The future of quantitative investing is going to be shaped by Artificial Intelligence (AI).

Quantitative investment strategies can identify better opportunities, and leverage them into those using mathematical models with the use of sophisticated algorithms.

Quantitative investing applies a mathematical model and algorithms to determine investment opportunities. About half of quantitative investors have implemented AI in their processes for investing. 

However, just 10% of those are actually deploying it at scale. It is much more cumbersome to solve puzzles relating to finance than any other machine learning or AI challenges. 

The foremost problem here is that AI models normally work well in static systems, and financial markets are anything but static. 

By its very nature, developing reliable models of finance and investment is extremely difficult. But if anyone can succeed, the rewards are pretty big.

Success stories already abound, and even small incremental enhancements in various steps of the investment process will result in better decision-making, leading to a better and more consistent performance. Not every innovation will result in superior investment outcome. That, however, is part of the deal as one embarks on a journey of exploration. The key is to have the modesty to learn from mistakes, and move forward. 

This commitment to learning serves to underscore the need for adaptability in the face of adversity a critical ingredient for driving innovation in finance. 

One of the most critical considerations and challenges in AI-based investment processes refers to being able to explain why a particular model chose one course of action over another. In fact, this is particularly important for those AI models that create customised investment solutions for each investor, representing one of the crucial advantages of AI.

If these customised outcomes were to vary significantly from one another, it would perhaps raise questions in investors’ minds regarding whether they have been treated fairly and with equity.

 These are rather simple investment processes when placed on paper; in practice, they are much more difficult since the individual may not fully understand just how his biases affect his investment decisions. 

AI seeks to emulate a human in decision-making, and be objective in this process without biases. 

Theoretically speaking, this is what traditional analysts try to do, and now AI allows us to automate that, taking away all the behavioural biases, and making the selection of stocks an affair far more objective. 

A well-designed model using AI will, therefore, cut down most of these biases, and possibly result in better decisions once experience is gathered. The chosen stock is more objective and constant. However, while explaining human decisions is often cumbersome, explaining AI-derived outcomes can be even more troublesome.

A growing body of research is, however, addressing this gap by employing probabilistic techniques to deconstruct the model’s decision-making process. Such progress most probably will encourage the receipt of such advanced AI techniques in the investment world.