London-based fintech company Algo-Chain has introduced a comprehensive set of ETF-focused portfolio tools powered by the GPT 3.5 Turbo artificial intelligence framework.
Allan Lane, CEO and co-Founder of Algo-Chain.
Available through online subscription, the ETF Portfolio AI Toolkit utilizes advanced data science to assist investment managers and financial advisors in designing and managing cost-efficient ETF-based investment strategies.
The toolkit was developed by Algo-Chain’s founding partners Dr. Allan Lane and Dr. Irene Bauer, both of whom hold PhDs in Mathematics.
Harnessing machine learning capabilities, the toolkit augments the portfolio construction process by drastically reducing the time it takes to identify and analyze potential investment opportunities and deploy them within target risk portfolios.
Algo-Chain’s platform allows investors to rapidly screen a comprehensive universe of ETFs and analyze a wide variety of macroeconomic data to try out new ideas in real-time.
The toolkit also comes with an extensive suite of risk-rated ETF model portfolios, including ESG-integrated portfolios and portfolios featuring crypto-related ETPs. Helping to reduce the decision burden to a minimum, the readymade model portfolios are based on asset allocations that have historically performed well under similar circumstances.
For those who wish to take a more active investment approach, Algo-Chain notes that the toolkit’s machine-learning capabilities may be deployed to analyze changing macroeconomic data, potentially helping to identify tactical opportunities on a daily basis.
Allan Lane, CEO and co-Founder of Algo-Chain commented: “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; however, so many new products coming to market can create a problem. Many investment managers will oversee multiple sets of Model Portfolios 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 $10 trillion ETF ecosystem, assisting in the process of tactical asset allocation.”