By Curt Custard, Chief Investment Officer, Newton Investment Management
The news is rife with articles covering the challenges that the active management industry faces: the rise of passive; jobs in active management at decade lows; increasing consolidation within the industry. It’s certainly true that the asset management industry is going through a period of disruption, just like the disruptions facing the media industry (streaming), the transportation industry (Uber and Lyft), and the automotive industry (electric vehicles and ride-sharing), to name a few.
As with any disruption, there will be winners and losers, but where there is change there is opportunity. These developments are definitely a step in the right direction for the long-term future of active management. As in other industries that have been disrupted, asset management has seen an exponential increase in the amount of data that we can capture, and in the new approaches to how that data is used.
Information that is differentiated and alternative has become available and it has opened up a whole new world of analytical possibilities. In addition to the volume of data available, there are new ways of processing this data, with users pushing past traditional time-series or company accounts analysis to incorporate machine-learning and pattern-recognition techniques that draw out important inferences about company and industry fundamentals.
This creates something that is exciting for active managers: renewed ‘information asymmetry’ – meaning one party has more information than another party. However, the explosion of data is adding to the cacophony of information that active managers are already immersed in. Therefore, how one filters and analyses the data, and how we draw inferences from it, rather than simply possessing it, will be our competitive advantage.
This idea of informational asymmetry is rooted in the origins of active management. If a potential investor has information that others do not, it has a competitive advantage. For example, a manager who can read company reports in a foreign language has an advantage. A similar argument can be made where a manager has access to information more quickly than someone else. These sources of competitive advantage are important, and have clear parallels with the law of comparative advantage in economics and skill in the interpretation of information.
Data overload to active management 2.0
Three factors have helped diminish information asymmetry over the last couple of decades: 1) the dispersion of information on the internet; 2) the ubiquitous use of English as a business language; and 3) regulation requiring standardisation of company accounts. The effect of the reduction in informational asymmetry has been that the competitive advantage has been reduced to delivering a superior interpretation of a limited number of commonly known facts. Essentially, investment managers have been forced to demonstrate their skill (and their competitive advantage) by being more insightful in their judgements on a relatively limited number of data points or variables.
The growth of data has now reached a tipping point. We live in an era of data deluge. Data abounds, and it is easier than ever to create new data sets. Firms can create proprietary data sets and establish proprietary ways of analysing data, both of which increase information asymmetry. For example, if one has access to a data set about a particular product or service, and can understand the usage (or the breakdown rate) of the product or service at a more detailed level than (or in advance of) the market, one has an informational advantage.
The explosion of data has pushed active managers into new ways of looking at the information they receive. It has required new techniques to filter, combine and analyse that information. Data science, machine learning, artificial intelligence, along with other related techniques allow us to obtain insights into what drives a company’s success in ways that were not possible before. This, in turn, is hugely exciting for those seeking to ‘beat the market’. In effect, the very same forces that made the market more efficient (a huge increase in publicly available information) now have the potential to make the market less efficient by increasing information asymmetry and greatly expanding the ability to evaluate companies in different ways. This potential for both acquiring an informational advantage and having larger degrees of freedom in the analysis could well usher in a new golden era of active management.
Good investors are inherently competitive, inquisitive, and diligent, and will always remain so. What has changed in our industry, is the tools being used to express those traits. The investment management industry has evolved from using an HP-12C calculator to work out the internal rate of return of an investment, to using Python code to scrape words from transcripts in multiple languages – if we harness data in the right way, then the future of active management will be a bright one.