11.11.2016

Alternative Data: The Hidden Alpha (by Robert Iati, Dun & Bradstreet)

11.11.2016

For professional traders, finding new methods of improving active return on investment – also known as Alpha – is more intense than ever. While this has always been the Holy Grail, contained by little or no boundaries, the dialogue around it has become more transparent in recent months.

Dun & Bradstreet believes that asset managers are gaining an edge on the competition using new sources of data, previously overlooked, to raise transparency on business performance for named companies as well as distinct industry segments. Creating predictive analytics from alternative data has become the current focus of the biggest quant trading firms in the industry.

The recent Markets Media story Data’s Holy Grail: Differentiation states, “The challenge is to find meaningful data that doesn’t hit every screen on every trading desk at the same time. The heaviness of the lift is about differentiated data being just a tiny sliver of the overall dataset available to traders.”

Data comes in all forms—social media, unstructured news, and ‘under the covers’ information about how companies operate, with whom they do business and how they pay their bills. This wave of big data has turned into a search for differentiating data and high-powered analytics used to correlate insights with investment returns – like a feverish pursuit of a pot of gold at the end of the rainbow.

Capital markets investing has become more efficient, and is now overwhelmingly reliant on exhaustive data analysis and advanced scenario modeling to find veiled correlations to predict market movements. Today, the professional investor leaves little to ‘gut feelings’. As more and more unsophisticated investors migrate to low-cost, passive strategies, only the most resourceful of active managers are consistently able to beat the market.

Many active managers use historical information to refine the universe of investment options, while a subset of highly quantitative managers utilize predictive models to build strategies capable of outperforming their benchmarks. Those strategies rely exclusively on data and analytics to build prescient investing models able to consistently outperform the market averages. The growth of these quantitative funds—totaling as many as two thousand, depending on the source—has led to an increased pressure for alpha generation strategies that have more than a few months of viability. This dynamic
Page 2

is creating an intense need for these ‘quants’ to find data that is differentiated and valuable.

Advantages of the past, such as speed, are now available to most participants, leveling the playing field and making the capture of alpha more difficult. As recently as 2010, high frequency trading accounted for 60% of U.S. equity trading volume. However, the decrease in market volatility and limitations caused by growing regulatory oversight has driven many firms to seek alternative strategies for alpha generation. The democratization of financial technology, which has enabled even the smallest boutique to easily access the same tools and sources of data used by larger competitors, compounds this challenge. In this new paradigm, market participants must revise their current strategies for alpha generation, focusing more on data intelligence and agility than on speed. Accomplishing this task requires that they seek out data providers that offer access to genuinely alternative data sets, as well as the tools and technology needed to extract valuable insights.

Again, from Markets Media “The value of alternative data isn’t just that it’s an alternative, i.e. another choice. The value lies in its differentiation, which means that not everybody is looking at the data. This sets the stage for alpha-capturing opportunities.”

Creating predictive analytics from alternative data has become the current focus of the biggest quant trading firms in the industry. Used in tandem with company fundamental data and today’s sophisticated programs to identify precise correlations, predictive analytics deploying this kind of exclusive company data can expose patterns that predict a business’s behavior. This intelligence can provide a valuable window into the company’s current valuation and projected growth that would not be possible using publicly available information. By incorporating predictive business analytics into meticulously validated models, the result is unique, actionable data.

A better 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.

The easy obtainability 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 payments data and other non-public information, from sources beyond the common channels, can be a predictive indicator
Page 3

of market performance; a difference maker in assisting firms as they develop models to evaluate their investments.

By collecting data covering more than 70 million public and private US businesses over the past ten years, Dun & Bradstreet proprietary datasets bring together business relationships, credit information, corporate linkage, and hierarchies, financial reports and predictive indicators of performance, providing special insight into the short- and long-term prospects for potential investments.

Dun & Bradstreet’s business performance data and analytical tools allow traders and managers to leverage data on company payment attributes, make peer-group comparisons and perform due diligence on public and private companies that exceeds that of other datasets.

Dun & Bradstreet is headquartered in Short Hills, NJ with offices around the globe. For more information on our capital markets offerings, please visit http://www.dnb.com/capital-markets

Pension funds, sovereign wealth funds, endowments and other institutional asset owners are sitting on vast troves of data -- but extracting value from that data is more challenging than ever.

#AssetOwners #DataQuality

Technology costs in asset management have grown disproportionately, but McKinsey research finds the increased spending hasn’t consistently translated into higher productivity.
#AI #Fiance

We're in the FINAL WEEK for the European Women in Finance Awards nominations – don't miss your chance to spotlight the incredible women driving change in finance!
#WomenInFinance #FinanceAwards #FinanceCommunity #EuropeanFinance @WomeninFinanceM

ICYMI: @marketsmedia sat down with EDXM CEO Tony Acuña-Rohter to discuss the launch of EDXM International’s perpetual futures platform in Singapore and what it means for institutional crypto trading.
Read the full interview: https://bit.ly/45xRUWh

Load More

Related articles

  1. The world’s largest investment firms are leveraging technology and partnerships to extract more value from t...

  2. Pyth aims to provide onchain prices for 10,000 instruments by the end of next year.

  3. Bringing government data onchain catalyzes a wave of new financial instruments.

  4. Data blind spots, specifically in private companies, have created challenges for institutions.

  5. Brokers want to focus on adding value, rather than collecting and cleaning data.

We're Enhancing Your Experience with Smart Technology

We've updated our Terms & Conditions and Privacy Policy to introduce AI tools that will personalize your content, improve our market analysis, and deliver more relevant insights.These changes take effect on Aug 25, 2025.
Your data remains protected—we're simply using smart technology to serve you better. [Review Full Terms] | [Review Privacy Policy] By continuing to use our services after Aug 25, 2025, you agree to these updates.

Close the CTA