The big data race reaches the City

The big data race reaches the City

The big data race reaches the City
IBM’s Watson supercomputer, once known for winning the television quiz show Jeopardy! in 2011, is now sold to wealth management companies as an affordable way to dispense investment advice.

Twitter has introduced “ cashtags ” to its stream of social chatter so that investors can track what is said about stocks. Hedge funds are sending up satellites to monitor crop yields before even the farmers know how they’re doing.

The world is awash with information as never before. According to IBM, 90pc of all existing data was created in the past two years.

Once the preserve of academics and the geekiest hedge fund managers, the ability to harness huge amounts of noise and turn it into trading signals is now reaching the core of the financial industry.

Last year was one of the toughest since the financial crisis for asset managers, according to BCG partner Ben Sheridan, yet they have continued to spend on data management in the hope of finding an edge in subdued markets.
“It’s to bring new data assets to bear on some of the questions that asset managers have always asked, like macroeconomic movements,” he said.
“Historically, these quantitative data aspects have been the domain of a small sector of hedge funds. Now it’s going to a much more mainstream side of asset managers.”

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Banks are among the biggest investors in big data Credit: Jason Alden/Bloomberg
Even Goldman Sachs has entered the race for data, leading a $15m investment round in Kensho , which stockpiles data around major world events and lets clients apply the lessons it learns to new situations. Say there’s a hurricane striking the Gulf of Mexico: Kensho might have ideas on what this means for US jobs data six months afterwards, and how that affects the S&P stock index.

Many businesses are using computing firepower to supercharge old techniques. Hedge funds such as Winton Capital already collate obscure data sets such as wheat prices going back nearly 1,000 years, in the hope of finding patterns that will inform the future value of commodities.
Others are paying companies such as Planet Labs to monitor crops via satellite almost in real time, offering a hint of the yields to come. Spotting traffic jams outside Wal-Marts can help traders looking to bet on the success of Black Friday sales each year – and it’s easier to do this from space than sending analysts to car parks.

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Some funds, including Eagle Alpha, have been feeding transcripts of calls with company executives into a natural language processor – an area of artificial intelligence that the Turing test foresaw – to figure out if they have gained or lost confidence in their business. Trades might have had gut feelings about this before, but now they can get graphs.
There is inevitably a lot of noise among these potential trading signals, which experts are trying to weed out.

“Most of the breakthroughs in machine-learning aren’t in finance. The signal-to-noise ratio is a problem compared to something like recognising dogs in a photograph,” said Dr Anthony Ledford, chief scientist for th e computer-driven hedge fund Man AHL.
“There is no golden indicator of what’s going to happen tomorrow. What we’re doing is trying to harness a very small edge and doing it over a long period in a large number of markets.”

The statistics expert said the plunging cost of computer power and data storage, crossed with a “quite extraordinary” proliferation of recorded data, have helped breathe life into concepts like artificial intelligence for big investors.
“The trading phase at the moment is making better use of the signals we already know about.

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