With its orange text on black interface and colour coded keyboard, the Bloomberg professional services terminal – known simply as ‘The Terminal’ – doesn’t appear to have changed much since it was launched in the early ’80s.
But behind the retro (Bloomberg prefers ‘modern icon’) stylings, its delivery of financial markets data news, and trading tools has advanced rapidly.
The terminal’s 325,000 subscribers globally are now able to leverage on machine learning, deep learning, and natural language processing techniques developed by the company, as they seek an edge in their investment decisions. Bloomberg is also applying those same techniques to its internal processes.
Leading the company’s efforts in the area is Bloomberg’s head of data science Gideon Mann, who spoke with CIO Australia earlier this month.
While the look of The Terminal hasn’t changed significantly since it was launched in 1981, the data it deals with certainly has.
"Traditional financial models are very good at structured data. You have a quarterly economic indicator, something that comes out every three months and you have had that for the past 10 years. That's not a huge amount of data. So you can build a great financial model using traditional financial mathematics,” Mann explains.
“What changes is once you start to have huge amounts of data that comes through every second. Whether that's news data or social media data or receipt data or satellite images, that are not structured and that are high volume. How do you model that? In that context, machine learning is very applicable.”
Bloomberg has around 5,000 engineers worldwide who support the terminal and the products on it. A fast growing number of them are data science specialists, many of whom have been hired direct from academia.
Mann, who reports to chief technology officer Shawn Edwards, leads the technology strategy around machine learning, natural language processing and search. He joined in 2014 from Google where he was a research scientist.
"Over the past year or so global finance has gotten increasingly excited about using machine learning for various different kinds of things and now it's kind of reached a fever pitch. Everybody is very focused and interested on what does machine learning have to offer?” he says.
In the toolbox
Bloomberg was a pioneer of sentiment analysis, which it began developing around a decade ago, in which machine learning techniques are used to flag a news story or tweet as being relevant to a stock and assign a sentiment score.
Typically, if there is a positive news story on a company, its share price will rise and vice versa.
The ability to read hundreds of articles in less time than it would take a human to read just one, gives Terminal customers a distinct advantage, as Bloomberg Market Specialist Ian McFarlane explains.
“During the time you’ve got your nose buried in that piece, the stocks or bonds in your portfolio might have been mentioned in hundreds of social media posts and news articles. It’s impossible for a human to keep up with that deluge of real-time data. That’s where distilling sentiment from news and social media provides an advantage,” he said.
This tool is being further developed to make a judgement on the reliability of Tweets and social media posts, says Mann.
“We know how to vet a news story and a news organisation. How do you vet that stuff on Twitter for accuracy?” he says.
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