We use our algorithm+analyst approach to calculate accurate dividend forecast data at scale – with amount and date forecasts for over 25,000 equities and ETFs globally.
In an ever-changing world, dividend forecast data remains valuable to investment managers for stock evaluation and income planning (among many other use-cases). However, despite the accelerating growth of computing power over the past 15 years, existing offerings have not kept pace and suffer from a combination of :
- Limited coverage beyond key markets / major indices
- Inflexibility in terms of access and bundling of required coverage – Questionable accuracy in some instances
- … and uncompetitive pricing in others.
We have been forecasting dividends since 2011 and we launched our algorithm+analyst product ‘Woodseer’ in January 2017. Working from our partner EDI’s global actions corporate actions feed we apply our proprietary algorithm to calculate forecasts for over 25,000 equities and ETFs globally. Forecasts run two fiscal years ahead with dates and amounts delivered via API, FTP or client login. Our London-based analyst team work with the algorithm to review and enhance, giving us very solid headline accuracy numbers: 80% of our forecast amounts are exact or within 10%, and 80% of our ex-dates are exact or within 7 days.
Our hybrid, ‘man and machine’ methodology is the next generation approach, offering the best of both worlds in terms of accuracy at scale. Three core differentiators make Woodseer the disruptive leader over the out-dated, older forecasting approaches: 1) Coverage – 3x the coverage of other providers: very strong on both equities and ETFs 2) Accuracy – constantly improving as we implement new rules to enhance the algorithm 3) Flexibility – nimble in response to specific requirements 4) Cost-efficiencies – leveraging technology to automate, and passing savings onto our clients
We can be reached directly at email@example.com or via www.woodseer.global. We regularly provide interested clients with sample indices for review as well as detailed forecast history for back-testing and accuracy comparison.