How and why does production relocation affect stock
prices?
Faculty of Economics and Business, University of Groningen
Master Thesis: MSc Technology Management
Author: Ing. Lennart Bakker
Student number: S1920588
E-mail: lennart-bakker@hotmail.com
Supervisor: drs. B.N. Petkova
ABSTRACT
This paper enhances our understanding of how and why production relocation influences the financial performance of stock exchange listed companies. Since an increasing number of companies relocate their production a better understanding of such influence is needed. Till now it is unknown if a relocation will improve a companies’ financial performance. Our study is completely focused on the stock price reaction of manufacturing companies on relocation announcements. We have used the database of the European Monitoring Centre on Change (EMCC) to search for relocations. This has resulted in 549 production relocations. However we have focused our research on stock exchange listed companies and this has resulted in 245 unique relocations. With these 245 unique relocations, this study is very extensive. This paper is divided as follows: first, a literature study is conducted to define production relocation and investigate what the effects of production relocation on financial performance could be. In addition we have also looked if there are relocation characteristics (e.g. geographical considerations and specific firm characteristics) that contribute to the success of relocations. Second, the databases from EMCC, Datastream, Orbis and Yahoo Finance are used for collecting relocation data (i.e. ISIN numbers, NACE-codes, relocation dates, headquarters, countries, regions and relocation reasons) in the manufacturing sector. Thereafter a data analysis is done on the effects of relocation announcements on the stock price reaction of stock exchange listed companies. The results of this research indicate that a relocation announcement does not result in a significant stock price reaction. Furthermore the predetermined geographical considerations and firm characteristics do also not explain variations in stock price reactions.
Keywords: Relocation, production, production relocation, offshoring, backshoring, event study,
CONTENT
1. INTRODUCTION ... 1
2. THEORY ... 3
2.1 Production relocation and financial performance ... 3
2.2 Relocation characteristics and relocation success ... 4
3. METHODOLOGY ... 8
3.1 Data collection ... 8
3.2 Descriptives of the sample ... 9
3.3 Data analysis... 12
3.3.1 Calculating the Return Index of the stock price and Return Index of the market ... 13
3.3.2 Calculating the Abnormal Return ... 13
3.3.3 The normal return ... 13
3.3.4 Analyses ... 14
4. RESULTS ... 16
5. DISCUSSION AND CONCLUSION ... 23
5.1 Theoretical implications ... 23
5.2 Managerial implications ... 25
5.3 Limitations and directions for further research ... 25
5.4 Conclusion ... 27
6. REFERENCES ... 28
1.
1. INTRODUCTION
Although literature has indicated that firms frequently relocate their production, little is known about the effects of relocation on the financial performance of firms. In this paper we hypothesize that production relocation has a positive effect on the stock price of a company. We expect a positive effect on the stock price because of the fact that companies will relocate their production to improve their financial performance. However, we think that relocation itself does not automatically result in a positive stock price reaction. The success of relocation depends of several factors. We hypothesize that the characteristics of the relocation influence the size of the stock price reaction. First, we study if geographical considerations (e.g. relocate nearer to or further away from headquarter and relocate to specific regions) influence the size of the effect. Second, we study if the company size (smaller or larger sized companies) and the number of redundancies (number of fired employees during relocation) influence the size of the effect. Further, we study if the motivation for relocation (e.g. cost reduction, expanding market, quality and flexibility) influences the size of the effect.
Empirical studies are very ambiguous whether firms benefit financially from production relocations. On one hand, Rottman and Lacity (2006) argued that production relocation can offer benefits such as cost savings and increased flexibility. On the other hand production relocations frequently do not yield the expected gains because costs appear to be higher than yields, the products are often of lower quality, there is inflexibility in the new factories, the transportation costs are higher than expected and the logistics are poor. Holstein (2011) stated that there are other negative aspects of production relocation. Some of these negative aspects are the increased wages of employees and the increased prices of raw materials in low wage countries where is relocated to.
It is thus unclear to what extent companies actually benefit financially from production relocations. This current lack of understanding leaves practitioners with real dilemmas. Should they relocate their production? What factors influence the success or failure of production relocations? We fill this gap in literature by researching the effects of production relocations on the stock price of firms. We expect that in general, relocations will have a positive effect on stock prices. It is based on the fact that most of the time companies decide to relocate because they want to improve their current financial performance, which in turn would result in higher stock prices.
2.
Second, regarding region, we expect that when a company relocates to Eastern-Europe, the stock price reaction is higher than when is relocated to Asia. We expect this positive stock price reaction because of the risen collaborations between countries in Europe. It makes countries in Eastern Europe more and more attractive for relocation. Third, regarding the number of employees we expect that relocations in smaller sized companies will result in a higher stock price reaction than relocations in larger sized companies. The reason for this expectation is that we think that the impact of relocation is higher when a higher percentage of the number of employees is fired during the relocation. Fourth, regarding the number of redundancies we expect that as more absolute numbers of employees are moved, the stock price reaction is higher. The reason for this expectation is that we think that shareholders will interpret a redundancy of more absolute numbers of employees as lower costs. This will result in better financial performance. Fifth, we expect that companies with reducing costs as reason for relocation will have a higher stock price reaction. We expect that shareholders will react more positively on reducing costs as reason because of the fact that lower costs will result earlier in more profit than other reasons (e.g. expanding the market or quality improvement) to relocate.
Unique for this study is that it contributes to both theory and business practice. We extend the current literature on production relocation with a study of the effects of relocation announcements on the financial performance of a company. Our study is completely focused on the stock price reaction of manufacturing companies on relocation announcements and with its 245 unique relocations, this study is very extensive. This paper aims to enhance the understanding of the effects of production relocation on the stock prices of companies. Such increased understanding may aid managers in their choices regarding relocation of production.
3.
2. THEORY
Production relocation is moving production facilities from one specific country or region to another country or region. Companies may want to relocate their production to another country or region because of better manufacturing conditions. These better manufacturing conditions could be lower costs, better infrastructure, shifting of the market or expanding market share (Mucchielli and Saucier, 1997).
As we will lay out, firms generally expect to improve their financial performance by relocating their production. Yet, we will argue that there may be differences in how successful relocation is. This success depends on the specific characteristics of the production relocation.
2.1 Production relocation and financial performance
Researchers generally emphasize the benefits of production relocation. First, one of the main reasons for relocating production is a decline in costs (Berger, 2006). These costs consist of labour costs and transportation costs. The labour costs are in particular very important for relocation decisions (Massini et al. 2010). Massini et al. (2010) stated that 90% of the companies in the U.S.A. and between 70% and 76% of the European companies which relocate production are focused on reducing labour costs. When companies want to reduce the labour costs they will relocate the production to low wage countries. However, these low wage countries are mostly located in Eastern-Europe and Asia while the markets of these companies are located in Europe and the U.S.A. Thus relocating to low wage countries for reducing the labour costs will probably result in increased transportation costs. Therefore, when companies decide to relocate they have to make an accurate consideration between the different increased and decreased costs by relocation and calculate what the relocation costs itself are.
4.
Generally, firms that relocate production thus anticipate achieving significant economical benefits and thus improving their financial performance. Thus, based on literature, we expect that the stock price of a firm that relocates production should increase:
H1: Production relocation has a positive effect on the stock price.
2.2 Relocation characteristics and relocation success
Relocation is a continuous process. Within this process it might seem that the new location meets the requirements and expectations. However, after a period of time it may appear that the new location does not meet the requirements and expectations anymore. When this occurs, companies may decide to relocate their production again. With this new production location they expect a better financial performance than the previous location. As result of the relocation they will expect a positive effect on the stock prices. Yet, the question arises if relocation will automatically result in a success. We expect that specific characteristics of the announcement will determine the success of relocation.
In the next sections we will explore several characteristics of production relocation which could influence the final success (i.e. stock price reaction) after a relocation announcement. These characteristics are: headquarter, region, company size, number of redundancies, and reason of relocation. First, with headquarter we will investigate if there is a different reaction of the stock price when firms relocate nearer to or further away from headquarter. Second, with region we will investigate if there is a different reaction of the stock price when firms relocate to Eastern-Europe versus Asia. Third, we will investigate if there is a different reaction of the stock price for smaller versus larger sized companies. Fourth, we will investigate if there is a different reaction of the stock price when there are the more or less absolute numbers of employees fired. Fifth, we will investigate if there is a different reaction of the stock price if firms relocate for cost reasons versus other reasons.
Headquarter
Production relocation consists of offshoring and backshoring. Offshoring is moving the production away from the country of origin or headquarter (Monczka et al. 2005 and Levy, 2005). Backshoring is moving production back to the country/region from where it was first offshored (Kinkel and Maloca 2009).
5.
culture differences, other languages and possible time zones differences (Walsham 2001 and Carmel 1999). Furthermore it is very difficult to control the production process. Kinkel & Maloca (2009) stated that production in other regions could lead to “shortcomings in flexibility and ability to supply
in the international supply chain and quality problems at the foreign location”. Second, offshoring
will result in educational problems. “The diffusion of knowledge is still problematic” (Sahay et al. 2003) which could lead to unskilled people and result in quality problems. This lack of knowledge has resulted to backshoring production facilities from India to the U.S.A. (Prezas et al. 2010). Third, in contrary to offshoring, backshoring production will result in speeding up innovation and improving efficiency (Veltri et al. 2008).
Thus, based on the current literature that offshoring will result in different operational problems, we expect that backshoring will result in a higher stock price reaction than offshoring.
H2. Backshoring production announcements will have a more positive effect on the stock price than offshoring production announcements.
Region
More and more companies move their production to countries in Eastern-Europe and Asia. Research from Kinkel & Maloca (2008), Heininger, N., & Gehnen, R. (2008) and Statistisches Bundesamt (2008) shows that around 55% of relocations of German companies were moved to new European member states in Eastern-Europe. China became the second most important relocation country. The reason why countries or regions become more attractive for relocation is because of a combination of
manufacturing conditions. These conditions are costs, infrastructure, and
6.
together, which has resulted in the European Union and the Euro. Because of these collaborations countries in Eastern-Europe become more attractive than countries in Asia for production relocation. For companies in Europe it is very attractive to relocate to Eastern Europe because of the shorter distances, open borders, no import duties and the same currency. These benefits are not available for companies that have relocated their production to Asia. The attractiveness of Eastern-Europe is supported by Sass& Fifekova (2011); they stated that a lot of production is relocated to Asia but relocation to Eastern-Europe is because of increased potential growing since the 1990’s. This potential of Eastern Europe “is based on the availability of skilled labour and language skills, low costs,
favourable business and stable political environment, well developed infrastructure and geographical
and cultural proximity to Western Europe” (Sass& Fifekova, 2011).
Generally, literature stated that the combination of the better manufacturing conditions and the European Union, relocation to Eastern Europe will result in higher benefits. Thus, we expect that the stock price of a firm that relocates to Eastern-Europe will result in a higher stock price reaction than relocating to Asia.
H3. Relocation announcements to Eastern Europe will have a more positive effect on the stock price than relocation announcements to Asia.
Company size
There are not so many studies which have done research to the size of a company as a factor which can influence the relocation success. Shin Im et al. (2001) stated in their research on IT investments and stock price reactions that “firm size also affected the reactions of stock price to the
announcements”.
There are no studies found which have concluded that relocation announcements of smaller sized companies will have a more positive effect on the stock price. However, Prezas et al. (2010) & Daniel et al. (2009) concluded after their research that a relocation announcement of larger sized companies will result in a more positive effect on the stock price reaction instead of smaller sized companies. For our research we want to find out if relocation of smaller sized companies has more impact on the company than relocation of larger sized companies. We expect that a higher percentage of employees that lose their jobs will have more influence on the reaction of stockholders.
7.
H4. Smaller sized companies that announce production relocation will result in a more positive effect on the stock price than larger sized companies that announce production relocation.
Number of redundancies
After several studies it is still unclear if the number of redundancies has a negative or positive influence on the stock price reaction. Several researchers found different outcomes regarding the effects of redundancies on the stock price reaction. Lin and Rozeff (1993) found that the reaction of shareholders on a redundancy announcement is in general negative. They argued that shareholders think that the investments and growth opportunities for the company are much smaller than previously predicted. The findings of Lin and Rozeff (1993) are supported by Worrell, Davidson and Sharma (1991), Caves and Krepps (1993), Farber and Hallock (2004), Couderc and Capelle-Blancard (2004) and Lee (1997). They found that there is a negative reaction of the stock price after a redundancy announcement. This is in contrast to Chatrah, Ramchander and Song (1995), Palmon, Sun and Tang (1997). They found an overall positive stock price reaction to a redundancy announcement. In addition, Collet (2002 & 2004) found that in general, a redundancy announcement will result in benefits for shareholders.
There is a lot of disagreement about the effects of a redundancy on the stock price reaction. It is not clear if a high absolute number of redundancies will result in a higher stock price reaction. Financial markets will interpret a high number of redundancies in two different ways. The first way is that they expect that with a lot of redundancies the labour costs will be lower which in the end will result in better financial performance. The other way is that they think that a lot of redundancies indicate that the financial performance is very bad and they do not expect that it will be better in the future. As consequence shareholders will sell their stocks. For our research we expect that relocation will have positive influence on the stock price reaction. It is based on the fact that we think that with redundancies a company wants to try to improve their financial performance. We assume that the reaction of shareholders on this improvement will be positive. We want to study if a higher absolute number of redundancies will result in a higher stock price reaction.
8.
Reasons for relocation
Companies have a lot of different reasons to relocate their production. These reasons could be, to reduce costs, to have more qualified and educated employees, to expand the market, to have more flexibility or to solve current quality problems. In literature, there is no distinct conclusion about the effects of different reasons to relocate. First, relocation based on cost reasons is the only reason that will result in a positive reaction on the stock price (Prezas et al. 2010). This is supported by Caniato et al. (2012); they found that cost drivers (seeking for lower input and work cost) are the most important during offshoring. Kinkel & Maloca (2007) stated about reasons for relocation “the reduction of
labour costs is the most important single motive for production offshoring activities of manufacturing companies”. Second, Massini et al. (2010) stated that companies with an offshoring strategy are
looking beyond mere cost savings. They defined it as “an offshoring strategy is associated with
identifying a broader set of drivers, risks and concerns, and location factors”. An offshoring strategy
is focused on improving quality, more flexibility, employees and speeding up delivery time. These factors could also affect the stock price in a positive way. Massini et al. (2010) found that companies which have a special offshore strategy instead of only focusing on reducing costs will have better results after relocation.
In line with the findings of Prezas, et al (2010), Caniato, et al. (2012) and Kinkel & Maloca (2007) we want to find out if mere cost reducing reasons will result in a higher stock price reaction than other reasons (e.g. quality, flexibility, expanding the market) to relocate. Because shareholders are mostly looking to the short term we expect that shareholders will react more positively on a cost reason than other reasons. Relocating for cost reasons will result directly in lower costs. It remains to be seen whether for example market expansion or better quality eventually lead to a better financial performance.
H6. Reducing costs as reason to relocate results in a higher stock price reaction instead of other reasons to relocate.
3. METHODOLOGY
3.1 Data collection
9.
involve the announced loss of at least 50 jobs, or at least a workforce of more than 250 people. We searched the database for production relocation announcements (i.e. “off shoring / delocalization”) in the manufacturing industries. We examined all announcements between August 2002 and July 2012. This approach yielded 549 announcements of production relocations. From these 549 announcements 304 announcements were eliminated for three reasons; the announcing firms are not listed on a stock exchange, we could not find the company ISIN number or the relocation destination was not available. This has resulted in a final sample of 245 production relocation announcements. For these announcements, we recorded the first date that the European press reported on the production relocation.
The next step was collecting data for the hypotheses. For H1 we have used the website of Yahoo finance and the Datastream database to search for the ISIN numbers. We have also used Datastream to collect the stock price data of firms by using the ISIN numbers. Thereafter we have collected the other data relevant for H2-H6. For collecting these data we have used the Restructuring Events Database of EMCC, the database of Datastream and the Orbis database. With these databases we have found headquarters, the NACE-codes, the data of announcement dates, country where is relocated to and from, planned job reductions and reasons to relocate.
3.2 Descriptives of the sample
Figure 1 and Table 1 show the number of relocations per year between the period of August 2002 and July 2012. The total number of relocations in this period relevant to our research is 245. Table 1 shows a continuous pattern of an increase and a decrease in relocations. From 2003 until 2006 and in 2009 there was a strong increase in relocations. The years 2007, 2008, 2010 and 2011 show a strong decrease in relocations. The decrease in these years and the peak in 2009 in between these years can be explained by the upcoming crisis in 2007/2008.
10. % West Europe 134 52.96 North Europe 53 20.95 South Europe 33 13.04 Central Europe 19 7.51 East europe 6 2.37 U.S.A. 3 1.19 Africa 1 0.40 South America 1 0.40
Not provided by Eurofond 3 1.19
Total 253
Countries where is relocated from Figure 1: Number of relocations per year
Table 2 and Figure 2 show from which countries these relocations come from. Table 2 shows that most of the countries where is relocated from are located in the regions West, North, South and Central Europe. The total number of countries where is relocated from is 253. This is higher than the total relocations announcements. This is because during some relocation announcements, companies have relocated from more than one country. Figure 2 shows how much relocation took place in the various regions each year. It also gives a clear overview that West and North Europe have the same waves in increased and decreased relocations. Table 2 and Figure 2 also show relocations originating from states within the U.S.A. and countries within Africa and South America while our research is focused on Europe. The reason for relocation from these continents is because headquarters of some companies are located in Europe and these companies decided to relocate their production facilities which were located in other continents. Another explanation is; companies from the U.S.A., Africa and South America have relocated from these regions to Europe. All the regions and the countries where is relocated from are shown in Appendix I.
Figure 2: Countries where is relocated from
Number of relocations per year Year number of relocations
2002 3 2003 11 2004 22 2005 32 2006 50 2007 30 2008 24 2009 38 2010 12 2011 12 2012 11 Total 245
Table 1: Number of relocations per year
11.
Table 3 and Figure 3 show to which countries relocation takes place. It shows that countries in Asia, West, Central and Eastern Europe are very popular to relocate production to. Central Europe and Asia are by far the most popular relocation destinations. The high number of relocations to West Europe can possibly be explained by the fact that more and more companies have experienced that manufacturing in low wage countries does not lead to the expected results and thus have decided to backshore production to West Europe. The total number of countries to which is relocated to is 326. This is higher than the total relocations announcements since some companies have relocated to more than one country. All regions and countries to which is relocated are shown in Appendix II.
% Central Europe 97 29.75 Asia 85 26.07 West Europe 37 11.35 East europe 33 10.12 South Europe 23 7.06 South America 11 3.37 North Europe 7 2.15 U.S.A. 5 1.53 Africa 6 1.84 Central America 1 0.31
Not provided by eurofond 21 6.44
Total 326
Countries where is relocated to
We have also looked at the number of redundancies and the number of employees within the companies of our sample. Table 4 shows that during relocations most of the companies have fired between 51 and 300 employees. During a single relocation around 23% of the companies of our sample have fired between 101 and 150 employees. The average number of redundancies in our sample is 374. Table 5 shows the number of employees of the companies of our sample. The smallest companies of our sample have between 501 and 750 employees. Most of the companies have between 10.001 and 20.000 employees and above 100.000 employees. Over 10% of the companies of our sample have between 10.001 and 20.000 employees. Over 22% of the companies of our sample have more than 100.000 employees. The average number of employees is 77.323. Based on these facts it can be said that companies in our sample are in general large enterprises. This is within the expectation since our research focuses solely on companies that are listed on the stock exchange. Further, there is an average of only 0,48% of the total workforce fired during relocations. This is relatively low.
12.
Number of redundancies Number of companies %
0 - 50 3 1.22 51 - 100 19 7.76 101 - 150 58 23.67 151 - 200 40 16.33 201 - 250 19 7.76 251 - 300 24 9.80 301 - 350 14 5.71 351 - 400 16 6.53 401 - 450 3 1.22 451- 500 11 4.49 501 - 550 4 1.63 551 - 600 7 2.86 601 - 650 4 1.63 651 - 700 3 1.22 701 - 750 1 0.41 751 - 800 2 0.82 801 - 850 1 0.41 851 - 900 3 1.22 901 - 950 0 0.00 951 - 1000 2 0.82 >1000 11 4.49 total 245
Number of employees Number of companies %
501 - 750 2 0,82 751 - 1000 8 3,27 1001 - 1500 3 1,22 1501 - 2000 5 2,04 2001-3000 3 1,22 3001 - 4000 6 2,45 4001 - 5000 1 0,41 5001 - 10000 20 8,16 10001 - 20000 26 10,61 20001 - 30000 17 6,94 30001 - 40000 8 3,27 40001 - 50000 8 3,27 50001 - 60000 15 6,12 60001 - 70000 6 2,45 70001 - 80000 5 2,04 80001 - 90000 7 2,86 90001 - 100000 13 5,31 > 100000 54 22,04 N.A. 38 15,51 total 245
During our research we have also looked at the sector where companies are operating in (i.e. NACE-code). The NACE-codes are found in Orbis. A NACE-code list is a European list with activity descriptions. It is used to classify companies in different sectors. The NACE-code of a company shows the main activity that generates the most turn-over for a company. The main activity is based on data from financial statements. Table 6 shows the most common NACE-codes of our sample. The table shows that most of the companies of our sample have their most turn-over from activities in electrical/electronic equipment. The top 5 of this list consist 3 categories which are related to electrical/electronic equipment and are responsible for over 25% of the total sample. All the NACE Codes of the companies in our sample are shown in Appendix III.
Nace Code Definition Number of companies %
2611 Manufacture of electronic components. 26 10.92 2751 Manufacture of electric domestic appliances. 25 10.50 2932 Manufacture of other parts and accessories for motor vehicles. 20 8.40 2612 Manufacture of loaded electronic boards. 15 6.30 2651 Manufacture of instruments and appliances for measuring, testing and navigation. 10 4.20
3.3 Data analysis
For this paper we have used the event study methodology of MacKinlay (1997). During this event study, the Abnormal Returns are calculated. For calculating the Abnormal Returns, first the stock price reaction is calculated by estimating the expected return for a company. Thereafter the difference of the actual return with the estimated normal return is calculated for the event day, and the days surrounding this event day. This is called the event window. The event day is the day of the relocation announcement. The event window is the time period before and after an announcement and the event day itself which is used for testing hypothesis 1. The difference per day is called the Abnormal Return per day. MacKinlay (1997) mentioned that researchers have to assess the Abnormal Return on the
Table 5: Number of employees Table 4: Number of redundancies
13.
event day. However when there is a chance of leakage before the event day or when the information is slowly incorporated, researchers have to take an event window of several days. This is called the Cumulative Abnormal Return (CAR). The CAR is the sum of a specific number of Abnormal Returns. The sum of number of Abnormal Returns is dependent on the chosen time window. During this research we have excluded all the non-trading days. If there was an announcement on a non-trading day we took the next trading day as event day.
3.3.1 Calculating the Return Index of the stock price and Return Index of the market
First, we have calculated the Return Indexes of the stock price and the Return Indexes of the market. To calculate these Return Indexes we have used Datastream to get the Return Indexes of the stock price and the market. The Return Indexes we have searched for are from day -100 till day 20. To calculate the return of the stock and the market we have used the following formula: Ri,t = (Pi,t-P i,t-1)/Pi,t-1. Where Ri,t is the return of stock i on date t, where Pi,t is the price of stock i on day t and where Pi,t-1 is the price of stock i on day t-1 (MacKinley, 1997).
3.3.2 Calculating the Abnormal Return
The next step during the analysis is to calculate the Abnormal Returns of the Return Indexes. MacKinley (1997) defined the Abnormal Return as “the actual ex post return of the security over the
event window minus the normal return of the firm over the event window”. The formula to calculate
the Abnormal Return is: ARi,t = Ri,t-E(Ri,t | Xt). Where ARi,t is the AR of stock i on day t. Ri,t is the actual return of stock i on day t and E(Ri,t | Xt) is the normal return of stock i on day t.
3.3.3 The normal return
To calculate the Abnormal Return we first have to calculate the normal return. The normal return can be calculated in two different ways, the constant mean return and market model.
The constant mean return model
The constant mean return model is possibly the simplest model to calculate normal returns. Nonetheless this model often finds the same results as more sophisticated models. The expected return with this model is based on the average Return Indexes from the past.
The formula for calculating the constant mean return model is:
14.
The market model
With using the market model, a statistical model which relates the return of any given security to the return of the market portfolio is used.
The formula for calculating the market model is:
Where E(Ri,t) is the expected return of stock i on day t. are the alpha and beta of stock i,R m,t is the Return Index of the market and is the error term.
The difference between the constant mean return and market model lies in the fact that the constant mean return model is only using the average market Return Indexes from the past. This is the average return of all stock listed companies on a specific stock exchange over a specific period. For our research we have used the market Return Indexes from the American stock exchange S&P500. The market model is using a combination of the Return Indexes of the market and the individual stock Return Indexes of the specific company. Both models should result in approximately the same findings. By using both models we could check if we used both models correctly.
3.3.4 Analyses
After calculating the Abnormal Return we want to check if there are significant deviations in the sample. For our research we have chosen for the period of day -10 till day 20. To test if there is a significance difference during this time period we use the z-test. We take the average Abnormal Return of all companies of dayt divided by the square root of the variance of the average return Abnormal Return of all companies on dayt. We take a significance level of 95%. To be sure that a relocation announcement has significant influence on the stock price the z-test must exceed the boundaries of -1,96 or 1,96.
Calculating the Cumulative Abnormal Return (CAR)
15.
Narrow event windows will reduce the effect of unexpected happenings around the announcement on stock prices. Wide event windows are more accessible for unexpected happenings (e.g. leakage) around the announcement. A positive feature of a wider event window is that it can also cover possible overreacting on the first day after an announcement (McWilliams and Siegel 1993, Carow et al. 2004 and Krivin et al. 2003). Prior event studies by DeAngelo, DeAngelo & Rice (1984), Marais, Schipper, and Smith (1989) and Esty, Narasimhan & Tufano (1999) used a 3-day window from -1 to +1 to measure the abnormal returns. Based on the different pros and cons of wide and narrow event windows we have chosen to look to two different event windows. The first event window is a 5-day event window from -2 to +2. The second window is a 7-day event window from -4 to +2. In this event window is day 0 the day of the announcement, the days after the announcement will be indicated with “+” and the days before the announcement will be indicated with “-“.
Regression analyses
We will test hypotheses number 4 and 5 with linear regression analyses in SPSS. To test hypotheses with regression analyses, the used data has to be continuous or ordinal and not categorical. For these two hypotheses the data is continuous (number of employees and number of redundancies). The formula of the regression analyses is Y=b+aX, where Y is the dependent variable (Abnormal Return), b is constant, a is the slope, X the independent variable (firm characteristics). The regression analyses will show if there is a significant relation between the firm characteristics and a higher stock price reaction after a relocation announcement. The regression analyses are done based on the natural log of the firm characteristics (log of number of employees and log of number of redundancies). The natural log is used if a variable grows at a constant percentage rate, the log of that variable will be a linear function of time, and the coefficient of time will be the percentage growth rate. The variable itself will exhibit exponential growth. For this regression analyses we have also used a significance level of 95%. This means that the significance in the test results has to be lower than 0,05.
The regression analysis is bilateral, there is tested on a positive and negative relation. However our hypothesis is unilateral, we expect a positive relation. Therefore the significance of the 2-tailed test must be halved.
Independent sample t-test
16.
Mean model Market model
Z-test -0,3044 -0,5594 Average -0,0009 -0,0016 Max 0,1253 0,1081 Min -0,1023 -0,1023 Median -0,0006 -0,0013 St. dev. 0,0271 0,0264 Data AR on day 0
independent samples t-test we have also used a significance level of 95%. This means that the significance in the test results has to be lower than 0,05.
To analyze the results of the independent samples t-test, first we use Levene’s test for equality of variances. With this test we make a distinction between equal variances and unequal variances. When significance is lower than 0,05 it is assumed that the variances of both groups are equal. When significance is higher than 0,05 it is assumed that the variances of both groups are unequal. It depends on the significance if we use the table of equal or unequal variances to analyze the results.
The independent samples t-test is bilateral, there is tested on a positive and negative relation. However our hypothesis is unilateral, we expect a positive relation. Therefore the significance of the 2-tailed test must be halved.
4. RESULTS
From our final sample consisting of 245 relocation announcements the Abnormal Returns are calculated. These Abnormal Returns are calculated for both the mean and market model. In Table 7 and Figure 4 the results are shown. Figure 4 shows that both models have the same wave movements in increasing and decreasing of the Abnormal Return. It also shows that the Abnormal Return on day 0 is negative. Thereby the Abnormal Return on day 1 is quite high compared to the average Abnormal Return on day 0. However on day 2 the Abnormal Return is already decreasing to a lower level. This wave in Abnormal Return means that there is a reaction from the market on the decision to relocate. Table 7 also shows the standard deviation. This standard deviation shows that the stock price reactions on the event day are not homogeneous. It means that investors or companies react differently on the relocation announcements. Table 7 and Figure 4 show that the results of the Abnormal Returns of the constant mean return and market model are substantiality equal. For our further research we have chosen to continue with the market model.
17.
4.1 Results for hypothesis 1:
“Production relocation has a positive effect on the stock price”.
For testing Hypothesis 1, we have used the average Abnormal Return and the variance to do the z-test for t=-10 until t=20. We have done the z-test on the data of the mean and market model. In table 8 are shown the results for the days of our event windows (-4 until 2). All the results are shown in appendix IV. This table shows that on day 0 the result of the z-test for the market model -0,559. This is within the significance limits of -1,96 and 1,96. However around the event day 0 there are some fluctuations by the Abnormal Return.
To check if there is a significant difference in the period around the relocation we have calculated the CAR. In figure 5 and 6 the CAR’s of time period -2 till 2 and -4 till 2 are shown. The different CAR’s do not show another difference about the significance of the Abnormal Return. The CAR’s doesn’t exceed the significance borders of -1,96 and 1,96. This means that production relocation does not have
Figure 4: Abnormal Returns for mean and market model
18.
a significant effect on the stock price reaction. Based on the result we could not say that relocation will result in a significant better financial performance. H1 is thus rejected.
4.2 Results for hypothesis 2:
“Backshoring production announcements will have a more positive effect on the stock price than offshoring production announcements.”
Figure 5: CAR 5 day time period
19.
Table 9 shows some statistical data about backshoring and offshoring in relation to the Abnormal Return. Under ‘Mean’ is shown that backshoring has a positive mean of 0,006 and offshoring a negative mean of -0,002 for the Abnormal Return. The standard deviation for backshoring is 0,029 and for offshoring 0,026.
Table 9 also shows the results of the independent sample t-test. The Levene’s test for equality of variances shows variance 0,189 this means that the variances are unequal. The significance of our test is 0,214/2=0,107 this is higher than 0,05. This means that backshoring announcements do not have a more positive effect on the stock price reaction than offshoring announcements. Based on this result we could not say that backshoring will result in a significant better financial performance. H2 is thus rejected.
Group Statistics
N Mean Std. deviation Std.Error Mean Backshoring 23 ,006 ,029 ,006
Offshoring 198 -,002 ,026 ,002
Independent Samples Test
Levene's Test for Equality of Variances
t-test for Equality of Means
F Sig. t df Sig. (2-tailed) Mean Differ-ence Std. Error Differ-ence 95% Confidence Interval of the Difference Lower Upper Equal variances 1,732 ,189 1,364 219 ,174 ,008 ,006 -,004 ,020 Unequal variances 1,274 26,486 ,214 ,008 ,006 -,005 ,021
20.
4.3 Results for hypothesis 3:
“Relocation announcements to Eastern Europe will have a more positive effect on the stock price than relocation announcements to Asia.”
Table 10 shows some statistical data about relocating to Eastern Europe and Asia in relation to the Abnormal Return. Under ‘Mean’ is shown that relocating to Eastern Europe has a positive mean of 0,00078 and relocating to Asia a negative mean of -0,00284 for the Abnormal Return. The standard deviation for Eastern Europe is 0,031 and for Asia 0,033.
Table 10 also shows the results of the independent sample t-test shown. The Levene’s test for equality of variances shows variance 0,709 this means that the variances are unequal. The significance of our test is 0,616/2=0,308. This is higher than 0,05. This means that announcements relating to offshoring to Eastern-Europe do not have a more positive effect on the stock price than announcements regarding offshoring to Asia. Based on this result we could not say that offshoring to Eastern Europe will result in a significant better financial performance. H3 is thus rejected.
4.4 Results for hypothesis 4:
“Smaller sized companies that announce production relocation will result in a more positive effect on the stock price than larger sized companies that announce production relocation”.
Table 11 shows the results of the regression analysis. The significance of our test is 0,266/2=0,133 this is higher than 0,05. This means that relocation within a smaller sized company does not have a more positive effect on the stock price reaction than relocation within a larger sized company. Based on this result we could not say that relocation within a smaller sized company will result in a significant better financial performance. H4 is thus rejected.
4.5 Results for hypothesis 5:
“The stock price reaction is higher as companies reduce more absolute number of employees during the relocation.”
21.
Group Statistics
N Mean Std. Deviation Std. Error Mean Eastern Europe 26 ,00078 ,031 ,006
Asia 70 -,00284 ,033 ,004
Independent Samples Test
Levene's Test for Equality of Variances
t-test for Equality of Means
F Sig. t df Sig. (2-tailed) Mean Differ-ence Std. Error Differ-ence 95% Confidence Interval of the Difference Lower Upper Equal variances ,140 ,709 ,492 94 ,624 ,004 ,007 -,011 ,018 Unequal variances ,505 47,251 ,616 ,004 ,007 -,011 ,018
Model Unstandardized Coefficients Standardized Coefficients T Sig. B Std. Error Beta (Constant) -,015 ,012 -1,310 ,192 LN Number of employees ,001 ,001 ,078 1,115 ,266 Dependent Variable: AR
22.
Model Unstandardized Coefficients Standardized Coefficients T Sig. B B Std. Error Beta (Constant) -,002 ,012 -,188 ,851 LN Planned job reductions ,000 ,002 ,003 ,053 ,958 Dependent Variable: AR
4.6 Results for hypothesis 6:
“Reducing costs as reason to relocate results in a higher stock price reaction instead of other reasons to relocate”.
Table 13 shows some statistical data about relocations relating to cost reasons and relocating because of other reasons in relation to the Abnormal Return. Under ‘Mean’ is shown that cost reasons has a negative mean of -0,00059 and other reasons a negative mean of -0,00526 for the Abnormal Return. The standard deviation for cost reasons is 0,022 and for other reasons 0,030.
Table 13 also shows are the results of the independent sample t-test shown. The Levene’s test for equality of variances shows variance 0,179 this means that the variances are unequal. The significance of our test is 0,523/2=0,2615 this is higher than 0,05. This means that cost reasons for relocation do not have a more positive effect on the stock price reaction than other reasons. Based on this result we could not say that cost reasons will result in a significant better financial performance.. H6 is thus rejected.
Group Statistics
N Mean Std. Deviation Std. Error Mean Cost reasons 102 -,00059 ,022 ,002
Other reasons 37 -,00526 ,030 ,005
23.
Independent Samples Test
Levene's Test for Equality of Variances
t-test for Equality of Means
F Sig. t df Sig. (2-tailed) Mean Differ-ence Std. Error Differ-ence 95% Confidence Interval of the Difference Lower Upper Equal variances 1,82 1 ,179 ,728 137 ,468 ,004 ,005 -,006 ,013 Unequal variances ,643 52,006 ,523 ,004 ,006 -,008 ,015
5. DISCUSSION AND CONCLUSION
This paper contributes to literature and business by gaining a better understanding of how production relocation influences the financial performance of stock listed companies. This influence on financial performance is tested by looking at the stock price reaction of a company after a relocation announcement. During the literature study the definition of production relocation is formulated, reasons why a company wants to relocate, the advantages and disadvantages of production relocation and factors that could influence the final success of a production relocation are described. These factors are tested by performing a quantitative research whilst using several databases. During our research, we have not found any significant evidence that production relocation will result in a higher stock price reaction. Furthermore, we have also not found any significance that during a relocation the specific relocation characteristics will result in a higher stock price reaction. In this chapter the theoretical and managerial implications of these findings, limitations and suggestions for further research and the conclusions are presented.
5.1 Theoretical implications
This study has several theoretical implications. First, this paper is an addition to existing literature on production relocation. It is the first paper which is completely focused on the effects of production relocation announcements on the financial performance of companies. With its 245 unique relocation announcements this study is very extensive.
24.
Second, this paper extends the current production relocation literature on how relocation announcements influence the financial performance. Current literature suggests relocation will result in a better financial performance. Berger (2006) stated that the main reason for relocation is the decline in costs. After declining the costs we expected that there will be more profit which results in a better financial performance. Looking to all these factors the expectation rises that after relocation the financial performance will be improved. Our research shows a different result. There is no significant relation between relocation and the stock price reaction.
Third, the current view is that offshoring will give more operational problems than backshoring. These problems are caused by communication problems as a result of culture differences, other languages, less flexibility. Further there are quality problems and problems with the diffusion of knowledge (Walsham 2001, Carmel 1999, Kinkel & Maloca 2009 and Sahay et al. 2003). The literature creates the expectation that during offshoring production there are a lot more operational problems than with backshoring. As a result, the expectation was that backshoring results in a better financial performance. This is in contrast with our findings. Our study shows that there is no significant relation between backshoring production and the stock price reaction.
Fourth, due to raised labour, energy and transportation costs Asia became less attractive to relocate to than Europe. Another reason why Eastern Europe became more attractive is because of the European Union and the Euro (Powell, 2011; McCutcheon et al., 2012 and Sass & Fifekova, 2011). Based on the literature it was expected that relocation to Eastern Europe will result in better financial performance. From our research it appears that there is no significant relation between relocating to countries in Eastern Europe and the stock price reaction.
Fifth, current literature states that the size of a company is of influence on the stock price reaction after relocation (Shin Im et al., 2001). However such standpoint is not in line with our findings. Our understanding, as evidenced by our research, is that there is no significant relation between the size of a company and the stock price reaction.
25.
Seventh, in literature there is still debate whether the reasons for relocation influence the stock price reaction. On one hand it is written that only cost reduction as reason to relocate will have a positive influence on the stock price reaction (Prezas, et al., 2010; Caniato, et al., 2012 and Kinkel & Maloca, 2007). However it is also written that the so called “offshoring strategy” will result in a positive influence on the stock price reaction (Massini, et al., 2010). For our research we have followed the literature which relates cost reasons to a higher stock price reaction. Our results are not in line with this literature. The result of our study shows that there is no significant relation between reducing costs as relocation reason and the stock price reaction.
5.2 Managerial implications
The purpose of this paper was to provide useful insights for managers of companies with plans to relocate their production facilities. First, it shows managers there is no significant relation between relocating the production and the stock price reaction. Second, it also shows that there is no significant relation between the relocation characteristics (nearer to or further from headquarter, relocation to Eastern-Europe or Asia, company size, number of redundancies and reason for relocation).
Finally, the knowledge that relocation of production itself does not always result in a significant better financial performance can aid managers by their decision to relocate or not. This research shows relocation is not always the answer to improve the current financial performance. This research gives managers the possibility to look beyond the option of relocation in order to improve the current financial performance.
Thereby, the knowledge that the relocation characteristics do not influence the size of the stock price reaction can also aid managers by their decision to relocate. It shows that the success of relocation is not dependent on one specific point. An eventual success of the relocation success depends on a combination of several relocation characteristics.
5.3 Limitations and directions for further research
This study has several limitations and this result in several directions for further research. The first limitation is that we have only used companies listed on a stock exchange. This results in a restricted number of relocations. Research to the financial performances of companies which are not listed on a stock exchange will result in a larger research sample. This will be beneficial for strengthening our conclusions.
26.
Further research on the financial performance of companies after relocation in other continents will result in a larger research sample. This will be beneficial for strengthening our conclusions.
The third limitation could be that we have not found any firm characteristic that has a significant influence on the stock price reaction after a relocation announcement. We have based our firm characteristics on the information of the database from the European Monitoring Centre. This data is supplemented with data from Datastream and Orbis. For further research it is advisable to search in other sources to find some other possible firm characteristics that could lead to a significant correlation.
The fourth limitation is that our research is purely focused on the manufacturing sector. This means that the results may not be applicable in other sectors. This is because it could be that shareholders of companies in other sectors will react completely different on relocation. Therefore the stock price reaction after a relocation announcement will also be different, which will lead to other results than in our study. For a complete overview of the effects of relocation announcements, a larger study covering several sectors is needed.
The fifth limitation is that our research data is based on the fact that we expect a stock price reaction after relocation is announced and published in newspapers. However, there is always the possibility that relocation information was already available to shareholders before the announcement. As consequence of leakage it could be that shareholders have already bought or sold the stocks days before the announcement and beyond our time window. If this has happened, it has influenced our results.
The sixth limitation is that we have looked at the stock price reaction on the day and the days around the announcement. There is no research done to the stock price reaction on the day of implementation or manufacturing start date of the new production location. It is possible that shareholders will react differently on the actual start date than on the day of announcement.
The seventh limitation for this study is that we have used data from different stock exchanges. All these stock exchanges have their own currency (e.g. Dollar, Euro, Yen and GBP). For our research we have all these currencies exchanged to Euro’s. It could result in some exchange differences in our sample.
27.
5.4 Conclusion
This study adds to the existing literature by investigating the effects of production relocations on the stock price. First, this paper is purely focused on the effects of relocations on manufacturing companies. Second, this paper provides an extensive empirical research on how production relocation affects the performance of stock listed firms. Third, the results of this paper could be used as a guide by managers that are deciding to relocate.
The results of the study show that: (1) Relocation does not have a significant effect on the stock price reaction. (2) Relocation characteristics (nearer to or further from headquarter, relocation to Eastern-Europe or Asia, company size, number of redundancies and reason for relocation) do not have a significant effect on the stock price reaction.
28.
6. REFERENCES
Berger, S. (2006). How we compete: What companies around the world are doing to make it in
Today’s global economy (1st ed.). New York: Doubleday.
Bramlage, J. C. (1988). All the right moves: A new strategy for successful relocation. Personnel, (65(5)), 40-43.
Caniato, F., Golini, R., & Kalschmidt, M. G. M. (2011). Offshoring and nearshoring success: The importance of strategic reasons.
Capelle-Blancard, G., & Couderc, N. (2007). How do shareholders respond to downsizing? A meta-analysis. Pantheon Sorbonne Economie,
Carmel, E. (1999). Global software teams: Collaborating across borders and time zones . (1st ed., pp. 200). Englewood Cliffs NJ: Prentice-Hall.
Carow, K., Heron, R., & Saxton, T. (2004). Do early birds get the returns? an empirical investigation of early-mover advantages in acquisitions. Strategic Management Journal, 25(6), 563-585.
Caves, R. E., & Krepps, M. B. (1993). FAT: The displacement of nonproduction workers and the
efficiency of U.S. manufacturing industries.
Chatrath, A., Ramchander, S., & Song, F. (1995). Are market perceptions of corporate layoffs changing?, Economic Letters, 47, 335-342.
Collett, N. (2002). Reactions of the international stock exchange to company employment announcements: Redundancies and new jobs. Journal of Business Finance & Accounting, 29(9), 1181-1208.
Collett, N. (2004). Shareholders and employees: The impact of redundancies on key stakeholders.
Business Ethics: A European Review, 13(2), 117-126.
29.
Elizabeth Daniel, Devendra Kodwani, & Sanjukta Datta. (2009). The impact of ICT-enabled offshoring announcements on share prices. Journal of Enterprise Information Management,
22(3), 241-256.
Esty, B., Narasimhan, B., & Tufano, P. (1999). Interest-rate exposure and bank mergers. Journal of
Banking & Finance, 23(2-4), 255-285.
Farber, H. S., & Hallock, K. F. (2009). The changing relationship between job loss announcements and stock prices: 1970–1999. Labour Economics, 16(1), 1-11.
Feldman, D. C., & Bolino, M. C. (1998). Moving on out: When are employees willing to follow their organization during corporate relocation. Journal of Organizational Behavior, 19(3), 275-288.
Heininger, N., & Gehnen, R. (2008). Business Forum China, special edition. German business expansions in china 2008–2010: Results of survey among german operations in china on market potential, business barriers and future business outlook (shanghai: German chamber of commerce).
Holstein, W. J. (2010). The case for backshoring; which manufacturing operations should return to the
united states? Retrieved January/25, 2010, from
http://www.strategy-business.com/article/00017?gko=b0c37
Kinkel, S., Lay, G., & Maloca, S. (2007). Development, motives and employment effects of manufacturing offshoring of german SMEs. International Journal of Entrepreneurship and Small
Business, 4(3), 256-276.
Kinkel, S., & Maloca, S. (2008). Produktionsverlagerungen rückläufig: Ausmaß und motive von produktionsverlagerungen und rückverlagerungen im deutschen verarbeitenden gewerbe.
Fraunhofer Institut System Und Innovationsforschung, 45
Kinkel, S., & Maloca, S. (2009). Drivers and antecedents of manufacturing offshoring and backshoring—A german perspective. Journal of Purchasing & Supply Management, 15(3), 154-165.
30.
Lee, P. M. (1997). A comparative analysis of layoff announcements and stock price reactions in the united states and japan. Strategic Management Journal, 18(11), 879-894.
Levy, D. L. (2005). Offshoring in the new global political economy. Journal of Management Studies,
42(3), 685-693.
Lin, J., & Rozeff, M. S. (1993). Capital market behaviour and operational announcements of layoffs, operation closings and pay cuts. Review of Quantitative Finance and Accounting, 3, 29-45.
MacCarthy, B., & Atthirawong, W. (2003). Border control. Manufacturing Engineer, 82(1), 9-13.
MacKinlay, A. C. (1997). Event studies in economics and finance. Journal of Economic Literature,
35(1), 13-39.
Marais, L., Schipper, K., & Smith, A. (1989). Wealth effects of going private for senior securities.
Journal of Financial Economics, 23(1), 155-191.
Mason, J. C. (1996). Corporate relocations: On the move. Management Review, 85(11), 33.
Massini, S., Perm-Ajchariyawong, N., & Lewin, A. Y. (2010). Role of corporate-wide offshoring strategy on offshoring drivers, risks and performance. Industry & Innovation, 17(4), 337-371.
McCutcheon, R. W., Pethick, R., Burak, M., Scamuffa, A. J., Hoover, S. T., & Bono, R. B. (2012). A
homecoming for US manufacturing? Why a resurgence in US manufacturing may be the next big
bet. Price Waterhouse Coopers,
McWILLIAMS, A., & SIEGEL, D. (1997). Event studies in management research: Theoretical and empirical issues. Academy of Management Journal, 40(3), 626-657.
Monczka, R. M., Markham, W. J., Carter, J. R., Blascovich, J. D., & Slaight, T. H. (2005).
Outsourcing strategically for sustainable competitive advantage
Mucchielli, J. L., & Saucier, P. (1997). European industrial relocations in low-wage countries: Policy and theory debates. Multinational firms and international relocation (pp. 5-33). Cheltenham: Buckley, P.J. and Mucchielli, J.L.
31.
Powell, B. (2011). The end of cheap labor in china. Time, 177(26), 1-4.
Prezas, A. P., Simonyan, K., & Vasudevan, G. (2010). Does offshoring create value for shareholders?
Review of Financial Economics, 19(4), 179-191.
Rottman, J. W., & Lacity, M. C. (2006). Proven practices for effectively offshoring IT work. MIT
Sloan Management Review, 47(3), 56-63.
Roza, M., Van, d. B., & Volberda, H. W. (2011). Offshoring strategy: Motives, functions, locations, and governance modes of small, medium-sized and large firms. International Business Review,
20(3), 314-323.
Sahay, S., Nicholson, B., & Krishna, S. (2004). Global IT outsourcing. software development across
borders. (1st ed.). Cambridge: Cambridge University Press.
Sass, M., & Fifekova, M. (2011). Offshoring and outsourcing business services to central and eastern europe: Some empirical and conceptual considerations. European Planning Studies, 19(9), 1593-1609.
Shin Im, K., Dow, K. E., & Grover, V. (2000). Research report: A reexamination of IT investment and the market value of the firm–An event study methodology. Information Systems Research, 12(1), 103-117.
Statistisches Bundesamt (2008). Verlagerung wirtschaftlicher aktivitäten (Wiesbaden: Statistisches Bundesamt)
Su, H. C., Gau, G. W., & Wang, K. (1995). Stock market reaction to capital investment decisions: Evidence from business relocations. Journal of Financial & Quantitative Analysis, 30(1), 81-100.
Veltri, N. F., Saunders, C. S., & Kavan, C. B. (2008). Information systems backsourcing: CORRECTING PROBLEMS AND RESPONDING TO OPPORTUNITIES. California
Management Review, 51(1), 50-76.
Walsham, G. (2001). Making a world of difference: IT in a global context. Chichester: John Wiley and Sons, LTD.
32.
7. APPENDIXES Appendix I: Countries where is relocated from.
Eastern europe North Europe Central Europe South Europe Western Europe U.S.A Africa South America Not provided
Romania 2 Denmark 7 Polen 6 Italy 17 Ireland 21 U.S.A 3 Morocco 1 Mexico 1 3
Slovenia 1 Sweden 22 Czech Republic 4 Portugal 5 United Kingdom 38
Estonia 2 Finland 24 Slovakia 4 Malta 1 France 21
Hungary 6 Spain 10 The Netherlands 14
Belgium 11 Luxembourgh 2 Germany 18 Austria 9 Total 5 53 20 33 134 3 1 1 3 253 Total
Appendix II: Countries where is relocated to.
Eastern europe North Europe Central Europe South Europe Western Europe U.S.A Africa South America Asia Central America Not provided
Estonia 6 Finland 1 Czech Republic 26 Italy 9 Austria 2 Canada 1 Africa 1 Brazil 1 Asia 17 Central America 1 Not provided by Eurofond 21 Lithuania 1 Sweden 4 Hungary 22 Malta 1 France 7 U.S.A. 4 Morocco 1 Costa Rica 3 China 39
Romania 9 Denmark 2 Poland 44 Portugal 3 Germany 18 South Africa 1 Dominican Republic 1 Hong Kong 1
Russia 6 Slovakia 5 Spain 10 Ireland 1 Tunisia 3 Mexico 5 India 9
Serbia 2 Schweiz 1 South America 1 Indonesia 1
Slovenia 2 The Netherlands 2 Malaysia 5
Turkey 4 United Kingdom 5 Middle East 2
Ukraine 1 Wales 1 Singapore 4
33.
Nace Code Number of companies %
2611 26 10,61 2751 25 10,20 2932 20 8,16 2612 15 6,12 2651 10 4,08 2620 9 3,67 2630 8 3,27 1089 7 2,86 2120 7 2,86 1051 6 2,45 2211 6 2,45 1200 5 2,04 2041 5 2,04 2042 4 1,63 2640 4 1,63 2712 4 1,63 1413 3 1,22 2434 3 1,22 2910 3 1,22 3091 3 1,22 1105 2 0,82 1320 2 0,82 1729 2 0,82 1920 2 0,82 2030 2 0,82 2059 2 0,82 2815 2 0,82 3109 2 0,82 3250 2 0,82 4673 2 0,82 6201 2 0,82 7112 2 0,82 600 1 0,41 1310 1 0,41 1512 1 0,41 1610 1 0,41 1629 1 0,41 1711 1 0,41 1724 1 0,41 2011 1 0,41 2229 1 0,41 2311 1 0,41 2442 1 0,41 2512 1 0,41 2521 1 0,41 2550 1 0,41 2660 1 0,41 2670 1 0,41 2711 1 0,41 2720 1 0,41 2740 1 0,41 2790 1 0,41 2811 1 0,41 2813 1 0,41 2822 1 0,41 2823 1 0,41 2825 1 0,41 2829 1 0,41 2830 1 0,41 2899 1 0,41 3011 1 0,41 3030 1 0,41 3230 1 0,41 3511 1 0,41 4771 1 0,41 6491 1 0,41 7219 1 0,41 N.A. 13 5,31 Total 245
34.
Appendix IV: Z-test.
Days AR market model var Z-test
