# Equity Datasets

- [Accounting Data](https://docs.sov.ai/realtime-datasets/equity-datasets/accounting-data.md): Standardized financial accounting data across multiple US publicly traded firms.
- [Bankruptcy Predictions](https://docs.sov.ai/realtime-datasets/equity-datasets/bankruptcy-predictions.md): Chapter 7 and Chapter 11 bankruptcy predictions made easy for over 5,000 US publicly traded stocks.
- [Employee Visa](https://docs.sov.ai/realtime-datasets/equity-datasets/employee-visa.md): The H1B dataset offers quarterly insights into foreign hiring trends, job details, and wages for informed decision-making.
- [Earnings Surprise](https://docs.sov.ai/realtime-datasets/equity-datasets/earnings-surprise.md): Earnings announcements are obtained from external sources as well as estimate information leading up to the actual announcement.
- [Congressional Data](https://docs.sov.ai/realtime-datasets/equity-datasets/congressional-data.md): From filings we collect and match trades in the Senate and House and make them available within a day of processing.
- [Factor Signals](https://docs.sov.ai/realtime-datasets/equity-datasets/factor-signals.md): A financial factor dataset for in-depth company analysis and investment strategies.
- [Financial Ratios](https://docs.sov.ai/realtime-datasets/equity-datasets/financial-ratios.md): More than 80+ financial ratios calculated from financial statement and market data.
- [Government Contracts](https://docs.sov.ai/realtime-datasets/equity-datasets/government-contracts.md): The government spending data provides comprehensive information about government contracts, transactions, product specifications, entity details, locations, competition, and compensation.
- [Institutional Trading](https://docs.sov.ai/realtime-datasets/equity-datasets/institutional-trading.md): The dataset provides a comprehensive analysis of institutional investment behaviors, strategies, and portfolio dynamics assist professional investors in making informed decisions.
- [Insider Flow Prediction](https://docs.sov.ai/realtime-datasets/equity-datasets/insider-flow-prediction.md): More than 60+ insider trading features helpful for machine learning, including a flow prediction value.
- [Liquidity Data](https://docs.sov.ai/realtime-datasets/equity-datasets/liquidity-data.md): Various dataset that could help with the assesment of security liquidity to inform trading decisions.
- [Lobbying Data](https://docs.sov.ai/realtime-datasets/equity-datasets/lobbying-data.md): A ticker matched lobbying data to see fine-grained corporate lobbying behaviour.
- [News Sentiment](https://docs.sov.ai/realtime-datasets/equity-datasets/news-sentiment.md): Two types of news datasets have been developed, one is ticker-matched, and the next is theme-matched.
- [Price Breakout](https://docs.sov.ai/realtime-datasets/equity-datasets/price-breakout.md): A dataset with daily updated predictions of price breaking upwards for US Equities.
- [Risk Indicators](https://docs.sov.ai/realtime-datasets/equity-datasets/risk-indicators.md): Here we develop three tables to develop a final score of corporate risk to US equities.
- [SEC Edgar Search](https://docs.sov.ai/realtime-datasets/equity-datasets/sec-edgar-search.md): State of the art notebook tools to designed to search, retrieve, and analyze financial data from the SEC's EDGAR database. This is a work-in-progress.
- [SEC 10K Filings](https://docs.sov.ai/realtime-datasets/equity-datasets/sec-10k-filings.md): A very easily digestable dataframe format for all 10-K filings, with multiple sections, categories, and textual datapoints. This is not yet available, a work-in-progress.
- [Short Selling](https://docs.sov.ai/realtime-datasets/equity-datasets/short-selling.md): This section covers the usage of various short-selling datasets for risk analysis.
- [Wikipedia Views](https://docs.sov.ai/realtime-datasets/equity-datasets/wikipedia-views.md): A look at some of the largest firms and their daily wikipedia page views and trends.
- [Patents Data](https://docs.sov.ai/realtime-datasets/equity-datasets/patents-data.md)


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