About Brain Language Metrics on Company Filings

The Brain Language Metrics on Company Filings dataset provides the results of an NLP system that monitors several language metrics on 10-K and 10-Q company reports for US Equities. The data covers 5,000 US Equities, starts in January 2010, and is delivered on a daily frequency. The dataset is made of two parts; the first one includes the language metrics of the most recent 10-K or 10-Q report for each firm, namely:

  1. Financial sentiment
  2. Percentage of words belonging to financial domain classified by language types (e.g. “litigious” or “constraining” language)
  3. Readability score
  4. Lexical metrics such as lexical density and richness
  5. Text statistics such as the report length and the average sentence length

The second part includes the differences between the two most recent 10-Ks or 10-Qs reports of the same period for each company, namely:

  1. Difference of the various language metrics (e.g. delta sentiment, delta readability score, delta percentage of a specific language type etc.)
  2. Similarity metrics between documents, also with respect to a specific language type (for example similarity with respect to “litigious” language or “uncertainty” language)

The analysis is available for the whole report and for specific sections of the report (e.g. Risk Factors and MD&A).

For more information, refer to Brain's summary paper.

This dataset depends on the US Equity Security Master dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.

About Brain

Brain is a Research Company that creates proprietary datasets and algorithms for investment strategies, combining experience in financial markets with strong competencies in Statistics, Machine Learning, and Natural Language Processing. The founders share a common academic background of research in Physics as well as extensive experience in Financial markets.

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Algorithm Example

from AlgorithmImports import *
from QuantConnect.DataSource import *

class BrainCompanyFilingNLPDataAlgorithm(QCAlgorithm):
    def initialize(self):
        self.set_start_date(2010, 1, 1)
        self.set_end_date(2021, 7, 8)
        self.set_cash(100000) 
        
        # Requesting data
        self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
        self.dataset_symbol = self.add_data(BrainCompanyFilingLanguageMetrics10K , self.aapl).symbol
        
        # Historical data
        history = self.history(self.dataset_symbol, 365, Resolution.DAILY)
        self.debug(f"We got {len(history)} items from our history request for {self.dataset_symbol}")
        
        
    def on_data(self, data):
        if data.contains_key(self.dataset_symbol):
            sentiment = data[self.dataset_symbol].report_sentiment.sentiment
            self.set_holdings(self.symbol, int(sentiment > 0))

Example Applications

The Brain Language Metrics on Company Filings dataset enables you to test strategies using language metrics and their differences gathered from 10K and 10Q reports. Examples include the following strategies:

Pricing

Provider offers 2 licensing options

Pricing