Thomson Reuters has made a major upgrade to its market feeds by adding analysis of content from social media sites.
The Machine Readable News service now delivers traders with analytics from 50,000 news sites and four million social networks, in a format promised to be digestible and to highlight the key information.
Analytics include sentiment, relevance and novelty indicators that capture market opinion, for algorithmic trading systems as well as risk management and human decision support processes. Thomson Reuters said the development would help traders to be better informed as they shape their strategies.
Data can be aggregated at the stock, sector, market, and country levels to track sentiment on desired parameters. It can also be filtered to hone in on specific information sources, with tools spotting trends and anomalies.
The new function will mine the "expansive wealth of social media and blog content", Thomson Reuters said, delivering "digestible analytics on selected companies and market segments".
The News Analytics software is available as a separate system - or within Elektron, Thomson Reuters' vast data and trading infrastructure. The new system uses information from supplier Moreover Technologies, an aggregator of global news and social media.
Financial markets had seen a "dramatic rise in the volume and influence of industry blogs, social-networking and commentary websites", Thomson Reuters said.
Some 35% of quantitative trading firms are using of machine readable newsfeeds, up from just two percent three years ago, according to Aite Group research.
"Investment firms are embracing new data, tools and techniques to help make sense of the massive amounts of unstructured data available on the internet," said Rich Brown, head of quantitative and event driven trading solutions, at Thomson Reuters. "When properly analysed and understood, this data can complement a firm's trading and investment strategies and give it a competitive edge."
This story, "Thomson Reuters feeds tap stock sentiment from social networks" was originally published by Computerworld UK.