Alternative data - the future of Alpha generation? | Calamatta Cuschieri

Capital market investors have two main strategies that can be followed for return generation, namely active and passive portfolio management. While a passive portfolio management strategy attempts to mimic the performance of the market or benchmark by allocating similar weights on a sectoral, country, etc. level in the portfolio, active management on the other hand pursues the goal of generating additional returns above what the wider market or its reference index – called its benchmark – does. Put it simply, while a passive strategy attempts to match market returns, an active strategy tries to beat them.

Beating the market, however, has proven to be very difficult on a consistent basis. This is due to the fact that Alpha generation – finance jargon for additional return over market return – is a zero-sum game: Alpha won by one market player means that it will be an underperformance suffered by another. Thus, there is fierce and ever-growing competition among market participants to achieve an edge over others for a limited amount of Alpha that is ‘out there’. Additionally, Alpha generating strategies can become obsolete over time as they become more widespread. A strategy that could generate extra returns in the past, if utilised by a substantial enough portion of market participants, will erode its capability to generate Alpha. Thus, investment managers continually need to find new ways to identify value in markets.

The field of alternative data is expected by many to become a new field that managers will be able to tap into for additional return generation, and it is expected to transform the industry in the next five years or so. What is alternative data, however? It is the usage of non-financial information in investment decisions away from its official or corporate sources. Examples of alternative data include mobile device data, data from the Internet of Things (IoT) sensors, credit card transactions, website data, online browsing activity, product reviews, internet activity, app store analytics, social media sentiment data, or even satellite imagery data. According to many, analysing these data sources could give an edge to managers in financial markets. By nature, alternative data is usually less organised and unstructured: it is easy to see that it is difficult to find structure in a big pile of satellite photos of shopping mall car parks in order to project revenue for example. Therefore big data analytics tools and knowledge is a key requirement to have on board if a manager wishes to dip their toes into this field.

Hedge funds have been at the forefront of applying alternative data analytics for their investment decisions. According to a research document issued by Deloitte, a hedge fund called MarketPsy, started to feed social-media sentiment data into its investment models as early as 2008. Alternative data was also brought to the attention of academia, and in 2010, a research study by Bollen, Mao, and Zeng found indications of a relationship between Twitter mood and the Dow Jones Industrial Average (DJIA), including an 87.6 percent accuracy rate of predicting the up and down movement in the DJIA a few days later.

Alternative data analytics is currently in its early adoption stage. There are several specialised data vendors selling it to investment management firms and they consistently remark that their prospect base is expanding from hedge funds into larger, more complex IM firms. Currently, however, it is mainly used as supplementary information to test their hypotheses derived from more traditional sources.

Those managers who get on board may see higher rewards and Alpha generating abilities for using sources that their competition doesn’t. On the other hand, there are a number of risk factors associated with it as well. To mention a few, in case utilised data is not of good quality, it could negatively impact the portfolio creation process. Also, data that is available for savvy programmers online, might not legally be public information so those who use that data for investment decisions could have legal concerns over trading on material non-public information (MNPI).

While there are certain risks associated with incorporating alternative data into the investment selection process, in the longer term there may also be risks with not doing so. As these applications become widespread, Alpha searching IM firms not adapting to the new trends might have a difficult time staying afloat in the future.

Disclaimer: This article was issued by Tamas Jozsa, Research Analyst at Calamatta Cuschieri. For more information visit, www.cc.com.mt. The information, view, and opinions provided in this article are being provided solely for educational and informational purposes and should not be construed as investment advice, advice concerning particular investments or investment decisions, or tax or legal advice.