Applying Alternative Data to Find Alpha in the Marketplace

My previous commentary introduced the idea that professional investors are using new sources of information—alternative data—to generate alpha returns. This blog is a summary of our recent research report Alternative Data:The Hidden Source of Alpha (click "Download Now" button below).

Here we’ll address the methods investment managers use to apply alternative data to their investing strategy in both the public and private markets, differentiating it from the datasets that are widely used in all investment models.

A clear understanding of public and private companies can provide investment managers with valuable insights. Professional investors seek new sources of actionable information that shine a light on opaque evidence and offer trends and patterns that are highly correlated with investment performance.

Two types of business performance data in particular can contribute to investment models but remain largely untapped because they have been so hard to obtain:

  1. Data on public companies that highlights the timing of payments made by those companies to their creditors, and
  2. Data from private companies, illustrating financial behavior patterns that are predictive indicators of performance of public companies

Creating predictive analytics from alternative data has become the current focus of the biggest quant trading firms in the industry.
Robert Iati, Capital Markets, Dun & Bradstreet

Professional investors thoroughly analyze petabytes of transparent information on publicly traded companies. They then intricately massage it in trading models designed to generate differentiated returns. Critical, albeit opaque, information on these companies, such as payment behaviors and corporate hierarchy ownership, is regularly overlooked.

Similarly, these investors often ineffectively deploy, or even ignore, private company data as a source for alpha generation. To underscore the tremendous potential of private company data as a source of alpha, consider this: among companies with 500 or more employees, over 85% are privately held (based on the Dun & Bradstreet database).


Alpha in Public Markets

This is where alternative business performance data on public and private companies – although hard to obtain – can play an important role. Using trends and patterns of these businesses, today’s refined analytics can identify correlations between their actions, such as their financial condition, payment behaviors, and customer trends and the performance of the public markets or particular sectors within those markets.

For example, access to alternative data for the public companies that service a large global corporation can yield information that is otherwise unavailable, such as the timing of the company’s payments to its suppliers. The signals derived from variations in that company’s payment behavior are of particular interest to analysts; a plus or minus change can provide useful insights into the health of the company’s business.

The ability to access this type of data can provide a definitive edge in investment performance. While a small number of providers have developed models that track widely available information such as SEC reports, venture capital financings and other limited financial information for private companies, these sources provide only a simple set of information.

Alpha in Private Markets

By nature, investing in private markets, consisting mainly of venture capital, private equity and private debt issuance is opaque. As such, those investing in private markets live and die on their ability to find irreplaceable data and analyze it in ways that enable them to realize distinct value in their investments.

As more companies choose to remain privately held, funding to fuel their growth will become more difficult to attract. This is in part because access to reliable financial data for these companies becomes more difficult for managers to assess a given company’s financial health, impeding their ability to accurately evaluate a potential investment. Distinctive data sets, such as payment timeliness, customer relationships and legal entity hierarchies that uncover patterns in that company’s financial performance and clarify its market position, are used as input to formulate alpha generation strategies, for valuation of debt issuances and in pricing IPOs.

For instance, as a business grows, new and potential trading partners and suppliers may formally inquire about its credit and payment experiences. Alternatively, increased credit inquiries may provide an indication of a business that is struggling to pay its bills. In both cases, payment inquiry data used in conjunction with other datasets can provide strong predictive indications of future share price.


The democratization of financial services data and technology, together with more intense competition, makes the needs of today’s market participants vastly different from those of previous generations. To successfully capture alpha in the current environment, firms must locate untapped sources of data for both public and non-public companies. This alternative data, such as payment data and other non-public information, from sources beyond the common channels, can be a predictive indicator of market performance; a difference maker in assisting firms as they develop models to evaluate their investments.

Dun and Bradstreet’s proprietary datasets provide comprehensive, historical company coverage of private companies, allowing market participants to take full advantage of this hidden potential for alpha. By combining our unique data sets with advanced analytics, traders, analysts and managers can seek predictive signals and actionable information utilizing their own models.  

View our research report: Alternative Data:The Hidden Source of Alpha to learn more how alternative data, our 'Information Alpha,' can help you earn differentiated investment returns.

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For more information, read about Dun & Bradstreet’s Capital Markets solutions.