Quants Think Like Amateurs in World’s Wildest Stock Market

(Bloomberg) --

Tom Zhou helps run a $500 million hedge fund and has degrees in civil engineering, economics and finance.

Like other quantitative trading whizzes attempting to make sense of China’s $6.6 trillion stock market, Zhou spends much of his time trying to think like a novice investor.

As quants adapt their models to a Chinese market where mom-and-pop investors drive more than 80% of trades, they’re coming up with clever ways to predict where the nation’s so-called dumb money is headed. Getting it right isn’t easy in a country with the world’s highest stock-market volatility and price swings that often appear to defy logic.

Quants Think Like Amateurs in World’s Wildest Stock Market

To anticipate how China’s 147 million retail investors will behave, quants are combing through social-media posts and using artificial intelligence to predict when popular technical indicators will spur waves of buying and selling. They’re buying troves of data from the likes of Tencent Holdings Ltd. to gauge investor sentiment, and weeding out factors that work well in the West but fail to outperform in China.

The efforts underscore how international investors will have to think differently as they increase exposure to Chinese stocks in the wake of the country’s entry into MSCI Inc.’s global indexes.

“In the U.S., quants are trying to make money off other institutional investors with complex models or automated transactions at lightening speed, but in China many strategies don’t work well and quants’ arch rivals are retail investors,’’ said Zheng Xu, a former portfolio manager at Millennium Partners who now teaches finance at Shanghai Jiao Tong University. “Understanding retail investors’ behavior and sentiment is extremely valuable here.’’

Read more: China Opens Door for Quants That It Slammed Shut in 2015

While quants are typically loathe to give away details of their approach, a few were willing to share the broad outlines of how they model the impact of China’s individual investors on the nation’s stock market.

Zhou, a former quantitative analyst at MSCI Barra who’s now a fund manager at Shanghai River East Asset Management, said one phenomenon that stands out is Chinese traders’ tendency to lock-in profits more quickly than their peers in the U.S. That means short-term price reversal factors tend to perform better in China than momentum factors, he said.

At High-Flyer Quant, which oversees more than 6 billion yuan ($870 million) in Hangzhou, traders use AI to anticipate as many as two days in advance when widely followed indicators including moving average convergence divergence, or MACD, will prompt individual investors to buy or sell, according to Simon Lu, High-Flyer’s deputy head of research.

BlackRock Inc., the global investing behemoth that has big plans to increase its presence in China, tracks changes in retail-investor sentiment by analyzing social media data, including about 100,000 chat-room posts a day on websites like Eastmoney.com and Xueqiu.com. The firm buys stocks that attract growing attention from investors and sells those that show waning interest.

Such factors aren’t always reliable. Wang Pei, chief executive officer of Shenzhen-based Focus Technology Ltd., said his fund gained an extra 7% to 8% annually in 2013 and 2014 by incorporating data on the most-viewed stocks in trading apps operated by Tencent and Hithink RoyalFlush Information Network Co. But the factor stopped working in 2015 as competitors copied the approach and China’s market crashed, Wang said.

The country’s unique market structure also poses challenges for quants. The biggest hurdles include a dearth of liquid hedging tools and a rule that prevents investors from buying and selling the same shares in a single day, which makes some high-frequency trading tactics unfeasible.

China’s censorship of social media -- and the constantly evolving online slang that netizens use to evade official monitors -- can also present challenges for firms using AI tools like natural language processing to monitor investor sentiment.

The median return among funds linked to China’s CSI 300 Index beat the benchmark by 3.63% in 2018, according to Citic Securities Co. In the first quarter of 2019, the funds underperformed the benchmark by 3.22%. The Shanghai Composite Index has gained about 18% this year.

Prospects for outsized returns are enticing enough that international quant shops including Boston-based PanAgora Asset Management Inc. are increasingly experimenting with new models to trade the Chinese market.

Mike Chen, a money manager at PanAgora who used to work for Google, developed a model to parse the slang Chinese retail investors use to discuss stocks on online message boards, which the government censors. He became interested in the challenge in part because of some quirky moves in Chinese stocks in the wake of the 2016 U.S. presidential election. When the result became clear, a listed Chinese company whose name sounds like “Trump Wins Big’’ in Mandarin surged, while a firm that sounds like “Aunt Hillary’’ slumped.

“There are a lot of inefficiencies, and many of these are driven by retail investors,’’ Chen said in an interview. PanAgora hasn’t yet used the model for trading. “You want to understand what they think, but you can’t do it through the traditional financial statements.’’

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