Top 4 Books for AI Driven Investing

As AI and machine learning have regained popularity over the last two decades, so has an interest in their application to financial prediction tasks. The two seem like a natural fit as data generated by markets have been scrutinized by investors for over a century in hopes of forecasting their way to financial success. A casual survey of the associated literature reveals there are generally two broad approaches to the topic. In one corner sits the astute STEM practitioners who view the task at hand as an engineering problem, preferring complex and novel architectures that minimize a nominated error metric. Whereas in the opposite corner resides the learned financial practitioner, who remains innately cognizant of efficient markets (EMH) and the need for explainability, in doing so, favoring simpler models infused with domain insights.