Last updated: 12:05 / Wednesday, 4 November 2015
Build on Insight, by BlackRock

Big Alpha in Big Data

Big Alpha in Big Data
  • The more participants an investment insights that exploit market inefficiencies has, the harder to reap alpha
  • Smart beta strategies have started to blur the line between active and pasive management
  • A data-driven, scientifically-based, technologically-aware research culture can produce sustainable alpha

Many investment insights that exploit market inefficiencies follow a fairly predictable performance arc. Those that are first to discover and implement effective insights tend to reap the most alpha from them in the early stages after implementation. However, as the insights become more widely known and followed by an increasing number of market participants, they start to deliver less alpha and may ultimately become commoditized. As competition in active management has grown more intense, the effective life span on many such insights has meaningfully decreased in recent years. The chart below* illustrates this.

This phenomenon is affecting the entire investment management landscape, most notably in recent years through the rise of smart beta strategies. Smart beta harnesses broad, persistent drivers of return such as quality, size and value that were once firmly the province of active investors—increasing the urgency for both quantitative and fundamental active managers to come up with innovative techniques to deliver differentiated alpha. So, as the traditional line between passive and active has begun to blur with the rise of smart beta strategies, true alpha has proven increasingly elusive. In response, managers across the spectrum from equity, to fixed income, to impact investing are looking to big data for an edge.

So, what exactly is “big data?”

Worldwide, humans are generating some 2.5 quintillion (2.5 x 1018) bytes of information every day, and IBM estimates that about 90% of this was created in the last two years. The potential that comes with an understanding of this data is practically unending, yet sifting through it is almost impossible without a solid understanding of what you are looking for and the ability to compute it. From an investment perspective, this mass of information is particularly valuable in how it relates to human economic behavior. To make sense of all the data some investors and asset managers are employing new approaches, and developing a different set of tools to search the entire universe of information for new and potentially market-moving information.

For example, text mining and machine reading algorithms are being developed to interpret vast quantities of written materials, such as company reports, regulatory filings, blogs, social media, etc. From this data, more accurate information on consumer confidence and corporate sentiment, among other things, can be derived. It is also possible to identify non-intuitive relationships between companies that are fundamentally related—and therefore are exposed to similar return drivers—despite differences in industry classification, country of domicile, market capitalization and supply chain position.

The above example harnesses the power of technology to create entirely new datasets, however techniques like machine learning can advance how we analyze existing datasets as well. For example, by employing machine learning to sort through data, find patterns and automate processes we can identify combinations of traditional investment signals (e.g. return on assets, ratios, momentum measures, etc.) in  a more effective way than when looking at these signals individually. This type of process is heavily reliant on computer processing, as there are simply too many permutations and interactions for humans to consider effectively.

The data revolution is not limited to these strategies alone—natural language processing, scientific data visualization, distributed computing and other techniques will also change how we handle information. A data-driven, scientifically-based, technologically-aware research culture can produce sustainable alpha. This is true not only for quantitative investment approaches, but also for fundamental managers that can use data analytics to make more informed decisions.

As the landscape of investing evolves, it will become increasingly important to consider the capabilities of who you chose to help you manage your assets. Investment managers that are able to unleash more of the power of data may be best positioned to rise to the top—uncovering important new insights and putting them to work for their clients.



*The strategies for the Tough Competition chart come from the following research papers: Do Stock Prices Fully Reflect Information in Accruals and Cash Flows about Future Earnings?, Richard G. Sloan, The Accounting Review, (July 1996); Accrual Reliability, Earnings Persistence and Stock Prices, Richardson, S. A., Sloan, R. G., Soliman, M. T., & Tuna, I, Journal of Accounting andEconomics (2005); and The Information in Option Volume for Future Stock Prices, Jun Pan, MIT Sloan School of Management and NBER and Allen M. Poteshman, University of Illinois at Urbana-Champaign, The Review of Financial Studies (2006).

This material is for educational purposes only and does not constitute investment advice nor an offer or solicitation to sell or a solicitation of an offer to buy any shares of any Fund (nor shall any such shares be offered or sold to any person) in any jurisdiction in which an offer, solicitation, purchase or sale would be unlawful under the securities law of that jurisdiction. If any funds are mentioned or inferred to in this material, it is possible that some or all of the funds have not been registered with the securities regulator in any Latin American and Iberian country and thus might not be publicly offered within any such country. The securities regulators of such countries have not confirmed the accuracy of any information contained herein.