M Angeles Carnero

Blanca Martínez

Rocío Sánchez-Mangas

Safety; Accidents; Industrial Health; Job Satisfaction; Public Policy < J2 -Time Allocation, Work Behavior, and Employment Determination/Creation < J -Labor and Demographic

Keywords: Bullying at workplace, Moral harassment JEL Classification:C20, J28 Bullying at workplace, Moral harassment

Introduction

The term Mobbing was popularized during the 80's by H. Leymann, who called mobbing a kind of long-term hostile behavior detected in employees at workplaces. Using Leymann's definition 1 , "psychological terror or mobbing in working life involves hostile and unethical communication which is directed in a systematic manner by one or more individuals, mainly toward one individual, who, due to mobbing, is pushed into a helpless and defenseless position and held there by means of continuing mobbing activities. These actions occur on a frequent basis (at least once a week) and over a long period of time (at least six months' duration). Because of the high frequency and long duration of hostile behavior, this maltreatment results in considerable mental, psychosomatic and social misery". Leymann considered 45 activities representative of mobbing which are contained in the LIPT -Leymann Inventory of Psychological Terrorization-questionnaire; see Appendix A. These activities include verbal aggressions, rumors, humiliations and so on. Three types of mobbing can be considered depending on the link between victims and aggressors: horizontal, up-down and down-up mobbing. Updown mobbing occurs when a superior harasses one of his subordinates. Down-up mobbing occurs when a worker or a group or workers harasses his/their superior. When mobbing occurs between co-workers at the same hierarchical level it is called horizontal mobbing.

The identification of mobbing is not a trivial task since hostile activities at work are sometimes of quite normal interactive behaviors. However, it is when such activities are used frequently and over a long period of time in order to harass, when they turn into dangerous communicative weapons. It is their systematic use what starts the mobbing process.

The objective of mobbing2 , following the Technical Prevention Note (NTP-476) of the Instituto Nacional de Seguridad e Higiene en el Trabajo, is to destroy the social net of the victim, to disrupt his job, to destroy his reputation and to achieve that the victim quits his job. So, mobbing could be a way of getting rid of the worker without paying any compensation. On the other hand, some authors, e.g. [START_REF] Lewis | Nurse Bullying: organizational considerations in the maintenance and perpetration of health care bullying cultures[END_REF], see mobbing as "learned behavior" within the workplace, rather than any predominantly psychological deficit within perpetrators and targets.

There are recent studies for most European countries which quantify the importance of mobbing, although the percentages of mobbing victims range quite a lot. For example, [START_REF] Hubert | Mobbing: Systematic harassment in organizations[END_REF] find that 1% of workers in the financial sector in Holland suffer from mobbing. [START_REF] Cowie | Comparing the nature of workplace bullying in two European countries: Portugal and UK[END_REF] find that 38% of international institutions workers are mobbed in England. However, since the methodology used and the sample of workers are different in the previous works, such percentages are not really comparable.

The Informe Cisneros is the first and most complete research about mobbing developed in Spain. The Barómetro Cisneros elaborated by Piñuel y Zabala and Oñate (2002), was carried out in 2001 and 2002. For the 2001 survey, around 1000 workers from Madrid and Guadalajara were interviewed. In order to avoid subjectivity, the questionnaire includes a first part in which the workers do not know that the behaviors cited in the questionnaire describe mobbing activities. Then, the workers are asked about the scale, intensity, causes and reactions of the mobbing experience. For the 2002 survey, a total of 2410 workers from an industrial area near Madrid were interviewed. The 2002 survey includes questions related to the physical and psychological problems derived from mobbing.

Understanding and quantifying the process of mobbing is important because of its socio-economic consequences. In fact, not only the victim is involved in this problem but also the firm and the society. [START_REF] Vega | Sticks and Stones may break your bones but words can break your spirit: Bullying in the workplace[END_REF] argue that mobbing activities, often accepted by the organization, can create an environment of psychological threat that diminishes productivity and inhibits individual and group commitment. Links have also been found between mobbing and mental and physical health problems. Piñuel y Zabala and Oñate (2002) found that around 16% of workers reported being subjected to moral harassment or mobbing, and over half of the mobbing victims answered that mobbing affects their physical and mental health. Finally there are some attempts to measure the economic consequences for the society. [START_REF] Pastrana | Cúanto cuesta el mobbing en España?[END_REF], focussing on one of the possible mobbing outcomes, disability, estimates the cost of mobbing in Spain. By analyzing a sample of 6500 temporary disability cases, he found that mobbing victims account for 1.71% of the temporary disability cases, which implies that, during the year 2002, 52 million Euros were lost in work compensation as a consequence of mobbing behaviors in Spain.

The objective of this paper is twofold. First, we quantify the importance of mobbing in Spain using the fifth Spanish survey on working conditions, VENCT (2003), which was conducted by the Instituto Nacional de Seguridad e Higiene en el Trabajo. It includes, for the first time, some questions related to psychological factors and violence at work. Secondly, we study which are the determinants of mobbing behaviors at the workplace and analyze which variables are significant at explaining the probability of being a mobbing victim. The causes of mobbing could be numerous and arise from the organization, the perpetrator or even from 4 F o r P e e r R e v i e w the victim. [START_REF] Hirigoyen | El acoso moral en el trabajo[END_REF] shows that the personality of the victims could explain the mobbing processes. [START_REF] Leymann | La persecution au travail[END_REF] identified two main sources as the cause of mobbing: work organization and poor conflict management. We will focus on Leymann's study and investigate if work organization and working conditions can explain the probability of being mobbed. Poor conflict management should be also important but unfortunately our data do not allow us to test this hypothesis.

The paper is organized as follows. Section 2 describes the data analyzed and reveals some empirical facts of mobbing in Spain. In section 3 several variables possibly related to the problem are considered and their impact on the probability of being a mobbing victim is estimated. Finally, section 4 contains the conclusions and further lines of research.

Data description

We have used data from the fifth Spanish survey on working conditions, VENCT (2003), which was conducted by the Instituto Nacional de Seguridad e Higiene en el Trabajo. It covers 5236 workers and provides detailed information on working conditions, including, for the first time, some questions related to psychological factors and violence at work. The questionnaire also includes questions on work and job characteristics as well as socio-demographic variables.

With the information available we can identify mobbing victims, following Leymann's definition, by focusing on the two questions related to violence behavior at work: P.79. During the last 12 months, have you been subjected at work to: physical violence from people form your workplace, from other people or unwanted sexual attention? P.80. During the last 12 months, have you and how often, while working, been silenced, ignored, isolated, humiliated or ridiculed in connection with your work or personal life, suffering from verbal and written threats, or other similar behaviors? P.80 is a multiple choice question. The possible answers are: yes, daily; yes, at least once per week; yes, several times per month; yes, several times per year; no. P.79 is a yes/no question and does not give information about the frequency of the violent behavior. Notice also that we do not know the duration of these hostile behaviors. Consequently, Leymann's definition can not be applied literally.

With the information we have, we select the mobbing victims as those workers 5 Among 5211 respondents we identified 254 workers as mobbing victims. That means that 4.87% of the workers declared being mobbed at their workplaces.

There are two different studies we can carefully compare these results with. The first one is the Third European survey on working conditions 2000 finding that 5% of the workers in Spain are subjected to intimidation. There are important variations between countries, ranging from 4% in Portugal to 15% in Finland, but such differences probably reflect awareness of the issue rather than reality, because the questionnaire does not specify any definition or behavior for intimidation. The second work is the Barómetro Cisneros pointing out that more than 16% of the workers interviewed suffer from moral harassment or mobbing. This huge figure is obtained from a specific questionnaire about forms of harassment asked to workers from an industrial area near Madrid. As we will see, the Comunidad de Madrid is the region where we estimate the highest proportion of mobbing victims (10.7%). However, such number is statistically smaller than the 16% estimated by the Barómetro Cisneros.

Next, we analyze how the percentage of mobbing victims we found in this study, (4.87%), changes with personal and job characteristics. This information is contained in Table 1 4 and can be interpreted as a first approximation to the potential factors which determine mobbing behaviors at the workplace. Standard errors for the corresponding percentages are in brackets. First, notice that a higher proportion of women (5.55%) had experienced hostile behaviors at workplace compared to men (4.47%). Being the household supporter does not show any difference in suffering from mobbing. We also find higher percentage of mobbing victims among those workers with more than three children and among foreign workers. However, as shown by the standard errors, the uncertainty in both cases is huge due to the small number of observations in these groups. With respect to job characteristics, data suggest that mobbing is much more prevalent in the services sector (6.13%), in particular, in social services (7.70%) and in administration and bank (7%). Relating to the size of the firm, mobbing is much more common activity in firms with more than 500 workers.

According to the data, working conditions have important effects on hostile behaviors. We found higher percentages of mobbing victims working during the weekend, earning a variable wage, and working more than 40 hours per week. Job training also seems to play an important role. Workers having been trained at the firm, (Job training and/or General Courses) seem to be less exposed to mobbing activities. Moreover, the percentage of mobbed workers are very different depending on the type of training.

As [START_REF] Leymann | La persecution au travail[END_REF] pointed out, apart from job characteristics and working conditions, work quality could be related to the process of mobbing. In order to explore this, we have summarized all information contained in the VENT (2003) on work quality in two variables: job quality and organizational factors. First, job quality is a variable reflecting a comfortable working environment. More specifically, it is a dummy variable taking value 1 when temperature and noise are adequate at the workplace. We have found big differences in the percentage of mobbed workers depending on the job quality. Second, the organizational factors variable includes information about the load of work and the degree of autonomy at the workplace. Only 2.83% of workers declaring positive organizational factors while working suffer from mobbing. However, this percentage raises dramatically to 9.07% when looking at workers with negative organizational factors while working.

Finally, it can be interesting to analyze the importance of geographical factors. Figure 1 shows wide variations in the proportion of mobbing victims by region. As we can see, with the exception of Asturias, where none worker has been identified as a mobbing victim, this figure varies from 0.85% in Navarra to 10.7% in Madrid.

To have a better understanding of the problem we concentrate next on analyzing the intensity and types of mobbing.

Behaviors, intensity and types of mobbing

As we said before, mobbing victims are identified as those workers suffering from at least one of the six behaviors contained in P.79 and P.80 and detailed in Table 2. The objective of this section is twofold. First, we study if there is a particular hostile behavior, among the previous six, which is commonly used by mobbers. And then, we focus on the intensity of mobbing, defined as the number of hostile behaviors suffered by the victim. Table 2 shows the proportion of the identified mobbed workers who suffer from the behaviors used to identify mobbing. It is surprisingly large the percentage of mobbed workers (at least 40%) who suffer from physical violence at their workplace. This violent behavior together with silencing, ignoring or isolating the victim are the activities which most mobbed workers suffer from. When looking at these numbers by gender, we can see that most mobbed male victims suffer from physical violence (more than 50%) while most mobbed female victims suffer from being silenced, ignored, isolated (51.68%). Therefore, it seems that male and female are mobbed in a different way. Notice also these gender differences in suffering from verbal and written threats and accidents. While just 2.88% of mobbed male workers suffer from this particular behavior, this number becomes 11.88% when looking at female victims. Note that the total sum of the percentages in Table 2 is greater than 100, reflecting that some workers suffer from more than one hostile behavior.

Intensity of mobbing can be thought in terms of the number of hostile behaviors suffered by the victim. The bigger the number of activities used to mobb, the higher the intensity of the mobbing process. Figure 2 plots the proportion of mobbing victims suffering from one, two, three and more than three mobbing activities. It is remarkable that most of the mobbing victims (71.1%) suffer from just one of the mentioned hostile behaviors, which could be interpreted as being at the first stage of mobbing. As it is shown in the graph, most of the mobbed workers at their workplace are weakly mobbed while few of them are strongly mobbed. This is expected given that, as mentioned before, the workers we consider are still 9 F o r P e e r R e v i e w at their workplace. Most of the workers strongly mobbed are expected to quit the job, to change jobs or to be absent from work due to disability. Differences by gender are also found in the intensity of mobbing. The percentage of mobbed female victims suffering from more than one hostile behavior (31.18%) is higher than the corresponding percentage of males (27.03%), meaning that, in general, more female than male are strongly mobbed.

%

Finally, we can know from the survey who is the mobber, although there is no information about his/her characteristics. Using P.81. Who is/are the mobber/s: a superior, a subordinate, co-worker, we can identify who is mobbed by whom. Depending on who is the mobber, three types of mobbing can be considered: (i) Up-Down mobbing, when being mobbed by a superior, (ii) Horizontal mobbing, when being mobbed by a co-worker and (iii) Down-Up mobbing, when being mobbed by a subordinate. This could give us an idea about possible differences Table 3 shows that, among mobbing victims, hostile behaviors occur mainly among co-workers: more than 64% of the mobbing victims declare being mobbed by co-workers. Up-Down mobbing has an incidence of 41.14%, and the less usual case is being mobbed by a subordinate (12.12%). Again, we have found gender differences. Although horizontal mobbing has similar prevalence in male and female victims, big differences are found for the other two types of mobbing. As we can see, the prevalence of Up-Down mobbing is higher in male than in female and the percentage of workers being mobbed by a subordinate is almost three times higher for females. Notice that, again, the percentages add up more than 100 which means that some workers are suffering from more than one type of mobbing.

Determinants of Mobbing in Spain

In this section we analyze which are the determinants of mobbing behaviors at workplace. To this aim, we focus on information about personal, firm and sector characteristics, labor conditions and work organization included in the VENT (2003). With this objective we consider the probit model given by P r(M obbed = 1|X) = Φ(Xβ) where the dummy variable M obbed takes on the value 1 if the individual declared being mobbed and 0 otherwise and Φ(•) is the standard cumulative normal probability distribution and X is a vector containing exogenous explanatory variables. These variables, included in probability (the reference category is elementary education). On the other hand, among job characteristics and working conditions only seniority, courses and timetable are not significant. It suggests that the number of years working for the same firm, general training and the type of timetable are not relevant when explaining being mobbed.

For all significant variables in the model, we have computed their marginal effects, which are shown in Table 5 (see the first two columns). We can see that Training, Soc-services, Admin-Bank, C. Madrid and W. Hours seem to be the ones with higher impact on the probability of suffering from mobbing. As it shown in the table, having received specific training at the firm decreases the probability of being mobbed almost 4%. On the other hand, working for the social services, in the Administration/Bank sector, in the Comunidad de Madrid and working more than 40 hours per week increase in a significant amount (3.46%, 2.62%, 2.33% and 2.16% respectively) the probability of being a mobbing victim. With respect to variables affecting negatively such probability, we found that having a good job quality and a flexible timetable are the ones with a significant impact.

When we look at personal characteristics, being a woman increases significatively the probability of being mobbed, which is estimated 1.05% higher than for men. Moreover, it seems that human capital accumulation decreases 0.82% the probability of suffering from hostile behaviors.

Finally, although organizational factors seem to be very much related to mobbing, we have not included this variable in our estimation due to possible endogeneity problems. 5 5 We have also estimated alternative models. An ordered probit where the dependent variable reflects the intensity of mobbing gave us similar results adding the problems derived from having few observations in some categories. In order to explore possible differences at explaining hostile behaviors depending on the type of mobbing, we have also estimated three probit models, one per each type. However, the sample size for the Up-Down and Down-Up mobbing are not big enough to draw conclusive results.Consequently, the results are not shown in this paper but they are available from the authors upon request.

Gender differences

Along this research we have noticed the existence of gender differences. First, we found a higher percentage of mobbing victims among women than among men, 5.55% versus 4.47%. We have also found that this difference can increase when focussing on particular characteristics. For example, the percentage of males suffering from mobbing in Madrid is 8.30% while this percentage changes dramatically to 14.2% for females. On the other hand when looking at the Administration-Bank sector we found that 8.04% of the male workers are mobbed while just 5.90% of the female are mobbing victims in this sector.

Second, it seems that gender differences are also relevant among mobbed workers. As seen in subsection 2.1 male and female are mobbed differently, with different intensity and by a different type of mobbing. Finally, we have found that gender is a relevant variable at explaining the probability of being mobbed.

Therefore, it will be interesting to explore in more detail the differences by gender and to analyze if the variables explaining hostile behavior at workplace are the same and have the same impact depending on the victim's gender. To this aim, the previous probit model has been estimated separately for female and male. The results are shown in the last columns of Tables 4 and5. They suggest there are indeed differences in the relevant variables. With respect to personal characteristics none of them is important at explaining mobbing for females while high-school is still significant for males with a similar negative impact than when looking at the whole sample. Related to job characteristics, only social services is significant for females with a similar positive impact. All job characteristics are relevant for males, although their impact on the probability of being mobbed changes slightly. Looking at working conditions, it is remarkable that Job quality and C. Madrid are not significant for males, while they are two of the most important variables at explaining mobbing among females. A female living in Madrid has a probability of being mobbed 6.54% higher than a female, with the same characteristics, living in another region. However, living in Madrid has no impact on this probability when dealing with males. Notice also that earning a variable wage and having a flexible timetable are significant variables for females but have no effect on the probability of being mobbed for males.

Finally, we have studied if women are still more likely to suffer from mobbing when the behavior Unwanted sexual attention is not considered in the definition of mobbing victims. The reason to do this is that such behavior is mostly related to women and we would like to test whether mobbers use mainly and only unwanted sexual behaviors when the victim is a woman. Hence, we have excluded from the mobbing victims group those workers declaring being subject 14 to just one hostile behavior at workplace: unwanted sexual attention. There are 246 mobbing victims with this definition, which means 4.72% of the total sample. We have estimated the same probit model as before finding similar results 6 with the exception of the significance of the variable gender. This variable is not significant anymore meaning that gender is not relevant at explaining mobbing behaviors. This result could suggest that gender differences found in the process of mobbing are mostly driven by hostile activities related to sexual behaviors. However, this should be taken carefully given that there are other aspects such as discrimination and/or labor market segregation which we are not taking into account.

Conclusions

In this paper we have analyzed the problem of mobbing in Spain during 2003. We found that around 5% of workers who are at their workplace suffer from hostile behaviors. Significant differences in the percentage of mobbed workers are found by region, sectors and size of the firms, being Madrid, the services sector and firms with more than 500 workers where we find the highest mobbing rates. With respect to the intensity of the mobbing process, we found that most of the victims suffer from just one hostile behavior, specially male. Moreover, horizontal mobbing is the most common one with similar prevalence in males and females.

A probit model was fitted to the data and the results suggest that working conditions and job characteristics are more important at explaining mobbing than personal characteristics. More precisely, working in the services sector, in large firms, earning a variable wage, working during the weekends, more than 40 hours per week and in Madrid are factors with a positive impact on the probability of being mobbed. On the other hand, having been trained at the firm, having a flexible timetable and good job quality have a negative impact on such probability.

Finally, we found that women are more likely to be mobbed than men and also there are significant differences in the variables explaining mobbing depending on the victim's gender. Personal and job characteristics are more relevant for males while working conditions are more relevant for females.

Measuring the effects of mobbing on victim's health, disability, and productivity loss are other important factors to explore which are in our research agenda. 6 The estimation results are avalaible upon request. 1. The aggressor or mobber gives the victim no possibility to communicate.

2. The victim is silenced or continuously interrupted.

3. Colleagues prevent the victim to communicate.

4. Colleagues scream and shout at the victim.

5. The victim suffers verbal attacks regarding work assignments.

6. The victim suffers verbal attacks regarding her/his personal life.

7. The victim is terrorized by means of phone calls.

8. The victim suffers verbal threats.

9. The victim suffers written threats.

10. People at work refuse to make any contact with the victim.

11. The victim's presence is ignored.

(B) Activities on the possibilities of the victim to maintain social contacts:

12. The aggressor does not talk to the victim.

13. The victim is forbidden to talk to the aggressor.

14. The victim is isolated in a room far away from others.

15. Colleagues are forbidden to talk to the victim. • Age: Age of the worker in years.

• Children: Discrete variable taking values from 0 to 5 which indicates the number of children younger than 18 who live with the worker. The value 5 refers to 5 or more.

• Foreigner : Dummy variable taking the value 1 if foreigner and 0 if from Spain.

• Elementary: Dummy variable taking the value 1 if worker's education is elementary school and 0 otherwise.

• High-school : Dummy variable taking the value 1 if worker's education is high school and 0 otherwise.

• College: Dummy variable taking the value 1 if worker's education is college and 0 otherwise.

• Household supporter (H.S.): Dummy variable taking the value 1 if worker's salary is the largest of the household and 0 otherwise.

• Tenure: Dummy variable taking the value 1 if having a permanent job and 0 otherwise.

• Fulltime: Dummy variable taking the value 1 if it is a fulltime job and 0 otherwise.

• Vblewage: Dummy variable taking the value 1 if worker's wage is not fixed and 0 otherwise.

• Team: Dummy variable taking the value 1 if the individual works with others and 0 otherwise.

• Experience: Number of years the individual has been working for the actual firm.

• Training: Dummy variable taking the value 1 if being trained for the job at the organization and 0 otherwise.

• Workplace: Dummy variable taking the value 1 if working at a closed space(office, room) and 0 otherwise. • Temperature: Dummy variable taking the value 1 if the temperature at the workplace is comfortable and 0 otherwise.

• Noise: Dummy variable taking the value 1 if working at a noisy workplace and 0 otherwise.

• Sitting: Dummy variable taking the value 1 if the position while working is sitting and 0 otherwise.

• Hours: Number of hours the individual works per week.

• Timetable: Discrete variable taking values from 1 to 3. The value 1 refers to split timetable, 2 refers to non split timetable and 3 refers to shifts.

• Weekend : Dummy variable taking the value 1 if working during the weekends and 0 otherwise.

• Flexible: Dummy variable taking the value 1 if flexible timetable and 0 otherwise.

• Courses: Dummy variable taking the value 1 if having received formation (courses, talks, ...) organized by the actual firm and 0 otherwise.

• Size: Discrete variable taking values from 1 to 5 increasing with the number of workers in the firm.

• Services: Dummy variable taking the value 1 if the individual works in the services sector and 0 otherwise.

• Admin-Bank : Dummy variable taking the value 1 if the individual works in Administration/Banks and 0 otherwise.

• Retail-Hosp: Dummy variable taking the value 1 if the individual works in Retails/Hospitality and 0 otherwise.

• Soc-services: Dummy variable taking the value 1 if the individual works in Social Services and 0 otherwise.

• Muchwork : Dummy variable taking the value 1 if the workload has been too much during the last three months and 0 otherwise.

7

 7

Figure 2 :

 2 Figure 2: Proportion of mobbing victims by number of hostile behaviors suffered

  Activities on the possibilities of the mobbed person or mobbing victim to communicate adequately:

••

  Lesswork : Dummy variable taking the value 1 if the workload has been too few during the last three months and 0 otherwise. Responsibility: Discrete variable taking values from 1 to 3 increasing with the job responsibility. The value 1 refers to no responsibility, 2 refers to few responsibility and 3 refers to much responsibility.• Mobbed : Dummy variable taking the value 1 if mobbed and 0 otherwise.

Page 4 of 20 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

	Submitted Manuscript
	answering yes to P.79 3 plus those workers who answered yes, daily or yes, at least
	once per week to P.80.
	F o r
	P
	e
	e r
	R e v
	i e
	w

Table 1 :

 1 Prevalence of Mobbing (%)

	Personal Characteristics	(%) Working Conditions
	Gender		Wage
	Female	5.55(0.523)	Variable wage	7.20(0.794)
	Male	4.47(0.359)	Fixed wage	4.29(0.317)
	H.S.		Seniority
	Yes	4.86(0.385)	Less than 1 year	4.41(1.033)
	No	4.89(0.475)	1-3 years	5.19(0.665)
	N. of children		3-10 years	3.82(0.546)
	0	4.70(0.396)	More than 10 years	5.29(0.453)
	1	5.42(0.645)	Job training
	2	4.50(0.675)	Yes	3.78(0.281)
	3	5.6(2.102))	No	12.25(1.381)
	4	8.33(6.703)	General courses
	5	16.1(12.89)	Yes	4.31(0.369)
	Nationality		No	5.65(0.496)
	Spanish	4.80(0.298)	Weekend
	Foreigner	11.6(4.206)	Yes	7.13(0.536)
	Education		No	3.10(0.323)
	Elementary	4.30(0.479)	Flexible
	Secondary	5.07(0.492)	Flexible	3.55(0.529)
	College	5.09(0.613)	Fixed	5.25(0.354)
			Timetable
			Split	3.38(0.353)
			Non split	5.27(0.526)
			Shifts	9.59(1.129)
			Working hours
			Up to 20	3.85(1.897)
			21-40	3.91(0.310)
			More than 40	8.42(0.827)
	Job Characteristics		Work Quality
	Sector		Job quality
	Industry	2.47(0.492)	Yes	3.64(0.394)
	Services	6.13(0.395)	No	6.34(0.473)
	Construction	0.50(0.308)
	Size		Organ. factors
	Less than 10	3.37(0.657)	Positive	2.83(0.603)
	10-49	3.35(0.492)	Negative	9.07(0.949)
	50-249	2.90(0.518)
	250-499 More than 500	3.36(0.945) 8.27(0.669) 8

Table 2 :

 2 Mobbing activities. (% among mobbing victims)

	Mobbing activities	Total Female Male
	Physical violence from people from the workplace	11.50	12.93	10.47
	Physical violence from other people	39.93	25.47	50.21
	Unwanted sexual attention	7.3	8.50	6.52
	Being silenced, ignored, isolated	41.07	51.68	33.44
	Being humiliated, ridiculed, questioned	27.98	32.25	24.78
	Suffering from verbal and written threats, accidents 6.63	11.88	2.88
	10

Table 3 :

 3 Proportion of mobbing victims by type of mobbing

	Total Female Male
	Up-Down mobbing 41.14	33	50
	Horizontal mobbing 64.63 65.43 63.76
	Down-Up mobbing 12.12 17.40	6.50
	among mobbed workers suffering from different types of mobbing.

Table 1 ,

 1 have been selected, among all variables in the VENT (2003), taking into account possible causes of hostile behavior given in the literature. Estimation results are shown in Table4(see the first two columns).

	They suggest that working conditions and job characteristics are more impor-
	tant at explaining mobbing than personal characteristics. Related to the latter,
	only gender and high-school are significant, that is, being a woman increases
	the probability of being mobbed and having high school education decreases this
	11

Table 4 :

 4 Probit estimation for the dependent variable Mobbed

		Total		Female	Male
	Variable Coefficient	Robust	Coefficient	Robust	Coefficient	Robust
		Std. Error		Std. Error	Std. Error
	Const.	-1.85 * * *	0.190	-1.50 * * *	0.341	-1.84 * * *	0.231
	Personal characteristics
	Gender	0.17 *	0.095	--	--	--	--
	Household Supporter	-0.03	0.089	0.05	0.135	-0.07	0.112
	N.Children	-0.02	0.044	0.04	0.081	-0.04	0.049
	Foreigner	0.31	0.305	0.06	0.457	0.49	0.362
	High-school	-0.15 *	0.088	-0.09	0.157	-0.18 *	0.109
	College	-0.13	0.120	-0.20	0.177	-0.09	0.163
	Job characteristics
	Admin-Bank	0.35 * * *	0.113	0.10	0.174	0.51 * * *	0.141
	Soc-services	0.43 * * *	0.119	0.38 * *	0.161	0.57 * * *	0.160
	Size5	0.21 * *	0.089	0.20	0.141	0.21 *	0.112
	Working Conditions
	Vblewage	0.19 * *	0.088	0.28 * *	0.140	0.16	0.111
	Seniority	0.00	0.044	-0.03	0.073	0.01	0.056
	Training	-0.47 * * *	0.102	-0.33 *	0.182	-0.56 * * *	0.125
	Courses	-0.12	0.079	0.11	0.131	-0.14	0.101
	Weekend	0.28 * * *	0.084	0.24 * * *	0.140	0.30 * * *	0.104
	Flexible	-0.19 *	0.105	-0.31 *	0.172	0.16	0.140
	Timetable	0.07	0.055	-0.07	0.092	0.13 *	0.067
	W. Hours (> 40)	0.31 * * *	0.091	0.29 *	0.154	0.34 * * *	0.113
	J. quality	-0.21 * *	0.086	-0.30 * *	0.123	-0.18	0.113
	C.Madrid	0.33 * * *	0.087	0.65 * * *	0.134	0.08	0.119
	Log pseudo-likelihood -612.17783		-227.93599		-373.53912
	Pseudo R 2	0.1097		0.1236		0.1201
	Number of Obs.	4498		1402		3096
	*, **, *** significance at the 10%, 5% and 1% level, respectively.

Table 5 :

 5 Marginal effects for significant variables (%)

		Total		Female	Male
	Variable Coefficient	Robust	Coefficient	Robust	Coefficient	Robust
		Std. Error		Std. Error	Std. Error
	Personal characteristics
	Gender	1.05	0.62	--	--	--	--
	High-school	-0.82	0.48	--	--	-0.86	0.50
	Job characteristics
	Admin-Bank	2.62	1.03	--	--	3.73	1.41
	Soc-services	3.46	1.23	3.20	1.63	4.57	1.87
	Size5	1.40	0.66	--	--	1.19	0.71
	Working Conditions
	Vblewage	1.20	0.62	2.36	1.36	--	--
	Training	-3.91	1.16	-2.91	2.02	-4.30	1.40
	Weekend	1.64	0.53	1.74	1.07	1.49	0.55
	Flexible	-0.97	0.48	-1.82	0.88	--	--
	Timetable	--	--	--	--	0.63	0.32
	W. Hours (> 40)	2.16	0.74	2.50	1.57	2.00	0.79
	J. quality	-1.16	0.46	-2.12	0.93	--	--
	C.Madrid	2.33	0.73	6.54	1.78	--	--

See, for example, The Mobbing Encyclopedia at http://www.leymann.se.

Moral harassment, victimization or psychological terror are other terms used in the literature to describe this hostile behavior. Bullying is the term used in English-speaking countries.

Some authors, e.g.[START_REF] Lee | Gendered worplace bullying in the restructured UK Civil Service[END_REF], argue that single incidents could be considered as mobbing. With this idea, when the victim suffers from physical violence, just once is enough for considering the worker as mobbed, because such experience is going to be easily repeated over again in the worker's mind.

A detailed description of variables inTable 1 can be found in Appendix B.6 Page

of 20 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript


Dong Loc

Ger Truong

Robert Lanjouw

Lensink

Dong Truong

Ger Loc

* Truong

Ger Lanjouw
email: g.j.lanjouw@rug.nl.

Robert Lensink

Stock-Market Efficiency in Thin-Trading Markets: The Case of the Vietnamese Stock Market

Keywords: stock-market efficiency, thin-trading stock markets, Vietnam stock-market efficiency, thin-trading stock markets, Vietnam JEL-codes: G14, P34

published or not. The documents may come   L'archive ouverte pluridisciplinaire

Introduction

A growing number of papers examine whether stock markets in emerging markets behave in line with the efficient-market hypothesis (EMH). Some of these studies reject the hypothesis that the stock markets in question are weak-form efficient 1 . For instance, [START_REF] Wheeler | The efficiency of the Warsaw Stock Exchange: the first few years 1991-1996[END_REF] fail to find support for weak-form efficiency in the case of the Warsaw Stock Exchange. Similarly, [START_REF] Grieb | Random walk tests for Latin American equity indexes and individual firms[END_REF] reject the hypothesis of market efficiency for all stock-market indexes and most individual stocks in Brazil and Mexico. In addition, [START_REF] Karemera | Random walks and market efficiency tests: Evidence from emerging equity markets[END_REF] find that stock-return series in Brazil, Chile, and Mexico in general are not weakform efficient; equally [START_REF] Mookerjee | An empirical analysis of the equity markets in China[END_REF], [START_REF] Groenewold | The efficiency of the Chinese stock market and the role of the banks[END_REF] and [START_REF] Lima | Tests of the random walk hypothesis for equity markets: Evidence from China, Hong Kong and Singapore[END_REF] document that the stock markets in China are not weak-form efficient, and [START_REF] Abeysekera | Efficient markets hypothesis and the emerging capital market in Sri Lanka: Evidence from the Colombo Stock Exchange -A note[END_REF] and [START_REF] Abraham | Testing the random walk behaviour and efficiency of the Gulf stock markets[END_REF] reject the hypothesis of weak-form efficiency for stock markets in Sri Lanka, Kuwait, Saudi Arabia and Bahrain.

Other studies, however, provide evidence that stock markets in some emerging market economies are efficient. [START_REF] Dockery | Testing the random walk hypothesis: Evidence for the Budapest Stock Exchange[END_REF] document that the Budapest Stock Exchange is efficient in the weak form. In addition, studies by [START_REF] Karemera | Random walks and market efficiency tests: Evidence from emerging equity markets[END_REF] and [START_REF] Buguk | Testing weak-form efficiency: Evidence from the Istanbul Stock Exchange[END_REF] support the null hypothesis of weak-form market efficiency for the stock market in Turkey. For Africa, [START_REF] Dickinson | Market efficiency in developing countries: A case study of the Nairobi Stock Exchange[END_REF], and [START_REF] Olowe | Weak form efficiency of the Nigerian stock market: Further evidence[END_REF] find that the Nairobi and Nigerian stock exchanges are efficient in the weak form. [START_REF] Seddighi | The Chinese stock exchange market: Operations and efficiency[END_REF] document that the Shanghai Stock Exchange is weak-form efficient for the period from Jan. 4, 2000to Dec. 31, 2000[START_REF] Fawson | The weak-form efficiency of the Taiwan Share Market[END_REF], [START_REF] Alam | An application of variance-ratio test of five Asian stock markets[END_REF], and [START_REF] Chang | A variance ratio test of the random walk hypothesis for Taiwan's stock market[END_REF] support the hypothesis that the Taiwanese stock market is efficient in the weak form. Similarly, the null hypothesis of market efficiency cannot be rejected for the Hong Kong stock market [START_REF] Karemera | Random walks and market efficiency tests: Evidence from emerging equity markets[END_REF][START_REF] Alam | An application of variance-ratio test of five Asian stock markets[END_REF][START_REF] Cheung | A note on weak form market efficiency in security prices: Evidence from the Hong Kong Stock Exchange[END_REF] [START_REF] Lima | Tests of the random walk hypothesis for equity markets: Evidence from China, Hong Kong and Singapore[END_REF]. In addition, it is documented that stock markets in the ASEAN region (Indonesia, Malaysia, Thailand and Singapore) behave in line with the weak form of the EMH [START_REF] Barnes | Thin trading and stock market efficiency: the case of the Kuala Lumpur Stock Exchange[END_REF][START_REF] Karemera | Random walks and market efficiency tests: Evidence from emerging equity markets[END_REF][START_REF] Alam | An application of variance-ratio test of five Asian stock markets[END_REF]. With respect to the Southern part of Asia, [START_REF] Sharma | A comparative analysis of stock price behaviour on the Bombay, London, and New York stock exchanges[END_REF] and [START_REF] Alam | An application of variance-ratio test of five Asian stock markets[END_REF] provide evidence supporting the hypothesis of market efficiency for the Bombay (India) and Dhaka (Bangladesh) Stock Exchange. Thus, results regarding market efficiency are mixed and country-dependent.

While studies on stock markets in emerging markets are widely available, so far not any study has focused on Vietnam. This is unfortunate, since the Vietnamese stock market, although still characterized by low capitalisation and thin trading, is rapidly developing. The Vietnamese stock market also is an interesting case since Vietnam is one of the world's few remaining communist countries and insight into the functioning of a typically capitalist institution like a stock market within such a country could give new perspectives on the possible co-existence of elements of different ways of organizing economic and political systems. The main Vietnamese stock market, formally known as the Securities Trading Centre (STC), located in Ho Chi Minh City, was launched on July 28, 2000. At the opening trading session only two stocks with a total market capitalisation of VND 444,000 million (about USD 28.20 million) were traded at the market. Over five years of operation (until the end of 2005), the number of listed companies has increased to 32, with a total market capitalisation of VND 6,337,480 million (about USD 402.38 million).

This paper aims to have a closer look at the stock market in Vietnam. We describe, in detail, the organisation of the Vietnamese stock market and its development. We focus on whether the Vietnamese stock market is weak-form efficient. The reason behind this is that if the evidence would fail to support the weak-form of market efficiency, it is not necessary to examine the EMH for the more demanding degrees of the semi-strong and strong form (Wong and [START_REF] Wong | The behaviour of Hong Kong stock prices[END_REF]. As such testing for weak-form efficiency is a logical first step in investigating market efficiency in a certain stock market. To test for market efficiency we apply autocorrelation tests, runs tests and variance-ratio tests. The data used for these tests consists of observed weekly returns of the market index and five individual stocks listed on the market. A special feature of the analysis is that we correct the data for thin (infrequent) trading, which is a prominent characteristic of the Vietnamese stock market.

The remainder of this paper is organised as follows. Section 2 provides an overall description of the organisation and operation of the Vietnamese stock market. Section 3 summarizes the performance of the market over the period from July 28, 2000 to December 31, 2005. Section 4 describes the data we have used 2 . Section 5 explains the tests that we use for examining the weak form of market efficiency. Section 6 presents the results. Section 7 concludes.

Organisation and operation of the stock market

The 2 There is a discrepancy between the time span of the data used in the more descriptive Sections 2 and 3, where we like to mention the latest available data, and the later sections of the paper where the formal empirical testing is reported (data period 2000-2004). A company has to fulfil some qualifications before it can be listed at the Vietnamese stock market. The main requirements are a minimum capital of VND 5 billion (USD 0.32 million) and having been profitable in the last two consecutive years before the year of applying for listing. Furthermore, members of the firm's Board of Directors, Board of Management, and Board of Supervisors have to hold at least 50 percent of their shares for three years from the date of listing, and there should be at least 50 outside investors, holding 20 percent of the firm's equity at minimum.

Companies that want to be listed are required to submit certain documents to the SSC, including financial statements approved by accepted auditing organisation, the firm's charter and a prospectus containing information similar to that required by regulatory bodies in developed countries. Within 45 days from the date of receiving a full and complete set of application documents, the SSC will decide about granting the listing license to the firm. daily with two order-matching sessions at 9h20 am and 10h30 am. At the STC securities transactions are executed through a fully-computerised trading system, the Automatic Order-Matching and Put-Through Trading System. The system performs the order-matching process according to price-then-time priority, without human intervention. After securities brokerage companies electronically send buy or sell orders from their offices to the STC's mainframe computer, the system automatically executes an order-queuing process and arranges the orders according to a price-then-time priority. This procedure means that orders are first grouped by price, with the best price (highest price for buying and lowest price for selling) taking precedence. Then, within each price group, orders are arranged according to time. The trading price of a given stock is determined at a specified time (currently at 9h20 am and 10h30 am daily) and at the price that generates the greatest trading volumes of the stock. In case of having two or more price levels that create the same trading volumes, the price closest to the previous trading session's closing price is chosen as the trading price.

F

Put-through trading is a method for securities trading at the STC that allows brokers to deal directly with each other, either on behalf of their clients or for themselves. The dealing price is negotiated between the two brokers and hence the executed price may not be the same as that of securities traded in the market on that day. Following the conclusion of negotiations and trading, dealers are required to send details of the negotiation results to the STC's mainframe for recording purposes.

Foreign investors (institutions and individuals) can buy or sell shares at the STC through securities companies. However, their ownership in a listed firm is limited to 30 percent of the firm's equity. Foreign investors who wish to participate in the STC are required to register through a licensed custodian who holds securities on behalf of foreign investors. services for foreign investors. Moreover, foreign securities business institutions are allowed to buy shares of Vietnamese securities firms and/or investment funds, or contribute capital to establish a joint-venture securities firm and/or investment fund with Vietnamese partners.

Currently

However, the proportion of capital contributed by foreign partners in a joint venture is maximized at 49 percent of the firms' chartered capital.

The performance of the Vietnamese stock market

The e.g., Truong, Lanjouw and Lensink, 2006). Out of the listed firms 20 operate in the manufacturing sector, while the rest belongs to the trading and services sector.

Data description

The data used in this study consist of weekly price series of the market index (VN-INDEX)

and the five oldest stocks listed at the STC, to wit REE, SAM, HAP, TMS and LAF 6 . We also have applied the same methodology to daily data, but since the results were similar we have not presented them here to save space. VN-INDEX is a composite index calculated from prices of all common stocks traded at the STC. Specifically, it is a market-capitalisationweighted price index which compares the current market value of all listed common shares to their value on the base date of July 28, 2000 when the first trading session took place. The weekly returns are calculated as the natural logarithm of the index and the stock prices from Wednesday's closing price minus the natural logarithm of the previous 6 The full firm names corresponding to the abbreviated firm names are given in Table 2. price (or Tuesday if Thursday is not available) is used. If both Tuesday and Thursday prices are not available, the return for that week is reported as missing. The choice of Wednesday prices aims to avoid the effects of weekend trading and to minimise the number of holidays [START_REF] Huber | Stock market returns in thin markets: Evidence from the Vienna Stock Exchange[END_REF].

Descriptive statistics for the weekly returns of VN-INDEX and the individual stocks are presented in Table 4.

Methodology

According to [START_REF] Fama | Efficient capital markets: A review of theory and empirical work[END_REF] market efficiency implies that successive price changes of a stock are independently and identically distributed. Thus, past price movements or trends of a stock or a stock market cannot be used to predict their future movement. We use several tests to examine market efficiency in the weak form, which form focuses on the information conveyed by past prices. We first use parametric autocorrelation tests to examine whether the consecutive stocks returns are independent. However, the results of the Jarque-Bera test (presented in Table 4), indicate that stock returns are not normally distributed. Therefore we also use a non-parametric test to investigate market efficiency. More specifically, we use the runs test. Furthermore the variance-ratio test, proposed by [START_REF] Lo | Stock market prices do not follow random walk: Evidence from a simple specification test[END_REF], is conducted to examine whether uncorrelated increments exist in the series, under the assumption of homoscedastic and heteroscedastic random walks.

Autocorrelation tests

The first approach to detect whether stock returns show a random walk summarized here is the autocorrelation test. Autocorrelation measures the relationship between the current stock return and its value in the previous period. It is calculated as: where k is the serial correlation coefficient of stock returns of lag k; N is the number of observations; r t is the stock return over period t; r t+k is the stock return over period t+k; r is the sample mean of stock returns; and k is the lag of the period.

The autocorrelation test aims to determine whether the serial-correlation coefficients are significantly different from zero. Statistically, the hypothesis of weak-form efficiency should be rejected if stock returns (price changes) are serially correlated ( k is significantly different from zero).

To test the joint hypothesis that all autocorrelations are simultaneously equal to zero, the Ljung-Box portmanteau statistic (Q) is used. The Ljung-Box Q-statistics are given by:

= + = k j j LB j N N N Q 1 2 ) 2 (
where j is the j th autocorrelation and N is the number of observations. Under the null hypothesis of zero autocorrelation at the first k autocorrelations ( 1 = 2 = 3 = . . . = k = 0), the Q-statistic is distributed as chi-squared with degrees of freedom equal to the number of autocorrelations (k).

Runs test

The runs test is a non-parametric test that is designed to examine whether or not an observed sequence is random. The test is based on the premise that if a series of data is random, the observed number of runs in the series should be close to the expected number of the runs. A run can be defined as a sequence of consecutive price changes with the same sign. Therefore, price changes of stocks can be categorized into three kinds of runs: an upward run (prices go is given by: ( )

2 1 2 3 1 3 1 3 3 3 1 2 2 ) 1 ( 2 1 + + = = = = N N N n N N N n n i i i i i i m
The standard normal Z-statistic that can be used to test whether the actual number of runs is consistent with the hypothesis of independence is given by:

m m R Z ± = 5 . 0
where R is the actual number of runs, m is the expected number of runs, and 0.5 is the continuity adjustment (Wallis and Roberts, 1956) in which the sign of the continuity adjustment is negative (-0.5) if R m, and positive otherwise. Since there is evidence of dependence among share returns when R is too small or too large, the test is a two-tailed one.

Variance -ratio test

The variance-ratio test, proposed by [START_REF] Lo | Stock market prices do not follow random walk: Evidence from a simple specification test[END_REF], is demonstrated to be more reliable and at least as powerful as the unit-root test [START_REF] Lo | Stock market prices do not follow random walk: Evidence from a simple specification test[END_REF] ). The test is based on the assumption that the variance of increments in the random-walk series is linear in the sample interval. Specifically, if a series follows a random-walk process, the variance of its q-differences would be q times the variance of its first differences.

Var(p t -p t-q ) = qVar(p t -p t-1 ) where q is any positive integer. The variance ratio, VR(q), is then determined as follows:

) 1 ( ) ( ) p - Var(p ) p - Var(p 1 ) ( 2 2 1 - t t q - t t q q q VR = =
For a sample size of nq + 1 observations (p 0 , p 1 , …, pnq), the formulas for computing ) ( 2 q and ) 1 ( 2are given in the following equations:

h q p p q nq q t q t t = = 2 2 ) ( ) ( µ where ) 1 )( 1 ( nq q q nq q h + = and ) ( 1 ) ( 1 ˆ0 1 1 p p nq p p nq nq nq t t t = = = µ ) 1 ( ) ( ) 1 ( 1 2 1 2 = = nq p p nq t t t µ
Under the assumption of homoscedastic and heteroscedastic increments, respectively, two standard normal test-statistics, Z(q) and Z * (q), developed by [START_REF] Lo | Stock market prices do not follow random walk: Evidence from a simple specification test[END_REF]

) 1 , 0 ( ) ( 1 ) ( ) ( 2 1 N q q VR q Z = [ ] ) 1 , 0 ( ) ( 1 ) ( ) ( 2 1 * * N q q VR q Z =
where ) (q is the asymptotic variance of the variance ratio under the assumption of homoscedasticity, and ) ( * q is the asymptotic variance of the variance ratio under the assumption of heteroscedasticity:

) ( 3 ) 1 )( 1 2 ( 2 ) ( nq q q q q = ) ( ) ( 2 ) ( 2 1 1 * j q j q q q j = =
where ) ( ˆj is the heteroscedasticity -consistent estimator, computed as follows:

Estimating the true returns: correcting for thin trading

As mentioned above, the Vietnamese stock market is characterized by thin and infrequent trading. Many studies have pointed out that thin or infrequent trading can cause a serious bias regarding the results on market efficiency (see [START_REF] Cohen | The returns generation process, returns variance, and the effect of thinness in securities markets[END_REF]Lo and MacKinlay, 1990a;[START_REF] Stoll | The dynamics of stock index and stock index futures returns[END_REF][START_REF] Miller | Mean reversion of Standard & Poor's 500 Index basis c hanges: Arbitrage-induced or statistical illusion?[END_REF]. The problem is that the absence of a price change between two moments may be interpreted as being caused by the absence of a price reaction to new information and consequently as a sign that the market is inefficient, whereas it may simply be caused by the absence of any trade due to trading taking place infrequently. To deal with the problem of thin trading, the methodology proposed by [START_REF] Miller | Mean reversion of Standard & Poor's 500 Index basis c hanges: Arbitrage-induced or statistical illusion?[END_REF] is employed in this study. To remove the effect of thin trading the model basically suggests that a moving-average model which reflects the number of non-trading days should be estimated, after which returns are adjusted accordingly. However, due to difficulties in determining non-trading days, [START_REF] Miller | Mean reversion of Standard & Poor's 500 Index basis c hanges: Arbitrage-induced or statistical illusion?[END_REF] show that it is equivalent to achieve the non-trading adjustment by estimating an AR(1) model. Specifically, the model can be stated in the following equation:

t t t R R + + = 1 1 0
Then, using the residuals from the above equation, adjusted returns are computed as follows:

1 1 = t Adj t R
where Adj t R is the adjusted return for thin trading at time t.

It is important to note here that the above model assumes the non-trading adjustment to be constant over time. The assumption may be correct for developed markets, but it is not likely to be the case for emerging markets [START_REF] Antoniou | Market efficiency, thin trading and non-linear behaviour: Evidence from an emerging market[END_REF]. Therefore, in this study the equation is recursively estimated on a yearly basis.

All tests are conducted with both observed and corrected data. The results of these tests are discussed in the following section.

Empirical findings

Autocorrelation tests

To test the weak form of market efficiency for the Vietnamese stock market, first the autocorrelation tests with 12 lags are performed for weekly returns of the VN-Index and five individual stocks. The results of these tests are summarized in Tables 5 and6.

The estimation results show that autocorrelation coefficients of the observed weekly index returns are significant with a positive sign at the 1 st , 2 nd , 3 rd , 4 th , and 5 th lags.

Additionally, based on the Q-statistics, the null hypothesis of absence of autocorrelation in the index returns for all lags selected is strongly rejected at the one percent significance level.

Furthermore, results of the autocorrelation tests on observed weekly returns for the individual stocks show significant autocorrelation coefficients for each individual stock-returns series. Specifically, significant autocorrelation coefficients are found at the 1 st , 2 nd , and 4 th lag for REE; at the 1 st , 2 nd , 4 th , and 5 th lag for SAM; at the 1 st and 2 nd lag for HAP; at the 1 st , 2 nd , 3 rd , 4 th , 5 th and 7 th lag for TMS; and at the 1 st , 2 nd , 3 rd , and 5 th lag for LAF. The Q-statistics fail to support the joint null hypothesis that all autocorrelation coefficients from lag 1 to 12 are equal to zero for the observed return series of all individual stocks.

The results of the autocorrelation tests for the corrected returns indicate that the random-walk hypothesis is still rejected for the market index and all selected individual stocks, except REE.

Runs test

To investigate weak-form efficiency of the Vietnamese stock market, the non-parametric runs test is also used in this study. The runs test is considered more appropriate than the parametric autocorrelation test since the observed series do not follow a normal distribution. Results of 7. The results indicate that the null hypothesis of independence among stock returns is rejected for the market index and all selected individual stocks, except HAP. However, when corrected returns are used, the results of the runs test reveal that the null hypothesis cannot be rejected for HAP, but it is to be rejected for REE and LAF. For the remaining series the rejection of the null hypothesis is unchanged, but the extent is less pronounced as compared with the results for the observed weekly data.

Variance -ratio tests

This study employs variance-ratio tests for both null hypotheses, namely the homoscedasticand heteroscedastic-increments random walk. In addition, the variance ratio is calculated for intervals (q) of 2, 4, 8, 16 and 32 observations. The results of the variance-ratio tests are reported in Tables 8 and9.

The estimation results again confirm that the null hypothesis of random-walk behaviour under the assumption of homoscedasticity is strongly rejected for all series for all values of q. Indeed, all Z-statistics are greater than the conventional critical value (1.96 for the five percent level). In addition, the heteroscedasticity-consistent variance-ratio test provides consistent evidence that the null hypothesis of random-walk behaviour cannot be accepted for any of the observed weekly-return series. Specifically, a comparison the Z * -statistic to the conventional critical value reveals that the random-walk hypothesis is rejected at q = 2, 4, 8, and 16 for TMS and REE, and at q = 2, 4, and 8 for VN-INDEX and LAF. In the case of HAP the evidence against the null hypothesis under the assumption of heteroscedasticity is weak because only two rejections (q=2 and q=4) are reported. Furthermore, when corrected returns are employed, similar results are obtained from the tests. Specifically, the null hypothesis of random-walk behaviour under the assumption of homoscedasticity is strongly rejected for all series for all values of q, while under the assumption of heteroscedasticity the null hypothesis cannot be accepted for all series for some values of q. The rejection of the null hypothesis is less pronounced for VN-INDEX, REE, TMS and LAF, but more pronounced for SAM and HAP as compared to the results for the observed weekly returns.

Conclusions

This paper provides a detailed description of the stock market in Vietnam and also examines whether the Vietnamese stock market is weak-form efficient. The weak form of market efficiency for the market index and five selected individual stocks is tested by using weekly return data for the period from July 28, 2000 to December 31, 2004. We apply three tests: the autocorrelation test, the runs test and the varianceratio test. To deal with the problem of thin or infrequent trading, which could seriously bias the results, the observed returns are corrected by using the methodology proposed by [START_REF] Miller | Mean reversion of Standard & Poor's 500 Index basis c hanges: Arbitrage-induced or statistical illusion?[END_REF]. The results obtained from the autocorrelation tests indicate that the null hypothesis of random-walk behaviour is conclusively rejected for the market index and four out of five selected individual stocks, even in the case that the returns are corrected for thin trading. In addition, the runs test provides evidence to reject the random-walk hypothesis for observed weekly returns of the market index and all selected individual stocks (except for HAP). However, when corrected returns are used, the results given by the runs tests fail to reject the randomwalk hypothesis for REE and LAF. The results of Lo and MacKinley's variance-ratio

  State Securities Commission (SSN), officially established in November 1996, is responsible for the organisation, development and supervision of the country's securities market. Before February 2004, the SSC had operated as an organ under the direct responsibility of the Prime Minister. However, the Prime Minister decided, on February 19, 2004, to hand over the task of managing the SSC to the Ministry of Finance. The SSC is responsible for four security-trading centres: the Ho Chi Minh City (HCMC) Stock-Trading Centre (STC), the Hanoi Stock Trading Centre, the Securities Information Technology Centre, and the Centre for Securities Research and Training. In this paper we concentrate on the Ho Chi Minh City Stock-Trading Centre, being the main stock market in the country.

  , three foreign banks (the Hong Kong and Shanghai Banking Corporation, Deutsche Bank AG and Standard Chartered Bank) are licensed by the SSC to provide custodian

  been negligible although it rose year by year. It goes up to 0.55 percent in 2004 from 0.24 percent in 2000. Trading at the market also is still rather thin. Trading value at the STC has been tiny; during the first year of trading the average daily trading value is only VND 1,385 million (USD 0.09 million). It rises significantly in 2001 to VND 6,128 million (USD 0.39 million). Secifically, daily trading extraordinarily peaks on June 11, 2001 at VND 30,459 million (USD 1.93 million). However, daily trading value remarkably declines in the two consecutive following years (2002 and 2003) before temporarily moving upwards in the first half of 2004 and, later, in the second half of 2005. The developments in daily trading value over the period from July 28, 2000 to December 30, 2005 are shown in Figure 1. Another indicator of the thinness of trading at the STC is the trading-value-to-GDP ratio. This ratio has been negligible, although it rose considerably in 2004, after a continuous decline over the period 2000-2003. Low market capitalisation and commensurately small trading volumes make the Vietnamese stock market the smallest in the Southeast Asian region. During the year following the STC's opening, the prices of all listed shares moved-up daily. Consequently, VN-INDEX considerably and continuously rose, moving from the initial base level of 100 to the record level of 571.04 on June 25, 2001. The main reason to explain the steep ascent of stock prices is the existence of an acute imbalance between supply of and demand for shares 5 . Since then, however, despite the government's great efforts in creating commodities for the market by stimulating joint-stock companies to have their stocks listed at the STC, the market index fell substantially. In fact, VN-INDEX has slipped-down from the top of 571.04 to a bottom of 130.9 on October 24, 2003. After falling to the bottom, 5 As of June 25, 2001, only six joint-stock companies with a total capital of VND 360,044 million (USD 22.87 million) had their stocks listed at the STC.

  has recovered and remained fairly stable at a level above 200 since January 2004. As of December 30, 2005 VN-INDEX stood at 307.50 points. The time pattern of VN-INDEX over the period from the STC's opening date to December 30, 2005 is graphically shown in Figure 2.

  a downward run (prices go down) and a flat run (prices do not change). Under the null hypothesis of independence in share-price changes (share returns), the total expected number of runs (m) can be estimated as: the total number of observations (price changes or returns) and n i is the number of price changes (returns) in each category (N = For a large number of observations (N > 30), the sampling distribution of m is approximately normal and the standard error of m ( m )

  ; Liu and He,

  for weekly returns of the index and the selected individual stocks are reported in Table

  homoscedasticity and heteroscedasticity assumptions for both observed and corrected returns fail to support the random-walk hypothesis for the market index and all selected individual stocks.In summary the predominant outcome of this study is that the Vietnamese stock market is inefficient in the weak form, even in case corrections are made for the thin trading that characterizes the market.

  As of December 31, 2005, 13 securities companies (see Table1) have been licensed, with a total registered capital of VND 605,750 million (USD 38.47 million). Securities companies can be established either as joint-stock or as limited-liability companies. The main business of the securities companies is brokerage, own-account trading 3 , securities investment portfolio management, underwriting, and financial and securities investment advice. In order to receive a license for the securities business the company should have a business plan that is in line with the objectives of Vietnam's socio-economic development and growth of the securities industry; it should have adequate technical facilities for securities-business

	F o r
	P
	e
	e r
	R e v
	i e
	w
	5

activities; it should have a certain minimum level of legal capital, dependent on the type of securities business; it should have a director, vice-director, and practitioners who are qualified for being granted the securities practitioner certificate by the SSC.

  Vietnamese stock market was launched on July 28, 2000 with just two firms listed,

Refrigeration Electrical Engineering Joint Stock Company (REE), and Saigon Cable and

Telecommunication Material Joint Stock Company (SACOM). The growth of the number of listed companies has been rather slow. At the end of 2000 still only five joint-stock companies were listed, to be joined by only five more in 2001. In 2002 10 more companies are listed. By the end of 2005, a total of 32 joint-stock companies have been given permission to float their shares on the STC (see Table

2

). All of the listed firms (except North Kinh Do Food Joint-Stock Company and Kinh Do Corporation) are former state-owned enterprises (SOEs) that were previously restructured by equitisation, the Vietnamese version of privatisation (see,

Table 3

 3

presents information on key indicators of the development of the STC over the period from the opening year (2000) through December 30, 2005. During this period market capitalisation has increased significantly and continuously, from VND 444,000 million (USD 28.20 million) at the first trading session (July 28, 2000) to VND 2,650,197 million (USD 168.32 million) at year-end 2002, and to VND 6,337,480 million (USD 396.06 million) on December 30, 2005. Moreover, the data in Table

3

reveal that the market capitalisation-to-

Table 2 :

 2 Companies listed at the STC (as ofDec. 31, 2005)

		No Date of listing	Company	Code	Sector	Number of outstanding common shares	Share price at IPO (1,000 VND)	Share price at Dec. 31, 2005 (1,000 VND)
	1	Jul. 28, 2000 Refrigeration Electrical Engineering Company	REE Manufacturing	15,000,000	16.0	34.4
	2	Jul. 28, 2000 Cable and Telecommunications Material Company	SAM Manufacturing	12,000,000	17.0	47.0
	3	Aug. 04, 2000 Hai Phong Paper Joint Stock Company	HAP Manufacturing	2,008,000	16.0	22.8
	4	Aug. 04, 2000 Transimex Joint Stock Company	TMS Transportation	3,300,000	14.0	43.4
	5	Dec. 15, 2000 Long An Food Processing Export Company	LAF Manufacturing	1,930,082	17.0	20.3
	6	Jul. 12, 2001 Saigon Hotel Corporation	SGH	Hotel	1,766,300	25.2	18.0
	7	Oct. 18, 2001 Halong Canned Food Joint Stock Company	CAN Manufacturing	3,500,000	27.1	17.2
	8	Nov. 26, 2001 Da Nang Plastics Joint Stock Company	DPC Manufacturing	1,587,280	35.0	12.2
	9	Dec. 17, 2001 Bien Hoa Confectionery Company	BBC Manufacturing	5,600,000	27.0	21.9
	10 Dec. 26, 2001 Saigon Beverages Joint Stock Company	TRI	Manufacturing	3,790,300	29.0	28.0
	11 Jan. 02, 2002 Binh Thanh Import-Export Production & Trade	GIL	Trade	1,700,000	38.0	32.0
		Joint Stock Company
	12 Jan. 17, 2002 Binh Trieu Construction and Engineering Joint	BTC Manufacturing	1,261,345	21.9	8.1
		Stock Company
	13 Apr. 11, 2002 Bim Son Packaging Company	BPC Manufacturing	3,800,000	25.0	16.2
	14 Apr. 18, 2002 Chau Thoi Concrete Corporation	BT6	Construction	5,882,690	23.9	31.0
	15 Apr. 22, 2002 General Forwarding & Agency Corporation	GMD Transportation	20,000,000	42.5	69.5

Page 25 of 35 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

	Submitted Manuscript
	Table 2 continued
	16 May 02, 2002 An Giang Fisheries Import & Export Joint Stock Company	AGF Manufacturing	4,179,130	30.0	42.0
	17 May 09, 2002 Savimex Corporation	SAV Manufacturing	4,500,000	25.0	31.0
	18 Aug. 29, 2002 Seafood Joint Stock Company No. 4 19 Aug. 19, 2002 Khanh Hoi Import Export Joint Stock Company 20 Dec. 18, 2002 Hanoi P&T Construction and Installation Joint Stock Company 21 Feb. 12, 2003 VTC Telecommunications Joint Stock Company 22 Nov. 04, 2003 Petroleum Mechanical Joint Stock Company 23 Mar. 15, 2004 Bach Tuyet Cotton Corporation 24 Apr. 14, 2004 Hoa An Joint Stock Company 25 Sep. 21, 2004 Saigon Fuel Joint Stock Company 26 Dec. 12, 2005 Kinh Do Corporation 27 Mar. 1, 2005 Southern Seed Joint-Stock Corporation F o r P F o r P e e r R e v i e w TS4 Manufacturing 1,500,000 KHA Trade 3,350,000 HAS Construction 1,200,000 VTC Manufacturing 1,797,740 PMS Manufacturing 3,200,000 BBT Manufacturing 6,840,000 DHA Manufacturing 3,500,000 SFC Trade 1,700,000 KDC Manufacturing 25,000,000 SSC Agriculture 6,000,000 28 Mar. 21, 2005 HaNoi Maritime Holding Company MHC Transportation 6,705,640 e	16.0 21.5 21.5 21.0 14.5 21.6 38.5 22.8 59.0 30.8 19.0	26.0 21.3 32.8 32.9 14.4 11.0 43.0 28.5 54.0 44.0 23.3
	29 Jul. 11, 2005 Phuong Nam Culture Joint-Stock Corporation 30 Jul. 20, 2005 Thien Nam Trading Import Export Corporation e r	PNC TNA	Trade Trade	2,000,000 1,300,000	16.0 30.0	16.6 31.0
	31 Dec. 12, 2005 Kinh Do Corporation 32 Dec. 16, 2005 Nhi-Hiep Brike-Tile Joint Stock Company Source: The State Securities Commission's website (www.ssc.gov.vn) R e v	KDC Manufacturing NHC Manufacturing	25,000,000 1,336,061	59.0 23,000	54.0 24.5
	i e
	w	26
	27

Table 3 :

 3 Key development indicators for the STC over the period 2000-2005Source: Own calculation on the basis of data obtained from the Bank for Investment & Development of Vietnam Securities Company's website, www.bsc.com.vn.

	Indicators	2000	2001	2002	2003	2004	2005
	Number of listed	5	10	20	23	26	32
	companies
	Market capitalisation	1,048.76 1,661.10 2,650.20	2,514.29 3,945.31	6,337.48
	(bil. VND)
	Market capitalisation	0.24	0.34	0.49	0.42	0.55	na
	on GDP (%)
	Yearly trading value	91.40	925.38	762.77	422.50 1,692.99	2,435.64
	(bil. VND)
	Trading value on GDP	0.02	0.19	0.14	0.07	0.24	na
	(%)
	Average daily trading	1.39	6.13	3.23	1.71	6.80	9.82
	value (bil. VND)
	VN-INDEX	206.83	235.40	183.33	166.94	239.29	307.50
	Percentage change in	-	+ 13.8	-22.1	-8.9	+ 43.3	+ 28.5
	VN-INDEX (%)
	Notes: All figures refer to the end of the year indicated.
	na: not available

Table 4 :

 4 Descriptive statistics for VN-INDEX and individual stock returns

		VN-INDEX	REE	SAM	HAP	TMS	LAF
	Observations	225	225	225	224	224	205
	Mean	0.0016	0.0007	0.0014	0.0007	0.0016	0.0013
	Median	0.0003	0.0000	0.0011	0.0011	0.0000	0.0000
	Maximum	0.0840	0.0834	0.0853	0.1718	0.2850	0.1567
	Minimum	-0.0894	-0.1774	-0.1768	-0.2553	-0.3010	-0.1467
	Std. Dev.	0.0189	0.0259	0.0240	0.0365	0.0376	0.0283
	Skewness	-0.4	-1.5	-2.0	-3.1	-0.97	-0.1
	Kurtosis	8.0	13.6	17.8	26.4	36.97	11.0
	Jarque-Bera	239.9 a	1,129.9 a	2,201.8 a	5485.9 a	10,808.3 a	543.5 a
	a : Indicates that the null hypothesis of normality is rejected at the 1% significant level

Table 5 :

 5 Results of autocorrelation tests for the observed weekly-return data

	Lag	VN-INDEX		REE		SAM	HAP		TMS		LAF
		AC	Q-stat	AC	Q-stat	AC	Q-stat	AC	Q-stat	AC	Q-stat	AC	Q-stat
	1	0.328 a 24.554 a	0.266 a 16.090 a	0.175 a 6.986 a	-0.188 a	8.016 a	0.200 a	9.106 a	0.164 b	5.588 b
	2	0.250 a 38.905 a	0.177 a 23.271 a	0.144 b 11.712 a	0.310 a 29.971 a	0.246 a	22.922 a	0.219 a	15.633 a
	3	0.155 b 44.434 a	0.120 26.589 a	0.040 12.085 a	-0.066 30.968 a	0.178 a	30.196 a	0.215 a	25.379 a
	4	0.206 a 54.280 a	0.201 a 35.946 a	0.172 a 18.921 a	0.063 31.883 a	0.151 b	35.418 a	0.097	27.372 a
	5	0.239 a 67.540 a	0.118 39.169 a	0.170 b 25.666 a	0.090 33.762 a	0.232 a	47.884 a	0.226 a	38.241 a
	6	0.075 68.838 a	0.077 40.550 a	-0.039 26.029 a	0.015 33.817 a	0.088	49.692 a	0.128	41.709 a
	7	0.089 70.685 a	0.066 41.577 a	0.064 26.993 a	-0.002 33.818 a	0.136 b	54.008 a	0.009	41.727 a
	8	-0.013 70.725 a	0.096 43.739 a	-0.031 27.214 a	0.008 33.833 a	0.078	55.431 a	-0.083	43.212 a
	9	0.098 72.993 a	-0.034 44.020 a	0.105 29.817 a	0.068 34.936 a	0.059	56.246 a	0.094	45.123 a
	10	-0.077 74.391 a	0.016 44.082 a	-0.091 31.773 a	-0.002 34.937 a	0.027	56.419 a	-0.097	47.192 a
	11	0.069 75.516 a	-0.007 44.093 a	0.018 31.850 a	0.061 35.809 a	0.051	57.037 a	0.022	47.297 a
	12	0.031 75.741 a	0.006 44.103 a	-0.007 31.861 a	0.048 36.369 a	0.047	57.561 a	0.021	47.393 a

a , b : Significant at the 1% and 5% levels, respectively.

Page 29 of 35 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

	Submitted Manuscript
	F o r P e e r R e v i e w
	30

Table 6 :

 6 Results of autocorrelation tests for the corrected weekly-return dataSignificant at the 1% and 5% levels, respectively.

	Lag	VN-INDEX	REE	SAM	HAP	TMS		LAF
		AC	Q-stat	AC	Q-stat	AC	Q-stat	AC	Q-stat	AC	Q-stat	AC	Q-stat
	1	-0.055	0.687	-0.033	0.244	-0.005	0.005	-0.010	0.022	-0.079	1.406	-0.046	0.448
	2	0.143 b	5.377	0.055	0.939	0.069	1.095	0.238 a 12.863 a	0.165 b	7.578 b	0.181 a	7.270 b
	3	0.017	5.442	0.047	1.455	-0.022	1.209	0.025 13.006 a	0.058	8.345 b	0.165 b	12.927 a
	4	0.099	7.707	0.100	3.761	0.162 b	7.213	-0.011 13.035 b	-0.008	8.360	-0.004	12.930 b
	5	0.178 a 15.016 a	0.081	5.265	0.162 b 13.305 b	0.089 14.846 b	0.243 a 21.917 a	0.211 a	22.340 a
	6	-0.040 15.386 b	0.008	5.280	-0.107 15.944 b	0.016 14.904 b	-0.011 21.945 a	0.062	23.152 a
	7	0.093 17.410 b	0.051	5.881	0.056 16.662 b	-0.053 15.569 b	0.147 b 26.992 a	0.026	23.300 a
	8	-0.104 19.948 b	0.006	5.889	-0.053 17.320 b	-0.006 15.576 b	-0.080 28.496 a	-0.171 b	29.549 a
	9	0.144 b 24.800 a	0.001	5.889	0.139 b 21.887 a	0.051	16.175	0.110 31.308 a	0.131	33.260 a
	10	-0.170 b 31.648 a	-0.044	6.345	-0.128 25.769 a	-0.018	16.249	-0.098 33.548 a	-0.139 b	37.422 a
	11	0.107 34.366 a	0.018	6.425	0.024 25.911 a	0.069	17.392	0.075 34.865 a	0.024	37.546 a
	12	-0.055 34.438 a	0.003	6.427	-0.000 25.911 b	0.045	17.872	0.123 38.488 a	0.045	37.986 a

a , b :

Table 7 :

 7 Results of the runs test for VN-Index and selected individual stocks

	Variables	(N) Obs.	Actual runs (R) Expected runs (m)	Z-statistic

Table 8 :

 8 Variance-ratio test results for the observed weekly-return dataNumber q of base observations aggregated to form variance ratio Variables : Significant at the 1% and 5% levels, respectively.

			Submitted Manuscript			Page 32 of 35
		Number nq
		of base
		observations	2	4	8	16	32
	VN-INDEX	224
	VR(q)		0.56	0.30	0.20	0.10	0.06
	Z(q)		-6.56 a	-5.59 a	-4.05 a	-3.05 a	-2.21 b
	Z * (q)		-2.92 a	-2.80 a	-2.19 b	-1.71	-1.39
	REE	224
	VR(q)		0.58	0.31	0.19	0.11	0.06
	Z(q)		-6.23 a	-5.49 a	-4.09 a	-3.01 a	-2.20 b
	Z * (q)		-3.11 a	-3.16 a	-2.68 a	-2.14 b	-1.76
	SAM	224
	VR(q)		0.52	0.25	0.17	0.08	0.05
	Z(q)		-7.14 a	-5.97 a	-4.18 a	-3.11 a	-2.22 b
	Z * (q)		-3.26 a	-2.89 a	-2.16 b	-1.71	-1.38
	HAP	223
	VR(q)		0.36	0.18	0.10	0.06	0.03
	Z(q)		-9.59 a	-6.57 a	-4.55 a	-3.19 a	-2.26 b
	Z * (q)		-2.63 a	-2.17 b	-1.95	-1.80	-1.60
	TMS	223
	VR(q)		0.52	0.31	0.18	0.10	0.06
	Z(q)		-7.20 a	-5.46 a	-4.11 a	-3.05 a	-2.20 b
	Z * (q)		-3.91 a	-3.32 b	-2.75 a	-2.22 b	-1.79
	LAF	204
	VR(q)		0.47	0.27	0.17	0.08	0.05
	Z(q)		-7.55 a	-5.55 a	-4.01 a	-2.98 a	-2.14 b
	Z * (q)		-3.80 a	-3.05 a	-2.29 b	-1.83	-1.51

a , b Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 9 :

 9 Variance-ratio test results for the corrected weekly-return dataNumber q of base observations aggregated to form variance ratio Variables : Significant at the 1% and 5% levels, respectively.Figure 1: Daily trading value over the periodJuly 28, 2000 -December 30, 2005

		Number nq
		of base
		observations	2	4	8	16	32
	VN-INDEX	223
	VR(q)		0.41	0.22	0.14	0.06	0.04
	Z(q)		-8.82 a	-6.24 a	-4.35 a	-3.18 a	-2.25 b
	Z * (q)		-3.33 a	-2.69 a	-2.08 b	-1.63	-1.32
	REE	223
	VR(q)		0.45	0.23	0.13	0.07	0.04
	Z(q)		-8.14 a	-6.17 a	-4.38 a	-3.14 a	-2.25 b
	Z * (q)		-3.11 a	-2.75 a	-2.24 b	-1.80	-1.52
	SAM	223
	VR(q)		0.46	0.21	0.14	0.07	0.04
	Z(q)		-7.99 a	-6.33 a	-4.34 a	-3.16 a	-2.24 b
	Z * (q)		3.68 a	3.14 a	2.35 b	1.85	1.49
	HAP	222
	VR(q)		0.45	0.20	0.11	0.06	0.04
	Z(q)		-8.18 a	-6.35 a	-4.46 a	-3.16 a	-2.25 b
	Z * (q)		-3.21 a	-2.96 a	-2.51 b	-2.15 b	-1.81
	TMS	222
	VR(q)		0.38	0.24	0.13	0.06	0.04
	Z(q)		-9.21 a	-6.03 a	-4.38 a	-3.18 a	-2.24 b
	Z * (q)		-3.05 a	-2.34 b	-1.96 b	-1.60	-1.30
	LAF	203
	VR(q)		0.39	0.24	0.15	0.07	0.04
	Z(q)		-8.62 a	-5.76 a	-4.11 a	-3.02 a	-215 b
	Z * (q)		-3.34 a	-2.48 b	-1.85	-1.48	-1.24

a , b

Page 34 of 35 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

  Figure 2: VN-INDEX changes over the periodJuly 28, 2000 to December 30, 2005

		Submitted Manuscript
		600
	VN-Index (points)	100 200 300 400 500
		0
		F 2 8 / 7 / 0 0 2 8 / 1 / 0 1 2 8 / 7 / 0 1 2 8 / 1 / 0 2 2 8 / 7 / 0 2 2 8 / 1 / 0 3 2 8 / 7 / 0 3 2 8 / 1 / 0 4 2 8 / 7 / 0 4 2 8 / 1 / 0 5 2 8 / 7 / 0 5 o r Time
		P
		e
		e r
		R e v
		i e
		w
		35

Page 35 of 35 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Own-account trading is defined as buying and selling activities of securities for the securities companies' own account.

The listed companies are required to disclose information within 24 hours if, e.g., a significant change in conditions of its business activities occurs; if it suffers from a loss equivalent to or more than 10 percent of its equity; if there is a change in the firm's business strategy or other important decisions on business activities are taken, such as major investments in other firms or in fixed assets; and if the company falls into bankruptcy, or makes a decision on corporate merger, acquisition, split, or dissolution. A company also has to disclose information if it signs a loan agreement or issues bonds, which is worth 30 percent or more of its equity; if it changes the Chairperson of the Board of Directors, or more than one-third of the members of the Board of Directors, or its Director (General Director); if a share split occurs, if it issues bonus shares or shares for paying dividends, which is worth more than 10 percent of the equity and if it applies for de-listing. Finally, the disclosure obligation concerns the suspicion of involvement in criminal acts, like violation of tax laws, of directors, supervisors, managers or the chief accountant.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript