Influence Brexit
International Financial
Centers
Master Thesis
Author: Jeffrey Vossen (10655182) Supervisor: Jan Lemmen
Date: 27-07-2017
University of Amsterdam
SCHOOL OF BUSINESS and ECONOMICS
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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.
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Table of Contents
Content
ABSTRACT ... 2 Table of Contents ... 3 1. Introduction ... 5 2. Literature Review... 9 2.1 Financial Center ... 92.2 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
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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
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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
6 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 01/06/2016-30/06/2016 23/06/2015-01/06/2016 % Increase 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
7 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
8 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.
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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.
10 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.
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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).
12 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
13 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
14 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
15 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
16 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
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.
1,1500 1,2000 1,2500 1,3000 1,3500 1,4000 1,4500 1,5000 30-5-2016 30-6-2016 31-7-2016 31-8-2016
GBP to US exchange rate
17
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”.
18 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).
19
Table 3: Areas of Competitiveness
Rank Business Environment Human Capital Infrastructure Financial Sector Development
Reputation 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
20 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
Source: Past 21 Global Financial Center Indices published by the Z/Yen Group 500 550 600 650 700 750 800 850 1- 3-2007 1- 8-2007 1-2008 1- 6-2008 1- 11-200 8 1- 4-2009 1- 9-2009 1- 2-2010 1- 7-2010 1- 12-201 0 1- 5-2011 1- 10-201 1 1- 3-2012 1- 8-2012 1-2013 1- 6-2013 1- 11-201 3 1- 4-2014 1- 9-2014 1- 2-2015 1- 7-2015 1- 12-201 5 1- 5-2016 1- 10-201 6 1- 3-2017
London Luxembourg Amsterdam Frankfurt
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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.
22 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
23
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 Competitiveness 168 5.38 0.20 4.85 5.74 Size of Government 168 18.61 4.57 9.06 26.48 Legal system and
property rights index
168 7.84 0.39 6 8.4
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
24
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.
25
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
Paris 279 -0.0385% 0.022 -8.15% 8.13%
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 sector London 279 -0.1292% 0.019 -10.29% 5.98% Return UK insurance sector London 279 -0.0546% 0.019 -11.77% 5.46%
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
26 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
27 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.
𝐺𝐹𝐶𝐼 𝐼𝑛𝑑𝑒𝑥 𝑖𝑗= β0+ β1∗ 𝐵𝑟𝑒𝑥𝑖𝑡 + β2∗ 𝐻𝑖𝑔ℎ − 𝑡𝑒𝑐ℎ 𝐸𝑥𝑝𝑖𝑗+ β3∗ 𝐼𝑛𝑡𝑒𝑟𝑛𝑒𝑡 𝑈𝑠𝑎𝑔𝑒𝑖𝑗+ β4 ∗ 𝐶𝑜𝑟𝑝𝑜𝑟𝑎𝑡𝑒 𝑇𝑎𝑥 𝑅𝑎𝑡𝑒𝑖𝑗+ β5∗ 𝐺𝑙𝑜𝑏𝑎𝑙 𝐶𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑖𝑣𝑒𝑛𝑒𝑠𝑠𝑖𝑗+ β6∗ 𝑆𝑖𝑧𝑒 𝑜𝑓 𝐺𝑜𝑣𝑖𝑗 + β7∗ 𝐿𝑆𝑃𝑅𝑖𝑗+ +β8∗ 𝐻𝑢𝑚𝑎𝑛 𝐷𝑒𝑣𝑒𝑙𝑜𝑝𝑚𝑒𝑛𝑡𝑖𝑗+ β9∗ 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝐹𝑟𝑒𝑒𝑑𝑜𝑚𝑖𝑗+ α𝑖 + δ𝑗+ 𝑒𝑖𝑗 (1)
Where;
The 𝐺𝐹𝐶𝐼 𝐼𝑛𝑑𝑒𝑥 𝑖𝑗 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 𝐻𝑖𝑔ℎ − 𝑡𝑒𝑐ℎ 𝐸𝑥𝑝𝑖𝑗 is the High-tech export in country j in time i
The 𝐼𝑛𝑒𝑟𝑛𝑒𝑡 𝑈𝑠𝑎𝑔𝑒𝑖𝑗 is the use of internet in country j in time i
The 𝐶𝑜𝑟𝑝𝑜𝑟𝑎𝑡𝑒 𝑇𝑎𝑥 𝑅𝑎𝑡𝑒𝑖𝑗 is the Corporate tax rate in country j at time i
The 𝐺𝑙𝑜𝑏𝑎𝑙 𝐶𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑖𝑣𝑒𝑛𝑒𝑠𝑠𝑖𝑗 is the global competitiveness index from country j in time i
The 𝑆𝑖𝑧𝑒 𝑜𝑓 𝐺𝑜𝑣𝑖𝑗 is the size of government in country j at the i
The 𝐿𝑆𝑃𝑅𝑖𝑗 is the legal and property right in country j at time i
The 𝐻𝑢𝑚𝑎𝑛 𝐷𝑒𝑣𝑒𝑙𝑜𝑝𝑚𝑒𝑛𝑡𝑖𝑗 is the Human development in country j at time i
The 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝐹𝑟𝑒𝑒𝑑𝑜𝑚𝑖𝑗 is the economic freedom in country j at time i
𝛼𝑖 controls for time fixed effects
𝛿𝑗 controls for fixed effects between financial centers
𝑒𝑖𝑗 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:
28 H0: β𝐵𝑟𝑒𝑥𝑖𝑡𝑗 = 0
H1: β𝐵𝑟𝑒𝑥𝑖𝑡𝑗≠ 0
Where β𝐵𝑟𝑒𝑥𝑖𝑡𝑗 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.
𝐺𝐹𝐶𝐼 𝐼𝑛𝑑𝑒𝑥 𝑖𝑗= β0+ β1∗ 𝐵𝑟𝑒𝑥𝑖𝑡 + β2∗ 𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐶𝑟𝑖𝑠𝑖𝑠 + β3∗ 𝐻𝑖𝑔ℎ − 𝑡𝑒𝑐ℎ 𝐸𝑥𝑝𝑖𝑗+ β4 ∗ 𝐼𝑛𝑡𝑒𝑟𝑛𝑒𝑡 𝑈𝑠𝑎𝑔𝑒𝑖𝑗+ β5∗ 𝐶𝑜𝑟𝑝𝑜𝑟𝑎𝑡𝑒 𝑇𝑎𝑥 𝑅𝑎𝑡𝑒𝑖𝑗+ β6
∗ 𝐺𝑙𝑜𝑏𝑎𝑙 𝐶𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑖𝑣𝑒𝑛𝑒𝑠𝑠𝑖𝑗∗ +β7∗ 𝑆𝑖𝑧𝑒 𝑜𝑓 𝐺𝑜𝑣𝑖𝑗+ β8∗ 𝐿𝑆𝑃𝑅𝑖𝑗+ β9 ∗ 𝐻𝑢𝑚𝑎𝑛 𝐷𝑒𝑣𝑒𝑙𝑜𝑝𝑚𝑒𝑛𝑡𝑖𝑗+ β10∗ 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝐹𝑟𝑒𝑒𝑑𝑜𝑚𝑖𝑗+ α𝑖+ 𝑒𝑖𝑗 (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: β𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐶𝑟𝑖𝑠𝑖𝑠 = 0 H1: β𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐶𝑟𝑖𝑠𝑖𝑠 ≠ 0
Where β𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐶𝑟𝑖𝑠𝑖𝑠𝑗 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.
𝐺𝐹𝐶𝐼 𝐼𝑛𝑑𝑒𝑥 𝑖 = β0+ β1∗ 𝐵𝑟𝑒𝑥𝑖𝑡 + β2∗ 𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐶𝑟𝑖𝑠𝑖𝑠 + β3∗ 𝐻𝑖𝑔ℎ − 𝑡𝑒𝑐ℎ 𝐸𝑥𝑝𝑖 + β4
∗ 𝐼𝑛𝑡𝑒𝑟𝑛𝑒𝑡 𝑈𝑠𝑎𝑔𝑒𝑖+ β5∗ 𝐶𝑜𝑟𝑝𝑜𝑟𝑎𝑡𝑒 𝑇𝑎𝑥 𝑅𝑎𝑡𝑒𝑖+ β6∗ 𝐺𝑙𝑜𝑏𝑎𝑙 𝐶𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑖𝑣𝑒𝑛𝑒𝑠𝑠𝑖 + β7∗ 𝑆𝑖𝑧𝑒 𝑜𝑓 𝐺𝑜𝑣𝑖+ β8∗ 𝐿𝑆𝑃𝑅𝑖+ β9∗ 𝐻𝑢𝑚𝑎𝑛 𝐷𝑒𝑣𝑒𝑙𝑜𝑝𝑚𝑒𝑛𝑡𝑖+ β10
∗ 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝐹𝑟𝑒𝑒𝑑𝑜𝑚𝑖+ β11∗ 𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐶𝑟𝑖𝑠𝑖𝑠 + 𝑒𝑖(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
29 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: β𝐵𝑟𝑒𝑥𝑖𝑡𝑗 = 0 H0: β𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐶𝑟𝑖𝑠𝑖𝑠𝑗 = 0 H1: β𝐵𝑟𝑒𝑥𝑖𝑡𝑗≠ 0 H1: β𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐶𝑟𝑖𝑠𝑖𝑠𝑗 ≠ 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:
𝑅𝑖,𝑡 = 𝛼𝑖+ 𝛽𝑖𝑅𝑚,𝑡+ 𝜖𝑖,𝑡(4)
Where 𝑅𝑖𝑡 and 𝑅𝑚𝑡 are the rates of return of stock market index 𝑖 and the MISC world index return at date 𝑡, respectively. The error term 𝜖𝑖𝑡 is assumed to be uncorrelated across the indices, have a zero mean and is independent of 𝑅𝑚𝑡. 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 𝑅𝑖𝑡 and 𝑅𝑚𝑡 are the rates of returns of the FTSE 350 listed stocks in sector 𝑖 and the FTSE 100 index return at date 𝑡, respectively.
