A machine learning powered unit trust fund has been launched in South Africa. The fund uses machine learning to drive research, analysis and stock selection.
The NMRQL SCI Balanced Fund, administered by the Sanlam Collective Investments platform, is a FSB-approved Collective Investment Scheme that is Regulation 28 compliant, and aims to achieve steady long-term growth of capital and income. This will be achieved by investing in a diversified portfolio of domestic and international assets, where the asset allocation and stock selection is systematically managed using machine learning algorithms.
The machine learning powered, computational investment process – the first of its kind in South Africa – allows NMRQL to discover hidden, patterns in underlying big data. Once discovered, these patterns can be exploited to forecast returns across all asset classes and markets, resulting in steady long-term growth of capital and income.
The fund is suitable for institutions, fund of funds and High Net-worth Individuals with a moderate aggressive risk appetite and an investment horizon of five years or longer. It may comprise a combination of assets in liquid form, money market and interest-bearing instruments, bonds, corporate debt, equity securities, property securities, preference shares and convertible equities. Its benchmark is the Multi-Asset High Equity category as recorded by the Association for Savings and Investment South Africa (ASISA). The annual investment fee of 0.9% is inclusive of a management and admin fee, with a 10% performance fee applied should the fund outperform the average performance of all funds within the Multi-Asset High Equity ASISA category.
According to NMRQL co-founder and CEO, Tom Schlebusch, this new investment philosophy essentially changes the investment management process from a biased, human-centric investment process to a non-emotive, unbiased algorithmic-driven process that is continuously learning and adapting to changing environments.
“Machine learning equips fund managers with the tools to assess historical and present data, to help predict future risks and returns based on large volumes of data. At NMRQL we process around 2 million data points each time we rebalance our portfolio. This could include quantitative, fundamental, economic or technical variables in order to discover and exploit repeatable patterns, helping us achieve our goal of delivering superior returns for clients.”
Michael Jordaan, co-founder of NMRQL, says that machine learning has already disrupted the fund management industry globally. “The launch of this fund in South Africa marks the start of a paradigm shift that the local investment management industry will soon experience.
“In addition to the vast amount of data that the algorithm is able to process, the investment philosophy eliminates emotive decision making, which allows the model to remain rational at all times.
“As humans we suffer from various cognitive biases. These biases negatively impact our objectivity and reasoning skills daily, and are compounded when financial repercussions are involved,” explains Jordaan.
Stuart Reid, Chief Engineer at NMRQL, says that the NMRQL algorithm is also testable, and uses a walk forward approach allowing the fund managers to use historical data to investigate exactly how the fund would have behaved using only information available at that point in time. By using more than 1000 different models and applying an algorithmic voting system, NMRQL is then able to produce portfolios with the best possible chance of outperformance.
The fund is currently available on the Glacier and Momentum platforms.