Investing in a rapidly-changing bond market
The only way is up - or so it seems for global bond yields.
Hedge funds have been trying to teach computers to think like traders for years. Now, an artificial intelligence technology called “deep learning” that loosely mimics the neurons in human brains is showing promise, according to Bloomberg.
WorldQuant is using deep learning for small-scale trading and Man AHL may soon begin using it, too. Two other hedge funds, Winton and Two Sigma, are also getting into the brain game.
The quant firms hope this technology will give them an edge in the escalating race in global finance. If they’re right, neural networks could help propel the transformation of finance, pitting machine against human and threatening old-school investing jobs. But today’s researchers are careful not to oversell the technology – it’s just another potential arrow in their quiver – after an earlier round of promotion and disappointment.
"Having witnessed in the 1990’s the hype and subsequent failure of hedge funds purporting to use neural networks, we tend to be skeptical of claims that ‘deep learning’ will solve the general problem of investment management,” said Winton, the $31.5 billion quant firm in London, in a statement.
Quant funds are following the lead of tech giants like Google, which have proved the mettle of deep learning. The technology, which needs super-powerful computers and troves of data to do its job, already enables Tesla’s self-driving cars and Amazon’s Echo, a voice-activated smart speaker.
Hedge funds lag in deploying deep learning because they lack expertise in applying it to complex financial data. Facebook’s image recognition – the ability to identify a dog in a photo, for instance – has succeeded because it draws on unlimited amounts of data, with fixed points like pixels, uploaded by social media users. Market data, on the other hand, is limited and constantly changing, making predictions of events like stock moves more challenging. (Source: Bloomberg)
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