Academia. Revista Latinoamericana de Administración ISSN: 1012-8255 [email protected] Consejo Latinoamericano de Escuelas de Administración Organismo Internacional

Fernández, Viviana Asymmetrical individual return herding Academia. Revista Latinoamericana de Administración, núm. 45, 2010, pp. 20-39 Consejo Latinoamericano de Escuelas de Administración Bogotá, Organismo Internacional

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Asymmetrical individual return herding* Viviana Fernández Pontificia Universidad Católica de Chile Santiago de Chile, Chile [email protected] Efecto rebaño asimétrico en retornos individuales

ABSTRACT

Herding takes place when individuals tend to rely on the consensus opinion and past trades rather than on fundamental asset pricing. In this study, we focus on 101 Chilean stocks over the period 1990-2009. Contrary to empirical evidence for the United States and international markets (e.g., Christie & Huang, 1995; Demirer & Kutan, 2006; Gleason, Lee & Mathur 2003; Gleason, Mathur & Peterson, 2004), we conclude that herding in individual stocks is more likely during extreme down markets, after controlling for trading volume. Our results also suggest an inverse relationship between volatility and trading volume, which contradicts the one-factor mixture-of-distribution hypothesis (MDH). Such an inverse association appears statistically more significant when choosing more extreme return thresholds.

Key words: Herding, market thinness.

* Funding from Fondecyt Grant No. 1090005 is gratefully acknowledged. The author wishes to thank two anonymous referees and participants at the BALAS 2010 Conference, held in Barcelona, Spain, for their helpful comments. The usual disclaimer applies.

20 Academia, Revista latinoamericana de administración, 45, 2010 Fe r n á n d e z

RESUMEN

El efecto manada ocurre cuando los individuos privilegian la opinión de consenso y las transacciones pasadas por sobre los fundamentos de los precios. En este estudio analizamos 101 acciones chilenas transadas durante el período1990 a 2009. Contrariamente a la evidencia empírica para los Estados Unidos y los mercados internacionales (véase, Christie y Huang, 1995; Demirer y Kutan, 2006; Gleason, Lee & Mathur 2003; Gleason, Mathur & Peterson, 2004), el efecto manada es más probable cuando el mercado accionario se encuentra deprimido, una vez que se considera el volumen transado. Nuestros resultados también sugieren una relación inversa entre volatilidad y volumen, lo que contradice la hipótesis de distribución de un factor (MDH). Esta asociación resulta particularmente significativa cuando se escogen umbrales más extremos para los retornos.

Palabras clave: efecto manada, mercados poco profundos.

1. Introduction

Herd formation has emerged recently as an alternative to the efficient markets hypothesis, which assumes that investors have fully rational expectations about future prices and that current prices reflect instantaneously all of the new incoming information. Herding is defined as the intent of investors to mimic the behavior of other investors over a period of time, so that investors alter their own beliefs to match those expressed publicly by others (e.g. Gleason, Mathur & Peterson, 2004). An important implication of herd formation is that economic agents tend to rely on the consensus opinion and past trades rather than on fundamental asset pricing. Consequently, herding could exacerbate asset returns volatility and destabilize financial markets, particularly under stressful conditions. From a theoretical viewpoint, the existence of herding can be rationalized in the context of informational advantages enjoyed by some investors (e.g., Hirshleifer, Subrahmanyam & Titman, 1994), informational externalities or cascades, which can influence capital structure, R&D investment or merger decisions (e.g., Devenow & Welch, 1996), panic episodes in the banking industry (e.g., Devenow & Welch, 1996), signaling by institutional investors (e.g., Trueman, 1988), anxiety experienced by investors due to conflicting opinions (e.g., Stickel, 1990; Olsen, 1996), and incentives to conceal low ability by mimicking the decision-making of higher ability managers (e.g., Zwiebel, 1995). Recent empirical evidence on herding is mixed. Olsen (1996) and Welch (2000) documented the existence of herding among analysts. Chang, Cheng and Khorana (2000) analyzed the US, Hong Kong, Japan, South Korea, and Taiwan markets, and they found no evidence of herding in the US and Hong Kong, and only partial evidence of it in Japan. However, Chang et al. reported significant evidence of herding for the emerging markets of South Korea and Taiwan. Kim and Wei (2002) studied the 1997 Korean currency crisis and found that non-residents tend

Consejo latinoamericano de escuelas de administración, Cladea 21 As y m m e t r i c a l i n d i v i d u a l r e t u r n h e r d i n g to herd more than residents. Additional evidence of herding in emerging markets is presented by Olivares (2008). He found that Chilean pension funds tended to exhibit similar asset allocations. Such behavior would be explained by the minimum-guaranteed-return regulation that pension funds had to meet. Other studies do not find support for herding formation. Indeed, Christie and Huang’s (1995) study of AMEX and NYSE firms, Gleason, Lee and Mathur’s (2003) analysis of commodity futures, Gleason et al. (2004) work on Exchange Traded Funds (EFTs), and Demirer and Kutan’s (2006) analysis of the Chinese market lend support to the rational asset pricing models, which would predict an increase in volatility during extreme market conditions. The aim of this study is to test for the existence of herding in the Chilean stock market. In particular, we analyze 101 Chilean stocks over the period 1990-2009 using a weekly frequency. The methodology of our study is similar to that of Christie and Huang (1995) and follow-ups, such as Gleason et al. (2003), Gleason et al. (2004) and Demirer and Kutan (2006) However, we add a twist by controlling for market liquidity - approximated by trading volume, given the thinness of the Chilean stock market. Indeed, the most actively traded stocks on the Santiago Exchange Trade, which are gathered in the IPSA (índice de Precios Selectivo de Acciones or Selective Stock Price Index), comprises only 40 stocks, out of which only about half is traded daily. By controlling for market liquidity the authors are also able to draw conclusions as to the relationship between volatility and trading volume. This subject has been the focus of several studies in finance (see, for instance, Fong, 2003; Ané & Ureche-Rangau, 2008; Fernandez, 2009 for recent insight). A traditional viewpoint is that return volatility and trading volume should be positively associated because they are driven by the same latent variable, which gauges the arrival of information relevant to the price-formation process. This perspective was first formalized by Clark’s (1973) mixture-of-distribution hypothesis (MDH). Our main findings can be summarized as follows. There exists herding behavior in individual stocks, but this is more likely to occur during extreme down markets, after controlling for trading volume. During extreme up markets, by contrast, the behavior of individual stocks return may be congruent with rational asset pricing models. Such an asymmetry may be rationalized by the major role played by institutional investors in the Chilean stock market. Moreover, we find an inverse relation between volatility and trading volume, which contradicts the MDH. More specifically, we conclude that it is more likely to observe a stronger feedback effect from trading volume to volatility under more extreme markets. These findings are subject to how market volatility is gauged, how an extreme market is defined, and what estimation technique one utilizes. The contribution of our study is twofold. Firstly, to our knowledge, this is the first study of its kind for a Latin American country. A key implication of our empirical results is that institutional investors may determine the stock market behavior. Secondly, related literature in this area has neglected the impact of trading volume on return volatility (e.g., Christie & Huang, 1995 and follow-ups). Such an omission could lead to erroneous conclusions when testing for rational against herding behavior.

22 Academia, Revista latinoamericana de administración, 45, 2010 Fe r n á n d e z

This paper is organized as follows. Section 2 presents the statistical tools utilized to test for the existence of herding behavior. Section 3 describes the dataset and discusses the empirical findings. Section 4 concludes by summarizing the main findings of this study.

2. Methodology

Following Christie and Huang’s (1995) methodology, we utilize the cross-sectional standard deviation and the mean absolute deviation of returns at time t. The former is given by

n ()rr- 2 å jt (1) s = j=1 t = 1,..,T t n -1

whereas the latter, a more robust measure of variability in the presence of outlying observations, can be expressed as:

n ||rr- å jt (2) s * = j=1 t = 1,..,T t n -1

where n is the number of sampled stocks at time t. In addition to the above two volatility measures, we fit a GARCH (1, 1) model to the cross-sectional average return series, rt , t = 1, ..., T, and obtain its estimated volatility:

r =+ 2 =+2 + b 2 (3) tttt--1 t 1

ee(|P),==0 (|P2 )  2 where , tt--1 tt 1 t , and Pt - 1 is the information set available at time (t - 1). We let

ˆ ˆ 2 ˆ 2 (4) S ,    t 1  S ,( 1) GARCH t  GARCH t The estimated regression models are a modified version of Christie and Huang’s, in which we also control for market volume:

sd=+bl ++bgdvU ln()+  tl11t 11Ut tt1 (5a)

sd* =+bl ++bgdvU ln()+  (5b) tl22t 22Ut tt2

l U sdGarcHt, =+b33lt++bg33Utdvln()tt+ 3 (5c)

Consejo latinoamericano de escuelas de administración, Cladea 23 As y m m e t r i c a l i n d i v i d u a l r e t u r n h e r d i n g

L Dt = 1 if the return on the aggregate market portfolio lies in the lower tail of the return u distribution at time t, and 0 otherwise; Dt = 1 if the return on the aggregate market portfolio lies in the upper tail of the return distribution at time t, and 0 otherwise. Vt is the average market volume at time t, and its corresponding coefficient, gi, i = 1, 2, 3, represents a semi-elasticity. Herd formation exists when the coefficients associated with extreme down and up markets, bL and bU, respectively, are negative and statistically significant. Intuitively, if economic agents herd when the market return is located in either tail of the distribution, volatility will be lower because they follow the market consensus. The reason for controlling for trading volume is that the Chilean stock market is relatively thin, as mentioned earlier. Our data source, which is described next, shows that only a few stocks were traded on a regular basis.

3. Empirical results: testing for herd formatioN

3.1. The data

We focus on 101 Chilean stocks traded on the Santiago Stock Exchange over the period 1990- 2009, whose prices are recorded at a weekly frequency, providing 1,072 observations of stock returns and trading volume. The data source is Economatica, a subscription-based service that includes information on publicly traded companies in the United States, Brazil, Argentina, Chile, Mexico, Peru, Colombia, and Venezuela (Economatica, 2010). The reason for relying on weekly rather than on daily returns is to circumvent, to some extent, the infrequent transactions of a considerable number of stocks, particularly those excluded from the Selective Stock Price Index, IPSA. As stated in the Introduction, only about half the stocks of the IPSA, which consists of the most liquid stocks traded on the Santiago Stock Exchange, exhibits daily transactions. Nevertheless, despite considering a weekly frequency, our dataset demanded some cleaning. Indeed, our final sample was obtained after eliminating highly illiquid stocks and stocks with no price information1. The sampled stocks started to be traded at different points in time. For instance, at the beginning of the sample period, in January 1989, about 35 stocks were traded. As Figure 1 shows, there was an increasing trend in the number of stocks traded between 1989 and 1997. From 1988 onwards, the number of stock traded weekly ranged between 75 and 95 approximately. A detailed description of the sampled stocks is given in the Appendix. Table 1 provides some information on weekly statistics of returns and number of trading stocks for the whole sample and for some specific economic sectors defined by the Standard Industrial Classification of allE conomic Activities (ISIC). For instance, the Agriculture, Mining & Manufacturing group corresponds with the 111 through 390 ISIC codes.

1 The total number of stocks reported for Chile in the Economatica database is around 300, including active and cancelled stocks. About 2/3 of these stocks include ones with only a few recorded transactions and/or missing price data. Such stocks were disregarded from the sample because they did not add much extra information. Therefore, our sample comprises IPSA stocks and some less liquid ones.

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figure 1 Sampled traded stocks per week: January 1989-July 2009.

120

100

80

stocks

60

traded

of

40

Number

20

0

-89 -90 -91 -92 -93 -94 -95 -96 -97 -98 -99 -00 -01 -02 -03 -04 -05 -06 -07 -08 -09

Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan

Date

Source: author’s own elaboration.

Table 1 Sample summary statistics: January 1990-July 2009. Economic sector Indicator Mean S.E Minimum Maximum All No. trading stocks 75 13.81 33 97 Average returns 0.004 0.023 -0.148 0.109 No. trading stocks 51 8.00 26 64 All but Financial sector Average returns 0.004 0.024 -0.154 0.132 Agriculture, Mining No. trading stocks 26 4.83 13 36 & Manufacturing Average returns 0.004 0.024 -0.153 0.116 Electrical Energy No. trading stocks 13 1.61 5 15 & Construction Average returns 0.005 0.029 -0.140 0.183 Retail, Transportation No. trading stocks 36 8.80 13 49 & Financial services Average returns 0.005 0.024 -0.147 0.145 Note: economic sectors are grouped according to Standard Industrial Classification of all Economic Activities (ISIC).

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When taking all of the economic sectors, the average number of trading stocks per week was 75, with a minimum and a maximum of 33 and 97 per week, respectively. The mean and standard deviation of the average weekly returns did not vary much across economic sectors. Indeed, the differences in the return distributions arose mostly at the extremes. For instance, the difference between the maximum and the minimum was greatest for the Electrical Energy & Construction group. Table 2 presents some statistics for the alternative volatility measures utilized in this * study. As we see, S and SGARCH are much more conservative estimates of return volatility than S, particularly so SGARCH. The economic groups that tended to exhibit more dispersion in their volatility estimates were Agriculture, Mining & Manufacturing, and Electrical Energy & Construction. This is not surprising because, according to Table 1, there were fewer trading stocks in these two categories, especially in the latter.

Table 2 Volatility measures. Agriculture, Mining All sectors All but Financial sector & Manufacturing * * * Statistics S S SGARCH S S SGARCH S S SGARCH Mean 3.18 4.63 2.18 3.20 5.63 2.29 3.23 8.00 2.35 Median 2.89 4.19 1.90 2.95 5.12 1.98 2.92 7.24 2.05 S.E. 1.21 2.16 0.90 1.23 2.53 0.95 1.53 3.64 0.93 Minimum 1.14 1.76 1.12 1.12 2.15 1.08 0.57 3.01 1.15 Maximum 12.10 37.07 7.63 14.97 41.75 7.90 27.10 58.37 7.51

Electrical Energy & Construction Retail * Statistics S* S SGARCH S S SGARCH Mean 3.53 12.06 2.80 3.16 6.96 2.26 Median 3.13 11.00 2.47 2.78 6.20 2.03 s.e. 1.76 5.32 1.05 1.40 3.56 0.83 minimum 0.64 4.33 1.48 0.77 2.58 1.26 maximum 15.91 87.58 7.33 15.13 64.18 6.68

Table 3, in turn, presents Granger causality tests between the natural logarithm of trading volume and the three alternative volatility measures under consideration. The reported F-statistics show strong evidence of a feedback effect from trading volume to volatility, but not vice versa. This finding is essential to treat trading volume as an approximately weakly exogenous variable in the regressions models (5a) through (5b).

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Table 3 Granger causality tests.

S causes S V causes Economic sector S* causes V V causes S* V causes S GARCH V causes V SGARCH All 2.65 [0.05] 11.47 [0.00] 1.86 [0.13] 19.02 [0.00] 1.72 [0.16] 5.20 [0.00] All but Financial sector 1.93 [0.59] 9.24 [0.00] 1.81 [0.14] 13.91 [0.00] 1.58 [0.19] 4.38 [0.00] Agriculture, Mining 1.92 [0.12] 7.50 [0.00] 2.00 [0.11] 15.60 [0.00] 2.28 [0.08] 4.17 [0.00] & Manufacturing Electrical Energy 0.09 [0.97] 6.01 [0.00] 0.63 [0.60] 5.77 [0.00] 3.11 [0.03] 6.54 [0.00] & Construction Retail, Transportation 4.29 [0.01] 13:01 [0.00] 1.26 [0.29] 25.72 [0.00] 0.91 [0.44] 4.35 [0.00] & Financial services Note: tests are carried out including 3 lags.

3.2. findings The regressions (5a) through (5c) were estimated by ordinary least squares (OLS) and least absolute deviations (LAD). The latter makes it possible to accommodate for a non-Gaussian error term in the regression model. The empirical results are reported in Table 4, Panels (a) through (e). We chose three alternative thresholds to define extreme returns: 5, 10, and 15 percent. The choice of such thresholds is due to the relatively small sample size of 1,072 return observations. In each panel, the estimated betas consistent with herd formation are in bold and highlighted in light grey, whereas those consistent with rational asset pricing models are in bold and highlighted in dark grey. Panel (a) shows the fitted regressions for all of the sampled stocks. Our findings depend on the estimation technique and on the selected threshold. Nevertheless, for the 10-percent extreme returns, under both estimation techniques, there is evidence of herding in the left tail. By contrast, at the 5-percent threshold, the evidence of herding in down markets becomes weaker, receiving more support when using LAD and the S* and S measures. Interestingly, when focusing on the 15 percent of the lower and upper tails, there is some evidence of herding in up markets. Moreover, there is a pattern observable in all of the fitted regressions of Panel (a), which refers to the inverse relation between volatility and the natural logarithm of trading volume. For instance, when focusing on the upper/lower 5-percent returns, the OLS estimate indicates that a 1-percent increase in trading volume would lead to a decrease of 2.39 points in S*. (LAD predicts a 2.54-point decrease). Such findings contradict the positive correlation between squared or absolute returns - proxies of volatility - and trading volume, documented by several empirical studies. Indeed, the positive association between volatility and trading volume would be the result of a correlated information arrival process, as formalized by Clark’s (1973) mixture-of- distribution hypothesis (MDH). The one-factor MDH states that the variance of returns and trading volume are driven by the same latent variable, which captures the arrival of information relevant to the price-formation process. A relatively recent article by Fong (2003) refutes the

Consejo latinoamericano de escuelas de administración, Cladea 27 As y m m e t r i c a l i n d i v i d u a l r e t u r n h e r d i n g 0.00 0.01 0.00 0.00 0.30 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.163 0.135 0.185 p-value p-value p-value = = = 2 2 2 LAD LAD LAD 0.66 1.40 0.44 0.08 6.81 7.72 7.19 b b b Upper/lower 15% –0.91 –0.83 –1.22 –0.07 –0.29 Adj. R Adj. R Adj. R Upper/lower 15% Upper/lower 15% 0.00 0.00 0.00 0.00 0.23 0.00 0.57 0.00 0.00 0.00 0.00 0.00 0.168 0.125 0.220 p-value p-value p-value = = = 2 2 2 LAD LAD LAD 0.09 0.07 0.38 b b b Upper/lower 10% 11.82 –2.54 –3.74 –2.26 –0.75 –1.04 –0.48 14.08 20.62 Adj. R Adj. R Adj. R Upper/lower 10% Upper/lower 10% 0.00 0.00 0.00 0.00 0.28 0.00 0.06 0.45 0.00 0.00 0.00 0.00 0.112 0.148 0.204 p-value p-value p-value = = = 2 2 2 LAD LAD LAD 0.12 0.31 0.45 9.70 b b b Upper/lower 5% 11.83 –2.04 –2.98 –1.78 –0.48 –0.77 –0.07 17.22 Adj. R Adj. R Adj. R Upper/lower 5% Upper/lower 5% GARCH 0.02 0.07 0.00 0.00 0.10 0.00 0.12 0.01 0.01 0.00 0.00 0.00 0.164 0.135 0.188 p-value p-value p-value Table 4 Table = = = 2 2 2 ols ols ols Dependent variable: S b b b Dependent variable: S* 0.92 1.46 0.41 0.21 6.40 7.39 Upper/lower 15% Dependent variable: S –0.77 –1.32 –1.22 –0.14 –0.21 10.19 (a) All sampled stocks. (a) Adj. R Adj. R Adj. R Upper/lower 15% Upper/lower 15% esting for herding behavior. esting for herding T 0.00 0.00 0.00 0.00 0.27 0.01 0.34 0.00 0.00 0.00 0.00 0.00 0.168 0.126 0.224 p-value p-value p-value = = = 2 2 2 ols ols ols b b b 0.12 0.16 0.48 Upper/lower 10% –3.22 –4.51 –2.61 –1.03 –1.00 –0.75 17.27 24.33 13.54 Adj. R Adj. R Adj. R Upper/lower 10% Upper/lower 10% 0.11 0.00 0.00 0.00 0.08 0.90 0.07 0.06 0.00 0.00 0.00 0.00 0.211 0.149 0.120 p-value p-value p-value = = = 2 2 2 ols ols ols b b b 0.30 0.09 0.39 0.75 Upper/lower 5% –2.39 –3.38 –1.95 –0.45 –0.30 13.58 19.31 10.66 Adj. R Adj. R Adj. R Upper/lower 5% Upper/lower 5% Regressor Regressor Regressor L U L U L U D log(volume) D log(volume) D log(volume) D D D Constant Constant Constant

28 Academia, Revista latinoamericana de administración, 45, 2010 Fe r n á n d e z 0.02 0.95 0.00 0.00 0.15 0.00 0.01 0.00 0.15 0.00 0.01 0.00 0.126 0.109 0.182 p-value p-value p-value = = = 2 2 2 LAD LAD LAD 0.78 1.89 0.52 0.07 5.32 5.09 7.94 b b b Upper/lower 15% Upper/lower 15% Upper/lower 15% –0.61 –0.03 –1.51 –0.10 –0.32 Adj. R Adj. R Adj. R 0.00 0.00 0.00 0.00 0.76 0.00 0.53 0.00 0.00 0.00 0.00 0.00 0.117 0.090 0.212 p-value p-value p-value = = = 2 2 2 LAD LAD LAD 0.03 0.09 0.22 b b b Upper/lower 10% Upper/lower 10% Upper/lower 10% –2.93 –4.55 –2.78 –0.95 –1.22 –0.48 14.69 23.37 13.10 Adj. R Adj. R Adj. R 0.00 0.00 0.00 0.00 0.02 0.00 0.03 0.53 0.00 0.00 0.00 0.00 0.101 0.081 0.188 p-value p-value p-value = = = 2 2 2 LAD LAD LAD 0.27 0.44 0.07 0.27 b b b Upper/lower 5% Upper/lower 5% Upper/lower 5% –1.97 –3.44 –2.12 –0.41 –0.75 10.80 18.83 10.43 Adj. R Adj. R Adj. R GARCH 0.11 0.47 0.53 0.00 0.00 0.00 0.23 0.01 0.02 0.01 0.04 0.00 0.110 0.127 0.185 p-value p-value p-value = = = 2 2 2 ols ols ols Dependent variable: S b b b Dependent variable: S* 1.06 2.07 0.44 0.22 4.25 7.67 8.20 Upper/lower 15% Upper/lower 15% Upper/lower 15% –0.30 –0.58 –1.52 –0.14 –0.20 Dependent variable: S Adj. R Adj. R Adj. R (b) All but F inancial stocks (b) 0.00 0.00 0.00 0.00 0.90 0.03 0.61 0.00 0.00 0.119 0.092 0.220 0.00 0.00 0.00 p-value p-value p-value = = = 2 2 2 ols ols ols b b b 0.11 0.01 0.44 Upper/lower 10% Upper/lower 10% Upper/lower 10% –3.34 –5.46 –3.25 –0.93 –1.03 –0.80 16.52 27.37 15.23 Adj. R Adj. R Adj. R 0.11 0.00 0.00 0.00 0.47 0.36 0.84 0.10 0.00 0.00 0.00 0.00 0.103 0.088 0.199 p-value p-value p-value = = = 2 2 2 ols ols ols b b b 0.29 0.17 0.45 0.70 Upper/lower 5% Upper/lower 5% Upper/lower 5% 11.40 –2.31 –4.00 –2.31 –0.23 –0.16 12.34 21.46 Adj. R Adj. R Adj. R Regressor Regressor Regressor L U L U L U D log(volume) D log(volume) D log(volume) D D D Constant Constant Constant

Consejo latinoamericano de escuelas de administración, Cladea 29 As y m m e t r i c a l i n d i v i d u a l r e t u r n h e r d i n g 0.92 0.57 0.00 0.00 0.06 0.00 0.02 0.00 0.25 0.00 0.00 0.00 0.065 0.132 0.195 p-value p-value p-value = = = 2 2 2 LAD LAD LAD 0.90 3.05 0.62 0.06 2.97 8.06 5.74 b b b –0.03 –0.33 –1.12 –0.16 –0.43 Adj. R Adj. R Adj. R Upper/lower 15% Upper/lower 15% Upper/lower 15% 0.00 0.00 0.00 0.00 0.04 0.00 0.49 0.00 0.00 0.00 0.00 0.00 0.067 0.106 0.234 p-value p-value p-value = = = 2 2 2 LAD LAD LAD 0.20 0.15 0.30 b b b –2.19 –5.17 –2.37 –0.99 –0.98 –0.45 10.34 24.61 10.04 Adj. R Adj. R Adj. R Upper/lower 10% Upper/lower 10% Upper/lower 10% 0.00 0.00 0.00 0.07 0.00 0.00 0.05 0.37 0.00 0.00 0.00 0.00 0.054 0.098 0.231 p-value p-value p-value = = = 2 2 2 LAD LAD LAD 0.46 0.58 0.09 0.53 7.22 8.39 b b b –1.27 –4.71 –1.89 –0.30 –1.04 23.02 Adj. R Adj. R Adj. R Upper/lower 5% Upper/lower 5% Upper/lower 5% GARCH 0.76 0.25 0.00 0.00 1.00 0.00 0.42 0.09 0.03 0.01 0.00 0.00 0.067 0.133 0.205 p-value p-value p-value = = = 2 2 2 ols ols ols Dependent variable: S b b b Dependent variable: S* 1.07 0.00 3.20 0.25 0.20 3.48 7.96 –0.13 –1.01 –1.71 –0.19 10.91 Dependent variable: S Adj. R Adj. R Adj. R Upper/lower 15% Upper/lower 15% Upper/lower 15% 0.00 0.00 0.00 0.00 0.06 0.23 0.46 0.00 0.00 0.00 0.00 0.00 0.070 0.107 0.237 p-value p-value p-value (c) Agriculture, Mining & Manufacturing stocks Agriculture, (c) = = = 2 2 2 ols ols ols b b b 0.27 0.22 0.50 11.01 –2.81 –6.76 –2.61 –0.98 –0.87 –0.49 12.64 30.53 Adj. R Adj. R Adj. R Upper/lower 10% Upper/lower 10% Upper/lower 10% 0.00 0.00 0.00 0.96 0.01 0.69 0.07 0.06 0.00 0.00 0.00 0.00 0.058 0.107 0.236 p-value p-value p-value = = = 2 2 2 ols ols ols b b b 0.03 0.63 0.48 0.73 0.33 0.81 8.81 8.41 –1.69 –5.50 –1.84 26.21 Adj. R Adj. R Adj. R Upper/lower 5% Upper/lower 5% Upper/lower 5% Regressor Regressor Regressor L U L U L U D log(volume) D log(volume) D log(volume) D D D Constant Constant Constant

30 Academia, Revista latinoamericana de administración, 45, 2010 Fe r n á n d e z 0.00 0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.18 0.03 0.00 0.71 0.163 0.072 0.389 p-value p-value p-value = = = 2 2 2 LAD LAD LAD b b b 5.01 2.52 2.02 1.57 1.63 21.20 –0.69 –0.35 –0.07 –0.74 –4.01 –0.06 Adj. R Adj. R Adj. R Upper/lower 15% Upper/lower 15% Upper/lower 15% 0.00 0.00 0.00 0.00 0.14 0.69 0.00 0.00 0.64 0.00 0.00 0.00 0.107 0.051 0.183 p-value p-value p-value = = = 2 2 2 LAD LAD LAD 6.53 4.05 0.13 1.25 1.27 0.03 b b b 30.50 –0.77 –0.66 –1.31 –7.54 –0.65 Adj. R Adj. R Adj. R Upper/lower 10% Upper/lower 10% Upper/lower 10% 0.11 0.00 0.00 0.00 0.00 0.10 0.05 0.00 0.00 0.00 0.00 0.00 0.092 0.046 0.158 p-value p-value p-value = = = 2 2 2 LAD LAD LAD 6.51 7.76 0.87 1.70 0.54 b b b 30.34 –0.78 –1.03 –0.16 –1.31 –7.49 –2.04 Adj. R Adj. R Adj. R Upper/lower 5% Upper/lower 5% Upper/lower 5% GARCH 0.00 0.01 0.00 0.00 0.00 0.24 0.00 0.00 0.80 0.23 0.04 0.74 0.164 0.072 0.390 p-value p-value p-value = = = 2 2 2 ols ols ols b b b 6.25 2.77 2.41 1.45 1.82 0.02 26.23 –0.83 –0.47 –1.09 –5.63 –0.10 Adj. R Adj. R Adj. R Dependent variable: S Upper/lower 15% Upper/lower 15% Upper/lower 15% Dependent variable: S* Dependent variable: S 0.00 0.00 0.00 0.00 0.75 0.88 0.00 0.00 0.69 0.01 0.00 0.00 0.110 0.053 0.192 p-value p-value p-value = = = (d) Electrical Energy & Construction stocks 2 2 2 ols ols ols 9.55 7.00 0.86 0.96 0.04 b b b 37.64 –0.98 –0.20 –0.07 –2.33 –9.94 –1.67 Adj. R Adj. R Adj. R Upper/lower 10% Upper/lower 10% Upper/lower 10% 0.00 0.00 0.00 0.00 0.00 0.16 0.13 0.00 0.31 0.01 0.00 0.00 0.094 0.058 0.163 p-value p-value p-value = = = 2 2 2 ols ols ols 9.21 8.47 1.22 2.15 1.02 0.76 0.18 b b b 29.58 –1.01 –2.21 –6.88 –2.23 Adj. R Adj. R Adj. R Upper/lower 5% Upper/lower 5% Upper/lower 5% Regressor Regressor Regressor L U L U L U Constant Constant Constant D D D log(volume) D log(volume) D D log(volume)

Consejo latinoamericano de escuelas de administración, Cladea 31 As y m m e t r i c a l i n d i v i d u a l r e t u r n h e r d i n g 0.00 0.00 0.60 0.00 0.124 0.206 0.187 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 p-value p-value p-value = = = 2 2 2 6.50 0.51 0.04 LAD LAD LAD –1.02 b b b 6.07 1.81 0.20 0.17 16.28 –1.13 –0.46 –2.80 Adj. R Adj. R Adj. R Upper/lower 15% Upper/lower 15% Upper/lower 15% 0.00 0.00 0.00 0.00 0.134 0.199 0.228 0.00 0.00 0.00 0.00 0.00 0.00 0.40 0.00 p-value p-value p-value = = = 2 2 2 0.26 10.03 –0.70 –1.97 LAD LAD LAD 7.77 0.46 0.16 b b b 25.24 –1.47 –0.47 –1.59 –5.23 Adj. R Adj. R Adj. R Upper/lower 10% Upper/lower 10% Upper/lower 10% 0.00 0.00 0.00 0.00 0.127 0.188 0.227 0.00 0.00 0.00 0.00 0.01 0.00 0.08 0.00 p-value p-value p-value = = = 2 2 2 8.91 0.35 –0.61 –1.67 LAD LAD LAD 6.75 0.67 0.45 b b b 23.15 –1.13 –0.23 –1.31 –4.67 Adj. R Adj. R Adj. R Upper/lower 5% Upper/lower 5% Upper/lower 5% GARCH 0.00 0.00 0.44 0.00 is the absolute deviation estimate. The OLS standard errors are heteroscedastic are errors standard OLS The estimate. deviation absolute the is 0.126 0.206 0.192 0.00 0.00 0.00 0.00 0.00 0.00 0.14 0.00 LAD p-value p-value p-value b = = = 2 2 2 5.96 0.78 –0.07 –0.81 ols ols ols b b b 5.03 1.92 0.22 0.44 Dependent variable: S 18.56 –0.80 –0.30 –3.30 Adj. R Adj. R Adj. R Dependent variable: S* Dependent variable: S Upper/lower 15% Upper/lower 15% Upper/lower 15% 0.00 0.00 0.01 0.00 0.135 0.200 0.231 0.11 0.00 0.00 0.02 0.00 0.00 0.00 0.00 p-value p-value p-value = = = 2 2 2 0.28 11.97 –1.02 –2.44 ols ols ols (e) Retail, Transportation & F inancial services stocks Transportation (e) Retail, b b b 8.72 0.37 0.55 30.10 –1.38 –0.69 –1.80 –6.42 Adj. R Adj. R Adj. R Upper/lower 10% Upper/lower 10% Upper/lower 10% is the ordinary least-squares estimate while estimate least-squares ordinary the is OLS 0.00 0.01 0.01 0.00 b 0.127 0.195 0.232 0.00 0.00 0.78 0.05 0.00 0.00 0.00 0.00 p-value p-value p-value = = = (1) (e) : 2 2 2 0.43 10.36 –2.00 –0.82 ols ols ols b b b 7.48 0.32 0.61 0.87 25.55 –0.50 –1.46 –5.20 Adj. R Adj. R Adj. R Upper/lower 5% Upper/lower 5% Upper/lower 5% Regressor Regressor Regressor L U L U L U D Constant Constant Constant D D D log(volume) D log(volume) D log(volume) consistent. (2) Estimated betas consistent with herd formation are in bold and highlighted in light grey; those betas consistent with rational asset pricing models are in bold and highlighted dark grey through (a) Panels to N otes

32 Academia, Revista latinoamericana de administración, 45, 2010 Fe r n á n d e z one-factor MDH based on evidence that volume and volatility are dynamically asymmetric. A more recent article by Ané and Ureche-Rangau (2008), which tests for a refinement of the MDH, shows that volatility and volume may share common short-term movements, but that their long-run behavior is fundamentally different. Fernandez (2009) also presents evidence against the one-factor MDH by concluding that shocks to volatility and volume are in general dynamically asymmetric for stocks belonging to the mining industry. When excluding financial firms from the sample (Panel (b)), the above findings do not change dramatically, except for the fact that evidence in favor of herding, in either down or up markets, becomes weaker for the OLS estimates. A rather different pattern arises when focusing exclusively on Electrical Energy & Construction stocks (Panel (d)). For this group of firms, the fitted models for *S , as the dependent variable, lend support to the existence of herding in up markets. No evidence of herding in either returns distribution tail is found for the two other alternative volatility measures. The only exception is some evidence, at the 10-percent significance level, of herding in down markets when S is the dependent variable and LAD is the estimation procedure. Another pattern worth highlighting is that, when taking the 15 percent of the lower and upper tails, the coefficient on log(volume) becomes statistically insignificant in some instances (e.g., Panels (b) and (c): LAD estimates and S as the dependent variable; Panel (d): OLS estimates and S* or S as the dependent variable). In other words, it seems more likely to observe a feedback effect from volume to volatility in conjunction with more extreme markets. Panels (a), (b), (d), and (e) also report evidence in favor of rational asset pricing models in both down and up markets (i.e., both bL > 0 and bU > 0). As we see, such findings appear more likely to arise when utilizing the SGARCH and a 15-percent threshold. Moreover, it appears to be case that the Chilean stock market herds when located in the left tail of the returns distribution, and it behaves as the rational asset pricing model would predict when located in the right tail of the returns distribution. In order to rationalize the above results one can appeal to institutional aspects surrounding the Chilean financial market. In particular, pension fund administrators (AFP) play an important role in the domestic financial market. Indeed, in recent years, AFPs equity holdings have amounted to about eight percent of the total domestic market capitalization (source: World Bank Development Indicators). Under the current multi-fund system2, which has been in operation since September 2002, and under the past two-fund system, AFPs have been legally obliged to meet minimum guaranteed returns on their managed funds. As mentioned in the Introduction, evidence of herding behavior in AFP has been reported by Olivares (2008) for the period prior to the multi-fund system. Specifically, by analyzing Type I-fund data (i.e., fixed- and variable-income assets) for the period June 1997 to December 2001, Olivares concluded that pension fund managers had an incentive to mimic other managers’ investment strategies to avoid falling below the minimum guaranteed return in place at the

2 In February 2002, a system of five multi-funds (A-E) was introduced, which differ primarily in the proportion of their financial resources invested on fixed-income securities. Fund type A has the smallest share of resources allocated to such securities, which gradually increases in Funds B, C, D and E.

Consejo latinoamericano de escuelas de administración, Cladea 33 As y m m e t r i c a l i n d i v i d u a l r e t u r n h e r d i n g time3. Under the current multi-fund system, a minimum guaranteed return applies to each fund, by following a rule similar to that established in the past4. Therefore, it is likely that pension fund managers tend to herd under the current system as well. The question that needs to be answered is why herding appears to be asymmetrical in the Chilean stock market. One could hypothesize that individual stocks are more likely to exhibit herding formation in extreme down markets because pension fund managers are more uncertain about meeting a minimum portfolio return under such scenarios and, hence, they are more inclined to herd. This could be particularly the case for those funds comprising a sizable percentage of corporate stocks, such funds A, B, and C, under the current multi-fund system. However, this conjecture deserves further research. A policy implication of our results is that herding in extreme down markets could be attenuated by the creation of circuit breakers, such as those set by the New York Stock Exchange (NYSE) and the Sao Paulo Stock Exchange5. Circuit breakers are procedures for coordinated cross-market trading halts if a severe market price decline leads to an exhaustion of market liquidity. Such procedures may stop trading temporarily or, under extreme circumstances, close the market prior to the end of normal closing.

4. Conclusions

The aim of this study was to test for the existence of herding in the Chilean stock market by resorting to a sample of 101 Chilean stocks over the period 1990-2009 at a weekly frequency. Our methodology was based on Christie and Huang’s (1995) and follow-ups. However, we added a twist by controlling for market liquidity, given the thinness of the Chilean stock market. We concluded that there exists herding behavior in individual stocks, but this is more likely to take place during extreme down markets. Moreover, we found that the Chilean stock market tends to herd under down markets, but it is inclined to conform to the rational asset pricing models in extreme up markets. It is possible that this pattern arises because pension funds, key players of the Chilean stock market, are less likely to meet a minimum guaranteed return under extreme down markets, and, hence, they are more prone to herd under such scenarios.

3 The minimum guaranteed return could not be less than, whichever was lower: (i) the past 12-month annualized average return across funds of the same type minus 2-percentage points, or (ii) the past 12-month annualized average return across funds of the same type minus 50 percent of the absolute value of this average return. 4 The AFPs are responsible for ensuring a minimum level of profitability on every fund, which should not be less than that resulting from: a) The annualized real average return of the past 36 months on all funds of the same type, minus four percentage points for Type A and B, and minus two percentage points for the other funds, and, b) The annualized real average return of the past 36 months on all funds of the same type, minus the absolute value of 50 percent of such a profitability rate. 5 Circuit breakers also exist in other Latin American exchanges, such as the Buenos Aires Stock Exchange and the Colombia Stock Exchange (World Federation of Exchanges Circuit Breakers Report, 2008).

34 Academia, Revista latinoamericana de administración, 45, 2010 Fe r n á n d e z

In addition, we found an inverse relation between volatility and trading volume, which contradicts the one-factor mixture-of-distribution hypothesis (MDH). Such an inverse association appears statistically more significant when choosing more extreme return thresholds.

Viviana Fernández obtuvo su Ph. D. en la Universidad de California en Berkeley. Actualmente es profesora de jornada completa de la Escuela de Ingeniería de la Pontificia Universidad Católica de Chile. Recientemente ha publicado en The Journal of Forecasting, Quantitative Finance y Studies of Nonlinear Dynamics & Econometrics. Sus intereses de investigación incluyen econometría financiera, mercados de commodities y finanzas corporativas.

References

Ané, T., & Ureche-Rangau, L. (2008). Does trading volume really explain stock returns volatility? Journal of International Financial Markets, Institutions & Money, 18, 216-235. Chang, E., Cheng, J., & Khorana, A. (2000). Examination of herd behavior in equity markets: An international perspective. Journal of Banking and Finance, 24, 1651-1679. Christie, W., & Huang, R. (1995). Following the pied piper: Do individual returns herd around the market? Financial Analyst Journal, 51, 31-37. Clark, P. (1973). A subordinated stochastic process model with finite variance for speculative prices. Econometrica, 41, 135-156. Demirer, R., & Kutan, A. (2006). Does herding behavior exist in Chinese stock markets? Journal of International Financial Markets, Institutions and Money, 16, 123-142. Devenow, A., & Welch, I. (1996). Rational herding in financial economics.E uropean Economic Review, 40, 603-615. Economatica (2010). Economatica system: Tools for investment analysis, years 1986-2010, www. economatica.com. Fernandez, V. (2009). The behavior of stock returns in the mining industry following the Iraq war. Research in International Business and Finance, 23, 274-292. Fong, W. (2003). Time reversibility tests of volume–volatility dynamics for stock returns. Economics Letters, 81, 39-45. Gleason, K., Lee, C., & Mathur, I. (2003). Herding behavior in European futures markets. Finance Letters, 1, 5-8. Gleason, K., Mathur, I., & Peterson, M. (2004). Analysis of intraday herding behavior among the sector ETFs. Journal of Empirical Finance, 11, 681-694. Hirshleifer, D., Subrahmanyam, A., & Titman, S. (1994). Security analysis and trading patterns when some investors receive information before others. Journal of Finance, 49, 1665-1698. Kim, W., & Wei, S. (2002). Foreign portfolio investors before and during a crisis. Journal of International Economics, 56, 77-96. Olivares, J. (2008). Rear-view-mirror driving in defined contributed systems: The strange formula of the Chilean pension funds. Applied economics, 40, 2005-2015. Olsen, R. (1996). Implications of herding behavior for earnings estimation, risk assessment, and stock returns. Financial Analysts Journal, 52(4), 37-41. Stickel, S. (1990). Predicting individual analyst earnings forecasts. Journal of Accounting Research, 28, 409-417.

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Trueman, B., (1988). A theory of noise trading in securities markets. Journal of Finance, 43, 83-95. Welch, I. (2000). Herding among security analysts. Journal of Financial Economics, 58, 369-396. World Development Indicators (2009). CD-ROM 2009. World Bank, October. World Federation of Exchanges Circuit Breakers Report (2008). available at http://www.world-exchanges. org. December. Zwiebel, J. (1995). Corporate conservatism and relative compensation. Journal of Political Economy, 103, 1-25.

Recepción del artículo: 01/04/2010 Envío evaluación a autores: 21/06/2010 Recepción correcciones: 01/07/2010 Aceptación artículo: 26/07/2010

Appendix Sampled firms. Beginning Company Class Economic sector CIIU series 1 Afpcapital Ordinary Insurance and Employee Benefit Funds 820 30-Jun-89 2 Aguas A Water, Sewage and Other Systems 420 23-Feb-90 3 Almendral Ordinary Securities and Commodity Contracts Intermediation 810 6-Jan-89 and Brokerage 4 Andina A Beverage Manufacturing 313 11-Apr-97 5 Andina B Beverage Manufacturing 313 11-Apr-97 6 Antarchile Ordinary Other Financial Investment Activities 810 21-Apr-95 7 Banmedica Ordinary Offices of Physicians 933 22-Sep-89 8 Banvida Ordinary Other Investment Pools and Funds 810 16-Apr-99 9 Bbvacl Ordinary Banks (Depository Credit Intermediation) 810 21-Jun-96 10 Bci Ordinary Banks (Depository Credit Intermediation) 810 18-Oct-91 11 Besalco Ordinary Residential Building Construction 500 26-May-95 12 Bsantander Ordinary Banks (Depository Credit Intermediation) 810 23-May-97 13 Calichera A Other Investment Pools and Funds 810 9-Mar-90 14 Calichera B Other Investment Pools and Funds 810 18-Jul-97 15 Campos Ordinary Other Financial Investment Activities 810 24-Feb-89 16 Cap Ordinary Iron and Steel Mills and Ferroalloy 371 6-Jan-89 Manufacturing 17 Cct Ordinary Tobacco Manufacturing 314 6-Jan-89 18 Ccu Ordinary Beverage Manufacturing 313 6-Jan-89 19 Cem Ordinary Hardware, and Plumbing and Heating Equipment 610 6-Jan-89 and Supplies Wholesalers 20 Cementos Ordinary Cement and Concrete Product Manufacturing 369 6-Jan-89

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Appendix (continued) Sampled firms. Beginning Company Class Economic sector CIIU series 21 Cencosud Ordinary Other General Merchandise Stores 620 7-May-04 22 Cge Ordinary Electric Power Generation, Transmission and 410 6-Jan-89 Distribution 23 Chile Ordinary Banks (Depository Credit Intermediation) 810 7-Nov-97 24 Cholguan Ordinary Forest Nurseries and Gathering of Forest Products 121 6-Jan-89 25 Cic Ordinary Other Furniture Related Product Manufacturing 332 1-Sep-89 26 Cintac Ordinary Forging and Stamping 381 16-Jul-93 27 Cmpc Ordinary Pulp, Paper, and Paperboard Mills 341 3-Mar-89 28 Colbun Ordinary Electric Power Generation, Transmission and 410 6-Jan-89 Distribution 29 Coloso Ordinary Fishing 130 6-Jan-89 30 Conchatoro Ordinary Beverage Manufacturing 313 6-Jan-89 31 Conosur Ordinary Other Investment Pools and Funds 810 15-Dec-95 32 Copec Ordinary Gasoline Stations 620 6-Jan-89 33 Corpbanca Ordinary Banks (Depository Credit Intermediation) 810 29-Nov-02 34 Cristales Ordinary Glass and Glass Product Manufacturing 362 13-Jan-89 35 Ctc A Telecommunications 720 6-Jan-89 36 Ctc B Telecommunications 720 6-Jan-89 37 Cti Ordinary Household Appliance Manufacturing 381 6-Jan-89 38 Cuprum Ordinary Insurance and Employee Benefit Funds 820 13-Jan-89 39 D&S Ordinary Other General Merchandise Stores 620 3-Jan-97 40 Detroit Ordinary Motor Vehicle and Motor Vehicle Parts and Sup- 610 13-Dec-96 plies Wholesalers 41 Edelnor Ordinary Electric Power Generation, Transmission and 410 6-Jan-89 Distribution 42 Elecda Ordinary Electric Power Generation, Transmission and 410 3-Feb-89 Distribution 43 Elecmetal Ordinary Architectural and Structural Metals Manufactur- 381 6-Jan-89 ing 44 Eliqsa Ordinary Electric Power Generation, Transmission and 410 3-Feb-89 Distribution 45 Embonor A Beverage Manufacturing 313 19-Dec-97 46 Embonor B Beverage Manufacturing 313 16-Apr-99 47 Emelari Ordinary Electric Power Generation, Transmission and 410 3-Feb-89 Distribution 48 Enaex Ordinary Basic Chemical Manufacturing 351 28-May-93

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Appendix (continued) Sampled firms. Beginning Company Class Economic sector CIIU series 50 Enersis Ordinary Electric Power Generation, Transmission and 410 6-Jan-89 Distribution 51 Entel Ordinary Telecommunications 720 6-Jan-89 52 Eperva Ordinary Fishing 130 6-Jan-89 53 Esval A Water, Sewage and Other Systems 420 21-May-93 54 Falabella Ordinary Department Stores 620 29-Nov-96 55 Fasa Ordinary Health and Personal Care Stores 620 5-Dec-97 56 Fosforos Ordinary Other Wood Product Manufacturing 331 6-Jan-89 57 Gasco Ordinary Natural Gas Distribution 410 5-Jan-90 58 Gener Ordinary Electric Power Generation, Transmission and 410 6-Jan-89 Distribution 59 Habitat Ordinary Insurance and Employee Benefit Funds 820 28-Apr-89 60 Iansa Ordinary Sugar and Confectionery Product Manufacturing 311 6-Jan-89 61 Indisa B Offices of Physicians 933 5-Jan-90 62 Indiver Ordinary Other Investment Pools and Funds 810 6-Jan-89 63 Inforsa Ordinary Converted Paper Product Manufacturing 341 6-Jan-89 64 Invercap Ordinary Other Investment Pools and Funds 810 11-Nov-94 65 Itata Ordinary Fishing 130 14-Aug-92 66 Kopolar Ordinary Beverage Manufacturing 313 20-Mar-92 67 Lafarge Cl Ordinary Cement and Concrete Product Manufacturing 369 6-Jan-89 68 Lan Chile Ordinary Scheduled Air Transportation 713 28-Jul-89 69 Madeco Ordinary Steel Product Manufacturing from Purchased 371 6-Jan-89 Steel 70 Marinsa Ordinary Other Investment Pools and Funds 810 6-Jan-89 71 Masisa Ordinary Forest Nurseries and Gathering of Forest Products 121 6-Jan-89 72 Minera Ordinary Other Investment Pools and Funds 810 25-Sep-92 73 Nortegran Ordinary Other Investment Pools and Funds 810 11-Jan-91 74 Oroblanco Ordinary Other Investment Pools and Funds 810 17-Jul-92 75 Parauco Ordinary Lessors of Real Estate 831 6-Jan-89 76 Pasur Ordinary Other Investment Pools and Funds 810 23-Aug-91 77 Pehuenche Ordinary Electric Power Generation, Transmission and 410 10-Nov-89 Distribution 78 Pilmaiquen Ordinary Electric Power Generation, Transmission and 410 31-Mar-89 Distribution 79 Provida Ordinary Insurance and Employee Benefit Funds 820 3-May-91

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Appendix (continued) Sampled firms. Beginning Company Class Economic sector CIIU series 80 Pucobre A Metal Ore Mining 230 27-Jun-97 81 Quinenco Ordinary Other Investment Pools and Funds 810 25-Oct-91 82 San Pedro Ordinary Beverage Manufacturing 313 24-Jan-92 83 Santa Rita Ordinary Beverage Manufacturing 313 7-Jul-95 84 Security Ordinary Management of Companies and Enterprises 810 5-Jan-90 85 Siemel Ordinary Other Crop Farming 111 6-Jan-89 86 Sipsa Ordinary Other Investment Pools and Funds 810 6-Jan-89 87 Sm Chile B Management of Companies and Enterprises 810 15-Nov-96 88 Sm Chile D Management of Companies and Enterprises 810 15-Nov-96 89 Sm Chile E Management of Companies and Enterprises 810 9-May-97 90 Sm Unimarc Ordinary Fishing 130 12-Mar-93 91 Soquicom Ordinary Metal and Mineral (except Petroleum) 610 4-Jun-93 Wholesalers 92 Sqm A Nonmetallic Mineral Mining and Quarrying 290 6-Aug-93 93 Sqm B Nonmetallic Mineral Mining and Quarrying 290 13-Jan-89 94 Tattersall Ordinary Other Professional, Scientific, and Technical 810 5-Jan-90 Services 95 Telsur Ordinary Telecommunications 720 5-Jan-90 96 Tricahue Ordinary Other Investment Pools and Funds 810 6-Nov-92 97 Vapores Ordinary Deep Sea, Coastal, and Great Lakes Water 712 6-Jan-89 Transportation 98 Ventanas Ordinary Support Activities for Air Transportation 712 18-Oct-91 99 Watts A Beverage Manufacturing 313 6-Jan-89 100 Watts B Beverage Manufacturing 313 23-Feb-96 101 Zofri Ordinary Department Stores 620 27-Sep-91

Consejo latinoamericano de escuelas de administración, Cladea 39