How AI can inform a dividend strategy
That process is now at work in two new funds from Guardian Capital, the Guardian i3 Global Dividend Growth Fund and the Guardian i3 Canadian Dividend Growth Fund. Cilio explained that the AI tools used in managing these funds are designed to evaluate stocks against global peers. In the case of the Canadian fund, that helps correct against the tendency for Canadian equity strategies to be dominated by a handful of companies.
In both the Canadian and global equity strategies Guardian just launched, Cilio explained that the AI tools allow his team to predict forward dividend growth of a company over time, while they measure the sustainability of those dividends. This allows them to steer the portfolio towards companies that may pay out slightly less, but who grow their dividends while avoiding companies who may be unable to sustain high dividend growth over time. Those outcomes, he says, tend to correlate well to long-term equity performance, especially the protection of capital in the long-term.
The broader application of AI feeds into what Cilio calls the firm’s GPS investment philosophy. GPS stands for growth, payout, and sustainability of cash flows. The AI tools, he explains, allow his management team to focus on those three core metrics in their analysis. The result, he says, is that portfolios can be high conviction, with lower turnover, and a more concentrated set of names. All of that is informed by the application of the right AI for the job.
The right AI tool for the right AI task
The core AI tool that Cilio and his team have been using since 2017 is not an LLM. It’s a numerate machine learning model algorithm that goes beyond traditional linear statistical models. In the case of these products, Cilio and his team use that AI model to looks for dividend growth, dividend sustainability, and earnings growth. That more traditional AI tool tends to be more robust in the face of outliers, spurious data, and the risks of hallucination which can derail the newer LLMs. They can also control for biases in input data and see interrelationships between these data that linear models can’t.
There is also space, Cilio says, for the application of generative AI tools, namely in the digestion and analysis of unstructured text data. LLMs, he says, can replace the three analysts who would need days to pore over 500 documents. The LLMs can speed qualitative analysis while machine learning AI can power quantitative analysis. The whole process still relies on a human at the core.
