Biased Decisions: Machine Learning VS Human Behaviour

As humans, we are prone to make irrational decisions. It’s in our nature, and in recent years, behavioural economists and cognitive psychologists have shed light on the extensive range of irrational decisions taken by most humans (HBR). Identified in this range are things like loss aversion and confirmation bias.

How can Machine Learning help in this case?

By implementing ML into the investment process, asset managers can now eliminate systematic biases by stitching together a broad set of data sources about an individual or team’s trading history, communication patterns, psychometric attributes, and time-management practices. All of which allow firms to identify drivers of performance and behavioural root causes at a more granular and individualised level than previously (McKinsey) — see the summary graphic below from McKinsey.

Can ML replace humans in this case?

Although ML has huge potential to increase the ability of investors to find outperforming stocks, humans will be needed to develop the right algorithms and exercise fair investment judgement (FT). ML comes with some limitations, as it may have biases derived from the data used to train algorithms or statistical quirks in its methodologies, and in order to detect and limit these biases, companies need talented and trained data scientists to ensure that ML-supported decision making is fair (McKinsey).



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The Data Analysis Bureau

The Data Analysis Bureau

We are a Data Science and Data Engineering Innovation Agency specialising in Machine Learning.