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:
- Financial sentiment
- Percentage of words belonging to financial domain classified by language types (e.g. “litigious” or “constraining” language)
- Readability score
- Lexical metrics such as lexical density and richness
- 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:
- Difference of the various language metrics (e.g. delta sentiment, delta readability score, delta percentage of a specific language type etc.)
- 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:
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) _symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
_dataset10KSymbol = AddData<BrainCompanyFilingLanguageMetrics10K>(_symbol).Symbol;
_datasetAllSymbol = AddData<BrainCompanyFilingLanguageMetricsAll>(_symbol).Symbol;
_universe10k = AddUniverse<BrainCompanyFilingLanguageMetricsUniverse10K>(UniverseSelection);
_universeAll = AddUniverse<BrainCompanyFilingLanguageMetricsUniverseAll>(UniverseSelection);
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:
- Using the similarity among reports to determine position sizing of securities. Some examples are discussed in Lazy Prices, Cohen et al. 2018 and The Positive Similarity of Company Filings and the Cross-section of Stock Returns, M. Padyšák 2020.
- Using the sentiment of the latest report to determine the portfolio allocation to give to each security in the universe.
- Using levels of uncertainty, readability, or litigious language in the report to determine position sizing of securities.
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:
BrainCompanyFilingLanguageMetricsUniverse Attributes
BrainCompanyFilingLanguageMetricsUniverse objects have the following attributes:
Requesting Data
To add Brain Language Metrics on Company Filings data to your algorithm, call the AddDataadd_data method. Save a reference to the dataset Symbol so you can access the data later in your algorithm.
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 namespace QuantConnect
{
public class BrainCompanyFilingNLPDataAlgorithm : QCAlgorithm
{
private Symbol _symbol, _dataset10KSymbol, _datasetAllSymbol;
public override void Initialize()
{
SetStartDate(2010, 1, 1);
SetEndDate(2021, 7, 8);
SetCash(100000);
_symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
_dataset10KSymbol= AddData<BrainCompanyFilingLanguageMetrics10K>(_symbol).Symbol;
_datasetAllSymbol= AddData<BrainCompanyFilingLanguageMetricsAll>(_symbol).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.
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}") public override void OnData(Slice slice)
{
if (slice.ContainsKey(_dataset10KSymbol))
{
var dataPoint = slice[_dataset10KSymbol];
Log($"{_dataset10KSymbol} report sentiment at {slice.Time}: {dataPoint.ReportSentiment.Sentiment}");
}
if (slice.ContainsKey(_datasetAllSymbol))
{
var dataPoint = slice[_datasetAllSymbol];
Log($"{_datasetAllSymbol} report sentiment at {slice.Time}: {dataPoint.ReportSentiment.Sentiment}");
}
}
To iterate through all of the dataset objects in the current Slice, call the Getget method.
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}")
public override void OnData(Slice slice)
{
foreach (var kvp in slice.Get<BrainCompanyFilingLanguageMetrics10K>())
{
var datasetSymbol = kvp.Key;
var dataPoint = kvp.Value;
Log($"{datasetSymbol} report sentiment at {slice.Time}: {dataPoint.ReportSentiment.Sentiment}");
}
foreach (var kvp in slice.Get<BrainCompanyFilingLanguageMetricsAll>())
{
var datasetSymbol = kvp.Key;
var dataPoint = kvp.Value;
Log($"{datasetSymbol} report sentiment at {slice.Time}: {dataPoint.ReportSentiment.Sentiment}");
}
}
Historical Data
To get historical Brain Language Metrics on Company Filings data, call the Historyhistory method with the dataset Symbol. If there is no data in the period you request, the history result is empty.
# 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)
// Dataset objects
var tenKHistory = History<BrainCompanyFilingLanguageMetrics10K>(_dataset10KSymbol, 100, Resolution.Daily);
var allHistory = History<BrainCompanyFilingLanguageMetricsAll>(_datasetAllSymbol, 100, Resolution.Daily);
// Slice objects
var history = History(new[] {_dataset10KSymbol, _datasetAllSymbol}, 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 AddUniverseadd_universe method with the BrainCompanyFilingLanguageMetricsUniverseAll class or the BrainCompanyFilingLanguageMetricsUniverse10K class and a selection function.
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] private Universe _universe;
public override void Initialize()
{
_universe = AddUniverse<BrainCompanyFilingLanguageMetricsUniverseAll>(altCoarse =>
{
return from d in altCoarse.OfType<BrainCompanyFilingLanguageMetricsUniverseAll>()
where d.ReportSentiment.Sentiment > 0m &&
d.ManagementDiscussionAnalyasisOfFinancialConditionAndResultsOfOperations.Sentiment > 0m
select d.Symbol;
});
}
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 Historyhistory method with the Universe object and the lookback period. If there is no data in the period you request, the history result is empty.
var universeHistory = History(_universe, 30, Resolution.Daily);
foreach (var universeDay in universeHistory)
{
foreach (BrainCompanyFilingLanguageMetricsUniverse10K languageMetrics in universeDay)
{
Log($"{languageMetrics.Symbol} sentiment at {languageMetrics.EndTime}: {languageMetrics.ReportSentiment.Sentiment}");
}
} universe_history = self.history(self._universe, 30, Resolution.DAILY)
for (_, time), universeDay in universe_history.items():
for language_metrics in universeDay:
self.log(f"{language_metrics.symbol} sentiment at {language_metrics.end_time}: {language_metrics.report_sentiment.sentiment}")
Historical Universe Data in Research
To get historical universe data in research, call the UniverseHistoryuniverse_history method with the Universe object, a start date, and an end date. This method returns the filtered universe. If there is no data in the period you request, the history result is empty.
var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
foreach (var universeDay in universeHistory)
{
foreach (BrainCompanyFilingLanguageMetricsUniverse10K languageMetrics in universeDay)
{
Console.WriteLine($"{languageMetrics.Symbol} sentiment at {languageMetrics.EndTime}: {languageMetrics.ReportSentiment.Sentiment}");
}
} 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 Historyhistory method in Research.
Remove Subscriptions
To remove a subscription, call the RemoveSecurityremove_security method.
self.remove_security(self.dataset_10k_symbol) self.remove_security(self.dataset_all_symbol)
RemoveSecurity(_dataset10KSymbol); RemoveSecurity(_datasetAllSymbol);
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:
- Using the similarity among reports to determine position sizing of securities. Some examples are discussed in Lazy Prices, Cohen et al. 2018 and The Positive Similarity of Company Filings and the Cross-section of Stock Returns, M. Padyšák 2020.
- Using the sentiment of the latest report to determine the portfolio allocation to give to each security in the universe.
- Using levels of uncertainty, readability, or litigious language in the report to determine position sizing of securities.
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.
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