RavenPack, a financial technology company in the text-analytics space, has launched an Insider Transactions Data Solution.
Although RavenPack already provides news analytics data on insider transactions - that is, the buying and selling of shares held by executives in their own companies - it now combines this with over 20 years of detailed transaction data that companies must file with their respective regulatory bodies, adding depth and breadth to the offering.
“RavenPack Insider Transactions gives investors a great resource from which to generate orthogonal alpha. Combined with our award-winning news sentiment analytics, our new highly detailed insider dealings offering provides a very rich resource for active investors and traders.” Says Armando Gonzalez, CEO of RavenPack.
Research conducted by RavenPack’s Data Science Team shows that the behavior of corporate insiders provides consistent predictive power for the performance of a company and its stock price. Signs of increased insider buying often precede outperformance and vice versa for insider selling.
A strategy based on insider trading data alone achieved annualized returns of 8.6% and 13.9% with a Global Mid/Large-Cap and Small-Cap strategy, respectively, and Information Ratios of 1.82 and 2.64, based on a one-day holding period.
Adding a news sentiment overlay provided more reliable signals, especially insider buy signals following a period of negative sentiment, suggesting insiders knew company fundamentals were in the process of turning around and improving after a hiatus.
RavenPack Insider Transactions provides a comprehensive dataset of exceptionally fine granularity, including 23 different types of insider dealing, tracking 550,000 insiders, from 50,000 different companies - which is 90% of the investable universe - and includes details such as the number of shares transacted, the total transaction value, and the company sentiment at the time of the transaction.
In addition, the system tracks the positions of individuals within the company, any other positions they might have in other companies and incorporates a ranking system to distinguish insiders that carry the most weight and are privy to the most sensitive information within a company.
All transactions are tagged to the relevant security’s ticker and unique identifiers, mapped to RavenPack’s existing reference data service with over 310,000 entities, and the data is provided in an easily downloadable CSV format.
The data can be incorporated directly into databases for algorithmic strategies and systematic trading models. The large archive allows comprehensive backtests to be performed and the data’s low correlation with existing factors gives it strong additive value. Given its long historical archive it can be used by business analysts and academics for research purposes.
Why Choose RavenPack for Insider Transactions Data?
RavenPack’s Insider Transactions covers global stocks whilst many insider transaction data providers just focus on the US, Canada, and the UK where regulatory filings are easier to access. Most providers only track high-level executives whilst research shows that tracking lower-level and non-executive positions can also be important, especially in Asia.
RavenPack’s solution also includes benefits such as access to a self-service data platform that enables the creation of custom factors - either “pulled” on demand or pushed in real-time via our API.
We also offer 24/7 access to premium support from client services and our award-winning data science team, who also provide regular research and ideas. An additional QA process ensures data is available in mission-critical applications and production environments.
RavenPack (www.ravenpack.com) is the leading big data analytics provider for financial services. The company’s products allow clients to enhance returns, reduce risk and increase efficiency by systematically incorporating the effects of public information in their models or workflows. RavenPack’s clients include the most successful hedge funds, banks, and asset managers in the world.