Bad news and Dow Jones make the Spanish stocks go round

European Journal of Operational Research 163 (2005) 253–275 www.elsevier.com/locate/dsw

Bad news and Dow Jones make the Spanish stocks go round Natividad Blasco a

a,*

, Pilar Corredor b, Cristina Del Rio b, Rafael Santamar
ıa

b

Facultad de Econ
omicas, Department of Accounting and Finance, University of Zaragoza, Gran V
ıa 2, Zaragoza 50005, Spain b Department of Business Administration, Public University of Navarre, Campus de Arrosad
ıa, s/n., Pamplona 31006, Spain Available online 3 March 2004

Abstract This paper is a data-based attempt to analyse what kind of information basically affects close-to-open returns, opento-close returns, volatility and volume in actively traded individual securities on the Spanish stock market. Specifically, we are interested in detecting how these variables react to specific pieces of news considered as exogenous information. However, as volume itself could be interpreted as a proxy of the information flow, we first apply the linear and nonlinear Granger causality tests from volume to return and to volatility. We do not find evidence supporting this latter hypothesis. Furthermore, we only find significant evidence of linear causality from volume to volatility. The other major finding is that both bad news and the Dow Jones play a significant informational role in explaining price changes and volatility. As a consequence of these findings, we also test the residual role of volume as a proxy for noise/liquidity trading after filtering for news, although we do not find evidence in favour of this argument.
2004 Elsevier B.V. All rights reserved. Keywords: Finance; Stock markets

1. Introduction A securities market is generally defined as informationally efficient if prices fully and quickly reflect all available information. Difficulties arise with such definition with regard to the precise meaning of the terms ‘‘fully reflect’’ and ‘‘available information’’. Until now, many papers have tried to shed some light on this complex area in finance, the relationship between market prices and information. However, most of the financial literature dealing with the market response to new information has focused on the adjustment of financial asset prices or volatilities following earnings, dividend, merger or acquisition announcements as well as the response to macroeconomic announcements (see, among others, Almeida et al., 1998; Ederington and Lee, 1993, 1995; La Porta et al., 1997). Little consideration has been given to the impact of a wider range of specific economic news. This paper aims to consider a wide variety of economic news to

*

Corresponding author. Tel.: +34-76-762156; fax: +34-76-761769. E-mail addresses: [email protected] (N. Blasco), [email protected] (P. Corredor), [email protected] (C. Del Rio), [email protected] (R. Santamar
ıa). 0377-2217/$ - see front matter
2004 Elsevier B.V. All rights reserved. doi:10.1016/j.ejor.2004.01.001

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investigate whether the evidence supports the existence of some patterns of processing and analysing daily economic information by investors in the Spanish stock market. Since public information is freely observed by everyone in the economy, a particularly interesting strand of the financial literature has addressed the problem of how costly information is revealed by prices. Uninformed traders learn about the information only through observing the price of the risky asset. However, the price does not perfectly reveal the information that informed traders have acquired because of noise in the form of a random supply of the risky asset. On average, when informed traders receive favourable news about individual stocks or firms, the corresponding price will be bid up. But prices could also be high because a supply shortage. Furthermore, if stock markets are informationally efficient and prices are a sufficient statistic for all private signals, we would not expect to observe any causal relationship at all between volume and stock prices. In conventional models of asset prices, the representative agent paradigm and the rational expectations paradigm explain trading by the allocation and diversification of risks. Nevertheless, there is a widely held belief that price and volume data provide useful indicators of future price movements or trading activity and this has led financial researchers to develop models with heterogeneous investors and incomplete asset markets. According to Gallant et al. (1992) the study of the joint dynamics of stock prices and trading volume can be more useful than focusing only on the univariate dynamics of stock prices. Supporting this argument there have been some important results in the financial literature in recent years reporting a significant relationship between price and volume in stock markets (see, among others, Hsu, 1998). However the theoretical research has not found a plausible and commonly accepted explanation for this behaviour observed in financial markets. The sequential information arrival models of Copeland (1976) and Jennings et al. (1981) suggest that lagged stock returns and volume could have predictive power in either direction since the pattern of information arrival produces a sequential stock-price–volume equilibrium before a final and complete equilibrium. Lakonishok and Smidt (1989) argue that tax-related motives could predict a negative lagged relation between those variables whereas nontax-related motives such as portfolio rebalancing predict a positive association. In the mixture model of Epps and Epps (1976) trading volume is a measure of the degree of disagreement among traders as new information arrives. From a practical point of view, and taking news as exogenous data, if volume can be considered as a proxy of the information flow that, in turn, is going to affect prices, both volume and stock returns should have common explanatory factors. However, if volume and stock returns respond to a different information set, then it may happen that volume represents an additional source of information. Another explanation for the relationship between return and volume is provided by noise-liquidity trader models. Noise-liquidity traders do not trade on the basis of economic fundamentals. Their strategies can cause stock prices to move and their decisions may also be conditioned by past stock price movements. Campbell et al. (1993) develop a model in which market makers take the opposite decision to noninformational traders only if they are compensated with an increase in stock returns. Abnormally large increases in volume are followed by stock return reversals, whereas informed traders move price to equilibrium without reversals. For example, a fall in stock prices could be due both to bad news reaching the market or an exogenous selling pressure by noninformational traders. In the latter case, market makers will require a higher expected return, so there will tend to be price increases on subsequent days. Blume et al. (1994) present a model where volume provides information about the quality or precision of information about past price movements, and thus traders can learn valuable information for their technical analysis. Since nonlinear structures mean richer types of asset behaviour (Brock et al., 1991), there is another variable deserving attention in the financial literature: return volatility. On the one hand, some papers report an asymmetric volatility response to bad news (e.g. Blasco et al., 2002). On the other hand, high stock market volume is usually associated with volatile returns. For example, He and Wang (1995) find that

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high volume generated by new exogenous private or public information is accompanied by high volatility, whereas high volume caused by existing information is not accompanied by high volatility. In spite of these theoretical proposals, there is no clear conclusion about whether trading volume may be a proxy of the information flow, its speed, whether it is only associated to volatility effects and its reaction to the information or whether it is a proxy for noise trading behaviour. In this context, this paper is a data based-attempt to analyse what kind of information basically affects stock returns, volatility and volume in actively traded individual securities on the Spanish stock market. The simple intuition underlying our work is as follows. Firstly, according to the proposals developed by Hiemstra and Jones (1994), we will test the nonlinear causality (after controlling the linear dependence) between ‘‘stock returns and volume’’ and ‘‘volatility and volume’’ for those individual securities used as underlying assets of option contracts in the Spanish market. It could be considered as a widely general test of the relationship between these variables and, perhaps, one of the most reasonable test to conclude whether volume may be considered as a proxy of the informational content reaching the market that will be reflected in prices or vice versa. Secondly, we study the information set affecting close-to-open returns, open-to-close returns, volatility and volume. This aim requires the identification of the main general and particular, scheduled and unscheduled news affecting each stock. When employing news as exogenous variables we should question whether the close-to-open stock returns and the open-to-close stock returns are influenced by the same information set, given that economically relevant events occurring during the trading session are supposed to be rapidly and efficiently reflected in prices (and therefore on the open-to-close return), whereas closeto-open returns are mainly supposed to be affected by the confirmation of such events (i.e. news published by financial newspapers) or, for example, the close returns of the New York Stock Exchange. The news database and the application used in this stage represents what is, to the best of our knowledge, the main contribution of this paper. Finally, if we consider that there are two basic reasons for trading, namely information and liquidity, and, after analysing information variables we conclude that volume and stock returns respond to different information sets and that volume cannot be taken as a proxy of the information flow, then the natural extension is to provide evidence of the additional usefulness of volume as an informative source. More specifically, if volume can be considered as a proxy for liquidity-noise trading behaviour. To sum up, the aim of this paper is to present evidence about the informational role of exogenous information set and volume to stock return and volatility (and vice versa) in the Spanish stock market using, to our knowledge, some unusual data sources: daily close-to-open and open-to-close returns for individual securities and for the Ibex35 index, their corresponding volatilities and volumes and exogenous general interest news as well as exogenous firm specific news. The remainder of the article is organised as follows. In Section 2 we describe the data sources and the adjustments made to remove systematic calendar and trend effects. In Section 3 we present the empirical strategy and review the methodology proposed for testing nonlinear Granger causality. Results are described in Sections 4 and 5. Finally Section 6 summarises our findings.

2. Data base Since assets do not all trade at the same time (nonsynchronous trading), those that are actively traded will reflect the more recent information in their prices better than thinner stocks. Chordia and Swaminathan (2000), for example, find that daily returns of stocks with high trading volume respond promptly to market-wide information and therefore lead daily returns of stocks with low trading volume. Following these arguments our raw data consist of daily open and closing prices and volume of the Ibex35 and eleven large individual securities that besides being heavily traded on the Madrid Stock Exchange and being

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almost permanently included in the Ibex35 composition, 1 were identified as underlying assets in the Spanish stock option market during the period January 1, 1997 and March 31, 1999. The latter characteristic let us homogenise to some extent the volatility behaviour, given some empirical results presented for the Spanish option market by Corredor and Santamar
ıa (2002) about the introduction effect, and following the argument exposed by Lamoureux and Lastrapes (1990) that stocks with traded option contracts present higher trading activity and lower potential asymmetry between positive and negative returns. Furthermore, Poon (1994) suggests that upon the introduction of option trading, there is a structural shift in the relation between stock return volatility and trading volume. Other important criteria were capitalisation volume and higher frequency of particular information flows as described below. The news database is taken from the headlines of the Spanish leading financial newspaper, Expansi
on, in order to consider, at least, those highly significant events that could influence prices. News belongs to the following categories: good general news (GG), bad general news (BG), good and bad particular news (GP and BP, respectively), particular news on investment (IP) and particular news on financing (FP). The GG category includes positive scheduled and unscheduled news that is supposed to be of general interest for investors and could affect all stocks (e.g. the anticipation/confirmation of positive movements of economic indicators such as the employment report, the interest rate or the consumer price index as well as relevant events that are supposed to benefit the local economy such as joining the EU or relevant expectations made by leading practitioners and academics). On the other hand, BG news includes those events that with a significant degree of consensus, are supposed to determine bad expectations for investors. GP collects good news dealing with a specific firm, such as profit increases, upward movements in specialised ratings, favourable legal resolutions, success in cost reduction process, favourable results in open competitions, auctions or licences that have been heavily competitive. . ., i.e., those events from which stockholders and investors may infer positive consequences. BP reflects the situation in which they infer negative consequences. IP deals with the proposal of firm acquisitions, wider product supply, investments associated with concessions or licences. . .. And finally, FP includes decisions such as debt facilities, capital increases or selling shares as disinvestment decisions. Another important question related to the information flow is the interaction between stock markets. The great improvement in the telecommunication technology and/or a major trend towards increasing both the commercial and business relationships between different countries strengthens international links. Studies such as those by Arshanapalli and Doukas (1993), Espitia and Santamar
ıa (1994) or, more recently, Dickinson (2000) find the significant presence of a leading market, New York, suggesting the possibility of anticipation strategies depending on the previous behaviour of the leader. Taking into account these findings, the closing prices of the Dow Jones are collected for the period under study. The raw price series are conveniently differenced in the logs to create return series as follows:
 Poit Rcoit ¼ Log ; Pcit
1
 Pcit þ Dit þ Sit Rocit ¼ Log ; Poit
 Pcdjt Rccdjt ¼ Log ; Pcdjt
1

1

These stocks belong to different activity sectors: Argentaria, BBV and Santander are banks, Endesa, Fecsa, Iberdrola, Sevillana and Uni
on Fenosa belong to the electricity/energy sector, ACESA is related to highways/communications, Repsol is devoted to petrol/ crude material and, finally, Telef
onica belongs to the telecommunication sector and is also related to high-tech.

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where Rco means close-to-open return, Roc means open-to-close return, D denotes dividends, S denotes subscription rights, Rcc means close-to-close return, Po means open price, Pc means closing price, i denotes each individual stock and dj indicates Dow Jones. Trading volume is initially expressed as VLit ¼ logðVit Þ with Vit being the traded shares of stock i on day t. With respect to the news database, dichotomic dummies are employed. 3. Empirical design and methodology Although not presented in this paper, the first preliminary analysis of this work was carried out using the Dickey–Fuller test, augmented Dickey–Fuller test and Phillips–Perron test, without finding any evidence against stationary return and log(volume) series. Many authors have noted systematic calendar effects in the mean and the variance of stock returns and in the trading volume. In order to adjust for these documented shifts, we first perform the two-stage adjustment process proposed by Gallant et al. (1992) using the following set of dummy and time-trend variables to capture these systematic effects: day-of-the-week dummies (one for each day, from Tuesday to Friday) and dummy variables for months from February to December. To perform the adjustment, we first regress returns or VLit on the total set of dummy variables. The intercept is also included. ð1Þ w ¼ x0 b þ u; where w is the series to be adjusted, x0 contains the dummy regressors and b collects the parameter estimates (intercept included). The residuals u are used to construct the variance equation: logðu2 Þ ¼ x0 c þ n:

ð2Þ

Once c is estimated by a regression procedure, these results are used to standardise the residuals from the mean equation. The linear transformation to calculate the adjusted w is as follows: wadj ¼ a þ bð^ u= expðx0 c=2ÞÞ; ð3Þ where a and b are chosen so that the sample means and variances of w and wadj are the same to facilitate interpretation of our empirical results. Let Rcoit , Rocit and VLit be the adjusted series. As Hsieh (1991) points out, much of the nonlinear dependence in stock returns is related to ARCH structures. Since we are interested in detecting causality from volume to current stock variance or vice versa, we fit two alternatives of the family of ARCH models to individual stock return series in order to obtain the variance series: (a) A GARCHðp; qÞ model given by Rit ¼ eit ; r2git ¼ a0 þ a11 e2it
1 þ a12 e2it
2 þ    þ a1q e2it
q þ a21 r2git
1 þ a22 r2git
2 þ    þ a2p r2git
p ;

ð4Þ

where Rit represents any of the Rcoit , Rocit , time series and r2g the variance associated with a GARCH process. (b) An EGARCHðp; qÞ model given by Rit ¼ eit ; lnðr2eit Þ ¼ a3 þ a41 lnðr2eit
1 Þ þ a42 lnðr2eit
2 Þ þ    þ a4p lnðr2eit
p Þ 
qffiffiffiffiffiffiffiffiffiffiffi
 qffiffiffiffiffiffiffiffiffiffiffi    pffiffiffiffiffiffiffiffi þ a51 / eit
1 r2eit
1 þ u eit
1 r2eit
1 
2=p þ    h  .qffiffiffiffiffiffiffiffiffiffiffi  .qffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffi i   r2eit
q þ u eit
q r2eit
q 
2=p ; þ a5q / eit
q

ð5Þ

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where Rit represents any of the Rcoit , Rocit , time series and r2e the variance associated with a EGARCH process. The empirical design proposed in this paper is developed in three stages, as described below. Since our main aim is to detect what type of information basically affects close-to-open returns, open-to-close returns, volume and volatility in the Spanish stock market, we should first consider the possibility of volume as being a proxy of the information flow. Therefore, in a first stage we should test whether lagged values of volume become significant in explaining price changes or volatility. In order to carry out this first stage, we use the procedure based on the Baek and Brock (1992) approach presented by Hiemstra and Jones (1994) to test for nonlinear causal relations. Consider two strictly stationary and weakly dependent time series Xt and Yt . Let Xtm Ytm denote the m-length lead vector of Xt and Yt Ly Lx and Xt
Lx and Yt
Ly the Lx-length and Ly-length lag vectors of Xt and Yt respectively. The strict Granger noncausality condition (Y does not strictly cause X ) is expressed as C1ðm þ Lx; Ly; eÞ C3ðm þ Lx; eÞ ¼ : C2ðLx; Ly; eÞ C4ðLx; eÞ

ð6Þ

For given values of m, Lx and Ly P 1 and e > 0. Both the LHS and RHS of this equation are ratios of joint probabilities defined as

Ly

 mþLx Ly

mþLx

C1ðm þ Lx; Ly; eÞ  Pr Xt
Lx
Xs
Lx < e; Yt
Ly
Ys
Ly
Ly

 Lx Ly

Lx

C2ðLx; Ly; eÞ  Pr Xt
Lx
Xs
Lx < e; Yt
Ly
Ys
Ly
 mþLx mþLx


Xs
Lx


C4ðLx; eÞ  Pr X Lx
X Lx < e ; t
Lx

s
Lx

where PrðÞ denotes probability and k  k denotes the maximum norm. Correlation-integral estimators of the joint probabilities are used to test the noncausality condition. Let IðZ1 ; Z2 ; eÞ denote a kernel that equals 1 when two vectors Z1 and Z2 are within the maximum-norm distance e of each other and 0 otherwise. Then, XX   Ly 2 Ly mþLx I xmþLx C1ðm þ Lx; Ly; e; nÞ  t
Lx ; xs
Lx ; e I yt
Ly ; ys
Ly ; e ; nðn
1Þ t
ð9Þ

where r2 ðm; Lx; Ly; eÞ can be estimated following the Appendix in Hiemstra and Jones (1994). A significant positive test statistic implies that lagged values of Y help to predict X , whereas significant negative values suggest that lagged values of Y rather confuse the prediction of X . According to these authors, the test is applied to the two estimated residual series from a VAR (vector autoregression), in order to previously remove any linear predictive power. Note that the parameter esti-

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mates of this VAR can be interpreted as positive or negative evidence of linear causality if significant. The well-known linear bivariate Granger causality test involves estimating a VAR and a standard joint test (F test usually) to statistically determine whether the coefficients associated with the lag operators are significantly different from zero. After filtering for linear dependence, we will first apply the nonlinear causality test to ‘‘stock returns and volume’’ series (Rcoit or Rocit versus VLit ) and ‘‘volatility and volume’’ series (r2git or r2eit versus VLit ) trying to provide general evidence about whether volume may play any informational role in determining stock returns or return volatility and whether the informational content comes from return or volatility to volume. Our second stage focuses on the influence of basic news on returns, volatility and volume. If the results of the first stage show evidence in favour of the hypothesis that volume is a proxy of the information flow, we can confirm these findings by detecting which pieces of information are common explanatory variables amongst volume, return or volatility. By contrast, if the results in the first stage reveal negative evidence, our second stage may provide some explanation about the different behaviour of the variables under study. In this latter regard, it could be more useful to filter returns and volume variables for conditional heterokedasticity and work with trading volume changes 2 calculated as RVit ¼ log ðVLit =VLit
1 Þ. Furthermore, it could be more interesting to use the unexpected volume change (hereafter denoted by ERVit ), particularly if our first volume measure is not found to convey information. To compute unexpected volume changes we use RVit minus its expected value. From this viewpoint, our proposal is as follows: (a) For close-to-open return. Rcoit ¼ d10 þ d11 Rcoit
1 þ d12 GGt þ d13 BGt þ d14 GPit þ d15 BPit þ d16 IPit þ d17 FPit þ d18 DJt þ t1it ; t1it ! N ð0; r2g1it Þ; r2g1it ¼ a10 þ a11 t21it
1 þ a12 r2g1it
1 þ a13 BGt þ a14 BPit þ a15 DJt þ m1it : ð10Þ The subscript t in each type of news denotes the day on which information is published by the financial newspaper. The subscript t of DJ indicates that the information about last close returns from the New York market reaches the Spanish market at time t, although it belongs to date t
1 in New York. For simplicity conditional heteroskedasticity is controlled by a GARCHð1; 1Þ. 3 To avoid an unnecessary computational burden, only bad news is included in the variance equations, taking into account the results of Blasco et al. (2002) and other preliminary estimates. 4 (b) For open-to-close return. Rocit ¼ d20 þ d21 Rocit
1 þ d22 GGtþ1 þ d23 BGtþ1 þ d24 GPitþ1 þ d25 BPitþ1 þ d26 IPitþ1 þ d27 FPitþ1 þ d28 DJt þ t2it ; t2it ! N ð0; r2g2it Þ;

ð11Þ

r2g2it ¼ a20 þ a21 t22it
1 þ a22 r2g2it
1 þ a23 BGtþ1 þ a24 BPitþ1 þ a25 DJt þ m2it :

Although tests using log ðVit =Vit
1 Þ were also computed, the choice of log ðVLit =VLit
1 Þ is due to a better efficiency in the equation estimation process, since results did not change either in sign or significance. 3 There are no significant changes in the reported results if orders p or q vary. 4 We did not find any other clear pattern that could improve the final estimates. These results are available from the authors upon request. 2

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It is worth mentioning that news included as explanatory variables corresponds to the news published in the next dayÕs press. The argument underlying this choice is that most of the news published in the economic press on day t þ 1 would have taken place during the interval from the open of day t to the close of day t. This means that the subscript t þ 1 of each type of news is interpreted as the day on which information is published by the financial newspaper although information is very likely to reach the market on day t by other information servers. 5 In other words, we consider that if there are two motives for trading, information and noise-liquidity, informed traders observe the public information one period in advance. Since that information will be published on t þ 1, the informational advantage is short-lived, and informed traders have no incentive to restrict their trading in order to have a larger informational advantage on the next day. (c) For unexpected volume changes. ERVit ¼ d30 þ d31 ERVit
1 þ d32 ERVit
2 þ d33 GGtþ1 þ d34 BGtþ1 þ d35 GPitþ1 þ d36 BPitþ1 þ d37 IPitþ1 þ d38 FPitþ1 þ d39 DJt þ t3it t3it ! N ð0; r2g3it Þ

ð12Þ

r2g3it ¼ a30 þ a31 t23it
1 þ a32 r2g3it
1 þ a33 BGtþ1 þ a34 BPitþ1 þ a35 DJt þ m3it : Note that in this case, two lags of the dependent variable have been included to account for most of the autocorrelation. At this point, it is worth mentioning that GARCH models have been proposed to model the variance equations since the existence of ARCH effects is clearly detected through an Engle test. Our third stage involves the standardised residuals from previous equations (equations belonging to the second stage). Our purpose here is to analyse whether unexpected volume may play some explanatory role as a proxy of noise/liquidity trading, according to the suggestions in Campbell et al. (1993). Consequently, the linear and nonlinear causality tests should be applied to a different information set, once the content of at least basic news has been eliminated from the variables under study. Since our interest lies only in testing the potential nonlinear relation between ‘‘noise return’’ and ‘‘noise volume’’ it could also be more useful to filter such variables for conditional heterokedasticity and work with unexpected volume, as mentioned above. In summary, we should eliminate those components or relations that cannot be directly attributed to noise or liquidity. For comparison, all the results for the Spanish Ibex35 index data will be included, in order to determine whether aggregation leads to different conclusions. In this case, monetary units measure the volume Vibext .

4. Results 4.1. Results for the first stage After filtering for both stationarity and the potential seasonal effects and before applying the nonlinear causality test, some preliminary vector autoregressions are computed. The order of the bivariate VAR has been selected using the Information Akaike criterion and the Schwarz criterion. The results indicate, on the one hand, that volume autocorrelation is quite strong and lasts over several trading sessions. On the other hand, as we mentioned above, some parameter estimates of these VAR can be interpreted as positive or

5 According to the results in Blasco et al. (2002), we consider that confirmed information does not induce any effect on prices unless the market is not efficient. Our choice has been to propose, for open-to-close return and volume, the model including news in advance. Moreover, these results support the findings in Ederington and Lee (1993, 1995). These authors conclude that although the impact of news on volatility lasts longer than on mean, it does not last longer than one session.

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negative evidence of linear causality if significant. Table 1 reports the linear bi-directional Granger causality test associated with each proposed VAR. Generally speaking Rcoit and VLit as well as Rocit and VLit do not seem to exhibit any linear causality in either direction, since the coefficients associated with the cross lag operators are basically not significant. These results partially support those found in Lee and Rui (2002) for aggregate data. These authors report that trading volume does not linearly Granger cause stock market returns in the three largest stock markets (New York, Tokio and London). They also found that stock returns does not linearly Granger cause volume in the UK market, although there is a linear causality from return to volume in the US and Japanese markets. Our findings are also contrary to those reported in

Table 1 Linear Granger causality (before controlling for news) Close-to-open returns/volume

Open-to-close returns/volume

Close-to-open GARCH volatility/volume

Close-to-open EGARCH volatility/volume

ARGENTARIA

1.0437 1.0777

2.0664 0.5698

4.0407 0.3972

6.3424 3.8099

ACESA

0.2407 1.1478

0.5431 3.0682

1.2548 0.7824

BBV

1.1499 1.0381

1.3198 0.7087

ENDESA

3.6014 2.6505

3.8215 1.2420

FECSA

1.8950 0.3903

IBERDROLA

Open-to-close GARCH volatility/volume

Open-to-close EGARCH volatility/volume

5.9505 0.1077

3.8004 3.5241

0.2786 0.6582

31.0819 0.2118

28.3767 0.3512

4.9686 0.8240

8.5287 1.3215

30.7496 26.1083

17.6324 32.6307

3.0822 3.4150

0.6976 0.2911

2.7718 1.1446

2.6185 0.1832

1.5681 1.8401

17.8095 5.1085

1.6027 1.7975

11.5103 1.7219

12.2425 0.7280

0.5431 0.3320

1.1353 0.8880

6.2110 0.7442

0.8993 1.5739

3.6772 0.3793

0.6208 0.7416

REPSOL

0.3892 0.3064

0.7702 1.7122

9.2660 0.0000

2.6192 1.6058

18.8841 0.4045

3.2185 1.7625

SANTANDER

0.4984 0.8212

0.8160 0.8542

3.7870 0.3192

1.7330 0.4563

10.3116 0.8381

5.2257 0.7499

SEVILLANA

0.5482 2.1874

1.0816 0.5273

2.8542 0.5605

2.9683 2.6500

30.1104 0.2434

1.0775 0.1387

TELEFONICA

0.2493 1.4004

1.3644 1.6106

6.4823 1.4046

8.8498 0.2773

11.1719 2.7507

7.7160 3.6312

UNION FENOSA

2.3211 0.8552

0.1003 2.0687

1.9826 0.5862

1.5156 1.3637

2.9987 1.2780

1.7933 0.8586

IBEX35

3.3589 2.0927

0.1587 0.7018

0.4843 0.5255

0.1684 2.7583

5.3688 0.2781

6.1007 0.3765

This table reports the results of the linear Granger causality test applied to Rcoit and log ðVit Þ, Rocit and log ðVit Þ, r2gcoit and log ðVit Þ, r2ecoit and log ðVit Þr2gocit and log ðVit Þ, r2eocit and log ðVit Þ. Each cell contains two rows: • The first reports the F-statistic result of testing the null ‘‘volume does not linearly Granger cause returns (or volatility)’’.  Denotes significance at the 5% level and  denotes significance at the 1% level. • The second reports the F-statistic result of testing the null ‘‘return (or volatility) does not linearly Granger cause volume’’.  Denotes significance at the 5% level and  denotes significance at the 1% level.

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N. Blasco et al. / European Journal of Operational Research 163 (2005) 253–275

Z
arraga (1998) for the Spanish stock market using aggregate data, uncovering unidirectional linear causality from daily close returns to volume. However, we find a common pattern of linear causality from volume to volatility for both single shares and Ibex35, which is particularly observable when the return variance is calculated through GARCH models. 6 Furthermore, one lagged value of VLit seems to be significantly useful to predict volatility. Taken together, the results in Table 1 suggest that trading volume can contain information about returns indirectly through volatility, but not directly about the return itself. This result could be related to some findings documenting that despite filtering for some volatility persistence, trading volume serves as a proxy of the information flow in the process of generating stock return variances. Clark (1973) and Tauchen and Pitts (1983) give one theoretical explanation for this association: over the day, a random number of individual pieces of information impinge on the market, which triggers independent price movement and transactions. The variance of daily return is a variable with a mean proportional to the mean number of daily transactions. Similar relations are documented in papers such as Gallant et al. (1992) or Lamoureux and Lastrapes (1990). Lee and Rui (2002) also document a positive linear feedback between trading volume and volatility, although they suggest that linking volatility to trading volume does not extract all the information. Phylaktis et al. (1996) also find significant results in explaining the variance of daily returns using trading volume as a proxy for information flow in the Athens Stock Exchange. However, our results are not in line with those reported in Lee et al. (2001) for ChinaÕs stock markets. These authors find that volume, used as a proxy for information arrival time, has no significant explanatory power for the conditional volatility of daily returns. As new information arrival is by definition unexpected, it could be thought that the proper volume measurement for carrying out the causality tests should be unexpected volume. Thus we computed daily volume increases or decreases minus the mean value of daily volume changes, in order to define abnormal change in volume which could be due to new information flows reaching the market. The causality test is then repeated and results do not vary significantly, excepting for open-to-close returns/volume relationship. For seven out of the twelve series (Acesa, Endesa, Fecsa, Iberdrola, Repsol, Union Fenosa and Ibex35) we find that open-to-close return linearly causes unexpected volume, according to the results from other papers cited above. Table 2 displays the results of the modified Baek and Brock (1992) test proposed by Hiemstra and Jones (1994). The parameter choice in this paper sets m ¼ 1, Lx ¼ Ly ¼ 2, e ¼ 1:5. 7 The statistic is used firstly to test the null hypothesis that volume does not nonlinearly Granger cause stock close-to-open return, open-to-close return and their corresponding volatilities (through GARCH and EGARCH models) and secondly to test the opposite direction, from each of the just mentioned stock returns and volatilities to volume. As can easily be concluded, we do not find evidence of nonlinear causality with the exception of Endesa which significantly shows nonlinear causality from close-to-open GARCH volatility to volume whereas the opposite direction suggests a significant level of confusion. More specifically, the difficulty of improving the predictability of returns by adding public information about trading volume is confirmed. These results are corroborated by repeating the nonlinear causality test using unexpected volume. The standardised test statistics for Endesa even enhance their values ()6.059 and 10.308 respectively). Overall, these results agree with those reported by Z
arraga (1998) for some portfolios in the Spanish stock market. Throughout this first stage, we have not found empirical evidence to support the idea that volume could be a proxy of information flow. Perhaps, as Bennett and Sias (2001) suggest, money flows do a much better job of explaining longer-term returns than do shorter-term money flows.

6 7

Orders p and q are initially chosen following maximum likelihood criteria. Some random sensitivity analysis did not change the sense and significance of results.

Table 2 Nonlinear Granger causality (before controlling for news) Open-to-close returns/volume

Close-to-open GARCH volatility/ volume

Close-to-open EGARCH volatility/ volume

)1026 ()0.7831) )0.0367 ()0.6063)

)0.0547 ()0.6492) )0.1114 ()1.4482)

0.0271 (0.0715) )0.0262 ()0.0550)

)0.0231 ()0.2548) )0.0987 ()1.9085)

0.0338 (0.3343) 0.0039 (0.0758)

)0.0761 (1.189) )0.0106 ()0.1109)

ACESA

0.0098 (0.1140) 0.0187 (0.2065)

)0.0770 ()0.6440) )0.1265 ()1.1086)

)0.0106 ()0.5878) 0.0035 (0.2919)

0.0097 (0.0696) )0.0973 ()0.8826)

0.01686 (1.0374) )0.0260 ()0.1959)

)0.0035 ()0.0218) )0.0863 ()0.6649)

BBV

0.0649 (0.8005) 0.0849 (1.0038)

)0.1159 ()1.2339) 0.0076 (0.0889)

0.0357 (0.3341) 0.0174 (0.2851)

0.0236 (0.1944) )0.0385 ()0.5615)

0.0380 (0.2922) )0.0450 ()0.6115)

0.0587 (0.4732) )0.0467 ()0.5648)

ENDESA

0.0770 (0.8885) 0.0155 (0.1657)

0.1097 (0.6603) )0.0192 ()0.2468)

)0.0256 ()3.8509) 0.0699 (8.6593)

0.0619 (0.4179) 0.0883 (0.8302)

0.1006 (0.5865) )0.0198 ()0.2541)

0.0501 (0.3237) 0.013 (0.1289)

FECSA

0.0486 (0.3693) 0.0327 (0.3529)

)0.0763 ()0.7433) 0.0185 (0.1615)

0.0876 (0.5688) 0.0276 (0.0081)

0.0831 (0.5594) )0.0571 ()0.6029)

0.0686 (0.4527) )0.0780 ()0.5963)

)0.0012 ()0.0081) )0.1072 ()0.7573)

IBERDROLA

)0.0923 ()1.1517) 0.0458 (0.5175)

)0.0011 ()0.0875) )0.1422 ()1.3301)

0.0953 (1.0817) 0.0678 (0.7108)

)0.0093 ()0.0762) )0.0319 ()0.2141)

0.0973 (1.0864) 0.0496 (0.5180)

)0.0017 ()0.0191) 0.0300 (0.1958)

REPSOL

0.0546 (0.4181) )0.0355 ()0.4269)

0.0072 (0.0682) )0.0252 ()0.2232)

0.0723 (0.1711) )0.02463 ()0.0596)

0.0467 (0.0893) )0.0043 ()0.0086)

0.0608 (0.1553) 0.0093 (0.0233)

0.1373 (0.3344) )0.0639 ()0.1563)

SANTANDER

0.0277 (0.3055) )0.0427 ()0.7210)

)0.0099 ()0.0807) )0.0225 ()0.2029)

)0.0019 ()0.0489) )0.0326 ()1.4973)

0.0515 (0.5817) 0.0047 (0.0495)

0.0123 (0.0885) 0.0509 (0.4786)

)0.0688 ()0.8077) 0.0447 (0.4738)

)0.0818 ()0.6988) )0.06863 ()0.8197)

0.0213 (0.1930) )0.0078 ()0.0939)

)0.0744 ()0.5399) )0.0691 ()0.6473)

)0.0232 ()0.1746) )0.0400 ()0.4507)

)0.0591 ()0.5603) )0.0798 ()0.7017)

ARGENTARIA

SEVILLANA

0.0983 ()1.0149) 0.0315 (0.3326)

Open-to-vlose GARCH volatility/ volume

Open-to-close EGARCH volatility/ volume

TELEFONICA

)0.0229 ()0.2089) )0.1202 ()1.8036)

0.0769 (0.5170) )0.0121 ()0.1720)

)0.0783 ()0.7116) )0.0410 ()0.5317)

0.0777 (0.63107) )0.0167 ()0.4262)

0.0487 (0.3025) 0.0110 (0.1186)

0.0777 (0.4674) 0.0081 (0.0757)

UNION FENOSA

)0.0102 ()0.1335) )0.0028 ()0.0335)

)0.0468 ()0.4546) )0.1023 ()1.2321)

0.1028 (0.8496) )0.0202 ()0.4180)

0.0793 (0.6353) 0.0079 (0.1144)

0.0451 (0.5161) )0.0371 ()0.9823)

0.0216 (0.3711) )0.0322 ()0.9198)

IBEX35

)0.0634 ()1.0072) 0.0293 (0.3752)

)0.0765 ()1.0064) 0.0124 (0.1819)

)0.0128 ()0.2674) )0.0303 ()1.1452)

)0.0452 ()0.6002) )0.0816 ()0.9759)

0.0199 (0.2209) )0.0305 ()0.5086)

)0.0091 ()0.0954) 0.0339 (0.3421)

This table reports the results of the nonlinear Granger causality test proposed by Hiemstra and Jones (1994) applied to the residuals from VAR specifications with Rcoit and log ðVit Þ, Rocit and log ðVit Þ, r2gcoit and log ðVit Þ, r2ecoit and log ðVit Þr2gocit and log ðVit Þ, r2eocit and log ðVit Þ. Each cell contains two rows: • •

N. Blasco et al. / European Journal of Operational Research 163 (2005) 253–275

Close-to-open returns/volume

The first reports the result of testing the null ‘‘volume does not nonlinearly Granger cause returns (or volatility)’’. Numbers without parenthesis are the values of the test statistic proposed. Numbers in parenthesis are the standardised test statistics, under the null asymptotically distributed N ð0; 1Þ. The second reports the result of testing the null ‘‘return (or volatility) does not nonlinearly Granger cause volume’’. Numbers without parenthesis are the values of the test statistic proposed. Numbers in parenthesis are the standardised test statistics, under the null asymptotically distributed N ð0; 1Þ. 263

m ¼ 1, Lx ¼ Ly ¼ 2, e ¼ 1:5.

264

N. Blasco et al. / European Journal of Operational Research 163 (2005) 253–275

4.2. Results for the second stage Table 3(Panels A and B) display the parameter estimates for each type of news considered in the mean equation and the variance equation for close-to-open return (Eq. (10)). The Dow Jones plays a significant role both for the mean and the volatility. In fact, this is a logically expected result, since the close of New York is chronologically the ‘‘most relevant financial event’’ at the opening of the Spanish market. This result agrees with the conclusions in Espitia and Santamar
ıa (1994) and Olmeda and Moreno (2001) about the great influence of the leader market, New York, on other developed stock markets. The high degree of significance of good general news in the mean equation and bad general news in volatility should also be noted. Conversely, individual news seems to be poorly reflected. Table 4(Panels A and B) show the parameter estimates for each type of news considered in the mean equation and the variance equation for open-to-close return (Eq. (11)). The Dow Jones remains significant but negative in the mean equation and quite significant (there are four exceptions) in the variance equation.

Table 3 Parameter estimates for close-to-open return equations (equations of the mean (Panel A) and equations of the variance (Panel B)) Panel A LAGt
1

C ARGENTARIA ACESA BBV ENDESA FECSA IBERDROLA REPSOL SANTANDER SEVILLANA TELEFONICA UN. FENOSA IBEX35

GGt

BGt

GPt

BPt

IPt

FPt

DJt

)0.0053 0.0000 0.0011 0.0011 )0.0110 0.0007 )0.0019 0.0068 )0.0031 0.0049 )0.0006

)0.0038 0.0000 0.0000 0.0020 0.0000 0.0016 )0.0036 )0.0051 )0.0048 0.0001 )0.0010

0.2531 0.0662 0.4157 0.1408 0.0826 0.2657 0.1853 0.3364 0.0805 0.3402 0.2298 0.4079

)0.0025 )0.0003 0.0001 )0.0018 0.0006 )0.0027 )0.0008 0.0015 )0.0028 )0.0021 )0.0041 )0.0010

)0.0319 )0.0098 )0.1818 )0.0271 )0.0186 )0.1009 )0.0434 )0.1402 )0.0991 )0.1102 )0.0766 )0.2768

0.0034 0.0006 0.0014 0.0031 )0.0005 0.0027 0.0020 0.0004 0.0019 0.0028 0.0021 0.0024

0.0010 0.0017 0.0000 )0.0013 0.0005 0.0022 0.0007 )0.0007 0.0012 0.0008 0.0016 0.0000

0.0024 0.0030 )0.0011 0.0012 )0.0017 0.0031 0.0018 )0.0002 )0.0009 0.0022 0.0017

)0.0063 0.0000 )0.0064 )0.0022 )0.0079 )0.0007 )0.0008 )0.0065 0.0000 )0.0007 0.0017

C

ARCH(1)

GARCH(1)

BGt

BPt

DJt





Panel B

ARGENTARIA ACESA BBV ENDESA FECSA IBERDROLA REPSOL SANTANDER SEVILLANA TELEFONICA UN. FENOSA IBEX35 



0.00004 0.00000 0.00005 0.00001 0.00000 0.00001 0.00005 0.00018 0.00004 0.00001 0.00001 0.00002



0.06835 0.00351 0.08532 0.0081 0.03360 0.0094 0.2079 0.3758 0.09745 0.09152 0.04689 0.12405

Denotes significance at the 10% level,





0.76290 0.96220 0.79034 0.8786 0.8624 0.9283 0.4211 0.24456 0.58498 0.85028 0.85585 0.74731

0.00008 )0.00006 )0.00003 0.0000 0.00003 0.00024 0.00002 0.00003 0.00001 0.00000 0.00000 0.00000 )0.00005 0.00000 )0.00004 0.00025 0.00003 0.00021 0.00000 0.00001 0.00002 )0.00004 )0.00000 

denotes significance at the 5% level and





)0.0035 )0.0004 )0.00192 )0.00087 )0.0017 )0.0014 )0.0016 )0.00767 )0.00199 )0.0013 )0.0014 )0.0021

denotes significance at the 1% level.

Rcoit ¼ d10 þ d11 Rcoit
1 þ d12 GGt þ d13 BGt þ d14 GPit þ d15 BPit þ d16 IPit þ d17 FPit þ d18 DJt þ t1it ; t1it ! N ð0; r2g1it Þ; r2g1it ¼ a10 þ a11 t21it
1 þ a12 r2g1it
1 þ a13 BGt þ a14 BPit þ a15 DJt þ m1it :

N. Blasco et al. / European Journal of Operational Research 163 (2005) 253–275

265

Table 4 Parameter estimates for open-to-close return equations (equations of the mean (Panel A) and equations of the variance (Panel B)) Panel A LAGt
1

C ARGENTARIA ACESA BBV ENDESA FECSA IBERDROLA REPSOL SANTANDER SEVILLANA TELEFONICA UN. FENOSA IBEX35

GGtþ1

0.007 0.0910 0.0038 )0.0009 0.0833 0.0025 0.0035 0.1144 0.0021 0.0025 0.0094 0.0020 )0.0011 0.0763 0.0045 0.0016 0.0350 0.0008 0.0004 )0.0448 0.0018 0.0006 0.1061 0.0028 0.0026 )0.0293 0.0024 0.0025 0.0160 0.0001 0.0043 0.1479 )0.0004 0.0018 )0.0173 0.0010

BGtþ1

GPtþ1

BPtþ1

IPtþ1

FPtþ1

DJt

)0.0075 )0.0043 )0.0069 )0.0050 )0.0028 )0.0065 )0.0024 )0.0041 )0.0058 )0.0069 )0.0063 )0.0055

0.0065 0.0000 0.0040 0.0006 0.0054 0.0046 0.0018 0.0027 0.0020 0.0022 0.0003

)0.0083 0.0000 )0.0166 )0.0041 )0.0029 )0.0026 )0.0025 )0.0008 )0.0003 )0.0039 )0.0034

)0.0008 0.0000 )0.0000 )0.0010 0.0016 0.0005 0.0010 )0.0030 0.0000 0.0005 0.0026

0.0011 0.0071 )0.0009 0.0010 0.0145 )0.0051 )0.0027 )0.0019 )0.0032 0.0065 0.0033

)0.1650 )0.1090 )0.1902 )0.1374 )0.2048 )0.3007 )0.1252 )0.1346 )0.1903 )0.1217 )0.2994 )0.1514

Panel B

ARGENTARIA ACESA BBV ENDESA FECSA IBERDROLA REPSOL SANTANDER SEVILLANA TELEFONICA UN. FENOSA IBEX35 

C

ARCH(1)

GARCH(1) BGtþ1

BPtþ1

DJt

0.00008 0.00001 0.00002 0.00005 0.00011 0.00005 0.00009 0.00005 0.00002 0.0000 0.00002 0.00001

0.0675 0.0808 0.0750 0.1216 0.1112 0.0057 0.1937 0.1620 0.1173 0.1009 0.0747 0.1315

0.7428 0.8901 0.8183 0.6265 0.6127 0.8106 0.3717 0.6443 0.8031 0.8340 0.8643 0.7264

)0.0001 0.0000 0.0002 0.0001 )0.0002 0.00004 )0.00007 0.0003 )0.00003 0.00008 )0.0001

)0.0035 )0.0002 )0.0004 )0.0014 0.0000 )0.0043 )0.0019 0.0005 )0.0017 )0.0020 )0.0046 )0.0009

Denotes significance at the 10% level, Rocit

¼ d20 þ d21 Rocit
1 t2it ! N ð0; r2g2it Þ;



0.0001 0.0000 0.00008 )0.00000 0.00000 0.00002 )0.00002 0.0001 0.00003 0.00002 0.00005 0.00005

denotes significance at the 5% level and



denotes significance at the 1% level.

þ d22 GGtþ1 þ d23 BGtþ1 þ d24 GPitþ1 þ d25 BPitþ1 þ d26 IPitþ1 þ d27 FPitþ1 þ d28 DJt þ t2it ;

r2g2it ¼ a20 þ a21 t22it
1 þ a22 r2g2it
1 þ a23 BGtþ1 þ a24 BPitþ1 þ a25 DJt þ m2it :

We think that this strong influence along the Spanish trading session may be due either to a correction effect diminishing the extreme importance assigned to such data in the close-to-open returns, or to the link between the last Dow Jones close-to-close return and the next Dow Jones open price, information which is available to Spanish traders before the market closes. 8 With respect to general news, bad news occurring during the trading session plays a major role in the equation of the mean, whereas its importance is reduced compared to bad particular news for individual stocks in the volatility equation. Basically, bad general and/or specific firm news increases current volatility.

8

We also considered the possible multicollinearity between Roct
1 and Rccdjt . The sign and significance of the estimates concerned were corroborated using as explanatory variables: • •

Rccdjt and the residuals from regressing Roct
1 on Rccdjt , Rccdjt and eliminating Roct
1 as an explanatory variable.

266

N. Blasco et al. / European Journal of Operational Research 163 (2005) 253–275

According to a volatility feedback perspective, investors will revise upwards their estimates of future conditional variance which will increase the required risk premium and will lead to the price drop reflected in the mean equation. Nevertheless, since there are some significant negative estimates in firm specific news in the variance equation and there are three stocks (ACESA, Iberdrola and Sevillana) with no significant estimates for bad news variables, we can not give empirical support to the feedback argument just mentioned. In the case of news related to investment and financing decisions, we have not found significant results. Our conclusion is that investors need some additional information or time to determine whether such decisions generate good or bad expectations. Table 5(Panels A and B) report the parameter estimates of the equations of the abnormal volume change (Eq. (12)). There is no other clear pattern except strong autocorrelation in the equation of the mean. The lack of significant news in the equation of the mean can be interpreted as a preliminary result supporting Table 5 Parameter estimates for unexpected volume change equations (equations of the mean (Panel A) and equations of the variance (Panel B) Panel A LAGt
1

LAGt
2

GGtþ1

BGtþ1

0.0013

)0.5003

)0.2858

)0.0013

)0.0062

)0.0028 )0.0025 0.0017 )0.0039 )0.0036 0.0009 )0.0001 0.0036 )0.0017

)0.5709 )0.4998 )0.4915 )0.5272 )0.5151 )0.5604 )0.4929 )0.4534 )0.4475

)0.2768 )0.1553 )0.2255 )0.2438 )0.3068 )0.3507 )0.2683 )0.2679 )0.2880

0.0016 0.0384

)0.5025 )0.3724

)0.2802 )0.1824

C ARGENTARIA ACESA BBV ENDESA FECSA IBERDROLA REPSOL SANTANDER SEVILLANA TELEFONICA UN. FENOSA IBEX35

GPtþ1

BPtþ1

IPtþ1

DJt )0.3077

0.0021 0.0070 0.0241 0.0148 )0.0420 0.0006 0.0002 0.0061 0.0057 )0.0022 )0.0047 )0.0043 0.0019 0.0105 )0.0001 0.0024 0.0053 )0.0117 0.0564 )0.0349 )0.0004 0.0053 0.0074 0.0127 ? 0.0052 )0.0081 0.0051 0.0029 0.0121 0.0026 )0.0044 0.0071 0.0046 )0.0068 )0.0072 0.0040 0.0019 0.0476 0.0423 0.0134 )0.0009 0.0018 0.0074 )0.0044 0.0029

)0.0126 0.0029 )0.0052 0.0985 0.0008 )0.0144 0.0101 )0.0111 0.0087

)0.4082 )0.3706 0.0733 )0.0120 0.0231 0.0769 )0.1506 0.0691 )0.1017

)0.0032 )0.0501

)0.0005

)0.2874 )4.3840

0.0045 0.0280

)0.0084

0.0225 )0.0084

FPtþ1 0.0027

0.0015

)0.0308

0.0043

Panel B

ARGENTARIA ACESA BBV ENDESA FECSA IBERDROLA REPSOL SANTANDER SEVILLANA TELEFONICA UN. FENOSA IBEX35 

¼ d30 þ

DJt

0.0007

0.2112

0.6022

)0.0007

0.00007 0.0001 0.0001 0.0002 0.0001 0.00005 0.0013 0.0020 0.00002

0.0261 0.2232 0.2257 0.1261 0.0569 0.0314 0.1260 0.5156 0.2165

0.9398 0.7761 0.6926 0.8328 0.8536 0.9397 0.5679 0.1733 0.7822

)0.00002 )0.0001 0.0041 )0.0002 )0.0002 0.0074 )0.0002 0.0012 0.0066 )0.0001 0.0006 )0.0120 )0.00007 )0.0001 )0.0008 )0.0001 0.0004 )0.0116 )0.0010 )0.0011 0.0019 )0.0006 0.0020 0.0015 )0.00004 0.0001 0.0018

0.0005 0.1809 0.1833 0.3758

0.5975 )0.0234

)0.0003 )0.0004 0.0266 0.3435 )2.9315





Denotes significance at the 10% level, ERVit

BPtþ1

ARCH(1) GARCH(1) BGtþ1

C

d31 ERVit
1

þ







)0.0003



0.0142

denotes significance at the 5% level and

d32 ERVit
2



denotes significance at the 1% level.

þ d33 GGtþ1 þ d34 BGtþ1 þ d35 GPitþ1 þ d36 BPitþ1 þ d37 IPitþ1 þ d38 FPitþ1 þ d39 DJt þ t3it ;

t3it ! N ð0; r2g3it Þ; r2g3it ¼ a30 þ a31 t23it
1 þ a32 r2g3it
1 þ a33 BGtþ1 þ a34 BPitþ1 þ a35 DJt þ m3it :

N. Blasco et al. / European Journal of Operational Research 163 (2005) 253–275

267

the argument of volume being a proxy for noise-liquidity trading rather than a proxy of news information flows. This finding disagrees with the suggestion made in Cremers and Mei (2002) that monthly turnover for some US markets is not purely random, but driven by macroeconomic and firm-specific news. Or perhaps abnormal volume depends more on the precision or quality of information flows rather than on the informational content itself. Another explanation would be related to herding (i.e. doing the same). Although agents do not neglect their own information, herding may be induced by reputational effects or the existence of ‘‘fashion leaders’’. Bad general news is the most important dummy variable in the equation of the abnormal volume variance and exhibits a negative sign. Although it is not a common finding for all stocks, this result can be justified if prices react quickly to this kind of information and traders do not think unstable volume movements are necessary to assume the arrival of bad news. Summarising the results of the estimates of Tables 3–5, we can conclude that both the Dow Jones and bad news significantly help to explain movements in the Spanish stock market. This is an interesting point, which deserves further attention. The influence of the Dow Jones may also be analysed through causality test, and bad news can be considered common feature for some of the series under analysis. Some results along these lines will be presented in next section (Further results). 4.3. Results for the third stage Given the results of stages 1 and 2, we are now interested in detecting additional evidence about the explanatory role of volume. In order to test whether volume could be a proxy for noise-liquidity trading, after filtering for basic news arrival, vector autorregressions with five fixed lags are estimated for the standardised residuals of Eqs. (10)–(12), and the corresponding linear Granger causality test is applied. Table 6 reports the results. At five percent significance, the null hypothesis of strict linear Granger noncausality is only rejected in both directions for ‘‘open-to-close noise return/volume’’ for Endesa. Although there are three other rejections for single stocks, the overall conclusion is the nonexistence of significant linear causality. Hence, similar results have been found before and after filtering for news and conditional volatility. Finally, the Hiemstra and Jones (1994) test is applied to the estimated VAR residuals just mentioned. The results are reported in Table 7. We have not found positive evidence in favour of nonlinear causality in either direction. There is no difference in the statistical significance of these results compared to those in Table 2. However, since we previously found a common pattern of linear causality from volume to GARCH volatility, in Table 8 we report the bi-directional Granger linear causality test using two fixed lags 9 between standardised extra-volume and GARCHð1; 1Þ volatilities in Eqs. (10) and (11). As can easily be seen, standardised abnormal volume change continues to be important in determining open-to-close volatilities although its significance level decreases in determining close-to-open volatilities. This finding supports that volume, before and after filtering for news (noisy or not), is a key explanatory element of open-to-close volatility. In order to support this set of results and to provide evidence, if significant, of a contemporaneous relationship between extra volume and open-to-close return, we reestimate Eq. (11) adding unexpected volume change as an explanatory variable both in the mean and in the variance. Table 9 shows the estimates. The news variables basically remain significant. Extra volume does not represent valuable information for daily open-to-close return directly but clearly influences its variance. Contrary to the conclusion reported by Lamoureux and Lastrapes (1990), the ARCH/GARCH effects do not tend to disappear when

9

Results have also been calculated for five lags and do not change significantly.

268

N. Blasco et al. / European Journal of Operational Research 163 (2005) 253–275

Table 6 Linear Granger causality (after filtering for news and conditional volatility) Close-to-open noise returns/extra volume change

Open-to-close noise returns/extra volume change

ARGENTARIA

1.2143 2.4298

1.0492 1.8550

ACESA

0.0511 0.5713

0.6660 1.1907

BBV

0.8597 1.4831

0.4460 0.5299

ENDESA

2.5697 1.3191

2.8322 2.2536

FECSA

0.4503 1.0149

0.9648 1.3406

IBERDROLA

1.1463 0.3946

1.6185 0.6153

REPSOL

0.4884 0.3685

0.6112 2.2769

SANTANDER

0.7795 0.2843

0.2414 0.7538

SEVILLANA

0.7782 1.0129

0.2069 0.9388

TELEFONICA

0.2604 0.4998

0.5479 1.2765

UNION FENOSA

0.7710 0.6394

0.8770 1.3543

IBEX35

1.6103 0.9009

0.9470 1.6227

This table reports the results of the linear Granger causality test applied to the standardised residuals of the following Eqs. (10)–(12): Rcoit ¼ d10 þ d11 Rcoit
1 þ d12 GGt þ d13 BGt þ d14 GPit þ d15 BPit þ d16 IPit þ d17 FPit þ d18 DJt þ t1it ; t1it ! N ð0; r2g1it Þ; r2g1it ¼ a10 þ a11 t21it
1 þ a12 r2g1it
1 þ a13 BGt þ a14 BPit þ a15 DJt þ m1it : Rocit ¼ d20 þ d21 Rocit
1 þ d22 GGtþ1 þ d23 BGtþ1 þ d24 GPitþ1 þ d25 BPitþ1 þ d26 IPitþ1 þ d27 FPitþ1 þ d28 DJt þ t2it ; t2it ! N ð0; r2g2it Þ; r2g2it ¼ a20 þ a21 t22it
1 þ a22 r2g2it
1 þ a23 BGtþ1 þ a24 BPitþ1 þ a25 DJt þ m2it : ERVit ¼ d30 þ d31 ERVit
1 þ d32 ERVit
2 þ d33 GGtþ1 þ d34 BGtþ1 þ d35 GPitþ1 þ d36 BPitþ1 þ d37 IPitþ1 þ d38 FPitþ1 þ d39 DJt þ t3it ; t3it ! N ð0; r2g3it Þ; r2g3it ¼ a30 þ a31 t23it
1 þ a32 r2g3it
1 þ a33 BGtþ1 þ a34 BPitþ1 þ a35 DJt þ m3it : Each cell contains two rows: • The first reports the F-statistic results of testing the null ‘‘extra volume change does not linearly Granger cause related to noise returns’’.  Denotes significance at the 5% level and  denotes significance at the 1% level. • The second reports the F-statistic results of testing the null ‘‘related to noise return does not linearly cause extra volume change’’.  Denotes significance at the 5% level and  denotes significance at the 1% level.

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269

Table 7 Nonlinear Granger causality (after filtering for news and conditional volatility) Close-to-open returns/volume

Open-to-close returns/volume

ARGENTARIA

)0.0815 ()0.7104) )0.0078 ()0.1428)

0.0669 (0.6941) )0.0556 ()0.6468)

ACESA

)0.064 ()0.7687) 0.0206 (0.2058)

0.0106 (0.0983) )0.1284 ()1.2905)

0.0388 (0.3559) 0.1183 (1.3072)

)0.0400 ()0.4433) 0.0091 (0.1011)

0.02879 (0.2668) )0.0234 ()0.2411)

0.1575 (0.9381) )0.1321 ()1.2977)

0.01938 (0.1397) 0.0054 (0.0582)

)0.073 ()0.6660) 0.0011 (0.0096)

IBERDROLA

)0.1214 ()1.2233) 0.0738 (0.7951)

0.0483 (0.3590) )0.0303 ()0.2674)

REPSOL

)0.0163 ()0.1391) )0.0296 ()0.3282)

)0.0131 ()0.1377) )0.0495 ()0.4305)

SANTANDER

)0.0103 ()0.1092) 0.0095 (0.1428)

)0.0243 ()0.2166) )0.0088 ()0.0860)

SEVILLANA

)0.0290 ()0.2660) 0.1465 (1.3027)

0.0227 (0.2106) )0.0673 ()0.7533)

0.0749 (0.6729) 0.0078 (0.0761)

)0.0268 ()0.2492) 0.0162 (0.1922)

UNION FENOSA

)0.0262 ()0.3149) 0.0153 (0.1963)

)0.0569 ()0.5853) )0.1090 ()1.2832)

IBEX35

)0.0997 ()1.4696) 0.0112 (0.1052)

)0.0919 ()1.0430) )0.0600 ()0.7382)

BBV ENDESA FECSA

TELEFONICA

This table reports the results of the nonlinear Granger causality test proposed by Hiemstra and Jones (1994) for the residuals from VAR applied to the standardised residuals of the following Eqs. (10)–(12): Rcoit ¼ d10 þ d11 Rcoit
1 þ d12 GGt þ d13 BGt þ d14 GPit þ d15 BPit þ d16 IPit þ d17 FPit þ d18 DJt þ t1it ; t1it ! N ð0; r2g1it Þ; r2g1it ¼ a10 þ a11 t21it
1 þ a12 r2g1it
1 þ a13 BGt þ a14 BPit þ a15 DJt þ m1it : Rocit ¼ d20 þ d21 Rocit
1 þ d22 GGtþ1 þ d23 BGtþ1 þ d24 GPitþ1 þ d25 BPitþ1 þ d26 IPitþ1 þ d27 FPitþ1 þ d28 DJt þ t2it ; t2it ! N ð0; r2g2it Þ; r2g2it ¼ a20 þ a21 t22it
1 þ a22 r2g2it
1 þ a23 BGtþ1 þ a24 BPitþ1 þ a25 DJt þ m2it : ERVit ¼ d30 þ d31 ERVit
1 þ d32 ERVit
2 þ d33 GGtþ1 þ d34 BGtþ1 þ d35 GPitþ1 þ d36 BPitþ1 þ d37 IPitþ1 þ d38 FPitþ1 þ d39 DJt þ t3it ; t3it ! N ð0; r2g3it Þ; r2g3it ¼ a30 þ a31 t23it
1 þ a32 r2g3it
1 þ a33 BGtþ1 þ a34 BPitþ1 þ a35 DJt þ m3it : Each cell contains two rows: • The first reports the result of testing the null ‘‘extra volume change does not nonlinearly Granger cause related to noise returns’’. Numbers without parenthesis are the values of the test statistic proposed. Numbers in parenthesis are the standardised test statistics, under the null asymptotically distributed N ð0; 1Þ. • The second reports the result of testing the null ‘‘related to noise return does not nonlinearly cause extra volume change’’. Numbers without parenthesis are the values of the test statistic proposed. Numbers in parenthesis are the standardised test statistics, under the null asymptotically distributed N ð0; 1Þ. m ¼ 1, Lx ¼ Ly ¼ 2, e ¼ 1:5.

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Table 8 Linear Granger causality (after filtering for news) Close-to-open GARCH volatility/extra volume

Open-to-close GARCH volatility/extra volume

ARGENTARIA

1.2686 0.0607

4.4952 0.1380

ACESA

1.1741 0.4570

6.6337 0.7235

BBV

4.5229 2.3989

ENDESA

0.5929 1.4222

5.1502 1.9805

FECSA

4.3385 3.5707

8.3642 5.7443

IBERDROLA

4.4367 0.4332

11.3173 0.7068

REPSOL

2.3084 4.5526

5.8316 5.0842

SANTANDER

0.3706 1.6665

7.2692 2.1354

SEVILLANA

3.0136 1.0277

6.7206 3.1376

TELEFONICA

1.7288 0.7376

16.0514 0.3665

UNION FENOSA

1.1875 1.7373

3.1451 2.0035

IBEX35

0.0704 0.6518

4.2653 5.0842

10.5277 0.2879

This table reports the results of the linear Granger causality test applied to the standardised residuals of the following equation (12): ERVit ¼ d30 þ d31 ERVit
1 þ d32 ERVit
2 þ d33 GGtþ1 þ d34 BGtþ1 þ d35 GPitþ1 þ d36 BPitþ1 þ d37 IPitþ1 þ d38 FPitþ1 þ d39 DJt þ t3it ; t3it ! N ð0; r2g3it Þ; r2g3it ¼ a30 þ a31 t23it
1 þ a32 r2g3it
1 þ a33 BGtþ1 þ a34 BPitþ1 þ a35 DJt þ m3it ; and the GARCHð1; 1Þ variance series (from Eq. (10) and (11)) r2g1it ¼ a10 þ a11 t21it
1 þ a12 r2g1it
1 þ a13 BGt þ a14 BPit þ a15 DJt þ m1it : r2g2it ¼ a20 þ a21 t22it
1 þ a22 r2g2it
1 þ a23 BGtþ1 þ a24 BPitþ1 þ a25 DJt þ m2it : Each cell contains two rows: • The first reports the F -statistic results of testing the null ‘‘extra volume change does not linearly Granger cause GARCHð1; 1Þ volatilities (after news)’’.  denotes significance at the 5% level and  denotes significance at the 1% level. • The second reports the F -statistic results of testing the null ‘‘GARCHð1; 1Þ volatilities (after news) does not linearly cause extra volume change’’.  denotes significance at the 5% level and  denotes significance at the 1% level.

volume is included in the variance equation. Extra volume variations can rather be considered as a source of volatility. Up to this point, our findings document that in the Spanish stock market, volume or unexpected volume do not seem to be an accurate proxy for basic information flows. Volume cannot either be considered

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271

Table 9 Parameter estimates for open-to-close return equations (equations of the mean (Panel A) and equations of the variance (Panel B) Panel A GGtþ1

LAGt
1

C



ARGENTARIA 0.0020 0.0815 ACESA )0.0009 0.0740 BBV 0.0031 0.1234 ENDESA 0.0025 0.0035 FECSA )0.0010 0.0681 IBERDROLA 0.0022 0.0325 REPSOL 0.0002 )0.0508 SANTANDER 0.0001 0.0983 SEVILLANA 0.0025 )0.0313 TELEFONICA 0.0032 0.0147 UN. FENOSA 0.0039 0.1129 IBEX35 0.0018 )0.0278

BGtþ1

GPtþ1

BPtþ1

IPtþ1

0.0036 0.0029 0.0021 0.0015 0.0042 0.0021 0.0022 0.0022 0.0024 0.0003 )0.0002 0.0008

)0.0075 )0.0040 )0.0063 )0.0050 )0.0027 )0.0080 )0.0022 )0.0039 )0.0052 )0.0066 )0.0061 )0.0053

0.0058 0.0000 )0.0025 0.0008 0.0061 0.0053 0.0017 0.0043 0.0022 0.0015 )0.0010

)0.0084 0.0000 )0.0163 )0.0040 )0.0005 )0.0009 )0.0024 )0.0013 0.0018 )0.0043 )0.0036

GARCH(1)

BGtþ1

BPtþ1

DJt









)0.0010 0.0000 )0.0001 )0.0004 0.0021 0.0005 0.0004 )0.0013 )0.0000 0.0007 )0.0017

ERVt

FPtþ1

DJt

0.0020 0.0076 )0.0004 )0.0001 0.0132 )0.0064 )0.0006 )0.0015 )0.0025 0.0061 0.0045

)0.1371 )0.0030 )0.1043 )0.0089 )0.1861 )0.0242 )0.1458 0.0072 )0.1858 )0.0005 )0.3729 0.0260 )0.1120 )0.0002 )0.1013 0.0035 )0.1786 )0.0003 )0.0920 )0.0152 )0.3099 0.0212 )0.1404 )0.0002 

Panel B ARCH(1)

C ARGENTARIA ACESA BBV ENDESA FECSA IBERDROLA REPSOL SANTANDER SEVILLANA TELEFONICA UN. FENOSA IBEX35 



0.00008 0.00003 0.00001 0.00005 0.00009 0.00004 0.00008 0.00005 0.00001 0.00002 0.00003 0.00002

0.0689 0.0842 0.0466 0.0956 0.0946 0.0158 0.1414 0.1422 0.0615 0.1319 0.0598 0.1471

Denotes significance at the 10% level, Rocit

¼ d20 þ d21 Rocit
1 t2it ! N ð0; r2g2it Þ;







0.7252 0.8006 0.8830 0.6675 0.6534 0.8767 0.4635 0.7230 0.8869 0.7441 0.8168 0.6688

ERVt

0.00012 )0.0001 )0.0031 0.00000 0.0000 0.00006 0.00007 0.0002 0.0003 )0.00002 0.0001 )0.0014 )0.00001 )0.0002 0.0004 0.00001 )0.00002 )0.0024 )0.00001 )0.00008 )0.0014 )0.0008 0.0003 0.0015 0.00001 )0.00004 )0.0004 0.00001 )0.00009 )0.0002 0.00007 )0.0001 )0.0031 0.00005 )0.0005 



denotes significance at the 5% level and





0.0004 0.0012 0.0006 0.0009 0.0006 0.0019 0.0005 0.0015 0.0006 0.0007 0.0005 0.0002

denotes significance at the 1% level.

þ d22 GGtþ1 þ d23 BGtþ1 þ d24 GPitþ1 þ d25 BPitþ1 þ d26 IPitþ1 þ d27 FPitþ1 þ d28 DJt þ d29 ERVit þ t2it ;

r2g2it ¼ a20 þ a21 t22it
1 þ a22 r2g2it
1 þ a23 BGtþ1 þ a24 BPitþ1 þ a25 DJt þ a26 ERVit þ m2it :

strictly as a proxy for noise-liquidity trading affecting price changes. Unexpected trading seems to be more related to other factors such as the quality or intensity of news, or herding. . .that may also influence return volatility.

5. Further results In this section we return to the role of bad news and Dow Jones as key variables for the Spanish stock market. To confirm this assessment some additional analysis has been carried out. First, a linear Granger causality test with two lags from Rccdjtþ1 to returns, unexpected volume and volatilities before filtering for news (Rcoit , Rocit , ERVit and r2gcoit and r2gocit following Eq. (4)). In the way it is constructed, the test implies testing whether the Dow Jones known by Spanish traders before opening a trading

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session as well as the Dow Jones known the previous day help to predict such returns, volume and volatilities (Table 10). Second, (Table 11) a linear Granger causality test with two lags from Rccdjtþ2 to open-to-close returns, unexpected volume and open-to-close and volume variability before controlling for news (Rcoit , Rocit , ERVit and r2gcoit and r2gocit following Eq. (4)). In the way it is constructed, this test implies testing whether the Dow Jones known by Spanish traders before opening a trading session as well as the Dow Jones that will be known by Spanish traders before opening tomorrowÕs trading session are significant, the latter through the informational advantage that informed traders infer from the opening of USA markets which is released before the Spanish close. Note that close-to-open return and the corresponding volatility do not have any time link with the explanatory variable.

Table 10 Linear Granger causality test from Rccdjþ1

ARGENTARIA ACESA BBV ENDESA FECSA IBERDROLA REPSOL SANTANDER SEVILLANA TELEFONICA UN. FENOSA IBEX35

Rcot

Roct

ERVt

Close-to-open GARCH volatility

Open-to-close GARCH volatility

Volume GARCH volatility

8.7539 3.3836 25.898 13.5124 5.9700 15.9772 12.4961 7.3976 5.1078 16.2390 14.441 31.2328

0.7402 0.8630 2.1705 6.7250 4.3364 14.5787 4.7157 0.5413 2.0520 0.5980 12.9540 1.7962

1.2149 4.5552 1.5279 1.2330 0.6853 0.3391 0.3577 0.4960 0.1839 0.5117 0.7846 2.0634

15.1627 0.6825 2.2329 12.8697 8.5819 15.0162 11.2188 6.6607 5.9469 11.4564 18.3446 14.0591

16.8019 3.6195 7.4085 5.0624 0.7684 14.9213 10.2470 6.8085 12.8616 1.7053 10.5855 17.1391

1.3050 0.3056 0.2616 0.2386 0.0026 0.6533 0.7594 0.3074 0.4093 1.2453 0.1982 4.6091

This table reports the F-statistic results of testing the null ‘‘Rccdjþ1 does not linearly Granger cause. . .’’.  Denotes significance at the 5% level and  denotes significance at the 1% level.

Table 11 Linear Granger causality test from Rccdjþ2

ARGENTARIA ACESA BBV ENDESA FECSA IBERDROLA REPSOL SANTANDER SEVILLANA TELEFONICA UN. FENOSA IBEX35

Roct

ERV

Open-to-close GARCH volatility

Volume GARCH volatility

7.5513 5.5414 11.6285 12.9650 9.0431 18.7698 9.1122 11.2983 8.3953 11.4898 13.6594 20.7889

1.8158 4.8302 2.0806 0.9163 1.0464 0.5466 0.6474 1.3895 0.2583 0.5211 1.6046 1.2637

6.8686 7.9307 4.8498 1.3627 1.5016 2.6785 3.5862 5.9528 7.7130 2.3321 2.8945 10.6726

0.2141 0.3277 0.3131 0.2856 0.0105 0.6486 0.7097 1.4216 0.4433 1.1271 0.0670 3.8116

This table reports the F-statistic results of testing the null ‘‘Rccdjþ2 does not linearly Granger cause. . .’’.  Denotes significance at the 5% level and  denotes significance at the 1% level.

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273

Table 12 d estimates and Wald statistics (test of common features) Group 1 BGt þ 1 common feature in Rocit , r2g3it ARGENTARIA ACESA BBV ENDESA FECSA IBERDROLA REPSOL SANTANDER SEVILLANA TELEFONICA UN. FENOSA IBEX35

Group 2 BGt þ 1 common feature in r2g2it , r2g3it

Group 3 BPt þ 1 common feature in r2g2it , ERVit

5.7820 (0.1727)

)0.0198 (0.7256)

)0.0022 (0.9711)

8.2836 (0.3404) 15.6462 (0.1587)

)0.1221 (0.7879)

Group 4 BPt þ 1 common feature in r2g2it , r2g3it 0.29064 (0.7053)

0.0132 (0.6853) )0.0004 (0.5762)

17.8622 (0.8595) 18.7378 (0.2860) 6.3434 (0.8527)

)0.3404 (0.8620)

26.6963 (0.5665) )0.6944 (0.9251)

)0.1766 (0.7748) 0.0031 (0.9327)

0.0591 (0.8640) )0.1067 (0.8928) )0.0047 (0.8167)

This table reports d estimates to construct the linear combination of two series and the Wald statistic (in parenthesis) for the corresponding news variable parameter estimates in the linearly combined series.

Results in Table 10 indicate that the Dow Jones is a basic information source for close-to-open return and return volatility. Results in Table 11 suggest that Dow Jones returns at the open of USA markets are quite relevant for returns at the close of the Spanish market. And third, the regression-based procedure proposed by Engle and Kozicki (1993) is applied to test whether bad general or bad particular news published on t þ 1 could be considered common features in two series. Very briefly and following these authors, two series Y1 and Y2 have a common feature if each of them has been positively tested for the feature and there is a d such that ut ¼ y1t
dy2t does not have the feature. Four groups have been considered: • Group 1: those cases (individual firms or Ibex35) where bad general news published on t þ 1 was found to be significant both in Rocit and r2g3it (in Eqs. (11) and (12) respectively), • Group 2: those cases where bad general news published on t þ 1 was found to be significant both in r2g2it and r2g3it (in Eqs. (11) and (12) respectively), • Group 3: those cases where bad particular news published on t þ 1 was found to be significant both in r2g2it and ERVit (in Eqs. (11) and (12) respectively), • Group 4: and those cases where bad particular news published on t þ 1 was found to be significant both in r2g2it and r2g3it (in Eqs. (11) and (12) respectively). Table 12 shows the parameter estimates of d and the significance of the Wald statistic in the regression based tests, according to the classification proposed. None of the Wald statistics for the proper news variables are significant at the usual significance levels. These results are maintained before and after filtering for first order autocorrelation. This finding suggests that in those cases analysed, bad general news is a common feature for some Rocit , r2g3it and r2g2it series and that bad particular news is a common feature for some r2g3it , r2g2it , or ERVit series. Although we have not been able to provide accurate evidence about the characteristics linking price changes and volume, it seems clear that volume affects stock return volatility over and above exogenous and endogenous (e.g. prices) information arrival. Bad news and Dow Jones are two main sources of exogenous information for the return and volatility of actively traded stocks in the Spanish stock market. The complex relationship between so many variables affecting the price process in stock markets is surely one reason why this merits interest and examination.

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6. Conclusions To sum up, we conclude that our results can be interpreted as follows: • Firstly, we find that bad news and the Dow Jones play a highly significant informational role in the Spanish market. On the one hand, Dow Jones data strongly and linearly causes close-to-open returns and its volatility, and is very likely to cause open-to-close return and its volatility through the open of the U.S. market because of the time overlap with the last hour and a half of the Madrid Stock Exchange. On the other hand, bad news appears to be a common feature among return, price volatility and volume change series, particularly between open-to-close return and unexpected volume variability. • Second, we find evidence that volume has no significant linear and nonlinear predictive power for explaining close-to-open and open-to-close returns, neither as informational content, nor as a proxy for noise/liquidity trading. Volumen has much to do with volatility effects in large individual stocks in the Spanish market. • Third, we have not found significant evidence of causality from returns to volume although some linear causality has been reported for unexpected trading volume changes. • And fourth, these results do not change when considering aggregated data for the period under analysis.

Acknowledgements The authors would like to thank two anonimous referees for their helpful comments and suggestions on an earlier version of this paper. Similarly, they are grateful to the Spanish Ministry of Science and Technology (SEC-2003-07808-C03) for financial support.

References Almeida, A., Goodhart, C., Payne, R., 1998. The effects of macroeconomic news on high frequency exchange rate behavior. Journal of Financial and Quantitative Analysis 33 (3), 383–408. Arshanapalli, B., Doukas, J., 1993. International stock market linkages: Evidence from the pre and post-October 1987 period. Journal of Banking and Finance 17, 193–208. Baek, E., Brock, W., 1992. A general test for nonlinear Granger causality: Bivariate model. Working paper, Iowa State University and University of Wisconsin, Madison. Bennett, J., Sias, R., 2001. Can money flows predict stock returns? Financial Analysts Journal 57 (6), 64–77. Blasco, N., Corredor, P., Santamar
ıa, R., 2002. Is bad news cause of asymmetric volatility response? A note. Applied Economics 34, 1227–1231. Blume, L., Easley, D., OÕHara, M., 1994. Market statistics and technical analysis: The role of volume. Journal of Finance 49 (1), 153– 182. Brock, W., Hsieh, D., LeBaron, B., 1991. A Test of Nonlinear Dynamics, Chaos and Instability: Statistical Theory and Economic Evidence. The MIT Press, Cambridge, MA. Campbell, J., Grossman, S., Wang, J., 1993. Trading volume and serial correlation in stock returns. The Quarterly Journal of Economics 108 (November), 905–939. Chordia, T., Swaminathan, B., 2000. Trading volume and cross-autocorrelations in stock returns. Journal of Finance 55, 913–935. Clark, P., 1973. A subordinated stochastic process model with finite variance for speculative prices. Econometrica 41, 135–155. Copeland, T., 1976. A model of asset trading under the assumption of sequential information arrival. Journal of Finance 31, 135–155. Corredor, P., Santamar
ıa, R., 2002. Does derivatives trading destabilize the underlying assets? Evidence from the Spanish stock market. Applied Economics Letters 9, 107–110. Cremers, K.J., Mei, J., 2002. A new approach to the duo-factor model of return and volume. Working paper, New York University, Available from . Dickinson, D., 2000. Stock market integration and macroeconomic fundamentals: An empirical analysis, 1980–95. Applied Financial Economics 10, 261–276.

N. Blasco et al. / European Journal of Operational Research 163 (2005) 253–275

275

Ederington, L.H., Lee, J.H., 1993. How markets process information: News releases and volatility. The Journal of Finance 48 (4), 1161–1191. Ederington, L.H., Lee, J.H., 1995. The short-run dynamics of the price adjustment to new information. Journal of Financial and Quantitative Analysis 30 (1), 117–134. Engle, R., Kozicki, S., 1993. Testing for common features. Journal of Business and Economic Statistics 11 (4), 369–383. Epps, T., Epps, M., 1976. The stochastic dependence of security price changes and transaction volumes: Implications for the mixture of distributions hypothesis. Econometrica 44, 305–321. Espitia, M., Santamar
ıa, R., 1994. International diversification among the capital markets of the EEC. Applied Financial Economics 4, 1–10. Gallant, R., Rossi, P., Tauchen, G., 1992. Stock prices and volume. Review of Financial Studies 5 (2), 199–242. He, H., Wang, J., 1995. Differential information and dynamic behavior of stock trading volume. Review of Financial Studies 8 (4), 919–972. Hiemstra, C., Jones, J.D., 1994. Testing for linear and nonlinear Granger causality in the stock price–volume relation. Journal of Finance 49 (5), 1639–1664. Hsieh, D., 1991. Chaos and nonlinear dynamics: Application to financial markets. Journal of Finance 46, 1839–1877. Hsu, C., 1998. Volume and the Nonlinear Dynamics of Stock Returns. Lecture Notes in Economics and Mathematical Systems, Springer-Verlag. Jennings, R., Starks, L., Fellingham, 1981. An equilibrium model of asset trading with sequential information arrival. Journal of Finance 36, 143–161. La Porta, R., Lakonishok, J., Shleifer, A., Vishny, R., 1997. Good news for value stocks: Further evidence on market efficiency. Journal of Finance 52 (2), 859–874. Lakonishok, J., Smidt, S., 1989. Past price changes and current trading volume. The Journal of Portfolio Management 15, 18–24. Lamoureux, C., Lastrapes, W., 1990. Heteroskedasticity in stock return data: Volume versus GARCH effects. Journal of Finance 45 (1), 221–229. Lee, B.S., Rui, O.M., 2002. The dynamic relationship between stock returns and trading volume: Domestic and cross-country evidence. Journal of Banking and Finance 26, 51–78. Lee, C., Chen, G., Rui, O., 2001. Stock returns and volatility on ChinaÕs stock markets. Journal of Financial Research 24 (4), 523–543. Olmeda, I., Moreno, D., 2001. Are the emerging stock markets more predictable than the developed ones? Proceedings IX Foro de Finanzas, Pamplona, November 2001. Phylaktis, K., Kavussanos, M., Manalis, G., 1996. Stock prices and the flow of information in the Athens Stock Exchange. European Financial Management 2 (1), 51–78. Poon, R., 1994. An empirical examination of the returns volatility–volume relation in related markets: The case of stocks and options. Financial Review 29, 473–496. Tauchen, G., Pitts, G., 1983. Variability–volume relationship on speculative markets. Econometrica 51, 485–505. Z
arraga, A., 1998. An
alisis de causalidad entre rendimientos y volumen. Investigaciones Econ
omicas 22 (1), 45–67.