How did Swiss companies react to the unexpected Swiss Franc - EUR peg release?

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UNIVERSITY OF AMSTERDAM AMSTERDAM BUSINESS SCHOOL

Master in International Finance

How did Swiss companies react to the unexpected Swiss

Franc – EUR peg release?

R.P. Homan

Student number 10901671 7-7-2017

Supervisor: Dr. T. Ladika

Abstract:

In this empirical research, the aim is to analyze what the impact of the depegging announcement of the Swiss National Bank on the 15th of January 2015 had on the stock return of Swiss companies. Based on previous theory (Jorion, 1990), it is analyzed whether the stock of companies with a high ratio of foreign sales and domestic assets are more sensitive to this type of events. The significant change appears to be negative for Swiss companies with a high ratio of foreign sales. For Swiss companies a high domestic asset ratio appears to have a positive effect on the cumulative abnormal return. This is analyzed by using the ratio of two independent variables: foreign sales/ total sales and Swiss assets/ total assets.

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Preface

After studying at the University of Amsterdam for almost three years this thesis will bring this period to an successful end. It have been three very educational years for me in which my perseverance skill is moved to a new level.

With the help, believe and support of several persons the goal of reaching the master degree has helped me a lot.

Therefore, I would like to thank first of all my supervisor professor T. Ladika for the great support in writing this research. With the feedback on my work I was able to make and revise certain choices.

Furthermore, I would like to thank my employer for the interest, support and the opportunity to spent time on this study next to my job. This opportunity has helped me to a very great extent to accomplish this task.

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1 Introduction and relevance

1.1 Introduction

The Swiss economy and the government bonds of the United States were considered the world’s most secure places for savings. With the turmoil affecting the Eurozone and other world economies,

Switzerland was a safe haven for many investors during the crisis that started in 2008. This country was considered a good option to escape the extremely low interest rates of the Eurozone, wherefore lots of investments flowed into Switzerland. As a consequence, the Swiss authorities moved to action, because massive overvaluations were expected to occur. When in 2011 the financial markets became confused, the Swiss National Bank introduced the exchange rate peg between the Swiss franc and euro. The aim of this was to protect Swiss’s competitiveness and prevent the country from a debt crisis (The Guardian, 2011).

On January 15th 2015, the Swiss central bank announced that it would no longer keep the fixed exchange rate that was introduced in 2011. Chairman Thomas Jordan of the Swiss National Bank announced that after three years in force, the peg wasn’t needed anymore. The Swiss franc responded on this news by jumping up 30% percent against the euro. This is also shown in figure 1.

Figure 2 shows that the foreign currency position of the Swiss national bank was increased after the Swiss national bank decided to depeg the Swiss franc. After the depegging announcement, the Swiss national bank started to intervene in the foreign exchange markets to prevent the franc from

appreciating above its euro exchange rate cap, which was set at 1.20 francs per euro. As a result of this, the Swiss national bank started purchasing purchasing foreign exchange currencies to keep the Swiss franc in control and strike a delicate balance in the exchange rates (The economist). Maintaining foreign currency reserves could translate into big losses when the foreign rates are depreciating, but most likely prevented bankruptcy for the majority of Swiss companies.

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6 Figure 1: Increase of the CHF on January 15th Figure 2: Increasing foreign currency positions of the SNB

Since currency rate swings of 2% were already considered as big in the exchange markets, companies called the reaction of the Swiss franc a ‘’tsunami’’ (The Guardian, 2015). Due to the unexpectedness of this event, companies did not have the opportunity to anticipate beforehand. Therefore the impact of the news was enormous. After the announcement, the worst performing stock a suffered a loss of almost 18% while the best performing stock gain almost 7%. As in 2015 63,5% of the Swiss GDP is gained by export, the rising CHF caused a lot of trouble (Data source: The world bank, exports of goods and services). Export-oriented companies in the Swiss industry, particullary companies focused on mechanical engineering, electronics industry, metal industry and hotels or catering, have suffered mainly in 2016 from the peg release (Data source: Credit-Suisse, 2016). Reasons of removing the cap were: protests against the accumulation of foreign currency reserves and the introduction of the European bank of ‘quantitative easing’.

All together, there are reasons to believe that the cap has affected the market value of Swiss companies. Therefore, the aim of this research is to carefully examine the impact of the unexpected depegging of the Swiss franc from the Euro on the market value of Swiss companies. This will be achieved by looking at the stock return values at different time horizons. This relationship has been studied several times on micro- and macro level and is extremely important for multinationals (Dominguez & Tesar, 2006; Jorion & Multinationals, 2016).

The contribution of this research is as follows. First of all, the results serve to predict the impact of the exchange rate. Which means that it should be examined if a relation can be established among the increasing Swiss franc and stock behaviour. Furthermore, a deeper understanding of exchange rates can help companies manage their exchange rate risk in foreign contracts. This contributes in stabilizing company earnings.

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7 Establishing a relation beteen exchange rate exposure and market value will be clarified by defining the core concepts of exchange rate exposure and their underlying variables: foreign sales and domestic assets. The level of impact that exchange rate changes will have on the stock return value company can be explained by looking at the two exchange rate variables. The concept is that the exchange rate has impact on companies that are export focused and have large domestic assets. In respons to the direction of the exchange rate move and the ratio of sales and asset location a company maintains, income and value of companies change. In chapter three and fourth the relation of sales and assets will be

extensively discussed from a theoretical and practical point of view.

1.2 Relevance of the topic

History has shown that unexpected outcomes of elections, unannounced political or financial decisions have had striking impacts on the market value of large companies. Next to the CHF-EUR peg, the Brexit and the US elections are examples of similar situations. In this thesis the impact of this, with focus on the CHF-EUR depegging, is analyzed. Although there is academic proof that the relationship between exchange rate exposure variables and market value of companies exists, no contemporary research on this particular topic exists nor is there literature covering the impact of the unexpected news event on this relation.

The research of jorion (1990) examined the exposure of U.S. multinationals on foreign currency risk. However, in this research no attention was paid to the effect of unexpected events. Jorion (1990) used the level of foreign sales and asset location as exchange rate variables to analyze the market value behavior of U.S. multinationals. He found a significant result for the two variables. The relation of the two exchange rate variables in combination with unexpected news announcements provides a new angle for research on the two exchange rate variables. For today’s Swiss companies, it is first of all important to know if there is a relation between exchange rates variables and the value of their companies. Secondly, it is interesting to see whether the exchange rate variables, sales and assets, supposed to impact the stock value of Swiss companies. Companies can use this information to insure their positions when monetary policies will be changed in the near future.

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1.3 Research questions

The relevance of this topic has eld to the following research question:

How did Swiss companies react to the unexpected Swiss Franc – EUR peg release? To answer the sesearch question, the following sub-questions will be answered

Sub-question 1: What is the impact of the Swiss franc – Euro depegging announcement on the stock value of Swiss companies?

H0: The depegging announcement has nog significant impact on the stock returns of Swiss companies. H1: There is a significant change in stock value of Swiss companies after the depegging announcement Positive: the overall result shows a decreasing line in the stock returns after the announcement Negative: the overall result shows a decreasing line in the stock returns after the announcement This question will be answered by looking at the percentage change in stock return of the Swiss

companies. Returns will be calculated by using days in advance and after the depegging announcement was published by the Swiss national bank. Graphs that represent information on the overall average expected and cumulative abnormal returns should indicate the impact of an increasing Swiss franc on the market value of companies. As a second step it will also be calculated per group of companies per industry.

Sub-question 2: Can certain behavior in stock returns, as a result of the depegging announcement, being explained by the two predictor variables: foreign sales and domestic assets?

H0: No relation can be found between the two predictor variables foreign sales, domestic assets and the change in stock return value of Swiss companies.

H1: The two predictor variables have a significant impact on the stock return change of Swiss companies.

Positive: the lower the ratio of foreign sales and Swiss assets, the higher the cumulative abnormal return

Negative: the higher the ratio of foreign sales and Swiss assets, the lower the cumulative abnormal return

After the relation in the first question is being examined and is proved to be significant, this sub-question will focus on the explainatory power of the exchange rate variables foreign sales and domestic

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9 assets. According to Jorion (1990), the impact should be higher when a company maintains a high ratio of foreign sales and domestic assets. Hence, the regression will be performed for the different

combinations of foreign sales and domestic assets.

2 Theoretical framework

2.1 The definition of exchange rate exposure

The exchange rate is one of the most important price indicators in the world. It is often defined as the relationship between excess returns and the change rate (Adler & Dumas, 1984). Put differently, an exchange rate is the price of one country’s currency in terms of another, and it converts prices

denominated in one currency into prices denominated in another currency (Bartov & Bodnar, 1994). It is believed that changes in the exchange rate have a significant effect on the performance of firms

involved in international activities (Shapiro, 1975). The impact of a given exchange rate change on a company, is determined by whether it has a long or short position in a foreign currency. On the one hand, companies with a net long position in a foreign currency will benefit from a depreciation of the home currency. On the other hand, companies with a net short position will suffer from a depreciation of the home currency (Bartov & Bodnar, 1994).

The variables foreign sales and domestic assets appear to be significant variables in this relation (Bodnar & Marston, 2002; Dominguez & Tesar, 2006; Forbes, 2002; Jorion & Multinationals, 2016). Switzerland is known for its high ratio of export and due to the appreciation of the Swiss franc on the 15th of January, Swiss companies in all likelihood experienced a bigger disadvantage than countries with low ratios of export.

Some empirical studies point out to have weak relations between exchange rate fluctuations and stock prices of companies (Bartov, Bodnar, & Kaul, 1996; Bartov & Bodnar, 1994; Griffin & Stulz, 2001; Jorion, 1990). Nevertheless, a significant relation between exchange rate exposure and the market value of companies is missing in these articles. To name some, Bodnar and Gentry (1993), Amihud (1993) and Griffin and Stulz (2001) found a weak relation between these two aspects. The relationship as indicated by Griffin and Stulz (2001), was found when they examined the impact of competition between similar industries located in different countries on the stock value. Using the returns measured over longer horizons, the importance and impact of exchange rate shocks on market value of companies and

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10 industries increases. Griffin and Stulz (2001) also found that large companies with significant foreign revenues and costs in foreign currency have a low exchange exposure. This is due to the netting method that companies use to match their proportion of foreign currency revenues with their costs. In case there is an unbalanced revenue or cost stream, companies should apply hedge methods to reduce this risk (G.M. Bodnar & Marston, 2002).

Likewise, Bartov & Bodnar (1994) extended the research of Jorion (1990) and failed to find a significant correlation when using the predictor variables foreign sales and domestic assets. They analyzed the exchange rate exposure on a broader scale by changing sample selection and reexamined the relation between dollar changes and equity value for possible mispricing. Their regression results show that dollar changes have little power in explaining abnormal stock returns for the selected companies in their sample.

The majority of the literature that concludes research on this topic, establishes a relationship between exchange rate exposure and the market value of companies. In contrast to the previously mentioned authors, the following researchers prove that exchange rate movements indeed have an impact on the return of company stocks (Adler & Dumas, 1984; Bailey & Chung, 1995; Dominguez & Tesar, 2005; Hodder, 1982; Jorion, 1990; Jotikasthira, Lundblad, & Ramadorai, 2013; Bartram, Brown, 2010; Priestley & Ødegaard, 2004). Relationship of the latter was most common for companies that are defined as multinationals. In his research, Jorion (1990) has a clear and straight approach where sales and assets were used as exchange rate exposure variables. According to him, net monetary assets and the value of real assets or fixed assets that are held by a company explain why some companies will react stronger than others on exchange rate exposure. All together it seems that an important consequence of greater foreign production costs, measured by the ratio of fixed assets to total assets, lead to a greater decrease in the firm value in response to depreciation of the home currency (Bodnar & Marston, 2002; Desai, Foley, & Forbes, 2008; Forbes, 2002). One of the consequences of the abrupt appreciation of the Swiss franc on the 15th_{of January 2015, is debiliation in competiveness of export driven Swiss companies.}

Domestic production costs in Switzerland increased compared to competition of companies with production assets located in foreign countries. The latter results in increasing prices since goods and services became more expensive to import for foreign countries.

For monetary assets, a clear distinction was made between short term foreign monetary assets and domestic monetary assets (Jorion, 1990). Foreign monetary assets are fully exposed to exchange risk, whereas domestic monetary assets are not. The value of real fixed assets are always exposed to

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11 exchange rate risk. Assets will always be affected by exchange movements trough effects on aggregated demand or on the costs of traded input (Jorion, 1990). However, it is also found that firms can combine three mechanisms to mitigate the exchange rate exposure. Companies can pass it through to customers, affect their exchange rate exposure by choosing the location and currency of costs and utilize an array of financial products as exchange rate risk management tools (M. Bartram, Gregory W. Brown, 2010).

Dominguez & Tesar (2005) found that exchange rate exposure has impact on the profitability of

companies. In their research, linking industries with firm-level exposure resulted into weak evidence. At the same time, they found a strong relation between the firm size and the level of foreign sales, in particular for non-U.S firms in developed countries. Like Dominguez & Tesar (2005), Shapiro (1974) focused on multinationals. In principal, he looked at major factors that affect a multinationals exchange rate risk, including the distribution of sales between domestic and foreign markets and the volume that multinationals imported. In response to a depreciation of the home currency, companies that manage large ratios of foreign sales show a decrease in firm value. When exchanging their revenue into the domestic currency, the value of sales will decrease. This is equal to what Jorion (1990) and Dominquez & Tesar (2005) also found, like described previously.

Differences between industries

Many studies on the exchange rate exposure between different industry’s show interesting results. The relation between an industry’s stock value and changes in the value of the home currency, should depend on where the focus of the companies within a particular industry lies (M. Bodnar & Gentry, 1993). The extent to which an industry’s export or import, the type of markets on which it obtains inputs and its foreign investements, all affect an industry’s linkage to the international environment. It is precisely for that reason that its foreign currency exposure size changes (M. Bodnar & Gentry, 1993; J. M. Griffin & Stulz, 2001; Jorion, 1990).

Griffin and Stulz (2001) found that the importance of exchange rate shocks are financially still small for industries (J. Griffin & Stulz, 2001). However, in their study, the general perspective is that effects of an appreciation of the home currency on the value of industries were found. A positive effect on the company value was found for the non-traded good producer, also known as the importer, and the user of internationally priced inputs. A negative effect on the company value was found for the exporter, import competitor and foreign investor. Bodnar and Gentry (1993) found that for an increasing home

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12 currency the following results in exchange exposure for the different industries in the USA, Canada and Japan. The latter is illustrated in table 1.

Sector / Country USA Canada Japan

Basic materials - - -

Consumer goods + + +

Consumer services - - +

Financials n/a n/a n/a

Health care n/a n/a n/a

Industrials - - -

Oil & Gas - + +

Technology - - -

Telecommunications - - +

Utilities + + +

Table 1: Impact of increasing home currency on different type industries

2.2 Predictor and control variables

According to most of the written theory, exchange rate exposure of public listed companies are measured by the two variables foreign sales and assets and are controlled by three other variables. As explained in the previous chapter, a number of studies in the literature performed research for exchange rate exposure of large companies (G.M. Bodnar & Marston, 2002; E. Bartov & Bodnar, 1994; Jorion & Multinationals, 2016). This chapter will elaborate on the two exchange rate variables: foreign sales and Swiss assets.

2.2.1 Foreign sales

The first predictor variable of the outcome variable is the ratio foreign sales to total sales. This term could also be indicated as the percentage export a company has. When companies frequently sell outside of their country of origin, companies become exposed to different exchange rates. Exchange rate changes in turn, can affect a transaction effect (Adler & Dumas, 1984). Transaction risk arises when a company is committed to a continuous flow of foreign valuta. When goods are sold on credit, it could experience a big disadvantage when the domestic currency depreciates. Companies with more export experience, are better positioned to benefit from the relative cost impact of a depreciating home currency (Bartov & Bodnar, 1994; Forbes, 2002). Forbes (2002) did research on firm performance and

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13 found that firms with high foreign sales have better performance after depreciation of the foreign currency or when the home currency increase against foreign currencies.

In the situation of Switzerland, where the Swiss franc appreciated adds up to the high ratio of export, companies are ought to experience disadvantages. When the ratio of the foreign sales ratio is significant and positive, it indicates that the more foreign sales a firm has, the higher is the probability that it uses financial hedges (Allayannis, Ihrig, & Weston, 2016). This could mean that companies with a high ratio of foreign sales, perhaps are better resisted against exchange rate fluctuations.

2.2.2 Assets

The second predictor variable is the ratio in domestic asset to total assets. Compared to the straight forward approach of foreign sales, opinions differ slightly in literature about assets. Some conjecture that companies with foreign sales are more exposed to exchange rates (Allayannis et al., 2016) while others take domestic assets when measuring exchange exposure for multinationals (Jorion, 1990). Dominguez and Tesar (2006) for example, argue that assets and sales will increase in value with a depreciation of the home currency relative to the foreign currency. The ratio of foreign assets to total assets is used as a firm’s fraction of marginal costs in the foreign market due to foreign currency inputs (M. Bartram, Gregory W. Brown, 2010). As the export of Switzerland is approximately between the 65 and 70 percent of their gross domestic product, it would be obvious that Swiss companies would produce abroad, in order to have production assets more close by their market. This means that companies acquire costs in a foreign currency and subsequently have a higher foreign exchange exposure.

Where the afore mentioned authors use the foreign assets, in most of the literature domestic assets are used. In the research of Jorion (1990) indicates that multinationals from the United States that rely heavily on exports, see the value of their domestic assets unfavorably affected by an appreciations of the dollar. Since Switzerland also relies heavily on export and endures a massive increase in the Swiss franc, the theory of Jorion (1990) predicts that domestic assets will be affected. Assets will be affected in value by exchange-rate movements, whatever their location is (Jorion, 1990). Domestic assets, may be affected by exchange-rate movements through effect on aggregate demand or on the cost of traded inputs. Companies with only assets located domestically compete with companies that import and will anyway be exposed to exchange-rate movements (Jorion, 1990). This exposure could have big impact on the production and employment decisions of multinational companies with domestic assets (Hodder,

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14 1982). Due to the competition factor and exchange rate impact on assets, domestic assets will be used as one of the predictor variables in this research.

2.2.3 Control variables

Next to the main predictor variables sales and assets– most studies also include variables to control the relation between the predictor and outcome variable for biases. The control variables will explain why some firms are exposed and others are less exposed to exchange rate changes. According to the following three control variables, companies are diffently exposed to exchange rates.

Trade volume also underlines the importance when investigating the impact of inflation or exchange rate changes (Shapiro, 1974). Results indicate that companies with a higher trading volume are more sensitive to liquidity when exchange rates are fluctuating (Dominguez & Tesar, 2006). Therefore, the average trade volume over a long period provides the best information.

According to the perspective of Dominguez & Tesar (2005), firm size is an ambiguous variable when analyzing exchange rate exposure. When doing research on exchange rate exposure, they found that for six of the eight countries, firm size is statistically significant The sign on the coefficients in this research suggests that larger firms are more engaged in international activities and are likely more affected by the exchange rate movements (Dominguez & Tesar, 2006). Additionally, larger firms are more likely to have borrowed valuta in foreign currency and therefore they experience negative balance-sheet effect form depreciations. Moreover, larger firms are more likely to hedge exchange rate risks than smaller firms (Forbes, 2002; Nance, Smith, & Smithson, 1993). So, for many reasons, larger firms could exhibit better or worse performance than smaller firms after depreciation. Still it is difficult to predict which of these effect dominates a priori (Forbes, 2002).

Firm value, as proxied by market-to-book ratios, is expected to be positive when looking at the relation between stock values and exchange rate exposure (Allayannis et al., 2016). In most research, to-book value make an adjustment for the effect of leverage on the return data. When adding the market-to-book ratio in the multiple regression model, it provides the exposure elasticity for an unleveraged firm. To correctly perform the multiple regression of the current research, the equity return data needs to be adjusted by an appropriate leverage factor (Bodnar, Dumas, & Marston, 2002). In Bloomberg this ratio is provided for all of the Swiss companies. If not, it will be calculated as the sum of total assets and total liabilities divided by total equity liability.

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2.3 Theoretical findings

The main assumption drawn in the above cited papers , aren’t decisive about the relation between the exchange rate exposure and stock value of companies. At country level the extent of exposure is robust, although the direction of the exposure and the type of firm that is affected depends on the type of exchange rate an varies over time. It is assumed that it may be linked to the time period the data is taken from and the type, country or sector of the companies. Furthermore, it is found that the exposure is higher for small-sized companies and companies that are engaged in international activities.

The structure as used by Jorion (1990), is the most common in the above analyzed literature. Most of the event studies look at the level of sales and assets to explain certain behavior of company market values. Therefore, the method invented by Jorion (1990), is applied as global structure for the current research. Therefore, the output of the current event study is expected to be in line with

with the papers that have shown an significant relation between the exchange rate exposure variables foreign sales and domestic assets and the market value of companies. As reported by these researches, the prospected outcome of the current study is that Swiss companies maintain a higher ratio in foreign

sales and assets in Switzerland, to show a higher significant relation with stock returns compared to

companies that do not.

3 Practical research

3.1 Hypothesis

Based on the theoretical framework in the previous chapter a practical approach of this theory is disclosed in this chapter. In particular the focuss will be on the financial metrics between the exchange rate exposure and the behavior of company stocks.

Running metrics and analysis on the data will provide an answer on the following research questions:

1. What is the impact of the Swiss franc – Euro depegging announcement on the stock value of Swiss companies?

 The null hypothesis: the major swing of the Swiss franc had no significant impact on the stock return of Swiss companies.

 The alternative hypothesis: the swing in the Swiss exchange rate causes a significant impact on the stock values of Swiss companies. It simply entails that the effect could be negative or positive.

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16 Positive: the overall result shows a decreasing line in the stock returns after the announcement Negative: the overall result shows a decreasing line in the stock returns after the announcement

This hypothesis will be answered by looking at the percentage change in stock value of a Swiss

companies around the depegging announcement, that was published by the Swiss national bank on thte 15th of January 2015. By applying calculation on the percentage change in the stock returns of the companies, figures will show wat the effect of an increasing exchange rate is on the stock returns of Swiss companies.

This question will be examined for companies that operate in all Swiss sectors. In the theoretical framework it was mentioned that several researches also tried to find difference in exposure per industry. After finding a relation, the results will also be analyzed per industry in Switzerland.

2. Can certain behavior in stock returns, as a result of the depegging announcement, being explained by the ratio of the two exchange rate exposure variables: foreign sales / total sales and domestic assets / total assets?

 The null hypothesis: no significant relation can be found between the two exchange rate variables foreign sales, domestic assets and the change in market value of Swiss companies.  The alternative hypothesis: the two exchange rate variables foreign sales and domestic assets

have a significant relation with the degree of change in stock return value of Swiss companies. Positive: the lower the ratio of foreign sales and Swiss assets, the higher the cumulative abnormal return

Negative: the higher the ratio of foreign sales and Swiss assets, the lower the cumulative abnormal return

After the relation in the first question is being examined and is proved to be significant, this sub-question will focus on the explainatory power of the exchange rate variables sales and asset location. According Jorion (1990),the impact should be higher when a company maintains a high ratio of foreign sales and domestic assets. Hence, the sample data should include clear information on the level of foreign sales and domestic assets.

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3.2 Research methods

In this part of the proposal the research method and techniques are defined. It will have more or less the same structure that is slightly touched on in the previous chapter. As introduced in the previous chapter, the research method of Jorion (1990) will be applied as a global structure.

Figure 3. Independent, predictor and dependent variables

Independent variable Outcome variable

Predictor variable 1 Predictor variable 2

In the first part the behavior of the market return of Swiss companies will be analyzed. The second part attemps to explain the market return behaviour of Swiss companies in terms of the two variables sales and assets.

3.2.1 Sub-question one – Market adjusted model

In this chapter the research method for the first sub-question will be defined. Aim of this research question is to find out whether the depegging announcement that was published on the 15th of January 2015 had any impact on the stock returns of Swiss companies. Finance theory suggest that the stock prices reflect all the available information about the prospect of a firm. Given this fact, the depegging event can be studied by quantifying the impact on a companies stock price. It isn’t necessary to include the exchange rate change around the event date. Since the direction of the change in exchange rate is known only the stock return has to be analyzed. For this reason the event study methodology will be applied in this sub-question to perform such an analysis. The market adjusted model is the general method that is used for event studies to get a useful first impression of exchange rate exposure impact (Benninga, 2014). Theoretically this model analyses the distinction between the returns that would have been expected if the event would not have taken (no depegging) place and the returns that are caused by the specific event (depegging announcement). It aims to separate company-specific events form market- and/or industry-specific events. The relation will be examined for all the companies overall but also splitted per industry.

Exchange rate EUR-CHF Return of a Swiss company Foreign Sales Domestic Assets

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Estimation window

Analyzing the depegging event will be done by using two time frames: the estimation window, also cited as the control period, and the event window. The estimation window is used to conclude the normal behavior based on 250 trading days in advance of the depegging event. This period stops about 30 trading days before the event to avoid contamination. This means that the coefficients will be calculated over the estimation window that starts -280 trading days and ends -30 trading days prior to the

depegging event. The regression formula 𝑅𝑖𝑡 = 𝛼 + 𝛽 𝑅𝑚𝑡 is used to determine what this normal behavior is. Rit and Rmt present the stock return of a Swiss company and the market return of a Swiss company on day t. By using the data of the estimation window the coefficients alpha (intercept), beta (slope), standard error and r-square will be estimated by running an ordinary least-square regression. For calculating the coefficients the Swiss market index is used together with a companies return. This is Switzerland’s stock market index and is the most broad-based value-weighted index for Switzerland.

When there are less than 126 observations available for daily stock prices per Swiss company it will not be used in the estimation window. This is done to avoid having coefficients that not indicate the true market movements between the stock returns and the market returns under normal conditions. After collecting the stock data and eliminating companies that do not reach the barrier of at least 126 observations prior to the even, 235 companies are left for use in the first sub-question.

Event window

In the event window the depegging announcement is anticipated or leaked. In combination with the slope and intercept that are calculated by using the estimation window it will be determined whether: the announcement was anticipated or leaked and will be analyzed how long it took for the occurence information to be absorbed by the market. With the regression formula mentioned before that was used for calculating the coefficients in the estimation window, the impact of the depegging on the Swiss stock’s return is calculated in the event window. For 29 days before and 31 days after the

announcement the expected return, abnormal return, accumulative return and the t-test on the abnormal return will be calculated.

- The expected return is the predicted return by using the stocks intercept and slope and the market return. In this research the regression intercept will be replaced by the risk-free rate. For a 10-year bond the yield is under zero. Therefore the intercept will be ignored in the

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19 - The abnormal return is defined as the difference between the actual stock and its predicted

return

- The cumulative abnormal return is the sum of the abnormal returns during the event window. It is the sum of all the abnormal returns from the start until a particular day t in the event window. First this research it starts six days before the event on the 15th of January.

The expected return will be showed by using a line graph and the cumulative abnormal return by using a bar chart. Reason for choosing different types of graphs is because the expected return shows how much stock prices change on a day-to-day basis while the cumulative abnormal return is a buy-and hold return. The cumulative abnormal return measures the total impact of the depegging event trough a particalur period. In this research the period starts three days in advance and stops three days after the event date.

3.2.2 Sub-question two – Multiple linear regression

In the second sub-question the reaction of the stock value on the depegging announcement will be analyzed by including two predictor variables and three control variables. The two predictor variables are foreign sales and domestic assets. To get reliable data before doing analysis it will be important to have segmented data for the two predictor variables. The first variable is foreign sales and will be presented as a ratio foreign sales/ total sales (FS). When a company only operates in foreign countries it will be more exposed to exchange rate risk compared to companies that solely operates domestically. The second variable is Swiss assets/ total assets (SA). In the theoretical framework was mentioned that companies will be exposed to exchange rates when have assets are located domestically.

The control variables are firm size, liquidity of the stock and price to book ratio of the stock and will be discussed more extensively at the end of this chapter.

The multiple linear regression method will be used for this sub-question. This method attempts to model the relationship between two or more predictor variables and the outcome variable. The correlation coefficient shows the strength of the correlation and the direction of the relation. The correlation coefficient is always between -1 and 1. A result below 0 indicates a negative relation wheras results above the 0 indicate a positive relation.

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20 The linear regression has an equation of the form:

𝑌 = 𝑎 + 𝑏𝑋

- Y is the predicted cumulative abnormal return (CAR) and is measured on continuous scale - X are the predictor and control variables

- a is the constant (intercept) - b is the slope of the regression

Subsequently, the regression equation for this research can be formulated as:

𝐶𝐴𝑅 = 𝑎 + 𝑏 ∗ 𝑓𝑜𝑟𝑒𝑖𝑔𝑛 𝑠𝑎𝑙𝑒𝑠 + 𝑏 ∗ 𝑆𝑤𝑖𝑠𝑠 𝑎𝑠𝑠𝑒𝑡𝑠 + 𝑏 ∗ 𝑡𝑟𝑎𝑑𝑒 𝑣𝑜𝑙𝑢𝑚𝑒 + 𝑏 ∗ 𝑝𝑟𝑖𝑐𝑒 𝑏𝑜𝑜𝑘 𝑟𝑎𝑡𝑖𝑜 + 𝑏 ∗ 𝑡𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠

SPSS statistics will be used to perform the linear regression test. The regression will be performed three times for different compositions of the predictor variable because, foreign sales and Swiss assets can be indicated as high and low. If the percentage foreign sales or Swiss assets of a Swiss company is above the sample median it will be labelled as high. If not, foreign sales or Swiss assets will be labelled as low. In table 2 the four combinations can be found. The first time the regression will performed with only the four combinations. Subsequently, an additional control variables is added in everyone after one of the control variables will be added.

Dependent Variable 1:CAR 2:CAR 3:CAR 4:CAR

High FS / High SA X X X X Low FS / Low SA X X X X High FS / Low SA X X X X Low FS / High SA X X X X Trade Volume X X X Price / book X X Total assets X

Table 2: Composition 1, multiple linear regression on four combinations of foreign sales and Swiss assets

Since the expected sample size for companies that do report on both foreign sales and Swiss assets is not to big, the composition of the two predictor variables that are presented in table 2 will be changed into smaller groups. Table 2 will be re-estimated to see whether the predictor variables have a stronger relation to the cumulative abnormal return.

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21 Table 3: Composition 2, multiple linear regression on two combinations of foreign sales and Swiss assets

In table 4 the composition is shown of the regression that will be performed with the raw data of foreign sales and Swiss assets. Different from the previous two compostition is the missing of subdividing the predictor variables in high and low.

High FS / Low FS X X X X

High SA / Low SA X X X X

Trade volume X X X

Price/book ratio X X

Total Assets X

Foreign sales X X X X

Swiss assets X X X X

Trade volume X X X

Price/book ratio X X

Total assets X

Table 4: Composition 3, multiple linear regression with raw data on foreign sales and Swiss assets

The output of the multiple linear regression is interpreted in two steps. First the model summary is of interest. This provides information about the r-square and adjusted r-square and explains how well a regression model fits the data. The r-square is the proportion of variance in the cumulative abnormal return that is explained by foreign sales and Swiss assets. Second, the statistical significance of each predictor variable is tested. This can be concluded by looking at the t-value and p-value.

Control variables

Control variables are added in the multiple linear regression to better assess the relationship between the predictor and outcome variable. There is no direct interest in the control variables but they influence the results of the research. The variables are constant when testing the relationship.

The first control variable is the size of a company. It will be measured in total assets of the company. Second control variable is the liquidity of the stock that is traded. Liquidity is often described as the ease to convert a stock into cash. In this research liquidity will be indicated as the traded volume. Usually

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22 large companies are traded more frequently and so will have a bigger impact on the stock return. This will be measured by adding the traded volume of the company stock. The volume in itself doens not have an impact on the stock price directly but is does have an impact on the direction the share moves. It will be measured by the average of the daily number of shares traded.

The final control variable is the market-to-book ratio. This ratio is used to compare its book value against the market value and to determine whether a company is under- or overvalued. This ratio has impact on the stock prices of Swiss companies as undervalued stocks tend to be traded more often and therefore change. It is calculated as the total market capitalization plus total liabilities divided by the total equity liability.

3.3 Dataset and selection process of data

To assess the two hypotheses that were formulated in the second chapter, it is necessary to have a dataset that contains solid and structured data. Selection criteria are needed to find companies with proper data that can be used for testing. This part of the chapter describes why companies in the dataset are selected and why other firms aren’t selected as being part of the dataset. Data for the sub-questions is extracted from the Bloomberg terminal that is available at my work. First step in creating a sample is to collect as many data as possible on all of the existing companies in Switzerland. This is a time absorbing process because data demands correction and companies that contain inconsistencies or missing figures have to be taken out of the sample. In the next part the process for obtaining reliable data is explained separately for each sub-question.

Dataset 1 - sub-question one

The first step was creating a list with all companies that are classified as Swiss companies. Result of applying this criteria in Bloomberg, was a list of approximately 12 thousand companies. For this group of companies the following selection criteria are applied to get a data set with companies that have high probability of reporting data on all elements that are required for the two sub-questions.

- Halted, pending symbol or listing, suspended, postponed, unlisted, when issued, delisted - Real estate investment firms /mutual funds and cross-listed companies

- Market capitalization of 20 million Swiss franc. Expectation is that smaller companies report data inconsistently.

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23 After applying the aforementioned criteria the sample had 613 companies . At this stage, a list with all of the company names and corresponding tickers was created in Excel. A ticker is a string of characteristics that represents a unique company or entity in the Bloomberg terminal. Most of the times this is made up of an exchange code, ticker of Bloomberg and the type of market sector the company is active in.

A third column was added with the main industry in which the company was active in. The industry type was added to the sample to assess which industry is more impacted by the depegging event than others. Categorizating the companies in industries was done by using the Industry Classification Benchmark (ICB). Applying the ICB enables to compare companies across four levels and boundaries. Companies are allocated in an industry that most closely represent the origin of its business. This is determined by the source of revenue or where the majority of its revenue is coming from and is represented by a code. The first number represents the industry, the second represents the supersector, the third represents the sector and the fourth represents the subsector. In this research the 10 industries in the bottom of the funnel are used as level of aggregation. In figure 4, the manner of dividing companies into sectors and the names of the 10 industries are presented.

1 Basic Materials 2 Consumer Goods 3 Consumer Services 4 Financials 5 Health Care 6 Industrials 7 Oil & Gas 8 Technology 9 Telecommunications 10 Utilities

Figure 4. Different Swiss industries according to the ICB

For the list of 615 companies the stock returns were extracted from the Bloomberg terminal. This was collected by using a designed excel tool with a VBA script. Certain criteria are included in the VBA script to have better aligned data. Stock returns were for example expressed in CHF/EUR and non-trading days were excluded for all companies. After extracting the data for these firms many of the company tickers did not report constant data or prices were missing. Tickers were marked green or red to distinquish companies that publish regular data from companies that publish unreliable data. When a company ticker is marked green the stock is traded constant over time and no prices are missing. If the ticker is marked red, the stock is traded irregular or not traded at all. Selecting the companies that are marked green narrowed the sample down to only 251 companies.

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24 Some of the companies did not report stock prices that fall within the range of the estimation window. As already mentioned in the second chapter the estimation window starts 280 trading days in advance of the announcement date and ends 30 trading days in advance of the announcement date. After excluding these companies, the sample exists of 235 companies that are used for the first sub-question.

Next step was calculating the different types of return for all of the 235 companies. First step is

calculating the daily return by using the closing stock price. This is the final price that is paid for the stock on a particular day and consists of the latest available information for a particular trading date. The daily return of the closing price is calculated by:

((Close price T) – (Close price T-1)) (Close price T -1)

Next step is calculating the slope, r-square and standard error for the event window. For the intercept, also known as alpha, the risk-free rate of Switzerland will be used instead of the regression intercept. The risk-free rate for Switzerland was negative, hence zero will be used in this research.

Final step before interpretating the relation explained in the next paragraph is calculating the expected-, abnormal- and cumulative abnormal return in the event window. The expected return was calculated by multiplying the return of the Swiss market index times the sum of the intercept and slope of a specific company. The abnormal returm was calculated as the difference between the actual return and expected return and the cumulative abnormal return is the sum of all the abnormal returns that fall in the event window.

Dataset 2 - sub-questions two

For sub-question two, additional data is needed. Information on the two predictor variables foreign sales and Swiss assets need to be verified from annual reports and Bloomberg. Information about their foreign sales and domestic assets will be used of the year 2014. Motivation for not using this

information for the year 2015, is because companies probably removed assets or ended selling in certain regions. It was a time consuming process to open the annual report for all companies to check whether data was reported on either their foreign sales or Swiss assets ratio. Since large companies are obliged to report this data, most of the companies publish information on both variables. The calculated

fractions of foreign sales and Swiss assets per company can be found in appendix 1. In total, the amount of companies that is part of the sampe for this sub-questions exists of 147 companies.

All of the 147 companies report their sales segmented per region. Approximately 10 companies

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25 are naturally exempted of reporting their assets per region. For this reason, a ratio of 1 is maintained.

For compiling the control variables the identical VBA model for gathering stock returns is used. For the missing figures, annual reports are used to make sure that all data iss captured. Trade volume is defined per company as the average of daily traded volume for the year 2014. It is used to measure the stock liquidity per company. Price to book ratio is calculated per company as the sum of market capitalization and liabilities divided by the equity liability. The firm size is defined per company by using the value of total assets. Eventually, the logarithm for trade volume and total assets is calculated to correct the data for possible outliers.

3.4 Did the depegging announcement had any impact on the stock return of Swiss companies?

For this sub-question, dataset 1 has been analyzed using the output of the maket adjusted model. For this dataset it is assumed that the data is normally distributed. The mean, expected- and the cumulative abnormal return are used in this part of the chapter that will be used for further analysis. The analysis will be done by looking at the overall results and for each of the industries individually.

Average result for all companies

In the below tables and graphs the output of the market adjusted model for dataset 1 can be found. It can be easily concluded that the announcement had an impact on the stock return of Swiss companies when taking a look at the data. When looking at the data on the event date, the average return for 235 companies in dataset 1 is -6.8%. Company with the worst result had a loss of -22% compared to the closing price on the day before the event date. The best result was a gain of 13% compared to the closing price on the day before. Comparing the expected figures to the actual figures a few things paid the attention. The expected return with -5.11% is higher than the actual figure while the maximum and minimum are expected to be lower. This indicates that the actual return on the event date is -1,68% lower than the expected return for the Swiss companies. On the day after the event date the actual return is -0.75% lower than the initial expected return.

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26 Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

Return 235 -,22 ,13 -,0680 ,05440

Valid N (listwise) 235

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

Return 235 -1,02 ,07 -,0511 ,07408

Valid N (listwise) 235 Table 5: Descriptive statistics of dataset 1

With regard to the expected return and cumulative abnormal return, table 5 shows the overall result of the 235 companies that are part of dataset 1. The column that is labelled with total show the sum of the expected return of cumulative abnormal return. For the expected return the event date the result has the most negative score. This is represented in table 6 and 7.

Table 6 & 7: Results for expected and cumulative abnormal return

Regarding the expected return, the bar chart in figure 5 illustrates the overall expected between the 5th and 27th of January. It can be seen that the expected return on the 15th and 16th of January are relatively low compared to the other days. However, while the numbers have a steep decrease on these days, the expected return seems to recover in the days after. Overall, we can see a slight downward trend.

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27 Figure 5: The average expected return for 235 Swiss companies

The line graph represented in figure 6 illustrates the average of all cumulative abnormal returns. This is the sum of the abnormal returns for the period of 5th of January till the 27th of January. Overall, it can be determined that the dotted line shows a decreasing trend in the cumulative abnormal return.

Figure 6: The average cumulative abnormal return for all 235 Swiss companies Average result per industry

In figure 7 and 8 the expected return and cumulative abnormal return is depicted for the ten industries around the event date. As shown in the first bar chart on the expected return, the results varies

-0,00295 -0,00425 0,00177 0,01581 -0,00218 0,00307 0,00855 -0,00554 -0,05114 -0,03515 0,01893 0,00189 -0,01227 -0,00065 0,01191 0,00979 0,00755 -0,06000 -0,05000 -0,04000 -0,03000 -0,02000 -0,01000 0,00000 0,01000 0,02000 0,03000 5- 1-2015 6- 1-2015 7- 1-2015 8- 1-2015 9- 1-2015 10 -1-20 1 5 11 -1-201 5 12 -1-201 5 13 -1-201 5 14 -1-201 5 15 -1-201 5 16 -1-201 5 17 -1-201 5 18 -1-201 5 19 -1-201 5 20 -1-201 5 21 -1-201 5 22 -1-201 5 23 -1-201 5 24 -1-201 5 25 -1-201 5 26 -1-201 5 27 -1-201 5

Average E[r]

0,00198 0,00929 0,01086 0,00658 0,00807 0,00818 0,00931 0,00736 -0,00949 -0,01696 -0,02265 -0,02253 -0,02356 -0,02251 -0,02567 -0,02112 -0,02495 -0,03500 -0,03000 -0,02500 -0,02000 -0,01500 -0,01000 -0,00500 0,00000 0,00500 0,01000 0,01500 5- 1-2015 6- 1-2015 7- 1-2015 8- 1-20 15 9- 1-2015 10 -1-201 5 11 -1-201 5 12 -1-201 5 13 -1-201 5 14 -1-201 5 15 -1-201 5 16 -1-201 5 17 -1-201 5 18 -1-201 5 19 -1-201 5 20 -1-201 5 21 -1-201 5 22 -1-201 5 23 -1-201 5 24 -1-201 5 25 -1-201 5 26 -1-20 1 5 27 -1-201 5

Average CAR

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28 considerably per industry. The industry that has the lowest mean on the expected return ar the

companies that have the technology label. Health care, energy, materials and the industrial companies also reach large negative expected returns. The lowest expected return that is recorded in the chart is -13.86%.

Figure 7: The expected return for the ten industries

In the below line graph the cumulative abnormal return of the industries is illustrated. The energy sector has the lowest score of -15.26% on the event date. Astonishing is the result of the health care sector on the cumulative abnormal return. On the contrary of the expected results, the health care together with the technology sector shows a positive and constant cumulative abnormal return. The energy, financials, consumer staples and industrial sector show the lowest returns. The extreme low result for the energy industry is explainable due to the low number of energy labelled companies in the sample.

Figure 8: The cumulative abnormal return for the ten industries Conclusion for sub-question 1

Recall that the first sub-question of this research is ‘’ What is the impact of the Swiss franc – Euro depegging announcement on the stock value of Swiss companies? ’’

 The null hypothesis: the major swing of the Swiss franc had no significant impact on the stock return of Swiss companies.

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29  The alternative hypothesis: the swing in the Swiss exchange rate causes a significant impact on

the stock values of Swiss companies. This means that we can not make a choice over the direction that the effect of the depegging takes. It simply entails that the effect could be negative or positive.

Positive: the overall result shows a decreasing line in the stock returns after the announcement Negative: the overall result shows a decreasing line in the stock returns after the announcement

For this sub-question the market adjusted model is used. The model is used to calculate the expected and cumulative abnormal return for 235 Swiss companies. Concerning the returns that are calculated in the event window the 235 companies in the sample for this sub-question suffered a big loss. The cumulative abnormal return shows a positive result on the 8 days in advance of the event day. An average positive result of 0.0077 was calculated on the eight days in advance of the event date. On the event date an average result of -0.00949 is calculated and an average result of –0.023 for the eight days after the event.

With regards to the impact of the depegging event on the industries. Similarity is established between the impact on the industrial sector. In Switzerland the industrial sector is impacted heavily by the event. This was also forecasted by similar research in the USA, Canada and Japan.

3.5 Can certain behavior in stock returns, as a result of the depegging announcement, being explained by the two predictor variables: foreign sales and domestic assets?

The output of the regressions between the predictor variables and outcome variable will be presented in this chapter. For the three compositions, discussed in chapter 3.2.2., the model summary and statistical significance of the linear regression will be interpreted.

Model summary and statistical significance

In figure 9 and 10 the coordinates are depicted between the cumulative abnormal return and the two predictor variables foreign sales and Swiss assets. A correlation coefficient of -0.496 is measured between the variable foreign sales and the cumulative abnormal return. The R square (R2) of the coefficient is -0.4962= -0.246 and designates a moderate downhill linear line in CAR in proportion to an increase of foreign sales. This is also visible in the location of the coordinates in figure 9. On the other hand, Swiss assets don’t show a significant relationship. In figure 10 the correlation between Swiss

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30 assets and the CAR is represented. A correlation coefficient of 0,068 is measured and the R square (R2) of the coefficient is 0.0682= 0.0046.

Figure 9: Correlation scatterplot between FS and CAR Figure 10: Correlation scatterplot between SA and CAR Composition 1:

The linear regression of the first composition, gives an R of 0.465, R2 of 0.216 and an adjusted R2 of 0.182. The adjusted R2_{is 0.184 and is therefore lower than the R}2_{. However, this can be easily explained}

by the additional variables that are added to the relation. This decrease could indicate that adding a control or predictor variable, does not have an extra causal impact on the cumulative abnormal return.

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31 As a result of the coefficients in table 8, the regression equation for composition 1 will be:

CAR = -0.59 + -0.028 *HighHigh + 0.031 * LowLow + 0.037* LowHigh + 0.003 * Volume + -0.002 * Assets + 0.003 * Price book ratio

The constant in this equation is -0,59 and can be described as the mean response in the cumulative abnormal return when all other variables are set to zero. For all other variables the cumulative abnormal return deviates by the multiplier when all other variables are equal. Briefly, while comparing two

companies with both a high level of foreign sales and Swiss assets but differ one unit of trade volume the cumulative abnormal return differ -0,028.

Beta for all variables can be found in the column of standardized coefficients. The most negative beta is -0,198 and the most positive is 0,300 for the combination of low foreign sales and high Swiss assets. When a beta is less than one means that the security is less volatile than the market. For a negative beta it means that the return will show the opposite behaviour as the market. When the currency increases a negative beta for high foreign sales and Swiss assets ratio will cause a stock return decrease of a Swiss company. The beta coefficient of 0,300 means that when the foreign sales goes down by 1 standard deviation and the ratio of Swiss assets goes up by 1 standard deviation, the cumulative abnormal return increases with 0,300.

Table 9: T- and p-values of multiple regression 1

The t- and p-values of the first regression can be found in table 9. The t-value can be easily interpreted by using a bold curve. If the t-value falls within one of the rejection regions of the bold curve, the null-hypothesis can be rejected. On the inside, this region is demarcated by critical values. For this regression the critical values are -1.96 and 1.96. The critical values are determined by applying the t-distribution

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32 with two-tails and infinite degrees of freedom. The t-value can be controlled by looking at the p-value in table 9. When the p-value is below 0.05 the null-hypothesis can be rejected as well.

Composition 2:

Compared to the first regression the type of predictor variable is different. For the second regression the dummy variables for foreign sales and Swiss assets are replaced by the actual data for both variables. As the amount of predictor variables is reduced from 4 to 2, the R2 is probably higher than the R2 of the first regression. This is confirmed by the output in the model summary in table 10.

The R of this regression is 0.606, the R2 is 0.367 and the adjusted R2 is 0.345. Comparing the model summary with the first regression, it appears that R2 of is 0.151 higher and the adjusted R2 is 0.163 higher. According to the adjusted R2, foreign sales and Swiss assets explain 35% of the variance in the cumulative abnormal return.

Table 10: Model summary of regression regression 2

The beta coefficient for the two predictor variables are both negative. Overall, the impact of foreign sales will have a more negative impact compared to having assets in Switzerland.

As a result of the coefficients table, the regression equation for composition 2 would be:

CAR = 0.40 + -0.120 * ForeignSales + 0.050 * SwissAssets + 0.003 * Price book ratio + 0.004 * Volume + -0.001 * Assets

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33 Table 11: T- and p-values for regression 2

For the t-values and p-values in table 11 the same critical values apply as for the first regression. 10 out of the 14 t-values fall within critical region. For these values the null-hypothesis can be rejected.

Composition 3

The final composition is a re-estimation of the first regression. Foreign sales and Swiss assets are only split into high and low. Since the sample only exists out of 147 companies, splitting the sample into 2 groups might lead to stronger results compared to splitting the sample into 4 groups. Although in theory it should provide stronger regression values, in practice the result for R, R2 and adjusted R2 are lower. The R of this regression is 0.442, the R2 is 0.195 and the adjusted R2 is 0.167. Comparing the model summary in table 12 with the first regression, it appears that R2 and adjusted R2 are both lower for this regression. According to the adjusted R2, foreign sales and Swiss assets explain 17% of the variance in the cumulative abnormal return. Additionaly, the beta coefficients show again that foreign sales will have a higher negative impact.

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34 As a result of the coefficients table, the regression equation for composition 3 would be:

CAR = -0.019 + -0.050 * HighFS LowFS + -0.009 * HighSA LowSA + 0.003 * Price book ratio + 0.003 * Volume + -0.002 * Assets

For the t-values and p-values in table 13, the same critical values apply as for the first and secon regressions. 10 out of the 14 t-values fall within critical region. 8 out of the 14 t-values fall within the critical region.

Table 13: T- and p-values of regression 3 Conclusion for sub-question two

Recall that the first sub-question of this research is ‘’Can certain behavior in stock returns, as a result of the depegging announcement, being explained by the ratio of the two exchange rate exposure

variables: foreign sales / total sales and domestic assets / total assets?’’

 The null hypothesis: no significant relation can be found between the two exchange rate variables foreign sales, domestic assets and the change in market value of Swiss companies.  The alternative hypothesis: the two exchange rate variables foreign sales and domestic assets

have a significant relation with the degree of change in stock return value of Swiss companies. Positive: the lower the ratio of foreign sales and Swiss assets, the higher the cumulative abnormal return

Negative: the higher the ratio of foreign sales and Swiss assets, the lower the cumulative abnormal return

A multiple linear regression is performed for the second sub-question. The multiple regression measures the relation between the predictor variables foreign sales and Swiss assets and the outcome variable cumulative abnormal return over the time frame of three days in advance and three days after the event date. The regression is performed three times for three different types of measurement units. In other

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35 words, the regression is performed with the ratios of the two predictor variables and dummy variables that symbolize a high or low score on the predictor variables.

The correlation coefficient for the three compositions of variables are: Regression 1: R coefficient is 0.465 and the R2 is 0.216

Regression 2: R coefficient is 0.606 and the R2 is 0.367 Regression 3: R coefficient is 0.442 and the R2 is 0.195

For the first and second regression the R2 value of the regression falls outside the region of 0 and 0.2. As a result, it can be concluded that there is a relation between the two predictor variables and the

cumulative return. Therefore, the null-hypothesis can be rejected. In view of the betas for the first and second regression Swiss assets seems to have a more positive impact on the cumulative abnormal return of Swiss companies.

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4 Discussion and interpretation

This chapter conducts a summary of the findings, interpretation, limitations and recommendation regarding the results of this research.

4.1 Findings and interpretation of conclusions

This study observed the stock return of Swiss companies before and after the depegging announcement in January 2015. It was found, based on observing the two variables foreign sales and Swiss assets, that companies in Switzerland are highly impacted if they maintain a high ratio of both variables. Some results are very accurate whereas others are less. The purpose of this section is to highlight the statistical results from this research and underpin the results with other theory.

The literature review focuses on the causal relationship between the two variables foreign sales and Swiss assets and the performance of companies. To put it very briefly, the review focused on exchange rate exposure. Research on this topic was conducted by Shapiro (1974), Hodder (1982), Jorion (1990), Forbes (2002), Bodnar (2002), Dominguez & Tesar (2006) and many others. In most studies was found that the ratio of foreign sales to total sales and domestic assets to total assets are variables that show causality with the performance of companies. However, the relation has not yet been analyzed in combination with unexpected news events.

The link between exposure and assets is an area of contrast between independent literature. By way of contrast, some authors observed foreign assets instead of domestic assets. In the current study is emphasized on domestic assets as the aspect of exchange rate exposure. It is recognized that surges in ‘foreign competition’ and domestic commodity price booms are regularly related to exchange rate fluctuations.

Another area of divergence is why some companies experience a bigger impact of exchange rate movements than others. One reason could be, that bigger companies have the financial resources to hedge their foreign positions. On the other hand, large companies can control their assets since their costs are lower compared to smaller companies. Upon which part of the researchers believe that even inventories that not engage in foreign trade may well be influenced by exchange rates.

How did Swiss companies react to the unexpected Swiss Franc - EUR peg release?