For an investment specialist, an appreciation of how the world is changing is vital, and indeed we use technology more and more to help identify investment opportunities and trends. This is exemplified by the rise of artificial intelligence and machine learning and a whole new breed of investment options, based on data analysed almost solely by machines.

Computer-based investment programmes do of course have their advantages and disadvantages. A computer reduces staff costs and can operate from anywhere. It can also react more quickly to events and news flow and make decisions in fractions of a second. You could say a computer’s lack of emotion is a strong positive attribute when it comes to investment decisions, however understanding emotions and human nature can be essential to investment success, and these are hard things for a computer to model.

Let’s look at some specific issues of today’s market. We all know electric vehicles are going to play a much more significant role in meeting our future transport needs. But how does a company like Tesla, who manufacture only a fraction of the number of cars of any other leading brand, justify a valuation far greater than its competitors. Can they fill their potential and maintain a technological edge over the likes of Volkswagen or Ford? How vulnerable is the Tesla brand to Elon Musk’s image? On the opposite side, is the oil industry now so vulnerable that peak oil (the theoretical point when production declines because there is little left to find) is irrelevant?! These questions really base their answers on assumptions and the forward thinking of investors. Something a machine will struggle with.

The Tesla example is a story about valuing growth aspirations; however there are a multitude of investment selection processes that are driven by historical data. In these scenarios, a computer can have a major role to the point that human intervention is hardly required. In part, the recent rise of Exchange Traded Funds (ETF’s) has been driven by something called factor investing. Principally factor investing uses data to identify sectors of a market that have historically grown faster than the leading indices. For example, research shows that mid-sized companies have tended to grow faster (albeit with higher risk) than large capitalisation companies. Companies can also be found that clearly have strong earnings momentum or are undervalued by historical standards (note there might be good reason technology changes!). It might be that a recent dip in share price created an opportunity and it takes a computer to find that opportunity quickly enough to profit from it.

In summary, technology is changing the investment landscape dramatically. It can help lower the cost of your investment choices and broaden the opportunities you have available. It should not though, be the only approach to follow, as it is unlikely to respond to short/medium term economic changes. Understanding how an economic cycle works and how we react in different circumstances is where your investment specialist will add value to your portfolio. 

– Report by Will Roberts

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