THE SMART MONEY EFFECT IN TWO MAJOR MUTUAL FUND EUROPEAN INDUSTRIES

THE SMART MONEY EFFECT IN TWO MAJOR MUTUAL FUND EUROPEAN INDUSTRIES Laura Andreu landreu@unizar.es Assistant Lecturer Department of Accounting and Finance. Universy of Zaragoza (Spain) Gran Vía, 2 50005, Zaragoza (Spain) Telephone: +0034 976 762308 Fax number: +0034 976 761769 Cristina Ortiz* cortiz@unizar.es Assistant Lecturer Department of Accounting and Finance. Universy of Zaragoza (Spain) Gran Vía, 2 50005, Zaragoza (Spain) Telephone: +0034 976 761000 Ext. 4652 Fax number: +0034 976 761769 José Luis Sarto jlsarto@unizar.es Professor of Finance Department of Accounting and Finance. Universy of Zaragoza (Spain) Gran Vía, 2 50005, Zaragoza (Spain) Telephone: +0034 976 762308 Fax number: +0034 976 761769 Luis Vicente lavicent@unizar.es Associate Professor Department of Accounting and Finance. Universy of Zaragoza (Spain) Gran Vía, 2 50005, Zaragoza (Spain) Telephone: +0034 976 761000 Ext. 4659 Fax number: +0034 976 761769 *corresponding author 1

THE SMART MONEY EFFECT IN TWO MAJOR MUTUAL FUND EUROPEAN INDUSTRIES This study compares investor abilies in two European countries wh relevant mutual fund industries: Germany and Spain. A complete dataset of domestic equy funds from each country was collected to test the smart money effect from an individual and an aggregate perspective. The results show ltle influence of previous fund flows on subsequent returns for German funds. In Spain, however, there is significant influence for investor timing abilies, though in selection abilies the influence is sometimes contrary. The findings of this study support the idea of the need of analyses on individual basis to complement aggregate portfolio analysis. Keywords: Investor abilies, smart money, fund flows JEL codes: G11, G23 2

THE SMART MONEY EFFECT IN TWO MAJOR MUTUAL FUND EUROPEAN INDUSTRIES 1. INTRODUCTION This paper further investigates whether flows into and out of equy funds have some predictive power over future returns in them. Detecting and analysing mutual fund investors abilies has been an issue of discussion in the lerature through time. Among the first pieces of research to be concerned wh the growth of mutual funds, s determinants and consequences is Gruber (1996). In his paper, he supports the idea of the rationaly of the investors given that they take advantage of some existing predictors of mutual fund return and, therefore, he finds a justification for the growth of mutual funds despe their inferior performance to index funds. Zheng (1999) confirms the results of Gruber (1996) and names the abily of investors to choose the moment to invest as smart money effect. These two seminal studies provide some conjectures about the source of the smart money effect, including momentum. Indeed, Sapp and Tiwari (2004) find that new cash flows do not subsequently perform better after controlling for Jegadeesh and Tman (1993) stock price momentum. In short, investors do not identify fund managers wh superior abily. Whereas the influence of return on investment decisions (Ippolo, 1992) 1 is clear; there is some controversy about the possibily of investors anticipating fund returns. Recent studies arise against the conclusion of Zheng (1999) on fund selection investor abilies. Ke, Ng and Wang (2005) not only find evidence of the absence of smart money effect but they conclude that this is due to investor across-style investing. 1 Other papers that also contrast and confirm this result are: Smh (1978), Gruber (1996), Sirri and Tufano (1998) and Guercio and Tkac (2002). 3

Investors try to chase hot styles and this search adversely affects their performance. Braverman, Kandel and Wohl (2007) conclude that mutual fund investors are bad performers. In order to further investigate this bad performance, they propose rational market explanations (based on time-varying risk premiums) and behavioural explanations (based on investor sentiment). Our study adds to the lerature empirical evidence on two major European mutual fund industries. While above referred pieces of research focus on the U.S. market, very ltle attention has been paid to the European market to date. Keswani and Stolin (2008) examine, in a recent paper, U.K. data and provide evidence of smart money, which is specifically driven by fund purchases of both individuals and instutions. In Spain, Ciriaco et al. (2003) observe this phenomenon wh a different methodology. The two countries analysed, Germany and Spain, are representative of the European mutual fund industry given that together they manage almost 20% of the total net assets. Germany represents a mature market at the beginning of the sample, whereas Spain experiences a deep evolution during the sample period. According to the results, German investors show ltle investment abilies, both in timing and selection approaches. On the other hand, Spanish fund flows seem to have influence on subsequent results, not only getting superior performance but also inferior performance. This influence is especially discouraging in the cross-section analysis, given that larger investment flows are followed by inferior returns. Tradionally, aggregate data has been analysed to draw conclusions about investor performance. However, this approach may lead to some biases. Firstly, the consideration of timing and selection abilies together, and secondly, most of previous studies consider long periods of time. In this sense, only recently, Friesen and Sapp (2007) present the advantages of the analysis on an individual fund basis. According to 4

them, there are two main restrictions when working on an aggregated level. First, the potential information lost by adding posive and negative fund flows, and second, the increase in the possibilies of the study in the sense that you can make differences among various fund clienteles. This paper aims at contributing wh further detail in the analysis of investor performance. Therefore, we first analyse investor abilies through time wh individual time-series regressions and we then test investor selection abilies wh a cross-section analysis. In addion, we evaluate whether the computing method is a cause of different conclusions, including the aggregate portfolio examination. Both approaches, on an individual or on an aggregate basis, offer a different complementary perspective. From the aggregate analysis; is shown that German funds perform better than Spanish funds wh regard to the market benchmark. It is also shown that the results only provide evidence of investors abily to select funds to Spanish investors. However, this abily is not significant for the whole sample in the period analysed due to the wide range of results found in the individual analysis. The rest of the paper is organised as follows. The next section describes the mutual fund data sample and the methodology. Section 3 examines the relation between flow and subsequent return on an individual basis. Section 4 further examines the performance of the new money portfolios. A final section concludes. 2. DATA AND METHODOLOGY 2.1 Data description This study is focused on mutual funds due to their importance in financial markets as investment vehicle for individual investors; specifically we analyse two major 5

European mutual fund industries: Germany and Spain. At the end of 2005, Spain and Germany were ranked in the sixth and third place in terms of assets under management by the European Fund and Asset Management Association (EFAMA) just behind Luxembourg and France in the case of Germany. In 2005 the European market share held by Spain and Germany was about 4 per cent and 15 per cent, respectively. The analysis of different mutual fund industries demands the consideration of the special features in each market in order to compare them. In this sense, is important to bear in mind that the Spanish industry only considers UCITS (Undertaking for Collective Investment in Transferable Securies) whereas the German industry also includes Non-UCITS or Spezialfonds, which are very important in terms of asset under management (53% of the total industry). 2 At the end of the sample period, in 2005, 545 billion were managed by approximately 2,450 German UCITS (Publikumsfonds), in contrast wh 244 billion managed by 2,600 Spanish funds. These figures show that the average assets managed by each Spanish fund are one of the lowest in the European Union, reporting a market map where a small number of large funds coexist wh a vast majory of small funds. Under this circumstance, the most important Spanish financial groups present a dominant role in the market. A sample of quarterly net returns, net flows and total net assets of all German and Spanish mutual funds listed in the category of domestic equy funds from September 1995 to December 2005 is analysed in the study. This sample, free of survivorship bias, consists of 179 and 114 Spanish equy mutual funds and German Aktienfonds. The dataset has been provided by Spanish Securies and Exchange Commission (CNMV) and by Bundesverband Investment und Asset Management e.v. (BVI), respectively. 2 Non-UCITS funds can not been compared to UCITS funds due to their specific characteristics and constraints. 6

Table 1: Cross-section descriptive magnudes This table shows descriptive magnudes of the sample in two periods: December 1995 and December 2004. It reports the number of listed funds, the total net assets managed by the funds, the average net money flow of the last quarter of the year; and the average annual net return of the following year. GERMANY SPAIN Dec-95 Dec-04 Dec-95 Dec-04 Number of funds 74 70 86 153 Total net assets (euro millions) 27717.36 25488.00 1398.11 10473.15 Average net flow (last quarter, euro thousands) 10294.31 14975.49-385.20-225.27 Average net return (following year) 24.44% 25.84% 31.02% 16.82% Table 2: Addional sample descriptive statistics This table reports the sample descriptive statistics. Panel A reports 114 German domestic equy funds and Panel B reports 179 Spanish domestic equy funds. Mean Std. deviation First quartile Median Third quartile Panel A: Germany Number of funds per quarter 84 8 77 84 92 Number of funds wh posive cash flow 44 12 38 42 50 Number of funds wh negative cash flow 39 11 31 37 47 Total net assets (millions of euros) 32295 11859 24668 29626 36537 Quarterly Net cash flow (thousands of euros) 214808 1015515-231845 349759 761779 Panel B: Spain Number of funds per quarter 138 28 118 153 157 Number of funds wh posive cash flow 60 26 41 57 84 Number of funds wh negative cash flow 78 43 38 80 111 Total net assets (millions of euros) 9572 4261 7192 9556 13006 Quarterly Net cash flow (thousands of euros) 105086 550693-198914 48563 240588 Tables 1 and 2 provide descriptive information about the sample being analysed in two senses. Table 1 shows the suation of the industry of the category domestic equy funds in each country in two cross-section moments. The results confirm how the German industry was already consolidated in 1995; however, Spain experiences a great 7

increase, both in the number of funds and in the total net assets. Table 2 shows some descriptive statistics of the sample for the whole period. We highlight the standard deviation in the number of funds in the Spanish industry given that the sample period is an expansive moment. 2.2 Measurement of money flows In order to test whether investor decisions entail a superior fund performance, we first calculate the flow measures. We then evaluate the fund performance as a function of the money flow into each fund. The first of these measures is net money flow (MF ) into and out of a fund; which is defined as the quarterly change in total net assets (TNA) net of fund returns (R ) during that period. On a quarterly basis, the net money flow to fund i during quarter t is measured as follows: MF = TNA TNAi, t 1(1 + R ) (1) The calculation of money flows requires making an assumption about the timing of these flows. As we can not know the exact moment of investment, Equation 1 assumes that the new money invested or whdrawn from the fund, as well as the reinvestment of dividends, occur at the end of period we compute the flows. This assumption is also contended in the studies of Gruber (1996), Ke, Ng and Wang (2005), and Zheng (1999). The latter compares his results wh the assumption of investment at the beginning of a quarter obtaining similar conclusions. A second measure of flow considered is percentage money flow (PMF ). It is calculated as the ratio between the net money flow and the amount of TNA at the beginning of the quarter of reference. This measure accounts for a possible size effect on the results and is shown following: 8

i, t 1(1 + R ) TNA TNA PMF = (2) TNA i, t 1 The central aim whin the studies of the so-called smart money effect is finding out whether these flows are able to earn subsequently superior returns. In order to investigate this effect, the usual practice is evaluating performance of portfolios based on investor money flows. In addion to that approach, this paper provides further evidence by obtaining results on an individual fund basis. A major concern about the individual fund analysis is detecting whether investors present superior timing abilies. We propose an addional approach to measure investor abilies, where funds aim at not only obtaining posive returns, but a superior performance than the average fund 3. The interest is, therefore, not focused on the value of the flow but on the relative posion wh the rest of the funds. We consider that a more accurate analysis should consider the relationship wh the average return and money flow of all existing funds on each quarter. According to above comments, the hypothesis will be tested wh the following time-series model 4 : R R t 1 1 ( MF 1 MF t 1 ) ε = α + β + (3) where the annual return a fund obtains above the mean ( R ) each quarter is a function of the superior flow of the previous quarter wh respect to the average money flow ( MF t 1 ) for all the funds. A posive and significant β 1 parameter would indicate that investors have time abilies, that is to say, that they are able to choose the best moment to invest in a concrete fund. t 3 In a context of bear markets, getting negative returns but above their peers, may be considered a good performance. 4 In the time-series analysis, a minimum of 30 observations is usually required in financial lerature. 9

Similarly, Equation 4 represents the model that includes the percentage money flow as explanatory variable. R R t 1 1 ( PMF 1 PMF t 1 ) ε = α + β + (4) Investors may have further abilies when taking investment decisions. Another major concern on this study is examining their selection abilies. That is whether in a given moment, they do the correct choice among the larger and larger number of listed funds. For that purpose, we carry out a cross-section individual analysis. R R t 2 2 ( MF MF t ) ε = α + β + 1 1 (5) On each quarter, β 2 establishes the relationship among the difference of return and money flows wh the average value. In this case, a posive and significant β 2 parameter would be a sign of selection abilies whin investors. Similarly when percentage money flows are computed: R R t 2 2 ( PMF 1 PMF t 1 ) ε = α + β + (6) 2.3 Portfolio formation In order to compare the results of this work wh the pioneering studies, we also test the smart money effect wh aggregate data. New portfolios are formed based on money flow values and, subsequently, the annual excess return of each of the different money flow portfolios is computed. The reference portfolio includes in the same proportion all existing funds during the holding period examined (equally-weighted portfolio). At the beginning of each quarter funds are separated into two groups, those that experience posive net money flows the previous quarter, and a second group that includes funds that present negative money flow the previous quarter. Following the procedure explained above for the individual analysis, we again consider the importance 10

of proposing portfolios that are formed wh a crerion referred to the average instead of to zero. Therefore, we create two addional groups wh posive or negative relative money flows, that is, whether the fund flow is above or below the average flow on the previous quarter. The portfolios being evaluated are described following: Portfolio 1: Equally-weighted portfolio Portfolio 2: All existing funds weighted by total net assets Portfolio 3: Equally weighted in all net money inflow funds (posive money flow) Portfolio 4: Equally weighted in all net money outflow funds (negative money flow) Portfolio 5: Equally weighted in all relative money inflow funds (money flow above the average fund flow) Portfolio 6: Equally weighted in all relative money outflow funds (money flow below the average fund flow) Portfolio 7: All net money inflow funds weighted by total net assets Portfolio 8: All net money outflow funds weighted by total net assets Portfolio 9: All net money inflow funds weighted by the amount of inflow Portfolio 10: All net money outflow funds weighted by the amount of outflow Once the money flow portfolios have been established, we develop the performance calculation method for these portfolios. The performance is calculated for a holding period of one year wh two measures, the excess return over the market and the excess return from the single factor model (CAPM): R R ft 1 i 1 i ( Rmt R ft ) ε = α + β + (7) 11

where R is the rate of return of fund i in year t, R ft is the risk-free interest rate in year t, R mt is the rate of market return in year t, and α measures the excess return of the fund. Wh regard to risk-free interest rate, Fibor and Mibor are included for Germany and Spain, respectively. Dax index and Ibex-35 index are selected as benchmarks of reference for the two markets. The calculation of performance follows the fund regression approach as described in Zheng (1999). We estimate the one-factor α for each fund and we then calculate the performance for the portfolios cross-sectionally period by period by averaging the α estimates for individual funds. This approach, as opposed to the portfolio regression approach, is able to pick up time-varying levels of risk. Notwhstanding, suffers from look-ahead bias given that certain number of observations is required to obtain the α estimates. The comparison of different performance of the new money portfolios will provide information about investor abilies. 3. INDIVIDUAL FUND ANALYSIS 3.1 Time-series approach Empirical analyses are carried out following Equations 3 and 4 for the two measures of flow considered (money flows, MF; and percentage money flows, PMF). Under the hypothesis that investors are able to earn superior returns, finding posive significant β 1 parameters would confirm the null hypothesis; whereas finding negative significant β 1 parameters would mean contrary timing abilies. 12

Table 3: Timing abily by country The restriction of a minimum number of 30 observations reduces the inial sample. 63 and 88 German and Spanish funds, respectively, are included in the time-series analysis. Numbers in parentheses indicate the percentage of funds wh significance at a 5% level. The t-statistic has been obtained using the Newey and West (1987) covariance estimator that is consistent in the presence of both heteroskedasticy and autocorrelation of unknown form. The average β 1 is calculated for every β 1 that presents a given sign. Numbers in parentheses following indicate the average β 1 only for significant funds of a given sign. Posive β 1 Negative β 1 GERMANY MF - MF 30 (10.00%) Average β 1 = 3.44E-07 (6.18E-07) 33 (12.12%) Average β 1 =-5.27E-07 (-9.21E-07) PMF - PMF 20 (20.00%) Average β 1 = 8.73E-02 (1.77E-01) 43 (23.26%) Average β 1 =-1.18E-01 (-2.24E-01) SPAIN MF - MF 49 (22.45%) Average β 1 = 3.72E-06 (7.45E-06) 39 (17.95%) Average β 1 =-3.30E-06 (-5.94E-06) PMF - PMF 37 (32.43%) Average β 1 = 1.14E-01 (1.63E-01) 51 (21.57%) Average β 1 =-9.48E-02 (-1.59E-01) Table 3 reports the number of funds in each country whose cash flows lead to superior (left-side) or inferior (right-side) returns. In view of the results, in Germany, for both measures of flow, the number of significant parameters is slightly higher for negative β 1 implying ltle abilies of investors to choose the best moment to invest in a given fund. Contrarily, in Spain, seems that funds that receive flows above the mean are also able to get return above the mean. Addionally, the parameter β 1 is higher in Spanish significant parameters, that is, the number of smart flows into funds is higher and wh a higher impact. Even though in both countries there is a high percentage of funds showing non-significant parameters what leads to non-clear conclusions. 13

3.2 Cross-section approach Another goal set in the introduction is detecting investor abilies to correctly choose among all existing funds at any given moment. To further explore the investors abily to pick mutual funds, we provide a cross-section analysis. Whin this framework, the parameters β 2 of Equation 5 and 6 test whether investors choose smartly among all available listed funds. Table 4: Selection abily by country 37 quarters are analysed. Each quarterly regression includes all existing funds at that time. Numbers in parentheses indicate significance at a 5% level. The t-statistic has been obtained using the Newey and West (1987) covariance estimator that is consistent in the presence of both heteroskedasticy and autocorrelation of unknown form.the average β 2 is calculated for every β 2 that presents a given sign. The average β 2 is calculated for every β 2 that presents a given sign. Numbers in parentheses following indicate the average β 2 only for significant funds of a given sign. Posive β 2 Negative β 2 GERMANY MF - MF 18 (11.11%) Average β 2 = 5.22E-08 (7.36E-08) 19 (15.79%) Average β 2 =-2.25E-07 (-2.25E-07) PMF - PMF 22 (18.18%) Average β 2 = 1.04E-01 (2.12E-01) 15 (6.67%) Average β 2 =-6.44E-02 (-7.78E-02) SPAIN MF - MF 25 (20.00%) Average β 2 = 9.85E-07 (1.46E-06) 12 (50.00%) Average β 2 =-9.27E-07 (-1.47E-06) PMF - PMF 25 (36.00%) Average β 2 = 8.85E-02 (1.33E-01) 12 (25.00%) Average β 2 =-5.27E-02 (-8.08E-02) Spain shows, according to the results of Table 4, a greater significance of the parameters than Germany. However, this significance occurs for posive and negative parameters, showing a wide range of results for this analysis. In six of the 37 quarters of the sample, Spanish funds receiving money above the average flow obtain the following year bad returns even below their peers average. Similarly to the timing abilies analysis, when the posive β 2 are obtained, they are higher for Spanish funds. Looking 14

in depth at individual fund regressions, is worth noting that most of the posive significant parameters are concentrated on the last quarters of the sample. In this analysis, we find again a lack of significant results. 4. NEW MONEY PORTFOLIOS EXAMINATION Recent research supports the idea of the validy of individual fund approach; however, we provide results on an aggregate level for comparative purposes. Table 5 reports the performance results of the portfolios based on money flows wh the method of buy and hold the funds during the following year. 5 The holding period varies across previous studies, Gruber (1996) considers inially quarterly periods and enlarges the results for one and three years; Zheng (1999) calculates the performance of the next quarter after the formation of the portfolios; and Ke, Ng and Wang (2005) prefer longer holding periods of one, two or three years. Table 5 also reports the average annual excess returns over the market and the results of the average α of the tradional one-factor model. Table 5: Performance of portfolios The excess return is calculated as R - R mt, where R is the annual return of the fund i at each quarter and R mt is the annual return of the benchmark at each quarter. Dax and Ibex- 35 are considered, respectively, for German and Spanish markets. The t-statistics in parentheses test whether the performance of a particular portfolio is different from the average mutual fund. The t-statistics in brackets test whether the performance of a particular portfolio is different from the market. Alpha 1 is the average of the individual 1 fund alphas calculated from the time series regression R R ft = α i + βi ( Rmt R ft ) + ε 1. R ft is the risk-free interest rate, Fibor and Mibor for the German and the Spanish sample. The t-statistics in parentheses test whether alpha 1 of each portfolio is significantly 5 Portfolios are adjusted every quarter. 15

different from the average mutual fund. The t-statistics in brackets test whether alpha 1 of a particular portfolio is significantly different from zero. For Panel B, the t-statistics in parentheses test whether the portfolio difference is significantly different from zero. Performance measures are expressed on an annual basis. Panel A Germany Spain Average Std. deviation Average Std. deviation Net return Excess return Alpha 1 Net return Excess return Alpha 1 Portfolio 1 0.1135 0.2960-0.0102-0.0030 0.1060 0.2175-0.0368-0.0115 (N/A) (N/A) (N/A) (N/A) [-1,4919] [-0,7258] [-2,8230]** [-3,1436]** Portfolio 2 0.1115 0.2934-0.0122-0.0042 0.1077 0.2178-0.0355-0.0079 (-0,2337) (-0,2278) (-0,0698) (-0,8174) [-2,2486]* [-1,2057] [-2,9003] [-3,1613]** Portfolio 3 0.1155 0.2966-0.0082-0.0020 0.1155 0.2211-0.0272-0.0046 (-0,1992) (-0,1698) (-0,5140) (-1,1465) [-1,1252] [-0,4866] [-2,0576]* [-0,9744] Portfolio 4 0.1096 0.2914-0.0141-0.0056 0.1011 0.2174-0.0417-0.0089 (-0,3820) (-0,4088) (-0,2651) (-0,5054) [-1,8457] [-1,1370] [-3,1779]** [-2,6062]* Portfolio 5 0.1131 0.2995-0.0076-0.0028 0.1144 0.2251-0.0178-0.0076 (-0,0414) (-0,0253) (-0,4686) (-0,6750) [-1,5094] [-0,6768] [-2,2903]* [-1,7005] Portfolio 6 0.1147 0.2939-0.0072-0.0018 0.0994 0.2164-0.0302-0.0135 (-0,1166) (-0,1911) (-0,3524) (-0,4154) [-1,2735] [-0,4041] [-3,2569]** [-4,0156]** Portfolio 7 0.1093 0.2937-0.0144-0.0040 0.1184 0.2252-0.0276-0.0033 (-0,4848) (-0,1960) (-0,5140) (-1,8150) [-2,6226]* [-1,1984] [-2,2506]* [-1,2504] Portfolio 8 0.1135 0.2885-0.0102-0.0035 0.1046 0.2172-0.0374-0.0061 (-0,0043) (-0,9262) (-0,0330) (-1,1645) [-1,5733] [-0,8797] [-2,9369]** [-2,1078]* Portfolio 9 0.0990 0.3033-0.0247-0.0025 0.1172 0.2241-0.0302-0.0042 (-1,1152) (-0,0854) (-0,3678) (-1,6085) [-2,2301]* [-0,7784] [-2,4289]* [-1,5814] Portfolio 10 0.1131 0.2824-0.0106-0.0041 0.1105 0.2264-0.0317-0.0064 (-0,0351) (-0,2186) (-0,2912) (-1,1288) [-1,1913] [-1,2708] [-2,7262]** [-2,4439]* Panel B Germany Spain Excess return Alpha 1 Excess return Alpha 1 Portfolio 3 - Portfolio 4 0.0059 0.0036 0.0145 0.0043 (-0.5590) (-0.5628) (-0.7753) (-0.7393) Portfolio 5 - Portfolio 6-0.0004-0.0010 0.0125 0.0059 (-0.1555) (-0.1653) (-0.8244) (-1.0650) Portfolio 7 - Portfolio 8-0.0042-0.0005 0.0098 0.0028 (-0.4944) (-0.0973) (-0.5544) (-0.7078) Portfolio 9 - Portfolio 10-0.0141 0.0016 0.0016 0.0022 (-0.9944) (-0.3464) (-0.0902) (-0.5875) * denotes significance at 5% level ** denotes significance at 1% level 16

In aggregate, mutual fund allocation decisions are not smart given that, on average, portfolios underperform the stock market benchmark, both in Germany and in Spain. The first difference between the two countries is that, overall, German funds perform better, though they present higher levels of volatily. In Spain, the excess return of the portfolios is negative for the testing period and significantly different from zero in mostly all the portfolios. Another worth being comment is about the results of the second portfolio, the TNA-weighted portfolio. Whereas German funds obtain even worst results after weighting by TNA, Spanish funds slightly improve their results after weighting by TNA. It seems that Spanish larger funds obtain better results than smaller funds. Regarding the performance of posive and negative portfolios, we observe interesting differences between Germany and Spain. Posive portfolios are those that include funds wh posive (portfolios 3, 7 and 9) or above the mean (portfolio 5) flows, and negative portfolios are those formed wh funds wh negative (portfolios 4, 8 and 10) or below the mean (portfolio 6) flows. In Spain, all posive portfolios have superior excess return and alpha than negative portfolios. This result is not maintained for the German sample of funds and posive portfolios get only superior performance in equally weighted portfolios separated by inflows and outflows. Addionally, in Spain, alpha 1 is only significantly different from zero in negative portfolios. Table 5, Panel B, shows the results of the long-short strategies of buying the posive money flow portfolios and selling their corresponding negative money flow portfolios. Evidence in favour of investors abilies to select funds would be denoted if the excess returns or alphas of these strategies were posive, but this is not always valid for the German investors. In the Spanish case, even though the excess return and alphas 17

are posive, they are not significant in any of the long-short strategies of the different portfolios. If we compare the aggregate money flow portfolio analysis wh the individual analysis of the previous section we could affirm that working in aggregate terms makes up the complexy of the phenomenon. The non-significant results in both countries are a consequence of the various results when we analyse smart money by funds or by a time period. 5. CONCLUSION This paper aims at providing empirical analysis on the smart money effect in two major European fund industries: Germany and Spain. By smart money, previous lerature refers to investors abilies when taking investment decisions. In this sense, funds that received superior fund flows are supposed to subsequently earn superior returns. Analyses on individual and aggregate fund level are carried out for two samples of domestic equy funds in Germany and Spain from 1995 to 2005. From the results of the time-series regression, even wh lack of significance in some funds, we can infer, in global terms, that Spanish investors show superior timing abilies, on one hand due to higher number of funds whose returns match wh the smart money hypothesis; on the other hand the magnude of this effect is also higher. According to the results of the cross-section regression, the levels of significance of the results are also higher for Spanish funds; however the influence of fund flows on subsequent returns is in both senses, they can get eher superior or inferior returns on the different quarters analysed. 18

The comparison of the two methodologies arises interesting results, analysing new money portfolios provide a global view of the phenomenon. If we only focused on that, we would have concluded that even not significant the smart money effect is superior on Spanish funds. However, analysing funds individually have also advantages. First, we differentiate timing and selection abilies; and second, we show that the smart money effect detected in the aggregate portfolio analysis is not significant because of the contrary subsequent performance of funds wh superior flows. 19

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