ETF model portfolio provider, Algo-Chain, has announced the launch of its ETF Portfolio AI Toolkit available as on online subscription.
The firm writes that the ETF Portfolio AI Toolkit is a one-stop shop for investment managers and financial advisers who are looking to deploy the benefits of automation and machine learning tools as they design and manage ETF strategies. Built around a framework of machine learning signals and traditional technical signals, the toolkit reduces the time it takes to identify and analyse potential investment opportunities and deploy them within target risk portfolios.
Augmenting the portfolio construction process with AI based tools and techniques, saves investors extensive time as they research which exposures to use, the firm says. “What used to take days can now be achieved in minutes. Algo-Chain’s platform allows an investor to rapidly screen large sets of ETF data and macro-economic data and instantly try out new ideas in real time.
“The tool provides an extensive collection of ETF model portfolios with the aim to increase the likelihood of delivering top quartile performance by selecting asset allocation models that have worked best in similar situations. Technical signals and machine learning signals based on macro-economic data allow the user to monitor and identify tactical opportunities on a daily basis. The framework enables wealth managers to offer easily bespoke solutions to their clients.”
Allan Lane, CEO and co-Founder of Algo-Chain says: “We are excited with this latest launch of our Model Portfolios platform which extends the offering with an AI ETF Search capability. As a fund selector it is no longer an option to ignore ETFs, but with so many new products coming to the market, that in itself creates its own problem. Many investment managers will oversee multiple sets of Model Portfolios, with and without an ESG theme, across multiple risk categories and in different currencies, resulting in a large number of permutations that need addressing at the portfolio construction stage.
“Given that a portfolio’s asset allocation accounts for the lion’s share of a portfolio’s return, it seemed only natural to design an algorithm that searches through an extensive range of portfolio allocations that can give the manager an edge. The portfolios can be augmented by an intelligent search process that can make sense of the vast amount of data embedded in the close to USD10 trillion ETF ecosystem, assisting in the process of tactical asset allocation.”
