Using proprietary technology including machine learning and smart scraping coupled with an international team of expert data analysts, 2iQ Research has built a comprehensive global database tracking corporate insider transaction information. Corporate Insiders have the most timely and accurate information about the prospects of their companies and their trading activity offers an informational advantage to both fundamental and systematic investors. The database is fully point-in-time.
The product information and content statistics contained in this document are as of April 2019.
The 2iQ Research Global Insider Transaction data covers over 60,000 securities across 50 countries with an average of 12 years history across regions. The database contains more than 8 million transactions by over 450,000 corporate insiders and 2iQ processes an average of 3,000 daily transactions. The chart below highlights the regional coverage by number of companies with transactions by year through 2018.
The chart below highlights the number of transactions per region through 2018.
2iQ Research focuses exclusively on collecting the most accurate and timely global insider trading data and has extensive experience working with this kind of information. The process begins with advanced machine learning techniques to collect, screen, and clean the data. 2iQ’s automated process reads all filings related to a company, regardless of category, to ensure all possible information related to trading in a company’s shares is captured. Over 100,000 sources are tracked and information is captured from filings in over 20 languages. Once the data collection effort is complete, the next step in the process is a rigorous review of the content for accuracy.
Over the 15+ years 2iQ has been focused on insider transaction data, it has developed an extensive set of proprietary tests that review the data for accuracy and match transaction entries to ensure proper identification and alignment between trades and the company and corporate insider. This sophisticated verification, matching, and error-checking process flags suspicious transactions and triggers manual review by industry experts.
In addition to collecting the data, 2iQ provides standardized data representations through time and across regions. This includes normalizing an insider’s hierarchical position to one of a hundred Insider Relation descriptions (e.g. CEO, CIO, 5% Shareholder, Head of Division, etc.) and further categorizing those Insider Relations into groups based on where in a company’s corporate hierarchy an insider sits. This allows for both quick and easy filtering alongside much more granular searches to be completed. 2iQ also detects and includes useful information on each trade, including labels for Options Related Transactions, M&A, Automated (10b5-1) trades, and other relevant events.
Example Use Case
There is a substantial body of academic and practitioner research focused on the linkages between corporate insider’s trading patterns and the future returns of their companies. Much of the work shows that while insider transaction data has strong predictive power when applied correctly, it is important to properly filter out the signal from the transactions that don’t include pertinent information. This includes identifying and ignoring transactions that are motivated by a need for liquidity, risk diversification, or are forced due to option expiration, M&A, or other structural reasons.
One application of insider transaction data is identifying patterns of insider buying and selling that can help predict future M&A. A large reduction in the number and value of insider sale transactions is one potential signal of future M&A activity. The chart below shows the aggregate insider transactions for E.I. du Pont de Nemours and Company for the quarters preceding and following the December 11, 2015 merger announcement with Dow Chemical.
Notice that the first three quarters of 2014 averaged $31.5 million in Insider Sales. Compare this to 2015 where there was about $4.7 million in sales in Q1, less than $500,000 sales in Q2, and zero insider sales in Q3. Additionally, both Q2 and Q3 saw insider purchases on top of the dramatically reduced insider selling. Identifying these patterns in insider transaction activity can help identify companies who may be takeover candidates.
A second example of how the content can be used is predicting future company performance based on specific kinds of insider activity. One strong signal of positive future corporate performance is when open-market purchases are made by company insiders, particularly when the transactions are of substantial size and the insider is a top-level executive at the company. One of the primary advantages of the 2iQ DataFeed is the work it has done classifying insiders by position in the company. The chart below shows the distribution of trades by corporate insider hierarchy level, along with a description of who is included in each of the different levels. More senior ranking insiders are most likely to have access to the best information on the future prospects of their company, by only including transactions made by insiders in the A or B bucket a user can narrow the focus to those trades most likely to include forward-looking information.
Thanks to the normalization performed by 2iQ, it is easy to identify a person’s position within the firm and to filter down to the highest-level insiders. The last step is to identify instances where top-level insiders have made a substantial personal investment. To do so, only trades where the purchase price is greater than $250,000 will be included.
The chart above shows each region ranked by the number of trade ideas per year generated by the top-level insider open-market purchase signal. Asia has consistently produced the largest number of trade ideas per year with Europe and North America each having a substantial number of trade ideas as well.
The 2iQ Research Global Insider Transaction DataFeed gives users the ability to understand corporate insider’s trading activity for a global universe of listed securities.
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