Over the past few years, the adoption of Artificial Intelligence (AI) and Machine Learning has been a key driver of innovation in many industries. Tasks like facial recognition or assisted medical imaging ― unthinkable just a few years ago ― are now possible thanks to models that learn from data to understand and predict the behavior of complex systems.
Similarly, investing ― and its vast toolkit of quantitative techniques ― is greatly benefiting from the introduction of AI. Indeed, the new landscape is taking shape at a time of increasing complexity and competition in financial markets, making the quest to find information hidden in the noise more important than ever. Yet, as we will see, AI and machine learning mark the pivotal transition from human (and statistical) reasoning to a new era of assisted decision-making.
They are the new forces shaping what investing is going to look like: a tech challenge. Their value ― instead of revolutionary ― should be considered evolutionary, i.e. sophisticated tools to tackle known problems.
Introduction
Recently, the application of AI to investing has become a hot topic in financial literature. Behind that, the availability of big data and the ever-increasing computing power are making the decades-long research in the field of AI finally deliver on its promise of performing human-like tasks.
