After spending a decade focusing on the pursuit of the fastest data, Max says firms are seeking a new edge by turning to older and slower datasets, delivered in new ways.
There’s something akin to the “slow food movement” going on in the capital markets.
For the past decade, most cutting-edge technical innovation in the market data industry has primarily been around the pursuit of low latency—in some cases, to the detriment of the development of other fundamental datasets that might arguably have delivered better long-term returns. But as it has become harder and more expensive to compete in the latency race, firms have begun looking for other sources of insight. As a result, the models by which datasets such as old-school fundamental research are produced and distributed are getting a facelift.
Years ago, Multex (now part of Thomson Reuters) revolutionized the delivery of broker research by providing access through a single portal. Then vendors such as Alacra pioneered the concept of online marketplaces for non–real-time premium content, while UK-based software consultancy Worldflow took this a step further with research apps and mobile access to research, responding to the emergence of wireless tablet devices that made research consumption feasible via mobile devices.
More recently, a new generation of content providers have begun bringing completely new approaches to the generation of old-style content. For example, New York-based Estimize—which recently sold a 10 percent stake in its business to Euromoney Institutional Investor—has brought crowd-sourcing to the earnings estimates space, and is consistently proving more accurate than consensus Wall Street estimates. Meanwhile, vendors such as Nous and Invstr are creating new sources of price forecasts by persuading retail investors to contribute their predictions through trading game apps.
A Step Further
London-based crowd-sourced investment platform StockViews is now taking research to a new level with a series of new initiatives: First, the vendor—which is now starting to charge fees to access its platform—is improving real-time tracking of the research and recommendations on StockViews, adding a social media-style immediate feedback element to the research and analyst rating capabilities of the platform.
In addition, StockViews is not limiting clients’ research choices to established firms or individual analysts, but is introducing features that allow users to search for analysts by their area of specialty or based on specific expertise, and the ability for users to pitch specific research projects and find the best-placed analysts with the most relevant experience to produce the research, essentially creating not just an online marketplace for research, but an interactive venue for custom content creation.
Once you have these “traditional” datasets in a new format, you can utilize them in different ways, such as how New York-based AnalytixInsight is leveraging its database of research generated from fundamental company data and other metrics to create actionable indicators that firms can directly incorporate into trading strategies, in response to increased use of its CapitalCube platform by buy-side firms.
The upshot of all this is that, in my opinion, greater importance will be placed going forward on well-established types of data, although it will be created and delivered in new ways, leaving vendors such as those described above well-placed to deliver unique sources of alpha. Meanwhile, to extract value, search tools—such as those from search providers like 9W Search and AlphaSense—will become critical to finding the right piece of data. Once users have the right data, they can turn to analytics providers to correlate it with other datasets, or store it for future use. And to bring these capabilities and content sets together, look perhaps not to traditional vendor aggregators, but to on-demand platform providers such as Xignite and others, who can create professional-level portals using web services and widgets.
All this is technologically possible. Now all we need are commercial models that make it economically feasible so that the providers can make money while consumers save it.
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