"Behind the hype: Machine learning in investment management" (Barclays June 15 report)

From City AM, August 11:

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.

Big data: Costly, but is it useful?
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).

The Barclays report states that 54% of surveyed investment managers use alternative data, such as web crawling social media data, satellite data, or credit card data. This finding suggests it is less prevalent than tick data (100% usage) or fundamental data (62% usage), such as balance sheet or income statement data, but more prevalent than economic data (38% usage), such as employment or inflation figures, or sell-side data (31% usage), such as analyst reports or broker recommendations.

Despite the widespread use of alternative data, 80% of surveyed investment managers in the Barclays report said that their biggest challenge was in assessing the usefulness of the data. Other concerns managers have are that the price of big data is typically greater than its usefulness, and it is difficult to clean and process for analysis. The key issue here is that the cost of the dataset is not merely its up front cost but also the opportunity cost of time spent cleaning, filtering, and analysing a dataset that may ultimately not yield any actionable recommendations. 

Machine learning does the dirty work
Interestingly, machine learning may help reduce this opportunity cost of alternative data by improving and automating the data gathering, processing, and cleaning procedures. Existing sources of data can also be rendered cheaper and more effective by these improvements....MORE