Which Asset Managers Will Be The Winners And Losers In The Machine Learning Age?

Many asset managers are now investing in machine learning (ML) technology. And with good reason, as ML is a valuable weapon in the battle to raise investment and make profitable decisions.

But ML is extremely complex technology and – if it isn’t used correctly – can do more harm than good.

So, who are the winners and losers in the asset management space when it comes to ML?

In the initial phase, it is likely to be the mid to large-sized asset managers that are the winners in the ML game. It is these companies that have both the reputation and money to be able to attract and keep the very best data scientists – people who are employed to set up ML and utilise the systems. Being able to employ top data science talent and encourage them to stay around and ensure that the process is built upon properly and that success is sustained, is crucial.

The smaller asset managers are likely to lose out in this respect as they won’t be able to attract the same quality of talent, thus won’t be able to manage ML as effectively – or analyse results as efficiently. Even if smaller firms do attract good data scientists, they are likely to be lured away to the bigger firms before long as the top people in the profession are in such demand. As with so many things, money talks.

However, it’s worth pointing out that it’s not quite as simple as winners and losers when it comes to ML. There is another factor at play that could jeopardise investment in ML across the whole sector. This is the fact that the adoption of ML may not lead to a long-term sustainable business benefit. In fact, any gains that a firm makes by using ML could be short-term, as other firms are likely to simulate the investment methods being used, which could then lead to everyone buying or selling at the same time, which could have the effect of wiping out gains for all concerned.

So, while ML may look like the saviour of active investing – and will undoubtedly allow early adopters to find new sources of alpha and outperform indexes – if insights are merely copied across the board by other managers then it will become more difficult to identify investments that outperform benchmarks. Which in turn could exacerbate the shift to passive investing.