITY
|
Float | 0.4384 | |
MD_DELTA_LEXICAL_RICHNESS
|
Float | -0.0246 | |
MD_DELTA_LEXICAL_DENSITY
|
Float | -0.0079 | |
MD_DELTA_SPECIFIC_DENSITY
|
Float | -0.007 | |
MD_SIMILARITY_ALL
|
Float | 0.9329 | |
MD_SIMILARITY_POSITIVE
|
Float | 0.9457 | |
MD_SIMILARITY_NEGATIVE
|
Float | 0.3103 | |
MD_SIMILARITY_UNCERTAINTY
|
Float | 0.6267 | |
MD_SIMILARITY_LITIGIOUS
|
Float | 0.9129 | |
MD_SIMILARITY_CONSTRAINING
|
Float | 0.5 | |
AQ_DELTA_PERC_N_CHARACTERS
|
Float | 0.0966 | |
AQ_DELTA_SENTIMENT
|
Float | 0.034 | |
AQ_DELTA_SCORE_UNCERTAINTY
|
Float | -0.1088 | |
AQ_DELTA_SCORE_LITIGIOUS
|
Float | -0.0003 | |
AQ_DELTA_SCORE_CONSTRAINING
|
Float | 0.0104 | |
AQ_SIMILARITY_ALL
|
Float | 0.7433 | |
AQ_SIMILARITY_POSITIVE
|
Float | 0.5107 | |
AQ_SIMILARITY_NEGATIVE
|
Float | 0.9522 | |
MA_DELTA_PERC_N_CHARACTERS
|
Float | -0.1508 | |
MA_DELTA_SENTIMENT
|
Float | 0.0831 | |
MA_DELTA_SCORE_UNCERTAINTY
|
Float | 0.0012 | |
MA_DELTA_SCORE_LITIGIOUS
|
Float | -0.005 | |
MA_DELTA_SCORE_CONSTRAINING
|
Float | 0.0182 | |
MA_DELTA_READABILITY
|
Float | 2.0079 | |
MA_DELTA_LEXICAL_RICHNESS
|
Float | 0.0113 | |
MA_DELTA_LEXICAL_DENSITY
|
Float | 0.0082 | |
MA_DELTA_SPECIFIC_DENSITY
|
Float | 0.0073 | |
MA_SIMILARITY_ALL
|
Float | 0.7444 | |
MA_SIMILARITY_POSITIVE
|
Float | 0.7035 | |
MA_SIMILARITY_NEGATIVE
|
Float | 0.8473 | |
MA_SIMILARITY_UNCERTAINTY
|
Float | 0.5565 | |
MA_SIMILARITY_LITIGIOUS
|
Float | 0.7303 | |
MA_SIMILARITY_CONSTRAINING
|
Float | 0.8165 |
Description
Country Coverage
History
Volume
4,500 | stocks covered |
Pricing
License | Starts at |
---|---|
One-off purchase | Not available |
Monthly License | Not available |
Yearly License | Available |
Usage-based | Not available |
Suitable Company Sizes
Delivery
Use Cases
Categories
Related Products
Frequently asked questions
What is Brain Language Metrics on Earnings Calls - 4500+ US Stocks?
The Brain Language Metrics on Earnings Calls Transcripts (BLMECT) dataset has the objective of monitoring several language metrics the quarterly earnings call transcripts for 4500+ US stocks.
What is Brain Language Metrics on Earnings Calls - 4500+ US Stocks used for?
This product has 5 key use cases. Brain Company recommends using the data for Alpha Generation, Systematic Trading, Quantitative Investing, Alternative Investment, and Earnings Calls Analysis. Global businesses and organizations buy Stock Market Data from Brain Company to fuel their analytics and enrichment.
Who can use Brain Language Metrics on Earnings Calls - 4500+ US Stocks?
This product is best suited if you’re a Medium-sized Business, Enterprise, or Small Business looking for Stock Market Data. Get in touch with Brain Company to see what their data can do for your business and find out which integrations they provide.
How far back does the data in Brain Language Metrics on Earnings Calls - 4500+ US Stocks go?
This product has 10 years of historical coverage. It can be delivered on a daily basis.
Which countries does Brain Language Metrics on Earnings Calls - 4500+ US Stocks cover?
This product includes data covering 1 country like USA. Brain Company is headquartered in Italy.
How much does Brain Language Metrics on Earnings Calls - 4500+ US Stocks cost?
Pricing information for Brain Language Metrics on Earnings Calls - 4500+ US Stocks is available by getting in contact with Brain Company. Connect with Brain Company to get a quote and arrange custom pricing models based on your data requirements.
How can I get Brain Language Metrics on Earnings Calls - 4500+ US Stocks?
Businesses can buy Stock Market Data from Brain Company and get the data via S3 Bucket. Depending on your data requirements and subscription budget, Brain Company can deliver this product in .csv format.
What is the data quality of Brain Language Metrics on Earnings Calls - 4500+ US Stocks?
You can compare and assess the data quality of Brain Company using Datarade’s data marketplace.
What are similar products to Brain Language Metrics on Earnings Calls - 4500+ US Stocks?
This product has 3 related products. These alternatives include Brain Language Metrics on Company Filings for 6000+ US Stocks, Crawlbee Asset Pricing Data Bankruptcy Data Foreclosure Data Historical Dataset, and EDI Global End-of-Day/Closing Price Data for Stocks Shares, Equities from Over 170 Exchanges Worldwide. History/Time Series available. You can compare the best Stock Market Data providers and products via Datarade’s data marketplace and get the right data for your use case.