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.
Watch this video – Gain an Edge on the Marketplace with Information Alpha from Dun & Bradstreet
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 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.”
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 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.
View our alpha video to learn more about how alternative data, our 'Information Alpha,' can help you earn differentiated investment returns.