Foreign versus Local Investors: Who Knows More? Who Makes More?

Transcription

1 Foreign versus Local Investors: Who Knows More? Who Makes More? Petko S. Kalev*, Anh H. Nguyen, Natalie Y. Oh Department of Accounting and Finance Monash University First Draft: 18 August 2006 Current Draft: 4 December 2006 We thank the Nordic Central Securities Depository and Reuters (SIRCA) for proving the data. We are also thankful to Joakim Westerholm and Emily Lok for their help and support in combining and matching the ownership data with trade data from HEX and Reuters. *Corresponding author: Petko Kalev, Department of Accounting and Finance, Faculty of Business and Economics, Monash University; P.O. Box 197, Caulfield East, VIC 3145 Australia. Tel: Fax: ; Petko.Kalev@BusEco.Monash.edu.au

2 Foreign versus Local Investors: Who Knows More? Who Makes More? ABSTRACT This paper examines the trading profitability of local and foreign investors on the Helsinki Stock Exchange for the period 1999 to Based on unique ownership data, we are able to capture precisely the per-transaction profit of any investor s type without making assumptions about the investors initial holdings. We find some evidence in support of the of information asymmetry hypothesis between foreign and local investors. Foreign investors tend to choose stocks with an international profile and transparent information. They significantly outperform local investors in global stocks such as Nokia. Excluding Nokia, local investors gain more. This evidence is strongest in intra-month and longer periods. JEL Classification: G10 Keywords: Foreign and local investor; Ownership; Per-transaction profit; Spectral decomposition

3 1. Introduction Who has more information and, therefore, gains more: Foreign or local investors? Although this question has been addressed in a number of studies using diverse approaches, the answer is far from conclusive. On one hand, it is argued that the locals do not face distance, linguistic or cultural barriers; thus they are likely to have more understanding about firms specifics. Such potential information advantage would lead to better performance in the domestic stock market in comparison to foreign investors (see, among others, Hau (2001); Dvořák (2005); Brennan & Cao (1997)). On the other hand, foreign investors are normally sophisticated institutional investors who possess superior analytical skills and investment experience. Thus, they are able to analyse market conditions, make optimal investments and trading decisions and perform better on average than local investors (see Froot & Ramadorai (2001); Grinblatt & Keloharju (2000); Karolyi (2002), among others). This controversy poses the need for further study since this issue is linked with the home bias puzzle, where investors overweigh their portfolios in domestic stocks (see Levy & Sarnat (1970); Cooper & Kaplanis (1994); and Stulz (2005), among others). Home bias is justified if local investors are more informed than their foreign counterparts. In contrast, if foreign investors are more informed, the information asymmetry hypothesis no longer satisfactorily explains the full extent of the home bias phenomenon. 1 1 Recent studies offer to explain home bias in the light of behaviour finance. See Grinblatt & Keloharju (2001) and Huberman (2001), among others. 1

4 We address the above question by directly comparing the trading profits made by foreign investors to those earned by local investors on the Helsinki Stock Exchange (HEX) for the period 1999 to Our aim is to investigate and compare the profitability between foreign and local investors and provide robust empirical evidence on the nature of information asymmetry by utilising a unique data set and directly measuring performance. The data set identifies not only the identity of the investor behind each trade, but also the investor s level of holdings in each stock. This paper is, to the best of our knowledge, the first to capture precisely the per-transaction profit without making assumptions, like previous studies. Building on the methodology developed by Hasbrouck and Sofianos (1993), we recognize the group with information advantage as the one with higher average profits, i.e., the one who knows more gains more. The nature of information asymmetry is further analysed on short-, medium- and long-term horizon by using a spectral decomposition technique. Unlike previous studies in this area, which have applied a similar analysis and utilized the same technique (Hau (2001) and Dvořák (2005)), we do not have to make any critical assumptions regarding the level of holdings. For example, Hau (2001) uses average position as a replacement for initial holdings; Dvořák (2005) assumes that the initial position of share holdings is zero for any investor. Potential measurement errors caused by these assumptions are overcome in this study. Our ownership data contain share registry from the beginning of From this initial data, we work out the holdings for each investor group at the start of 1999, taking into account share deposits, withdrawals, splits and buy-back. By doing this, we ensure that the initial holdings at the start of our sample in 1999 represent the true and fair position for each group of 2

5 investors. Utilising this recent data set, our study unbiasly examines the nature of information asymmetry between foreign and local investors for the Finnish market. The results from earlier studies by Grinblatt and Keloharju (2000, 2001) and Liljeblom and Löflund (2005) are likely to be affected by the abolition of foreign ownership restrictions in Finland in The documented foreigners superior performance may be the results of price increases, which were induced by large buying activities of foreign investors after the financial liberalization. Our sample period, starting from 1999, is free from these abnormal effects. Together with a longer period of investigation, our data set allows for a better comparison of the performance between foreign and local investors. The results suggest that information asymmetry exists between local and foreign investors. Consistent with prior literature, foreign investors concentrate their holdings on stocks with an international profile and reputation, where information is more apparent. In terms of profits, the results show that foreign investors perform better than their local counterparts in global stocks, such as Nokia. Excluding Nokia, local investors are the ones that possess information advantage, on average. The evidence is strongest for the medium- and long-term horizon. Additionally, the local profits come mostly from domestic retail investors. Domestic institutions are relatively inactive in trading, which result in lower profits. The rest of the paper is organized as follows. Section 2 provides a review of the related literature; Section 3 outlines the methodology; Section 4 introduces the data; the results together with a detailed discussion are presented in Section 5; and Section 6 concludes. 3

6 2. Related Work One reason for mixed empirical results regarding the question of who are more informed, local or foreign investors, is the diverse methodology used to measure information advantages. The choice of methodology depends largely on availability and characteristics of the data. In general, however, it is possible to divide the choice into methodologies using international investment data and methodologies using trading data. Based on a model of international investment flows, Brennan and Cao (1997) observe that US investors tend to purchase foreign equities if the foreign market return is high. Brennan et al. (2005) extend the 1997 paper to analyse investors responses to information signals from foreign markets. They show that global financial institutions are more optimistic if the foreign market return increases, and vice versa. These findings, according to the authors, are consistent with the assumption that foreign investors are less informed, since they react on lagged information. Froot and Ramadorai (2001) also use an aggregate data approach, examining the impacts of US institutional equity flows on prices of closed-end country funds of 25 different countries. They show that foreign activities can predict the equity performance of these countries and prices of associated closed-end funds in the US market. Their findings mean that foreign investors are more informed. While aggregate investment data provide macro analysis, most recent studies have focused on comparing trading behaviour and performance between foreign and local investors in one particular market environment. The most direct approach is comparing trading profits, which is also the methodology of 4

7 this study. Hau (2001) and Dvořák (2005) apply the methodology from Hasbrouck and Sofianos (1993) to compare marked-to-market profits of each investor group. Spectral decomposition is then used to further analyse profits in the short-, medium- and longterm. Analysing the German market, Hau (2001) finds that foreign traders have significantly lower profits than domestic ones for all time horizons. Similarly, Dvořák (2005) documents that domestic investors are generally more informed, with strong evidence for the medium-term horizon. Besides trading profits, comparison of trading styles has been used in several studies. Grinblatt and Keloharju (2000) compare between foreign and local investors the tendency to buy (sell) future winning (losing) stocks. The approach is straightforward and intuitive. However, it requires a subjective judgment on the definition of winning/losing stocks and appropriate investment horizons. Grinblatt and Keloharju (2000) find strong evidence that foreign investors outperform local investors. Karolyi (2002) finds some evidence of an informed trading strategy employed by foreign investors in the Japanese market from the start of the Asian financial crisis until the end of Foreign investors continue employing positive-feedback trading, while domestic institutions follow negative-feedback or contrarian strategies. The author estimates that foreign investors would have earned a cumulative of 1,200 billion yen by 2001 (i.e., postcrisis effects), while domestic institutions would have cumulatively lost 240 billion yen for the same period. Choe et al. (2005) compare daily trading price differences between foreign and local investors in the Korean market. They show that foreign investors pay more for purchases 5

8 and receive less for sales of stocks. Thus, foreign investors are at a disadvantage compared to local investors. In a comprehensive analysis, Seasholes (2004) uses the combination of three different approaches to analyse the Taiwanese market. First, event study is applied to examine the reactions of two investor groups against an earning surprise. The result indicates that foreigners react more appropriately, buying (selling) before a positive (negative) earning surprise. Second, daily return on foreign portfolio is calculated. Regressing this on a constant and market return, it is evident that foreigners earn above-market returns. Finally, a bivariate VAR model is employed, which indicates that foreign net inflow can predict market returns. From the results of these three tests, Seasholes conclude that foreign investors are more informed. In a recent paper using the same data as the current study, Kalev et al. (2006) also utilise a VAR model to examine the dynamic relationship between price and holdings of foreign and local investors. Unlike Seasholes (2004), they find weak evidence that daily net inflows affect future price movements. 3. Methodology Following Hasbrouck and Sofianos (1993), for any particular stock, the marked-tomarket trading profits are defined as the function of changes in price and net inventory holdings. Thus:, t+ 1 = t Pt +1 Q (1) where is the profit at t+1, Q t is the number of shares held at time t, P t+1 is the change in share prices and t refers to the time of each transaction. The profit position of each group will be marked-to-market each time the stocks are traded. After per-market 6

9 transaction profits are derived, spectral decomposition is used to separate these profits into short-, medium- and long-term horizons. The intuition and mathematical expression of this approach are described in detail in Hasbrouck and Sofianos (1993) and Hau (2001). We employ intraday transaction data in the spectral decomposition analysis, which means that the frequency band is defined in terms of number of transactions, not calendar time. Following Dvořák (2005), the spectral horizon for each stock is defined using the average number of transactions per day. Reinterpreting the frequency bands in terms of calendar time enhances interpretation of results. 4. Data and Descriptive Statistics The paper employs the data set of stocks listed on the HEX. The data are the combinations of trading data from the HEX, the bid ask quotes from Reuters and the ownership statistics from the Nordic Central Securities Depositary (NCSD). The NCSD is the central ownership register through which the clearing and settlement of all share transactions occur, and these trades are attributable to both local and foreign investors. 2 Our sample stocks come from the OMX Helsinki 25 Index at the beginning of Being the most liquid stocks, they allow examination of stocks at high levels of frequencies (i.e., intraday). Additionally, foreign investors are more likely to invest in liquid and larger stocks. Restricting the sample to the top 25 stocks provides an equal 2 The NCDS system keeps all records of share ownership: every change in share holdings and all deposits, withdrawals and adjustments that occur during the sample period. 7

10 ground to contrast performance between foreign and local investors. The final sample consists of 21 stocks. Four stocks were excluded due to incomplete or missing data. The three main groups of investors are identified as domestic financial institutions, domestic retail investors and foreign investors. 3 Foreign investors are foreign-owned banks and companies and those who trade through nominee accounts. 4 Domestic institutions include domestic banks or financial institutions, insurance companies, credit institutions and brokerage houses. The rest is classified as domestic retail investors, which include non-financial firms, non-profit institutions, local government and household investors. Table 1 shows the list of stocks included in our sample together with some descriptive statistics. In terms of ownership, both foreign and local retail investors exhibit high levels of ownerships in the sample firms. Local institutions, on the other hand, show relatively smaller yet representative holdings. To effectively compare the characteristics of each group of investors, the sample is divided into three categories based on the relative portion of share ownership by the respective groups of investors in each stock. This classification will aid the analyses of information asymmetry, since level of ownership is a key indication of information advantage possessed by investors (Kang & Stulz (1997)). The three categories used to 3 Due to an insignificant percentage of foreign retail investors, we do not further partition foreign investors into institutional and retail foreign investors. This follows Choe et al. (2005). 4 The only traders with nominee accounts on the HEX are foreign investors. All domestic investors are given a specific account to trade. 8

11 classify the sample are: stocks with strong foreign holdings (more than 60% foreign ownership), stocks with strong domestic holdings (more than 60% domestic ownership) and stocks with balanced holdings between foreign and domestic investors. For the foreign-dominated, domestic-dominated and balanced holding groups, we identify 5, 6 and 10 stocks, respectively. Looking more closely at the category of companies with strong foreign ownership, the top two companies in this group (Nokia and Stora Enso) are international stocks, which are listed in other stock exchanges. 5 The characteristics described above validate earlier findings that firms with international profiles and more apparent information seem to be more appealing to foreign investors (Dahlquist & Robertsson (2001); Kang & Stulz (1997)). 6 [INSERT TABLE 1] In Tables II and III we present statistics on trading activities. While the figures in Table 2 are calculated across stocks on a year-by-year basis, the trading statistics presented in Table 3 are aggregated according to each group of investors. Table 2 Panel A presents the number of stocks that are active in each particular year. There is a decrease in the number of stocks over the year because some stocks are de- 5 Nokia is listed in three other major stock exchanges, including the New York Stock Exchange (United States), Frankfurter Wertpapierborse (Germany) and Stockholmsborsen (Sweden). Stora Enso is listed in two other stock exchanges besides the HEX, namely the New York Stock Exchange (United States) and Stockholmsborsen (Sweden). 6 Dahlquist & Robertsson (2001), however, suggest that this finding may also be due to the institutional investor bias, since most foreign investors are financial institutions. 9

12 listed, taken over or merged. Table 2 Panel B looks at the total of number of transactions per stock in each year. On average, the number of transactions is relatively stable in each year, except for The statistics show some clear tendencies. The trading intensity of Nokia is far beyond that of any other stocks in the sample. The figures reported in Table 2 Panel C, which is by number of trading days per stock, indicate a similar story. [INSERT TABLE 2] The trading statistics by each group of investors are reported in Table 3. The daily number of transactions per stock reported in Table 3 Panel A reveals that foreign investors are by far the most active traders on the HEX. By contrast, the Finnish domestic institutions do not engage much in trading. Separating the figures according to the level of ownership, the results show that foreign investors remain the most active, even in stocks with strong domestic holdings. Nevertheless, foreign investors tend to be more active in the stocks that they own exclusively. These findings suggest that foreign investors might be more comfortable trading stocks about which they have more information. Table 3 Panel B reports the trade size. Domestic institutions have the largest size trades. The trade size used by domestic retail investors is predictably small, since they have less financial resources. Foreign investors, on the other hand, use more sophisticated trading 7 The reason for the drop in number of transactions in 1999 is the late start of Nokia. In April 1999, the two stock classes of Nokia were merged into one. The current analysis only includes this single stock, which starts from April Since Nokia is the largest stock on the HEX, excluding its transactions in the first three months of 1999 affects the total transactions in that year. 10

13 techniques This group mainly utilizes medium-sized trades, with frequent trading intensity. The trading behaviour of the three investor groups is further explored by looking at the average initiation rate (Table 3 Panel C). 8 These results show that foreign investors initiate trade more often than the two domestic groups. Furthermore, domestic institutions average initiated rate is larger than that of retail investors. This confirms previous findings that retail investors act as liquidity providers, and institutions tend to be less patient in executing transactions (Dvořák (2005)). [INSERT TABLE 3] 4. Main Results In order to identify the groups with information advantage, we analyse trading performance between each group of investors at four levels. First, we compare the results of per-market transaction profits across all time horizons. Then, the performance of each group is compared according to short-, medium- and long-term horizon. Finally, a comprehensive picture of comparative performance between each group of investors is drawn. 8 Following Lee & Ready s (1991) algorithm, we identify the initiated trades based on bid and ask quote provided by Reuters. 11

14 4.1 Average Trading Profits The results of trading profits across all time horizons for each group of investors are reported in Table 4. When Nokia is included, foreign investors outperform both domestic institutions and retail investors on average (Panel A). Excluding Nokia, domestic retail investors are the best performers and domestic institutions are the worst performers. However, the performance of domestic institutions is steadier across stocks. This result is consistent with their constant shareholdings and low trading intensity strategy. The sources of profits for each group of investors are better revealed when segregating the sample into the three categories based on the relative level of ownerships (Table 4 Panel A). In foreign-dominated stocks, foreign investors outperform in terms of number of winning stocks and magnitude of profits. This result persists when Nokia is excluded. Similarly, domestic retail investors outperform in stocks where their ownership is most concentrated. Performance in the balance-holding category, on the other hand, is more uniform. Our results conform with the prediction that the level of ownership is a key indication of information advantage (see Kang & Stulz (1997); Dahlquist & Robertsson (2001), among others). Consistent with Ivkovic and Weisbenner (2005), these findings suggest that local investors retain their information advantage for firms where foreign investors are reluctant to invest, most likely due to limited information. [INSERT TABLE 4] 4.2 Results for Spectral Decomposition Table 5 reports the results for spectral decomposition. Summary statistics and statistical 12

15 testing results are reported separately for short-, medium- and long-term horizons in Panels A, B and C. The results for short-term profits depict a similar picture to the results for average trading profits across all time horizons. No one group of investors significantly outperforms the others. Foreign investors perform well even in stocks in which they do not have large holdings. Unlike foreign investors, domestic retail investors main sources of profit are from those stocks in which they have large shareholdings. This finding suggests the possibility that these investors are able to take advantage of foreign investors less patient behaviour (Dvořák (2005)), as indicated by the comparative initiation rates. In general, the statistical tests show no significant differences in short-term profits between the three groups of investors. When comparing the performance of any two groups, the t-test of the difference between foreign and local investors (including both local institutions and retail customers) is significant at the 10% level. Together with the summary statistics, it indicates that the domestic investors do better in the short term. The medium-term profits, however, show some distinguishable differences in performance. Domestic retail investors outperform both foreign investors and local institutions (see Table 5 Panel B). Domestic institutions continue to exhibit stable profits patterns. The Kruskal Wallis test statistics show a significant difference in the performance of the three investor groups. When comparing the performance between foreign investors and combined groups of local investors, the difference is significant at the 5% level, whether Nokia is included or not. The overall intra-month performance of local investors is better than that of foreign investors. 13

16 We observe similar results for long-term profits (Table 5 Panel C). Nokia continues to be the biggest winning stock for foreign investors. Without Nokia, foreign investors are the worst performers in the long-term. This provides empirical evidence that local investors perform better than their foreign counterparts over the long-term. The pattern is further supported when we compare these investor groups in pairs. 9 [INSERT TABLE 5] 4.3 Evidence on Information Asymmetries Information asymmetries between foreign and local investors are illustrated in Figures 1 and 2 by computing difference in profits between domestic and foreign investors gains across all time horizons, and then across short-, medium- and long-term horizons. Figure 1 presents results based on all stocks, while Figure 2 excludes Nokia. Across all time horizons, the biggest winning stock for foreigners is Nokia. Excluding Nokia, domestic investors perform better in 13 out of 20 stocks. Additionally, domestic investors tend to gain more in the domestic-dominated stocks, which are located on the far-right side of the graphs. In the short term, there is no clear different in the performance of the two groups. In medium- and long-term horizons, however, domestic investors are more profitable. They outperform in 13 stocks in medium term and 14 stocks in the long term, respectively. In terms of magnitude, the profit differences are 9 The summary statistics for profits show deviation from normal distributions, since the medians are far from the means. The sample size is also relatively small. Accordingly, the results from non-parametric tests are more suitable in this case. 14

17 more profound in those stocks with larger domestic holdings. The difference in profits, however, is very small in magnitude. It suggests that if transaction costs are taken into account, these superior trading profits from local investors may disappear. In summary, our results indicate the existence of information asymmetry between local and foreign investors. Information advantage can explain why domestic investors outperform foreign investors. The evidence is especially strong in the longer trading horizon. This finding is similar to the results of Dvořák (2005), where local investors are documented to perform significantly better than foreign investors in the medium term. Furthermore, domestic investors excel in stocks in which they have strong holdings. This confirms the information advantage of local investors in local-dominated stocks (Ivkovic & Weisbenner (2005)). Nevertheless, the local information advantage does not help domestic investors to gain more at the intraday level. One possible explanation for the comparable profits between foreign and local investors at the intraday level is the trading effects. Foreign investors trade with high intensity and initiate more transactions than do domestic investors. These strong activities may immediately influence the prices of certain stocks in a direction that benefits the foreigners. These effects, however, wear off over time, leading to weaker performance of foreign investors in the longer period. This explanation seems to confirm to the findings of Westerholm (2006). Studying the same data set, Westerholm concludes that foreign investors have trading characteristics of more sophisticated investors, but they are not necessarily more informed. [INSERT FIGURE 1 & 2] 15

18 Our results differ from some earlier studies utilising Finnish ownership data, such as Grinblatt and Keloharju s (2000) and Liljeblom and Löflund (2005). First, we employ a different methodology. Second, while Grinblatt and Keloharju s (2000) and Liljeblom and Löflund s (2005) sample periods cover 1994 to 1996 and 1992 to 1997, respectively, we examine a more recent period ( ). It is worth mentioning that, at the beginning of 1993, the foreign ownership restriction was removed in Finland, leading to a rapid increase in foreign shareholdings in Finnish-listed firms (Ylä-Anttila et al. (2004)). According to Dahlquist and Robertsson (2004), after the abolition of foreign restriction, net buying from foreign investors caused permanent price increases. 5. Conclusion This study investigates the information asymmetry between foreign and local investors on the HEX for the period from 1999 to The results indicate evidence of information asymmetry between local and foreign investors. Foreign investors tend to choose stocks with international profiles and transparent information. In terms of trading performance, foreign investors significantly outperform local investors in Nokia, the largest stock on the HEX. Excluding Nokia, local investors tend to do better. Results from spectral decomposition show small differences in short-term profits between the two groups. In the medium and long term, local investors have stronger performance. However, these differences in performance are not economically significant. Further inquiries into this line of research would take into account transaction costs associated with each type of investor. Such an approach enables the comparison of economic profits. Another direction would be to examine the contagion and learning 16

19 effects between foreign and domestic investors. The idea is that, in an integrated equity market where active trading interaction between foreign and local investors occurs, there should exist a learning and spillover process among market participants. This latter line of research (contagion and learning effects) would deepen the understanding about the nature of information asymmetry between foreign and local investors. We leave these for future research. 17

20 References Brennan, M. J., Cao, H. H., International Portfolio Investment Flows. Journal of Finance, 52(5), Brennan, M. J., Cao, H., Strong, N., Xu, X., The Dynamics of International Equity Market Expectations. Journal of Financial Economics, 77(2), Choe, H., Bong-Chan, K., Stulz, R. M., Do Domestic Investors Have an Edge? The Trading Experience of Foreign Investors in Korea. Review of Financial Studies, 18(3), Cooper, I., Kaplanis, E., Home Bias in Equity Portfolios, Inflation Hedging, and International Capital Market Equilibrium. Review of Financial Studies, 7(1), Dahlquist, M., Robertsson, G., Direct Foreign Ownership, Institutional Investors, and Firm Characteristics. Journal of Financial Economics, 59(3), Dahlquist, M., Robertsson, G., A Note on Foreigners Trading and Price Effects across Firms. Journal of Banking and Finance, 28(3), Dvořák, T., Do Domestic Investors Have an Information Advantage? Evidence from Indonesia. Journal of Finance, 60(2), Froot, K. A., Ramadorai, T., The Information Content of International Portfolio Flows. NBER Working Paper No Grinblatt, M., Keloharju, M., The Investment Behavior and Performance of Various Investor Types: A Study of Finland's Unique Data Set. Journal of Financial Economics, 55(1), Grinblatt, M., Keloharju, M., How Distance, Language, and Culture Influence Stockholdings and Trades. Journal of Finance, 56(3), Hasbrouck, J., Sofianos, G., The Trades of Market Makers: An Empirical Analysis of NYSE Specialists. Journal of Finance, 48(5), Hau, H., Location Matters: An Examination of Trading Profits. Journal of Finance, 56(5), HEX, Helsinki Stock Exchange. Retrieved 13 January 2006, from Huberman, G., Familiarity Breeds Investment. Review of Financial Studies, 14(3), Ivkovic, Z., Weisbenner, S., Local Does as Local Is: Information Content of the Geography of Individual Investors' Common Stock Investments. Journal of Finance, 60(1), Kalev P.S., Lok, E., Westerterholm, P.J., Dynamics between Prices and Equity Holdings. Working Paper, Monash University. Kang, J.-K., Stulz, R. M., Why Is There a Home Bias? An Analysis of Foreign Portfolio Equity Ownership in Japan. Journal of Financial Economics, 46(1),

21 Karolyi, G. A., Did the Asian Financial Crisis Scare Foreign Investors out of Japan? Pacific-Basin Finance Journal, 10(4), Lee, C. M. C., Ready, M. J., Inferring Trade Direction from Intraday Data. Journal of Finance, 46(2), Levy, H., & Sarnat, M., International Diversification of Investment Portfolio. American Economic Review, 60(4), Liljeblom, E., & Löflund, A., Determinants of International Portfolio Investment Flows to a Small Market: Empirical Evidence. Journal of Multinational Financial Management, 15(3), Nokia, Stock Information [Electronic version]. Retrieved 30 April 2006 from Seasholes, M. S., Smart Foreign Traders in Emerging Markets. Working Paper, University of California at Berkeley. Stora-Enso., Stora Enso in Brief [Electronic version]. Retrieved 30 April 2006 from Westerholm, P. J., Do Uninformed Internalized Trades Tap into Unexpressed Liquidity? Working Paper, University of Sydney. Ylä-Anttila, P., Ali-Yrkkö, J., & Nyberg, M., Foreign Ownership in Finland - Boosting Firm Performance and Changing Corporate Governance [Electronic version]. ELTA (The Research Institute of the Finnish Economy). Retrieved 2 May 2006 from 19

22 Table 1 Summary statistics at the beginning of sample period Panel A: Holdings for individual stock Stock Code Industry Mkt Cap FInv DIns DRet Nokia NOK Telecommunication 88,441,293, % 0.46% 2.96% Stora Enso Oyj B EGR Forest Industry 831,536, % 2.05% 14.49% KCI Konecranes Inter KONC Metal & Engineering 525,000, % 5.48% 13.07% Raisio Yhtym Vaih-os RTEV Food Industry 1,108,564, % 2.11% 18.41% Pohjola B POBR Insurance 941,489, % 10.15% 24.22% Stora Enso Oyj A EGA Materials 2,628,662, % 4.75% 42.11% Sampo A SAMA Insurance 1,805,293, % 6.69% 40.52% Metsa-serla Oyj B MSB Forest Industry 831,536, % 3.69% 46.67% UPM-Kymmene Oyj UPM Forest Industry 7,588,928, % 10.25% 40.58% Amer AMA Sports 284,624, % 9.91% 41.98% Huhtamaki Oyj I HUIF Food Industry 591,815, % 12.16% 42.00% Helsingin Puhelin HEP Telecommunication 2,059,617, % 9.47% 59.62% Outokumpu Oyj A OUTO Metal & Engineering 1,351,146, % 5.27% 70.97% Kesko KESK Trade 876,848, % 15.81% 60.81% Kemira Oyj KEMR Materials 792,120, % 9.35% 70.35% Orion-yhtyma B ORIB Health Care Industry 626,104, % 10.72% 70.42% Rautaruukki Oyj K RRUK Materials 892,630, % 7.59% 73.78% Oyj Hartwall Abp A HART Food Industry 562,065, % 2.07% 84.44% Sonera-yhtyma Oyj SONE Telecommunication 105,500, % 2.10% 86.48% Fortum Oyj FORT Energy 3,963,152, % 4.00% 91.33% Partek PAR Manufacturing 512,575, % 10.91% 85.47% Panel B. Summary statistics on initial holdings Total (21) Mean Holding (%) 42% 7% 51% Median Holding (%) 46% 7% 47% Standard Deviation 28% 4% 27% Foreign Dominated (5) Mean Holding (%) 85.25% 2.53% 12.22% Domestic Dominated (10) Mean Holding (%) 14.34% 6.50% 79.16% Balanced Holding (6) Mean Holding (%) 47.80% 7.95% 44.25% Holdings at the beginning of sample period in January 1999 are presented for each investor group: foreign investors (FInv), domestic institutions (DIns) and domestic retail investors (DRet). The holdings are calculated using share registry data from Finnish Central Security Depository (FCSD). Panel A provides the detailed holdings for each stock. Panel B presents the summary statistics for initial holdings. Companies also are divided into groups with foreign- or domestic-dominated holdings (representing at least 60% of total holdings) or balanced. Market capitalization (Mkt Cap) is in euro (EUR). All holding figures are reported in percentage (%) of total shares on issues. 20

23 Table 2 Year-by-year trading statistics Year Aggregate Figures Panel A: Number of stocks Panel B: Number of transactions With Nokia Mean 55, , , , , , ,949 Median 17,088 18,689 14,464 21,007 26,755 52, ,480 Stdev 106, , , , , ,822 2,002,486 Without Nokia Mean 34,439 60,926 48,302 49,737 59,476 83, ,429 Median 16,339 17,963 13,606 20,286 26,128 48, ,856 Stdev 42, , ,258 67,073 82,335 83, ,205 Panel C. Number of trading days With Nokia Mean ,347 Median ,449 Stdev Without Nokia Mean ,342 Median ,445 Stdev Trading statistics are provided on year-by-year basis. Panel A presents the number of sample stocks that are active on HEX for any particular year. Panel B provides the total number of transactions per stock for any year. Panel C reports the total of days the stock is traded in a particular year, averaged across stocks. Trading figures are presented in two cases: with Nokia and without Nokia. 21

24 Table 3 Trading statistics for each investor group Panel A: Number of daily transactions With Nokia Without Nokia FInv DIns DRet FInv DIns DRet Total Foreign Dominated Domestic Dominated Balanced Holdings Mean Median Stdev Mean Median Stdev Mean Median Stdev Mean Median Stdev Panel B. Trade size With Nokia Without Nokia FInv DIns DRet FInv DIns DRet Total Mean 2,454 3,438 1,324 2,404 3,422 1,309 Median 2,224 3,194 1,092 2,131 3,104 1,084 Stdev 2, ,205 1, Foreign Dominated Mean 3,372 4,347 1,797 3,345 4,548 1,855 Median 3,572 4,691 1,839 3,691 5,634 2,052 Stdev 1,478 1, ,809 2,387 1,044 Domestic Dominated Mean 2,488 3,584 1,389 Median 2,224 3,194 1,050 Stdev 1,021 1, Balanced Holdings Mean 1,956 2,818 1,015 Median 1,954 2,970 1,084 Stdev 1,095 1, This table provides the results for trading statistics for each group of investors foreign investors (FInv), domestic institutions (DIns) and domestic retail investors (DRet) for the entire period. Panel A presents the statistics on number of daily transactions across stocks, which is calculated as total transactions divided by total trading days. Panel B presents the results on trade size, which is the volume per transaction. Panel C reports the average initiation rate, which is the ratio of initiated trade (both buy-initiated and sellinitiated) and total number of trades, averaged across all sample stocks. All statistics are calculated using data from the Helsinki Stock Exchange for the period from January 1999 to December 2004 for 21 stocks in the sample. Whenever possible, the figures are reported both with and without Nokia in the sample. (continued on next page) 22

25 Table 3 (continued) Panel C: Average initiation rate With Nokia Without Nokia FInv DIns DRet FInv DIns DRet Total Mean 42% 24% 16% 41% 24% 16% Median 40% 22% 12% 39% 24% 13% Stdev 16% 9% 11% 15% 9% 11% Foreign Dominated Mean 49% 20% 15% 44% 21% 17% Median 50% 19% 11% 43% 20% 13% Stdev 22% 6% 15% 23% 7% 16% Domestic Dominated Mean 36% 24% 18% Median 37% 24% 15% Stdev 12% 10% 12% Balanced Holdings Mean 46% 26% 12% Median 47% 25% 13% Stdev 14% 10% 4% 23

26 Table 4 Average profit Panel A: Summary statistics With Nokia Without Nokia FInv DIns DRet FInv DIns DRet Total Mean 4, , , , Median , Stdev 11, , Foreign Dominated Mean 10, Median 1, Stdev 21, , , , Domestic Dominated Mean 1, , Median , Stdev 2, , Balanced Holdings Mean 4, , Median Stdev 7, , Panel B:.Statistical testing ANOVA Kruskal-Wallis T-test Wilcoxon rank sum test With Nokia All Groups FInv - Local FInv - DIns FInv -DRet DInt - DRet 0.03** 0.41 Without Nokia All Groups FInv - Local FInv - DIns FInv -DRet DInt - DRet 0.03** 0.51 This table provides results of the average trading profit, in euros per transactions, for the three groups of investors (FInv, Dins & DRet). Profits are calculated as the average product of cumulative changes in holdings and price changes for the entire period. Panel A presents the summary statistics mean, median, standard deviation (Stdev), minimum and maximum values. Whenever possible, the summary statistics are calculated both with and without Nokia. Panel B provides results of the hypothesis testing on the differences of average profits between the respective groups of investors. Profit figures are in euros. Results for hypothesis testing are p-values. (*), (**) and (***) denote significance at the 1%, 5% and 10% levels, respectively. 24

27 Table 5 Results from spectral decomposition Panel A: Short-term profits With Nokia Without Nokia FInv DIns DRet FInv DIns DRet Total Mean 1, , , , Median Stdev 4, , , , Foreign Dominated Mean (327.07) Median (150.97) Stdev 2, , , , Domestic Dominated Mean 1, , Median (17.68) Stdev 2, , Balanced Holdings Mean 3, , Median (159.28) Stdev 6, , ANOVA Kruskal-Wallis T-test Wilcoxon rank sum test With Nokia All Groups FInv - Local FInv- DIns FInv DRet DInt - DRet 0.06*** 0.46 Without Nokia All Groups FInv - Local FInv- DIns 0.09*** 0.19 FInv - DRet DInt - DRet 0.07*** 0.52 This table presents the results from spectral decomposition of trading profits into short-, medium- and longterm horizons. Time series are resolved into periodic components. The frequency of each component is the number of transactions required for that component to complete one cycle. For a given stock, the short-term profit (corresponding to intraday profit) is estimated as the summation of the co-spectrum between price change and inventory holdings series over periods of less than its number of transactions per day. Mediumterm profits are computed over the frequency ranges from average number of transactions per day to 20 times the number of transactions per day. Long-term profits include wavelengths that are longer than 20 times the number of transactions per day. Panel A represents summary results for short-term horizons, Panel B represents summary results for medium-term horizons, and Panel C represents summary results for long-term horizons. Hypothesis tests are included in each panel. Profit figures are in euros. Whenever possible, the summary statistics are calculated both with and without Nokia. Results for hypothesis testing are p-values. (*), (**) and (***) denote significance at the 1%, 5% and 10% levels, respectively. (continued on next page) 25

28 Table 5 (continued) Panel B: Medium-term profits With Nokia Without Nokia FInv DIns DRet FInv DIns DRet Total Mean Median Stdev 3, , , Foreign Dominated Mean 2, Median Stdev 6, Domestic Dominated Mean , Median Stdev , Balanced Holdings Mean Median Stdev ANOVA Kruskal-Wallis T-test Wilcoxon rank sum test With Nokia All Groups *** FInv - Local ** FInv - DIns ** FInv -DRet DInt - DRet Without Nokia All Groups ** FInv - Local ** FInv - DIns 0.04** 0.01** FInv -DRet *** DInt - DRet (continued on next page) 26

29 Table 5 (continued) Panel C: Long-term profits With Nokia Without Nokia FInv DIns DRet FInv DIns DRet Total Mean 1, Median Stdev 7, Foreign Dominated Mean 7, Median Stdev 16, Domestic Dominated Mean Median Stdev Balanced Holdings Mean Median Stdev ANOVA Kruskal-Wallis T-test Wilcoxon rank sum test With Nokia All Groups ** FInv - Local ** FInv - DIns ** FInv -DRet ** DInt - DRet Without Nokia All Groups ** FInv - Local ** FInv -DIns ** FInv -DRet ** DInt - DRet

30 Average Profits Per Transaction Profits (in EUR) 20, , , , , , , NOK EGR KONC RTEV POBR EGA SAMA MSB UPM AMA HUIF HEP OUTO KESK KEMR ORIB RRUK HART SONE FORT PAR Short-term Profits Per Transaction Profits (in EUR) 12, , , , , , , , , , NOK EGR KONC RTEV POBR EGA SAMA MSB UPM AMA HUIF HEP OUTO KESK KEMR ORIB RRUK HART SONE FORT PAR Medium-term Profits Per Transaction Profits (in EUR) 15, , , , , , , NOK EGR KONC RTEV POBR EGA SAMA MSB UPM AMA HUIF HEP OUTO KESK KEMR ORIB RRUK HART SONE FORT PAR Long-term Profits Per Transaction Profits (in EUR) 5, , , , , , , , , NOK EGR KONC RTEV POBR EGA SAMA MSB UPM AMA HUIF HEP OUTO KESK KEMR ORIB RRUK HART SONE FORT PAR Figure 1. Difference between domestic and foreign profits, by stock and horizon (with Nokia). The height of each bar is the difference between domestic and foreign per-market transaction profits, in euros, for each of the 21 stocks. 28

31 Average Profits Per Transaction Profits (in EUR) 15, , , , , EGR KONC RTEV POBR EGA SAMA MSB UPM AMA HUIF HEP OUTO KESK KEMR ORIB RRUK HART SONE FORT PAR Short-term Profits Per Transaction Profits (in EUR) 12, , , , , , , , , , EGR KONC RTEV POBR EGA SAMA MSB UPM AMA HUIF HEP OUTO KESK KEMR ORIB RRUK HART SONE FORT PAR Medium-term Profits Per Transaction Profits (in EUR) 14, , , , , , , , EGR KONC RTEV POBR EGA SAMA MSB UPM AMA HUIF HEP OUTO KESK KEMR ORIB RRUK HART SONE FORT PAR Long-term Profits Per Transaction Profits (in EUR) 2, , , , EGR KONC RTEV POBR EGA SAMA MSB UPM AMA HUIF HEP OUTO KESK KEMR ORIB RRUK HART SONE FORT PAR Figure 2. Difference between domestic and foreign profits, by stock and horizon (without Nokia). The height of each bar is the difference between domestic and foreign per market transaction profits, in euros, for each of the 20 stocks. 29