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This article was downloaded by: [Washington State University Libraries ] On: 07 November 2014, At: 20:44 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Applied Financial Economics Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/rafe20 The differentiation of 'emerging' equity markets P. C. KUMAR & GEORGE P. TSETSEKOS Published online: 07 Oct 2010. To cite this article: P. C. KUMAR & GEORGE P. TSETSEKOS (1999) The differentiation of 'emerging' equity markets, Applied Financial Economics, 9:5, 443-453, DOI: 10.1080/096031099332104 To link to this article: http://dx.doi.org/10.1080/096031099332104 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

The differentiation of 'emerging' equity markets

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Page 1: The differentiation of 'emerging' equity markets

This article was downloaded by: [Washington State University Libraries ]On: 07 November 2014, At: 20:44Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: MortimerHouse, 37-41 Mortimer Street, London W1T 3JH, UK

Applied Financial EconomicsPublication details, including instructions for authors and subscriptioninformation:http://www.tandfonline.com/loi/rafe20

The differentiation of 'emerging' equity marketsP. C. KUMAR & GEORGE P. TSETSEKOSPublished online: 07 Oct 2010.

To cite this article: P. C. KUMAR & GEORGE P. TSETSEKOS (1999) The differentiation of 'emerging' equity markets,Applied Financial Economics, 9:5, 443-453, DOI: 10.1080/096031099332104

To link to this article: http://dx.doi.org/10.1080/096031099332104

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”)contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensorsmake no representations or warranties whatsoever as to the accuracy, completeness, or suitabilityfor any purpose of the Content. Any opinions and views expressed in this publication are the opinionsand views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy ofthe Content should not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings,demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arisingdirectly or indirectly in connection with, in relation to or arising out of the use of the Content.

This article may be used for research, teaching, and private study purposes. Any substantial orsystematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distributionin any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found athttp://www.tandfonline.com/page/terms-and-conditions

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*Author to whom all correspondence should be addressed.1 The International Finance Corporation in its publication, Emerging Stock Markets Factbook 1996, de ® nes an emerging’ market as anymarket in a developing economy, with the implication that all have the potential for development’ (p. 2). The same publication uses theterm developed markets’ to refer to stock markets in industrialized nations. This paper follows these de® nitions.2 Ibid.

Applied Financial Economics, 1999, 9, 443–453

The di� erentiation of ‘emerging’ equitymarkets

P. C. KUMAR* and GEORGE P. TSETSEKOS ³

Kogod School of Business, American University, Washington , DC 20016, USAand ³ College of Business Administration, Drexel University, Philadelphia, PA 19104,USA

We argue that emerging’ security markets, as de® ned by IFC, have characteristicsdi� erentiated from their counterparts in industrialized nations not only due todi� erential levels of economic development, but also because their origins are morerecent. Consequently, the institutional infrastructure comprising a broad legal frame-work recognizing property rights, disclosure requirements, accounting practices con-forming to international standards, supervision and regulation of these markets, maybe inadequate or even absent in emerging’ markets. Our study develops a positive(descriptive) framework of the qualitative (institutional infrastructure) and quantita-tive features that classi® es and predicts the relative development of securities marketsacross countries. Discriminant and logit analyses using IFC data indicate that theemerging’ equity markets as a class are dissimilar from developed’ markets. These® ndings lend support to the premise that the two sets of markets are segmented. Thereis weak evidence of convergence in the characteristics of the two sets of markets.However, it is expected that as the institutional infrastructures in emerging’ marketsimprove, there will be stronger evidence of the trend towards convergence in thesemarkets.

I. INTRODUCTION

The term emerging equity markets’ is used as a descriptionof stock markets in developing countries by the Inter-national Finance Corporation (IFC).1 `Emerging’ marketshad gained prominence in the 1980s when both the erst-while socialist economies and developing nations opted formarket-based decision systems to replace centralized plann-ing. These markets have grown signi® cantly since then. In1995, their market capitalization represented 8% of the worldstock market capitalization, their trading amounted to 10%of the value traded in shares in the world stock markets, andtheir listed companies comprised 50% of all the listed com-

panies in the world. Emerging’ markets have been instru-mental in foreign direct investment growing from US$8.55billion in 1983 to US$90.30 billion in 1995; portfolio equity¯ ows to these nations which were virtually nonexistent in1983 increased to US$22 billion in 1995. It is estimated in1994 equity issues in emerging’ markets raised US$19.80billion and these markets raised US$10.30 billion in 1995.2

Three broad areas of enquiry have been developed in theemerging markets literature. The ® rst question relates to theinherent volatility of these markets, while the second focuseson the informational e� ciencies of these markets. The ® nalline of research investigates whether emerging markets aresegmented or integrated with other international equity

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markets. Investigating the ® rst question, Bekaert andHarvey (1997) conclude that volatility in fully integratedmarkets, will be in¯ uenced by world factors, whereas it islikely to be in¯ uenced by local factors in segmented mar-kets. More open economies (in terms of trade) are associatedwith lower volatilities in their equity markets. Finally, capi-tal market liberalization measures reduce volatilities inemerging markets. Divecha et al. (1992) characterize emerg-ing markets as having volatile returns which are homogene-ous within each market, but heterogeneous across markets.Correlations between stock returns in these markets tend tobe uniformly higher than those observed in developed mar-kets (homogeneity e� ect); whereas the correlations betweenemerging market stock returns as well as between emergingmarket and developed market stock returns tend to be low(heterogeneity e� ect). Emerging markets have concentratedstructures in that a small number of stocks have dominantproportions of market capitalization and the returns ofthese stocks tend to in¯ uence the overall market return. Thedominant factor in¯ uencing individual stock returns inemerging markets is the aggregate market return whichaccentuates volatility.

On the topic of informational e� ciency of emerging mar-kets, Antoniou et al. (1997) investigate whether returns inthe Istanbul stock market can be predicted by technicalanalysis of both past volume and past return data. Theauthors rationalize that emerging markets have informed aswell as uninformed investors both of whom operate ina relatively unreliable information environment. Whereasinformed investors discern fundamental asset values frommarket prices, uninformed investors are not equally percep-tive. However, by observing the volume of trades takingplace, they gain insights into the fundamental value imputedto an asset by the informed traders. The authors concludethat volume is an useful predictor of returns in thinly tradedequity markets. Sewell et al. (1993) reject the hypothesis thatreturns in a sample of markets are independently and inden-tically distributed. Their ® nding implies that the time-seriesof returns are nonstationary perhaps because information isnot available to all traders simultaneously (lack of pricetransparency).

Finally, in investigating the third issue of market segmen-tation, Domowitz et al. (1997) examine the relationshipbetween stock prices and market segmentation implicit inthe foreign equity ownership restrictions in Mexico. Theauthors report signi® cant stock price premia for shares notrestricted to any particular investor group. In addition toother economy-wide factors, they conclude that the segmen-tation re¯ ects the relative scarcity of unrestricted shares.Errunza et al. (1992) report that some emerging markets(Brazil, Chile, Korea, Greece and Mexico) exhibit mildsegmentation (i.e., between complete integration and com-plete segmentation), whereas markets in Argentina andZimbabwe are in between mild segmentation and completesegmentation.

Given the growing importance of the emerging’ markets,two questions arise. First, are emerging’ markets as de® nedby IFC di� erentiated from developed’ markets? It is recog-nized that emerging’ markets have more recent origins.Consequently, the institutional infrastructure comprisinga broad legal framework a� rming and protecting propertyrights, mandating disclosure requirements, promoting ac-counting practices conforming to international standards,supervising and regulating these markets, may be inad-equate or even absent. Second, if perceivable di� erences doexist, can we identify their distinguishing characteristics andquantify the extent of their dissimilarities? In this study wedevelop a positive (descriptive) framework of the qualitative(institutional infrastruture) and quantitative features of thetwo sets of securities markets. We then determine whetherthese features are su� cient to di� erentiate IFC’s categor-ization of equity markets as developed’ and emerging’. Ourprincipal ® nding is that emerging’ equity markets as a classare dissimilar from developed’ markets. Our empiricalanalysis of IFC data up to the early 1990s reveals weakevidence of convergence suggesting that these two setsof markets are not yet fully integrated but remain segmentedfrom each other. We conclude that as the institutionalinfrastructures in developed’ and emerging’ equitymarkets get aligned, there will be convergence of theirquantitative characteristics. We believe market analysts,portfolio managers, and specially policy designers examin-ing the role of securities markets in overall ® nancial sectordevelopment will be interested in the responses to thesequestions.

The rest of this paper is organized as follows. The follow-ing section identi® es some qualitative features of emergingmarkets’. Section III presents quantitative evidence distin-guishing emerging’ and developed’ markets in terms ofactivity, size, and pricing variables . Section IV presents theresults of empirical analyses (discriminant and logit ana-lyses) to classify the markets and to identify possible conver-gences. The paper is concluded in Section V.

II . QUALITATIVE FEATURES OF`EMERGING ’ MARKETS

Emerging’ markets have more recent origins thandeveloped’ markets. Furthermore, developing nationshave adopted di� ering policies relating to ® nancial sectordevelopment. Hence we infer that emerging’ markets di� erfrom their counterparts in developed nations in theirinformation-related attributes and their institutionalinfrastructures. In addition, developing nations are charac-terized by skewed wealth distributions that in¯ uence risktolerance levels and thus some of the characteristics of theiremerging’ markets. The following discussions elaborate onthese themes.

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3 It is possible that such institutional structure may not be optimal even in some developed ’ markets.4 For a detailed discussion, see O’Hara (1995, Chapter 9).5 Khambatta and Khambatta (1989) indentify necessary physical infrastructure consisting of an e� cient settlement system as well asa centralized depository system.6 Withholding taxes vary from zero per cent on dividends and capital gains in Argentina, Colombia, Croatia, Czech Republic, Ecuador,Jordan, Lithuania, Mauritius, Oman, Paraguay, Peru, South Africa, and Turkey to 35% on dividends and 35% on capital gains in Chile.See the Emerging Stock Markets Factbook 1996 for details.7 Di� erential entry and exit regulations exist in emerging markets, as described in the Emerging Stock Markets Factbook, 1996.8 See Emerging Stock Markets Factbook 1994. For detailed information on individual emerging markets, see Park and Van Agtmael (1993).Glen (1994) provides information on market microstructure in selected countries.

Information-related attributes and market microstructure

The free ¯ ow of information to all participants is a necessarycondition for market e� ciency. The institutional structurerequired for investors to have access to all relevant informa-tion may be inadequate at best in emerging’ markets.3 Thisshortcoming relates to the microstructure of emerging’ mar-kets. A speci ® c issue is price transparency, i.e., the ability ofmarket participants to observe the information driving thetrading process. Hence the information available in the trad-ing process dictates the strategies of the market participants.For example, if the list of limit orders was available only tothe market maker, then the actions of all participants(informed and uninformed traders) would di� er from thesituation where the list was made public. Since transparencya� ects the strategies of the participants, it in¯ uences marketequilibrium and thus market prices.4

Another information-related attribute deals with the ex-tent to which the size and direction of order ¯ ow is visible tomarket participants, thus a� ecting the viability of the mar-ket. Madhavan (1992) compares various market structuresand concludes that an order-driven (specialist) market ismore robust than a quote-driven (dealer) market. But even ifno equilibrium exists in either of the two market systems, itmay still exist in batch trading markets. This observationre¯ ects the aggregation ability of batch trading markets, i.e.,traders’ information is averaged over all trades, and marketprices relate to average of all trades rather than to themarginal trade. This aggregation carries the cost of reducedmarket transparency. Thus there is a tradeo� between mar-ket viability (which is particularly important in emergingmarkets) and market transparency. We use real change inmarket value to capture these information-related attributesin our empirical analyses.

Institutional infrastructure in emerging ’ markets

Institutional infrastructure is de® ned in terms of the tax(withholding) structure, regulations relating to market entryand exit, and factors relating to quality of informationdissemination.5

Investment tax structure. Taxes on investment returns in-hibit investor participation particularly by foreign investors.This factor in turn a� ects the liquidity and trading volume

in the market. Withholding tax structure varies among theemerging’ market nations.6

Market entry and exit regulations. Entry regulations relateto the free availability of stock investments to foreign inves-tors, whereas exit regulations refer to impediments to therepartriation of income and capital.7

Factors relating to quality of information dissemination. Allthe countries in our sample have at least one publicallyavailable market index. Security exchange publications areavailable on a daily basis. There is international electroniccoverage of all the exchanges. Market commentaries inEnglish are prepared by local and international brokerage® rms. Regular and comprehensive publications of theprice-earnings ratio and dividend yield are prepared byinternational brokerage ® rms for all developing nationswith the exception of Nigeria and Pakistan. All the emerg-ing markets specify annual ® nancial disclosure requirementsin the form of consolidated audited annual reports. Interim® nancial disclosure in the form of quarterly reports arerequired in Argentina, Brazil, Chile, Colombia, Indonesia,Mexico, Philipines, Taiwan, Thailand, and Turkey. Where-as, semiannual reports are required in Greece, India, Korea,Malaysia, Nigeria, Pakistan, Portugal, Zimbabwe, and inVenezuela (for banks only). Accounting standards in emerg-ing market nations are evaluated to be of internationallyacceptable quality in Brazil, Chile, India, Korea, Malaysia,Mexico, and Philippines, and of adequate quality in theremaining emerging market countries with the exception ofIndonesia where they are considered to be of poor quality.Investor protection is deemed to be of good internationallyacceptable quality in Brazil, Chile, India, Korea, Malaysia,and Mexico; whereas they are considered to be of acceptablequality in the other emerging’ markets. All these nationshave a functioning securities commission or a similargovernment agency regulating market activity.8 We uselevel of trading activity, price-earnings ratio, and dividendyield as variables representing the institutional infrastruc-ture.

We emphasize that di� erences in the institutional infra-structure of emerging’ and developed’ markets are attri-butable to the relative recent origins of the former. It isexpected that these di� erences will be minimized as theinstitutional infrastructure in emerging’ markets mature.

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9 See Brennan (1995) for a discussion on this aspect.1 0 These data have been collated from the Emerging Stock Markets Factbook 1994.1 1 The dispersion of T RACT among the emerging markets, measured by the range of 2.02 (di� erence between the highest and lowest meanvalues), is greater than the dispersion among developed markets (range = 0.88).

Wealth distributions

The distribution of wealth in the economy can a� ect marketrisk premia. For example, if risk tolerant individuals takeriskier investment positions they can in¯ uence pricing deci-sions and drive the market risk premium down. On theother hand, if risk averse individuals are dominant they candrive the market risk premium up. Thus the distribution ofwealth between risk tolerant and risk averse individualsin¯ uence overall market returns.9 Developing nations havemore skewed wealth distributions and this factor may a� ectthe quality (volatility) of emerging’ market returns.

III. QUANTITATIVE DESCRIPTIONS OF`DEVELOPED ’ AND `EMERGING ’MARKETS

This section describes some ® nancial characteristics ofdeveloped’ and emerging’ markets.

The data

The IFC data base covers equity markets in 16 developedand in 21 developing nations. The primary data, extendingover the thirteen year period between 1980 and 1992, in-clude annual values for variables, such as value traded,nominal market capitalization, nominal dividend yield, con-sumer’s price index, number of listed companies, marketprice-earnings ratio, market price-book value ratio andGDP.1 0 The IFC database is particularly useful in our studyof IFC’s de® nitions of developed’ and emerging’ markets.

Selected variables and their descriptive statistics

We consider three sets of attributes to di� erentiate emerg-ing’ from developed’ markets, namely, activity, size, andpricing. This subsection discusses the variables comprisingeach set. We also compare our a priori expectations of thesevariables with their observed values from the data.

Activity. Free ¯ ow of information to the market, its rapidassimilation, and immediate price reaction are the charac-teristics of an e� cient market. These features of institutionalinfrastructure are measured by the ratio of trading value tomarket capitalization (T RACT ), an indicator of the activitylevel in the market.

Size. This characteristic is a proxy for age or maturity of themarket. In general markets with older origins are likely to

have better institutional infrastructures. We use twomeasures of this attribute. First, the size of the securitymarket relative to the overall economy, measured by theratio of market capitalization to GDP (MV GDP), is anindicator of ® nancial deepening and is thus a proxy for® nancial sector development. Second, the size of the average® rm, measured by the logarithm of the average real marketcapitalization per ® rm (RMV CO), is an indicator of theextent of the corporate sector in the economy.

Pricing. The market valuation of earnings streams is deter-mined jointly by the dividend payout ratio, potential forgrowth in earnings, and riskiness of earnings. These factorsare re¯ ected in the price–earnings ratio (PE). In addition,the components of real total return – real dividend yield(RDY L D) and change in real market capitalization(RCMVAL ) – are measures of the pricing mechanism in themarket. These variables jointly represent the informationaland infrastructural features of the markets.

The combination of variables in the three categories pro-vides an adequate description of the emerging’ market.Descriptive statistics of these variables are summarized inTable 1. Table 2 presents the mean values for the two sets ofmarkets and the results of an univariate F-test which con-cludes that the di� erences of all variables except T RACTare statistically signi ® cant.

A priori , we would expect to ® nd asymmetric informationamong market participants in the emerging’ markets. In-siders in these markets would have access to informationnot available to the general investor. We would expecttrading in the emerging’ markets to be thin or sluggishcharacterized by lower levels of trading activity partly re-¯ ecting asymmetric information. Our expectations are notsupported by the data. The ratio of trading value to marketcapitalization (T RACT ) in the emerging’ markets is onaverage about as large as in the developed’ markets and thedi� erence is not statistically signi® cant (Table 2). However,the developed’ markets are more homogeneous in that thevariability of mean T RACT among them is smaller.1 1

We would expect ® nancial deepening as measured byMVGDP (ratio of market capitalization to GDP) to belower on average in the emerging’ markets due to theirmore recent origins. The observed values support this ex-pectation and MV GDP is more than twice as high in de-veloped markets as in the emerging markets (Table 2). Thedispersion in this variable among developed’ markets(range of 1.08) is greater than the dispersion among emerg-ing’ markets (range of 0.69) as reported in Table 1. Wewould also expect ® rm size to be smaller in the emerging’markets. The logarithm of real market value of the average

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Table 1. Summary of the descriptive statistics

Developed mkts. Emerging mkts.

Mean Std. devn. Mean Std. devn.Variable High Low High Low High Low High Low

T RACT 0.9939 0.1162 0.8322 0.0266 2.0277 0.0068 1.8368 0.0028Germany Belgium Germany Belgium Taiwan Nigeria Taiwan Nigeria

MV GDP 1.1458 0.0662 0.5677 0.0489 0.6991 0.0119 0.4692 0.0077S’pore Austria S’pore Austria Malaysia B’desh Taiwan Col’bia

RMVCO 6.6128 4.1692 1.0445 0.2280 5.2109 0.6580 1.4926 0.2177Japan Denmark Spain Canada Taiwan B’desh Turkey Jordan

PE 59.2111 8.4000 43.4353 1.4567 27.3667 6.3250 15.2558 0.8381Austria Norway Austria Belgium Korea Nigeria Taiwan Pakistan

RDY L D 0.0606 0.0091 0.0237 0.0039 0.0682 0.0030 0.0505 0.0028Spain Japan Spain Canada Nigeria A’tina Z’babwe A’tina

RCMVAL 0.3919 0.0936 0.6590 0.1412 1.2186 0.0011 2.8810 0.1485Austria Canada Austria Canada Prtgl. Trndd. Prtgl. Brazil

Note: This table summarizes the descriptive statistics, i.e., mean and standard deviation of the time series values of each variable for eachcountry. To conserve space, we display only the highest and lowest values of these sample statistics for a given variable. The particularcountries associated with these highest and lowest values are identi ® ed under the numbers. For example, among the developed markets,Germany has the largest mean value (0.9939) for the variable T RACT and Belgium has the smallest mean value (0.1162). The range forT RACT among developed markets is therefore 0.88. Germany also has the largest standard deviation (0.8322) and Belgium has thesmallest standard deviation of itstime series values (0.0266) .

Table 2. Mean values of variables

Mean value

Variable Developed Emerging F value

T RACT 0.3461 0.3022 0.1267MV GDP 0.3729 0.1546 16.1800***RMVCO 5.1568 2.7295 157.8000***PE 17.7233 14.6504 3.2170*RDY L D 0.0372 0.0406 3.5740*RCMVAL ** 0.2107 0.2970 4.0200**

Notes: Signi ® cance levels of univariate F-test for di� erences inmean values: *** 1% level; ** 5% level; * 10% level.

® rm (RMVCO) in developed’ markets is almost twice aslarge as its value in the emerging’ markets (Table 2). How-ever, the dispersion of this variable is higher among theemerging’ markets (range of 4.55) than among the de-veloped’ markets (range of 2.44) as reported in Table 1. Thedi� erences in the mean values of MVGDP and RMVCO arestatistically di� erent at the 1% level (Table 2).

We ® nd that on average the value for the PE ratio is 21%higher in developed’ markets than the value in emerging’markets (Table 2). The range for the PE variable is higher indeveloped’ markets (50.80) than the range among emerg-ing’ markets (21.04) as seen in Table 1. Mean PE is signi ® -cantly di� erent between the two markets at the 10% level.These di� erences in the PE ratio are attributed to theintrinsic di� erences in institutional infrastructure in the twosets of markets. We would expect annual growth of themarket index to be lower in thin or sluggish markets. We® nd that the annual growth in the real value of the marketindex (RCMVAL ) is about 40% higher in the emerging’markets (Table 2). However, this growth is uneven amongemerging’ markets – the range is 1.22 as against 0.30 amongdeveloped’ markets (Table 1). We would expect dividendyield to be higher in emerging’ markets as certain dividendincome mitigates uncertain capital appreciation given theirinformational ine� ciencies. Our expectations are supportedby the data. Real dividend yield (RDY L D) is on average10% higher in emerging’ markets (Table 2). The range is notreally di� erent between the two sets of markets (0.07 in

emerging’ markets and 0.05 in developed’ markets) as re-ported in Table 1. The di� erence in the mean values ofRCMVAL is statistically signi® cant at the 5% level and thedi� erence between the mean values of RDY L D is signi ® cantat the 10% level (Table 2).

IV. EMPIRICAL ANALYSES AND RESULTS

Multivariate methods – discriminant analysis as well aslogit analysis – are used to test the di� erentiation betweenthe two sets of securities markets. These methods of cate-gorical analyses have not been used in previous studies of

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Table 3. Discriminant function coe� cients

Variable Coe� cient

T RACT - 0.7888MV GDP - 1.4361RMVCO 1.0733PE 0.0065RDY L D 4.0372RCMVAL - 0.5372CONST ANT - 3.7702

Wilks’ K statistic ( = 0.4551) º v 2 ( = 173.98), df = 6x 2

0 . 0 1 , (df = 6) = 10.645

Table 4. Average discriminant scores (1980–90)

Developed market Emerging market

Australia 0.3454 Argentina - 3.3280Austria 0.4659 Bangladesh - 2.6330Belgium 1.0705 Brazil - 2.2741Canada 1.0124 Chile - 0.6795Denmark 0.0362 Colombia - 1.6749France 1.3987 Greece - 0.0368Germany 1.4462 India - 2.7272Italy 2.1787 Jordan - 1.0491Japan 1.7799 Korea - 0.0865Netherlands 1.2856 Malaysia 0.2103Norway - 0.0127 Mexico - 0.4859Singapore 0.1892 Nigeria - 1.1731Spain 0.7547 Pakistan - 2.1444Sweden 1.6331 Philippines - 0.8534UK 0.5976 Portugal - 1.1413USA 1.3258 Taiwan - 0.7019

Thailand - 0.3755Trinidad - 1.1543Turkey - 0.1790Venezuela - 0.6742Zimbabwe - 1.1423

Group mean 0.9692 - 1.1573

Notes: The Mahalanobis statistic is D2 , where D is the di� erence in discriminant scores between the two groups evaluatedat their centroids. These discriminant scores are 1.0239 and - 1.1591 respectively. The test statistic used isz = (n1 n2 /(n1 + n2 ))((n1 + n2 - p - 1)/(n1 + n2 - 2))(D2 /p), where n1 and n2 are the sample sizes of each of the two groups andp is the number of explanatory variables. z is distributed as an F-variable with p and (n1 + n2 - p - 1) degrees of freedom (seeDillon and Goldstein (1984, p. 368)). z = 43.7038 is signi® cant at the 0.01 con® dence level.

emerging markets. The following subsections discuss thetechniques, analyses, and results.

Discriminant analysis

Discriminant analysis is used to di� erentiate between twogroups by a set of explanatory variables, Xi, i = 1, 2, ¼ , M.The discriminant function, D = + M

i = 1 wiXi, is a linear combi-nation of the explanatory variables . Di� erentiation betweenthe two groups is optimized by assigning the weights (wis) to

maximize the ratio of the between-group variance to thewithin-group variance. The D-value for each country foreach year is computed by substituting the values of theexplanatory variables into the estimated discriminant func-tion.

The data are divided into two groups. The ® rst subgroup,which includes data from 1980 to 1990, is used to estimatethe discriminant and logistic functions. The second sub-group from 1991 to 1992 is used as a holdout sample tovalidate the models.

Estimation of the discriminant function. Table 3 presents theunstandardized discriminant function results. Unlike regres-sion analysis, discriminant function coe� cients cannot beinterpreted as suggesting economic causality. However, weo� er some intuitive observations on these coe� cients. Thereal market value of the average ® rm listed in developed’markets is signi ® cantly larger than its emerging’ marketcounterpart and RMVCO has a positive coe� cient inthe discriminant function. PE is also signi® cantly higherin developed’ markets and has a positive coe� cient.RCMVAL has a signi ® cantly smaller mean value in thedeveloped’ markets and its coe� cient is negative. However,MV GDP has a signi ® cantly larger mean value in de-veloped’ markets but has a negative coe� cient. Conversely,

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Fig. 1. Average discriminant scores: (Ð h Ð ) developed nations,(Ð m Ð ) emerging market ratios

Table 5. Market misclassi® cations

`Developed’ markets Emerging’ markets

Country Year D-score Country Year D-score

Austria 1984 - 0.1521 Greece 1988 0.2315Denmark 1982 - 0.2482 Greece 1989 0.4361Denmark 1983 - 0.2391 Greece 1990 0.3266Norway 1982 - 0.2393 Korea 1989 0.4242Norway 1983 - 0.3849 Korea 1990 0.7166Singapore 1982 - 0.3189 Malaysia 1984 0.4947Spain 1982 - 0.1230 Malaysia 1985 0.2995

Malaysia 1986 0.2645Malaysia 1987 0.1306Malaysia 1988 0.2280Malaysia 1990 0.1671Mexico 1989 0.5318Mexico 1990 0.8739Philippines 1990 0.5291Portugal 1988 0.3209Portugal 1989 0.2033Portugal 1990 0.3176Taiwan 1984 0.2508Taiwan 1985 0.5001Taiwan 1986 0.0156Thailand 1990 0.2486Turkey 1990 0.7058

Note: This list of misclassi® cations does not include two anomalies, Malaysia (1989:D = - 0.1128) and Nigeria (1985: D = - 0.1178). In both cases, the negative D-scores aregreater than the mean D-scores for that particular year.

1 2 Wilks’ K is a statistic that considers both the di� erences between groups and cohesiveness or homogeneity within groups. Cohesivenessimplies the degree with which cases cluster near their group centroid,’ (see Klecka, 1980, p. 54).1 3 Mahalanobis statistic permits a test which considers both the squared distance’ between the group centroids and the sample sizes of thegroups.

RDY L D has a signi ® cantly smaller mean value in de-veloped’ markets but has a positive coe� cient. The negativecoe� cient for T RACT may be only a statistical artefactsince the di� erence between the mean values for the two setsof markets is not statistically di� erent. The Wilks’ K statistic

(0.4551) for the discriminant function yields a x 2 value of173.98 with six degrees of freedom which is highly signi® -cant (< 0.01 con® dence level).1 2 This measure suggests thatthe discriminant function is signi® cant and that emerging’and developed’ markets are di� erentiated.

A discriminant metric. Table 4 reports the mean dis-criminant scores for the members of the two groups over theestimation period. It is clear from these values that theequity markets in the two groups of countries are welldi� erentiated with lower average values obtaining inemerging’ markets. `Developed’ markets have positive aver-age discriminant scores (except Norway), whereas emerg-ing’ markets have negative values (except Malaysia) . Thediscriminant scores evaluated at the centroids of the twogroups are 1.0239 and - 1.1591 for developed’ and emerg-ing’ markets respectively. The Mahalanobis test statisticindicates that the di� erence in scores is statistically signi® -cant at the 1% level (see Table 4).1 3 This test suggests thatthere is signi® cant distance’ between the group centroidsimplying that the discriminant function di� erentiates be-tween developed’ and emerging’ markets. Figure 1, whichdisplays the trajectories of the average discriminant scores

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Table 6. Holdout sample discriminant scores

`Developed’ market Emerging’ market

1991 1992 1991 1992

Australia - 0.3206 0.7905 Argentina - 5.9634 - 2.1741Austria 1.6209 1.6182 Bangladesh - 3.0843 - 3.2389France 2.2717 2.3453 Brazil - 3.0196 - 3.8680Germany 1.3015 0.9032 Chile - 3.0852 - 2.4992Japan 2.7615 3.0376 Colombia - 2.6909 - 2.0895Italy 2.5181 2.8967 Greece - 1.8426 - 1.5881Netherlands 2.0979 1.9990 India - 3.0227 - 2.8341Singapore 0.3529 0.7301 Jordan - 3.0595 - 3.4460Spain 1.9210 1.9333 Korea - 2.4341 - 2.6665Sweden 2.9549 2.6802 Malaysia - 3.1677 - 3.8097UK 1.4844 1.4251 Mexico - 2.5022 - 1.7046USA 1.6786 1.7822 Nigeria - 2.5842 - 2.7181

Pakistan - 3.4588 - 2.7727Philippines - 2.5732 - 2.3474Portugal - 2.2149 - 2.2032Taiwan - 4.0933 - 3.3016Thailand - 2.8531 - 3.2591Turkey - 2.1791 - 2.4752Venezuela - 2.1040 - 2.0172Zimbabwe - 2.3789 - 2.6571

Mean 1.7202 1.8452 - 2.9156 - 2.6835

Note: Data were not available to calculate the D-scores for Canada, Denmark, and Norway for 1991and 1992. In the case of Belgium, the D-score for 1991 is 2.3327; data were not available to calculatethe D-score for 1992. Among the emerging markets, data were not available for Trinidad for bothyears.

over the sample period, illustrates this distinction betweenthe two sets of markets. This ® gure also shows that theD-values for the emerging’ markets are increasing rapidlyover time, suggesting an evolutionary pattern leading toa convergence with the developed’ markets.

In-sample prediction. How successful is the discriminantmodel in predicting membership in the groups from thesample data? The model has a success rate of 94.2% inidentifying developed’ markets and a 77.4% success rate ofpredicting emerging’ markets within the sample. Table5 identi ® es speci ® c cases of misclassi® cation of developed’and emerging’ markets on the basis of their discriminantscores. A plausible explanation for the lower success rate inidentifying the emerging’ stock markets is that as thesemarkets become more active and open, they acquire theinstitutional characteristics of the more mature markets.This feature is observable in the markets of Greece,Malaysia, Korea, Mexico, Portugal and Taiwan.

Hold-out sample prediction. The predictions from the out-of-sample data of 1991 and 1992 are used to validate thediscriminant model. These D-scores are exhibited in Table6 and conform to the results in Table 4. In general, with theexception of Australia in 1991, developed’ markets have

positive D-scores while emerging’ markets have negativevalues. The holdout sample supports the ® nding that thediscriminant function successfully di� erentiates between thetwo sets of markets. We conclude from the discriminantanalysis that there is weak evidence of convergence in thecharacteristics of the two sets of markets. We supplementthe results of the discriminant analysis with those from logitanalysis in the following subsection.

L ogit analysis

Logit analysis posits that the probability a randonly drawnmarket belongs to the sample of developed’ markets isrelated to a vector of explanatory variables by the functionalform:

Pr(Ci = 1) = Pi = 1/(1 + e ± L i) i = 1, 2, ¼ , N

where L i = b0 + + Mj = 1 bjXi j is a linear combination of the

explanatory variables and a set of coe� cients B = (b0 ,b1 , ¼ , bm), which are to be estimated, Ci = 1 or 2 fordeveloped’ or emerging’ markets respectively, M is thenumber of explanatory variables, and N is the total numberof markets in the sample. We assume that there is somelinear combination L of the independent variables that ispositively related to the probability that the randomly

450 P. C. Kumar and G. P. T setsekos

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Table 7. L ogit function coe� cients

Variable Coe� cient

T RACT *** - 3.3893MV GDP*** - 4.5899RMV CO*** 3.3568PE 0.0137RDY L D 6.8294RCMV AL *** - 1.9531CONST ANT *** - 11.3266

Note: Model test: { - 2(log likelihood value)} ~ x 2 = 192.02****** Signi ® cant at 1% level.

Fig. 2. Predicted probability levels: (Ð h Ð ) C1, (´´´ m ´´´ ) C2, (-- j --)T1, (Ð 3 Ð ) T2Table 8. Probability matrix

Prediction (%)

EmergingDeveloped market

(i) Threshold level = 5%Developed C1 = 99.2 T 1 = 1.7

Observed %Emg. mkt. T 2 = 28.3 C2 = 55.7

(ii) Threshold level = 50%Developed C1 = 85.8 T 1 = 16.8

Observed %Emg. mkt. T 2 = 17.6 C2 = 79.2

drawn market has the characteristics of a developed’ mar-ket. Thus the higher values of L i indicate a higher probabil-ity that the market is from a developed’ market, conditionalon the speci ® c values of the explanatory variables for thatmarket. The coe� cient vector B is estimated from theknown values of the sample explanatory and dependentvariables.

Estimation of the logit function. The coe� cients of the logitmodel reported in Table 7 are maximum likelihood esti-mates. The model ® t is assessed by testing the hypothesisthat the coe� cients of all explanatory variables are zero.The test statistic used is { - 2 (log likelihood value)}, whichis distributed as a chi-squared variable, and is signi ® cant atthe 1% level ( x 2 = 192.03, df = 6). Thus the model ® t issatisfactory. The signs of the coe� cients obtained in thelogit estimation are similar to those obtained in the dis-criminant function estimation.

In-sample prediction. In order to assess the predictiveability of the model, we need to specify a threshold orcritical probability level above which a market may becharacterized as a developed’ market. We specify a range ofthreshold probability levels from 0.05 to 0.50 in incrementsof 0.05. Four elements are identi® ed in the probability

matrix. The probability of correctly predicting a developed’(or emerging’) market from its characteristics is designatedas C1 (or C2). In contrast, the probability of incorrectlypredicting a developed’ (or emerging’) market to be anemerging’ (or developed’) market from its characteristics isdesignated as a type 1 error: T 1 (or type 2 error: T 2). Theseprobabilities are calculated as follows. De® ne N1 1 = num-ber of observations when a developed’ market is correctlypredicted, N1 2 = number of observations when a de-veloped’ market is incorrectly predicted to be an emerging’market, N2 1 = number of observations when an emerging’market is incorrectly predicted to be a developed’ market,and N2 2 = number of observations when an emerging’market is correctly predicted. Then C1 = N1 1 /(N1 1 + N1 2 ),C2 = N2 2 /(N2 1 + N2 2 ), T 1 = N1 2 /(N1 2 + N2 2 ), andT 2 = N2 1 /(N1 1 + N2 1 ). Table 8 reports these probabilitiesfor thresholds levels of 5% and 50%.

Figure 2 displays graphs of C1 , C2 , T 1 , and T 2 plottedagainst the threshold probability level. C1 has a decliningrelationship indicating that with increases in the criticalprobability level, the standard for a market to be classi-® ed as developed’ market, based on its characteristics,becomes more stringent. Note from the logit model,P(C = 1) = 1/(1 + e ± L ), increased values of the left handside can be satis® ed only with high values of L , which isa linear combination of the variables describing the struc-ture of the market. In other words, there are fewer countrieswith su� ciently high values of L to satisfy this condition.The same argument holds for declining values of T 2 , i.e., anemerging’ market needs to have a high value of L for themarket to be classi ® ed as a developed’ market. For similarreasons, increases in the threshold probability are asso-ciated with increases in C2 , implying that as the criterionbecomes more stringent (increasing L ), a greater number ofemerging’ markets do not satisfy this condition and arehence classi ® ed correctly. T 1 is also seen as an increasingfunction, implying that with the stringent criterion there are

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Table 9. Classi ® cation probabilities

`Developed’ markets Emerging’ markets

1980–90 1991 1992 1980–90 1991 1992

Australia 0.7175 0.8658 0.9027 Argentina 0.0000 0.0000 0.0006Austria 0.7682 0.9914 0.9924 Bangladesh 0.0001 0.0000 0.0000France 0.9838 0.9990 0.9992 Brazil 0.0034 0.0000 0.0000Germany 0.9808 0.9062 0.6519 Chile 0.0679 0.0000 0.0004Italy 0.9985 0.9996 0.9999 Colombia 0.0062 0.0001 0.0014Japan 0.9937 0.9998 0.9999 Greece 0.0898 0.0033 0.0055Netherlands 0.9856 0.9980 0.9971 India 0.0000 0.0000 0.0001Singapore 0.5045 0.7050 0.8953 Jordan 0.0314 0.0000 0.0000Spain 0.8648 0.9969 0.9971 Korea 0.0626 0.0003 0.0001Sweden 0.9942 0.9998 0.9997 Malaysia 0.5572 0.0000 0.0000UK 0.8647 0.9871 0.9832 Mexico 0.0426 0.0003 0.0043USA 0.9781 0.9914 0.9839 Nigeria 0.0926 0.0003 0.0003

Pakistan 0.0007 0.0000 0.0000Philippines 0.0155 0.0003 0.0006Portugal 0.0027 0.0008 0.0008Taiwan 0.0292 0.0000 0.0000Thailand 0.1306 0.0000 0.0000Turkey 0.0470 0.0008 0.0003Venezuela 0.0992 0.0012 0.0019Zimbabwe 0.0083 0.0007 0.0003

Note: The probability of classi® cation as a developed market for Canada, Denmark, and Norway,calculated from the mean values of the variables, are 0.9591, 0.6782, and 0.6752 respectively. Data werenot available to calculate these probabiliti es for 1991 and 1992. In the case of Belgium, the probability ofbeing identi® ed as a developed market in 1991 is 0.9993; data were not available to calculate theprobability for 1992. Among the emerging markets, data were not available for Trinidad for both years.

greater chances for identifying a developed’ market as anemerging’ market.

Holdout sample prediction. Table 9 presents the probabilityof a security market being classi® ed as a developed’ marketfrom the mean values of the variables from the sampleperiod and from the data in the holdout sample (1991 and1992). The results are similar to those obtained with dis-criminant analysis. The developed’ markets have highprobabilities of being correctly classi® ed whereas theemerging’ markets have low probabilities of being classi® edas developed’ markets. The holdout sample indicates thatthe logit model successfully di� erentiates between the char-acteristics of developed’ and emerging’ markets. We at-tribute the di� erentiation between the two sets of markets tothe more recent origins of the emerging’ markets and thattheir characteristics have not evolved completely towardsthose of developed’ markets. However, there are some in-dications of convergence by emerging’ markets towardstheir developed’ market counterparts.

V. CONCLUSION

This paper has presented tests of the hypothesis that thecharacteristics of emerging’ and developed’ markets as

de® ned by the IFC are di� erentiated. The results of thediscriminant analysis and logit analysis support the pro-position. However, as the emerging’ markets mature inthe quality of their institutional infrastructure and theircharacteristics converge towards those in their developed’counterparts, this classi ® cation of markets will not hold.There is weak evidence of convergence of the characteristicsof the two sets of markets from the current IFC data. Anextension of this work is a detailed survey of the oper-ational, legal, institutional, and technological infrastructurein these economies which contribute to the current marketclassi ® cation.

ACKNOWLEDGEMENTS

Assistance and suggestions received from Dr Vivian Shayne,Director, Social Science Computer Laboratory, Ameri-can University, are gratefully acknowledged. ArindhamBhattacharjee and Pablo Gaya provided able researchassistance. Useful comments were received from ananonymous referee, Raj Agarwal, Andy Naranjo, and fromparticipants in the Eastern Finance Association AnnualMeeting, April 1995, the Second Annual Conference onMultinational Financial Issues, June 1995, and in researchseminars at American University, Drexel University,

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and Georgetown University. The usual disclaimer relatingto remaining errors applies.

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