Using Machines to Find Sustainable, Growing Dividends

Sri Iyer, Managing Director, and Head of Systematic Strategies at Guardian Capital joined us for an intriguing discussion about how he is spearheading his firm's leadership and long-term plan to use and teach machines, equipped with AI to solve one of the quintessential problems of the investment industry – can you create sustainable yield for the long term?

These days a lot of investors are chasing yield, as equity valuations are at historic high and both dividend and bond market yields are historically low, and they mostly approach the task as a near-term venture.

At the institutional and pension level, however, this is a very big problem to solve, and Iyer's work is the quest for a sustainable dividend strategy that will help satisfy, to a high degree, long term liabilities that lie 15-20 years out.

Events of the last 10-15 years, unrecovered market losses, and historic rock-bottom low and negative yields have forced the future valuation of unfunded liabilities faced by pensions, as well as individual investors, to stratospheric requirements, e.g. based on current yields of 1.7% for the U.S. 10-year treasury, it takes roughly $2.94-million in assets to fund a $50,000 annual pension. At a 4% yield it would take a considerably lower sum of $1.25-million. Finding a way to solve this income vs. asset requirements deficiency has big ramifications.

Iyer says that since all relevant, known, market financial data are, for the most part, arbitraged away, the edge lies in capturing intelligence from all the new (digital, non-financial) data that is not already part of the market's efficient value. For context, Iyer adds that, "90% of the world's data was created in the last two years."

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