At a Glance: S3 Short Interest and Securities Finance Data

This dataset provides accurate, transparent, and independent short interest and financing rates, along with a Richter scale for crowding and days-to-cover information.

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Basics

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S3's Short Interest and Securities Finance dataset provides accurate, transparent, and independent short interest and financing rates, along with a Richter scale for crowding and days-to-cover information.

Data is aggregated from a variety of sources, which include S3's anonymized Blacklight platform and regulatory reported numbers from every public exchange worldwide. By aggregating data directly from these unique sources and global exchanges, S3 eliminates the typical delays associated with receiving this data and the difficulty around commingling it.

S3's Enterprise dataset provides analytics on more than 40,000 securities. This data provides the necessary information to understand how short interest, finance rates, and crowded trades affect price action.

Data Overview

Asset Class: Public Companies 

Data Frequency: Daily

Delivery Frequency: Hourly

History: Data available back to 2015

aag s3 short interest

Data Methodology

To ensure timely and accurate data, S3 leverages data from multiple sources. One of the primary sources is S3's proprietary software and data, which provide a consolidated and anonymized platform featuring over five million daily transactions. Additionally, S3 sources its data using bank and broker inventory feeds from all major market participants, regulatory filings from every public exchange globally, and an in-house service desk that performs live confirmation of intraday rates from voice-brokered financing markets.

Data points are broken up into four categories:

  • Short Interest – Daily shorting activity for 40,000+ global securities
  • Financing Rates – Unbiased bid, offer and last rates from all sides of the market
  • Crowding – An indicator providing a measure of shorting and covering events relative to market float that predicts financing spikes in real-time
  • Days-to-Cover – Number of days to close out all short positions

Use Cases

Factor Creation and Alpha Generation 

  • Leverage measures of short interest shares and percent of float. This helps users predict future stock returns; short interest has been shown in academic research to be a strong predictor of them.

Identifying Short Selling, Covering, and Crowded Events 

  • Access a proprietary index score measuring the magnitude of shorting/covering activity relative to a security's float, borrow capacity, and financing rate. Short-sale constrained stocks have been shown to underperform in the market; this index score provides transparency to the true spread of the borrow/loan market.

Risk Exposure and Mitigation

  • Use short interest data as a means to predict future bad news, negative earnings surprises, and downward revisions in analyst earnings forecasts.

The details provided above are as of May 2020.

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