04.14.2016
By Rob Daly

Taming Research Overload

Wall Street may as well stop publishing the majority of its research for its institutional clients since almost four-fifths of it never gets read by its intended audience, according to fintech concern Street Contxt.

The Toronto-based enterprise content-management service provider found, after examining traffic on its cloud-based platform used by more than 100,000 institutional investors spread in more than 16,000 funds, that 78% of all formal research sent to institutional clients never gets opened.

Portfolio managers and buy-side analysts are oversubscribed to formal and informal research offerings and have trouble separating the wheat from the chaff, according to Blair Livingston, the CEO of Street Contxt. “Whether a portfolio manager or buy-side analyst, they will not consume content from half the people who provide it.”

Livingston attributes much of the unread content to the buy side’s lack of content management capabilities or reliance on point solutions that are designed to manage individual inboxes.

Starting two and a half years ago, Street Contxt has offered an enterprise-wide content management system that not only slims down users’ inboxes, but helps make sure that the proper research providers get paid for their content. “The platform helps ties communications with commission allocations,” Livingston said.

Content providers send their research to a firm’s virtual inbox, where users within the same organization can mark it up, make comments on it, and share it with colleagues.

“This can be formal research, but it also can be desk notes, traders, notes, morning-call notes, macroeconomic pieces as well as everything in between,” said Livingston.

Street Contxt also built a content contextualization index system into the platform that suggests available content to which the user is entitled.

“Any piece of content that is delivered by Street Contxt gets scanned in real-time for keywords, names, ticker symbols, asset classes, and named entities then scored,” he explained. “Instantly, the machine knows what the topic is, how important the article is to people and will recommend it to users based on analysis of what content to which they subscribe, receive, and consume.”

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