The offering will be available as an online subscription for investment managers and financial advisers, offering them the ability to design and manage ETF strategies with the help of automation and machine learning.
ETFs set to make up 24% of market AUM by 2027
The toolkit incorporates the GPT-3.5-Turbo framework to “augment the delivery of ETF-related knowledge and provide a collection of ready-made risk-rated model portfolios, including ESG and crypto portfolios”, the company said.
The augmentation of the portfolio construction process will save investors “extensive time, as they research which exposures to use”, Algo-Chain noted, considering the type of research and insight that would usually take days “can now be achieved in minutes”.
More specifically, the tool will provide an extensive collection of ETF model portfolios aiming to deliver top quartile performance by selecting asset allocation models “that have worked best in similar situations”.
Allan Lane, CEO and co-founder of Algo-Chain, said fund selectors can no longer afford to ignore ETFs but, with such a wide pool of options to choose from, “that in itself creates its own problem”.
Asset managers call for creation of equities and ETFs consolidated tape
He added there is a risk investment managers will “oversee multiple sets of model portfolios” which could lead to the tackling of a large number of permutations during the portfolio construction stage.
Lane continued: “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 $10trn ETF ecosystem, assisting in the process of tactical asset allocation.”
