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The leading knowledge platform for the financial technology industry

A-Team Insight Blogs

Market Signals and Risk Data Emerge As Opportunity

Demand from traders, portfolio managers and quants for better systematic data workflow management is an opportunity for service providers, according to Tom Doris, CEO and founder of OTAS Technologies, a market data analytics platform provider, who spoke in a panel discussion in New York on May 17.

“The challenge in the next couple years, if you have that long, is to get this interesting content — a proliferation of new data sets — into the trader workflow and discretionary workflow in intelligent fashion,” said Doris.

Market analysts are looking at signals of market activity and at data sources in an “ad hoc fashion,” he adds. “It’s not systematic, so there’s a huge potential win there for data and content providers who can get the content into a systematic workflow … for those who want to scale up strategies that already work.”

Data consumers in the industry are becoming more interested in information relevant to risk, rather than just achieving alpha, according to Doris. “They’re much more forgiving of a data set that picks 10 out of a list of 100 stocks, and then only one or two of those 10 are correct to act on,” he said. “Buyers of these data sets have to be more communicative around risk and downside protection. That should result in novel uses of new data sets in [the risk] space.”

OTAS, for its part, provides traders with signals and alerts based on the top of the market. Looking at market data in real-time is insufficient to properly support an order execution strategy, Doris said. “There are a small number of very sophisticated funds, with low latency news feeds and all the most sophisticated natural language processing,” he said. “Only really in the last year has there been recognition by providers that there’s a lot of untapped value from the raw market data that’s just out there already but not monetised. We started to see providers who do a decent job of doing sentiment analysis of structured and unstructured data sets.”

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