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Brain

Brain Language Metrics on Company Filings

Introduction

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.

For more information about the Brain Language Metrics on Company Filings dataset, including CLI commands and pricing, see the dataset listing.

About the Provider

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.

Getting Started

The following snippet demonstrates how to request data from the Brain Language Metrics on Company Filings dataset:

Select Language: 
self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
self.dataset_10k_symbol = self.add_data(BrainCompanyFilingLanguageMetrics10K , self.aapl).symbol
self.dataset_all_symbol = self.add_data(BrainCompanyFilingLanguageMetricsAll, self.aapl).symbol

self.universe_10k = self.add_universe(BrainCompanyFilingLanguageMetricsUniverse10K, self.universe_selection)
self.universe_all = self.add_universe(BrainCompanyFilingLanguageMetricsUniverseAll, self.universe_selection)

Data Summary

The following table describes the dataset properties:

PropertyValue
Start DateJanuary 2010
Asset Coverage*5,000 US Equities
Data DensitySparse
ResolutionDaily
TimezoneUTC
The coverage includes all assets since the start date. It increases over time.

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:

Disclaimer: The dataset is provided by the data provider for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor do they constitute an offer to provide investment advisory or other services by the data provider.

For more example algorithms, see Examples.

Data Point Attributes

The Brain Language Metrics on Company Filings dataset provides BrainCompanyFilingLanguageMetrics and BrainCompanyFilingLanguageMetricsUniverse objects.

BrainCompanyFilingLanguageMetrics Attributes

BrainCompanyFilingLanguageMetrics objects have the following attributes:

BrainCompanyFilingLanguageMetrics Select Language:
  • sentence_count: Int32
  • mean_sentence_length: decimal
  • sentiment: decimal
  • uncertainty: decimal
  • litigious: decimal
  • constraining: decimal
  • interesting: decimal
  • readability: decimal
  • lexical_richness: decimal
  • lexical_density: decimal
  • specific_density: decimal
  • similarity: BrainCompanyFilingLanguageMetricsSimilarityDifference

BrainCompanyFilingLanguageMetricsUniverse Attributes

BrainCompanyFilingLanguageMetricsUniverse objects have the following attributes:

BrainCompanyFilingLanguageMetricsUniverse Select Language:
    Language Metric score by report part
  • report_sentiment: BrainCompanyFilingLanguageMetrics
    • Language Metric score by risk factor statement part
  • risk_factors_statement_sentiment: BrainCompanyFilingLanguageMetrics
    • Language Metric score by Management Discussion Analyasis Of Financial Condition And Results Of Operations
  • management_discussion_analyasis_of_financial_condition_and_results_of_operations: BrainCompanyFilingLanguageMetrics
    • Time the data became available
  • end_time: DateTime
    • The associated underlying price data if any
  • underlying: BaseData
    • Gets or sets the contracts selected by the universe
  • filtered_contracts: HashSet<Symbol>
    • Gets the data list
  • data: List<BaseData>
    • Market Data Type of this data - does it come in individual price packets or is it grouped into OHLC.
  • data_type: MarketDataType
    • True if this is a fill forward piece of data
  • is_fill_forward: bool
    • Current time marker of this data packet.
  • time: DateTime
    • Symbol representation for underlying Security
  • symbol: Symbol
    • Value representation of this data packet. All data requires a representative value for this moment in time. For streams of data this is the price now, for OHLC packets this is the closing price.
  • value: decimal
    • As this is a backtesting platform we'll provide an alias of value as price.
  • price: decimal

Requesting Data

To add Brain Language Metrics on Company Filings data to your algorithm, call the add_data method. Save a reference to the dataset Symbol so you can access the data later in your algorithm.

Select Language: 
class BrainCompanyFilingNLPDataAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2010, 1, 1)
        self.set_end_date(2021, 7, 8)
        self.set_cash(100000)
        
        self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
        self.dataset_10k_symbol = self.add_data(BrainCompanyFilingLanguageMetrics10K, self.aapl).symbol
        self.dataset_all_symbol = self.add_data(BrainCompanyFilingLanguageMetricsAll, self.aapl).symbol

Accessing Data

To get the current Brain Language Metrics on Company Filings data, index the current Slice with the dataset Symbol. Slice objects deliver unique events to your algorithm as they happen, but the Slice may not contain data for your dataset at every time step. To avoid issues, check if the Slice contains the data you want before you index it.

Select Language: 
def on_data(self, slice: Slice) -> None:
    if slice.contains_key(self.dataset_10k_symbol):
        data_point = slice[self.dataset_10k_symbol]
        self.log(f"{self.dataset_10k_symbol} report sentiment at {slice.time}: {data_point.report_sentiment.sentiment}")

    if slice.contains_key(self.dataset_all_symbol):
        data_point = slice[self.dataset_all_symbol]
        self.log(f"{self.dataset_all_symbol} report sentiment at {slice.time}: {data_point.report_sentiment.sentiment}")

To iterate through all of the dataset objects in the current Slice, call the get method.

Select Language: 
def on_data(self, slice: Slice) -> None:
    for dataset_symbol, data_point in slice.get(BrainCompanyFilingLanguageMetrics10K).items():
        self.log(f"{dataset_symbol} report sentiment at {slice.time}: {data_point.report_sentiment.sentiment}")

    for dataset_symbol, data_point in slice.get(BrainCompanyFilingLanguageMetricsAll).items():
        self.log(f"{dataset_symbol} report sentiment at {slice.time}: {data_point.report_sentiment.sentiment}")

Historical Data

To get historical Brain Language Metrics on Company Filings data, call the history method with the dataset Symbol. If there is no data in the period you request, the history result is empty.

Select Language: 
# DataFrames
ten_k_history_df = self.history(self.dataset_10k_symbol, 100, Resolution.DAILY)
all_history_df = self.history(self.dataset_all_symbol, 100, Resolution.DAILY)
history_df = self.history([self.dataset_10k_symbol, self.dataset_all_symbol], 100, Resolution.DAILY)

# Dataset objects
ten_k_history_bars = self.history[BrainCompanyFilingLanguageMetrics10K](self.dataset_10k_symbol, 100, Resolution.DAILY)
all_history_bars = self.history[BrainCompanyFilingLanguageMetricsAll](self.dataset_all_symbol, 100, Resolution.DAILY)

For more information about historical data, see History Requests.

Universe Selection

To select a dynamic universe of US Equities based on Brain Language Metrics on Company Filings data, call the add_universe method with the BrainCompanyFilingLanguageMetricsUniverseAll class or the BrainCompanyFilingLanguageMetricsUniverse10K class and a selection function.

Select Language: 
def initialize(self) -> None:
    self._universe = self.add_universe(BrainCompanyFilingLanguageMetricsUniverseAll, self.universe_selection)

def universe_selection(self, alt_coarse: List[BrainCompanyFilingLanguageMetricsUniverseAll]) -> List[Symbol]:
    return [d.symbol for d in alt_coarse \
                if d.report_sentiment.sentiment > 0 \
                and d.management_discussion_analyasis_of_financial_condition_and_results_of_operations.sentiment > 0]

For more information about dynamic universes, see Universes.

Universe History

You can get historical universe data in an algorithm and in the Research Environment.

Historical Universe Data in Algorithms

To get historical universe data in an algorithm, call the history method with the Universe object and the lookback period. If there is no data in the period you request, the history result is empty.

Select Language: 
# DataFrame example where the columns are the universe object attributes: 
history_df = self.history(self._universe, 30, Resolution.DAILY, flatten=True)

# Series example where the values are lists of the universe objects: 
universe_history = self.history(self._universe, 30, Resolution.DAILY)
for (_, time), universeDay in universe_history.items():
    for language_metrics in universeDay:
        self.log(f"{language_metrics.symbol} sentiment at {language_metrics.end_time}: {language_metrics.report_sentiment.sentiment}")

Historical Universe Data in Research

To get historical universe data in research, call the universe_history method with the Universe object, a start date, and an end date. This method returns the filtered universe. If there is no data in the period you request, the history result is empty.

Select Language: 
# DataFrame example where the columns are the universe object attributes: 
history_df = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)

# Series example where the values are lists of the universe objects: 
universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
for (_, time), universeDay in universe_history.items():
    for language_metrics in universeDay:
        print(f"{language_metrics.symbol} sentiment at {language_metrics.end_time}: {language_metrics.report_sentiment.sentiment}")

You can call the history method in Research.

Remove Subscriptions

To remove a subscription, call the remove_security method.

Select Language: 
self.remove_security(self.dataset_10k_symbol)
self.remove_security(self.dataset_all_symbol)

If you subscribe to Brain Language Metrics on Company Filings data for assets in a dynamic universe, remove the dataset subscription when the asset leaves your universe. To view a common design pattern, see Track Security Changes.

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:

Disclaimer: The dataset is provided by the data provider for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor do they constitute an offer to provide investment advisory or other services by the data provider.

For more example algorithms, see Examples.

You can also see our Videos. You can also get in touch with us via Discord.

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