When China’s stock-market was rocked by its biggest one-day selloff in three years on May 6, Chinese state media and local bloggers steered clear of the elephant in the room – President Donald Trump’s threat to hike tariffs on Chinese goods.
Quantitative investors say censorship around the chief source of China’s equity tumble highlights the limitations of algorithms used to sift through millions of social media posts and news articles in real time to mine investor sentiment. This comes as quantitative hedge funds and well-resourced money managers, such as BlackRock Inc. BLK, +1.71% advocate looking at social media data to check the pulse of unsophisticated retail traders in China’s stock markets.
The analysis of social media first got its start as part of a broad movement in Wall Street to analyze unconventional data sources such as satellite images and credit-card transactions. In developed markets like the U.S., where trade is largely driven by larger institutional investors, social media was mostly used to root out sources of undiscovered information that had yet to affect stock values.
But Phillip Wool, director of research at Rayliant Global Advisors, said that in China, quants use such data in China to find how much mom-and-pop investors, who often act more on speculation and not fundamentals, overreacted to market-moving events like a product launch or allegations of accounting manipulation.
He said there is plenty of opportunity to seize on investor mistakes when retail traders are responsible for more than 80% of turnover in China-listed stocks, according to the China Securities Regulatory Commission.
To do that, quants might train algorithms to scan Chinese-language blog posts on social networking platforms for the frequency of certain phrases that carry positive or negative implications to infer the overall tone of the writings, and then assess how spikes in the number of bullish or bearish blog posts influence stock prices.
Luo Yin, head of quantitative research at Wolfe Research, said stock pickers found social media algorithms especially effective when aimed at coverage surrounding individual Chinese companies. As these narrower issues attract less censorship, news articles and online discussions around such topics showed a richer variety of opinions and therefore gave a better indication of what local investors thought.
But Luo, the former global head of Deutsche Bank’s quantitative strategy team, said these same algorithms were less effective when they faced broader and more sensitive topics like the U.S.-China trade spat, discussions over which Beijing has attempted to keep a tight rein.
Parsing Chinese news and social media on politically sensitive matters such as the state of play in trade negotiations was less useful and revealing in part because nonofficial media sources and blog posts will toe Beijing’s official line, or risk being censored, said Luo.
“Given the censorship in China,...