Neuberger trusts market-timing model to hook China investors

Fund aims to smooth returns for buy-siders spooked by past market dips


Neuberger Berman is counting on a quant approach to smoothing out choppy stock returns to help win a share of China’s $11 trillion-plus in assets to be managed. 

The handful of foreign funds now operating in China – of which Neuberger is one – are taking on thousands of local managers in an equity market where conventional strategies have experienced periods of heavy losses.

Neuberger, which set up in Shanghai in 2017, believes a quant strategy with dynamically rebalancing market beta can smooth away the bumps in returns and win over investors made wary by past drawdowns.

The aim is to avoid the worst of the market dips but still capture market beta in good times, rather than neutralise market exposure completely. “Consistent outperformance against the benchmark while carrying zero beta is basically impossible,” says Zhou Ping, chief investment officer of quantitative investment at Neuberger Berman (China).

The firm registered a new quant product on May 6 with the Asset Management Association of China, a self-regulatory organisation for funds. The strategy will invest across six factors in China stocks using around 50 fundamental and technical signals.

Zhou Ping
Zhou Ping

It is Neuberger’s third private fund and can be offered to a maximum of 200 investors under the firm’s local private fund entity, following two renminbi-denominated bond funds. Neuberger says it plans to launch funds across all assets for Chinese investors over the next three to five years.

Despite gains in China equities, Chinese investors are wary about their home assets because of big drawdowns in recent years, Zhou explains.

The flagship China large-cap index CSI 300 is up 70% over the past five years, outperforming the S&P 500’s 43% and Topix’s 21%.

But the CSI 300 slid 72% in the year following October 2007. A second big drawdown wiped 47% from its value in late 2015. And most recently, the index slipped 32% from January last year to January this (see graph).

“How investors feel rewarded through A-shares investments does not match the actual performance,” Zhou says.

Neuberger’s quant model aims to generate absolute return through a strategy of “stock-picking alpha and dynamic market beta” with the objective of smoothing away some of those drawdowns.

The model tries to time the market, adjusting the notional value of the fund’s index futures exposure to balance its beta to the market, depending on the market outlook.

Valuations and the evolution of trends, in an equity market where mean reversion historically is strong, are key elements in the market timing part of the model, Zhou says.

“Dynamic beta does not equate to a market neutral strategy,” he says. “When the market outlook turns positive, the strategy’s beta exposure can reach up to 100%, rather than constantly hedging away the entire market exposure.”

Conversely, the flexible beta exposure can protect against drawdowns when markets sour.

Special dynamics

Meanwhile, the heavy involvement of retail investors in China’s markets should benefit active managers, Zhou says.

Retail investors account for about four-fifths of turnover in the mainland’s stock market but only take out one-tenth of returns, according to a survey conducted by Shenzhen International Exchange in 2017.

Retail buyers tend to prefer familiar investments, typically hold on too long to losing assets and move too fast to sell assets that have made gains. They also generally hold highly concentrated portfolios, the survey noted.

It is pretty easy to reject a factor that works widely elsewhere but not in China
Zhou Ping

On the other hand, reliable data on China’s stock market goes back little more than a decade, making it hard to develop and back-test quant strategies.

Datasets that are relevant to the current trading environment begin only in 2007 after the regulator revamped accounting and reporting standards.

“Limited data history is inevitable for investors in emerging markets, including China,” Zhou says. He acknowledges that overfitting – where managers design a strategy that works well for a sample of historical data but fails when put into live trading – is “the major risk” in quant investing.

The team cross-checks the signals it uses to make sure they work also in other economies, Zhou says, though sometimes that creates difficulties of its own.  

“It is pretty easy to reject a factor that works widely elsewhere but not in China,” he says. The trickier question is what to do when the firm finds a factor that empirically works in China but not elsewhere.

Those factors demand closer scrutiny, Zhou explains, to determine whether they are a unique factor that exclusively applies to China or merely a statistical fluke. “Signals are many and can be found in different forms but we have to be extra mindful when a potential signal lacks a reasonable explanation.”

Zhou’s studies of dynamics in China A-shares compared with US equities indicate stronger mean reversion, a stronger link between earnings growth and stock price movements, higher turnover and lower long-term returns.

Meanwhile, it will take time to turn those observations into funds under management, he expects. 

On the outlook for quant funds in China generally, Zhou points out that many domestic institutional investors are more trusting of human judgement than rule-based models – at least for now.

“Understanding of quantitative strategies will take time to develop,” he says. Neuberger plans to be there when it does.

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