Unit trust powered by machine learning

UNBIASED: USES NON-EMOTIVE ALGORITHM PROCESS

MRQL Research’s fund uses non-emotive algorithm investment process.

Machine learning ‘already disrupting’ the global asset management industry.

NMRQL Research, a financial technology start-up co-founded by former First National Bank CEO Michael Jordaan, has launched a unit trust fund that uses machine learning to drive research, analysis and stock selection.

The NMRQL SCI Balanced Fund, administered by the Sanlam Collective Investments platform, is a collective investment scheme approved by the Financial Services Board that 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,” NMRQL said.

The company says the fund is suitable for institutions, fund of funds and high-net-worth individuals with a moderately 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.

An annual investment fee of 0.9% is inclusive of costs, with a 10% performance fee applied should the fund outperform the average performance of all funds within the category.

Nonemotive

“This new investment philosophy essentially changes the investment management process from a biased, human-centric investment process to a nonemotive, unbiased algorithmic-driven process that is continuously learning and adapting to changing environments,” said NMQRL CEO Tom Schlebusch.

Jordaan, who co-founded NMRQL with Schlebusch, said machine learning has already disrupted the fund management industry globally. “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.

Stuart Reid, chief engineer at NMRQL, said the algorithm the company uses is “testable” and allows 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 1 000 models and applying an algorithmic voting system, it is then able to produce portfolios with the best possible chance of outperformance.”