Algos Scour Social Media for Clues to Cryptocurrency Moves

After months of relative calm in cryptocurrency markets, bitcoin exploded back into life in April with its sharpest price jump in over a year - but few people could convincingly explain why.

The 20% leap focused investors’ attention on one of the enduring mysteries of cryptocurrencies: what moves the price of an emerging asset in an opaque, largely unregulated market?

For some, the answer lies on social media. Hedge funds and asset managers seeking an edge are training computers to scrape social media sites for triggers that could move the price of digital currencies.

Their goal: crafting algorithms capable of picking out price “signals” from the background noise of sites ranging from Reddit and WeChat to Twitter and Telegram.

Many investors already use computer models to identify, and trade, price differences across hundreds of cryptocurrency trading exchanges.

But with opportunities for arbitrage narrowing as the nascent sector develops, big players are increasingly looking to build or buy more sophisticated robots to find market-moving signals online, according to interviews with six hedge funds and asset managers and three software developers.

Yet while the use of algorithms, or algos, for parsing social media may be growing, some of those interviewed said major challenges and risks remain to their wider deployment, from cost to complexity.

“It’s an arms race for money managers,” said Bin Ren, CEO of Elwood Asset Management, which specializes in digital assets and is owned by Brevan Howard founder Alan Howard.

“Very few players are able to implement and deliver it, but I believe it is highly profitable.”

Such “sentiment analysis,” as computer-driven reading of the social media mood is known, is used as a tool in traditional markets like equities and foreign exchange to trade on consumer feelings toward a company or asset.

But it could be of greater significance in cryptocurrency markets, where there are few authoritative sources of information, such as central banks, scarcely any reliable data to gauge asset value like economic indicators and financial statements, and a high proportion of individual investors.

It is also early days for the technique in the crypto sector, with scant industry-wide data on performance and many questions over its effectiveness. None of the institutions Reuters spoke to would give details of the performance of their algorithms, citing commercial confidentiality.

• Reuters