Behind the hype: Machine learning in investment management

In a recent article, I discussed some of the significant progress being made in machine learning–enabled artificial intelligence and some of its potential drawbacks as well as the challenges it poses for regulators. Now, I want to bring your attention to a very interesting Barclays report that looks at the deployment of quantitative fund strategies, and in particular, the role of machine learning in investment management. You can read more articles on technology's role in finance by Sviatoslav Rosov, PhD, CFA on the Market Integrity Insights blog. Although big data is usually directly associated with machine learning, there is still a debate whether new data sources, such as web crawling through news or social media, credit card data, geolocation data, and so on, is helpful in the investment process. Some specific examples of trading strategies based on such data include using Twitter sentiment to make bets on the equity market as a whole or individual stocks in particular or using geolocation data to estimate retail activity relevant to individual stocks (e.g., footfall at retail stores).