MARKET INSIGHTSThe Point of
No Returns:
Trading Halts and Other Market Structure
Considerations in China A-Shares Research
September 2021

Introduction
Dongxu Optoelectronic Technology Co. Ltd. is anelectronic component manufacturer headquartered in
Beijing, China. Its stock has been listed since 1996, tradeson the Shenzhen stock exchange, and, from June 2014 toJune 2020, was a constituent of the CSI 300® Index.

Investors in equity markets are accustomed to receiving
daily security prices, but, as Dongxu illustrates, such data
do not always exist for issuers in the China A-sharesmarket. Because return data are fundamental when
deriving quantities such as volatility, beta, and factor
loadings, active decisions regarding how to treat trading
halts for purposes of computing stock returns can affectkey downstream elements of the investment process aswell as portfolio outcomes.

Dongxu typifies issuers in the China A-shares market in atleast one important way: over time, its stock has been
subject to numerous trading halts. As reflected by thefrequency and duration of the periods captured by theorange bars in Figure 1, trading in the stock has been
halted, sometimes for extended periods, even as thebroader market remained active. 1

As we’ll show, given the unusual prevalence of trading
halts in Chinese equity markets, those active researchdecisions are particularly important when conducting
quantitative research on China A-shares.

Figure 1: Dongxu Cumulative Return(2010–2020)
500%

400%

300%

200%

100%

0%

-100%
2010

2011

2012

2013

2014

2015

Dongxu

2016

2017

2018

2019

2020

Stock Is Halted

Sources: Bloomberg (Dongxu pricing data); the D. E. Shaw group. Applicable data are used with permission of Bloomberg.

1

We chose Dongxu (or “Tungshu”—ticker: 000413:CH) as an example because we believe its lengthy trading experience is broadly
representative of the frequency and magnitude of the key issues surrounding trading halts and pricing in the China A-shares market.

MARKET INSIGHTS | The Point of No Returns: Trading Halts and Other Market Structure Considerations in China A-Shares Research1Understanding the Structureof the China A-Shares Market

When computing security- and market-level returns, it istherefore important to identify halts cleanly in thehistorical data.

Historically, Chinese stock exchange authorities—as well ascompany CEOs—have had the right to suspend the
trading of individual stocks for a variety of reasons. These
rules continue to evolve and are generally intended to limit
potential threats to orderly market function, such as insider
trading and panic-driven trading activity.

From an allocator’s perspective, interruptions in tradingand pricing data might not appear to impact the overall
return and risk profile of a long-term, strategic allocation.
But over shorter horizons, they are material to active
portfolio managers.

As Figure 2 shows, individual issuers of China A-shares
have been subject to trading halts at least an order ofmagnitude more frequently than constituents of otherglobal equity markets. For more than half of thesesecurity-level halts in China, the restriction remained inplace for longer than one day.
Although their prevalence has abated over the past few
years as regulators introduced changes intended to reduce
the frequency and duration of interruptions, trading halts
remain a salient feature in the historical track records ofChina A-shares. Additionally, halts are often associatedwith events or information material to the price of a stock.

Trading halts are just one of a number of structuralfeatures of Chinese equity markets that active managers
need to address. Others include limit-up/limit-down rules,
which are frequently binding and often interact directlywith halts, 2 as well as the effects of reverse mergers, stateowned enterprises, and limited free float.
When discussing these aspects of Chinese equity market
structure, market participants typically do so in terms of
implementing and executing a given investment strategy.
What many overlook is the extent to which thosestructural elements influence the very foundational aspects
of developing and designing an investment approach.

Figure 2: Stock Halts as a Percentage of Total Market(2013–2020)
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
2013

2014

2015

2016

Developed Markets

2017

2018

Emerging Markets ex China

2019

2020

China

The above graph reflects the dollar average daily volume (“DADV”) of stock halts as a percentage of the applicable market. The figuresreflected are calculated as the expected DADV of halted stocks divided by the DADV of the relevant market. The expected DADV of halted
stocks is calculated by propagating the last realized DADV adjusted by a cumulative multiplier that is proportional to the market-wide DADV.
The data reflected for “Emerging Markets ex China” represent countries that currently constitute approximately 85% of the weight ofapplicable markets.
Source: the D. E. Shaw group.
2

Because of relatively tight daily up and down limits on price changes—typically +/-10%—it often takes a number of trading days for a previously
halted security to reach its new equilibrium price.

MARKET INSIGHTS | The Point of No Returns: Trading Halts and Other Market Structure Considerations in China A-Shares Research2Computing Returns Is
Elemental, but Not Always
Elementary

Because of the trading halt, Stock A’s price doesn’t mark at
all for the first three days after the event. After the halt is
lifted, its price decreases by 10%, 10%, and 5% on
consecutive days—catching up to the market’s 25%
decline while still subject to daily limits of +/-10%.

In a liquid and efficient market, it is common to assume
that all securities are priced each trading day, and that asecurity’s price reflects the vast majority of known
information. Those assumptions do not hold for securities
whose trading activity is suspended or whose pricemovement is limited—factors that complicate daily return
computation.

By way of contrast, Stock B, which is not subject to a
trading halt, decreases 10%, 10%, and 5% on consecutive
days immediately following the event and then doesn’t
move on subsequent days, as no new information arrives
that would affect the market.

To illustrate, we consider a stylized example in which thetrading universe consists of only two equally weighted
stocks, Stock A and Stock B, each of which moves inconcert with the market.
Assume that the two stocks are at price parity before
entering a six-day trading period that begins with anexogenous event—say, a natural disaster or geopoliticalevent—that causes a 25% decrease in the fundamental
price of the market. 3 Additionally, assume that for reasons
unrelated to this event, trading in Stock A is suspended forthe first three days of the period, and a 10% daily pricechange limit applies to both stocks in the universe. Table 1
summarizes the hypothetical daily returns of Stocks A andB over the six days following the exogenous event. (In thisstylized example, we ignore compounding, idiosyncratic
returns, and other potentially confounding factors.)

In aggregate over the six-day period, both stocks fall by
25%, matching the overall decline in the market. Despite
this straightforward appearance, however, an active
manager would need to make some key methodologicaldecisions. First among them, how should market returns
be computed for each day? To account for the trading haltof Stock A, we consider below four potential approaches
that market participants might adopt, and which result inthe market returns shown in Table 2:
1) Zero Return: Often adopted by index providers (e.g.,
MSCI Inc.), this approach assumes a zero return for
Stock A on halted days. The full-period market returnreflects the true loss due to the exogenous event, but theeffect of assuming a zero return for Stock A on days 1–3
is that the market’s decline on each of those days, aswell the volatility computed for the market, are artificiallylow.

Table 1
Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Total
ReturnVolatility(all days)

Volatility(only traded days)

Stock A

halt

halt

halt

-10%

-10%

-5%

-25%

4.9%

2.9%

Stock B

-10%

-10%

-5%

0%

0%

0%

-25%

4.9%

4.9%

Market Return Approach

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Total
ReturnVolatility(all days)

Volatility(only traded days)

1) Zero Return

-5.0%

-5.0%

-2.5%

-5.0%

-5.0%

-2.5%

-25.0%

1.3%

1.3%

2) Perfectly Efficient Market-25.0%

0.0%

0.0%

0.0%

0.0%

0.0%

-25.0%

10.2%

10.2%

3) Adjust Universe-10.0%-10.0%

-5.0%

-5.0%

-5.0%

-2.5%

-37.5%

3.1%

3.1%

n/a

n/a

n/a

-5.0%

-5.0%

-2.5%

-12.5%

2.5%

1.4%

Table 2

4) Exclude Halted Days

3

Although our example is purely hypothetical, during the summer of 2015, trading was halted in approximately 50% of the China A-shares
universe following a market drawdown of approximately 43%. Such high-magnitude events may be rare, but they do occur.

MARKET INSIGHTS | The Point of No Returns: Trading Halts and Other Market Structure Considerations in China A-Shares Research32) Perfectly Efficient Market: This approach attributes theentire 25% decline resulting from the exogenous eventto the day news of the event became known. This
assumes near omniscience—noise-free estimates of eachstock’s market beta and precise estimates of changes inmarket fundamentals—as well as the assumption thattrading halts or limit-up/limit-down rules have no effect.
3) Adjust Universe: This approach dynamically excludes
suspended stocks from the universe and estimates
market returns accordingly. Although conceptuallysimple and perhaps accurate for each isolated day, thederived full-period market returns do not line up with the
cumulative weighted returns of the underlying securities
or, in most cases, those of the index provider.
4) Exclude Halted Days: In this approach, for days when astock is suspended, one either excludes those days
altogether or relies on an index provider to define
relevant market returns. In our stylized example, inwhich an index provider is not involved, days 1–3 would
be treated as missing observations, potentially ignoring
useful information.
We explore below how the choice of methodology—often
assumed as a given in the research process—affects risk
estimation and return forecasting.

Risk Estimation is Subject toEstimation Risk
Our stylized example hints at the challenges that arise
when calculating security-specific risk measures, especiallyas we apply more realistic assumptions to the factors that
influence stock prices. Managers must account for
estimation risk; that is, the reality that inputs to calculating
a given measure—like volatility, beta, or factor loading—
are themselves subject to uncertainty. As shown in Table 1
and Table 2, estimates of stock- and market-level volatility
can vary with the treatment of trading halts in the data.

Observed betas are also dependent on how we address
trading halts in computing stock- and market-level returnseries. Taking our example further, we see that the four
possible approaches to calculating market returns outlined
above, plus the variable of including or excluding halted
days when calculating individual stock returns, result ineight different estimates of Stock A’s market beta (ranging
from -0.93 to 2.0).
Returning to Dongxu Optoelectronic, Figure 3 illustrates
how estimates of risk measures can be sensitive to choices
in return calculation methodology at the stock level alone,using the CSI 300® Index as a proxy for market return.
The estimates of volatility and beta that follow rely on
three distinct approaches to assembling stock-level returnseries. Table 2 above refers to the first two of theseapproaches as “Zero Return” and “Exclude Halted Days,” 4
respectively. The third is a proprietary “Adjusted/Efficient”
approach, which seeks to move in the direction of, if not
to approximate, the approach referred to above as“Perfectly Efficient Market.” 5
The ultimate impact of applying divergent risk estimates toa broader portfolio depends on the magnitude of resulting
risk estimate variation across securities and through time.
To get a sense of this relationship, we can compare the“Zero Return” and “Adjusted/Efficient” approaches tocalculating security-level betas for constituents of the
CSI 300® Index to the index itself. We find that in roughly
8.8% of daily, stock-level observations between 2005 and2020, these approaches produce more than a 0.02
difference in resulting beta estimates.
Even small deviations in individual securities’ beta
estimates can compound quickly when seeking to manage
portfolio-level risk precisely, especially during periods ofmarket stress. Furthermore, this form of estimation risk
applies not only to beta, but also to the estimation of asecurity’s loadings to sector, industry group, and risk andstyle factors.

4

Our application of the “Exclude Halted Days” approach entailed excluding (1) halted days, (2) any subsequent days with a 10% or larger pricechange (either positive or negative), and (3) one additional day.

5

For reference, the third approach in Table 2, “Adjust Universe,” does not apply to Figure 3 because it is used to define the market return. By
contrast, the example in Figure 3 is focused on alternatives for calculating stock-level measures relative to the market, where we have chosen, forsimplification purposes, to use the CSI 300® Index as a proxy for the market return.

MARKET INSIGHTS | The Point of No Returns: Trading Halts and Other Market Structure Considerations in China A-Shares Research4Figure 3: Calculated Risk Measures for Dongxu
Rolling One-Year Beta to CSI 300® Index(2010–2020)

Rolling One-Year Standard Deviation(2010–2020)
1.6

75%

1.4

65%

1.2
1.0

55%

0.8
45%

0.6
0.4

35%

0.2

25%

0.0

Zero Return
Adjusted/Efficient

Exclude Halted Days
Stock Is Halted

Zero ReturnExclude Halted Days

Adjusted/Efficient

Stock Is Halted

Rolling One-Year Beta to CSI 300® Index(January–June 2017)

Rolling One-Year Standard Deviation(January–June 2017)55%

1.5
Stock Is Halted

Stock Is Halted
1.4

50%

1.3
1.2

45%

1.1
40%

1.0
Jan

Feb

Mar

Zero Return

Apr

May

Jun

Exclude Halted Days

Adjusted/Efficient

Jan

Feb

Mar

Zero Return

Apr

May

Jun

Exclude Halted Days

Adjusted/Efficient

Source: the D. E. Shaw group. The charts above reflect application of the “Zero Return,” “Exclude Halted Days,” and “Adjusted/Efficient”
approaches described above to the computation of the standard deviation or beta figures indicated.

Past Returns Matter When
Forecasting Future Returns
The effects of trading restrictions on return computation
also complicate the process for forecasting returns.
Consider an investment approach that deploys four equity
style factors: low volatility, value, momentum, and size.
Construction of these factors requires time series data ofshare prices and returns, and as discussed above, such data
depend on active choices regarding how to handle trading
halts.

For a single style factor and a single stock, using thesedifferent approaches to assemble time series factor
loadings does, in fact, yield significantly different results.
Figure 4 shows estimates of Dongxu's loading to the low
volatility factor using each of the “Zero Return,” “Exclude
Halted Days,” and “Adjusted/Efficient” approaches.
Especially during periods of higher market volatility, suchas 2015–2016, we see meaningful differences in loadings
across the approaches.

MARKET INSIGHTS | The Point of No Returns: Trading Halts and Other Market Structure Considerations in China A-Shares Research5Figure 4: Dongxu Loading to Low Volatility Factor by Approach(2010–2020)
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
2010

2011

2012
Zero Return

2013

2014

2015

Exclude Halted Days

2016

2017

Adjusted/Efficient

2018

2019

2020

Stock Is Halted

Source: the D. E. Shaw group. The chart above reflects application of the “Zero Return,” “Exclude Halted Days,” and “Adjusted/Efficient”
approaches described above to the computation of factor loadings.

Figure 5: Long-Short Portfolio Cumulative Returns by Approach(2010–2020)
50%
40%
30%
20%
10%
0%
-10%
-20%
2010

2011

2012

2013
Zero Return

2014

2015

2016

Exclude Halted Days

2017

2018

2019

2020

Adjusted/Efficient

The absolute results of a long-short portfolio are qualitatively similar to excess results of a long-only implementation. We have chosen longshort to illustrate these effects in order to avoid complications introduced by the benchmark in a long-only approach. The hypothetical portfolio
reflected here is intended for illustrative purposes and is simplified by, among other things, ignoring factors such as availability and borrow costs.
Source: the D. E. Shaw group.

When combining all four factors on an equal-weighted
basis to construct a simplified long-short portfolio, Figure 5
shows that significant performance differences accumulate
by the end of the period. Figure 5 reflects simulated
annualized returns from 2010 through 2020 that range
from +2.2% to +2.9% and Sharpe ratios that range from0.55 to 0.70.

To summarize, active research decisions that feed into
security and market return calculations are not trivial. In
fact, different decisions can yield materially different
outcomes.

MARKET INSIGHTS | The Point of No Returns: Trading Halts and Other Market Structure Considerations in China A-Shares Research

6Conclusion
For many investment applications, it is easy to overlook
the potential challenges of assembling time series returns.
But for active managers that deploy a quantitative
approach and seek to deliver consistent risk-adjusted
performance, research depends on high-quality datameasured over short horizons, as this enables better
calibration of risk estimates and return forecasts.
In the China A-shares market, data idiosyncrasies such as
those for Dongxu are widespread across securities andover time, requiring caution, a deep understanding ofmarket structure, and—as with any application ofquantitative techniques—relentless discipline in assemblingclean data to support both research and implementation.
As we have seen in a number of respects, active decisions
made when calculating and applying return series data for
China A-shares can have a meaningful impact on portfolio
risk and results.

MARKET INSIGHTS | The Point of No Returns: Trading Halts and Other Market Structure Considerations in China A-Shares Research

7

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MARKET INSIGHTS | The Point of No Returns: Trading Halts and Other Market Structure Considerations in China A-Shares Research8