“If we want things to stay as they are, things will have to change.”

We are all familiar with extraordinary statistics about the abundance of data.  With an estimated 90% of all data having been generated in the last year, and device connectivity in its nascent stage, data growth will continue exponentially.  Our data-rich world is spurring structural changes in the asset management industry.  Like other disruptive industry innovations,data brings opportunities for asset management firms to enhance their returns while at the same time creating challenges for those firms unprepared to deal with the data deluge.  For systematic firms, data analytics is at the core of their investment strategy.  Many of these firms are gaining assets faster than the industry overall, although there have been hiccups in performance. 

Other large asset managers, such as Fidelity, Goldman Sachs Asset Management and Capital Group, are developing well thought out data strategies to complement their current research processes.  “Quantamental” investment approaches where quantitative techniques are incorporated into fundamental investment research (and vice versa), is slowly garnering increased attention.  Yet many firms have not addressed the question of how they will take advantage of the amount of data available to analysts.

Traditionally the domain of fundamental investment analysts, data has now become too complex to be managed at the analyst level.  Even though there has been little change in traditional information sources such as financial reports, securities price data, and certain government data, there are many new data sources available that can be used to verify an investment thesis or evaluate business risks.  Many of these new data sources are unstructured, in the form of text and images, while other new data sources are large datasets.  This makes many of the new data sources out of reach for fundamental analysts who do not have access to data analytics resources.  

There has been a lot of buzz around “alternative data”, the term used for data that has not been traditionally used by asset managers.  It can be a matter of interpretation as to which data is “alternative” and which is “traditional”.  It is clear that alternative data is growing rapidly as new data, such as that generated by mobile phones, is appearing.  However, the distinction between the two data types is less important to investors as traditional data becomes increasingly big and new methods emerge to analyze it.

New analytical approaches to traditional data are yielding insights previously not available.  For example, graphical depictions of corporate connections derived from government filings are now available on a broad scale.  Sentiment analytics provider Prattle specializes in central bank statements and has generated a strong track record of predicting central bank actions.  Programs can scan pdf loan documents in a CDO and populate a spreadsheet with key data points from the documents.  Even without tapping into alternative data sources, the need for more data analytics capabilities at asset management firms still stands out.

Many investors believe there is no reward in chasing new data sources as the best data sources will survive the test of time.  Gains from being an early user of a new data source which contains “alpha” (value added information) can be eroded by losses from using new data sources that don’t provide reliable signals or information.  While there is merit in this argument, there is a growing amount of data that seems to be surviving the test of time.   It can be difficult to know which datasets those are, particularly as the volume grows.  Years ago there were smaller numbers of new data sources emerging, such as First Call Analyst Estimates, and it took time for investors to adopt them.  Now there are hundreds of new data sources and hundreds more in various stages of preparation for commercialization.  Gauging the value of data by its popularity is no substitute for thorough data analysis.

Because of the growth in data and the new ways of analyzing data, it’s clear that asset management firms need to have a data strategy, regardless of whether the firm has made a commitment to leverage alternative data.  The mission of a data strategy is to get the right data in front of analysts and portfolio managers, and to help them interpret it correctly. 

Information investors can glean from data includes:

• Current read on business and economic conditions.
• Insights into customer preferences, trends, and satisfaction.
• Industry dynamics including competition, pricing and supply chain issues.
• Management strategy and execution; human capital management.
• Macroeconomic and political trends.

This information is useful for verifying an investment thesis; producing accurate estimates of earnings and returns on capital; and for risk analysis.  These are all part of an asset manager’s fiduciary duty.  In the near future, asset managers will need to regularly address the question of how they are applying data to carry out their fiduciary duties.

Coming up next: Building an effective data strategy.