Enin.ai

  • Delivery Frequency/Timezone:

    Intraday or Daily/ UTC + 2

  • History:

    From 2012

  • Coverage:

    Norwegian Equities

  • Format:

    XML, JSON, CSV

Overview

Utilizing ML, Enin measures which news stories actually have predictive power, and to what degree. Despite sifting through the full breadth of available news and social media sources, they expose you to news which is both relevant and impactful. Enin analyzes the data from the perspective of risk, and displaying a unique approach to sentiment.Output sentiment is displayed in both raw & normalized format.

Data descriptions

Read_timestamp

Timestamp

Exchange_ticker

Abbreviation of the exchange

Instrument_ticker

Ticker symbol for the security

Instrument_name

Full name of the security

Exchange_name

Full name of the exchange

is_tradable

A boolean indicating whether or not the instrument was directly tradable in the time period

title

The title part of the article text

contents

The main content of the article

authors

The authors of the article, if found

combined_contents

All text of the article, combined

keywords

Topic keywords that are detected

summary

NLP-based article summary

positive_sentiment

Positive sentiment, measured as number of positive words used in the article

negatve_sentiment

Negative sentiment, measured as number of negative words used in the article

sum_sentiment

Summation of the positive and negative sentiment

normalized_positive_sentiment

Positive sentiment divided by number of words in the article

normalized_negative_sentiment

Negative sentiment divided by number of words in the article

normalized_sum_sentiment

Sum sentiment divided by number of words in the article

Benzinga’s data samples are intended to provide a data sample large enough for testing data quality and application for the financial markets.  These sample files demonstrate a sample of the formats and content that can be delivered