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Machine learning-powered unit trust launched in SA

The NMRQL SCI Balanced Fund’s asset allocation and stock selection is managed using machine-learning algorithms.

NMRQL Research, a new 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 in a statement.

“The machine learning-powered, computational investment process … allows NMRQL to discover hidden patterns in underlying big data,” it said. “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.” 

According to the company, 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.

Its benchmark is the multi-asset high equity category as recorded by the Association for Savings and Investment South Africa. An annual investment fee of 0.9% is inclusive of management and administration costs, with a 10% performance fee applied should the fund outperform the average performance of all funds within the category.


“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,” said NMQRL co-founder and CEO Tom Schlebusch. 

“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 two 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.”

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.

“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,” said Jordaan.

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 a thousand different models and applying an algorithmic voting system, NMRQL is then able to produce portfolios with the best possible chance of outperformance,” Reid said.

This article was first published on TechCentral. To access the original, please click here.

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I have seen this movie before. At the time, Long Term Capital Management was the largest bankruptcy in history in the USA. Some of the managers received the Nobel Price in Economic Sciences for the Black-and-Scholes formula. It was quantitative models and algorithms built by these true geniuses that almost took down the entire financial system in the USA. After that episode you won’t impress me with algorithms any more.

I won’t put my money on a logical, “non-emotive” algorithm for making decisions on my behalf in an illogical world that is ruled by emotions.

Handing over complete control to a machine, mmmh, not for the faint-hearted.
Like the technicals vs fundamentals fence, stock-picking is also moving to a human vs machine era.
I’d love to see historical comparisons and of course where the algo’s see opportunities right now.
As is, probably for the super-rich who can afford a loss here and there, but undoubtedly a sign of what’s to come.

Their Hedge Fund has been solidly negative over the past year (-20%)

If people are not clever enough to beat the market, then why would an algorithm written by people do any better.

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