Steinhoff: a case for investments powered by machine learning
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.
“The algorithms analyse financial statement line items alongside market data and aggregate them to make a prediction. Using this information, the algorithms actively try to predict what Steinhoff and other JSE-listed shares will do. In the case of Steinhoff, it predicted the stock would go down.”
However, according to Reid, the real question should be why fund managers failed to act despite the warning signs.
“We are wired to want to minimise cognitive dissonance - this is the discomfort we feel when we disagree with the 'status quo'. For example, many would feel uncomfortable not holding Steinhoff, as it was the seventh-biggest company in the FTSE/JSE Shareholders Weighted Index and present in many large managers’ portfolios,” he says. Reid believes this behaviour can result in sub-optimal decisions that hurt investors.
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“There were many warning signs about Steinhoff which fund managers did not heed, as they appear to have been more concerned with upholding the status quo than maximising shareholder value. This behaviour leads to group think and concentration risk,” says Reid. “And, as the saying goes: if everybody’s thinking alike, somebody isn’t thinking.”
Reid says the benefit of machine learning is that it is free from cognitive biases and makes decisions objectively without any regard for what everybody else is doing.
He says that NMRQL Research are working on ways to further understand the algorithms picks so that they can pick up warning signs early on. He believes that all fund managers, including those who avoided the Steinhoff debacle, should use this opportunity to interrogate their investment processes from first principles.
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