International cross-listings by Australian firms: A stochastic dominance analysis of equity returns

J. of Multi. Fin. Manag. 16 (2006) 494–508

International cross-listings by Australian firms:A stochastic dominance analysis of equity returns

Kamran Ahmed a, Jae H. Kim b, Darren Henry a,∗a School of Business, La Trobe University, Bundoora, Vic. 3086, Australia

b Department of Econometrics and Business Statistics, Monash University,Caulfield East, Vic. 3145, Australia

Received 24 March 2005; accepted 21 December 2005Available online 23 February 2006

Abstract

This paper examines the share return and risk exposure consequences of Australian companies decidingto cross-list on major international stock exchanges. Due to limited sample availability of cross-listingtransactions by Australian companies and concerns regarding non-normality, we apply bootstrapping andstochastic dominance techniques to evaluate share return and return variance changes following cross-listings.The findings provide evidence that firm share returns decline following the undertaking of cross-listings, withan associated fall in the post-listing variance in share returns. Measures of the importance and ownershipconcentration of stockmarkets in destination countries are found to be correlated with post-listing sharereturn outcomes, however, no evidence of superior return or risk reduction benefits from firms cross-listing incountries providing stronger investor protection or higher accounting reporting and disclosure requirementsis identified.© 2006 Elsevier B.V. All rights reserved.

JEL classification: G15; G32

Keywords: Cross-listing; Share returns and risk; Stochastic dominance

1. Introduction

With increasing globalization of trade and commerce, an expanding influence of multinationalcorporations and the need for investment capital, there has been tremendous growth in instances

∗ Corresponding author at: Department of Economics and Finance, School of Business, La Trobe University, Bundoora,Vic. 3086, Australia. Tel.: +61 394 791 730; fax: +61 394 791 654.

E-mail address: [email protected] (D. Henry).

1042-444X/$ – see front matter © 2006 Elsevier B.V. All rights reserved.doi:10.1016/j.mulfin.2005.12.001

K. Ahmed et al. / J. of Multi. Fin. Manag. 16 (2006) 494–508 495

of international cross-listings by companies. Statistics published by the International Federationof Stock Exchanges indicate that the number of foreign companies listed on the 10 largest stockexchanges around the world has almost tripled from 1594 in 1986 to 4703 in 1998 (Faff et al., 2002).

It has been argued that, by cross-listing their shares in a foreign country, firms broaden anddiversify their shareholder base, diversify their market risks, reduce the cost of international diver-sification to investors, reduce transaction costs to foreign investors, and increase media coverageand analyst following. Further, it has been shown that information asymmetry among investorstends to reduce following cross-listing and that firms experience an increase in trading volumeand the liquidity of shares. Refer to Karolyi (1998) for an in-depth discussion of cross-listingmotives and effects and early empirical evidence evaluating these motives. These advantagesmay be particularly important to firms based in smaller capital markets, such as Australia (seeDomowitz et al., 1998; Faff et al., 2002).

Previous research has examined market price behaviour around foreign listings (Alexanderet al., 1988; Foerster and Karolyi, 1999; Miller, 1999; Baker et al., 2002), the effect of foreignlistings on a stock’s risk and cost of capital (Barclay et al., 1990; French and Poterba, 1991;Noronha et al., 1996; Errunza et al., 1999; Stulz, 1999; Errunza and Miller, 2000; Kim and Mei,2001), the effect of listing on the liquidity of a firm’s shares (Kadlec and McConnell, 1994;Domowitz et al., 1998; Lins et al., 2005), the consequences of cross-listing on firm transparencyand visibility (Merton, 1987; Foerster and Karolyi, 1999; Pagano et al., 2002; Baker et al., 2002)and the valuation, information disclosure and governance characteristics of cross-listing firms(Saudagaran and Biddle, 1992; Lang et al., 2003a, 2003b; Doidge, 2004; Doidge et al., 2004).

Contrasting results are evident throughout this literature, and particularly in regards to the stockreturn and risk implications of cross-listing actions. Further, most prior research has been in thecontext of cross-listings between the major capital markets in the US, Canada, Europe and Japan.There is limited research that investigates the issue of international listings from smaller to largercapital markets, such as from Australia to the US and UK, with the exceptions of Alexander etal. (1988), Jayaraman et al. (1993), Ko et al. (1997), Foerster and Karolyi (1999), Errunza andMiller (2000), Kim and Mei (2001), Faff et al. (2002), and Doidge et al. (2004). These studiesgenerally document a return or valuation discount resulting from cross-listings by firms fromvarious countries, and specifically for Australian firms in Faff et al. (2002) and Doidge et al. (2004),and Faff et al. (2002) find that the risk profile of Australian stocks marginally increases followinginternational cross-listings. We extend this line of research by examining the cross-listing effecton the equity returns and risk of Australian firms which have listed their shares on overseas stockexchanges during the period from 1988 to 2000. In our study, unlike Faff et al. (2002) who used amultivariate GARCH model to measure risk, we use the distribution-free Stochastic Dominancecriteria, along with a bootstrapping application of the popular mean-variance criteria.

In recent years there has also been substantial growth in overseas listing on the ContinentalEuropean stock exchanges such as Germany, France and the Netherlands. Continental Europe ischaracterized by powerful financial intermediaries and the equity markets are relatively illiquidand trading activities are thin (Coffee, 1999). In contrast, the major US stock exchanges areconsidered to be the most efficient in the world and provide the most effective legal systemto protect minority equity-holders. Continental European countries, and especially French civilcode countries, in contrast, provide poor protection to these shareholders (La Porta et al., 1998;Demirguc-Kunt and Maskimovic, 1998; Coffee, 1999; Reece and Weisbach, 2002) and their stockexchanges adhere to lower accounting standards and disclosure requirements and have highertrading costs (Pagano et al., 2002; Radebaugh and Gray, 2002). Conversely, Baker et al. (2002)and Lang et al. (2003a) both report the existence of greater analyst coverage and improved forecast

496 K. Ahmed et al. / J. of Multi. Fin. Manag. 16 (2006) 494–508

accuracy for firms cross-listing on US stock exchanges, and Lang et al. (2003b) and Doidge (2004)provide empirical support for US cross-listings improving accounting and reporting quality andminority shareholder rights, respectively.

An Australian firm that decides to cross-list on the stock exchanges in countries such asthe US and UK is expected to benefit more than listing on those in the Continental Europeancountries, which should be reflected in higher post-listing returns and/or lower risk. However,this is an empirical issue which has not been examined before. In this study, we evaluate thisimportant issue by comparing the post-listing share return and risk measures for Australian firmswhich have cross-listed on Anglo-Saxon and Continental European stock exchanges. We alsoformally model the relationship between post-cross-listing share returns for sample firms andlisting country characteristics, including measures of legal tradition and enforcement, shareholderrights, accounting disclosure and sharemarket importance created by La Porta et al. (1997, 1998),to identify the potential source of any post-listing return movements.

The remainder of this study is organized as follows. Following the introduction and motivation,section two discusses data issues and structure and the research methodology associated withthe bootstrapping and stochastic dominance procedures employed, and section three presentsthe results of the analysis undertaken. In the final section we summarize the results and drawconclusions.

2. Data collection and research methodology

2.1. Data collection

We started by obtaining a list of ASX-listed firms that have cross-listed on overseas stockexchanges between 1986 and 2000, for which the Australian Stock Exchange claimed homeexchange responsibilities. The list contained the names of 134 firms with 300 listings during theperiod, without providing the overseas listing dates. We checked the web-pages of the relevantoverseas stock exchanges to obtain that information and, where no information was available, wecontacted the stock exchange listing officers. In this way, we obtained 116 listing dates for 85 firms.We extracted the daily share prices for firms for the period from 1 year before the listing date to 1year after the cross-listing from the Securities Industry Research Centre of Asia-Pacific (SIRCA)ASX Daily Data share price database. Data constraints (due to company delisting or share pricesnot being available in the database for this 2-year period) restrict our sample to 43 firms with 55listings, which forms the basis of our analysis. In the case of sample firms undertaking multiplecross-listings on stock exchanges in different countries, care was taken to ensure that the 2-yearreturn observation period for individual cross-listing events did not overlap. This resulted in thedeleting of one cross-listing transaction by Pacific Dunlop Limited (now Ansell Limited) whichcross-listed its equity securities on the Frankfurt Stock Exchange on 2 February 1987, which wasa month after undertaking a similar cross-listing on the London Stock Exchange.

The testing and analysis within the paper is conducted using market-adjusted (abnormal) dailyreturns and return variances.1 The daily return for each sample firm (Rit) was calculated as thedifference in the natural logarithm of consecutive prices, Rit = ln(Pt) − ln(Pt−1). The All OrdinariesIndex is used as the market return proxy in this study, with daily abnormal returns determined as

1 The analysis was also completed using unadjusted (or raw) daily returns for cross-listing firms with similar resultsobtained. For brevity purposes these results are not included in the paper, but can be obtained from the authors on request.


Table 1Descriptive statistics (means) for sample firms for the 7-year period surrounding cross-listing year

−3 −2 −1 0 1 2 3

Total assets ($million) 6726.975 6180.098 5842.224 5638.113 6175.287 8773.185 9776.872Total revenue ($million) 3059.068 2920.922 2847.520 2877.581 3163.936 3529.704 3673.022Net operating cash flows (NOCF)

($million)287.135 302.957 308.884 338.076 318.214 305.170 194.725

NOCF to total assets (%) 6.893 2.931 2.445 2.004 2.504 −1.105 −4.807Long-term debt to total assets (%) 24.853 21.598 21.813 20.215 18.762 20.513 21.553EBIT to total assets (%) 5.822 1.043 −6.531 −3.640 −9.778 −10.732 −9.828EBIT to capital employed (%) 7.802 2.160 −6.230 −3.428 −9.864 −10.720 −17.618Market-to-book value of equity 1.929 1.878 2.094 3.805 1.897 2.021 2.627Increase in long-term debt

($million)257.259 135.560 143.583 53.324 59.087 42.979 381.047

Increase in total shares (million) −375.418 43.609 88.209 2343.442 661.791 −2488.280 72.895

Note: Year 0 represents the financial reporting year-end containing the date of the respective sample firm cross-listingevents.

the difference between the sample firm return on day t (Rit) and the market return on day t (Rmt),with daily market returns calculated in the same manner as that described for sample firm returnsabove. The All Ordinaries Index data was also accessed from the SIRCA ASX Daily Data database.

Table 1 provides some descriptive statistics for the sample firms from 3 years before to 3 yearsafter cross-listing on the overseas stock exchanges. The table shows that the average total assetsof sample firms have increased over the 7-year period, from $6726.975 million 3 years prior tocross-listing to $9776.872 million 3 years subsequent to the listings. During the same period, totalrevenues have also increased, but the extent of the revenue increase is much less compared withthat of the sample firms’ total assets. Net operating cash flows (NOCF) have not increased greatlyover the period and the ratio of NOCF to total assets has fallen, with the mean ratio being negativein years 2 and 3 following cross-listing date. The leverage ratio has remained at approximately20% throughout the 7-year period, suggesting that cross-listing transactions are not undertakenfor capital structure modification reasons. The average change in long-term debt has been positivein each of the individual years, and there is a large increase in issued ordinary shares in year 0,which is consistent with cross-listing decisions being, at least partially, financially motivated. Theprofitability ratios, as measured by earnings before interest and taxes to total assets and to capitalemployed, indicate that sample firms, on average, have experienced relatively poorer operatingperformance over time and have incurred losses during and subsequent to cross-listings. However,despite these operating losses, the market value of equity has remained at or above two times thebook value of equity.

2.2. Research methodology

This section reviews the methodologies used in this paper. First, we will compare the mean andvariance of abnormal return distributions for sample firms before and after cross-listings. To testthe statistical significance of the differences in the abnormal return means and variances, we willuse statistical inference based on confidence intervals. For example, if the 90% confidence intervalsfor the mean of returns before and after the listing overlap, we cannot reject the null hypothesisthat the two means are equal at the 10% level of significance. Conventionally, confidence intervalsbased on normal approximations have been used for this purpose. However, return distributions


often show asymmetric shape or a high proportion of extreme values, which are stylized facts ofstock returns that are not consistent with the assumption of normality. In addition, stock returnscan exhibit variance which changes over time depending on their own past, which violates the keyassumptions associated with the conventional confidence intervals based on normal distributions.

To conduct more sensible and reliable statistical tests, we adopt the bootstrap method of Efron(1979), which is a computer-intensive statistical technique designed to approximate the unknownsampling distribution of a statistic. This technique has been used widely in economics and financeas an alternative to the conventional methods of statistical inference (see, for example, Li andMaddala, 1996; Berkowitz and Kilian, 2000; Ruiz and Pascual, 2002). It has been found thatthe bootstrap technique provides a superior alternative to the conventional methods in manyapplications, especially when the underlying distribution is non-normal. In this paper, we adoptwhat is called the moving block bootstrap (MBB), which is applicable to a set of time series datagenerated with possible dependence on its own past. With the MBB, the detailed dependencestructure is not required to be known.

Second, we compare stock return distributions before and after cross-listings using the stochas-tic dominance criteria (see, for example, Copeland and Weston, 1988; page 92). These criteriacompare the empirical cumulative distributions of two random variables in a non-parametric way.The first-order dominance criterion compares the two empirical cumulative distribution func-tions paying attention to the difference in means, while the second-order criterion measures thedifference in variances when the mean is identical.

Both methods mentioned are similar in the sense that they are non-parametric, but they arebased on somewhat different ideas. The stochastic dominance criteria make direct comparisonsbetween two empirical cumulative distribution functions. This is in contrast with the bootstrap,which approximates the sampling distributions of a statistic using pseudo-samples repeatedlygenerated from an empirical cumulative distribution function. Both methods are based on the pre-sumption that the empirical cumulative distribution is a good estimate of the unknown cumulativedistribution of the random variable of interest. A fundamental difference is that the bootstrapperforms statistical inference on the mean and variance using their interval estimates, while thestochastic dominance criteria merely compare sample estimates of cumulative distribution func-tions. Nevertheless, it seems highly likely that the two methods should provide good alternativesin comparing the features of two return distributions. In the following two sub-sections, briefreviews of the MBB method and stochastic dominance criteria are presented.

2.2.1. The moving block bootstrapThe bootstrap method provides a useful small sample alternative to the conventional statis-

tical inference based on normal approximations (see Efron, 1979; Freedman, 1984; Efron andTibshirani, 1993; among others). The bootstrap involves generation of a large number of pseudo-data sets, each obtained by re-sampling the original data with replacement. From each pseudo-dataset, the statistic of interest, such as the mean and variance, is calculated. A collection of thesestatistics calculated from a large number of pseudo-data sets forms a bootstrap distribution of thestatistic of interest, which can be used as an approximation of the true sampling distribution ofthe statistic. In generating pseudo-data sets in this paper, we resort to the technique of the movingblock bootstrap (MBB) developed by Kunsch (1989). This method involves re-sampling blocks ofadjacent sub-series from the observed data with replacement and produces pseudo-data sets thatcan replicate salient features of the underlying time series. Its advantage over the traditional boot-strap methods of Efron (1979) and Freedman (1984) is that the underlying dependence structureneed not be assumed or estimated when re-sampling is conducted.


The MBB can be summarized as follows:

(i) Consider (Y1, . . ., Yn), a realization of the strictly stationary, possibly dependent, process.Let B(I,h) = (YI, . . ., YI+h−1) denote a block of length h starting from YI, where h and I arepositive integers. For Yj ∈ B(I,h), if j > n, Yj is set to Yj−n (circular treatment of data).

(ii) Draw blocks from {B(1,h), B(2,h), . . ., B(n − h+1,h)}, randomly with replacement, to con-struct a bootstrap sample of size n* denoted as G = (S1, ..., Sn∗). Note that Si indicates amember of a randomly drawn block.

(iii) Repeat (ii) b times to obtain b sets of bootstrap samples{Gj}bj=1. For each bootstrap sample,the statistic of interest is calculated and this forms the bootstrap distribution of the statisticof interest.

When the statistic of interest is the mean, one can obtain {Xj}bj=1 where Xj is the samplemean calculated from Gj. If the statistic of interest is the variance, the bootstrap distribution is

{s2j}bj=1 where s2j denotes the sample variance calculated from Gj. For example, when n = n* = 100with h = 10, random selection of 10 blocks is required from {B(1,10), B(2,10), . . ., B(91,10)}.To ensure n* = n, re-sampling is stopped as soon as n* becomes greater or equal to n, and theresidual observations, if any, can be discarded (see, for example, Politis and Romano, 1994). The

bootstrap distributions {Xj}bj=1 and {s2j}bj=1 can be used to approximate the unknown samplingdistributions of the sample mean and sample variance.

The MBB confidence intervals are calculated from the bootstrap distribution of a statistic usingthe percentile method (Efron and Tibshirani, 1993). The lower and upper limits of confidenceintervals are constructed by taking appropriate percentiles from the bootstrap distribution. Thatis, the 100(1 − α)% confidence interval for the population mean is constructed as [Xτ, X1−τ],

where Xτ denotes the 100τth percentile of the bootstrap distribution {Xj}bj=1 where τ = 0.5α.For example, for the 90% confidence interval with α = 0.1 when b = 1000, Xτ represents the

5th percentile, which is the 50th element of {Xj}bj=1 sorted in an ascending order. The MBB

confidence interval for the population variance can be computed in a similar way, using {s2j}bj=1.As for the choice of b, a sufficiently large number should be used. In this paper, the number ofbootstrap iterations b is set to 1000.

Past studies on the MBB have found that the choice of block length h is important, as smallsample performances of the MBB are affected to a great extent by the choice of block length. In thispaper, we use the selection method proposed by Romano and Wolf (2001), which automaticallychooses the optimal block length by minimizing the volatility of the lengths of confidence intervals.

2.2.2. Stochastic dominanceLet X and Y be the random variables defined over the support denoted as W. The cumulative

probability functions of X and Y are denoted as F(W) and G(W), respectively. Then, X is said tobe first-order dominant over Y if

F (W) ≤ G(W) for all W (1)

with strict inequality for at least one value of W. In other words, F(W) always lies to the righthand side of G(W) over the support W. This means that the mean of X is always larger than thatof Y under the first-order stochastic dominance.


In some cases, however, the functions F(W) and G(W) can cross and stochastic dominance isindeterminate based on the first-order criterion. This is typical when two random variables havean identical mean. In this case, the decision of dominance should be based on the variance of thedistribution, and it can be captured by the second-order stochastic dominance criterion. X is saidto be second-order dominant over Y if∫ Wi

−∞[G(W) − F (W)] dW ≥ 0 for all Wi ∈ W (2)

The second-order stochastic dominance criteria require that the difference in areas under the cumu-lative distribution function be positive below the mean and negative above the mean. However,the sum of the differences should always be greater than or equal to 0.

In the next section, we will compare return distributions of various sample companies beforeand after the undertaking of cross-listings. Comparisons are made using the MBB confidenceintervals for the mean and variance, and using the first and second-order stochastic dominancecriteria.

3. Empirical results

3.1. Results for market-adjusted (abnormal) daily returns

The primary analysis results are reported in Tables 2 and 3, where the companies are classifiedinto six groups depending on the location of the market on which they cross-list. The mean dailyabnormal returns and return variances for sample firms for the 1-year periods before and aftercross-listing dates and the stochastic dominance analysis results are provided in Table 2. Table 3reports the 90% confidence intervals based on the MBB for the mean and variance of the abnormalstock returns before and after cross-listings.

We begin by looking at the mean abnormal daily return values reported in Table 2. By usingthe 90% confidence intervals for the mean returns, provided in Table 3, we can test the statisticalsignificance of these values. The starred values for the mean abnormal daily returns in Table 2indicate those found to be statistically different from zero. Based on the abnormal share returns,there is mixed evidence, across the six cross-listing regions, of performance change followingcross-listing transactions. For US cross-listings undertaken by Amcor Limited (AMC) and AtlasPacific Limited (ATP) there is evidence of negative performance effects, with statistically positivedaily abnormal returns for AMC prior to cross-listing in the US becoming significantly zero in thefollowing 1-year period and ATP exhibiting statistically zero daily abnormal returns prior to cross-listing which are significantly negative in the 1-year period after the cross-listing date. On the otherhand, statistically zero abnormal returns in the year prior to cross-listing for ANZ Banking GroupLimited (ANZ) become significantly positive in the year following cross-listing on the NYSE.Positive and statistically significant abnormal return effects are observed for Harvey NormanLimited (HVN) listing on the New Zealand Stock Exchange and for Broken Hill ProprietaryCompany Limited (BHP) from cross-listing their ordinary shares in Germany. None of the samplefirm cross-listings, however, exhibit first-order dominance based on abnormal returns.

The second-order stochastic dominance results in Table 2 are also varied, although risk effectsare more evident with 28 out of the 55 cross-listing transactions showing second-order dominance.For these 28 cross-listings, the return distribution after dominates that of before in only 10 casessuggesting, overall, that significant risk-reduction benefits do not eventuate for the large majorityof cross-listing firms. A comparison of the second-order stochastic dominance results across the


Table 2Basic statistics and stochastic dominance results based on sample company market-adjusted (abnormal) returns

Mean Variance FSD SSD

Before After Before After

USAMC 8.00* −4.48 0.50 0.79 ND BDAANI −2.67 −1.33 2.57 3.61 ND BDAANZ 0.17 10.07* 1.44 0.77 ND NDATP −12.30 −44.78* 15.49 16.24 ND BDACML 12.97 −7.35 2.25 1.70 ND NDCSR −8.93 −3.72 0.83 0.96 ND NDCTR −24.61* 14.61 20.29 249.05 ND BDACVI 120.55 −45.29 103.21 24.83 ND NDFNC −32.76 −92.77 58.90 55.36 ND NDGCM −36.40 0.35 76.08 19.57 ND NDNAB 12.06 3.20 3.30 1.00 ND ADBNCP −23.79 8.50 6.28 2.07 ND NDNRT 7.80 0.12 12.13 9.89 ND BDAOEC 10.86 −18.10 3.73 4.53 ND BDAPDP −1.83 −0.28 3.97 8.62 ND BDAWBC −3.86 −3.27 3.02 1.10 ND ADBWMC 7.75 −10.62 1.15 1.58 ND BDA

New ZealandAMC 11.57* 10.79* 0.69 0.52 ND ADBCML 6.41 −35.69 0.89 26.79 ND BDAEML 3.42 −2.74 2.66 1.76 ND NDFOA −13.54 15.19 16.42 2.28 ND ADBGPT −10.57 −1.76 1.14 1.21 ND NDHVN −3.10 29.31* 17.57 3.20 ND ADBMDC 16.80 57.73 26.54 96.87 ND NDNBH −1.20 −0.54 1.94 2.11 ND NDOMO 8.91 −2.68 25.43 11.45 ND NDRSG −4.63 −9.39 4.46 4.36 ND NDSUR −3.89 −10.53 32.11 34.50 ND NDVKI 19.21 45.08 32.42 22.16 ND ADB

CanadaFGL −1.27 −0.07 0.59 0.86 ND NDKCN 53.43 −62.73 34.46 47.98 ND BDALVG 19.80 −28.23 15.34 9.97 ND NDUKCSR 12.86 1.91 1.79 0.79 ND NDGMF −10.62 −21.86 6.78 4.86 ND NDPDP 1.86 1.08 3.67 9.44 ND BDA

GermanyAMG 5.39 32.52 57.34 65.93 ND NDAVL −14.27 12.01 19.24 29.42 ND NDAXR 23.35 −40.20 48.68 63.98 ND BDABHP −2.57 9.02* 0.35 0.40 ND NDCNM 63.15 −49.33 53.29 42.74 ND NDCSR −14.58 −5.73 3.03 3.04 ND NDDOM −13.11 46.19 19.15 46.27 ND BDAMIM 0.42 0.71 9.78 3.06 ND ADBMPH −26.65 35.94


Table 2 (Continued )

Mean Variance FSD SSD

Before After Before After

61.45 50.66 ND ND

OEC 9.11 −34.56 11.82 14.09 ND BDAPMM −39.65* −15.00 7.41 7.48 ND BDASSI 42.45 −30.59 39.52 50.18 ND BDATBR −3.57 22.10 123.46 35.61 ND ADBWMC 5.44 6.92 1.94 2.43 ND NDWRF −12.40 27.05 17.63 26.97 ND ND

JapanBHP −6.20 −1.34 4.18 0.63 ND ADBFGL −4.33 −1.23 6.06 0.56 ND ADBNAB −0.07 0.71 0.88 1.26 ND BDANCP −5.99 −6.80 1.59 2.52 ND NDPDP 4.82 −1.24 4.06 9.08 ND BDA

FSD: first-order dominance; SSD: second-order dominance; ND: no dominance; ADB: after dominates before; BDA:before dominates after. All numbers have been multiplied by 104. Starred values in the mean columns indicate those thatare statistically significant at the 10% level.

six location groups reveals a number of interesting traits, namely that the variability of returnsappears to generally increase for Australian companies cross-listing on US exchanges (beforedominates after in 8 out of the 10 cases of second-order dominance) and decrease for firms listingtheir securities in New Zealand (where 4 out of 5 of the return variances after dominate thoseevident before cross-listings). The second-order dominance patterns in the other four country orregion groups do not, however, demonstrate any clear outcomes of this nature.

Slightly different, and more consistent, results are identified from applying the alterna-tive evaluation technique. Using the confidence intervals determined by applying the bootstrapmethodology, as presented in Table 3, we find five cases which show a statistically significantdifference between the mean daily abnormal returns before and after cross-listing. These fivecases are for cross-listings on either US or German stock exchanges and all indicate lower meanabnormal returns in the post-listing period relative to those in the year prior to undertaking cross-listings. There are no observations in which returns are found to significantly increase followingcross-listing transactions. In relation to the variance of daily abnormal returns, the results inTable 3 identify nine cross-listings which result in a statistically significant negative differencein abnormal return variances and no instances where the variance in abnormal return movementssignificantly increases as a result of cross-listings. Instances of these significant reductions inabnormal return variance after cross-listing are spread across the six region classifications, withat least one cross-listing within each region group exhibiting this outcome. Overall, these resultssuggest that cross-listings do not provide abnormal return gains to shareholders of sample firms,however, there is evidence of risk reduction benefit, in terms of lower return variability, in the1-year period following these cross-listings.

3.2. Country-specific characteristics and post-listing share returns

An objective of this study was to examine whether institutional, and legal and accounting,differences across countries have any influence on the return or risk effects for cross-listing firms.Initially, we broadly classify countries into the Anglo Saxon and Continental Europe categories.


Table 390% confidence intervals for the mean and variance of market-adjusted (abnormal) returns: calculated using the movingblock bootstrap

Mean Variance Test the difference in

Before After Before After Mean Variance

USAMC 2.59 17.14 −9.84 0.91 0.42 0.58 0.53 1.08 – 0ANI −19.14 9.03 −14.74 14.35 2.18 2.98 2.34 3.86 0 0ANZ −19.69 7.12 0.44 17.37 1.04 1.87 0.56 0.97 0 –ATP −41.98 16.98 −75.62 −31.95 11.92 20.96 13.11 19.91 0 0CML 9.63 25.19 −20.31 1.84 1.50 2.57 0.66 2.71 – 0CSR −15.57 −1.41 −10.20 5.31 0.74 1.02 0.72 1.32 0 0CTR −53.73 10.77 −94.66 228.75 13.50 30.86 13.87 704.96 0 0CVI 44.77 223.28 −74.99 −14.41 18.55 265.01 16.65 28.33 – 0FNC −112.79 43.28 −152.02 −59.01 25.21 102.61 35.33 55.52 0 0GCM −140.92 26.81 −58.32 35.47 0.89 224.45 8.62 28.70 0 0NAB 2.61 36.34 −4.74 14.94 1.93 4.88 0.61 1.19 0 –NCP −35.56 −0.63 −8.29 23.20 1.30 10.27 1.47 2.78 0 0NRT −21.96 35.34 −28.52 25.39 8.68 16.85 7.18 12.08 0 0OEC −10.47 23.84 −30.25 −3.70 2.75 4.00 3.21 4.92 0 0PDP −20.13 16.29 −11.81 20.31 2.91 4.99 2.58 19.98 0 0WBC −17.30 5.61 −13.50 9.58 1.18 5.44 0.93 1.45 0 0WMC −3.21 17.50 −23.43 3.36 0.84 1.37 1.25 1.88 0 0

New ZealandAMC 3.55 17.32 7.41 16.45 0.55 0.86 0.41 0.58 0 0CML −0.97 15.10 −67.30 5.62 0.72 1.12 0.54 52.69 0 0EML −6.60 14.14 −17.72 4.49 2.06 3.16 1.25 2.06 0 0FOA −64.17 17.81 −2.84 30.71 1.35 45.61 1.80 2.96 0 0GPT −22.26 −5.82 −9.86 7.24 0.92 1.30 1.03 1.43 0 0HVN −59.19 30.82 18.52 41.21 1.34 48.49 2.32 3.95 0 0MDC −11.10 60.36 −5.96 161.94 20.23 32.12 8.76 269.20 0 0NBH −9.89 10.73 −13.16 12.05 1.60 2.24 1.51 2.86 0 0OMO −23.34 47.94 −22.66 21.83 14.33 40.21 9.71 13.92 0 –RSG −17.36 18.84 −24.84 3.99 3.80 5.35 3.76 4.87 0 0SUR −36.55 39.04 −54.92 31.70 20.44 44.20 27.16 44.05 0 0VKI −27.45 62.71 11.06 94.13 22.54 39.48 16.20 29.01 0 0

CanadaFGL −9.35 2.44 −9.88 8.62 0.47 0.70 0.46 1.51 0 0KCN −24.13 129.72 −91.98 2.71 18.95 52.89 27.87 65.22 0 0LVG −15.04 49.71 −44.26 −7.73 11.38 19.73 7.78 10.38 0 –

UKCRS −4.27 22.77 −5.14 9.75 1.00 2.32 0.60 0.95 0 –GMF −29.75 6.44 −26.61 1.15 3.19 12.23 1.96 6.82 0 0PDP −13.48 19.91 −16.13 20.99 3.04 4.85 3.12 19.98 0 0

GermanyAMG −35.66 56.85 −48.66 66.71 28.62 70.53 44.22 79.79 0 0AVL −53.27 13.07 −33.19 44.07 14.65 22.78 17.86 45.62 0 0AXR −47.94 93.88 −128.93 −10.59 37.35 58.95 33.51 90.50 0 0BHP −9.46 3.14 2.52 15.75 0.26 0.43 0.31 0.45 0 0CNM −12.23 154.62 −97.82 −20.05 37.43 72.92 35.06 55.75 – 0CRS −26.63 −6.21 −21.44 13.52 2.10 4.18 2.12 3.78 0 0DOM −49.09 23.35 −18.55 66.17 14.85 24.89 5.24 84.96 0 0


Table 3 (Continued )

Mean Variance Test the difference in

Before After Before After Mean Variance

MIM −33.20 19.55 −14.33 9.65 6.32 14.17 2.10 4.30 0 –MPH −102.52 37.50 −33.86 70.50 21.28 118.18 36.45 56.84 0 0OEC −10.13 30.73 −67.08 −12.78 9.42 14.20 8.92 16.75 – 0PMM −65.98 −16.80 −33.93 −0.02 5.48 9.85 5.57 8.94 0 0SSI −41.39 180.63 −88.73 38.10 15.50 73.75 27.68 76.93 0 0TBR −82.87 59.47 −6.02 80.45 60.02 196.77 27.46 45.66 0 –WMC −6.65 21.50 −7.75 21.80 1.52 2.35 1.83 2.88 0 0WRF −31.83 11.15 0.09 66.70 9.54 25.22 19.19 33.76 0 0

JapanBHP −18.67 13.33 −9.79 6.38 1.13 4.99 0.48 0.83 0 –FGL −53.97 23.90 −8.77 7.01 1.05 15.11 0.46 0.66 0 –NAB −8.11 13.32 −11.33 12.69 0.56 0.99 0.92 1.69 0 0NCP −21.56 16.67 −34.57 14.32 1.15 1.97 1.78 3.44 0 0PDP −15.06 10.98 −16.89 19.96 3.04 4.86 3.11 19.79 0 0

0: confidence intervals before and after overlap. No change in means or variances, at 10% level of significance; +:confidence intervals before and after do not intersect. Confidence intervals after imply higher mean or variance. Increasein the mean or variance, at 10% level of significance; −: confidence intervals before and after do not intersect. Confidenceintervals after imply lower mean or variance. Decrease in the mean or variance, at 10% level of significance. All numbershave been multiplied by 104.

The former category includes the US, UK, Canada, and New Zealand, while Germany belongsto the latter. Japan is not included in the comparison because it adopts a mixture of the twoclassifications. As clarification of this, the US-based accounting system was introduced in Japanafter World War II, and is monitored by the Ministry of Finance. The other system used is based onthe German system and is monitored by the Ministry of Justice. As such, a listed company in Japanhas to prepare two sets of financial statements to meet the requirements of the two ministries.

The mean daily abnormal return for sample firms in the year prior to undertaking cross-listings in Anglo-Saxon countries is 0.0396%, which decreases to an average abnormal returnof −0.0601% in the year following cross-listing. The pre-cross-listing mean abnormal return forcompanies listing on Continental European (represented by Germany for our sample) exchanges islower than that for Anglo-Saxon firms above at 0.0150%, and the average domestic daily abnormalshare return for these companies only decreased to 0.0114% in the year subsequent to cross-listing.These figures suggest that the negative return consequences for cross-listing firms are less pro-nounced for firm listing in Germany relative to Anglo-Saxon stock exchanges. The statistics forthe return variances for the two country classifications are particularly interesting, with com-panies that cross-list in Anglo-Saxon countries exhibiting substantially lower average pre-listingabnormal return variances (0.1543%) than those listing on Continental European stock exchanges(mean daily abnormal return variance of 0.3160%), although return volatility increased follow-ing cross-listing on Anglo-Saxon stock exchanges, whereas there is approximately a 0.0212%decrease in the variance of returns following cross-listing in Germany. This suggests that riskierstocks may choose to, or may find it easier to, list on international stock exchanges which havelower regulatory and disclosure requirements, but that this does not have an adverse impact ontheir stock return volatility in the post-listing period.

The above analysis does not, however, consider country-specific institutional characteris-tics that may explain post-listing share returns. For this purpose, we estimate four models by


Table 4The effects of country characteristics on post-listing equity returns

Model 1 Model 2 Model 3 Model 4

Constant 0.047 (0.119) 0.051 (0.099) 0.087 (0.069) 0.086 (0.091)Minright −0.011 (0.118) −0.012 (0.098) −0.021 (0.072) −0.021 (0.089)Legtraddum 0.004 (0.292) 0.004 (0.259) 0.008 (0.184) 0.007 (0.242)ImpEquity 0.002 (0.097) 0.002 (0.077) 0.003 (0.061) 0.003 (0.078)Owncon −0.074 (0.125) −0.080 (0.105) −0.139 (0.072) −0.139 (0.091)Disind 0.001 (0.568) 0.001 (0.503) 0.001 (0.467) 0.001 (0.700)

N 55 55 55 55Adjusted R2 (%) 4.5 7.3 1.7 1.2F-value 1.512 1.857 1.191 0.980

Dependent variables—Model 1: mean market-adjusted share returns for the 1-year period post overseas cross-listings.Model 2: mean raw (unadjusted) share returns for the 1-year period post overseas cross-listings. Model 3: mean market-adjusted share returns in the 1-year period post overseas cross-listing minus market-adjusted share returns in the 1-yearperiod before overseas cross-listings. Model 4: mean raw (unadjusted) share returns in the 1-year period post overseascross-listing minus raw share returns in the 1-year period before overseas cross-listings. Explanatory variables—Minright:outside investor rights is the “anti-director rights” index for the destination country created by La Porta et al. (1998).Legtraddum: a dummy variable in which one is assigned if the overseas cross-listing occurs within a common-lawcountry, otherwise zero, following classification by La Porta et al. (1998). ImpEquity: an average of three variablesmeasuring the importance of the stockmarket in the destination country, including (1) the ratio of market capitalizationheld by outside (minority) shareholders to gross domestic product, (2) the number of listed domestic firms relative to thepopulation, and (3) the number of initial public offerings relative to the population. All three variables have been takenfrom La Porta et al. (1997). Owncon: ownership concentration is measured as the median percentage of common sharesowned by the largest three shareholders in the 10 largest privately owned non-financial firms in the destination country(La Porta et al., 1998). Disind: the disclosure index measures the inclusion or omission of 90 items in the 1990 annualreports of destination country companies (La Porta et al., 1998). We deduct the disclosure score for Australia (75) fromthe country score to capture status as a higher or lower disclosure country relative to Australia.

regressing post-listing share returns on selected country-specific institutional characteristics andassess which of these characteristics are associated with share return movements. The institu-tional characteristics used in our study are outside investor rights (Minright), whether the countrybelongs to a common law or code-law classification (Legtraddum), the importance of the secu-rities market (ImpEquity), the degree of ownership concentration (Owncon), and accountingdisclosure level (Disind). We have sourced information on these institutional characteristicsfrom La Porta et al. (1997, 1998) and Leuz et al. (2002). Based on prior research we expectto find share returns following cross-listing to be related to the selected listing-country variablesand, specifically, to be positively associated with the level of outsider investor rights, the exis-tence of common law tradition, greater stockmarket importance and higher accounting disclosurerequirements and negatively related to the degree of ownership concentration in cross-listingstockmarkets.

The four dependent variables employed in this analysis are (1) the average market-adjusted(abnormal) share returns in the 1-year period after cross-listings, (2) the average unadjusted(raw) share returns in the 1-year period after cross-listings, (3) the difference between the meanmarket-adjusted (abnormal) share returns for the 1-year period after and the mean market-adjusted(abnormal) share returns for the 1-year period before overseas listings, and (4) the differencebetween the mean unadjusted (raw) share returns for the 1-year period following cross-listingsand the mean unadjusted (raw) share returns for the 1-year period before cross-listings. Theresults, presented in Table 4, show that the importance of the equity market within the listing


country is positively associated with post-listing share returns at the 10% significance level for allfour models. The outside investor rights (Minright) variable is statistically significant (p < 0.10)in three of the four models, however, the direction of the coefficient is contrary to the assumedexpectation and implies that a higher level of minority shareholder rights in the listing countrylowers company share returns following cross-listing events. Similarly, the degree of ownershipconcentration in listing countries is negatively associated with share returns in two of the fourmodels (p < 0.10), which is consistent with the suggestion that shareholder benefits are reducedwhen firms cross-list on more concentrated and less liquid markets. With respect to accountingdisclosure quality, we do not find any association between this variable and share returns. Althoughsuggesting evidence of correlation, our results are not strong enough to establish a definitive linkbetween the selected country-specific institutional variables and benefits expected to be derivedfrom international cross-listing.

4. Conclusion

This paper has attempted to evaluate the benefits, in terms of increased share returns andreduced risk, for Australian companies from cross-listing shares on major stock exchanges inthe US, UK, New Zealand, Canada, Germany and Japan, based on a sample of 55 cross-listingsundertaken by 43 companies. We address small sample and non-normality issues by adoptingthe moving block bootstrap method suggested by Kunsch (1989) and derive statistical inferenceusing first-order and second-order stochastic dominance criterion.

Although the findings are mixed, there is some evidence indicating that mean daily abnor-mal share returns tend to decline following cross-listing transactions and stronger, although notdefinitive, results supporting the conclusion that cross-listing lowers share return variability. Thedetrimental share return consequences appear to be more evident for cross-listings undertaken onUS or Germany stock exchanges, whereas risk-reduction benefits are observed across all the desti-nation markets included in the study. Multivariate analysis conducted provides weak evidence thatpost-listing share returns are influenced by the relative importance and degree of ownership con-centration of destination stockmarkets, however, we do not find any evidence suggesting superiorreturn or risk benefits from Australian companies specifically cross-listing in countries providingstronger shareholder protection or higher accounting standard and disclosure requirements. Theresults are generally comparable with the conclusions of Faff et al. (2002), which also examinedAustralian firms cross-listing on liquid international stock exchanges, and particularly in regardto the effect of the cross-listing process on post-listing abnormal returns and shareholder wealth.Overall, the findings cast doubts as to the obvious benefits for shareholders from companiesdeciding to cross-list on major international stock exchanges.

Acknowledgments

Comments and suggestions from Allan Hodgson, Ike Mathur, the journal editor, and an anony-mous reviewer are greatly appreciated.

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International cross-listings by Australian firms: A stochastic dominance analysis of equity returns

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