E/B/R ECONOMIC AND BUSINESS REVIEW | VOL. 15 | No. 2 | 2013 | 113-33 113 THE PERSISTENCE OF PRICING INEFFICIENCIES IN THE STOCK MARKETS OF THE EASTERN EUROPEAN EU NATIONS JAMES FOYE1 Received: 17 March 2013 DUŠAN MRAMOR2 Accepted: 2 September 2013 MARKO PAHOR3 ABSTRACT: This paper applies a range of metrics to test for the presence of weak form market efficiency in the Eastern European countries that joined the EU in 2004, we test both the years prior to and following accession. The results from our tests indicate that, despite the expectations of many previous studies, even after entering the EU the stock markets of these countries still do not conform to even the loosest form of market efficiency. We improve and extend previous studies by incorporating liquidity controls, applying a wider range of methodologies and by using individual stocks rather than indices. keywords: Emerging Stock Markets; European Union; Eastern European Transition Countries; Stock Market Ef- ficiency; Weak Form Market Efficiency je! classification: F36, G14 1. INTRODUCTION The debate over stock market efficiency is one of the central tenets of capital market theory. The issue is particularly pertinent for the Eastern European nations that joined the European Union in 20044 (hereafter the EE EU nations) because of the stock market's role in the ongoing privatization process and also as it serves as an important barom- eter with which to measure the progress made by these countries in the transition from planned to market economies. In this paper we examine weak form market efficiency (WFME) as defined by Fama (1970) which, as the loosest form of market efficiency, re- quires nothing more than current period returns "fully reflect" earlier period returns and thus successive price movements are independent of each other: failure to conform to WFME means that stronger forms of efficiency are not present and the stock market's pricing can be considered inefficient. 1 University of Ljubljana, Faculty of Economics, Slovenia, Ljubljana, e-mail: james.foye@ef.uni-lj.si 2 University of Ljubljana, Faculty of Economics, Slovenia, Ljubljana, e-mail: dusan.mramor@ef.uni-lj.si 3 University of Ljubljana, Faculty of Economics, Slovenia, Ljubljana, e-mail: marko.pahor@ef.uni-lj.si 4 These are the transition nations that joined the EU on 1st May 2004, namely Czech Republic, Estonia, Hun- gary, Latvia, Lithuania, Poland, Slovakia and Slovenia 114 E/B/R ECONOMIC AND BUSINESS REVIEW | VOL. 15 | No. 2 | 2013 | 114-33 113 A significant body of research into WFME in the EE EU nations exists. Jagric et al (2005) test for WFME in the region, the authors found that the stock market indices of Czech Republic, Hungary, Russia and Slovenia all exhibited weak form inefficiencies in the form of long memory in stock returns. Worthington and Higgs (2004) examined WFME in both developed and emerging stock markets in Europe, of the emerging markets cov- ered (Czech Republic, Hungary, Poland and Russia) only the Hungarian stock market could be considered weak form efficient. Gilmoore and McManus (2001) applied a range of WFME tests to the larger EE EU economies (Czech Republic, Hungary and Poland) over the period 1990 to 2000 and found that significant weak form inefficiencies exist in the stock exchanges of all three countries. Chun (2000) reported that while the Hungar- ian market may be weak-form efficient, the stock markets of the Czech Republic and Poland were inefficient. Nivet (1997) and Gordon and Rittenberg (1995) also found that the Polish stock market could not be considered weak form efficient. Ahmed, Rosser and Uppal (2010) found strong evidence of nonlinear speculative bubbles in Czech Republic, Hungary and Poland. Mihailov and Linowski (2002) and Dezelan (2000) find evidence of weak-form inefficiency in the Latvian and Slovenian stock markets respectively. We further the above studies in a number of ways. Firstly, we incorporate liquidity con- trols into our work. It is quite possible that illiquid shares exhibit properties consistent with weak form inefficiency; WFME tests, especially those in emerging markets, need to incorporate liquidity controls in order to ensure that the results are not distorted by apparently predictable returns from infrequently traded securities. In our view this is an omission in the studies listed above that reduces the robustness of results. Indeed, Benic and Franic (2008) found a substantial level of illiquidity in the stock markets of Central and Eastern Europe. Secondly, we include all eight transition countries that acceded to the EU in 2004, while the studies listed above include between one and five of the coun- tries: by considering the region in its entirety, we are able to ascertain a broader and more complete perspective of WFME in the EE EU nations. Thirdly, Jagric, Podobnik and Kolanovics's (2005) dataset ends in 2004, the datasets in the other papers cited end before this. In contrast, our dataset starts in 1999 and runs to the end of 2008. Fourthly, much of the previous work examining WFME in the EE EU nations has been based on stock market indices rather than individual stocks: previously reported findings that the stock markets are inefficient may be due to only a small proportion of the indices' constituents or simply the manner in which the indices are constructed. By using individual stocks, our work provides an important validation of previous work. Furthermore, using indi- vidual stocks provides a broader view than using indices alone and may help to provide insight into the underlying causes of the inefficiency. Finally, we use the same metrics as Worthington and Higgs (2004), this is a much broader range than the other cited papers use: our wider range of tests allows us to cross check and validate our results. Further- more, the results from our work further the existing literature by providing a pre- and post-EU accession comparison. While the majority of early studies found that returns on the newly-created stock ex- changes of the EE EU nations did not conform to WFME, many expected these ineffi- ciencies to disappear over time. Wheeler et al (2002) studied the Warsaw Stock Exchange J. FOYE, D. MRAMOR, M. PAHOR | THE PERSISTENCE OF PRICING INEFFICIENCIES 115 during its first five years of operation; the authors expected the exchange to become more efficient over time, citing increasing experience of market participants, more sessions per week, more analysts offering better research, and better investor relations departments. Rockinger and Urga (2001) surmised that their finding that the Hungarian market had a lower level of predictability than the markets of Czech Republic, Poland and Russia was partly due to the fact that the Budapest Stock Exchange had operated for a longer period of time. Again, suggesting that the stock markets of the EE EU nations should become more efficient simply due to the passage of time. Moor and Wang (2007) examined the volatility levels on the stock markets of the Czech Republic, Hungary, Poland, Slovenia and Slovakia and concluded that volatility declined as the nations moved into the EU. Worthington and Higgs (2004) hypothesised that there may be a link between the ab- sence of WFME and the small size of some stock markets in the EE EU; this implies pricing efficiency will improve with the growth of these markets. Jagric et al (2005) also proposed a tentative link between a stock market's size and its pricing efficiency. From a macroeconomic perspective, Claessens et al (2000) suggested that EU integration will drive the development process in the EU transition countries. Rapacki and Prochniak (2009) and Vojinovic, Oplotnik and Prochiniak (2010) examined real beta and sigma convergence in the EE EU nations during the process of EU accession, an important extension of this work is to question whether nations' stock markets are also converging as authors such as Csaba (2011, p11) report that "financial institutions play a pre-eminent role in all phases of transformation". Bekaert et al (2013) report that EU membership reduces equity market segmentation. We test WFME in the EE EU nations over periods 1.1.1999 to 31.12.2003 and 1.1.2004 to 31.12.2008 to determine whether the increasing experience of market participants over time, EU accession and the increasing number of stocks listed, larger market capitalisa- tions and increased turnover in the region has caused markets to become more efficient. Contrary to the expectations of the majority of studies listed above, our tests are all in broad agreement that the equity markets of the EE EU nations do not conform with WFME and this situation has not been substantially affected by accession to the EU. Therefore, none of the factors that previous researchers expected to become catalyst to drive the markets towards higher efficiency have materialized. Despite the passage of almost a generation since the creation of the EE EU stock markets, a significantly larger number of listed securities and 5 years since EU accession, these markets still cannot be considered to conform to WFME: these results pose the question of what changes are needed to improve efficiency of financial markets in these countries or whether these stock exchanges will ever attain pricing efficiency. 2. DATASET Our dataset consists of stocks included in the Dow Jones Stoxx EU Enlarged Total Market index, using data obtained from Bloomberg. This is a free-float capitalization-weighted index covering the countries have joined the EU since 2004. We excluded stocks from Bulgaria and Romania as the paper is concerned with the countries that joined the EU in 116 E/B/R ECONOMIC AND BUSINESS REVIEW | VOL. 15 | No. 2 | 2013 | 116-33 113 2004.Weexcludedstocks fromCyprusandMalta as we areonly investigating transition ^ounti^^ef^.Oi^rc^ataset:cove^^theperi^d fromthe P1 Jsnuary 1999 to 31st December 2008, oplitinto Jon2arye9°9 pocea Dccamber 2d03]pee-accession) and 1st Janu- ary 200Cto31stDecember 202a(oort-eccessionl. Theeeason for the use of subperiods lies in thruroadeeradbeofmethodologocmel oyed.rucleasKquidity controls and the use of individurl stoeko rktrlecr then ineüces.trhatedaes nop allowdnrct comparison of our post accescionresukswithprevioue p^uci ]er. nllU°ugnls] Mkuwas the actual accession date, tlreeffe oteaf aioo^i^onwei^rt^cirli^r- A.s^Aereasnnwhy we include the entirety of 200nen our dptasetWed10 08 because of the collapse in n4andalmar°.e ts. U2e owe dkilyB]oomUeru ^^^t^i^iees and log returns calculated as: Ay;t = logOit) - log (yit-i) Where: y = price of stock i at time t The descriptive statistics for the two datasets are shown in Table 1. The increasing number of IPOs caused the number of stocks in our post accession data- set to increase to 151 from 97 in our pre accession dataset. As Poland is by far the region's largest economy, it is logical that the country's stock exchange has the largest weight in our dataset; what is interesting is that the number of stocks quoted on the Warsaw Stock Exchange has almost doubled from 55 to 102 between 1999 and 2004, while few new stocks appeared on the other exchanges. Because our dataset contains only a single stock from each of Latvia and Slovakia we are sceptical that we can make any inferences about the stock markets of these countries. Average returns over the pre-accession period are positive, the financial crisis that began in 2007 resulted in negative returns over the post-accession period. Despite the volatil- ity ensuing from the stock market downturn that began in 2007, the standard deviation of our dataset for 2004-2008 is lower than for 1999-2003, with only Slovenia recording higher volatility. The skewness of our datasets moves from positive to negative, indicat- ing that while over period 1999-2003 there was a greater probability of a large decrease rather than a large increase in stock prices, the opposite was true for period 2004-2008. However, as the skewness readings for 1999-2003 and 2004-2008 are both close to zero, it is hard to draw any firm conclusions. The kurtosis of our dataset decreased significantly between 1999-2003 and 2004-2008, with only the single Latvian stock recording an in- crease. The Jacque-Bera statistic is used to test the null hypothesis that stock returns are normally distributed. From the associated p-values, it is clear that only stocks listed on the Budapest Stock Exchange over period 1999-2003 could have returns claonsidered to be normally distributed at any conventional level of significance. The results from the Jacque-Bera test are in broad agreement: returns on the stock markets of the EE EU na- tions are not normally distributed. However, it is clear the Jacque-Bera test is significant J. FOYE, D. MRAMOR, M. PAHOR | THE PERSISTENCE OF PRICING INEFFICIENCIES 117 Table 1: Descriptive Statistic CO CO CN LO LO CO CO t— t— t— CN C^ C^ C© CN CN C^ V!» c© lo oo ro v© CO CN CO CO CO cn oo LO oo O r^ cC C^ CO CN CN CO OO o t^ CN CN <"N <"N CN CN CN CO CO CO LO CO OO O co" <-N LO CN CN co> ro CN r— LO CN LO LO CO CO CO CO CO CO o LOLOCOOt— LO'— CN

3 C ^ od B C ai a '> % -S S ^ e 'S S rt ^ T3 s ECONOMIC ANDBUSINESS REVIEW| 3 due to high kurtoois, rathec than skewness, til^^i'ei-oii^ t:hs jearamu.riE models we apply still eoturn oobusS I'jesu^ttE. N METHODOVOGY The ieste wo employ fplL inio lowocapegories: iesee ob sui'ÌOÌ independonces uniU rooetests, mulliple parianpu i'ptio itsts snU liquiisSity. "We chose to eeplloaie the meihodelogy of i/VorSliéngton and Irtigfejs i2h0e) for ni eesiat iodenendencot uosS scmt teste sisiel muytiple ratianrs ralee Cesie beeause of the treed carge ofWnMU°eoteapphedEytheau°Aors and te hi a euE gmtio o er recetred tn tluelitwra° ure. Geiiffid et al (° OW) pserrEngeseEests similar to °htones em.ioyed in °hie pajoee, Sehen qaeElioa whethrr rt io frasfols tomake state- meaeo oOouC astuCive markrt tfficitneu iniernćitlonali1^ uuker one car eunrtol ioe Oho in- eoimctioa environment WUiis oiai rl^i:a^et sovers nstions whO SOSUSCUSCCÌ ^tmSlaskl£;s, uee auon or ruCe ossi: elea possibiliiy Oh^t our oee ìe Its nevi dcsc rt aisOool^d by Uhin.While opr eafasat ceeares a ^^ege jt^aosejar^julotc area, Che majority oe eiscks oo quoted otc She ^ossito^ Stock; Eccrhangs. Po conteoi Woe aon Volish bias, wc ]pe:rfodm iPi<2 lenti aoil üueoOSe l/Use ^ttj^iu^ ao a whvic enei Ihe indioidua i co^i^ieraeit a. 1 Tesio oU airiat itelSesieIodegou i tltefoc ee riot as e oSd to CO e eeiaHc c umse i aScd oi w atoreu sto U oh o timo norie e of sel urne WÌU Sr itn osen loco yieltoslotrctisallyBignificanC intuite: E{kyit\Ay^) = ßi + ßz^it-i COWeeiii ixe-yit|-yit_1 ) = theexpecled vaius of Ayit gtven H,yit_1 ßl = theregrennion intercept b2 = the regression slope Unlike serial correlation, the runs test is non-parametric and therefore does not require thereturnstobe normallydistributed.Runs tests determinewhetheratime serirsfol- towse random walkbycountingthenumber ofconeecubive pasétiveotneaariveopterva- CibdsanCcombariugiUtoan eopected value (E(R)): , N N + 2NuND E(R) = — V y N Where: N = Numberofobservations J. FOYE, D. MRAMOR, M. PAHOR | THE PERSISTENCE OF PRICING INEFFICIENCIES 119 N = Numberofpositiveo bservations Nd = Numberofnegatìveobaervations R= fumbfr ofouns We use the expected value a nd variance values (V(R)) to calculate a teststatlstic.Z: V(^=2NUNP (2NP N ~N) ( ) (N)2(N-l) R - E(R) Z= ' VV(R) The null hypothesis is that the returns can be considered to follow a random walk proc- ess. Rejection of the null hypothesis indicates that the stock's returns are non-random and contravene WFME. In order to test whether EU accession resulted in an increase in WFMEi we ase a z-test to Satetmine ifthe percentage ofsta pks considered statistically stgnificanl at a pacticdZar digniScance fuvnl iz^^^tSsticzlly tèffenent between the pre- and O ectias cessirn datcdcc- . 3.2 Unit root tests Unit root tests are used to determine whether the log returns of stocks in our dataset is stationary, i.e. whether it has constant statistical properties; if stocks follow a random wc skprocess, t tock cE^ic c Et choc Id b e non-rtutionar y. We uzeAr a e variants, Augmented DickeyFullrf-ADF^Phülipc - Paca on(fP) a nd KwaitokowH.Phillips, S chmidt and Shin -Kabli. ADS i-lbn mosS wclf-kdcwb udtt roditssl, thenull 1-ypzlhcsis is that the data is nonsta- i^c^iba^^.lte^rbi^^^i^^e^c balnulotcdCy risonlngiheCniiuwingregression: q Ayit = ßo + ß1tr + a^t-i + ap ^ ^Vit-p + £pt p=i Where: a = the coefficients to be estimated q = number of lagged terms b0 = intercept b = trend coefficient tr = trend MacKinnon's critical values are then applied to determine the significance of a. The PP test, developed by Phillips and Perron (1988), extends ADF to allow errors to be in- dependent and heteroscedastic. For a complete derivation, see Phillips and Perron (1988). 120 E/B/R ECONOMIC AND BUSINESS REVIEW | VOL. 15 | No. 2 | 2013 | 120-33 113 WhiletheADF and PP testsh lve r^i^Hti^pc^t h^^^^js of nonstationarity, the KPSS test has a nullhypothesisofstationarity. Reteeeingthe nulldrrothesis provides a useful validation check for the results from the ADF and PP tests. The reader should consult Kwiatkowski etal.(lP92)for etullPefivatioy.As withthefesfsof re rial independence, we apply a z- teetlodeptrmins erPotest ^lefsi^fu l^^fi^e^enth^ysu- fid post-accession datasets can be cynritleesi thatirti ca^U^erene 3.3 Multiple Variance Ratio Tests The third set of statistics employed are multiple variance ratio (MVR) tests. This approach wardevelopenUn Lo md MadVšnlpy((9PnerltUand Chow and Denning (1993) who coArtructed theMVR lette Snorder todetect I(^) h autocorrelation and heteroscedastic- itrr^jrr^^ye nf.^UpicSrimpo ftan(decpuuaifttockr dol low a random walk, the variance of returnr rhould rire ar a linear function to the number of obrervationr. That ir, the vari- z z ance ratio of the returnr over qq period murt be equal to qu q& . The variance ratio (VR) ir calculated ar: VR(q) = 2 T 2 o 2(q) o 2(i) Where: G2(1) = variance of daily log returns q = number of periods used for the sampling interval 02(q) = (1/ q)multiplied by the variance of q-daily returns If stocks conform to the random walk process, VR should not be statistically different to one. In line with the methodology of Worthington and Higgs (2004), the sampling inter- vals used for q were 2, 5, 10 and 20 days. For a more in depth overview of MVR method- ology or a complete derivation, the reader should consult Worthington and Higgs (2004) or Chow and Denning (1993) respectively. We also apply a z-test to determine whether the pre-andpost-accession results are statisticallydifferent. 3.4LiquidityControls Studies frequently conclude that liquidity is related to future returns. Examples of such work include Amihud and Mendelson (1986, 1989), Chordia et al (2001), Jones (2002), Amihud (2002), and Brennan et al (1998). Datar et al (1998) demonstrate a negative cor- relation between liquidity, as measured by turnover, and returns. Haugen and Baker (1996) found that liquidity is one of several generic factors that explain returns across global stock markets. Brzeszczynski et al (2011) found that trading intensity affected beta calculations for stocks listed on the Warsaw Stock Exchange and thus had serious ramifications for corporate finance decisions. J. FOYE, D. MRAMOR, M. PAHOR | THE PERSISTENCE OF PRICING INEFFICIENCIES 121 The relatively small size of the stock markets of the EE EU countries raises the concern that our results could be distorted by liquidity issues. Liquidity is an elusive concept, consequently in Table 5 we employ three widely used measures to control for it: i) Mar- ket capitalization ii) Average volume divided by shares outstanding iii) Bid-ask spread divided by share price. We create liquidity portfolios by assigning a rank (1 (low) to 5 (high)) to every stock for each of the three liquidity measures. Then we separate the combined results from Tables 2, 3, and 4 into five liquidity ranked portfolios in order to examine the effects of liquidity on the tests employed; we repeat this for each of market capitalization (Panel A) average volume divided by shares outstanding (Panel B) and Bid-ask spread (Panel C). 4. RESULTS The results from the tests of serial independence, unit root tests and multiple variance ratio tests are shown in Tables 2, 3 and 4 respectively. As we cover a large geographic region, each table also provides a geographic breakdown of the results. While around one-third of our dataset is listed outside Poland, the shares are listed on a lot of different exchanges; no exchange other that the Warsaw Stock Exchange has more than 14 shares in the dataset. This makes inferences for individual countries difficult. 4.1 Tests of serial independence Table 2 shows the results from the tests of serial independence, the serial correlation coefficient and the runs test. Looking at all the stock exchanges in the dataset, even at the 0.01 level of significance, almost one third of the stocks in our dataset return significant t-statistics from the serial correlation regressions for both the pre- and post-EU accession periods. Whilst there has been a marginal decrease in the number of stocks statistically significant at the 0.01 level between the pre- and post-accession datasets, the z-test reveals that the difference is not statistically significant. 43% of stocks in our dataset can be considered serially correlated at the 0.1 significance level for the pre-accession period; this rises to 66% for the post-accession period. The z-test reveals that the increase in the number of stocks exhibiting serial correlation at the 0.05 and 0.1 levels is statistically significant at 0.01, indicating that prices of stocks listed in the EE EU nations may have actually become less efficient. Looking at the individual stock exchanges, it can be seen that the results from the stock exchanges of other countries are largely consistent with those from the Warsaw Stock Exchange. Across the majority of stock exchanges most stocks exhibit properties consistent with serial correlation, at least at the 0.1 level. The z-test reveals no statistically significant difference between the pre- and post-accession datasets. Thus we can comfortably reject the null hypothesis that returns in the stock markets of the EE EU are not serially correlated. 122 E/B/R ECONOMIC AND BUSINESS REVIEW | VOL. 15 | No. 2 | 2013 | 122-33 113 Table 2: Tests of Serial Independence Serial Correlation T Statistic 1999-2003 2004-2008 Z Test 1999-2003 Runs Test 2004-2008 Z Test Entire Region % of Observations Significant at % of Negative Observations Czech Republic % of Observations Significant at % of Negative Observations Estonia % of Negative Observations Hungary % of Observations Significant at % of Negative Observations Latvia % of Observations Significant at % of Negative Observations Lithuania % of Observations Significant at % of Negative Observations Poland % of Observations Significant at % of Negative Observations Slovakia % of Observations Significant at % of Negative Observations Slovenia % of Observations Significant at % of Negative Observations 1% 31% 28% 0,41 22% 19% 5% 39% 54% - 2,23 38% 38% 10% 43% 66% - 3,45 46% 49% 15% 42% 64% 69% 1% 40% 29% 20% 0% 5% 60% 43% 40% 29% 10% 60% 71% 60% 43% 0% 29% 80% 57% 1% 60% 50% 40% 38% 5% 60% 63% 60% 50% 10% 60% 63% 60% 63% 60% 75% 20% 63% 1% 13% 38% 13% 38% 5% 38% 50% 13% 63% 10% 50% 63% 13% 75% 38% 50% 13% 38% 1% 0% 100% 0% 0% 5% 0% 100% 0% 0% 10% 0% 100% 0% 0% 0% 0% 100% 0% 1% 40% 70% 70% 30% 5% 70% 90% 90% 50% 10% 70% 90% 90% 70% 30% 20% 90% 90% 1% 13% 16% 11% 18% 5% 16% 48% 27% 35% 10% 22% 63% 40% 44% 11% 48% 71% 74% 1% 100% 0% 100% 100% 5% 100% 0% 100% 100% 10% 100% 0% 100% 100% 0% 0% 0% 100% 1% 100% 71% 25% 7% 5% 100% 71% 50% 36% 10% 100% 71% 50% 50% 0% 0% 58% 50% 0,47 - 0,04 - 0,40 All calculations are based on stock returns calculated on natural logarithms of Bloomberg last prices in local currencies. Serial correlation is calculated using one day lags Runs tests calculations are based on the sign of returns J. FOYE, D. MRAMOR, M. PAHOR | THE PERSISTENCE OF PRICING INEFFICIENCIES 123 When the runs test was applied to our dataset, about one fifth of stocks yielded statisti- cally significant results even at the most stringent 0.01 level for both the 1999-2004 and 2004-2008 datasets. Around half of both the pre- and post-accession datasets can be considered significant at the 0.1 level. Stocks listed on the Riga Stock Exchange perform poorly in the runs tests, but the dataset only contains one stock from this country; ex- cluding Latvia, the non-Polish stock markets have similar results to the entire dataset. 4.2 Unit root tests Table 3 shows the results from the three sets of statistics that form the unit root tests. The null hypothesis of the ADF and PP tests is that the time series has a unit root. The KPSS test reverses the null hypothesis and assumes that the time series has no unit root. Both the ADF and PP tests reject the null hypothesis, even at 0.01, for all stocks in both the pre- and post-accession datasets. We can comfortably reject the null hypothesis of nonstationarity for all stocks. Needless to say, there is no country variation here. Both tests clearly indicate that the returns of all stocks in the dataset are stationary, that is fol- low a deterministic rather than stochastic trend; inconsistent with a random walk. Out of all the metrics we employ, only the KPSS test indicates that stationarity may have declined between the pre- and post-accession periods. The KPSS statistic is insignifi- cant for less than half of all stocks at the 0.01 level of significance for the post-accession dataset, indicating that we cannot reject the null hypothesis of no unit root; yet for our pre-accession dataset, only 5% of stocks have KPSS statistics that can be considered statistically significant at the 0.01 level. Whilst almost three quarters of post-accession stocks have KPSS statistics that can be considered statistically significant at 0.1, the cor- responding figure for the pre-accession nations is only around one quarter. The z-test reveals that there is a statistically significant increase in the KPSS statistic between the pre- and post-accession datasets. The results from Poland are almost identical to those for the region as a whole, indicating little regional variation. While the KPSS statistic is less conclusive than ADF or PP, we can still confidently infer that all three unit root tests employed indicate that returns of many stocks listed in the EE EU nations are stationary, leading us to reject the null hypothesis that stocks follow a random walk. 4.3 Multiple Variance Ratio Tests Table 4 shows the results from the MVR tests using sampling intervals of two days, 5 five days, 10 days and 20 days; corresponding to one day, one week, one fortnight and one month. 124 E/B/R ECONOMIC AND BUSINESS REVIEW | VOL. 15 | No. 2 | 2013 | 124-33 113 Table 3: Unit Root Tests ADF Phillips-Perron Test KPSS Test 1999-2003 2004-2008 1999-2003 2004-2008 1999-2003 2004-2008 Z Test Entire Region % of Observations 1% 100% 100% 100% 100% 5% 46% - 6,81 Significant at 5% 100% 100% 100% 100% 13% 64% - 7,86 Significant at 10% 100% 100% 100% 100% 25% 72% - 7,31 Average -29,27 -28,88 -33,76 -31,09 0,26 0,79 - 7,31 Absolute Average 29,27 28,88 33,76 31,09 0,26 0,79 Czech Republic % of Observations 1% 100% 100% 100% 100% 0% 0% Significant at 5% 100% 100% 100% 100% 0% 14% Significant at 10% 100% 100% 100% 100% 20% 29% Average -31,61 -28,49 -33,00 -32,15 0,16 0,34 Absolute Average 31,61 28,49 33,00 32,15 0,16 0,34 Estonia % of Observations 1% 100% 100% 100% 100% 0% 63% Significant at 5% 100% 100% 100% 100% 0% 75% Significant at 10% 100% 100% 100% 100% 20% 75% Average -31,57 -28,50 -34,43 -30,91 0,24 0,88 Absolute Average 31,57 28,50 34,43 30,91 0,24 0,88 Hungary % ofObservations 1% 100% 100% 100% 100% 0% 50% Significant at 5% 100% 100% 100% 100% 0% 75% Significant at 10% 100% 100% 100% 100% 0% 75% Average -31,32 -31,86 -31,37 -35,00 0,13 0,65 Absolute Average 31,32 31,86 31,37 35,00 0,13 0,65 Latvia % of Observations 1% 100% 100% 100% 100% 0% 100% Significant at 5% 100% 100% 100% 100% 0% 100% Significant at 10% 100% 100% 100% 100% 0% 100% Average -36,60 -35,06 -36,58 -35,10 0,27 1,04 Absolute Average 36,60 35,06 36,58 35,10 0,27 1,04 Lithuania % of Observations 1% 100% 100% 100% 100% 30% 90% Significant at 5% 100% 100% 100% 100% 60% 100% Significant at 10% 100% 100% 100% 100% 60% 100% Average -18,52 -25,24 -30,20 -32,16 0,56 1,73 Absolute Average 18,52 25,24 30,20 32,16 0,56 1,73 Poland % of Observations 1% 100% 100% 100% 100% 4% 39% Significant at 5% 100% 100% 100% 100% 9% 60% Significant at 10% 100% 100% 100% 100% 22% 69% Average -30,73 -28,75 -33,88 -30,30 0,23 0,71 Absolute Average 30,73 28,75 33,88 30,30 0,23 0,71 Slovakia % of Observations 1% 100% 100% 100% 100% 0% 100% Significant at 5% 100% 100% 100% 100% 0% 100% Significant at 10% 100% 100% 100% 100% 0% 100% Average -22,35 -34,85 -22,24 -34,81 0,11 0,96 Absolute Average 22,35 34,85 22,24 34,81 0,11 0,96 Slovenia % of Observations 1% 100% 100% 100% 100% 0% 64% Significant at 5% 100% 100% 100% 100% 17% 79% Significant at 10% 100% 100% 100% 100% 33% 93% Average -28,19 -30,21 -38,55 -32,87 0,27 0,93 Absolute Average 28,19 30,21 38,55 32,87 0,27 0,93 All calculations were made on natural logarithms of Bloomberg last prices in local currency Augmented Dickey Fuller (ADF) test, H0: unit root, H1: no unit root (stationary) Phillips Peron (PP), H0: unit root, H1: no unit root (stationary) Kwiatkowski, Phillips, Schmidt and Shin (KPSS), H0: no unit root (stationary), H1: unit root J. FOYE, D. MRAMOR, M. PAHOR | THE PERSISTENCE OF PRICING INEFFICIENCIES 125 126 E/B/R ECONOMIC AND BUSINESS REVIEW | VOL. 15 | No. 2 | 2013 | 126-33 113 Even at the 0.01 level of significance, the MVR tests generally suggest that many stocks in our dataset do not follow a random walk process. While the percentage of stocks significant for at least one of the q levels is substantially higher for the post-accession dataset than the pre-accession dataset, the z-tests reveal that this is not statistically sig- nificant. At the 0.1 level of significance, more than half of all stocks do not conform to a random walk process for at least one of the sampling intervals applied, and the results are very similar for the pre- and post-accession nations. Excluding Czech Republic, Latvia and Slovakia (the small number of stocks listed in these nations makes inferences about them questionable anyway), there is not a large variation amongst the different countries in our dataset, with the results for Poland and the entire region being almost identical. 4.4 Liquidity Controls Table 5 shows the results from the liquidity controls employed: The results from using market capitalization as a proxy for liquidity are shown in Table 5 Panel A. For both the pre- and post-accession datasets, smaller capitalized stocks exhibit higher levels of serial correlation. Runs tests are also substantially affected by their mar- ket capitalization quintile, with the smaller market capitalization quintile stocks return- ing a higher proportion of significant results. The ADF and PP tests are both excluded from the table as every stock in our dataset can be considered statistically significant at the 0.01 level and thus there is no variation across any of the liquidity quintiles. For the KPSS tests, the results for the large market capitalization quintile are very similar to those from the small market capitalization quintile, therefore there is nothing to suggest that the KPSS tests is affected by liquidity (as measured by market capitalization). For the MVR tests, portfolio 5 actually has a higher percentage of stocks returning statisti- cally significant results than any of the other four quintiles: lack of liquidity is clearly not distorting results from the MVR tests. Whilst lack of liquidity associated with smaller market capitalization may have distorted some of the tests of serial independence, a sub- stantial number of stocks in the largest market capitalization portfolio still return sig- nificant results. Market capitalization does not have any meaningful effect on any of the three unit root tests of the MVR tests. The results from using average volume divided by shares outstanding as a liquidity con- trol are shown in Table 5 Panel B. For serial correlation, the number of stocks significant at each of the three significance levels we use is actually higher in the most liquid port- folio 5 than in the least liquid portfolio 1. Therefore, there is no indication that lack of liquidity, as measured by average volume divided by shares outstanding, is distorting the serial correlation tests. Whilst the runs tests return the highest percentage of significant results for the lowest-liquidity portfolio 1, there is not a huge amount of variation across the quintiles. In a similar manner to the serial correlation statistic, the percentage of stocks returning significant results for the KPSS tests actually increases as liquidity in- creases. The MVR tests return very similar results across the five quintiles. It is clear that J. FOYE, D. MRAMOR, M. PAHOR | THE PERSISTENCE OF PRICING INEFFICIENCIES 127 Table 5 Panel A: Liquidity Controls - Market Cap > • o U- .c C (Ü U o 4= o 'c C o M i/i (Ü U O (Ü V) J2 oo " S > o i- (N ® .i C O CT (N SI Is £ o ra fN " Ò o o (N " £3 |2 § « S vt O (U o C ^ 3 a OC o IN Ó O O (N 00 O >2 o § S £ S |S £ o 3 Z O o o (N H s: o u ununun o O unt— ^ i! ^ cu J3 2 O o c aj — 3 -D a o 128 E/B/R ECONOMIC AND BUSINESS REVIEW | VOL. 15 | No. 2 | 2013 | 128-33 113 Table 5 Panel B: Liquidity Controls - Average Volume Divided by Shares Outstanding o ^ > v a. (5 O jz C ro u O 4= > ^ cu " O -S £ "So. g % J2SE § ^ U — (0 (N '•C 0*i>n A 3 m o o o oc -o C tu . tu -o C LO ^ a. o ü O (N Ó O O = S 3 S ££ O Ó O O (N 00 O m O C = ^ ce ci v*r O O (N ® rt- c ^ t o 3 IN tU O U (j- l_p - (j- i_p - (j- «— LO «— < «— LO «— < «— tj- I_P ^^ -- (j- i_p -- «— LO «— < «— LO «— < > ^ il ^ v -O « o O O oöö c u oo 3 -Q a o C U 3 -Q a o ^ £ O ru := > £ s — ^ 3 .D a o C