on the right track: measuring tracking efficiency in chinese equity...

Measuring Tracking Efficiency in Chinese Equity ETFs

Morningstar ETF Research

May 2014

Authors:Jackie Choy, CFA: ETF StrategistBen Johnson, CFA: Director, Global Passive Fund ResearchHortense Bioy, CFA: Director, European Passive Fund Research

On The Right Track:

Executive Summary 3

The Subject of Our Study – Chinese Equity ETFs 4

The Common Metrics 5

Are These ETFs on Track? 6

Caveats: A Few Words of Caution on “Tracking” 18

Conclusion 21

Appendix 1: Methodology 22

Appendix 2: Defining Common Metrics: Tracking Error 23

Appendix 3: Defining Common Metrics: Tracking Difference 27

Appendix 4: Chinese Equity ETFs 28

Contents

On The Right Track: Measuring Tracking Efficiency in Chinese Equity ETFsMay 2014

©2014 Morningstar. All rights reserved. The information, data, analyses, and opinions contained herein (1) are proprietary to Morningstar, Inc. and its affiliates (collectively, “Morningstar”), (2) may not be copied or redistributed, (3) do not constitute investment advice offered by Morningstar (4) are provided solely for informational purposes and therefore are not an offer to buy or sell a security, and (5) are not warranted to be accurate, complete, or timely. Certain information may be self-reported by the investment vehicle and not subject to independent verification. Morningstar shall not be responsible for any trading decisions, damages, or other losses resulting from, or related to, this information, data, analyses or opinions or their use. Past performance is no guarantee of future results.

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sensitivity in a pair of hypothetical examples. In some cases, tracking difference can also be quite sensitive to changes in its inputs—particularly the chosen start and end dates used in the calculation. It is vital that investors use accurate data in making the calculations. Inexplicably high levels of tracking error should be carefully examined, as there is a chance that they are a result of data errors, and might not be a true indication of poor tracking performance.

While all the various tracking metrics we discuss in this paper are important to consider when evaluating an ETF, they are not the only metrics that investors should consider. Other key factors to assess are factors affecting trading costs such as commissions and bid-ask spreads, product and index construction, counterparty risk, and tax considerations, amongst others.

Lastly, we see a number of important developments on the horizon that should ultimately serve to improve these funds’ ability to track their benchmarks over time. We also believe that there are some key steps that these fund’s sponsors and regulators can take towards harmonising the calculation and dissemination of these metrics to allow investors to make more informed decisions:

The ongoing liberalisation of the Chinese capital markets could lead to more competition and potentially drive down transaction costs and expense ratios charged by these ETFs.

Harmonising the definitions of tracking error, tracking difference, expense ratio, etc, and the approach to calculating these figures would help investors to make apples-to-apples comparisons amongst different ETFs. We believe any move towards harmonising definitions and reporting will likely be driven by regulators. We believe the various ongoing fund passport initiatives could address these issues.

There is a need for more transparent and accurate disclosures of NAV and index values by ETF providers.

In February 2013, our European passive funds research team published a study titled “On the Right Track: Measuring Tracking Efficiency in ETFs” (the “2013 European Tracking Report”). The report examined the factors that influence the tracking performance of Europe-listed ETFs.

In this study, we shift our focus to Chinese equity ETFs listed in various markets around the world as the prominence of Chinese equities has been increasing in the global investment arena. The universe we have examined in this study includes 33 exchange traded funds (ETFs) tracking five popular Chinese equity indices.

In general, we have found that Chinese equity ETFs appear to be tracking their respective benchmarks reasonably well. As compared to the emerging markets equity ETFs we examined in our 2013 European Tracking Report, Chinese equity ETFs showed comparable levels of tracking efficiency. However, these funds have not tracked their benchmarks as closely as those ETFs tracking developed market equity benchmarks that we examined in our European study. These findings are not surprising as the Chinese equity market shares similar characteristics with its emerging-markets peers, characteristics which tend to lead to higher levels of tracking error and tracking difference as compared to ETFs tracking developed markets equity benchmarks.

We also found that synthetic replication ETFs have tended to have greater levels of tracking difference relative to physical replication ETFs, despite the fact that they have tended to have lower levels of tracking error relative to their physical replication counterparts. In some cases, we believe the cost of derivatives in sythetic replication ETFs, which come in addition to the funds’ expense ratio, is the primary source of these relatively higher levels of tracking difference.

Offshore Chinese equity ETFs have had lower levels of tracking error and tracking difference as compared to onshore Chinese equity ETFs. While onshore Chinese equity ETFs provide one of the few channels investors currently have to attain this unique exposure, investors are effectively paying more for this access. However, we believe this difference between offshore and onshore Chinese equity ETFs could be gradually narrowed as the Chinese onshore market opens further.

We also found that the tracking error calculation is extremely sensitive to minor changes in its inputs. We illustrate this

Executive Summary

http://media.morningstar.com/uk/MEDIA/Research_Paper/Morningstar_Report_Measuring_Tracking_Efficiency_in_ETFs_February_2013.pdf

http://media.morningstar.com/uk/MEDIA/Research_Paper/Morningstar_Report_Measuring_Tracking_Efficiency_in_ETFs_February_2013.pdf



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Offshore Chinese Equities: Companies listed outside of China with significant business exposure in China (whether or not they are incorporated in China). These are further classified into H-Shares, Red Chips and P Chips (listed in Hong Kong); and also L-Shares (listed on the London Stock Exchange), N-Shares (listed on the NYSE or NASDAQ) and S-Shares (listed in Singapore).

Our decision to narrow the scope of our study and focus on ETFs tracking Chinese equities was driven by a number of factors:

The number of these ETFs has continued to grow since we published “Morningstar’s Guide to Investing in Chinese Equities via ETFs”. Growth has been particularly pronounced in the RQFII space. As of March 2014, total AUM in RQFII ETFs stood at Rmb 35 bn (US$ 5.6 bn).

Chinese equities will likely continue to grow in prominence in the long run. In March 2014, MSCI launched a consultation on the proposed index inclusion roadmap for China A-Shares in the MSCI EM Index.

More countries are being granted RQFII quota to invest directly in the onshore Chinese equity market. These include: Hong Kong (Rmb 270 bn), Taiwan (Rmb 100 bn, based on a favourable response from the Chinese government but not yet granted officially), UK (Rmb 80 bn), Singapore (Rmb 50 bn) and France (Rmb 80 bn). A number of A-Share ETFs were launched in the past 6 months outside of Hong Kong.

In April 2014, the Shanghai-HK Stock Connect pilot programme was announced. The programme will allow domestic Chinese investors to invest in HK-listed stocks (a total quota of Rmb 250 bn has been granted) and Hong Kong investors to invest in Shanghai-listed stocks (a total quota of Rmb 300 bn). This marks another big stride towards opening up China’s capital market.

The Chinese equity market is becoming ever more important to the global investment landscape and the crop of ETFs offering exposure to Chinese equities continues to grow. In this context, we hope our various studies of this subject – Chinese Equity ETFs – will allow investors to gain a deeper understanding of these products as the industry continues to develop at a rapid pace.

In this study, we shift our focus to Chinese equity ETFs listed in various markets around the world.

Recall that, in general terms, Chinese equities are issued by companies doing business in the People’s Republic of China (PRC). In more granular terms, Chinese equities can be classified as (1) onshore or (2) offshore:

Onshore Chinese Equities: Companies incorporated in China and listed on the Shanghai or Shenzhen Stock Exchange. These are further classified into the A-Shares (quoted in Renminbi (Rmb) and only available to domestic Chinese investors, Qualified Foreign Institutional Investors (QFIIs) and RQFIIs (“R” stands for Rmb)) and B-Shares (quoted in Hong Kong dollars or US dollars; but open to foreign investors; relatively small in size).

The Subject of Our Study – Chinese Equity ETFs

China A-Shares B-Shares A-Shares(Shanghai & Shenzhen) + B-Shares

(in RMB,billions) 23,511 152 23,663

(in USD, billions) 3,781 24 3,806

Hong Kong H-Shares Red Chips Mainland H-Shares Total

Private + Red Chips Hong

Enterprises + Mainland Private

Enterprises

Kong

(in HKD, billions) 4,603 4,420 4,009 13,032 23,065

(in USD, billions) 593 570 517 1,680 2,973

Exhibit 1. Total Market Capitalisation of China and Hong Kong Markets

Exhibit 2. RQFII ETF AUM, Quota Granted and Number of RQFII ETF

Source: HKEx, Morningstar Research

Source: Morningstar Direct, Morningstar Research (Data to end-Mar 2014)

http://global.morningstar.com/GuideChineseEquityETFs

http://global.morningstar.com/GuideChineseEquityETFs

On The Right Track: Measuring Tracking Efficiency in Chinese Equity ETFs

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There are two widely used metrics of index funds’ tracking efficiency: tracking error and tracking difference. Here, we provide a brief definition of each (a more detailed version can be found in the Appendix).

Tracking ErrorTracking error is a measure of the standard deviation of an ETF’s excess returns. In this context, excess returns refer to the absolute difference between the ETF’s performance and that of its benchmark.

Lower tracking error is indicative of more consistency in the periodic deviations between the return of the fund and that of its benchmark.

Tracking DifferenceTracking difference is simply the annualised difference between a fund’s actual return and its benchmark return over a specific period of time. A small tracking difference indicates that the ETF has done a good job matching its index over the period in question.

The Common Metrics

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90

100

80

70

60

50

40

20

10

Tracking Error

-7.5 5 0

Tracking Difference

Exhibit 3. Frequency Distribution of Daily Tracking Difference of a MSCI China Index ETF in a Year

Source: Morningstar Direct, Morningstar Research (Data to end-Jan 2014)

Measures the volatility of these daily differences

Daily tracking difference (bps)

Number

of days

Differences in return of the ETF against the benchmark index. Also the geometric sum of these daily differences.



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In answering the big question “How well did these ETFs track their benchmark indices?” we will examine the two common metrics – tracking error and tracking difference – as well as a proprietary Morningstar data point – Estimated Holding Cost.

Tracking ErrorOn average, ETFs tracking Chinese equity indices exhibited tracking error ranging from 64 basis points (for synthetic replication funds) to 102 basis points, or bps, (for physically replicated funds) over the period we studied. This is a narrower range than that seen amongst ETFs tracking the MSCI Emerging Markets Index. In our 2013 European Tracking Report, we found that these funds’ tracking error ranged from 17 bps (synthetic replication) to 177 bps (physical replication). It is important to note that we are sharing this data for purposes of generic comparison only and that we examined different measurement periods in each report.

Synthetic Replication Offers Superior TrackingIn general, ETFs utilising synthetic replication have lower tracking error. The lone exception is the XACT KINA ETF, which is listed in Sweden and utilises options and

futures to replicate its benchmark, a process that results in high tracking error. On average, tracking error amongst synthetic ETFs was 15 – 68 bps lower than for physical ETFs. This is similar to what we found in Europe, where the difference was an average of 30 bps.

In addition, this finding was, in general, universal among offshore and onshore Chinese equity ETFs. Specifically, the tracking error of offshore synthetic ETFs was anywhere from 15 – 33 bps lower than that measured amongst physical ETFs whereas the same figure among onshore synthetic ETFs was 42 – 68 bps lower than that for physical ETFs tracking onshore benchmarks.

Offshore Chinese Equity ETFs Offer Superior TrackingGiven the nature of onshore Chinese equity ETFs — from the cross-market nature of the underlying securities to the more complex use of derivatives (China access products) by the synthetic ETFs — it is not surprising that the offshore Chinese equity ETFs produced lower tracking error than the onshore Chinese equity ETFs. On average across the physical and synthetic ETFs, the difference was in excess of 110 bps.

Are These ETFs on Track?

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Index tracked by ETFs (excluding feeder funds) Physical Replication Synthetic Replication

Daily Tracking Error %

Weekly Tracking Error %

Daily Tracking Error %

Weekly Tracking Error %

FTSE China 25 0.89 0.49 0.18 0.18

HSCEI 0.53 0.48 2.20 1.45

HSCEI (excl. XACT) 0.53 0.48 0.37 0.39

MSCI China 0.24 0.22 0.10 0.13

FTSE China A50 1.58 1.31 1.63 1.29

CSI 300 2.43 2.05 1.35 1.35

Average (excl. XACT) 1.02 0.80 0.64 0.62

Average Offshore Chinese Equity Indices (excl. XACT) 0.53 0.37 0.20 0.22

Average Onshore Chinese Equity Indices 2.09 1.75 1.42 1.34

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Exhibit 4. Average 1-Year Tracking Error for ETFs Tracking Chinese Equity Indices

Source: Morningstar Direct, Morningstar Research (Data to end-Jan 2014)


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Tracking Error – By Index at the ETF Level

ETF Listing Countries Replication Method

Daily Tracking Error % (since)

Weekly Tracking Error % (since)

Monthly Tracking Error % (since)

Feb 2011 Feb 2013 Feb 2011 Feb 2013 Feb 2011

CIMB FTSE China 25 Malaysia Physical 2.64 2.69 1.30 1.34 0.67

db x-trackers FTSE China 25 UCITS ETF HK,SG, EU Synthetic 0.11 0.13 0.15 0.16 0.20

EasyETF FTSE China 25 EU Synthetic 0.42 0.24 0.28 0.19 0.23

Hang Seng FTSE China 25 Index ETF HK Physical 0.25 0.26 0.27 0.26 0.26

iShares China Large Cap UCITS ETF EU Physical 0.42 0.26 0.29 0.16 0.24

iShares China Large-Cap US, Chile, Mex, Aus Physical 0.30 0.35 0.21 0.20 0.21

iShares China Index CA Feeder Fund 23.42 17.34 12.61 9.64 6.18

Average FTSE China 25 3.94 3.04 2.16 1.71 1.14

Average FTSE China 25 (excluding feeder funds) 0.69 0.66 0.42 0.39 0.30

Average Physical Replicator 0.90 0.89 0.52 0.49 0.34

Average Synthetic Replicator 0.26 0.18 0.21 0.18 0.21

Exhibit 5. Tracking Error for FTSE China 25 ETFs

Source: Morningstar Direct, Morningstar Research (Annualised data to end-Jan 2014)

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ComStage HSCEI UCITS ETF EU Synthetic 0.56 0.58 0.59 0.57 0.62

Hang Seng H-Share Index ETF HK, TW Physical 0.33 0.35 0.36 0.35 0.29

KODEX China H ETF KR Physical 0.80 0.72 0.59 0.62 0.45

Listed Index Fund China H-share (HSCE) JP Feeder Fund 36.97 32.57 16.32 17.67 9.78

Lyxor UCITS ETF China Enterprise (HSCEI) SG, EU Synthetic 0.18 0.16 0.22 0.21 0.29

XACT Kina ETF SE Synthetic N/A 5.87 N/A 3.57 N/A

Average HSCEI 7.77 6.71 3.61 3.83 2.28

Average HSCEI (excl. XACT) 7.77 6.87 3.61 3.88 2.28

Average HSCEI (excluding feeder funds) 0.47 1.53 0.44 1.07 0.41

Average HSCEI (excluding feeder funds and XACT) 0.47 0.45 0.44 0.44 0.41



Average Synthetic Replicator (excl. XACT) 0.37 0.37 0.40 0.39 0.45

Exhibit 6. Tracking Error for HSCEI ETFs

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db x-trackers MSCI China Index UCITS ETF HK, SG, EU Synthetic 0.10 0.10 0.11 0.13 0.15

Deka MSCI China UCITS ETF EU Physical 0.28 0.28 0.30 0.29 0.24

Horizons MSCI China ETF HK Physical Listed on 17 June 2013

HSBC MSCI CHINA UCITS ETF EU Physical 0.26 0.22 N/A 0.21 N/A

HSBC MSCI China ETF HK Physical N/A 0.25 N/A 0.27 N/A

iShares MSCI China US, Chile, Mex Physical N/A 0.26 N/A 0.19 N/A

iShares MSCI China Index HK Physical 0.27 0.18 0.27 0.14 0.32

Source MSCI China ETF EU Synthetic 0.09 0.10 0.11 0.13 0.15

Average FTSE China 25 0.20 0.19 0.20 0.19 0.22

Average FTSE China 25 (excluding feeder funds) 0.27 0.24 0.28 0.22 0.28


Exhibit 7. Tracking Error for MSCI China ETFs


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Bosera FTSE China A50 Index ETF HK Physical Listed on 9 December 2013

ComStage FTSE China A50 UCITS ETF EU Synthetic Listed on 8 October 2013

CSOP FTSE China A50 ETF HK, JP Physical N/A 1.79 N/A 1.74 N/A

CSOP Source FTSE China A50 UCITS ETF EU Physical Listed on 8 January 2014

iShares FTSE A50 China Index ETF HK Synthetic 1.80 1.63 1.45 1.29 1.14

KODEX FTSE China A50 ETF KR Physical N/A 5.87 N/A 3.57 N/A

Average FTSE China A50 1.80 1.60 1.45 1.30 1.14

Average Physical Replicator N/A 1.58 N/A 1.31 N/A



Exhibit 8. Tracking Error for FTSE China A50 ETFs

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Tracking Error – By Index at the ETF Level (continued)


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Key Findings from Tracking Error at the ETF Level:

Feeder funds exhibit very high daily tracking error These funds’ daily tracking error has ranged from 10% to 30% on an annualised basis over the trailing 1- and 3-year periods. This compares to a range of 0.10% to around 2% for their peers. Feeder funds generally invest in another ETF that tracks the index and hence the NAV of the feeder fund is based on the market price of the underlying ETF rather than the underlying securities of the index. The ETF that a feeder fund invests in may trade at a premium or discount to its NAV. This creates an additional source of tracking error for the feeder fund. The feeder fund’s

tracking error will likely be even larger if the trading hours for the exchange where the underlying ETF is listed do not overlap with the normal trading hours of the securities that comprise the fund’s benchmark index.

Daily/weekly/monthly data irrelevant In general, using weekly or monthly data instead of daily data results in similar annualised tracking error figures. The exception is once again the feeder funds. In their case, using weekly data tends to “smooth-out” the annualised daily tracking error and the monthly data further “smooth” this figure. This finding is somewhat similar to that which we shared in the 2013 European Tracking Report.






ChinaAMC CSI 300 Index ETF HK, JP Physical N/A 2.27 N/A 2.22 N/A

C-Shares CSI 300 Index ETF HK Physical Listed on 8 July 2013

db x-trackers CSI300 Index UCITS ETF HK, EU Synthetic 0.24 0.23 0.23 0.16 0.40

db X-trackers Harvest CSI 300 China A-Shares Fund US Physical Listed on 6 November 2013

db x-trackers Harvest CSI300 Index UCITS ETF EU Physical Listed on 7 January 2014

Fuh Hwa CSI300 A Shares ETF TW Physical N/A 2.37 N/A 1.71 N/A

Haitong CSI300 Index ETF HK Physical Listed on 7 March 2014

iShares CSI 300 A-Share Index ETF HK Synthetic 6.04 1.57 5.27 1.44 4.54

KINDEX China A CSI300 ETF KR Physical N/A 2.66 N/A 2.23 N/A

KraneShares Bosera MSCI China A ETF US Physical Listed on 5 April 2014

Listed Index Fund China A Share (Panda) CSI300 JP Feeder Fund 30.85 32.20 13.68 12.24 7.49

Lyxor UCITS ETF CSI 300 A-Share EU Synthetic Listed on 2 October 2013

Market Vectors ChinaAMC A-Share ETF

(formerly Market Vectors China ETF,

prior to 7 January 2014)

US Physical

(formerly

Synthetic)

7.68 4.35 3.97 2.37 1.50

TIGER China A300 ETF KR Physical Listed on 17 February 2014

W.I.S.E. - CSI 300 China Tracker HK Synthetic 1.84 2.24 1.77 2.46 1.64

W.I.S.E. KTAM CSI 300 China Tracker TH Feeder Fund 12.34 9.29 10.16 7.35 8.71

W.I.S.E. Yuanta/P-shares CSI 300 ETF TW Feeder Fund 11.82 9.33 9.83 7.35 9.44

Average CSI 300 10.12 6.65 6.42 3.95 4.82

Average CSI 300 (ex-feeder funds) 3.95 2.24 2.81 1.80 2.02

Average Physical Replicator (excl. Market Vector) N/A 2.43 N/A 2.05 N/A

Average Synthetic Replicator (excl. Market Vector) 2.71 1.35 2.42 1.35 2.19

Exhibit 9. Tracking Error for CSI 300 ETFs

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Tracking Error – By Index at the ETF Level (continued)



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Tracking Error Varies Over Time In general, there were no obvious differences between the 1-year and 3-year annualised tracking errors amongst the ETFs we examined. This is consistent with our findings on the use of daily/weekly/monthly data. However, Exhibit 10 illustrates that tracking errors can vary considerably over time. This variation depends largely on the chosen beginning and ending points of the dataset. In our view, considerable changes in tracking error over time could be driven by a variety of factors, such as cash drag,

NAV accounting adjustments, changes in replication methodology, inaccurate data, differences in data vendors, etc. For instance, the iShares CSI 300 A-Share Index ETF invested in other iShares sector ETFs to track its benchmark index prior to May 2012. It has since been utilising access products which has resulted in a significant reduction in tracking error. The fund’s trailing 1-year tracking error has fallen sharply (1.57%) relative to the trailing 3-year figure (6.04%).

Exhibit 10. Rolling One-Year Tracking Error for a Subset of MSCI China ETFs

Source: Morningstar Direct, Morningstar Research



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Tracking DifferenceTracking difference – the absolute level of annual out- or underperformance of each ETF relative to its benchmark –varies considerably depending on the reference index in question. On average, ETFs tracking the Chinese equity indices exhibited tracking difference ranging from -95 bps (physical replication) to -200 bps (synthetic replication) over the measurement period under examination. This range is wider than that we discovered amongst ETFs tracking the MSCI Emerging Markets Index of -92 (synthetic replication) to -103 (physical replication) bps in the 2013 European Tracking Report. It is important to note that we are sharing this data for purposes of generic comparison only and that we examined different measurement periods in each report.

Physical Replication Results in Lower Tracking DifferenceAmongst funds tracking all five indices within our study, physical replication ETFs exhibited lower tracking difference than synthetic replication ETFs. On average, tracking difference among physical ETFs was 105 bps lower than for synthetic ETFs. This difference was more pronounced amongst funds tracking onshore Chinese equity indices (-340 bps for synthetic replication funds; -104 bps for physical replication funds). We believe this is due to the embedded costs/spread associated with the use of derivatives to replicate the performance of onshore Chinese equity indices. As an example, the db x-trackers CSI300 Index UCITS ETF (listed in Hong Kong and various exchanges in Europe) discloses that there is an index replication cost of 2.65% in addition to the 0.50% TER that the ETF levies.

Index tracked by ETFs (excluding feeder funds) Tracking Difference %

Physical Replication Synthetic Replication Average of Physical & Synthetic

FTSE China 25 -1.14 -1.26 -1.18

HSCEI -0.88 -1.54 -1.28

MSCI China -0.73 -1.10 -0.87

FTSE China A50 -0.61 -1.89 -1.22

CSI 300 -1.33 -3.72 -2.36

Average -0.95 -2.00 -1.40

Average Offshore Chinese Equity Indices -0.91 -1.30 -1.07

Average Onshore Chinese Equity Indices -1.04 -3.40 -2.09

Exhibit 11. Average 1-Year Tracking Difference for ETFs Tracking Chinese Equity Indices

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Source: Morningstar Direct, Morningstar Research (Data as of end-Jan 2014)



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Tracking Difference – By Index at the ETF Level

ETF Listing Countries Replication Method Expense Ratio % Tracking Difference % (since)

Feb 2011 Feb 2013

CIMB FTSE China 25 Malaysia Physical 1.01 -1.84 -1.26

db x-trackers FTSE China 25 UCITS ETF HK,SG, EU Synthetic 0.60 -0.99 -1.05

EasyETF FTSE China 25 EU Synthetic 0.60 -1.34 -1.47

Hang Seng FTSE China 25 Index ETF HK Physical 0.89 -1.26 -1.35

iShares China Large Cap UCITS ETF EU Physical 0.74 -0.89 -0.96

iShares China Large-Cap US, Chile, Mex, Aus Physical 0.73 -0.91 -1.01

iShares China Index CA Feeder Fund 0.86 -1.65 -2.71

Average FTSE China 25 -1.27 -1.40

Average FTSE China 25 (excluding feeder funds) -1.21 -1.18

Average Physical Replicator -1.22 -1.14

Average Synthetic Replicator -1.17 -1.26

Exhibit 12. Tracking Difference for FTSE China 25 ETFs


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Feb 2011 Feb 2013

ComStage HSCEI UCITS ETF EU Synthetic 0.55 -1.41 -1.39

Hang Seng H-Share Index ETF HK, TW Physical 0.65 -0.91 -0.94

KODEX China H ETF KR Physical 0.37 -0.85 -0.83

Listed Index Fund China H-share (HSCE) JP Feeder Fund 0.55 -1.11 -1.12

Lyxor UCITS ETF China Enterprise (HSCEI) SG, EU Synthetic 0.65 -1.25 -1.43

XACT Kina ETF SE Synthetic 0.60 N/A -1.78

Average HSCEI -1.10 -1.25

Average HSCEI (excl. XACT) -1.10 -1.14

Average HSCEI (excluding feeder funds) -1.10 -1.28

Average HSCEI (excluding feeder funds and XACT) -1.10 -1.15



Average Synthetic Replicator (excl. XACT) -1.33 -1.41

Exhibit 13. Tracking Difference for HSCEI ETFs


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ETF Listing Countries Replication Method Expense Ratio % Tracking Difference %

Feb 2011 Feb 2013

db x-trackers MSCI China Index UCITS ETF HK, SG, EU Synthetic 0.65 -1.02 -1.26

Deka MSCI China UCITS ETF EU Physical 0.65 -0.95 -1.11

Horizons MSCI China ETF HK Physical 0.25 Listed on 17 June 2013

HSBC MSCI CHINA UCITS ETF EU Physical 0.60 -0.63 -0.50

HSBC MSCI China ETF HK Physical 0.50 N/A -0.55

iShares MSCI China US, Chile, Mex Physical 0.73 N/A -0.67

iShares MSCI China Index HK Physical 0.59 -0.84 -0.82

Source MSCI China ETF EU Synthetic 0.65 -1.20 -1.17

Average MSCI China -0.93 -0.87




Exhibit 14. Tracking Difference for MSCI China ETFs

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Feb 2011 Feb 2013

Bosera FTSE China A50 Index ETF HK Physical 1.19 Listed on 9 December 2013

ComStage FTSE China A50 UCITS ETF EU Synthetic 0.40 Listed on 8 October 2013

CSOP FTSE China A50 ETF HK, JP Physical 1.15 N/A 0.13

CSOP Source FTSE China A50 UCITS ETF EU Physical 1.11 Listed on 8 January 2014

iShares FTSE A50 China Index ETF HK Synthetic 1.39 -2.91 -2.44

KODEX FTSE China A50 ETF KR Physical 0.99 N/A -1.34

Average FTSE China A50 -2.91 -1.22

Average Physical Replicator N/A -0.61



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Exhibit 15. Tracking Difference for FTSE China A50 ETFs

Tracking Difference – By Index at the ETF Level (continued)


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Key Findings from Tracking Difference at the ETF Level:

There is a Fairly Weak Relationship between Tracking Difference and ExpensesIntuitively, an ETF’s expense ratio should represent a good portion of its tracking difference given that expenses are the most explicit and predictable detractor from an ETF’s performance. However, our samples showed that the relationship between tracking difference and expense ratios was fairly weak, contrary to our findings in the 2013 European Tracking Report. In our study, the two values

only exhibited a correlation of 44% and a coefficient of determination, or R-squared, of 0.196, implying that just under 20% of a funds tracking difference could be explained by its expense ratio (contrary to a figure of 50% from our European study).

Recall that other factors such as securities lending income, cash drag, taxes, accounting adjustments, rebalancing costs for physical ETFs and the cost of the derivatives underlying synthetic ETFs can all impact an ETF’s tracking performance.

In addition, our study has included ETFs which are


Feb 2011 Feb 2013

ChinaAMC CSI 300 Index ETF HK, JP Physical 0.85 N/A 0.87

C-Shares CSI 300 Index ETF HK Physical 0.79 Listed on 8 July 2013

db x-trackers CSI300 Index UCITS ETF HK, EU Synthetic 0.50 -2.01 -3.00

db X-trackers Harvest CSI 300 China A-Shares Fund US Physical 0.82 Listed on 6 November 2013

db x-trackers Harvest CSI300 Index UCITS ETF EU Physical 1.10 Listed on 7 January 2014

Fuh Hwa CSI300 A Shares ETF TW Physical 0.85 N/A -2.69

Haitong CSI300 Index ETF HK Physical 0.85 Listed on 7 March 2014

iShares CSI 300 A-Share Index ETF HK Synthetic 1.39 -4.68 -3.38

KINDEX China A CSI300 ETF KR Physical 0.70 N/A -2.17

KraneShares Bosera MSCI China A ETF US Physical 1.10 Listed on 5 April 2014

Listed Index Fund China A Share (Panda) CSI300 JP Feeder Fund 0.95 -0.66 -0.30

Lyxor UCITS ETF CSI 300 A-Share EU Synthetic 0.40 Listed on 2 October 2013

Market Vectors ChinaAMC A-Share ETF

(formerly Market Vectors China ETF,

prior to 7 January 2014)

US Physical

(formerly

Synthetic)

0.72 -1.55 -1.38

TIGER China A300 ETF KR Physical 0.70 Listed on 17 February 2014

W.I.S.E. - CSI 300 China Tracker HK Synthetic 1.39 -2.95 -4.77

W.I.S.E. KTAM CSI 300 China Tracker TH Feeder Fund 1.50 -6.73 -8.16

W.I.S.E. Yuanta/P-shares CSI 300 ETF TW Feeder Fund 0.40 -7.01 -8.97

Average CSI 300 -3.66 -3.39

Average CSI 300 (ex-feeder funds) -2.80 -2.36

Average Physical Replicator (excl. Market Vector) N/A -1.33

Average Synthetic Replicator (excl. Market Vector) -3.21 -3.72


Exhibit 16. Tracking Difference for CSI 300 ETFs

Ons

hore

Tracking Difference – By Index at the ETF Level (continued)


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15

domiciled in various countries and listed on a number of different exchanges around the world. The definition and calculation of an ETF’s expense ratio (ongoing charge, TER, etc.) may be different across geographies and go some way towards explaining the weaker statistical relationship. In particular, in many cases, the cost of swap and access products and some ancillary costs are not included in the stated expense ratio. In any case, our results underscore the fact that the stated expense ratio should not be the sole consideration when choosing an ETF.

Tracking Difference Unstable Over Time Because tracking difference is a snapshot of difference be-tween the return of the ETF and the benchmark over a spe-cific time period, it is not surprising that this measure can be quite volatile as it is very sensitive to the selected beginning and end dates used in the calculation. Exhibit 18 is a prime example of this volatility, depicting the rolling 1-year track-ing difference amongst a subset of ETFs tracking the MSCI China Index.

-2.0

0.0

2.0

4.0

6.0

0.5 1.0 1.5

Tracking Difference %

Expense Ratio %

Physical Synthetic

-2.0

0.0

2.0

4.0

6.0

0.5 1.0 1.5

Tracking Difference %

Expense Ratio %

Offshore Onshore


Exhibit 17. Estimated Holding Cost and Tracking Difference of ETFs


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Estimated Holding CostIn the 2013 European Tracking Report, we introduced a proprietary Morningstar data point – “Estimated Holding Cost”. We believe that estimated holding cost is a more reliable metric than tracking difference, as it takes a larger number of data points into account. Our methodology is based on twenty sets of trailing one-year returns derived from the prior twenty trading days. We then calculate twenty return ratios between the fund and its benchmark, and take the geometric average of these twenty return ratios. The result is a smoothed, annualised estimation of the divergence of the ETF’s performance relative to its benchmark index over a year.

Like tracking difference, Morningstar’s Estimated Holding Cost accounts for all the various explicit (e.g. expense ratio) and implicit (e.g. turnover, cash drag, spread/cost of derivatives, etc.) costs of holding an ETF. Estimated holding cost excludes any costs associated with buying and selling ETFs.

Our study shows that the Estimated Holding Costs for the ETFs we examined were fairly similar to their tracking differences. Similar to our findings on tracking differences, Estimated Holing Costs for onshore Chinese equity ETFs were higher as compared to the offshore Chinese equity

ETFs. In addition, physical replication ETFs exhibited lower Estimated Holding Costs with those tracking onshore Chinese equity indices in particular.

Exhibit 18. Rolling 1-Year Tracking Difference for a Subset of MSCI China ETFs



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Index tracked by ETFs (excluding feeder funds) Estimated Holding Costs % Tracking Difference %

FTSE China 25 1.37 -1.18

HSCEI 1.50 -1.28

MSCI China 0.95 -0.87

FTSE China A50 1.88 -1.22

CSI 300 3.06 -2.36

Average 1.73 -1.40

Average Offshore Chinese Equity Indices 1.23 -1.07

Average Onshore Chinese Equity Indices 2.91 -2.09

Exhibit 19. Estimated Holding Cost and Tracking Difference by Benchmark


Off

shor

eO

nsho

re


Tracking Difference % Tracking Difference %

Physical Synthetic

0.0 2.0 4.0 -2.0 6.0-2.0

0.0

2.0

4.0

6.0

-2.0

0.0

2.0

4.0

6.0

-2.0 0.0 2.0 4.0 6.0

Offshore Onshore Estimated Holding Costs % Estimated Holding Costs %

Exhibit 20. Estimated Holding Cost and Tracking Difference of ETFs


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Do we need accurate data?The answer is an unqualified “yes”, which would seem intuitive in conducting any form of analysis. However we have noticed that having accurate data is particularly important for purposes of analysing tracking error as this calculation is extremely sensitive to minor fluctuations in the values of its various inputs. Here, we illustrate the importance of accurate data with two hypothetical scenarios we implanted in the real life data set for the iShares MSCI China Index ETF which is listed in Hong Kong.

Hypothetical Case 1: Stale NAV DataIn this hypothetical example, we have built a scenario where the NAV of the iShares MSCI China Index ETF is stale by a single day. A stale NAV is not uncommon in the real world. It can arise from a number of sources, for example, exchange holidays might not be taken into account in the calculation. Errors might also arise at the level of the ETF and/or data provider.

In our hypothetical example, on the day (day T; we hypothetically chose it as three months into the third year of the measurement period) the stale NAV appeared in the data set, the benchmark index returned +1.1%. With a stale NAV, all else equal, on day T, it would appear that ETF lagged its benchmark index by 1.1%. On day T + 1, it the NAV would “catch up” and appear to generate an incremental 1.1 percentage points of performance. The end result of this hypothetical scenario is a fairly dramatic increase in the fund’s trailing 1-year tracking error. This single stale NAV figure would cause the ETF’s tracking error to increase from 18 bps to 156 bps. There would also be a less dramatic but also significant change for the fund’s 3-year annualised tracking error—which would rise from 27 bps to 93 bps.

Meanwhile, the fund’s tracking difference, being the product of a snapshot of a pair of year-on-year figures, was not affected, as the stale NAV did not fall on either of the dates at which the relevant data points were calculated. Should the stale NAV fall on the relevant data points for calculating tracking difference, tracking difference will be affected significantly.

Hypothetical Case 2: Number of Decimal PlacesIn this hypothetical example, we took the NAVs of the iShares MSCI China Index ETF during the measurement period and rounded the NAV to two decimal places from the four decimal places (in Hong Kong dollars) that were disclosed by the ETF provider.

This hypothetical exercise had a significant effect on this fund’s calculated tracking error. The fund’s trailing 1-year tracking error increased from 18 bps to 36 bps; and its trailing 3-year annualised tracking error rose from 27 bps to 44 bps. The impact is less dramatic than that seen in our first hypothetical case, but the incremental difference of 17-18 bps could have caused an investor to choose another ETF in the event that they relied heavily on tracking error figures to inform their decision.

On the other hand, tracking difference calculations were only slightly affected by the elimination of two decimal places from the raw NAV data.

Caveats: A Few Words of Caution on “Tracking”

Tracking Difference % Daily Tracking Error %

Scenario Since Feb 2011 Since Feb 2013 Since Feb 2011 Since Feb 2013

Real Life: iShares MSCI China Index ETF

-0.84 -0.82 0.27 0.18

Hypothetic Case 1:One Day of Stale NAV

-0.84 -0.82 0.93 1.56

Hypothetical Case 2:2 d.p. of Reported NAV

-0.85 -0.82 0.44 0.36

Exhibit 21. Impact of Data Accuracy



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Is it Desirable for an ETF to Outperform its Benchmark?As we mentioned in our conclusion of the 2013 European Tracking Report, in discussing tracking difference and Estimated Holding Cost, at some point it’s valid to ask: is it desirable for an ETF to outperform its benchmark?

Certainly, if you own the ETF, the extra basis points of return will augment your bottom line. And viewed through the traditional prism of investment management, a higher return is generally considered preferable to a lower one. But unlike actively-managed funds, ETFs have a clear objective to track—not outperform—a benchmark. Any deviation from that objective, on the upside or the downside, represents a subversion of their mission.

To see why that distinction might matter, consider the market participant that sells an ETF short. That investor will be hurt, not helped, by the outperformance, and might justly criticise the fund for falling short of its stated objective.

What Else Matters When Evaluating an ETF?Also from our conclusion of the 2013 European Tracking Report, it is important to mention that while all the tracking metrics we discussed in this paper, namely tracking error, tracking difference and Estimated Holding Cost are important factors to consider when evaluating an ETF, they are not the only metrics that investors should look at. Additional factors to take into consideration include, but

are by no means limited to:

Trading costs, including commissions, bid-ask spreads, and market impact

ETFs’ market price relative to NAV over time (i.e. premiums and discounts)

Counterparty risk

Tax considerations

Some of these considerations have been the subject of previous Morningstar studies, while others will be covered in future research papers.

Exhibit 22. 1-Year Rolling Tracking Error of the Hypothetical Scenarios



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Use accurate data sourcesAs illustrated in the hypothetical examples above, having accurate data sources is very important, both for the benchmark index and the ETF. Accurate data could be obtained from ETF providers (NAVs), index providers (index values) and trusted data providers (NAVs and index values).

In addition to using accurate data, it is also important to use the appropriate data. For example it is vital to measure the total return of an ETF (i.e. including the dividend distributions) against the appropriate version of its benchmark index (total return).

Understand what the numbers are sayingIn analysing ETFs’ tracking performance, it is crucial that investors understand what the numbers are telling them. It is important to make apples-to-apples comparisons and assess the risks represented by the numbers. For example, some cases of inexplicably high tracking error could be the result of data errors rather than a true indication of poor tracking performance.

This is just the startTracking error, tracking difference, and estimated holding cost are just a few of the key metrics investors should employ when assessing the total cost of owning an ETF. It is also important to asses other metrics that serve to measure the frictional/transactional costs of ETF ownership. These include bid/ask spread, market impact, premium/discount, taxation, trading commissions, etc.

Harmonise definitionsETF providers should align themselves in an effort to harmonise the definitions of the various metrics we’ve discussed. These items include the specifics of what should be included in a fund’s stated expense ratio, and could even go so far as regular disclosure of historical tracking error and tracking difference, and how they were calculated. We understand it might be difficult for ETF providers to form a consensus on definitions. As such, any move towards harmonising definitions and reporting will likely be driven by regulators.

Providing accurate dataNAVs and distributions should be disclosed accurately and consistently. If permitted, we would suggest ETF providers also provide index data as, in many cases; historical index data is not publicly available.

Tips for ETF Investors

Tips for ETF Providers


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Scenario Annualised Tracking Error % Annualised Tracking Difference %

Chinese Equity ETFs * 0.87 -1.40

- Offshore Chinese Equity ETFs 0.69 -1.07

- Onshore Chinese Equity ETFs 1.79 -2.09

- Physical replicators 1.02 -0.95

- Synthetic replicators 0.64 -2.00

MSCI Emerging Markets ETFs ** 0.77 -0.95

- Physical replicators 1.77 -1.03

- Synthetic replicators 0.17 -0.91

ETFs tracking various developed market indices ** 0.04 to 0.21 0.47 to -0.49

- Physical replicators 0.05 to 0.27 0.55 to -0.55

- Synthetic replicators 0.02 to 0.30 0.36 to -0.49

So, have Chinese Equity ETFs tracked their indices well?Offshore Chinese equities are included in MSCI’s emerging markets classification while onshore Chinese equities have been included in the consultation for potential inclusion in this category. Chinese equities share many common characteristics with those already included in MSCI’s emerging markets category, such as higher trading costs and lower levels of liquidity as compared developed equity markets. In addition, ETFs tracking Chinese equities tend to have similar fees as compared to emerging-markets ETFs, which tend to be higher than those levied by ETFs tracking developed market equities. All of these factors contribute to higher tracking error and/or tracking difference amongst ETFs tracking Chinese or emerging-markets equities as compared to ETFs tracking developed markets benchmarks. As a result, we judge that it is fairer to compare our findings on the tracking performance of Chinese equities ETFs against the tracking performance of emerging-markets ETFs.

As compared to ETFs tracking the MSCI Emerging Markets Index, we have found that physically replicated Chinese equity ETFs have tracked their benchmarks about as well as physically replicated ETFs tracking the MSCI Emerging Markets Index, albeit with less volatility. Meanwhile, the synthetic replication ETFs tracking Chinese equities that we examined have demonstrated inferior tracking performance relative to synthetic replication ETFs benchmarked to the MSCI Emerging Markets Index that we studied in our European tracking study.

Comparing to ETFs tracking various developed markets indices, Chinese equity ETFs produced less satisfactory results in terms of both tracking error and tracking difference. The chief drivers of this relatively poor performance include: these funds’ relatively higher expense ratios; their inability to benefit from or execute certain tax-optimisation techniques employed by other ETFs; less effective use of securities lending.

While synthetic replication ETFs have generally produced less favourable results relative to their physical replication counterparts, some of them have had reasonable tracking performance. Hence, investors should be careful about making broad generalisations pertaining to synthetic funds’ tracking performance. Nevertheless, we are seeing a trend towards switching from synthetic replication to physical replication amongst ETF providers in Europe, with db X-trackers being an activist in this shift.

ETFs tracking offshore Chinese equity indices have produced superior tracking performance relative to ETFs tracking onshore Chinese equity benchmarks. While these onshore Chinese equity ETFs provide one of the few channels through which investors can access this unique exposure, they are effectively paying a steeper price for this level of access. However, we believe this difference could be gradually narrowed as the Chinese onshore market opens further.

Overall, Chinese equity ETFs appear to be tracking their benchmarks reasonably well. Ultimately, our hope is that as the Chinese onshore market opens further, and these ETFs continue to grow in both number and size, transaction costs and management fees will be reduced. This, in turn, should serve to drive down the total cost of owning Chinese equity ETFs.

Conclusion

Exhibit 23. Comparing to MSCI Emerging Market ETFs

Notes: *As of end-Jan 2014, ** Results from the 2013 European Tracking ReportSource: Morningstar Direct, Morningstar Research


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22

(A large portion of this section was first published in the “On The Right Track: Measuring Tracking Efficiency in ETFs” dated February 2013).

For the purpose of this study we have tried to focus on ETFs covering a sample of the broadest and most widely used Chinese equity indices; hence our choice of the FTSE China 25, the Hang Seng China Enterprises Index (HSCEI), the MSCI China, the FTSE China A50 and the CSI 300.

For each of these benchmarks, we have included all the ETFs around the world (outside of China) for which we have data and for which there is sufficient history. In total, the funds in our study constituted, at the time of writing, roughly 87% of the assets under management within ETFs (outside of China) tracking the Chinese equity indices.

We have chosen a measurement period of three years.

Wherever possible we have used the capitalising share class of a fund to compare it to total return benchmarks. In all other cases we have used an ‘adjusted’ net asset value (NAV) that assumed immediate reinvestment of any distribution made by the ETF.

As well, we’ve excluded from our data set the values for any days when a ETF did not produce a net asset value but the index did produce a price, or vice versa. The “holiday effect” that would result from leaving this data in the calculation has a tendency to make tracking error look much higher than otherwise, when in fact it stems from the absence of a price on a particular day rather than reflecting the normal tracking error we are trying to measure.

In all of our calculations, we have used the ETFs’ net asset value, rather than their closing price. We made this decision in order to be consistent for funds that trade on multiple exchanges, as a better reflection of the quality of ETF management rather than secondary market support. It is highly likely that our results would have looked markedly different had we used price data instead of NAV data.

Tracking ErrorTo measure tracking error, we have calculated the standard deviation of return differences between each fund and its benchmark, and annualised it:

Tracking Error = std (Rnav – Rindex) x √n

Where:

Rnav is the single period total return of the fund’s NAV

Rindex is the single period total return of the index

n is the number of observations per year

For weekly results, we measured returns from Wednesday to Wednesday. For monthly tracking error, returns where calculated from the 13th of the month or preceding weekday.

Tracking DifferenceTo measure tracking difference, we have calculated the difference between the fund return and the benchmark return, and annualised the total period value:

Tracking Difference = (1 + Rnav – Rindex)1/N – 1

Where:

Rnav is the total return of the fund’s NAV over the entire measurement period.

Rnav is the total return of the index over the entire measurement period.

N is the number of years.

Appendix 1: Methodology


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Tracking ErrorTracking error is often cited as one of the most important considerations when selecting an ETF. It measures the quality of index replication, i.e. how well a fund manager replicates the performance of a specific index. Investors typically expect their ETF to adhere tightly to an index.

Different industry participants define tracking error in different ways. Some use the term to refer to the absolute difference in returns between an ETF and its benchmark over a period of time. In other words, they view it as a simple arithmetic exercise where the performance of the benchmark is subtracted from the fund’s performance, with the difference representing “tracking error”. While this definition is easy to understand, it is not the most widely recognised way of calculating tracking error and is fraught with issues. For our purposes, we will refer to the result of this simple arithmetic as an ETF’s “tracking difference”, which we will examine in more detail shortly. As it is more commonly defined, tracking error is a measure of the standard deviation of a fund’s excess returns. In this context, excess returns refer to the absolute difference between the fund’s performance and that of its benchmark. This is congruent with the definition that ESMA and IOSCO (International Organisation of Securities Commissions) communicated in their latest consultation papers on the subject: ESMA’s consultation paper states that tracking error is “the volatility of the difference of the returns of the fund and of the returns of the index.”

Lower tracking error is indicative of more consistency in the periodic deviations between the return of the fund and that of its benchmark.

Indices representing certain segments of the market are inherently more difficult for managers of ETFs and other index funds to track than others. This is particularly the case in those instances where the benchmark in question has a very large number of constituents or when the index’s components are illiquid or otherwise difficult to access. For example, as we saw in the 2013 European Tracking Report, ETFs tracking emerging market equities tend to exhibit higher tracking error than those based on

developed market large cap indices like the DAX or the S&P 500.

Sources of Tracking ErrorWhen it comes to index-tracking funds, tracking error is a risk, i.e. a risk that the fund’s performance will diverge from that of its benchmark. When this occurs, it is crucial to understand why. Tracking error can be caused by many factors, and some are more likely than others to cause mistracking because of their unstable, non-recurring nature. Here is a list of the key factors influencing tracking error.

Transaction and Rebalancing Costs Rebalancing costs are typically incurred by physical replication ETFs when an index’s methodology requires a reweighting of its constituents or when market events force the rebalancing of a fund. Other transaction costs like stamp duties can increase rebalancing costs.

Synthetic ETFs are not directly affected by transaction and rebalancing costs. The level of index turnover will be taken into consideration in the negotiation of the swap’s price. Transaction and rebalancing costs could also manifest themselves as a premium between the ETF’s market price and its net asset value in secondary market trading.

Cash Drag Cash drag can result from periods when funds are forced to hold a portion of their portfolio in cash. This may occur during index rebalancing or be a result of the fund’s dividend (or coupon) distribution policy. In the case that an index changes composition, for a physical replication ETF there may be a time lag between the liquidation of the index’s old constituents and the addition of its new constituents. During this span, the fund will hold cash.

Also, for those ETFs that regularly distribute income to shareholders, there can often be a lag between the time when the ETF receives dividends or coupon payments from its underlying holdings and the time that it ultimately distributes this income to its own investors. Between these dates, which can last from a few days to a few months, the pending distribution sits in an interest bearing

Appendix 2: Defining Common Metrics: Tracking Error


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account instead of being reinvested in the constituents of the benchmark index. In some cases, depending on the underlying index, the dividend income may be reinvested in futures contracts linked to the fund’s benchmark in order to limit cash drag. It is important to note that physical replication ETFs that capitalise income and synthetic ETFs are not affected by the timing of distribution payments.

In both of the cases outlined above, the difference in returns between the fund’s cash position and its underlying index can generate a cash drag over the period before the cash is either reinvested or distributed to shareholders. Cash drag will cause the fund to underperform its benchmark in the case that the index rises at a greater rate than the rate earned on cash during this time. Likewise it will cause the fund to outperform its benchmark in the event that its benchmark index falls during this time.

Funds with a high level of turnover and income-oriented funds with high distribution levels are most susceptible to cash drag.

Differing Dividend Reinvestment Assumptions Differences in the dividend reinvestment assumptions between the index and the fund can also lead to tracking error. For instance, while the index may assume an immediate reinvestment of dividends on ex-dividend dates, a physical replication fund will have to wait to receive the cash dividend and reinvest it.

Also, in the case of synthetic ETFs, the underlying total return swap(s) might not be based on the same assumptions and calculations as the reference index. For example, a fund’s NAV may be calculated on a total return basis but benchmarked against a price return index or a price return index plus dividends. The discrepancy between the two can be a source of tracking error.

Dividend Taxation The tax treatment of dividends and coupon payments received by an ETF may differ from that assumed in its benchmark index’s calculation, leading to another potential source of tracking friction. Some indices—typically Gross Total Return indices—assume that dividends are reinvested without any tax deduction, but the fund will be liable for tax on dividend distributions. Conversely, other indices—typically the Net Total Return indices—assume a withholding tax is paid on dividends, but in practice in

certain jurisdictions, either some portion of or all of this tax can be reclaimed by the ETF’s manager at its source jurisdiction, provided that there is a branch office in the country where the tax is levied. This activity, known as tax optimisation or dividend tax enhancement, can boost a fund’s return relative to its benchmark, but it can also increase its tracking error.

Both physical and synthetic replication ETFs can be affected by tax optimisation. In the case of ETFs employing physical replication, this activity will be carried out at the fund level, directly impacting fund returns, while in the case of synthetic ETFs, it will be implemented at the level of the bank(s) standing behind the swap(s) embedded in the fund, thereby indirectly impacting fund returns. The bank(s) providing the swap(s) can decide whether or not to pass on any revenues from dividend tax enhancements within the framework of the swap agreement between the fund manager and the swap providers.

Securities Lending Physical replication ETFs can lend securities from their portfolios to generate incremental revenue that can help to reduce the negative impact of costs like the total expense ratio (TER) on a fund’s performance relative to its benchmark. The revenues earned by a fund engaged in this activity will vary over time according to changes in the level of lending activity within the fund and market lending rates. As such, securities lending can reduce or increase tracking error.

It is important to bear in mind that this return enhancement activity doesn’t come without risk. Securities lending introduces counterparty risk which has to be mitigated. For a more detailed examination of this practice, see our report written by our European Passive Funds Research Team “Securities Lending in Physical Replication ETFs: A Review of Providers’ Practices”.

In the case of ETFs employing synthetic replication, securities lending is rarely carried out at the fund level. Rather, it is more often undertaken outside the fund by the swap counterparty. The bank providing the swap may lend the securities of its hedging baskets, i.e. those that the bank bought to hedge the exposure it has committed to deliver to the fund. The revenues derived from this practice can help the bank to cover the costs relating to the swap and/or reduce expenses charged to ETF shareholders. In


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this case, the risk of a borrower default is directly assumed by the bank, not the fund.

SamplingThe manner in which physical replication ETFs seek to replicate their benchmark can be a substantial source of tracking error. For instance, a physical replication fund that tracks a benchmark containing a number of small, illiquid components may use “sampling” techniques to replicate the returns of its reference index. Sampling involves investing in a select basket of only the largest and most liquid components of the benchmark index in an effort to improve the overall liquidity of the fund itself (it makes the ETF shares easier and cheaper for market makers to hedge or create) and to minimise costs. While sampling has some obvious advantages, by virtue of excluding some of the smaller, less liquid components of a fund’s reference index, it creates another potential source of tracking error as the fund could stray from its benchmark. In market stressed events, such replication technique may produce significant tracking error.

Variable Swap SpreadsProviders of synthetic ETFs commit to deliver the performance of a reference index through the use of total return swap agreements which are regularly renegotiated. These swap spreads depend on various factors, including the costs borne by the swap provider in hedging its exposure, the cost of collateral, and any revenue generated from techniques such as securities lending and tax optimisation. The absolute level of swap fees will not directly impact the tracking error to the extent that it is constant from one period to the next, but changes in swap spreads will have an impact on synthetic ETFs’ tracking error.

ETFs’ Stated Expense RatioThe ETF’s stated expense ratio is an annual expense charged to the fund to cover costs ranging from index licensing to custodial fees. This charge is deducted from a fund’s NAV on a daily basis. Expense ratio is often mistakenly cited as the biggest source of tracking error. But tracking error only results if expense ratio varies over time, which happens rather infrequently. Because tracking error is a measure of volatility, it is not affected by revenues and costs that remain constant over the calculation’s time period. TER will directly impact tracking difference though. For example, a fund that charges an expense ratio of 0.15% and suffers no other sources of return deviation

would have a tracking error of zero but a tracking difference of 0.15%. The same can be said about swap spreads. For example, a synthetic ETF with a expense ratio of 0.15% and a swap spread of 0.20% that is reviewed every year could claim to have a 1 year tracking error of zero during the year when its swap spread is fixed. Its tracking difference, however, would be 0.35%.

Operational RisksTracking error, as the name suggests, can also come from errors in the construction or management of the fund, such as execution-related errors that result in the wrong quantity of a security being bought or sold in the case of physical replication ETFs.

Is There a Relationship Between Replication Method and Tracking Error?As we have seen in the previous section, the choice of replication method (physical or synthetic) has an impact on tracking error. Synthetic ETFs typically offer superior tracking to physical ETFs. Tracking error is minimised by the fact that the fund’s return is guaranteed by a bank through a total return swap rather than being derived from a full or sampled physical portfolio of the reference index’s underlying components. For this same reason, synthetic ETFs are also generally less expensive to run (they don’t incur trading costs, experience cash drag, etc.), and these cost savings are in turn often shared with fund holders in the form of lower total expense ratios. However, there is no such thing as a free lunch. Improved tracking and lower fees come at the expense of counterparty exposure. Investors should ask themselves if they are being adequately compensated for assuming this additional source of risk.

Numerous Calculation MethodologiesAs previously mentioned, the most widely used calculation for tracking error is the annualised standard deviation of the return differences between a fund and its benchmark. If a fund’s tracking error is 0.50%, we can say that the fund’s annual return is expected to be within +/- 0.50% of its average deviation from the index return with 68% confidence and within +/- 1% of its average deviation from the index return with 95% confidence.

It is important to realise that the calculation of tracking error can result in different values depending on a variety


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of factors which include but are not limited to:

The frequency of observations, i.e. whether daily, weekly or monthly data is used

The day chosen as the starting point for the calculation when weekly data is used, i.e. whether weekly returns are calculated from Friday to Friday, Monday to Monday, etc., or also whether weekly average data is used

The time period, i.e. whether tracking error is calculated over one, three or five years, or longer

Again, we urge ETF providers to harmonise the way tracking error is calculated and disclose the figures regularly.

Challenges in Calculating Tracking ErrorAccurately measuring tracking error can be challenging for fund providers and investors alike. One of the challenges they face relates to data issues. The sheer volume of data necessary to compute tracking error leaves ample room for errors. In addition to mistakes in the calculation or reporting of the fund’s NAV, there could be issues posed by holidays, missing or misaligned data, outliers, rounding, etc. Because these factors can distort results significantly, it is important to ensure that the data series provided are accurate, complete and aligned. All told, an unusually high level of tracking error can often be attributed to data errors. Another challenge when measuring tracking error is choosing the right benchmark index for the calculation. For instance, a fund whose NAV is calculated on a total return basis, i.e. including reinvested dividends, should be compared to a total return index. Comparing it to a price return index would result in different, and most likely meaningless, tracking error numbers. Sometimes, both fund NAV and index data need adjusting, as we have done in a few cases for the purpose of this study. It is equally important that the ETF NAV and index data are expressed in the same currency.


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Sources Physical ETFs Synthetic ETFs

TER Negative Negative

Transaction and Rebalancing Costs Negative N/A*

Cash Drag Negative / Positive N/A*

Dividend Taxation Negative / Positive N/A*

Dividend Reinvestment Assumptions Negative / Positive Negative / Positive

Securities Lending Positive N/A*

Swap Spread / Embedded Cost of Derivatives N/A Negative / Positive

Sampling Negative / Positive N/A*


Tracking DifferenceWhen evaluating the tracking quality of an ETF, tracking error is not the only metric investors should look at. One limitation of tracking error is that it doesn’t capture the actual magnitude of underperformance or outperformance of an ETF. For this, investors should also look at tracking difference, also known as performance difference.

Tracking difference is simply the annualised difference between a fund’s actual return and its benchmark return over a specific period of time. A small tracking difference indicates that the ETF has done a good job matching its index over the period in question.

Tracking difference is usually negative, meaning that

the ETF underperforms its benchmark. This is a logical outcome. Assuming otherwise perfect tracking, an ETF should underperform its benchmark by an amount equal to its total expense ratio on an annual basis. For reasons we explained in the 2013 European Tracking Report, an ETF’s tracking difference can also be positive, in which case the ETF has outperformed its benchmark.

What are the Sources of Tracking Difference?Tracking difference is caused by the same factors as tracking error, i.e. transaction and rebalancing costs, cash drag, dividend taxation, securities lending, swap spreads, etc. But the biggest factor influencing tracking difference is the fund’s expense ratio. The expense ratio is also the most predictable and readily quantifiable source of tracking difference for both physical and synthetic ETFs.

The various factors affecting tracking difference are mostly negative, but some can be positive. We outline the impact

Exhibit 24. Direct Sources of Tracking Difference (Impact: Positive / Negative)

Note: *Factors embedded in swap pricing. ** Depending on the underlying index, the swap provider may decide to pass on all or part of the revenues generated from return enhancement techniques to the fund, sometimes resulting in negative swap spreads, i.e. a gain for the fund (in terms of its performance relative to its benchmark) rather than a cost.Source: Morningstar Research

of these factors on an individual basis in the table below.

What Matters More to Whom?The question we often hear from investors is what matters more, tracking error or tracking difference? Again, the answer mainly depends on the type of investor, their investment objective and investment horizon. Buy-and-hold investors, because they generally focus on

maximising return while minimising costs, do not need to be as concerned about tracking error as short-term investors. For these long-term investors, tracking difference is the more appropriate metric to consider. Asset managers concerned with tracking an index precisely by mandate or for hedging purposes will see tracking error as an important metric to monitor.

Appendix 3: Defining Common Metrics: Tracking Difference


May 2014


28

Name Ticker Domicile ETF Provider Underlying Index Type Onshore / Offshore China Exposure

Replication Method

AUM(USD m)

Expense Ratio (%)*

iShares China Large-Cap ETF IZZ Australia iShares FTSE China 25 Country Offshore Physical 5,086.0 0.73

Bosera FTSE China A50 Index ETF (HKD Counter) 02832 Hong Kong Bosera FTSE China A50 Country Onshore Physical 75.1 1.19

Bosera FTSE China A50 Index ETF (RMB Counter) 82832 Hong Kong Bosera FTSE China A50 Country Onshore Physical 75.1 1.19

ChinaAMC CES China A80 Index ETF (HKD Counter) 03180 Hong Kong China AMC CES China A80 Index Country Onshore Physical 12.4 0.99

ChinaAMC CES China A80 Index ETF (RMB Counter) 83180 Hong Kong China AMC CES China A80 Index Country Onshore Physical 12.4 0.99

ChinaAMC CSI 300 Index ETF (HKD Counter) 03188 Hong Kong China AMC CSI 300 Country Onshore Physical 1,045.3 0.85

ChinaAMC CSI 300 Index ETF (RMB Counter) 83188 Hong Kong China AMC CSI 300 Country Onshore Physical 1,045.3 0.85

CMS CSI Overseas Mainland Enterprises 02842 Hong Kong CMS AM CSI Overseas Mainland Enterprises Country Offshore Physical 2.3 0.80

C-Shares CSI 300 Index ETF (HKD Counter) 03008 Hong Kong China Universal CSI 300 Country Onshore Physical 34.5 0.79

C-Shares CSI 300 Index ETF (RMB Counter) 83008 Hong Kong China Universal CSI 300 Country Onshore Physical 34.5 0.79

CSOP CES China A80 ETF (HKD Counter) 03137 Hong Kong CSOP CES China A80 Index Country Onshore Physical 35.2 1.05

CSOP CES China A80 ETF (RMB Counter) 83137 Hong Kong CSOP CES China A80 Index Country Onshore Physical 35.2 1.05

CSOP FTSE China A50 ETF (HKD Counter) 02822 Hong Kong CSOP FTSE China A50 Country Onshore Physical 3,123.9 1.15

CSOP FTSE China A50 ETF (RMB Counter) 82822 Hong Kong CSOP FTSE China A50 Country Onshore Physical 3,123.9 1.15

Da Cheng CSI China Mainland Consumer Tracker 03071 wong Kong Da Cheng CSI China Mainland Consumer Sector Onshore Synthetic 1.5 1.49

Da Cheng CSI HK Private-owned Mainland Enterprises Tracker 03022 Hong Kong Da Cheng CSI HK Private-owned Mainland Enterprises Country Offshore Physical 0.4 0.80

Da Cheng CSI HK State-owned Mainland Enterprises Tracker 03077 Hong Kong Da Cheng CSI HK State-owned Mainland Enterprises Country Offshore Physical 1.4 0.80

db x-trackers CSI300 Banks UCITS ETF 03061 Luxembourg db X-trackers CSI 300 Banks Sector Onshore Synthetic 1.6 0.50

db x-trackers CSI300 Consumer Discretionary UCITS ETF 03025 Luxembourg db X-trackers CSI 300 Consumer Discretionary Sector Onshore Synthetic 7.6 0.50

db x-trackers CSI300 Energy UCITS ETF 03017 Luxembourg db X-trackers CSI 300 Energy Sector Onshore Synthetic 0.8 0.50

db x-trackers CSI300 Financials UCITS ETF 02844 Luxembourg db X-trackers CSI 300 Financials Sector Onshore Synthetic 8.8 0.50

db x-trackers CSI300 Health Care UCITS ETF 03057 Luxembourg db X-trackers CSI 300 Health Care Sector Onshore Synthetic 4.2 0.50

db x-trackers CSI300 UCITS ETF 03049 Luxembourg db X-trackers CSI 300 Country Onshore Synthetic 757.5 0.50

db x-trackers CSI300 Industrials UCITS ETF 03005 Luxembourg db X-trackers CSI 300 Industrials Sector Onshore Synthetic 3.0 0.50

db x-trackers CSI300 Materials UCITS ETF 03062 Luxembourg db X-trackers CSI 300 Materials Sector Onshore Synthetic 2.3 0.50

db x-trackers CSI300 Real Estate UCITS ETF 02816 Luxembourg db X-trackers CSI 300 Real Estate Sector Onshore Synthetic 1.2 0.50

db x-trackers CSI300 Transportation UCITS ETF 03063 Luxembourg db X-trackers CSI 300 Transportation Sector Onshore Synthetic 1.4 0.50

db x-trackers CSI300 Utilities UCITS ETF 03052 Luxembourg db X-trackers CSI 300 Utilities Sector Onshore Synthetic 1.3 0.50

db x-trackers FTSE China 25 UCITS ETF 03007 Luxembourg db X-trackers FTSE China 25 Country Offshore Synthetic 232.9 0.60

db x-trackers MSCI China Index UCITS ETF 03055 Luxembourg db X-trackers MSCI China Country Offshore Synthetic 205.9 0.65

E Fund CES China 120 Index ETF (HKD Counter) 03120 Hong Kong E Fund CES China 120 Index Country Onshore Physical 22.2 1.15

E Fund CES China 120 Index ETF (RMB Counter) 83120 Hong Kong E Fund CES China 120 Index Country Onshore Physical 22.2 1.15

E Fund CSI 100 A-Share Index ETF (HKD Counter) 03100 Hong Kong E Fund CSI 100 Country Onshore Physical 320.5 0.99

E Fund CSI 100 A-Share Index ETF (RMB Counter) 83100 Hong Kong E Fund CSI 100 Country Onshore Physical 320.5 0.99

Haitong CSI300 Index ETF (HKD Counter) 02811 Hong Kong Haitong CSI 300 Country Onshore Physical 257.1 0.85

Haitong CSI300 Index ETF (RMB Counter) 82811 Hong Kong Haitong CSI 300 Country Onshore Physical 257.1 0.85

Hang Seng China A Industry Top Index ETF (HKD Counter) 03128 Hong Kong Hang Seng Hang Seng China A Industry Top Index Strategic Onshore Physical 54.1 0.90

Hang Seng China A Industry Top Index ETF (RMB Counter) 83128 Hong Kong Hang Seng Hang Seng China A Industry Top Index Strategic Onshore Physical 54.1 0.90

Hang Seng FTSE China 25 Index ETF 02838 Hong Kong Hang Seng FTSE China 25 Country Offshore Physical 22.5 0.89

Hang Seng H-Share Index ETF 02828 Hong Kong Hang Seng HSCEI Country Offshore Physical 2,826.4 0.65

Harvest MSCI China A 50 Index ETF (HKD Counter) 03136 Hong Kong Harvest Fund MSCI China A 50 Country Onshore Physical 207.3 0.80

Harvest MSCI China A 50 Index ETF (RMB Counter) 83136 Hong Kong Harvest Fund MSCI China A 50 Country Onshore Physical 207.3 0.80

Harvest MSCI China A Index ETF (HKD Counter) 03118 Hong Kong Harvest Fund MSCI China A Country Onshore Physical 239.0 0.88

Harvest MSCI China A Index ETF (RMB Counter) 83118 Hong Kong Harvest Fund MSCI China A Country Onshore Physical 239.0 0.88

Horizons MSCI China ETF 03040 Hong Kong Mirae Asset MSCI China Country Offshore Physical 37.7 0.25

HSBC MSCI China ETF 03033 Hong Kong HSBC MSCI China Country Offshore Physical 3.6 0.50

iShares CSI 300 A-Share Index ETF 02846 Hong Kong iShares CSI 300 Country Onshore Synthetic 58.5 1.39

iShares CSI A-Share Consumer Discretionary Index ETF 03001 Hong Kong iShares CSI 300 Consumer Discretionary Sector Onshore Synthetic 9.7 1.39

iShares CSI A-Share Consumer Staples Index ETF 02841 Hong Kong iShares CSI 300 Consumer Staples Sector Onshore Synthetic 7.5 1.39

iShares CSI A-Share Energy Index ETF 03050 Hong Kong iShares CSI 300 Energy Sector Onshore Synthetic 4.0 1.39

iShares CSI A-Share Financials Index ETF 02829 Hong Kong iShares CSI 300 Financials Sector Onshore Synthetic 18.4 1.39

iShares CSI A-Share Infrastructure Index ETF 03006 Hong Kong iShares CSI 300 Infrastructure Sector Onshore Synthetic 6.2 1.39

iShares CSI A-Share Materials Index ETF 03039 Hong Kong iShares CSI 300 Materials Sector Onshore Synthetic 6.3 1.39

iShares FTSE A50 China Index ETF 02823 Hong Kong iShares FTSE China A50 Country Onshore Synthetic 6,901.1 1.39

iShares MSCI China Index ETF 02801 Hong Kong iShares MSCI China Country Offshore Physical 163.2 0.59

Hong Kong

Asia Pacific

Australia

Appendix 4: Chinese Equity ETFsListed outside of China


May 2014


29


Replication Method

AUM(USD m)

Expense Ratio (%)*

Ping An of China CSI RAFI A-Share 50 ETF 2818 Hong Kong Ping An of China CSI RAFI 50 Strategic Onshore Synthetic 1.2 1.91

Value China ETF 03046 Hong Kong Sensible AM (Value Partners) FTSE Value-Stocks China Strategic Offshore Physical 20.3 0.99

W.I.S.E. - CSI 300 China Tracker 02827 Hong Kong BOCI-Prudential CSI 300 Country Onshore Synthetic 893.4 1.39

W.I.S.E. - SSE 50 China Tracker 03024 Hong Kong BOCI-Prudential SSE 50 Country Onshore Synthetic 16.9 1.39

W.I.S.E. - CSI HK Listed Mainland Consumption Tracker 02817 Hong Kong BOCI-Prudential CSI HK Listed Tradable Mainland Consumption Sector Offshore Physical 1.6 0.89

W.I.S.E. - CSI HK Listed Mainland Real Estate Tracker 02839 Hong Kong BOCI-Prudential CSI HK Listed Tradable Mainland Real Estate Sector Offshore Physical 3.7 0.89

China H-share Bear -1x ETF 1573 Japan Simplex HSCEI Leveraged / Inverse Offshore Derivative-

Based 8.4 0.85

China H-share Bull 2x ETF 1572 Japan Simplex HSCEI Leveraged / Inverse Offshore Derivative-

Based 6.0 0.85

ChinaAMC CSI 300 Index ETF (JDR) 1575 Hong Kong China AMC CSI 300 Country Onshore Physical 1,045.3 0.85

CSOP FTSE China A50 ETF (JDR) 1576 Hong Kong CSOP FTSE China A50 Country Onshore Physical 3,123.9 1.15

Listed Index Fund China A Share (Panda) CSI300 1322 Japan Nikko AM CSI 300 Country Onshore Feeder Fund 35.5 0.95

Listed Index Fund China H-share (Hang Seng China Enterprises) 1548 Japan Nikko AM HSCEI Country Offshore Feeder Fund 2.9 0.55

Nomura SSE50 Index Link ETF 1309 Japan Nomura SSE 50 Country Onshore Feeder Fund 89.3 0.95

CIMB FTSE China 25 0823EA Malaysia CIMB-Principal FTSE China 25 Country Offshore Physical 2.8 1.01

db x-trackers CSI300 UCITS ETF KT4 Luxembourg db X-trackers CSI 300 Country Onshore Synthetic 757.5 0.50

db x-trackers FTSE China 25 UCITS ETF HD8 Luxembourg db X-trackers FTSE China 25 Country Offshore Synthetic 232.9 0.60

db x-trackers MSCI China Index UCITS ETF LG9 Luxembourg db X-trackers MSCI China Country Offshore Synthetic 205.9 0.65

Lyxor UCITS ETF China Enterprise (HSCEI) P58 France Lyxor HSCEI Country Offshore Synthetic 868.7 0.65

United SSE 50 China ETF JK8 Singapore UOB SSE 50 Country Onshore Synthetic 35.2 0.85

KINDEX China A CSI300 ETF A168580 South Korea KIM CSI 300 Country Onshore Physical 84.3 0.70

KODEX China H ETF A099140 South Korea Samsung HSCEI Country Offshore Physical 54.8 0.37

KODEX FTSE China A50 ETF A169950 South Korea Samsung FTSE China A50 Country Onshore Physical 103.8 0.99

KStar China A CSI100 ETF A174360 South Korea KB AM CSI 100 Country Onshore Physical 27.4 0.65

TIGER China ETF A117690 South Korea Mirae Asset Hang Seng Mainland 25 Country Offshore Physical 9.8 0.49

TIGER China A300 ETF A192090 South Korea Mirae Asset CSI 300 Country Onshore Physical 78.2 0.70

Fubon SSE180 ETF 006205 Taiwan Fubon SSE 180 Country Onshore Physical 295.3 1.09

Fuh Hwa CSI 300 A Shares ETF 006207 Taiwan Fuh-Hwa CSI 300 Country Onshore Physical 119.5 0.85

Hang Seng H-Share Index ETF 0080 Hong Kong Hang Seng HSCEI Country Offshore Physical 2,826.4 0.55

W.I.S.E. - SSE 50 China Tracker 008201 Hong Kong BOCI-Prudential SSE 50 Country Onshore Synthetic 16.9 1.39

W.I.S.E. Yuanta/P-shares CSI 300 ETF 0061 Taiwan Yuanta CSI 300 Country Onshore Feeder Fund 746.0 0.40

Yuanta/P-shares SSE50 ETF 006206 Taiwan Yuanta SSE 50 Country Onshore Physical 172.4 1.30

W.I.S.E. KTAM CSI 300 China Tracker CHINA Thailand Krung Thai CSI 300 Country Onshore Feeder Fund 7.5 1.50


Replication Method

AUM(USD m)

Expense Ratio (%)*

Lyxor UCITS ETF China Enterprise (HSCEI) ASI France Lyxor HSCEI Country Offshore Synthetic 868.7 0.65

Amundi ETF MSCI China UCITS ETF - EUR CC1 France Lyxor MSCI China H Country Offshore Synthetic 93.0 0.55

Amundi ETF MSCI China UCITS ETF - USD CC1U France Amundi MSCI China H Country Offshore Synthetic 93.0 0.55

db x-trackers FTSE China 25 UCITS ETF X25 Luxembourg Amundi FTSE China 25 Country Offshore Synthetic 232.9 0.60

DJ BRIC China 15 THEAM Easy UCITS ETF(Formerly Easy ETF FTSE China 25) EXC France BNP Paribas Dow Jones BRIC China 15 Capped Index

(from 18 Feb 2014) Country Offshore Synthetic 20.2 0.60

HSBC MSCI CHINA UCITS ETF CNY Ireland HSBC MSCI China Country Offshore Physical 149.5 0.60

iShares China Large Cap UCITS ETF FXC Ireland iShares FTSE China 25 Country Offshore Physical 817.4 0.74


Lyxor UCITS ETF CSI 300 A-Share CSIA France Lyxor CSI 300 Country Offshore Synthetic 61.4 0.40

Amundi ETF MSCI China UCITS ETF - EUR 18MG France Amundi MSCI China H Country Offshore Synthetic 93.0 0.55

ComStage FTSE China A50 UCITS ETF C024 Luxembourg Commerzbank FTSE China A50 Country Onshore Synthetic 43.6 0.40

ComStage HSCEI UCITS ETF C023 Luxembourg Commerzbank HSCEI Country Offshore Synthetic 19.1 0.55

db x-trackers CSI300 Banks UCITS ETF XCHB Luxembourg db X-trackers CSI 300 Banks Sector Onshore Synthetic 1.6 0.50

db x-trackers CSI300 Consumer Discretionary UCITS ETF XCHD Luxembourg db X-trackers CSI 300 Consumer Discretionary Sector Onshore Synthetic 7.6 0.50

db x-trackers CSI300 Energy UCITS ETF XCHE Luxembourg db X-trackers CSI 300 Energy Sector Onshore Synthetic 0.8 0.50

db x-trackers CSI300 Health Care UCITS ETF XCHC Luxembourg db X-trackers CSI 300 Health Care Sector Onshore Synthetic 4.2 0.50

db x-trackers CSI300 Real Estate UCITS ETF XCHR Luxembourg db X-trackers CSI 300 Real Estate Sector Onshore Synthetic 1.2 0.50

db x-trackers CSI300 UCITS ETF XCHA Luxembourg db X-trackers CSI 300 Country Onshore Synthetic 757.5 0.50

db x-trackers FTSE China 25 UCITS ETF DBX9 Luxembourg db X-trackers FTSE China 25 Country Offshore Synthetic 232.9 0.60

db x-trackers Harvest CSI300 Index UCITS ETF RQFI Luxembourg db X-trackers CSI 300 Country Onshore Physical 135.9 1.10

db x-trackers MSCI China Index UCITS ETF XCS6 Luxembourg db X-trackers MSCI China Country Offshore Synthetic 205.9 0.65

Europe

Asia Pacific (cont’d)

Hong Kong(cont’d)

Japan

Malaysia

Singapore

South Korea

Taiwan

Thailand

France

Belgium

Germany


May 2014


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Replication Method

AUM(USD m)

Expense Ratio (%)*

Deka MSCI China UCITS ETF EL46 Germany Deka ETFs MSCI China Country Offshore Physical 28.9 0.65

HSBC MSCI CHINA UCITS ETF H4ZP Ireland HSBC MSCI China Country Offshore Physical 149.5 0.60

iShares China Large Cap UCITS ETF IQQC Ireland iShares FTSE China 25 Country Offshore Physical 817.4 0.74

iShares DJ China Offshore 50 (DE) EXXU Germany iShares Dow Jones China Offshore 50 Country Offshore Physical 76.6 0.61

Lyxor UCITS ETF China Enterprise (HSCEI) L8I1 France Lyxor HSCEI Country Offshore Synthetic 868.7 0.65

Amundi ETF MSCI China ETF EUR A/I CC1 France Amundi MSCI China H Country Offshore Synthetic 93.0 0.55


db x-trackers FTSE China 25 UCITS ETF XX25 Luxembourg db X-trackers FTSE China 25 Country Offshore Synthetic 232.9 0.60

db x-trackers Harvest CSI300 Index UCITS ETF RQFI Luxembourg db X-trackers CSI 300 Country Onshore Physical 135.9 1.10


Lyxor UCITS ETF China Enterprise (HSCEI) CINA France Lyxor HSCEI Country Offshore Synthetic 868.7 0.65

db x-trackers CSI300 Banks UCITS ETF 159879 Luxembourg db X-trackers CSI 300 Banks Sector Onshore Synthetic 1.6 0.50

db x-trackers CSI300 Consumer Discretionary UCITS ETF 159882 Luxembourg db X-trackers CSI 300 Consumer Discretionary Sector Onshore Synthetic 7.6 0.50

db x-trackers CSI300 Energy UCITS ETF 159900 Luxembourg db X-trackers CSI 300 Energy Sector Onshore Synthetic 0.8 0.50

db x-trackers CSI300 Financials UCITS ETF 159903 Luxembourg db X-trackers CSI 300 Financials Sector Onshore Synthetic 8.8 0.50

db x-trackers CSI300 Health Care UCITS ETF 159891 Luxembourg db X-trackers CSI 300 Health Care Sector Onshore Synthetic 4.2 0.50

db x-trackers CSI300 Industrials UCITS ETF 159906 Luxembourg db X-trackers CSI 300 Industrials Sector Onshore Synthetic 3.0 0.50

db x-trackers CSI300 Materials Index UCITS ETF 159885 Luxembourg db X-trackers CSI 300 Materials Sector Onshore Synthetic 2.3 0.50

db x-trackers CSI300 Real Estate UCITS ETF 159888 Luxembourg db X-trackers CSI 300 Real Estate Sector Onshore Synthetic 1.2 0.50

db x-trackers CSI300 Transportation UCITS ETF 159894 Luxembourg db X-trackers CSI 300 Transportation Sector Onshore Synthetic 1.4 0.50

db x-trackers CSI300 UCITS ETF 1D 155651 Luxembourg db X-trackers CSI 300 Country Onshore Synthetic 757.5 0.50

db x-trackers CSI300 UCITS ETF 2D 155654 Luxembourg db X-trackers CSI 300 Country Onshore Synthetic 757.5 0.50

db x-trackers CSI300 Utilities UCITS ETF 159897 Luxembourg db X-trackers CSI 300 Utilities Sector Onshore Synthetic 1.3 0.50

db x-trackers MSCI China Index UCITS ETF 163350 Luxembourg db X-trackers MSCI China Country Offshore Synthetic 205.9 0.65


db x-trackers FTSE China 25 UCITS ETF DXX25 Luxembourg db X-trackers FTSE China 25 Country Offshore Synthetic 232.9 0.60


db x-trackers FTSE China 25 UCITS ETF XX25 Luxembourg db X-trackers FTSE China 25 Country Offshore Synthetic 232.9 0.60

XACT Kina ETF XACT/KINA Luxembourg Handelsbanken HSCEI Country Offshore Synthetic 21.9 0.60

Amundi ETF MSCI China UCITS ETF - USD CC1USD France Amundi MSCI China H Country Offshore Synthetic 93.0 0.55

ComStage ETF HSCEI I Cap CBHSCE Luxembourg Commerzbank HSCEI Country Offshore Synthetic 19.1 0.55

db x-trackers FTSE China 25 UCITS ETF XXXI Luxembourg db X-trackers FTSE China 25 Country Offshore Synthetic 232.9 0.60

db x-trackers MSCI China Index UCITS ETF XMCH Luxembourg db X-trackers MSCI China Country Offshore Synthetic 205.9 0.65

HSBC MSCI CHINA UCITS ETF HMCH Ireland HSBC MSCI China Country Offshore Physical 149.5 0.60


Lyxor UCITS ETF China Enterprise (HSCEI) LYASI France Lyxor HSCEI Country Offshore Synthetic 868.7 0.65

Amundi ETF MSCI China UCITS ETF - EUR CC1 France Amundi MSCI China H Country Offshore Synthetic 93.0 0.55

Amundi ETF MSCI China UCITS ETF - USD CC1U France Amundi MSCI China H Country Offshore Synthetic 93.0 0.55

CSOP Source FTSE China A50 UCITS ETF CHNA Ireland Source FTSE China A50 Country Onshore Physical 240.2 1.11


db x-trackers FTSE China 25 UCITS ETF XX2D Luxembourg db X-trackers FTSE China 25 Country Offshore Synthetic 232.9 0.60

db x-trackers Harvest CSI300 Index UCITS ETF RQFI / ASHR Luxembourg db X-trackers CSI 300 Country Onshore Physical 135.9 1.10

db x-trackers MSCI China Index UCITS ETF XCX6 / XCS6 Luxembourg db X-trackers MSCI China Country Offshore Synthetic 205.9 0.65

HSBC MSCI CHINA UCITS ETF HMCD/ HMCH Ireland HSBC MSCI China Country Offshore Physical 149.5 0.60

iShares China Large Cap UCITS ETF IDFX Ireland iShares FTSE China 25 Country Offshore Physical 817.4 0.74

Lyxor UCITS ETF China Enterprise (HSCEI) ASIL / ASIU France Lyxor HSCEI Country Offshore Synthetic 868.7 0.65

Lyxor UCITS ETF CSI 300 A-Share CSIA / CSIL France Lyxor CSI 300 Country Onshore Synthetic 61.4 0.40

Source MSCI China UCITS ETF MXCS Ireland Source MSCI China Country Offshore Synthetic 4.8 0.65


Replication Method

AUM(USD m)

Expense Ratio (%)*

BMO China Equity Index ETF ZCH Canada BMO BNY Mellon China Select ADR Country Offshore Physical 15.1 0.65

iShares China All-Cap Index (Advisor Series) CHI.A Canada iShares AlphaShares China All Cap Country Offshore Feeder Fund 11.6 1.49

iShares China All-Cap Index (Common Shares) CHI Canada iShares AlphaShares China All Cap Country Offshore Feeder Fund 11.6 0.70

iShares China Index XCH Canada iShares FTSE China 25 Country Offshore Feeder Fund 23.3 0.86

iShares China Large-Cap FXI United States iShares FTSE China 25 Country Offshore Physical 5,086.0 0.73

iShares MSCI China Index MCHI United States iShares MSCI China Country Offshore Physical 878.8 0.61

iShares MSCI China Small Cap Index ECNS United States iShares MSCI China Small Cap Country Offshore Physical 32.3 0.61

Market Vectors ChinaAMC A-Share ETF PEK United States Van Eck CSI 300 Country Onshore Physical 33.1 0.72

Europe (cont’d)

Germany(cont’d)

Italy

Luxembourg

Netherlands

Spain

Sweden

Switzerland

UK

Canada

Chile

Americas


May 2014


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Replication Method

AUM(USD m)

Expense Ratio (%)*

CHNTRAC 11 CHNTRAC.11 Mexico BBVA BMV China SX20 Country Offshore Physical 1.4 0.50

Global X China Consumer ETF CHIQ United States Global X Management Solactive China Consumer Country Offshore Physical 170.0 0.65


iShares FTSE China FCHI United States iShares FTSE China (HK Listed) Country Offshore Physical 27.2 0.73



PowerShares Golden Dragon China PGJ United States PowerShares NASDAQ Halter USX China Country Offshore Physical 307.3 0.70

SPDR S&P China GXC United States State Street S&P China BMI Country Offshore Physical 775.5 0.59

db X-trackers Harvest CSI 300 China A-Shares Fund CN United States db X-trackers CSI 300 Country Onshore Physical 143.4 0.82

db X-trackers Harvest MSCI All China Equity Fund (Launched on 30 April 2014) ASHR United States db X-trackers MSCI All China Country Onshore Physical / Feeder 0.0 0.71

Direxion Daily China Bear 3X Shares YANG United States Direxion BNY Mellon China Select ADR Leveraged / Inverse Offshore Derivative-

Based 19.2 0.95

Direxion Daily China Bull 3X Shares YINN United States Direxion BNY Mellon China Select ADR Leveraged / Inverse Offshore Derivative-

Based 102.2 0.95

EGShares China Infrastructure CHXX United States Emerging Global Advisors INDXX China Infrastructure Sector Offshore Physical 9.1 0.85

First Trust China AlphaDEX FCA United States First Trust Defined China Strategic Offshore Physical 4.4 0.80

Global X China Consumer ETF CHIQ United States Global X Solactive China Consumer Sector Offshore Physical 170.0 0.65

Global X China Energy ETF CHIE United States Global X Solactive China Energy Sector Offshore Physical 4.3 0.65

Global X China Financials ETF CHIX United States Global X Solactive China Financials Sector Offshore Physical 21.8 0.65

Global X China Industrials ETF CHII United States Global X Solactive China Industrials Sector Offshore Physical 4.1 0.65

Global X China Materials ETF CHIM United States Global X Solactive China Materials Sector Offshore Physical 2.1 0.65

Global X NASDAQ China Technology ETF QQQC United States Global X NASDAQ OMX China Technology Sector Offshore Physical 20.8 0.65

Guggenheim China All-Cap YAO United States Guggenheim Alphashares China All Cap Country Offshore Physical 55.4 0.70

Guggenheim China Real Estate TAO United States Guggenheim AlphaShares China Real Estate Sector Offshore Physical 26.4 0.70

Guggenheim China Small Cap HAO United States Guggenheim AlphaShares China Small Cap Country Offshore Physical 230.6 0.75

Guggenheim China Technology CQQQ United States Guggenheim AlphaShares China Technology Sector Offshore Physical 82.5 0.70


iShares FTSE China FCHI United States iShares FTSE China (HK Listed) Country Offshore Physical 27.2 0.73



KraneShares Bosera MSCI China A ETF KBA United States KraneShares MSCI China A Country Onshore Physical 11.6 1.10

KraneShares CSI China Five Year Plan ETF KFYP United States KraneShares CSI Overseas China Five-Year Plan Sector Offshore Physical 3.1 0.68

KraneShares CSI China Internet ETF KWEB United States KraneShares CSI Overseas China Internet Sector Offshore Physical 77.2 0.68

Market Vectors ChinaAMC A-Share ETF PEK United States Van Eck CSI 300 Country Onshore Physical 33.1 0.72

PowerShares China A-Share CHNA United States PowerShares Actively managed Country Onshore Derivative-Based 2.2 0.51

PowerShares Golden Dragon China PGJ United States PowerShares NASDAQ Halter USX China Country Offshore Physical 307.3 0.70

ProShares Short FTSE China 25 YXI United States ProShares FTSE China 25 Leveraged / Inverse Offshore Derivative-

Based 5.3 0.95

ProShares Ultra FTSE China 25 XPP United States ProShares FTSE China 25 Leveraged / Inverse Offshore Derivative-

Based 39.7 0.95

ProShares UltraShort FTSE China 25 FXP United States ProShares FTSE China 25 Leveraged / Inverse Offshore Derivative-

Based 106.0 0.95

RBS China Trendpilot ETN TCHI United States RBS RBS China Trendpilot Index Country Offshore Derivative-Based 19.5 1.10

SPDR S&P China GXC United States State Street S&P China BMI Country Offshore Physical 775.5 0.59

WisdomTree China Dividend ex-Fincls CHXF United States WisdomTree WisdomTree China Dividend ex-Financials Country Offshore Physical 17.4 0.63

Americas (cont’d)

Mexico

US

* Being the stated expense ratio of the ETFs (excluding: CIMB FTSE China 25 being its Management Expense Ratio, W.I.S.E. Yuanta/P-shares CSI 300 ETF being the sum of management fee and trustee fee) Source: Morningstar

Direct, Morningstar Research (Data as of end-March 2014)


May 2014


32

Contacts

Jackie Choy, CFA, ETF [email protected]

Ben Johnson, CFA, Director, Global Passive Funds [email protected]

Hortense Bioy, CFA, Director, European Passive Funds [email protected]

on the right track: measuring tracking efficiency in chinese equity...

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