JOHANNESBURG - When the news broke last week of alleged accounting irregularities involving retail giant Steinhoff, its share price plummeted amid a flurry of investor fear and panic selling. According to Stuart Reid, the chief engineer at NMRQL Research, the warning signs were there but were ignored by many fund managers because of their inherent cognitive biases, highlighting the benefits of machine-learning-powered investing.
“Most stock crashes are preceded by warning signals, but fund managers’ inherent cognitive biases prevent them from seeing the wood for the trees,” says Reid.
This is why the algorithms behind the NMRQL SCI Balanced Fund, South Africa’s first machine-learning-powered unit trust, administered by the Sanlam Collective Investments platform, were not invested in Steinhoff. “Our algorithms stopped picking Steinhoff in 2016,” says Reid.
Although the algorithms did not know, and could not have known, about the fraud allegations, Reid says the algorithms assess a combination of structured data that includes market data, public company information, currencies, indices, economic data and risk metrics.