March 24, 2011, 9:19 AM — Vince Fioramonti had an epiphany back in 2001. He realized that valuable investment information was becoming increasingly available on the Web, and that a growing number of vendors were offering software to capture and interpret that information in terms of its importance and relevance.
"I already had a team of analysts reading and trying to digest financial news on companies," says Fioramonti, a partner and senior international portfolio analyst at Hartford, Conn.-based investment firm Alpha Equity Management. But the process was too slow and results tended to be subjective and inconsistent.
The following year, Fioramonti licensed Autonomy Corp.'s semantic platform, Intelligent Data Operating Layer (IDOL), to process various forms of digital information automatically. Deployment ran into a snag, however: IDOL provided only general semantic algorithms. Alpha Equity would have had to assign a team of programmers and financial analysts to develop finance-specific algorithms and metadata, Fioramonti says. Management scrapped the project because it was too expensive.
(For more information about semantic technologies, including search, see Part 1 of this story, "The semantic Web gets down to business.")
The breakthrough for Alpha Equity came in 2008, when the firm signed up for Thomson Reuters' Machine Readable News. The service collects and analyzes online news from 3,000 Reuters reporters, and from third-party sources such as online newspapers and blogs. It then analyzes and scores the material for sentiment (how the public feels about a company or product), relevance and novelty.
The results are streamed to customers, who include public relations and marketing professionals, stock traders performing automated black box trading and portfolio managers who aggregate and incorporate such data into longer-term investment decisions.