Influence Brexit International Financial Centers Master Thesis

University of Amsterdam Author: Jeffrey Vossen (10655182) SCHOOL OF BUSINESS and ECONOMICS Supervisor: Jan Lemmen

Date: 27-07-2017 MSC Finance - Asset Management

ABSTRACT

This study examines the competitiveness power of the International Financial Centers (IFC) of London, Paris, Frankfurt, Luxembourg, Amsterdam, Singapore, Tokyo, and New York. The sample period used in this study ranges from March 2007 until March 2017, which contains the recent financial crisis and the Brexit. Furthermore, this study introduces an event study on the stock markets indices of the European IFCs and on the stocks of the banks and insurers listed in the FTSE 350. The results show that the Brexit and the financial crisis have a negative influence on the total competitiveness power of the selected IFCs mentioned above. Furthermore, the results show that the Brexit has a significant negative impact on the IFC of Luxembourg. The IFCs of Paris and surprisingly London improve their competitiveness power by the Brexit, but the improvement is not statistically significant. The competitiveness of the IFCs of Amsterdam and Frankfurt is negatively affected by the Brexit, but again the negative effect is not statistically significant. Singapore is the only IFC which is significantly negatively affected by the financial crisis. The event study provides evidence that the Brexit referendum has a significant impact on the abnormal returns of the stock market indices of the European IFCs in several days of the thirty-one days enclosing the referendum day (i.e., t= -15,…,0…,+15). Moreover, this study provides strong statistical evidence that the stocks of banks and insurers listed in the FTSE 350 were hit hardest by Brexit referendum at short-term.

Keywords: International financial center, Brexit, Brexit referendum, London, financial crisis, European Union, Competitiveness power, Event study, Abnormal returns, Stock markets

JEL Classification: G01, G15, G18

Statement of Originality This document is written by Jeffrey Vossen who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

Table of Contents

Content ABSTRACT ...... 2 Table of Contents ...... 3 1. Introduction ...... 5 2. Literature Review...... 9 2.1 Financial Center ...... 9 2.2 Competitiveness between financial centers ...... 11 2.3 History of the world leading financial centers ...... 13 2.4 The Brexit ...... 14 2.5 The Economy of London after the referendum date of the Brexit ...... 16 2.6 The Global Financial Center Index ...... 18 2.7 Event studies ...... 21 3. Data ...... 23 3.1 Panel Data ...... 23 3.2 Event Study Data ...... 25 4. Methodology ...... 26 4.1 Panel Data Methodology ...... 26 4.2 Event Study Methodology ...... 29 5. Results ...... 32 5.1 Influence of the Brexit and the Financial Crisis on the competitiveness power of International Financial Centers ...... 32 5.2 Results of the Brexit and Financial Crisis on the competitiveness power of European International Financial Centers...... 34 5.3 Influence of the Brexit and the Financial Crisis on individual International Financial Centers ...... 36 5.4 Influence of Brexit referendum on the stock market indices of the IFCs within the EU .... 39 5.5 Influence of Brexit referendum on the stock market indices of the Banking and Insurance sector of the FTSE 350 ...... 42 6. Robustness Check ...... 45 6.1 The Hausman Test ...... 45 6.2 The Woolridge Test ...... 46 6.3 The Modified Wald Test ...... 47 6.4 The White Test ...... 47 7. Discussion and Conclusion ...... 49 References ...... 53

Appendix A: IFCs used in this Study ...... 57 Appendix B: Banking and Insurance companies within the FTSE 350 used in the event study ...... 58 Appendix C: Stock market returns of the European indices beyond Brexit referendum ...... 59 Appendix D: Time line of events after Brexit Referendum ...... 61 Appendix E: Definitions and Sources of Explanatory Variables ...... 62 Appendix F: Results from the Hausman Test ...... 63 Appendix G: Results from the Modified Wald Test and the Woolridge Test ...... 65 Appendix H: Results from the White Test ...... 67 Appendix I: List of Figures and Tables ...... 68

1. Introduction

Research in previous studies on financial centers has come along with broader urban fields of studies. Wójcik (2013) states that since the 1980s, the study of relations between financial centers has experienced a shift from the focus on national levels to the focus at an international level. Micheal Porter posed a fundamental and challenging question in 1990 – why do some nations and their financial centers succeed in international competition while other fail? Recent processes about the financial deregulation have consecrated the consolidation of financial centers (Karreman & Van der Knaap, 2007). Daly (1984) indicates that a financial center can be classified in different terms of geographic influence. He mentioned the different terms of influence as national, regional, zonal or global. Mainelli (2006) defines a global financial center as “an intense concentration of a wide variety of international financial businesses and transactions in one location”. In general, financial centers are cities and countries which mobilize and reallocate a substantial volume of financial sources. Ogloblina (2012) indicates that financial centers “usually combine the complexity of banking and financial institutions, carrying out foreign exchange, financial and credit operations, different securities, and transactions with commodities”. While previous research has a strong tendency to focus on the role of banking activities, Poon (2003) argues that the competition between financial centers appears the most in securities and capital markets (Poon, 2003). In the first decades of the 18th century, Amsterdam had become the world’s leading financial center until the 19th century (Cassis, 2010). Early in the 19th century, Amsterdam was officially replaced by London as the world leading financial center. Moreover, New York City replaced London as the world’s leading financial center in the years following the World War II. Since the 19th centuries, New York and London are the leading global financial centers, where they’re battling for the leading position. The financial center of New York City has surrendered its world’s leading position to London since the events of “9/11” in 2001 and the “Enron” scandal in 2002 (Zhao, 2010). To maintain or improve the position of financial centers, Karreman & Van der Knaap (2007) suggest that “financial centers should be flexible and able to adjust to changing market conditions. Therefore, the competitive power of financial centers depends upon their adjustability in providing the conditions of existence needed for the profitable production of financial services. The success of financial centers is their flexibility” International financial centers (IFCs) are not easily affected since they are developed over cycles. The balance of international competitiveness power is developed with trends about technology, economic, and demographic. The loss in the competitiveness power of an IFC is generally

5 precipitated by a shock, a disadvantageous event that could destroy the continuity of financial services and confidence within an IFC. For example, New York lost its world leading position after events like “9/11” in 2001 and the “Enron” scandal in 2002. After an event with enough magnitude and intensity, it is worthwhile to examine the potential impact on the structure and business prospect of the major financial centers around the world (Deutsche Bank, 2010). This paper will investigate the competitiveness between major IFCs through the cycle of the past 10 years, including events like the Brexit and the financial crisis. The Brexit, a combination of ‘Britain’ and ‘exit’, is widely known as the withdrawal of the United Kingdom (UK) from the European Union (EU). The UK held a referendum on 23 June 2016, in which 52% of the British people voted in favor of leaving the EU, while 48% of the British people vote against the Brexit. In following of this referendum, the government of the UK invoked article 50 of Treaty on European Union by end of March 2007. Europa recognized a large majority vote in favor of leaving the EU, resulting in volatility and uncertainty in global financial markets. Table 1 shows that the volatility increases for all the stock markets within the EU since Brexit referendum.

Table 1: Increase in Return Volatility due to uncertainty by Brexit referendum

IFC 01/06/2016- 23/06/2015- % Increase 30/06/2016 01/06/2016 in Volatility London 0.0127 > 0.0123 3,252% Paris 0.0224 > 0.0218 2,752% Luxembourg 0.0161 > 0.0154 4,605% Frankfurt 0.0160 > 0.0152 5,263% Amsterdam 0.0151 > 0.0146 3,424% Note: The period from 01/06/2016 until 30/06/2016 includes 15 trading days before and after Brexit referendum date

At the moment, the IFC of London executes the role as the world’s leading financial center. However, the Britain’s role as the world leading financial center is at risk because of the Brexit. The divorce from the EU costs banks their ability to help clients across the region. For example, banks from the US sell their services at the moment through the center of London. However, these US banks would be unable to extend these services because of the losing European passporting rights caused by the Brexit (Bloomberg, 2016). The passporting rights will be explained in more details further in this paper. The aim of this paper is to investigate whether the Brexit and the financial crisis have an influence on the competitiveness power of IFCs. Furthermore, this paper wants to investigate whether the Brexit has an influence on the IFC of London and whether other major financial centers within the EU could benefit from the potential effect of the Brexit. Moreover, this study introduces an event study on the abnormal returns of the stock market indices of the European IFCs and on the abnormal returns of the stocks of the banks and insurers listed in the FTSE 350. This study expects to

6 find a significant negative influence of the Brexit and the financial crisis on the competitiveness power of IFCs. Furthermore, it expects to find a significant influence of the Brexit and the financial crisis on the competitiveness power of an individual IFC. However, this study doesn’t expect a significant influence for all the IFCs because the Brexit is in its begging stage of its process. This study expects to find more significant results when the Brexit is in a later stadium of its process. Moreover, this study expects to find significant negative abnormal returns right after Brexit referendum for all the stock market indices.

This paper sought to make a number of contributions to the existing literature. First, this study is the first study that is empirically testing the competitiveness of IFCs. Studies before are mostly theoretically. This study provides evidence for a significant influence of the Brexit and the financial crisis on the competitiveness power of IFCs. Second, this study examines the influence of the Brexit on the competitiveness power of the IFC of London. The results of this study provide evidence that the Brexit doesn’t have a significant influence on the IFC of London. Third, this study compares the potential effect of the Brexit and the financial crisis between the major financial centers within the European Union. This creates a possibility to overview the potential benefits for European financial centers, because of the potential negative effect of the Brexit on the IFC of London. The results show that the Brexit has a significant negative influence on the competitiveness power of the IFC of Luxembourg. Moreover, the IFCs of London and Paris take an economic advantage in their competitiveness power by the Brexit. The IFCs of Amsterdam and Frankfurt take an economic disadvantage by the Brexit. Furthermore, this study provides an overview of the competitiveness of eight major IFCs through the last ten years, including events like the financial crisis and the Brexit. The results from this overview indicate that the financial crisis has a significant negative influence on the competitiveness power of Singapore. Fourth, this study employed an event study to examine the impact of Brexit referendum on the stock market indices of the European IFCs and the stocks of banks and insurers listed in the FTSE 350. The results provide evidence that Brexit referendum has a significant impact on the abnormal returns of the stock market indices of the European IFCs in several days of the thirty-one days enclosing the referendum (i.e., t= -15,…,0…,+15). Furthermore, the results provide strong evidence that the stocks of banks and insurers listed in the FTSE 350 are hit hardest by Brexit referendum.

This study uses panel data regressions with fixed effects to estimate the coefficients of the explanatory variables and the dummy variables of the Brexit and the financial crisis. To determine whether the panel data regressions have to be regressed with fixed or random effects, this study runs a Hausman test. The results of this test provide evidence that the panel data regressions in this study have to be regressed with fixed effects. This study introduces a Woolridge test and a Modified

Wald test in order to test whether the data contains autocorrelation and/or heteroscedasticity. Appendix G provides evidence for the existence of autocorrelation and heteroscedasticity in the database. This study uses clustered standard errors to control for autocorrelation and heteroscedasticity. The null hypothesis from the panel data regression model is tested with a T-test. To test whether an individual IFC has taken an advantage or disadvantage by the Brexit, this study introduces a multiple regression model. This study introduces a White Test to test for heteroscedasticity in the error terms of the datasets for individual IFCs. The results of this test are provided in Appendix H. This study compares the regression results of the eight different IFCs to determine which IFC has taken an advantage or disadvantage by the Brexit. To examine the impact of Brexit referendum on the stock market indices of the selected European IFCs used in this study and the stocks of the banks and insurers listed in the FTSE 350 the event study methodology has been employed. The abnormal returns will be standardized to control for the induced uncertainty caused by the Brexit. Data from the dependent variable is obtained from the past 21 Global Financial Center Index reports. Data on the control variables corresponding the eight IFCs used in this study were achieved by different organizations including the World Bank, the World Economic Forum, and KPMG. The data of the European stock market indices and the stocks of banks and insurers listed in the FTSE 350 were obtained from DataStream. In the conclusion, this paper hopes to get insight into whether events like the Brexit and the financial crisis could have an influence on the status of an IFC. After the review of previous studies in chapter 2, chapter 3 presents the data used in this study. Chapter 4 will explain the methodology used for this research. Chapter 5 provides the empirical results and chapter 6 gives an overview of the robustness checks. This study provides a conclusion in chapter 7.

2. Literature Review

In the past decades, a lot of research has been done about the competitiveness between IFCs. Through the time, there were different world leading financial centers. In the past literature, researchers evaluate the performance between IFCs and suggest whether a financial center performs better than their rivals. Researchers suggest that financial centers are not easily affected by macroeconomic events, but Deutsche Bank (2010) indicates that events with a high magnitude, like the financial crisis, are interesting to investigate whether these events could affect the competitiveness power of an IFC.

The first subsection explains what a financial center is and what it does. This paper discusses the competitiveness between financial centers in the second subsection. Subsection three gives an overview of the history of the world’s leading financial centers. The Brexit and his potential consequences with respect to trading within the European Union will be discussed in the fourth subsection. The fifth subsection gives an overview of the economy in London after the referendum date of the Brexit. The Global Financial Center Index will be discussed in the sixth subsection. The seventh subsection provides an overview of previous event studies.

2.1 Financial Center

In the past literature, researchers define the term ‘International Financial Center’ as a location where international financial services are produced on a large scale (Imad et al., 2016). In general, IFCs are geographic locations which include an urban area of banks and other financial intermediaries, like investment managers and stock exchanges. IFCs transfer money from savers to investors, take care of the balance between savings and investments over time, and act as a medium-of-exchange1. Definitions and characteristics of IFCs can be found in the literature on financial history and geography. For example, Jao (1997) defines an IFC as a “place in which there is a high concentration of banks and other financial institutions, and in which a comprehensive set of financial markets are allowed to exist and develop, so that financial activities and transactions can be effectuated more efficiently than at any other locality”. Cassis (2010) defines a financial center as “the place where intermediaries co-ordinate financial transactions and arrange for payments to be settled”. Based on the geographical area concentration of the financial services within an IFC, he makes a distinguish between national, regional and international financial services.

1 “Financial Centers: What, Where and Why”. The University of Western Ontario. Retrieved on 24 May 2015. 9

To further become an IFC, Cheung and Yeung (2007) indicate that an economy of an IFC will need to appeal the bearing of international financial activities. For example, Cheung and Yeung (2007) indicate that the status of in IFC can be determined by the number of different international activities which are locally managed. A paper by the International Monetary Fund defines an IFC as a large international full- services center with advanced payment systems and settlement. Furthermore, within an IFC, deep and liquid markets are both the sources and uses of funds and an IFC supports the large domestic economy. Kindleberger (1974) states that an IFC has “the same concentration that produces a single dominant financial center within a country tends to result in the emergence of a single worldwide center with the highlyo specialized functions of lending abroad and serving as a clearing house for payments among countries”. He suggests that IFCs have a similar process as national financial centers. However, IFCs are forestalled from being carried before the 1980s, because of barriers to higher transaction costs and exchange risk. In the beginning of the 20th century, the development of new IFCs results in a special economic environment (Kindleberger, 1974). Since March 2007, the taxation and ranking of financial centers are taken further by Z/Yen Group. They classify financial centers into five different categories.

• Global: Wide range of financial services (London, New York) • International: Significant of cross-border transactions conducted (Hong Kong, Singapore, Tokyo) • Niche: Special financial services like financial center of Zurich in private banking (Zurich, Luxembourg, Sydney, San Francisco) • National: Act as hub for financial services in one country (Paris, Frankfurt, Amsterdam) • Regional: Regional businesses within a country (Athens, Helsinki, Milan)

The classifications and rankings provided by the Z/Yen Group are known as The Global Financial Centers Index (GFCI), which is provided semi-annually since March 2007. This index is a ranking of the competitiveness power of IFCs and is widely quoted as a source for ranking IFCs, the index will be discussed in more details later in this paper.

2.2 Competitiveness between financial centers

It is an interesting fact that before the 1980s the formation of financial centers is not studied yet in economics, perhaps because it falls between two stools, urban and regional economics (Kindleberger, 1974). Kindleberger (1974) investigates the evolution of financial centers within major national economies. However, Wójcik (2013) states that since the 1980s, the study of relationship and competition between financial centers has experienced a shift from the focus on national levels to the focus at an international level. A major breakthrough in the past literature focused on the international level was introduced by John Friedmann (1986), who introduced the ‘world city hypothesis’ in 1986. Friedman (1986) defines the world city hypothesis as “the spatial organization of the new international division of labor. It helps us to understand what happens in the major global cities of the world economy and what political conflict in these cities is about”. This hypothesis linked urbanization to global economic process (Friedmann, 1986). The next milestone in the literature was induced by Manuel Castells’ (1996) paper which introduced a work about the network society. He indicates that his work is “stressing the prevalence of the space of flows over the space of places in the contemporary economy” (Castells, 1996). This drives to rethinking about the system of financial centers as a network. The position and power of a city are determined by its connection with other financial centers, whereby the relation between financial centers are pictured as a complex combination of competition (Wójick, 2013). The Z/Yen Group (2007) argues in The Global Financial Centers Index 1 (GFCI 1) that financial services are an attractive business sector. Cities are seeking to develop their financial services because the services in the financial sector have been high growing and successfully for the past quarter of the century. The financial services sector is a highly mobile sector, which can be directly influenced by policy and planning (Z/Yen Group, 2007). Therefore, the financial services sector is highly attractive and of great relevance to regulators and government official. Karreman & Van der Knaap (2007) indicate that most of the success of financial centers is driven by their flexibility. Global financial markets are integrated due to recent financial deregulation processes. Obstfeld and Taylor (2004) said in their paper that “financial deregulation processes, together with the development of information and communication technology, have generated a significant improvement of capital mobility and an increasing volume of international capital”. When market conditions are changing, an IFC should be flexible and able to set aright these changing market conditions to maintain or improve their position in the global market (Karreman & Van der Knaap, 2007).

As mentioned earlier, due to data limitations, most of the researchers introduced descriptive studies which investigate the status of an IFC. However, there are descriptive studies in the literature which compare different IFCs with each other. Poon (2003) has examined the evolution of world cities as centers of capital from 1980 to 1998. He provides evidence that world city centers of capital are interconnected within a system of vertical relationships that intensified over time. On the global arena, London, New York, and Tokyo are battling to be the world leading center of global finance (Poon, 2003). Furthermore, he indicates that deregulation has sped up in Europe and several cities are attempting to imitate London’s 1986 Big Bang. The Big Bang refers to the deregulation of the security market in London at 27 October 1986, in which the London Stock Exchange became a private company. Poon (2003) suggests that major trends describe the increasing differentiation among the cities, in which London has overtaken the position of Tokyo and shares the world’s leading position with New York. Kasimoglu et al. (2016) have compared the IFC of Istanbul with twelve other selected IFCs. They use a ranking analysis to compare all these IFCs with Istanbul. They compare nine indicators from the banking sector and thirteen indicators from the financial markets. They examine the following research question; What is the competitiveness of Istanbul among IFCs? In their paper, they conclude that the IFC of Istanbul can’t compete yet with the compared IFCs. Karreman & Van der Knaap (2007) investigate the competitiveness between the financial centers of Hong Kong and Shanghai. They suggest that financial centers try to outperform their rivals in market segments and geographical areas in which they have a comparative advantage. Cheung & Yeung (2007) construct a simple model to determine the determinants of the financial sector in 18 OECD countries for the period 1998 until 2003. They mention that given the estimated determinants of the formation of IFCs, it would be very interesting to investigate how the IFC of Hong Kong deals to these factors. They provide evidence that Hong Kong and Singapore are the most successful in most of the competitiveness factor of an IFC. However, they also mention that Hong Kong still lags well behind the major IFCs such as London and New York, particularly in the financial market activities. Their findings suggest that an economy can improve its competitiveness rating by increasing the level of international activities in the stock markets (Cheung & Yeung, 2007). Deutsche Bank (2010) investigates whether the financial crisis has an influence on the global financial centers. Furthermore, they examine the impact of the global financial crisis on the competition between IFCs between 2007 and 2010. They mention that financial centers aren’t easily shaken because they’re developed over long time periods. However, they suggest that it’s worthwhile to examine the potential impact of an event, with such a magnitude and intensity of the financial crisis, on the structures and business prospects of the major world financial centers. In their paper, they provide evidence for a decline in the stock markets in the US and the EU in the period

12 following the financial crisis. However, neither the ranking or the competitiveness rating of the typical top 10 world leading centers has changed during the financial crisis. However, they provide evidence that emerging IFCs such as Shenzhen, Dubai, and Beijing have strongly increased their global ranking since 2007. Deutsche Bank (2010) indicates that the European IFCs like London, Paris, and Amsterdam have lost competitive power compared to other advanced and emerging financial centers. Most importantly, Beijing, Dubai, and Shanghai have successfully achieved to the top group of competitive IFCs (Deutsche Bank, 2010). After all, Deutsche Bank (2010) concludes that the competitiveness ratings of traditional financial centers have not changed significantly over the period of 2007 until 2010. On the other hand, the US and EU financial markets continue at a significantly lower overall level of market activity in many market segments after the financial crisis. According to CBRE Group, Inc. (2015), office occupancy costs are increasing across the globe as the service sector accelerates away from the recession, with eight British cities featuring the top 50 priced prime office locations. They describe occupancy costs as “costs related to occupying a space including rent, real estate taxes, insurance on building and contents, and personal property taxes” (CBRE Group, Inc., 2015). They mention in 2015 in their “Global Prime Office Occupancy Cost” report that London is the most expensive market in the UK, with costs of almost 180 pound per sq ft. This means that the UK is one of the most expensive places to locate an office, especially London (CBRE Group, Inc., 2015).

2.3 History of the world leading financial centers

The financial center of Amsterdam was the first IFC in the beginning of the 18th century. The IFC of Amsterdam and his developed financial system was the world’s leading financial center for more than a century. The City of London replaced Amsterdam as the world’s leading financial center by the early 1800s. London was the world’s leading financial center until 1840, ahead of Paris, followed by a group of important secondary financial centers, including Amsterdam, Brussels, Geneva, and Frankfurt. Cassis (2010) suggests that the fall of the financial center of Amsterdam was one of the worst events in the history of global finance. Before 1870, Paris and London existed as the two world’s leading financial centers. However, New York and Berlin grew soon after this period to become important IFCs2. Things were not basically different around 1875; The City of London was still the world leading financial crisis, followed by Paris. The House of Rothschild is the largest bank in Europe and private bankers control

2 London and Paris as International Financial Centers in the Twentieth Century, Oxford:OUP Oxford. 2005. ISBN 9780191533471 13 over large international financial transactions (Cassis, 2010). Two major changes occurred during the second third of the 19th century, the increased concentration of capital and the strong growth of capital exports. During the mid-1850s, foreign investment began to grow more substantially from $1 billion in 1855 to $7.7 billion in 1870 (Barth, 1998). This capital was mainly exported from the markets of Paris and London. The City of London remained to be the world’s leading financial center but was strongly challenged by Paris. London was for a long time the world’s leading IFC, however in the years following the World War II, New York City became the world’s leading IFC. London and New York are the leading global financial centers since the 19th centuries, where they’re in competition for the leading position. The financial center of New York has surrendered its world’s leading position to London since the events of “9/11” in 2001 and the “Enron” scandal in 2002 (Zhao, 2010). Since 2002 is London the world’s leading IFC, except from March 2014 to September 2015, where London gives its position away to New York City. Table 2 provides an overview of the world’s leading IFC in each time period.

Table 2: Leading Financial Centers over Time

Time Period Financial Center Leading Period 18th century Amsterdam 1780-early 1800s Early 1800s London Early 1800s-1945 1945 New York 1945-2002 2002 London 2002- March 2014 March 2014 New York March 2014 - Sept 2015 Sept 2015 London Sept 2015 – April 2017

2.4 The Brexit

On 23 June 2016, the UK held a referendum about whether the UK should stay in or divorce from the EU. The results of this vote were published on 24 June 2016. Within the UK, 51.9% of the votes cast were in favor to leave the EU, while 48.1% chose to remain in the EU. The outcome of the UK’s referendum to leave the membership of the EU will outline the future of the British relationship with the EU and refers to the ‘Brexit’. As a member of the EU, the UK benefits from a harmonized internal and external market regime. Within the EU, there is a customs union between EU members, which mean that all tariff barriers have been eliminated within the EU. This means that countries within the EU could trade in goods and services for free (Dhingra et al., 2016). Companies allocated in the UK can invest and sell

14 their goods, services, and labor in the other 27 EU member states. Furthermore, the EU has exclusive power to legislate on trade matters and to conclude free trade agreements (FTAs) on its 28 EU members (Vergano & Dolle, 2016). An FTA is an agreement between countries to reduce trade barriers between them (Das, 2012). Vergano & Dolle (2016) indicate that, because of the Brexit, the UK will lose these passporting rights. This means that the UK has no longer the ability to take an advantage from the FTAs concluded and negotiated by the EU and other benefits from being an EU member state. This could hurt the competitiveness power of the IFC of London. In 2014, followed by the global financial crisis, the EU introduced the Alternative Investment Fund Managers Directive (AIFMD). The AIFMD is an EU law on the financial regulation of hedge funds, real estate funds, private equity and other AIFMs in the EU. The AIFMD is intended to protect alternative private investment fund managers (AIFMs) (Lee, 2015). However, when the UK leaves the EU, investors will lose their AIFMD passport, which should have consequences for investors who are located in the UK. Lee (2015) suggests that AIFMs are overwhelmingly concentrated in the UK. Therefore, it is even more interesting whether the Brexit has an influence on the performance of the IFC of London. In short, members of the EU have a common trade policy and are presented by the EU in all international trade negotiations. However, the UK would become an independent player after the Brexit, free to seek its own trade deals with other countries all over the world. The UK could use these potential trade deals to look for new agreements with countries such as the United States, China, and India. The performance of the IFC of London could be positively or negatively affected by these new potential trade deals. Because of the Brexit, the UK will become less attractive as gateway to the EU for foreign investors. The European IFCs could benefit due to the fact that foreign companies want to transfer themselves to other countries within to EU. In a recent study of Thesqua.re, Stapley (2017) indicates that Frankfurt is uniquely positioned to benefit from the Brexit and is expected up to 10.000 jobs from some diverse businesses could move to Frankfurt since the European Central Bank is stationed here. Furthermore, among 8 of the 10 major banks in London have subsidiaries in Frankfurt. Luxembourg is also mentioned as a potential financial center to be the next financial center after London, post-Brexit. AIG, the insurance giant, said to open a subsidiary in Luxembourg and JPMorgan could also shift resources to Luxembourg (Stapley, 2017). However, Stapley (2017) suggests that it could still be difficult for Luxembourg to absorb even a small percentage of London’s financial services sector. Paris is also seen as a rival between the IFCs. The France chief financial regulator said, “The international banks based in the UK are planning to shift their operations to London braces for the impact of the Brexit”. To stay near their clients, these banks plan to visit Paris and make business in the French capital (Stapley, 2017). Dhingra et al. (2016) suggest that all EU member are worse off after the Brexit. However, countries that lose the most are those currently trade the most with the

UK. The Central Plan Bureau (2016) suggests that the financial center of Amsterdam will be hurt more by the Brexit than other financial centers in the EU. This is because the business in the Netherlands is more interweaved with the UK.

2.5 The Economy of London after the referendum date of the Brexit

The Brexit brings a lot of uncertainty which has many consequences for the economy of London. One of such consequences is the fact that the pound fell dramatically after Brexit referendum. Since the referendum date, the pound has been trading around 12% lower compared to the euro and 15% lower compared to the dollar. Furthermore, the interest faces a low record, which also contributes to a weaker pound. Plakandaras et al. (2017) investigate the effect of the Brexit in the significant depreciation in the USD/GBP exchange rate. In their paper, they provide evidence that most of the depreciation is based on the uncertainty caused by the Brexit. Figure 1 below shows the deprecation of the Sterling pound in comparison with the US dollar. The line in the figure represents Brexit referendum held on 23 June 2016. As the figure shows, the USD/GBP exchange rate fall dramatically after Brexit referendum vote.

Figure 1: The Deprecation of the Pound Post Brexit Referendum

GBP to US exchange rate 1,5000 1,4500 1,4000 1,3500 1,3000 1,2500 1,2000 1,1500 30-5-2016 30-6-2016 31-7-2016 31-8-2016

Source: The European Central Bank

Nevertheless, the confidence of investors (measured by the UK share prices) is holding up well because the UK stock markets have risen since Brexit referendum. Figure 2 on the next page shows that the day after Brexit referendum on 23 June 2016, the three biggest stock exchanges of the London stock market plunged but quickly recovered and ended 2016 at an all-time peak.

Figure 2: FTSE Stock Prices Post Brexit Referendum

Source: DataStream

The rise in the London stock market was driven in part by the pound’s weakness. This weakness helps the many companies in the London stock market indices that report in dollars and those that export from the UK. The FTSE 100, FTSE 250, and FTSE 350 indices are respectively almost around 19%, 25%, and 15% above its level on the night of the Brexit vote. Moreover, the stock market crashed by eight percent in response to the stock, such that the stock returns of the FSTE 100, FTSE 250, and FTSE 350 declined immediately in response to the referendum vote. The figures in Appendix B provide evidence that almost all the stock market indices of the IFCs within the EU have been directly negatively affected by the referendum vote of the Brexit. Figures 3, 4, 5, and 6 show that the stock market returns of the indices of London, Paris, Luxembourg, and Frankfurt have a negative pattern instantaneously after 23 June 2016, the referendum date of the Brexit. This means that the London stock market was not the only index within the EU which was negatively affected by the referendum vote of the Brexit. However, all the stock market returns of the indices of the European IFCs rises within a few days to their old returns or even higher. As shown in Appendix C, the FTSE 100 and the LuxX, the stock market indices of London and Luxembourg, were the only indices within the EU which generate in a few days higher returns than before Brexit referendum date. KPMG (2017) suggests that the banking and insurance sectors are hit hard by Brexit referendum vote. They said the following for the banking sector “without passporting rights, the desired fall-back option is that the EU regulator grants the UK ‘regulatory equivalence’. Without equivalence, firms may have to split their capital and business models to try and maintain coverage, clients, and footprints. Others may cut their European operations and retrench to the US and Asia”.

Furthermore, KPMG (2017) indicates that without the market access that passporting right provides the insurance groups also faces the challenge of splitting their capital and business models (KPMG, 2017). This study introduces further in this paper an event study methodology to investigate the impact of Brexit referendum on the stock market indices of the selected European IFCs used in this study and the stocks of banks and insurers listed in the FTSE 350. The Bank of England took steps in order to boost the economy after the referendum of the Brexit. For example, the bank has cut the interest rates to a new low record from 0.5% to 0.25% in August 2016. Moreover, the Office for National Statistics provided evidence about the fact that net migration has dropped with 49.000 people from the previous year. Djankovic (2017) provides evidence in his paper about that the Brexit has a significant direct effect on the financial sector in the City of London. The city will lose a 12 to 18 percentage of its revenue and a 7 to 8 percent drop in employment. He suggests that the biggest uncertainty is whether the UK government will be able to deliver on the various promises made with Brexit. For example, Djankovic (2017) is questionable about the consequences of the reform of immigration. He doubt about whether it possible to maintain the inflow of talent to the city of London and more generally to universities and the British market (Djankovic, 2017)? Appendix D provides a graphical overview of the events after Brexit referendum which affects the London stock market.

2.6 The Global Financial Center Index

The Global Financial Center Index (GFCI) is a ranking of competitiveness of financial centers. The most recent index was compiled using 101 instrumental factors. These quantitative measures are provided by third parties including the World Bank, the Economist Intelligence Unit, the Organization for Economic Co-operation and Development (OECD), and the United Nations. The instrumental factors were combined with financial center assessments provided by the response to the GFCI online questionnaire (Z/Yen Group, 2017). The number of financial centers in the main index has increased to 88. The classifications and rankings of the GFCI were created in 2005 and first published by Z/Yen Group in March 2007. The GFCI is updated and republished each September and March. The instrumental factors used in the GFCI model are grouped into five broad factors of competitiveness. Business Environment, Human Capital, Infrastructure, Reputation, Development, and Financial Sector. To estimate how IFCs perform in each of these areas, the GFCI factor assessment model is run with only one of the five groups of areas of competitiveness. Table 3 on the next page shows the top 15 ranked centers in each sub-index in the most recent GFCI (GFCI 21).

Table 3: Areas of Competitiveness

Rank Business Environment Human Capital Infrastructure Financial Sector Reputation Development 1 London (-) New York (-) London (-) London (-) New York (+1) 2 New York (-1) London (-) New York (-) New York (-) London (-1) 3 Hong Kong (+1) Hong Kong (-) Hong Kong (-) Hong Kong (+1) Singapore (-)

4 Singapore (-1) Singapore (-) Singapore (-) Singapore (-1) Hong Kong (-) 5 Toronto (+3) Toyko (-) Toyko (-) Boston (-0) Chicago (-) 6 Toyko (-1) Chicago (+1) Shanghai (+3) Toyko (+1) Boston (-) 7 Chicago (-1) Los Angles (+1) Bejing (+3) San Fransisco (-) Toyko (+7)

8 Los Angeles (-1) San Francisco (-1) San Fransico (-2) Chicago (-) Sydney (+2)

9 Montreal (+3) Boston (+1) Taipei (+5) Washington DC (-) San Francisco (-2)

10 Sydney (-) Shanghai (+4) Dubai (+3) Shanghai (+18) Zurich (+4)

11 Boston (-1) Washington (-1) Boston (-4) Zurich (-1) Washington DC (-3)

12 Luxembourg (+3) Shenzen (+7) Washington DC (-3) Frankfurt (-) Toronto (-)

13 Zurich (-4) Zurich (-2) Paris (+5) Los Angles (-2) Dublin (+15)

14 San Fransisco (-1) Luxembourg (-1) Sydney (-4) Toronto (+1) Los Angles (-6)

15 Washington DC (-1) Toronto (+1) Zurich (-3) Edinburg (+4) Shanghai (-3) Source: GFCI 21

The GFCI 20 didn’t contain the results of the referendum already. This is because of the fact that the GFCI 20 was calculated based on data up to the end of June 2016, a few days after the results of the referendum on 24 June 2016. However, the Z/Yen Group (2017) suggests that the GFCI 21 may show some significant changes based on the Brexit and indicates in the GFCI 21 that the Brexit has become a major source of uncertainty for all centers, not just London. However, the financial center of London is most damaged by the Brexit in comparison with the other financial centers. The Z/Yen Group (2017) provides evidence in the GFCI 21 that the Brexit and the US election had a significant impact on the competitiveness of the IFCs of London and New York. London and New York fell 13 and 14 points respectively right after these events. Furthermore, five of the top six Eastern European centers rise in their GFCI rating. Luxembourg and Dublin show strong rises in their ratings while Geneva and Amsterdam fall. Because of Brexit referendum, investors are looking around and considering Luxembourg as potential if they need to leave the UK (Z/Yen Group, 2016). Furthermore, an investment banker based in London said “First we had the Scottish referendum and then the general election and now the Brexit referendum. What bankers want is certainty no uncertainty! I feel like packing my bags and going off to Singapore” (Z/Yen Group, 2016, p.9). This makes it interesting to investigate whether the Brexit leads to a decline in the competitiveness power of the

IFC of London and whether other IFCs could benefit from this potential decline in competitiveness power. Figure 3 shows the competitiveness power of the eight IFCs used in this study since March 2007. As figure 3 shows, London and New York are the top leading IFCs since 2007. However, their competitiveness power decreases during the financial crisis. Meanwhile, Luxembourg increases its competitiveness power during the financial crisis. Figure 3 provides evidence of the fact that the competitiveness power of London and New York stays quite constant over time, while the other six IFCs increased their competitiveness power relative to New York and London over time.

Figure 3: Competitiveness Between International Financial Centers Used In This Study

850

800

750

700

650

600

550

500

1-3-2007 1-8-2007 1-1-2008 1-6-2008 1-4-2009 1-9-2009 1-2-2010 1-7-2010 1-5-2011 1-3-2012 1-8-2012 1-1-2013 1-6-2013 1-4-2014 1-9-2014 1-2-2015 1-7-2015 1-5-2016 1-3-2017

1-11-2008 1-12-2010 1-10-2011 1-11-2013 1-12-2015 1-10-2016

London Luxembourg Amsterdam Frankfurt Paris New York Singapore Toyko

Source: Past 21 Global Financial Center Indices published by the Z/Yen Group

2.7 Event studies

The random-walk theory presumes that stock prices are uncorrelated with historical prices. The theory assumes that there is no trend in stock price movements and that they are independent. Therefore, the Efficient Market Hypothesis (EMH), introduced by Fama (1970), indicates that historical prices have no predictive capacity over the future prices. Therefore, the shift in prices should be random (Fama, 1970). The Brexit is expected to have a negative influence on stock prices, which is caused by an increase in uncertainty. Uncertainty is one of the main reasons for instability in financial markets. The uncertainty caused by the Brexit will most likely spill over to other EU countries. In economics, it is widely known that during political events volatility increases and spills over across markets which will affect other countries indirectly. Belke et al. (2016) investigate the increase in uncertainty caused by the Brexit on European financial markets. Furthermore, they examine the impact of the Brexit on the European stock market returns, the pound, and the euro. Their results suggest that the high uncertainty within the UK, caused by the Brexit, spills over to other European financial markets. Therefore, this study wants to examine the impact of Brexit referendum on the European stock market indices of the IFCs used in this study and the stocks of banks and insurers listed in the FTSE 350 employed by an event study. The event study methodology is one of the most used tools in financial research. James Dolley (1973) was the first researcher who introduced the event study methodology in financial literature. He investigates how share prices react to stock splits announcements and found a significant impact to the extent of 60 percent (Dolley, 1973). Since Dolley (1973), many other researchers have employed this methodology. Sathyanarayana & Gargesha (2016) used the event study methodology to examine the impact of Brexit referendum on the Indian stock market. They chose the Sensex and Nifty Fifty indices of the Indian stock market for the purpose of their study. The daily returns used in their event study are calculated as logarithmic differences of daily closing prices. Their results provide evidence that both stock market indices generate negative abnormal returns the day right after Brexit referendum. This means that both indices generate the trading day after Brexit referendum a significantly lower return than their expected returns. Moreover, the Nifty Fifty Index generates a significant positive abnormal return twelve trading days after Brexit referendum (Sathyanarayana & Gargesha, 2016). They also examine whether the pre-event window abnormal returns are significantly different from the post-event window abnormal returns in the event window periods of 7 and 15 days. However, they didn’t find any significant result. Ramiah et al. (2017) used recently the event study methodology to investigate the sectoral

21 effect of the Brexit on the British economy. They examine the stock markets of several sectors within the UK. The daily returns used in their event study are calculated as logarithmic differences of daily closing prices. Ramiad et al. (2017) provide evidence that the Brexit will have varying sectoral effects. Although most sectors reacted negatively as indicated by significant negative abnormal returns after Brexit referendum. Furthermore, their results indicate that the banking sector was hit hardest by Brexit referendum.

3. Data

3.1 Panel Data

The dependent variable (the status of the IFC) is obtained from the past 21 Global Financial Center Indices. The GFCI report, which is published by the Z/Yen Group, was first published in March 2007 and is updated and provided semi-annually. The index is compiled based on two sources: financial center assessments (survey data) and instrumental data (statistical data). The most recent index was compiled using 101 instrumental factors. The survey data is obtained from financial center assessment, based on responses to an online questionnaire completed by international financial services professionals (Z/Yen Group, 2016). Since 2007, the Z/Yen Group included more financial centers to the Global Financial Center Index. However, this study only uses IFCs which are included in the index since March 2007. Data on the explanatory variables corresponding the eight IFCs used in this study were achieved by different organizations including the World Economic forum, the World Bank Forum, and KPMG. The values of the eight explanatory variables are calculated in such a way as to make sure that they are semi-annually and corresponding to the values of the semi-annually dependent variable. The listing of IFCs used can be found in Table 14 in Appendix A. Appendix E provides the explanatory variables and their sources. This study is done over the time period from March 2007 until March 2017. This time period is divided into three different time periods; financial crisis period, non-crisis period, and Brexit period. This study uses three sub-periods so that it can test the influence of the Brexit and the financial crisis period on the status of an IFC. Table 4 provides the descriptive statistics for the variables used in this study.

Table 4: Descriptive Statistics Variables Panel Data

Variable Obs Mean Std.Dev Min. Max Financial Center 168 4.5 2.30 1 8 Year 168 11 6.073 1 21 GFCI 168 697.46 63.80 570 807 Internet Usage 168 80.59 10.68 55.7 97.33 Tax Rate 168 0.31 0.06 0.20 0.41 Global 168 5.38 0.20 4.85 5.74 Competitiveness Size of Government 168 18.61 4.57 9.06 26.48 Legal system and 168 7.84 0.39 6 8.4 property rights index Economic Freedom 168 74.92 6.48 61.2 89.4 Human Development 168 0.90 0.01 0.83 0.93 High Tech Exports 168 9.98e+10 5.53e+10 6.49e+08 2.21e+11 Brexit 168 0.14 0.35 0 1

Financial Crisis 168 0.29 0.45 0 1

Table 4 shows the descriptive statistics of the dependent and independent variables of the panel data used in this study. As Table 4 shows, this study investigates the status of eight different IFCs over 10 years, divided into semi-annual periods. The GFCI is an index from 0 to 1000. The mean of the GFCI over the eight financial centers from March 2007 until March 2017 is 697.45 and varies between 570 and 807. Furthermore, there is also quite some variation in the GFCI, as can be seen from the standard deviation, which is around 63.79. This study uses 10 independent variables to measure the GFCI. Each variable has 168 observations. Appendix E provides the definitions, sources and expected signs of the variables. The mean of the internet users per 100 people is 80.58 persons in each financial center. The minimum is 55.7 per 100 people, where the maximum value measured is 97.334 internet users per 100 people. This maximum indicates that almost all the people in a financial center use the internet. During the sample period, Luxembourg is the financial center with the highest percentage of 100 people who use the internet. The tax rate is a variable which is measured in percentages and is at average 30.75%, which varies between 20.0% and 40.69%. During the Brexit, London has the lowest observed tax rate of 20%, whereas New York faces the highest tax rate of 40%. The global competitiveness is an index scaled from 1 to 7. The average over the financial centers is 5.38, which is quite high. This is because this study only uses high ranked IFCs. The size of government has a high difference of 17.424 between the minimum and the maximum. This is because of this study uses relatively big IFCs like New York and London and relatively small IFCs like Luxembourg and Paris. The legal system and property rights index is an index measured between 1 and 10. The mean of this index over the IFCs is 7.844, where Amsterdam faces the highest index rates over time and Paris the lowest index rates. The economic freedom index is an index between 0 and 100 and has a median of 74.919. The difference of 28.2 between the minimum and the maximum of this index is quite high. This can be explained by the fact that the economic freedom in Paris is relatively low and in Singapore relatively high. The human development index in the IFCs is at average 0.899 and measured as an index between 0 and 1. The standard deviation is very low, which is around 0.017. This means that most of the IFCs have a human development index around the median. The high-tech exports by IFCs are measured in US dollars. The standard deviation in the high-tech exports is quite high, as can be seen from the standard deviation reported in Table 4. This variable generates high differences between the minimum and maximum. This can be explained by the fact that study uses relatively big IFCs and relatively small IFCs. For example, high ranked IFCs like Tokyo, New York, and London generate way more high-tech exports than Amsterdam and Paris.

3.2 Event Study Data

This study introduces an event study to investigate the impact of Brexit referendum, held on 23 June 2016, on the stock market indices of the selected European IFCs used in this study and the stocks of banks and insurers listed in the FTSE 350. Table 5 below provides the descriptive statistics for the stock market returns of the indices and listed stocks used in this study.

Table 5: Descriptive Statistics of the Stock Market Indices used in the Event Study

Variable (Indices) IFC Obs Mean Std.Dev Min Max Return FTSE 100 London 279 -0.0078% 0.013 -4.78% 3.51% Return Euronext Paris 279 -0.0385% 0.022 -8.15% 8.13% Paris Return Luxx SE Luxembourg 279 -0.0458% 0.016 -6.30% 3.86% Return DAX Frankfurt 279 -0.0499% 0.016 -7.01% 4.85% Return AEX Amsterdam 279 -0.0342% 0.015 -5.87% 3.97% Return UK banking London 279 -0.1292% 0.019 -10.29% 5.98% sector Return UK London 279 -0.0546% 0.019 -11.77% 5.46% insurance sector Return FTSE 350 London 279 -0.0097% 0.012 -4.64% 3.45%

As Table 5 shows, this study uses the stock market indices of the five selected European IFCs used in this study and the UK banking and insurance sectors. The stock market index selected for the IFC of London is the FTSE 100. This index generates an average return of -0.008% over the sample period of 279 trading days from 23/06/2015 until 19/07/2016. The stock market index selected for the IFC of Paris is the Euronext Paris. This index generates its lowest return at -8.15%, while it generates its highest return at 8.13%. This means that there are big differences between the returns in this index. Moreover, this index has a standard deviation of 2.23%, which is quite high. The stock market index selected for the IFC of Luxembourg is the Luxx SE. The lowest observed return over the sample period of this index is generated at -6.30%, while the highest observed return is generated at 3.86%. The mean return is observed at -0.046%. This means that this index generates on average a negative return over the sample period. The stock market index selected for the IFC of Frankfurt is the Dax. This index generates on average a negative return of -0.049%. The difference between the highest and lowest observed return is -2.16%. The stock market index selected for the IFC of Amsterdam is the AEX. This index generates on average a negative return of -0.0342%. The difference between the highest and lowest observed return is -1.90%. The stocks of banks and insurers listed in the FTSE 350 are based on all the banking and insurance companies listed in the FTSE 350 index. This study selected the companies in the FTSE 350 index instead of the FTSE 100 index because there were just a few banking and insurance companies listed in the FTSE 100. The listed companies used in this

25 study are provided in Appendix B. Moreover, Table 5 shows that the stocks of banks and insurers listed in the FTSE 350 generate on average a negative return of -0.129 % and -0.05%, respectively. Furthermore, these sectors generate respectively high negative returns of -11.77% and -5.46%. However, the FTSE 350 index generates on average a smaller negative average return than the banking and insurance sector. Moreover, both sectors generate larger negative returns and larger positive returns than the FTSE 350 index. This means that stocks of banks and insurers listed in the FTSE 350 contain a higher return volatility which results in more uncertainty. This can be seen from Table 5 which shows that the stocks of banks and insurers listed in the FSTE 350 have a higher standard deviation than the complete FTSE 350 index.

4. Methodology

4.1 Panel Data Methodology

This study introduces a newly developed method to test whether events like the Brexit and the financial crisis could have an influence on the status of an IFC. This method is based on the paper of Imad et al. (2016), who investigate whether explanatory variables have a (robust) effect on the status of IFCs. Their paper delivers a nice contribution to the existing literature on IFCs because the literature is somewhat thin, especially when it comes to the determinants of the status and rank of an IFC (Iman et al., 2016). They provide evidence about which explanatory variables are important for determining the status of an IFC as measured by the Global Financial Center Index (GFCI). This study uses the evidence of the study of Imad et al. (2016) to determine the control variables in equation (1). Imad et al. (2016) use extreme bounds analysis to find out whether determinants of the dependent variable are robust or not. They consider the relationship between the dependent variable (GFCI) and a given explanatory variable to be robust if “the estimated coefficient on that variable remains statistically significant without a change of sign when the set of explanatory variables changes” (Imad et al., 2016). They indicate that it’s a very typical finding in studies that very few (or no) variables are robust, using the criteria mentioned in the paper of Imad et al. (2016). They consider 24 possible determinants of the status of an IFC while trying to avoid the problem arising from subjective model selection and data mining using the extreme bounds analysis (Imad et al., 2016). In their results, they provide evidence that only two variables turned out to be robust: occupancy costs and global competitiveness. However, they also run a cumulative distribution function approach suggested by Sala-i-Martin (1997) to determine the determinants of an IFC. They provide evidence that eight explanatory variables have a higher CDF (0) than 0.95. This indicates that

26 these eight variables are important for determining the status of an IFC as measured by the GFCI (Imad et al., 2016). These variables are High-Tech Exports, Internet Usage, Corporate Tax Rate, Global Competitiveness, Global Credit rating, Occupancy cost, the Size of Government, and the Legal System and Property Rights. Furthermore, Imad et al. (2016) suggest that a country also should pay attention to the following three variables; Capital Access, Human Development, and Economic Freedom. Because of data limitations, this study uses eight of the eleven variables in equation (1) as control variables. As earlier mentioned, Appendix E provides the definitions, sources, and the expected sign of the explanatory variables used in this study. The expected sign of these variables, positive or negative, is based on intuition and previous studies. To empirically test whether the Brexit has an influence on the status of the IFCs, this study uses the following panel data regression with fixed effects. To check whether the data from equation (1) contain random effects or fixed effects, this study introduces the Hausman test. This test will be explained in more details further in this paper. Furthermore, this study controls for heteroscedastic and auto correlated standard errors within the model. This study uses a Woolridge test to provide evidence that the data contains auto correlated standard errors and a Modified Wald test to provide evidence that the data contains heteroscedastic errors. These tests will be explained in more details further in this paper.

퐺퐹퐶퐼 퐼푛푑푒푥 푖푗 = β0 + β1 ∗ 퐵푟푒푥𝑖푡 + β2 ∗ 퐻𝑖푔ℎ − 푡푒푐ℎ 퐸푥푝푖푗 + β3 ∗ 퐼푛푡푒푟푛푒푡 푈푠푎푔푒푖푗 + β4 ∗ 퐶표푟푝표푟푎푡푒 푇푎푥 푅푎푡푒푖푗 + β5 ∗ 퐺푙표푏푎푙 퐶표푚푝푒푡𝑖푡𝑖푣푒푛푒푠푠푖푗 + β6 ∗ 푆𝑖푧푒 표푓 퐺표푣푖푗 + β7 ∗ 퐿푆푃푅푖푗 + +β8 ∗ 퐻푢푚푎푛 퐷푒푣푒푙표푝푚푒푛푡푖푗 + β9 ∗ 퐸푐표푛표푚𝑖푐 퐹푟푒푒푑표푚푖푗 + α푖 + δ푗 + 푒푖푗 (1)

Where; The 퐺퐹퐶퐼 퐼푛푑푒푥 푖푗 is the Global Financial Center Index in country j in time i The Brexit is a dummy variable which equals 1 when it’s in the time period of the Brexit, 0 otherwise. The 퐻𝑖푔ℎ − 푡푒푐ℎ 퐸푥푝푖푗 is the High-tech export in country j in time i The 퐼푛푒푟푛푒푡 푈푠푎푔푒푖푗 is the use of internet in country j in time i The 퐶표푟푝표푟푎푡푒 푇푎푥 푅푎푡푒푖푗 is the Corporate tax rate in country j at time i The 퐺푙표푏푎푙 퐶표푚푝푒푡𝑖푡𝑖푣푒푛푒푠푠푖푗 is the global competitiveness index from country j in time i The 푆𝑖푧푒 표푓 퐺표푣푖푗 is the size of government in country j at the i The 퐿푆푃푅푖푗 is the legal and property right in country j at time i The 퐻푢푚푎푛 퐷푒푣푒푙표푝푚푒푛푡푖푗 is the Human development in country j at time i The 퐸푐표푛표푚𝑖푐 퐹푟푒푒푑표푚푖푗 is the economic freedom in country j at time i 훼푖 controls for time fixed effects 훿푗 controls for fixed effects between financial centers 푒푖푗 is the error term in country j at time i

Because the estimated coefficient of the Brexit reflects the difference in the GFCI value of the selected IFCs between the time period during the Brexit and the time period before the Brexit, the hypothesis to test the influence of the Brexit on the status of the financial centers is set up as follow:

H0: β퐵푟푒푥푖푡푗 = 0

H1: β퐵푟푒푥푖푡푗 ≠ 0

Where β퐵푟푒푥푖푡푗 is the coefficient of the dummy variable Brexit.

In order to test whether the financial crisis period has a significant effect on the status of IFCs, this study expands equation (1) with a dummy variable for the financial crisis. This dummy variable equals 1 when the time period is during the financial crisis, 0 otherwise.

퐺퐹퐶퐼 퐼푛푑푒푥 푖푗 = β0 + β1 ∗ 퐵푟푒푥𝑖푡 + β2 ∗ 퐹𝑖푛푎푛푐𝑖푎푙 퐶푟𝑖푠𝑖푠 + β3 ∗ 퐻𝑖푔ℎ − 푡푒푐ℎ 퐸푥푝푖푗 + β4 ∗ 퐼푛푡푒푟푛푒푡 푈푠푎푔푒푖푗 + β5 ∗ 퐶표푟푝표푟푎푡푒 푇푎푥 푅푎푡푒푖푗 + β6 ∗ 퐺푙표푏푎푙 퐶표푚푝푒푡𝑖푡𝑖푣푒푛푒푠푠푖푗 ∗ +β7 ∗ 푆𝑖푧푒 표푓 퐺표푣푖푗 + β8 ∗ 퐿푆푃푅푖푗 + β9 ∗ 퐻푢푚푎푛 퐷푒푣푒푙표푝푚푒푛푡푖푗 + β10 ∗ 퐸푐표푛표푚𝑖푐 퐹푟푒푒푑표푚푖푗 + α푖 + 푒푖푗 (2)

The estimated coefficient of the financial crisis reflects the difference in the GFCI value of the selected IFCs between the time period during the financial crisis, from 2007 until 2009, and the time period after the financial crisis. The hypothesis used in this study to test whether the financial crisis influences the competitiveness power of IFCs is set up as follow:

H0: β퐹푖푛푎푛푐푖푎푙 퐶푟푖푠푖푠 = 0 H1: β퐹푖푛푎푛푐푖푎푙 퐶푟푖푠푖푠 ≠ 0

Where β퐹푖푛푎푛푐푖푎푙 퐶푟푖푠푖푠푗 is the coefficient of the dummy variable Financial Crisis.

The results from the panel data regressions from equation (1) and (2) provide evidence whether events like the Brexit and the financial crisis have a statistical impact on the competitiveness power of IFCs. However, these results don’t provide evidence which IFC has taken a (dis)advantage of these events. This study uses a multiple regression model for each IFC to examine whether an individual IFC has taken a gain or loss by the Brexit. Equation (3) will be used for regressions for the eight different IFCs. To check whether the data from equation (3) contain homoscedastic or heteroscedastic errors, this study introduces the White test. This test will be explained in more detail further in this paper.

퐺퐹퐶퐼 퐼푛푑푒푥 푖 = β0 + β1 ∗ 퐵푟푒푥𝑖푡 + β2 ∗ 퐹𝑖푛푎푛푐𝑖푎푙 퐶푟𝑖푠𝑖푠 + β3 ∗ 퐻𝑖푔ℎ − 푡푒푐ℎ 퐸푥푝푖 + β4

∗ 퐼푛푡푒푟푛푒푡 푈푠푎푔푒푖 + β5 ∗ 퐶표푟푝표푟푎푡푒 푇푎푥 푅푎푡푒푖 + β6 ∗ 퐺푙표푏푎푙 퐶표푚푝푒푡𝑖푡𝑖푣푒푛푒푠푠푖

+ β7 ∗ 푆𝑖푧푒 표푓 퐺표푣푖 + β8 ∗ 퐿푆푃푅푖 + β9 ∗ 퐻푢푚푎푛 퐷푒푣푒푙표푝푚푒푛푡푖 + β10

∗ 퐸푐표푛표푚𝑖푐 퐹푟푒푒푑표푚푖 + β11 ∗ 퐹𝑖푛푎푛푐𝑖푎푙 퐶푟𝑖푠𝑖푠 + 푒푖(3)

When the coefficient of the Brexit or the financial crisis significantly differs from zero, this implies that the status of an IFC is different because of the Brexit or financial crisis. This study expects to find

28 a negative influence of the financial crisis on the status of all the IFCs. However, this study expects different signs of the influence of the Brexit on the status of each IFC. This study provides the expected influence of the Brexit for each IFC in Table 6 below.

H0: β퐵푟푒푥푖푡푗 = 0 H0: β퐹푖푛푎푛푐푖푎푙 퐶푟푖푠푖푠푗 = 0 ≠ H1: β퐵푟푒푥푖푡푗 ≠ 0 H1: β퐹푖푛푎푛푐푖푎푙 퐶푟푖푠푖푠푗 0

Table 6: Expected Influence of the Brexit on the Status of each IFC

International Financial Center Expected influence of the Brexit on their status London Negative (significant) Luxembourg Negative (significant) Amsterdam Negative (non-significant) Paris Positive (non-significant) Frankfurt Positive (significant) New York Positive (non-significant) Singapore Positive (non-significant) Tokyo Positive (non-significant)

4.2 Event Study Methodology

To examine the impact of Brexit referendum on the stock market indices of the selected European IFCs used in this study and the stocks of banks and insurers listed in the FTSE 350 the event methodology has been employed. The event study methodology is one of the most used tools in economics, accounting, and financial research. The first event study documented in the financial literature was by James Dolley (1993). Moreover, Sathyanarayana & Gargesha (2016) used this methodology very recent to examine the impact of the Brexit on the stock market indices of India. For the purpose of the study, this study used the same methodology to investigate the impact of Brexit referendum on various selected European stock indices and the stocks of banks and insurers listed in the FSTE 350. The data has been collected from 24-06-2015 to 13-06-2017 from DataStream. Daily returns are calculated as logarithmic differences of daily closing prices. The standard market model is used to estimate the daily expected returns for the stock market indices:

푅푖,푡 = 훼푖 + 훽푖푅푚,푡 + 휖푖,푡(4)

Where 푅푖푡 and 푅푚푡 are the rates of return of stock market index 𝑖 and the MISC world index return at date 푡, respectively. The error term 휖푖푡 is assumed to be uncorrelated across the indices, have a zero mean and is independent of 푅푚푡. The same market model is used to estimate the expected daily returns of the stocks of banks and insurers listed in the FTSE 350. Where 푅푖푡 and 푅푚푡 are the rates of returns of the FTSE 350 listed stocks in sector 𝑖 and the FTSE 100 index return at date 푡, respectively.

The error term 휖푖푡 is assumed to be uncorrelated across the listed banks and insurers stocks, have a zero mean and is independent of 푅푚푡.

An OLS regression is run to estimate the normal returns. Abnormal returns (ARs) are calculated as the difference between the realized return and the estimated return from the OLS regression:

퐴푅푖,푡 = 푅푖,푡 − 훼̂푖 − 훽̂푖푅푚,푡(5)

This study introduces a t-test for each event day to check whether the ARs in the event window are significantly different from zero:

퐴푅푖,푡 푇푖,푡 = ~푡(푇 − 2) 휎̂푖,푡

A standard cross-sectional test will not give the right results because of the sharp rise in the return volatility caused by the Brexit. This will result in a low power of the test. This study will standardize the ARs to control for the event induced uncertainty. The purpose of standardization is to ensure that each AR will have the same variance, which is estimated over the estimation period (Boehmer et al., 1991). To ensure a higher power of the test the standard deviations of the ARs over the estimation period are used.

The ARs will be tested for each trading day of the event window of Brexit referendum. When the null hypothesis is rejected, the stock market index estimates ARs on that particular day because of Brexit referendum.

In order to ascertain any significant difference between the pre-event window ARs and post-event window ARs for several event windows, this study introduces a student t-test.

퐻0: 퐸(퐴퐴푅푝푟푒−퐵푟푒푥푖푡 푝푒푟푖표푑푡,푖) = 퐸(퐴퐴푅푃표푠푡−퐵푟푒푥푖푡 푝푒푟푖표푑푡,푖)

퐻0: 퐸(퐴퐴푅푝푟푒−퐵푟푒푥푖푡 푝푒푟푖표푑푡,푖) ≠ E(퐴퐴푅푃표푠푡−퐵푟푒푥푖푡 푝푒푟푖표푑푡,푖 )

This test compares the average abnormal return (AAR) of the pre-Brexit period with the AAR of the post-Brexit period within a particular event window. The AARs are calculated in the following way:

푁 1 퐴퐴푅 = ∑ 퐴푅 (6) 푖,푡 푁 푖,푡 푖=1

This test will be done for several event windows and all the stock market indices of the European IFCs used in this study and the stock of banks and insurers listed in the FTSE 350.

The cumulative abnormal returns (CARs) over the event window are calculated as follows:

푡2

퐶퐴푅푖(푡1, 푡2) = ∑ 퐴푅푖푡 (7)

푡=푡1

Taking the average of the CARs result in the cumulative average abnormal returns (CAARs) for each stock market index:

푁 1 퐶퐴퐴푅 = ∑ 퐶퐴푅 (8) 푖,푡 푁 풊,풕 푖=1

This study will plot the values of the CAARs in the event window to get graphically insight in the impact of Brexit referendum on the selected stock market indices used in this study and the stocks of banks and insurers listed in the FTSE 350.

5. Results

5.1 Influence of the Brexit and the Financial Crisis on the competitiveness power of International Financial Centers

Table 7 provides the results of the test for the influence of the Brexit and the financial crisis on the status of IFCs. This test is based on the regressions from equations (1) and (2). This study regressed all the variables of equation (2) on the GFCI in regression (1). The results from regression (1) provide evidence that the Brexit has a significant negative influence on the competitiveness power of IFCs at the 5% level. This means that during the Brexit, IFCs have a lower competitiveness power in comparing with the time period before the Brexit. This study has also included the financial crisis period in regression (1). The results from regression (1) provide evidence that the financial crisis has a significant negative influence on the competitiveness power of IFCs at the 5% level. This means that during the financial crisis, IFCs have a lower competitiveness power in comparing with the time period after the financial crisis. Furthermore, regression (1) included all the eight control variables, where Internet Usage, Global Competition, Size of Government, and Tax Rate have a significant effect on the GFCI. The other variables don’t have any significant explanatory power on the GFCI. In regression (2), this study excludes the dummy variable of the financial crisis from equation (2) and regressed all the variables of equation (1) on the GFCI. The Brexit has the same statistical negative influence on the GFCI as in regression (1), however, the coefficient increased more negatively. This means that during the Brexit, IFCs have a lower competitiveness power in comparing with the time period before the Brexit, including the financial crisis. Furthermore, the same control variables have a significant impact on the GFCI, but the coefficients of these variables differ from the coefficients of regression (1). The regression coefficients differ from each other since regression (2) compares the Brexit with the time period before the Brexit, including the financial crisis, and regression (1) compares the Brexit with the time period before the Brexit, excluding the financial crisis. In regression (3), this study excludes the control variables which don’t have any significant explanatory power in regression (1) and (2). The results of this regression are quite the same as regression (1), this study observes some small changes in the coefficients and statistical explanation power of the variables. However, the adjusted R-squared decreases from 0.730 to 0.710. This indicates that the excluded control variables had an explanatory power of 0.20 together, which is quite low. In regression (4), this study excludes the control variables which don’t have any significant explanatory power in regression (1) and (2) and the dummy variable of the financial crisis. The results

32 of this regression provide quite the same results of regression (2), this study observes again only some small changes in the coefficients of the variables and some variables lose significant explanatory power. In conclusion, the regression results from equation (2) provide evidence that the Brexit and the financial crisis have a significant impact on the competitiveness power of IFCs at the 5% level. Furthermore, the control variables Internet Usage, Global Competition, and Size of Government have a significant influence on the competitiveness power of IFCs. The variables Legal System and Property Rights, Economic Freedom, High-Tech Export, Tax Rate, and Human Development don’t have any statistical explanatory power on the competitiveness of IFCs. However, when the financial crisis period is excluded from equation (2), the Tax Rate and Human Development variables turned into significant explanatory variables. Moreover, the economic signs of the variables were the same as found in the study by Imad et al. (2016), except for the variable High-Tech Export. This study uses the regression with the highest adjusted R-squared from Table 7 to indicate whether the Brexit and the financial crisis have a statistical influence on the competitiveness power of IFCs. The highest adjusted R-squared is measured at 0.730 in regression (1), so that the conclusion about whether the Brexit or the financial crisis have an influence on the competitiveness power of IFCs is based on the results of regression (1) in Table 7.

Table 7: Test on the Effect of the Brexit on the Status of International Financial Centers

푮푭푪푰 푰풏풅풆풙 풊풋 = 훃ퟎ + 훃ퟏ ∗ 푩풓풆풙풊풕 + 훃ퟐ ∗ 푭풊풏풂풏풄풊풂풍 푪풓풊풔풊풔 + 훃ퟑ ∗ 푯풊품풉 − 풕풆풄풉 푬풙풑풊풋 + 훃ퟒ ∗ 푰풏풕풆풓풏풆풕 푼풔풂품풆풊풋 + 훃ퟓ ∗ 푪풐풓풑풐풓풂풕풆 푻풂풙 푹풂풕풆풊풋 + 훃ퟔ ∗ 푮풍풐풃풂풍 푪풐풎풑풆풕풊풕풊풗풆풏풆풔풔풊풋 + 훃ퟕ ∗ 푮풍풐풃풂풍 푪풓풆풅. 푹풂풕풊풏품풊풋 + 훃ퟖ ∗ 푶풄풄풖풑풂풏풄풚 푪풐풔풕풊풋 + 훃ퟗ ∗ 푺풊풛풆 풐풇 푮풐풗풊풋 + 훃ퟏퟎ ∗ 푳푺푷푹풊풋 + 훃ퟏퟏ ∗ 푪풂풑풊풕풂풍 푨풄풄풆풔풊풋 + 훃ퟏퟐ ∗ 푯풖풎풂풏 푫풆풗풆풍풐풑풎풆풏풕풊풋 + 훃ퟏퟑ ∗ 푬풄풐풏풐풎풊풄 푭풓풆풆풅풐풎풊풋 + 훂풊 + 풆풊풋(1)

Variable (1) (2) (3) (4) GFCI GFCI GFCI GFCI Brexit -12.84** -16.77* -8.266* -8.366 (3.525) (5.258) (2.624) (6.881) Internet Usage 1.621** 3.504*** 1.099 3.202*** (0.894) (0.934) (0.482) (0.660) Global Competition 84.49*** 52.23 82.70* 69.63 (11.71) (27.36) (30.37) (41.81) Size of Government -9.518*** -8.734** -9.825** -6.388* (1.337) (2.060) (2.521) (3.286) Tax Rate 189.9 256.6** (132) (149.0) Human Development -194.5 -375.7** (172.8) (221.1) Legal system and 3.902 -3.435 Property Rights (3.362) (5.677) Economic Freedom 1.113 -1.406 (2.153 (2.156) High Tech Export -1.87e-10 -1.23e-10 (1.31e-10) (1.78e-10)

Financial Crisis -28.55** -29.10*** (7.674) (4.059) Constant 320.5 702.6** 513.8** 635.2** (196.3) (238.2) (192.5) (215.4) Fixed Effects Yes Yes Yes Yes Clustered errors Yes Yes Yes Yes N 168 168 168 168 R-Squared 0.746 0.677 0.719 0.622 Adj R-squared 0.730 0.659 0.710 0.613 Note: * significant at 10%, ** significant at 5%, *** significant at 10%. T-values are based on clustered standard errors

5.2 Results of the Brexit and Financial Crisis on the competitiveness power of European International Financial Centers.

Table 8 shows the results of the test for the influence of the Brexit and the financial crisis on the competitiveness power of IFCs within the EU. This test is based on the regressions from equations (1) and (2). However, instead of the regressions from Table 7, the regression results from Table 8 are based on the dataset of IFCs within the EU. This means that the IFCs of Tokyo, Singapore, and New York are excluded from the dataset. This study regressed all the variables of equation (2) on the GFCI in regression (1). The results from regression (1) provide evidence that the Brexit has a significant negative influence on the competitiveness power of IFCs within the EU at the 1% level. This means that during the Brexit, European IFCs have a lower competitiveness power compared to the time period before the Brexit. Furthermore, the results from regression (1) provide evidence that the financial crisis doesn’t have any significant influence on the competitiveness power of European IFCs. This means that the competitiveness power of IFCs within the EU doesn’t suffer by cause of the financial crisis. Furthermore, regression (1) included all the eight control variables, where Internet Usage, Global Competition, Size of Government, Tax Rate, and Human Development have a significant effect on the GFCI. The other variables don’t have any significant explanatory power on the GFCI. In regression (2), this study excludes again the dummy variable of the financial crisis from equation (2). The Brexit has the same statistical negative influence on the GFCI as in regression (1), however, the coefficient increased more negatively. This means that during the Brexit, European IFCs have a lower competitiveness power compared to the time period before the Brexit (including the financial crisis). Furthermore, the same control variables have a significant impact on the GFCI, but the coefficients of these variables differ from the coefficients of regression (1). The regression coefficients differ from each other because that regression (2) compares the Brexit with the time period before the Brexit, including the financial crisis, and regression (1) compares the Brexit with the time period before the Brexit, excluding the financial crisis. In regression (3), this study excludes the control variables which don’t have any significant

34 explanatory power in regression (1) and (2). The results of this regression are quite the same as regression (1). However, this study observes some small changes in the coefficients and statistical explanation power of the variables. Furthermore, the adjusted R-squared increased from 0.776 to 0.782. This indicates that the excluded control variables don’t have any explanatory power together on the competitiveness power of European IFCs ` In regression (4), this study excludes the control variables which don’t have any explanatory power in regression (1) and (2) and the dummy variable of the financial crisis. The results of this regression provide quite the same results of regression (2). However, this study observes again some small changes in the coefficients of the variables and some variables lose significant explanatory power. In conclusion, the regression results from equation (2) provide evidence that the Brexit has a significant impact on the competitiveness of European IFCs at the 1% level. The financial crisis has an economical negative impact on European IFCs, but not statistically significant. Furthermore, the control variables Internet Usage, Global Competition, Size of Government, Tax Rate, and Human Development have a significant effect on the competitiveness of IFCs. The variables Legal System and Property Rights, Economic Freedom, and High-Tech Export don’t have any statistical explanatory power on the competitiveness of IFCs. However, the economic signs of the variables were the same as found in the study by Imad et al. (2016), except for the variable High-Tech Export. This study uses the regression with the highest adjusted R-squared from Table 8 to indicate whether the Brexit and the financial crisis have a statistical influence on the competitiveness power of IFCs. The highest adjusted R-squared is measured at 0.782 in regression (3), so that the conclusion about whether the Brexit or financial crisis have an influence on the competitiveness power of European IFCs is based on the results of regression (3) in Table 8.

Table 8: Test on the Effect of the Brexit on the Status of European International Financial Centers

Variable (1) (2) (3) (4) GFCI GFCI GFCI GFCI Brexit -21.27*** -22.14*** -19.99*** -21.42*** (5.873) (5.863) (5.452) (6.881) Internet Usage 1.651* 2.202*** 1.772* 3.202*** (0.706) (0.577) (0.676) (0.660) Global Competition 104.7*** 104.4*** 100.5*** 69.63 (22.53) (27.36) (20.77) (41.81) Size of Government -4.449* -3.942 -4.851** -6.388* (2.160) (2.136) (2.041) (3.286) Tax Rate 426.4*** 452.3*** 466.5*** 458.5***

(132) (85.81) (81.34) (80.86) Human Development -1905.4*** -1942.8*** -1738.9*** -1855.4*** (172.8) (499.7) (423.5) (413.0) Legal system and 2.011 0.543 Property Rights (5.017) (1.278) Economic Freedom 0.646 0.244 (1.307) (1.278) High Tech Export -1.15e-10 -5.90e-11 (2.01e-10) (1.98e-10) Financial Crisis -8.204 -6.803 (6.119) (5.710) Constant 1619.2*** 1623.26*** 513.8** 635.2** (474.5) (476.6) (192.5) (215.4) Fixed Effects Yes Yes Yes Yes Clustered errors No No No No N 104 104 104 104 R-Squared 0.806 0.802 0.805 0.802 Adj R-Squared 0.776 0.774 0.782 0.781 Note: * significant at 10%, ** significant at 5%, *** significant at 10%. T-values are based on clustered standard errors

5.3 Influence of the Brexit and the Financial Crisis on individual International Financial Centers

Table 9 shows the results of the test for the influence of the Brexit and the financial crisis on the competitiveness power of an individual IFC. This test is based on the regressions from equation (3). In regression (1), this study regressed all the variables of equation (3) on the GFCI of London. The results provide evidence that the Brexit hasn’t any significance explanatory power on the competitiveness power of the IFC of London. However, economically increased the competitiveness power of London with 33.29. Furthermore, regression (1) in Table 9 provides evidence that the financial crisis hasn’t any significant influence on the competitiveness power of the IFC of London. This indicates that the IFC of London is stable during the financial crisis. In regression (2) and (3), this study regressed all the variables of equation (3) on the GFCI of Luxembourg and Amsterdam. The results provide evidence that the competitiveness power of Luxembourg and Amsterdam economically decreased respectively with -11.86 and -18.94 by the Brexit, but not statistically significant. In regression (4), this study regressed all the variables of equation (3) on the GFCI of Frankfurt. The results provide evidence that the Brexit has a significant negative influence on the competitiveness power of the IFC of Frankfurt at the 10% level. This indicates that the competitiveness power of the financial center of Frankfurt suffers with -26.28 during the Brexit. In regression (5), this study regressed all the variables of equation (3) on the GFCI of Paris. The results provide evidence that the competitiveness power of Paris economical increased with 5.66, but not statistically significant. This provides evidence that the IFC of Paris takes an economical advantage of the Brexit.

In none of the European IFCs has the financial crisis significant explanatory power. This means that the competitiveness powers of the individual European IFCs are stable during times of the financial crisis. This corresponds with the results from Table 8 which provide evidence that financial crisis hasn’t any significant influence on the status of a European IFCs. In regression (6) and (7), this study regressed all the variables of equation (3) on the GFCI of Singapore and Tokyo. The results provide evidence that the Brexit hasn’t any significant influence on the competitiveness power of the IFCs of Singapore and Tokyo. As expected, the Brexit hasn’t any explanatory power on the IFCs of Singapore and Tokyo because they aren’t financial centers of the EU. Furthermore, the financial crisis has a significant negative effect on the competitiveness power of Singapore at the 10% level. In regression (8), this study regressed all the variables of equation (3) on the GFCI on New York. The results provide evidence that the Brexit and the financial crisis don’t have any significant influence on the competitiveness power of New York. This means that the status of the IFC of New York is stable during times of a financial crisis. As expected, the Brexit hasn’t any explanatory power since the IFC of New York isn’t an IFC of the EU. In conclusion, the results from Table 9 provide evidence that the competitiveness power of the IFC of London didn’t significantly suffer from the Brexit. Furthermore, the IFC of Frankfurt has taken a disadvantage of the Brexit. Furthermore, no other European IFCs could already take a significant (dis)advantage of the Brexit on their competitiveness power. However, Luxembourg and Amsterdam economically decreased in their competitiveness power, while Paris economically increased in its competitiveness power. As Table 9 shows, the IFC of Singapore loses competitiveness power during the financial crisis. As earlier mentioned, Yeandle (2016) suggests in the GFCI 21 that the IFC of London suffers in his competitiveness power right after the announcement of Brexit referendum held on 23 June 2016. However, regression (1) in Table 9 doesn’t show any significant negative influence of the Brexit on the competitiveness power of London. This study concludes that the Brexit doesn’t have enough statistical explanatory power on the competitiveness power of London because the Brexit is in its beginning stage of its process. When the Brexit is in a later stadium of its process, this study expects more statistical evidence about whether the Brexit has a significant influence on the competitiveness power of London and other European IFCs.

Table 9: Test on the Effect of the Brexit on the Status of an Individual International Financial Center

푮푭푪푰 푰풏풅풆풙 풊 = 훃ퟎ + 훃ퟏ ∗ 푩풓풆풙풊풕 + 훃ퟐ ∗ 푭풊풏풂풏풄풊풂풍 푪풓풊풔풊풔 + 훃ퟑ ∗ 푯풊품풉 − 풕풆풄풉 푬풙풑풊 + 훃ퟒ ∗ 푰풏풕풆풓풏풆풕 푼풔풂품풆풊 + 훃ퟓ ∗ 푪풐풓풑풐풓풂풕풆 푻풂풙 푹풂풕풆풊 + 훃ퟔ ∗ 푮풍풐풃풂풍 푪풐풎풑풆풕풊풕풊풗풆풏풆풔풔풊 + 훃ퟕ ∗ 푺풊풛풆 풐풇 푮풐풗풊 + 훃ퟖ ∗ 푳푺푷푹풊 + 훃ퟗ ∗ 푯풖풎풂풏 푫풆풗풆풍풐풑풎풆풏풕풊 + 훃ퟏퟎ ∗ 푬풄풐풏풐풎풊풄 푭풓풆풆풅풐풎풊 + 훃ퟏퟏ ∗ 푭풊풏풂풏풄풊풂풍 푪풓풊풔풊풔 + 풆풊(ퟑ)

Variable (1) (2) (3) (4) (5) (6) (7) (8) GFCI GFCI GFCI GFCI GFCI GFCI GFCI GFCI London Luxembourg Amsterdam Frankfurt Paris Singapore Tokyo New York Brexit 32.296 -18.95 -11.86 -26.28* 5.66 2.51 15.09 5.00 (26.74) (19.98) (7.80) (11.33) (4.92) (21.77) (15.45) (19.67) Internet Usage -6.97 -5.63 -6.76 -0.79 -4.30 -4.367 3.18 -1.55 (16.54) (7.47) (3.64) (4.54)) (4.13) (16.79) (3.731) (2.3468) Global Competition -264.06 144.21 93.46 16.98 -143.68 254.98** -97.72 76.91 (274.94) (250.32) (115.19) (88.29) (101.88) (128.84) (359.37) (75.99) Size of government 26.08 -5.30 -12.83 -9.61 -7.005 -27.82 -3.38 -1.50 (18.18) (10.26) (9.95) (6.14) (8.30) (25.49) (36.06) (8.31) Tax rate -55.59 3015.03 190.9 178.69 304.92 -22.51 (1722.76) (2612.79) (69.75) (369.03) (2215.44) (742.10) Human 9785.18 -7138.37* -147.88 -3750.04* -3733.13 -132.90 -528.67 104.34 Development (9421.30) (3111.89) (129.3) (1680.17) (2977.72) (834.18) (3191.07) (297.63) Legal system and -282.25 -23.50 182.47 7.25 -12.97 -106.85 -83.13 -1.02 Property Rights (278.69) (14.59) (161.55) (27.34) (51.32) (91.88) (127.06) (26.78) Economic Freedom -51.82 34.14 -4.33 1.90 -3.96 15.37 6.59 -3.31 (51.45) (27.13) (7.93) (6.22) (3.05) (11.84) (23.77) (3.84) High Tech Export -1.25e-08 -6.04e-08* 2.64e-09 4.76e-11 -1.87e-10 -2.15e-10 3.64e-10 2.50e-11 (1.20e-08) (1.02e-07) (1.81e-09) (2.77e-10) (1.02e-10) (5.30e-10) (9.14e-10) (1.50e-10) Financial Crisis 6.80 -13.81 -32.50 -13.97 -22.39 -40.93* -37.42 -4.88 (21.74) (19.44) (14.01) (12.50) (11.41) (16.79) (25.65) (17.31) Constant -10243.9 2959.82 5975.54* 3657.14 5464.88 -551.59 1717.63 654.95 (12194.19) (2959.82) (2192.67) (1967.08) (2683.96) (1457.17) (3821.19) (887.30) Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Heteroscedasticity No Yes No No No No No No N 168 21 21 168 168 21 21 168 R-Squared 0.413 0.975 0.947 0.967 0.746 0.975 0.943 0.746 Note: * significant at 10%, ** significant at 5%, *** significant at 10%. T-values are based on homoscedastic errors, except for regression (7) which are based on heteroscedastic errors.

5.4 Influence of Brexit referendum on the stock market indices of the IFCs within the EU

To examine the impact of Brexit referendum on the stock market indices of the selected European IFCs used in this study the event study methodology has been employed. In Table 10 below can be seen at which date a particular stock market index generates its highest and lowest abnormal return (AR). Furthermore, it presents all the days in which Brexit referendum had a significant impact on a particular stock market index.

Table 10: Abnormal returns (AR) per Stock Market Index

IFC AR Min T-stat AR Max T-stat Date min Date max Impact days

London -1.62% -1.62 2.42% 2.41** 20/06/16 14/06/16 -7 Paris -6.57% -3.19*** 4.39% 2.13** 27/06/16 14/07/16 2, 15 Luxembourg 3.91% -2.77*** 3.91% 2.77*** 24/06/16 01/07/16 -4, 1, 2, 6

Frankfurt -3.50 -2.81*** 2.65% 2.10** 24/06/16 20/06/16 -3, 1 Amsterdam -2.55% -2.22** 2.74% 2.29** 24/06/16 20/06/16 -3, 1

Note: * significant at 10%, ** significant at 5%, *** significant at 10%. T-values are based on normal standard errors

Table 10 provides evidence that the returns of the European stock market indices of the IFCs used in this study are affected by the stock market. In the case of London, it was observed that the highest AR recorded in the event period was generated at 2.42%, 7 event days before Brexit referendum. The lowest AR was observed 3 event days before the referendum at -1.62%. However, the only significant AR in the event window was observed at event day -7. The lowest AR observed in Paris was generated at -6.57% on event day 2 and the highest AR was generated at 4.39% on event day 15 of the event window. This means that the recovery period of Paris took a while until the 15th trading day after Brexit referendum. This study observed that the lowest ARs for Luxembourg, Frankfurt, and Amsterdam were generated the day right after Brexit referendum respectively at 3.91%, 3.50%, and 2.55%. This indicates that they were hurt immediately because of Brexit referendum vote at 23 June 2016 with significant negative AR in their stock indices. Furthermore, Amsterdam and Frankfurt generate respectively on event day -3 an positive AR of 2.74% and 2.65%, which are the highest observed ARs in the event window of these stock indices. The highest AR observed for Luxembourg is generated at 3.91%. Moreover, the results provide evidence that the stock market index of Luxembourg also generates a significant positive AR of 3.22% at event day -4 and a significant negative AR of -3.81% at event day 2. As can be seen from Table 10, all the stock indices of the European IFCs faces significant positive and negative ARs during the thirty-one days enclosing Brexit referendum (i.e., t= -

15,…,0…,+15), except the stock market index of London. Therefore, this study can conclude that Brexit referendum has an impact on the European stock market indices at several days of the thirty- one days enclosing the referendum. Moreover, Table 10 provides all the event days at which Brexit referendum had a significant impact on the European stock market indices. As can be seen from Table 10, most of the stock market indices generate a significant negative AR at the day right after Brexit referendum. In order to ascertain any significant difference between the pre-event window ARs and post-event window ARs for 15, 7, and 3 days event windows a student t-test was run. Table 11 provides the results of the t-test for the different event windows. As can be seen from the table, none of the stock indices of the European IFCs have significant differences between pre-event window ARs and post- event window ARs in a window period of 15 and 7 days. However, in an event period of 3 days, the post-event window ARs are significantly different from the pre-event window ARs at the stock market indices of Amsterdam and Frankfurt at the 5% and 10% level, respectively. Therefore, this study concludes that because of Brexit referendum the stock market indices of Amsterdam and Frankfurt generate lower post-event window ARs than pre-event window ARs in an event window of 3 days. However, Brexit referendum didn’t have any significant impact for the 3 days event window at the stock market indices of London, Paris, and Luxembourg. Furthermore, Brexit referendum hardly had any impact for the fifteen and seven days event windows at all the stock market indices of the selected European IFCs used in this study.

Table 11: Difference in Abnormal returns (AR) in pre-event and post-event Period

15 days event window 7 days event window 3 days event window Diff in AR T-Stat Diff in AR T-Stat Diff in AR T-Stat London -0.0002 -0.0515 0.0003 0.0472 -0.0123 -1.2742 (0.004) (0.007) (0.010) Paris -0.002 -0.2159 -0.018 -1.6379 -0.312 -1.3865 (0.008) (0.011) (0.225) Luxembourg -0.003 -0.3999 -0.003 -0.2473 -0.209 -1.3345 (0.007) (0.128) (0.016) Frankfurt -0.004 -0.8519 -0.013 -1.6544 -0.279 -2.2269* (0.004) (0.007) (0.125) Amsterdam -0.003 -0.6466 -0.008 -1.0031 -0.026 -2.6871** (0.004) (0.008) (0.010) Note: * significant at 10%, ** significant at 5%, *** significant at 10%. T-values are based on normal standard errors

Figure 4 on the next page provides graphical insight into the impact of Brexit referendum on the European stock market indices. The figure presents a 30-day window for the cumulative average abnormal returns (CAARs) for these indices.

Figure 4: The Impact of Brexit Referendum on the European Stock Market Indices

First of all, in the ten days prior Brexit referendum (date 0) the stock market indices seem to have ARs around 0 at the same level, except for Paris which generates negative ARs. In the five days before the event date, the ARs increases for all stock market indices. However, the ARs sharply drop after the event date for the stock market indices of Luxembourg, Amsterdam, Paris, and Frankfurt. The stock market index of London faces a small drop in its AR right after the event day. Moreover, after event day 2, the stock market indices of London, Luxembourg, and Amsterdam recovered and generate upwards ARs. However, the stock market index of Luxembourg still generates small negative ARs until event day 14. As Figure 4 shows, the stock market index of Paris is hit hard by Brexit referendum. After the referendum, it generates high downward sloping negative ARs. The index is in its recovery period after event date 12 since when the ARs are upward sloping. In conclusion, Figure 4 shows graphically that Brexit referendum has a high impact on the European stock market indices.

5.5 Influence of Brexit referendum on the stock market indices of the Banking and Insurance sector of the FTSE 350

As earlier mentioned in this study, the banking and insurance sectors were hit hard by Brexit referendum. To examine the impact of Brexit referendum on the stocks of banks and insurers listed in the FTSE 350 event study methodology has been employed. Table 12 provides at which date the stocks within the FTSE 350 banking and insurance sectors generate its highest and lowest abnormal return (AR). Furthermore, it presents all the days in which Brexit referendum had a significant impact on the stocks of banks and insurers listed in the FTSE 350.

Table 12: Abnormal returns (AR) in the Stocks of Banks and Insurers listed in the FTSE 350

Sector AR Min T-stat AR Max T-stat Date min Date max Impact days

Banking -10.29% -5.99*** 4.19% 2.55** 24/06/16 20/06/16 -3, -2, 1, 2 Insurance -11.78% -7.42*** 4.66% 2.96** 24/06/16 28/07/16 -9, -3,1,2,3,4, 8, 9 Note: * significant at 10%, ** significant at 5%, *** significant at 10%. T-values are based on normal standard errors

Table 12 provides evidence that the returns of the stocks of banks and insurers listed in the FTSE 350 are hardly affected by Brexit referendum. In the case of the banking sector, it was observed that the lowest significant AR recorded in the event window was generated the day right after Brexit referendum at -10.29%. The highest significant AR observed was generated on event day 3 at 4.19%. In case of the insurance sector, it was observed that the lowest AR was generated at the day right after the referendum at -11.78%. The highest AR observed in this sector was generated on event day 3. This means that both sectors generate the trading day after Brexit referendum a significantly lower return than their expected returns and 3 trading days after Brexit referendum a significant higher return than their expected returns. Furthermore, as can be seen from Table 12, Brexit referendum has on several days significant impact on the stocks of banks and insurers listed in the FTSE 350 (especially insurers listed stocks) with strong significance. This provides evidence that the stocks of banks and insurers listed in the FTSE 350 are hit way harder by Brexit referendum than the other sectors within the London stock market. In order to ascertain any significant difference between the pre-event window ARs and post- event window ARs for the 7, 3, and 2 days event windows a student t-test was run. Table 13 provides the results of the t-test for the different event windows. As can be seen from the table, none of the stock market indices have significant differences between pre-event window ARs and post-event window ARs in a window period of 5 and 3 days. This indicates that the ARs in the pre-event window aren’t significantly different from the ARs in the post-event window for window periods of 7 and 3

42 days. Nevertheless, for the 2 days window period, companies within the FTSE 350 banking and insurance sectors have significant differences between the pre-event window ARs and the post-event window ARs at the 1% level. This means that the stock of banks and insurers listed in the FTSE 350 generate on average a significant lower AR in the 2 days after Brexit referendum in comparison with the 2 days before Brexit referendum. The stocks of banks and insurers listed in the FTSE 350 generate respectively a lower AAR of -9.7% and -11.9% in the 2 days after Brexit referendum in comparison with the AAR in the 2 days before the referendum. This provides evidence that Brexit referendum has a tremendous negative short-term impact on the stock returns of banks and insurers listed in the FTSE 350.

Table 13: Difference in Abnormal returns (AR) in pre-event and post-event Period

7 days event window 3 days event window 2 days event window Sector Diff in AR T-Stat Diff in AR T-Stat Diff in AR T-Stat Banking -0.027 -1.29 -0.069 -1.66 -0.097 -7.17*** (0.021) (0.042) (0.014) Insurance -0.024 -0.90 -0.077 -1.42 -0.119 -7.99*** (0.027) (0.054) (0.014) Note: * significant at 10%, ** significant at 5%, *** significant at 10%. T-values are based on normal standard errors

Figure 5 provides graphical insight into the impact of Brexit referendum on the stocks of banks and insurers listed in the FTSE 350. The figure presents a 30-day window for the cumulative average abnormal returns (CAARs) for these listed stocks.

Figure 5: The Impact of Brexit Referendum on the Stocks of Banks and Insurers Listed in the FTSE 350

Figure 5 shows that the stocks of banks and insurers listed in the FTSE 350 generate downward sloping negative ARs in the event days before Brexit referendum until event day -6. After event day - 6, the ARs are upward sloping in both sectors, but still negative until the day before the referendum. As can be seen from Figure 5, the stock of banks and insurers listed in the FTSE 350 are hit hard by Brexit referendum. As expected, the day right after the referendum, the ARs of the stocks in both sectors decrease sharply to their lowest observed AR in the event window. The days after the first trading day after Brexit referendum, the ARs are upward sloping but still stay negative in both sectors. In conclusion, Figure 5 shows graphically that Brexit referendum has a tremendous short- term impact on the stocks of banks and insurers listed in the FTSE 350.

6. Robustness Check

6.1 The Hausman Test

Empirical analysis with panel data requires the choice between whether to use fixed effects (FE) or random effects (RE) (bole & Rebec, 2013). The analysis on the influence of the Brexit and the financial crisis on the competitiveness power of IFCs is conducted with the use of entity fixed effects. In the case of this study, the decision between FE or RE depends on the correlation between the unobserved effect and the eight explanatory variables used in this study (Bole & Rebec, 2013). The Hausman test, which is used in this study, is the standard test for the distinction between FE and RE in panel data models. Stock & Watson (2012) defines this test as “a statistical hypothesis test in econometrics to test the consistency of an estimator when compared to an alternative, less efficient, estimator which is already known to be consistent”.

Bole & Rebec (2013) indicate that the Hausman statistic is defined as follow;

In the panel data regression model used in this study;

Where the error term 푒푖푗 and the individual-specific effect α푖 are individual and independent distributed such that the covariance between them should be zero, 푐표푣(α푖, 푒푖푡) = 0. Moreover, 2 2 Var(α푖) = 휎 α, Var(푒푖푡) =휎 푒

The Hausman test for the regression model is based on the distance between fixed 훽퐹퐸 and random

훽푅퐸 effects estimators (Bole & Rebec, 2013):

푇 2 푇 −1 푇 −1 −1 퐻 = (훽푅퐸 − 훽퐹퐸 ) (휎 푢(푍 푄푍) − 푍 훺 푍) (훽푅퐸 − 훽퐹퐸 )

Where Z=[1푥푖푡]NT*(K+1) is a design matrix, Q is within projector, and 훺 = Cov([푐푖 + 푢푖푡]NT*1) covariance matrix of disturbance vector.

Both 훽푅퐸 and 훽퐹퐸 estimators are consistent under the null hypothesis and 훽푅퐸 is also efficient under the null hypothesis.

퐻표: 퐸(푐푖|푥푖) = 0

2 The statistic is asymptotically chi-squared distributed (푋퐾) and the number of degrees of freedom of the statistic is equal to the rank of the matrix (푉푎푟(훽퐹퐸) − 푉푎푟(훽푅퐸)). If this study accepts the null hypothesis, RE are favored due to higher efficiency than FE. However, when the alternative hypothesis is accepted, fixed effects are preferred due to it is at least consistent. When the null hypothesis is rejected, the random effect becomes inconsistent. When the null hypothesis is accepted, this study runs the panel data regressions with random effects, otherwise with fixed effects. The results in Appendix F show evidence that this study must run the panel data regressions from equation (1) and (2) with fixed effects.

6.2 The Woolridge Test

Empirical analysis with panel data requires a decision on how to treat with autocorrelation within the particular dataset. Consider the panel data regression model used in this study.

Where the 퐺퐹퐶퐼 퐼푛푑푒푥 푖푗 is the dependent variable and the other variables are the explanatory variables. α푖 is the individual-level effect and 푒푖푡 is the error term. In a panel data regression model like above with time-varying explanatory variables, Woolridge (2002) indicates that the error term 푒푖푡 should be uncorrelated with the time-varying explanatory variables in order to estimate efficient and consistent coefficients. This means that the error terms are not serial correlated.

This study uses Woolridge’s method to test whether the error terms are correlated or not. This method uses a regression in the first differences to test whether the data contains serial correlation. The formula below provides the regression in first differences, which removes the individual effect.

푌푖푡 − 푌푖푡−1 = (푋푖푡 − 푋푖푡−1)훽1 + 푒푖푡 − 푒푖푡−1 (11)

Moreover, this method regresses the change in the dependent variables on the change in the explanatory variables to estimate the parameter 훽1.

The Woolridge test is defined as followed;

∑푁 . ∑푇̂−1. ∑푇 .푢̂ 푢̂ 푊 = 푡=1 푡=1 푠=푡+1 푖푡 푖푠  N(0,1) 푁 푇−1 푇 2 1/2 [∑푡=1.(∑푡=1 . ∑푠=푡+1.푢̂푖푡푢̂푖푠) ]

Where 푢̂푖푡 are the pooled OLS residuals. The test can detect many types of serial correlation in the error term 푒.

2 퐻0: σ푦 = 푂

When the null hypothesis is rejected, this study should control for autocorrelated error terms within the panel data regression model. The results in Appendix G provide evidence whether this study must control for autocorrelated error terms in the panel data regression model for a particular data sample.

6.3 The Modified Wald Test

Empirical analysis with panel data requires a decision on how to treat with potential groupwise heteroscedasticity in the error terms. As provided in appendix F, this study has to run the panel data regression model with fixed effects. The OLS estimator estimates the coefficients under the classical assumptions that the error terms are independent and identical distributed. However, Greene (2000) indicates in his paper that the assumptions could be violated in certain ways in the pooled time cross-section time series. He suggests that the error term could be homoscedastic but its standard deviation may vary beyond units. This is known as groupwise heteroscedasticity and can be tested with the Modified Wald test. Within a fixed effect model, this test estimates the Modified Wald test for groupwise heteroscedasticity.

The Modified Wald test statistic is determined as follow:

푁 (푒2 −σ̂2)2 푊 = ∑ 푔 푖푡 푖 푖=1 푉푖

The null hypothesis of this test indicates that the variance of every individual cross-sectional unit is equal to the total variance, such that the variances in the dataset don’t differ across several units.

2 2 2 퐻o: σ푖 = σ 푓표푟 𝑖 = 1, … , 푁푔 χ [푁푔].

When the null hypothesis is accepted, this study should control for groupwise heteroscedastic errors in the panel data regressions. The results in Appendix G provide evidence whether this study should control for groupwise heteroscedasticity in the panel data regression model for a particular data sample.

6.4 The White Test

Empirical analysis with a multiple OLS regression model requires a decision on how to treat with potential heteroscedasticity in the error terms. The regressions from equation (3) can be regressed with homoscedastic or heteroscedastic standard errors. Heteroscedasticity means that several observations have different variances in the error terms. When the data contains heteroscedastic

47 error terms, the estimator from the OLS regression is still unbiased but the estimator is inefficient because the true variance and covariance are underestimated (Goldenberg, 1964). This study uses the White test to test whether the standard errors contains heteroscedasticity or not. This test examines whether the variances of the error terms are constant or not. When the null hypothesis is accepted, the data contains homoscedastic error terms, otherwise heteroscedastic error terms (Daniel, 2013).

The White test uses the following linear regression:

2 u = α0 + α1푍1 + ⋯ + α푝Z푝 (12)

Where the explanatory variables Z1, Z2,…., Zp are as follows:

• If the number of explanatory variables (k) in the regression is 1 and we denote by X = X1, the

above explanatory variables are Z1 = X1 and Z2 = X2, and p = 2.

2 2 • If k = 2, the above explanatory variables are Z1 = X1, Z2 = X2, Z3 = X 1 , Z4 = X 2 and Z5= X1 ·X2, and p=5.

2 2 • If k ≥ 3, we have the explanatory variables Z1 = X1, Z2 = X2,…, Zk = Xk, Zk+1 = X 1 , Zk+2 = X 2,…, Z2k

2 = X k , and p = 2·k

2 2 2 This study considers that n · R has the distribution χ p, where R is the coefficient of determination

2 for the regression. Homoscedasticity is accepted if n ·R2 < χ p;ε (Daniel, 2013).

2 2 2 퐻o: σ푖 = σ 푓표푟 𝑖  χ [퐾].

When the null hypothesis is accepted, this study runs the regressions from equation (3) with homoscedastic standard errors, otherwise with heteroscedastic standard errors. The results in Appendix H provide evidence whether this study should control for heteroscedasticity in the multiple regression model for a particular dataset.

7. Discussion and Conclusion

This study examined the influence of the Brexit and the financial crisis on the competitiveness power of the international financial centers (IFCs) of London, Paris, Frankfurt, Amsterdam, Luxembourg, Singapore, and New York. Although the analysis has mainly focused on the influence of the Brexit, this study examined the evolution of the competitiveness power over the last 10 years, including the time periods of the Brexit and the financial crisis. Furthermore, this study introduces an event study to examine the impact of Brexit referendum on the stock market indices of the European IFCs mentioned above and the stocks of banks and insurers listed in the FTSE 350. The thirty-one days enclosing the referendum (i.e., t= -15,…,0…,+15) is labeled as the event window. More specifically, this paper addressed the following research questions: (1) Does the Brexit and the financial crisis have an influence on the competitiveness power of IFCs?; (2) Does the Brexit and the financial crisis have an influence on the competitiveness power of IFCs within the EU?; (3) Does the Brexit have influence on the IFC of London?; (4) Could any IFC within the EU take an advantage of the potential effect of the Brexit on the IFC of London? (5) Does Brexit referendum tend to abnormal returns in the stock market indices of the European IFCs and the stocks of banks and insurers listed in the FTSE 350?

In the past decades, a lot of research has been done about the competitiveness between international financial centers (IFCs). Through the time, there were different world leading financial centers. In the past literature, researchers evaluate the performance between IFCs and suggest whether a financial center performs better than their rivals. Imad et al. (2016) define the term ‘International Financial Center’ as a location where international financial services are produced on a large scale. Cassis (2010) defines a financial center as “the place where intermediaries co-ordinate financial transactions and arrange for payments to be settled”. Before the 1980s, the formation of financial centers is not studied yet in economics. However, since the 1980s, the study of relationships and competition between cities and financial centers has experienced a shift from the focus on national urban systems to an international level. However, most of the research done before was theoretically. Deutsche Bank (2010) investigates theoretically whether the financial crisis has an influence on the competition between IFCs. In their paper, they conclude that the competitiveness ratings of the traditional financial centers have not changed significantly over the period from 2007 until 2010. Furthermore, Karreman & Van der Knaap (2007) suggest that IFCs try to outperform their competitors in the market segments and geographical areas in which they carry a comparative advantage. This study is the first paper which empirically test the competitiveness power of IFCs and

49 whether events like the Brexit and the financial crisis have an influence on their competitiveness power. Data on the explanatory variables corresponding the eight IFCs used in this study were achieved by different organizations including the World Bank, the World Economic Forum, and KPMG. Data on the independent variable is obtained from the past 21 Global Financial Center Indices (GFCI). Appendix A provides the IFCs used in this study over the sample period from 01/03/2007 until 01/03/2017. This study introduces a newly developed method to test whether events like the Brexit and the financial crisis could have an influence on the status of an IFC. This method, which is based on panel data regression models, is based on a paper of Imad et al. (2016). They investigate whether explanatory variables have an influence on the competitiveness power of an IFC. They found for 8 of the 24 variables used in their study significant results. Furthermore, they provide 3 more variables which should be considered in the competitiveness power of an IFC. However, due to data limitations, this study uses 8 of the 11 variables provided by the study of Imad et al. (2016). This study observed that 5 out of the 8 explanatory variables have a significant influence on the competitiveness power of IFCs. These variables are the Internet Usage, Global Competition index, Size of Government, Tax Rate, and Human Development index. The regression results of equation (1) and (2) in Table 7 provide evidence about the fact that the Brexit and the financial crisis have a significant influence on the competitiveness power of IFCs within and outside the EU. Furthermore, the results of equation (1) and (2) in Table 8 provide evidence that the Brexit also has a significant influence on the competitiveness power of only European IFCs. However, the financial crisis doesn’t have any significant influence on the competitiveness power of European IFCs. The empirical results of equation (3) provide evidence that the Brexit hasn’t any significant explanatory power on the competitiveness power of London. This is not in line with the expectations of this study. This study suggests that the results aren’t significant because the Brexit is in its beginning stadium of its process. When the Brexit is in a later stadium of its process, this study expects to find a significant negative influence of the Brexit on the competitiveness power of the IFC of London. This could be examined in future research. Furthermore, the results of equation (3) provide evidence that the Brexit has a significant negative influence on the IFC of Frankfurt. This means that Frankfurt has a lower competitiveness power because of the Brexit. Moreover, the results provide evidence that the competitiveness power of Amsterdam and Luxembourg have decreased economically, but not statistically significant. The competitiveness power of Paris increases economically, but again not statistically significant. This provides evidence that the IFC of Paris takes an economic advantage of the Brexit, while Amsterdam and Luxembourg take an economic disadvantage of the Brexit. The results also provide evidence that the financial crisis has a significant negative influence on the IFC of Singapore. This suggests that

Singapore has a lower competitiveness power because of the financial crisis. The competitiveness powers of the other IFCs are not affected by the financial crisis. This is in line with the findings of Deutsche Bank (2010). Furthermore, this study also focused on the impact of Brexit referendum to leave the EU and its impact on the stock market indices of the European IFCs used in this study and the stock of banks and insurers listed in the FTSE 350. To realize the stated objectives this study has collected the data from 23-06-2015 to 19-07-2016 from the DataStream database. To examine the impact of Brexit referendum on the stock market indices of the selected European IFCs used in this study and the stocks of the banks and insurers listed in the FTSE 350 the event study methodology has been employed. The results of the event study provide evidence that Brexit referendum had significant impact on all the European stock market indices. The results revealed that the lowest significant AR was recorded the trading day right after Brexit referendum in case of the IFCs of Amsterdam, Frankfurt, and Luxembourg. The stock market index of Paris generated the lowest significant AR two trading days after Brexit referendum, while London observed its lowest significant AR three trading days before the referendum. Moreover, the highest ARs of the indices of Frankfurt and Amsterdam were observed three trading days before Brexit referendum, while the indices of Paris and Luxembourg generate their highest significant ARs fifteen and six trading days after the referendum, respectively. Furthermore, the highest significant AR of London was observed seven trading days before the referendum. Therefore, we can conclude that Brexit referendum has an impact on the European stock market indices at the event days mentioned above for each index apart. Moreover, the referendum also has a significant impact on the index of Luxembourg two trading days before and four trading days after the event date. To investigate the significant difference between the pre-event window ARs (-15 to -1) and post-event window ARs (15 to 1) for fifteen, seven and three days event windows a student t-test was run. This study is unable to reject the null hypothesis in case of the fifteen and seven days event windows at all the stock market indices. However, this study reject the null hypothesis for the stock market indices of Frankfurt and Amsterdam at the three days event window. Meaning that there was significant difference between pre-event window returns and post-event ARS at the stock market indices of Frankfurt and Amsterdam in the three days event window. Furthermore, this study provides evidence that the stocks of British banks and insurers listed in the FTSE 350 were hit hardest by Brexit referendum. The results provide strong significant evidence for negative and positive ARs at several event days in the event window. The lowest ARs of the stocks of banks and insurers listed in the FTSE 350 are observed at the trading day right after Brexit referendum at -10.29% and -11.78%, respectively. Figure 5 provides graphically evidence that Brexit referendum has a tremendous short- term impact on the stocks of banks and insurers listed in the FTSE 350. Moreover, the results provide

51 evidence that the stocks of banks and insurers listed in the FTSE 350 generate on average a significant lower AR in the two days after Brexit referendum in comparison with the two days before Brexit referendum. In advance, this study was expected to find more significant results of the influence of the Brexit on the eight individual IFCs. The results indicate that the Brexit doesn’t have that much influence on the competitiveness power of a particular individual IFC as expected. However, this study can conclude with these results that the Brexit is in a too early stadium of its process to suggest whether an individual IFC within the EU could take a significant advantage of the Brexit. When the Brexit is in a later stadium of its process, this study expects to find more significant results for the IFCs within the EU. This can be expected since the Brexit has more consequences for the IFC of London and the other IFCs within the EU when the Brexit is in a later stadium of its process. It is important to note that this study has some limitations in the data. The data limitation occurs because this study uses 8 instead of the 11 explanatory variables provided by Imad et al. (2016). This is because the other 3 variables weren’t available for the sample period of this study. Furthermore, the Brexit is in the beginning stadium of its process during this study. This study could make a better estimation of the effect of the Brexit on an individual IFC when the Brexit is in a later (end) stadium of its process. Therefore, it is interesting for future research to examine again the influence of the Brexit on the competitiveness power of London and all other high ranked IFCs within the EU when the Brexit is in the end stadium of its process.

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Appendix A: IFCs used in this Study

Table 14: A list of the International Financial Centers used in this Study

City Country London United Kingdom New York United States Paris France Amsterdam Netherlands Luxembourg Luxembourg Frankfurt Germany Tokyo Japan Singapore Singapore

Appendix B: Banking and Insurance companies within the FTSE 350 used in the event study

Table 15: Listed Banking and Insurance companies within the FTSE 350 Index

Banking sector companies Insurance Sector Companies ➢ HSBC HDG. ➢ Prudential ➢ Lloyds Banking Group ➢ Aviva ➢ Barclays ➢ Legal & General ➢ Royal Bank of Scotland Group ➢ Old Mutual ➢ Standard Chartered ➢ Standard Life ➢ CYBG ➢ RSA Insurance Group ➢ Metro Bank ➢ ST.James’s Place ➢ Aldermore Group ➢ Admiral Group ➢ BGEO Group HDG ➢ Direct Line IN.Group ➢ Shawbrook Group ➢ Jardine Lloyd Thompson ➢ Virgin Money Holdings ➢ Beazley ➢ TBC Bank Group ➢ Hiscox (D ➢ Phoenix Group HDG. ➢ Hastings Hroup HDG ➢ Just Group ➢ Esure Group ➢ Lancashire Holdings

Appendix C: Stock market returns of the European indices beyond Brexit referendum

In Appendix B, this study shows the returns of the stock market indices of the European IFCs used in this study. The returns are calculated from 1 January 2016 until 1 June 2017, such that this study can take a look at the development of the stock returns of the market indices of the European IFCs after the referendum date of the Brexit. The adjusted close prices of the indices, which are already adjusted for dividends, are used to calculate the stock returns and are obtained from Datastream. The following formula is used to calculate the stock returns of the market indices.

푃푟𝑖푐푒 푛푒푤 − 푃푟𝑖푐푒표푙푑 ∗ 100% = 퐷푎𝑖푙푦 푆푡표푐푘 푀푎푟푘푒푡 퐼푛푑푒푥 푅푒푡푢푟푛 푃푟𝑖푐푒표푙푑

The following figures provide the development of the stock market indices beyond the referendum date of the Brexit for all the European IFCs used in this study.

Figure 6: Return overview of the FTSE 100 Index, London

Figure 7: Return overview of the Euronext Paris Index, Paris

Figure 8: Return overview of the LuxxSE Index, Luxembourg

Figure 9: Return overview of the DAX Index, Frankfurt

Figure 10: Return overview of the AEX Index, Amsterdam

Appendix D: Time line of events after Brexit Referendum

Figure 11: Time Line of Events which Affects the London Stock Market after Brexit Referendum

The Brexit process started on 20 February 2016 when then Prime Minister David Cameron called a referendum to vote whether the UK should leave or maintain in the EU. The results of this vote were announced on 24 June 2016, were 51.9% of the voters chooses to leave the EU. Moreover, on 24 June, David Cameron resigns as Prime Minister, the pound plunges to a three-decade low, and the Bank of England says it’s ready to support the financial system. On June 25, the UK’s Jonathan Hill resigns as EU financial-service chief in the wake of Brexit vote. The German Chancellor Angela Merkel tells Cameron ahead of Brussels summit on 28 June that there will be no informal talks before the UK triggers to break up and that the UK can’t be cherry picking of the best bits of the EU membership. The Home Secretary Theresa May declares her candidacy for the Conservative Party leadership on 30 June and says “Brexit means Brexit”. On 5 July, the three largest UK real-estate funds freeze assets after the referendum vote sparked a bustling of amortizations. May becomes prime minister on 13 July 2013 and she pledges a new industrial program to get the economy ‘firing’ on 2 August 2016. Moreover, the Bank of England cuts its benchmark interest rate to a record level of 0.25% and expands its bond-buying program on 4 August. May said on 31 August to her cabinet that she’s going to make a success of the Brexit and won’t try to keep the UK in the EU by the “back door”. Cameron said on 12 September 2016 that he’s quitting as a member of the UK parliament. Furthermore, Davis, the Brexit secretary, said that the UK divorces from the EU and a new trade deal can be completed inside two years. The US presidential election on 8 October has a negative effect on the stock market index of London, but the stock market recovers fast to a record high peak level.

Appendix E: Definitions and Sources of Explanatory Variables

Based on the paper of Imad et al. (2016), Table 16 provides for each explanatory variables their definitions and sources of the data. Furthermore, Table 16 provides the expected positive or negative sign of the variables which are based on previous research and economic intuition.

Table 16: Definitions and Sources of Explanatory Variables.

Variable Description Source Expected Sign High-Tech Exports High-technology exports are products United Nations, Comrade Positive with high R&D intensity, such as in database aerospace, computers, scientific instruments and electrical machinery Economic Freedom An index that is built upon the The Heritage Foundation Negative Index analysis of ten components of economic freedom, some of which are themselves composites of additional measures Internet Usage The number of internet users per 100 World Bank Positive people. Internet users are people with access to the worldwide network Corporate Tax Rate The tax imposed on corporate KPMG Negative earnings Global A comprehensive indicator that World Economic Forum Positive Competitiveness measures the microeconomic and Index macroeconomic foundations of national competitiveness Size of Government An aggregate rating of government World Bank Positive consumption to total consumption, transfers and subsidies to GDP, government enterprises and investment to total investment, and top marginal tax rate Legal system and An aggregate rating of judicial Institute for Liberty and Negative Property Rights independence, impartial courts, Democracy Index protection of property rights, military interference in the rule of law and politics, integrity of the legal system, legal enforcement of contracts, regulatory restrictions on the sale of real property, reliability of police, and business costs of crime Human A composite index measuring average United Nations Positive Development Index achievement in three basic Development Program dimensions of human development: a long and healthy life, access to knowledge, and a decent standard of living Brexit The referendum date where 51.9% of Wikipedia Negative the people of the United Kingdom votes to leave the European Union. This period is from the announcement date of the referendum at 20 February 2016 until March 2017 Financial Crisis The global financial crisis period starts Wikipedia Negative in this study from March 2007 until September 2009. Source: Paper of Imad et al. (2016), p. 2094-2095.

Appendix F: Results from the Hausman Test

Hausman Test Results (dataset with all international financial centers).

This study uses the Hausman test to determine whether the panel data regressions from Table 7 have to be regressed with fixed effects (FE) or random effects (RE).

Table 17: Results of the Hausman Test

Coefficients Variable (b) (B) (b-B) Sqrt(diag(V_b- FE RE Difference V_B)) S.E. Internet Usage 1.62 1.38 0.11 1.32 Tax rate 189.91 138.79 51.12 203.44 Global 97.24 112.12 -24.07 47.44 Competitiveness Size of Government -9.52 -4.73 -6.63 5.41

LSPR 5.88 -57.49 52.12 7.94 Economic Freedom 1.71 3.02 -3.56 2.94 Human -194.49 -716.84 522.35 223.75 Development High tech Export -1.87e-10 3.60e-11 -1.23e-10 2.72e-10 Brexit -12.84 -11.88 -0.96 5.62 Financial Crisis -28.55 -15.19 -13.36 8.41

b = consistent under Ho and Ha; obtained from xtreg

B = inconsistent under Ha, efficient under Ho; obtained from xtreg

Ho: Difference in coefficients is systematic

2 ′ −1 Chi (7) = (b − B) [(Vb − VB) ] ∗ (b − B)

Chi2(7) = = 140.05

Prob>Chi2(7) = 0.000

This study rejects the null hypothesis of the Hausman test and accepts the alternative hypothesis. This provides evidence that the panel data regressions in this study have to be regressed with fixed effects.

Hausman Test Results (dataset with all international financial centers within the EU).

This study uses the Hausman test to determine whether the panel data regressions from Table 8 have to be regressed with fixed effects (FE) or random effects (RE).

Table 18: Results of the Hausman Test

Coefficients Variable (b) (B) (b-B) Sqrt(diag(V_b- FE RE Difference V_B)) S.E. Internet Usage 1.65 -7.23 8.88 1.21 Tax rate 426.42 -392.589 819.00 121.83 Global 104.68 281.68 -176.99 33.56 Competitiveness Size of Government -4.45 -2.33 -2.12 4.51

LSPR 2.01 10.43 -8.52 3.19 Economic Freedom 0.65 11.13 -10.49 2.41 Human -1905.41 -5460.74 3555.32 972.77 Development High tech Export -1.15e-10 1.20e-11 -1.03e-10 4.22e-10 Brexit -21.27 -42.28 21.015 5.83 Financial Crisis -8.20 -35.67 27.48 4.74 b = consistent under Ho and Ha; obtained from xtreg

B = inconsistent under Ha, efficient under Ho; obtained from xtreg

Ho: Difference in coefficients is systematic

2 ′ −1 Chi (7) = (b − B) [(Vb − VB) ] ∗ (b − B)

Chi2(7) = = 74.13

Prob>Chi2(7) = 0.000

Appendix G: Results from the Modified Wald Test and the Woolridge Test

Results Modified Wald test (dataset with all international financial centers).

Modified Wald test for groupwise heteroscedasticity in fixed effect regression model.

2 2 Ho = σi = σ for all i

Chi2(8) = 31.20 Prob>Chi2(8) = 0.0001

This study rejects the null hypothesis of the Woolridge test at the 1% level and accepts the alternative hypothesis. This provides evidence that this study has to control for heteroscedastic standard errors in the panel data regressions from Table 7. This study controls for the heteroscedasticity in the standard errors with the clustered standard errors option. The standard errors are clustered between the IFCs.

Results Modified Wald test (dataset with all international financial centers within the EU).

Modified Wald test for groupwise heteroscedasticity in fixed effect regression model.

2 2 Ho = σi = σ for all i

Chi2(8) = 5.28

Prob > Chi2(8) = 0.3821

This study accepts the null hypothesis of the Woolridge test at the 1% level. This provides evidence about the fact that this study has to control for homoscedastic standard errors in the panel data regressions from Table 8.

Results Woolridge test (dataset with all international financial centers):

Woolridge Test for autocorrelation in panel data:

Ho: no first − order autocorrelation

F(1,7) = 25.429

Prob>F = 0.0015

This study rejects the null hypothesis of the Woolridge test at the 1% level and accepts the alternative hypothesis. This provides evidence that this study has to control for autocorrelation in the panel data regressions from Table 7. This study controls for the autocorrelation with the clustered standard errors option. The standard errors are clustered between the IFCs.

Results Woolridge test (dataset with all international financial centers within the EU).

Woolridge Test for autocorrelation in panel data:

Ho: no first − order autocorrelation

F(1,7) = 8.633

Prob>F = 0.0425

This study accepts the null hypothesis of the Woolridge test at the 1% level. This provides evidence about the fact that the dataset in this panel data regression model doesn’t suffer from autocorrelation.

Appendix H: Results from the White Test

White Test Results

White test to examine the standard errors of the regressions in all the international financial centers:

2 u = α0 + α1푍1 + ⋯ + α푝Z푝 (4)

퐻표: 퐻표푚표푠푐푒푑푎푠푡𝑖푐

퐻1: 푈푛푟푒푠푡푟𝑖푐푡푒푑 ℎ푒푡푒푟표푠푐푒푑푎푠푡𝑖푐

Model:

퐺퐹퐶퐼 퐼푛푑푒푥 푖 = β0 + β1 ∗ 퐵푟푒푥𝑖푡 + β2 ∗ 퐹𝑖푛푎푛푐𝑖푎푙 퐶푟𝑖푠𝑖푠 + β3 ∗ 퐻𝑖푔ℎ − 푡푒푐ℎ 퐸푥푝푖 + β4

∗ 퐼푛푡푒푟푛푒푡 푈푠푎푔푒푖 + β5 ∗ 퐶표푟푝표푟푎푡푒 푇푎푥 푅푎푡푒푖 + β6 ∗ 퐺푙표푏푎푙 퐶표푚푝푒푡𝑖푡𝑖푣푒푛푒푠푠푖

+ β7 ∗ 푆𝑖푧푒 표푓 퐺표푣푖 + β8 ∗ 퐿푆푃푅푖 + β9 ∗ 퐻푢푚푎푛 퐷푒푣푒푙표푝푚푒푛푡푖 + β10

∗ 퐸푐표푛표푚𝑖푐 퐹푟푒푒푑표푚푖 + β11 ∗ 퐹𝑖푛푎푛푐𝑖푎푙 퐶푟𝑖푠𝑖푠 + 푒푖(3)

Table 19: Results of the White Test

IFC Chi2 value Degrees of freedom P- value Heteroscedastic errors Paris 0.00 9 0.9578 No Luxembourg 4.88 10 0.0271 No Amsterdam 0.13 10 0.7142 No Frankfurt 1.14 10 0.2862 No London 58.80 10 0.000 No New York 2.34 9 0.1265 No Singapore 0.96 10 0.3269 No Tokyo 8.22 10 0.0041 Yes

The IFCs of Paris and New York have a degree of freedom of 9 instead of 10. This is due to the fact that the control variable ‘Tax Rate’ is omitted from the regression for these IFCs. This variable is omitted because of that these IFCs have the same tax rate over the whole sample period, such that it hasn’t any explanatory power on the competitiveness power of the IFCs of Paris and New York.

Appendix I: List of Figures and Tables

List of Figures:

Figure 1: The Deprecation of the Pound Post Brexit Referendum ...... 16 Figure 2: FTSE Stock Prices Post Brexit Referendum ...... 17 Figure 3: Competitiveness Between International Financial Centers Used In This Study ...... 20 Figure 4: The Impact of Brexit Referendum on the European Stock Market Indices ...... 41 Figure 5: The Impact of Brexit Referendum on the Stocks of Banks and Insurers Listed in the FTSE 350 ...... 43 Figure 6: Return overview of the FTSE 100 Index, London...... 59 Figure 7: Return overview of the Euronext Paris Index, Paris ...... 59 Figure 8: Return overview of the LuxxSE Index, Luxembourg ...... 60 Figure 9: Return overview of the DAX Index, Frankfurt ...... 60 Figure 10: Return overview of the AEX Index, Amsterdam ...... 60 Figure 11: Time Line of Events which Affects the London Stock Market after Brexit Referendum .. 61

List of Tables:

Table 1: Increase in Return Volatility due to uncertainty by Brexit referendum ...... 6 Table 2: Leading Financial Centers over Time...... 14 Table 3: Areas of Competitiveness ...... 19 Table 4: Descriptive Statistics Variables Panel Data ...... 23 Table 5: Descriptive Statistics of the Stock Market Indices used in the Event Study ...... 25 Table 6: Expected Influence of the Brexit on the Status of each IFC ...... 29 Table 7: Test on the Effect of the Brexit on the Status of International Financial Centers ...... 33 Table 8: Test on the Effect of the Brexit on the Status of European International Financial Centers35 Table 9: Test on the Effect of the Brexit on the Status of an Individual International Financial Center ...... 38 Table 10: Abnormal returns (AR) per Stock Market Index ...... 39 Table 11: Difference in Abnormal returns (AR) in pre-event and post-event Period ...... 40 Table 12: Abnormal returns (AR) in the Stocks of Banks and Insurers listed in the FTSE 350 ...... 42 Table 13: Difference in Abnormal returns (AR) in pre-event and post-event Period ...... 43 Table 14: A list of the International Financial Centers used in this Study ...... 57 Table 15: Listed Banking and Insurance companies within the FTSE 350 Index ...... 58 Table 16: Definitions and Sources of Explanatory Variables...... 62 Table 17: Results of the Hausman Test ...... 63 Table 18: Results of the Hausman Test ...... 64 Table 19: Results of the White Test ...... 67