Commonality in Liquidity: Evidence from the First Transatlantic Exchange

Pankaj Jain Department of Finance, Insurance and Real Estate The University of Memphis U.S. Securities and Exchange Commission Email: [email protected]

Mohamed Mekhaimer Department of Finance, Insurance and Real Estate The University of Memphis Email: [email protected]

Sandra Mortal Department of Finance, Insurance and Real Estate The University of Memphis Email: [email protected]

This Draft: August 2013

Comments/Corrections welcome

Abstract

In this paper, we use the introduction of the first transatlantic exchange, NYSE Arca

Europe (NAE), to examine the impact of transatlantic trading on commonality in liquidity. We find that transatlantic trading bring up a new source of commonality in liquidity, after controlling for home market co-movement. Using a control sample, we show that the new source of commonality cannot be attributed to regional or market- specific factors. The results reveal that the magnitude of common liquidity factor for

NAE market is greater than the respective home market common factor. We also show that co-movement with NAE increases with firm size.

Keywords: Commonality in Liquidity, Multi-Market Trading, Stock Market Integration, NYSE Arca Europe, NYSE-Euronext.

JEL Classification: G11, G12, G14

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1. Introduction

The evolution of competition and integration of financial markets in recent years have shaken the stock exchange landscape, particularly in the USA and Europe1. While the words of former CEO of New York Stock Exchange John Thain ―Every country has a flag, an army and an exchange‖ were true ten years back, today, we have a different landscape with international stock exchange groups such as NYSE-Euronext and

NASDAQ-OMX, London Stock Exchange-Borsa Italiana Groups, in addition to

Alternative Trading Systems (ATS) and Multi-lateral Trading Facilities (MTF) that are powered, regulated and have staff and customers from different countries all over the world. In such an integrated complex stock market environment, the significance of a single physical national stock exchange has eroded significantly and multi-market trading becomes more common. In this paper, we take advantage of the introduction of the first transatlantic exchange, NYSE Arca Europe (NAE), to investigate the effect of multi- market trading on commonality in liquidity2.

We find that trading in multi-market trading setting bring up a new source of commonality, in addition to the reported home market commonality (Chordia, Roll, and

Subrahmanyam 2000; Hasbrouck and Seppi 2001; Huberman and Halka, 2001; Kamara,

1 Aggarwal and Dahiya (2005) provide a discussion of the main drivers of world stock market integration. Shahrawat (2008) show that, only between 2005 and 2007 the global exchange market saw 15 major M&A deals, valued at $42 billion. He further states that the two common aspects of these transactions are the combination of exchanges across countries and the coming together of firms trading different products. About 33% of the 15 transactions were cross-border deals, and over 50% of them were between firms trading different products. 2 NYSE Arca Europe is a traditional Multi Trading Facility (MTF) that aims to extend the exchange's European reach beyond Euronext-listed securities to compete with pan-European MTFs. Before March 09, 2009, stocks traded in NAE were traded only on its home exchange. After March 2009, these stocks are traded not only on the home exchange, but also traded on NAE. 2

Lou and Sadka, 2008; Brockman, Chung and Perignon 2009, Corwin and Lipson 2011)3.

Simply stated, changes in NYSE Arca Europe individual firm‘s liquidity are significantly influenced by a common liquidity factor in the transatlantic market NAE after controlling for the home market co-movement. In addition, we find that firm size plays an important role in commonality in liquidity. Consistent with Chordia, Roll and Subrahmanyam

(2000) and Kamara, Lou and Sadka (2008) we find that firm co-movement with NAE market wide liquidity increases with firm size. Moreover, the results reveal that, for most of the studied markets, the magnitude of common liquidity factor for NAE market is greater than the respective home market common factor. With respect to systematic liquidity risk, one can conclude that using the home market portfolio only to assess the systematic liquidity risk does not capture the true market portfolio that affect the systematic liquidity risk.

The introduction of the Multi-Lateral Trading Facility (MTF) NYSE Arca Europe provides an ideal setting to answer our research questions. The market provides its traders, for the first time, the opportunity to trade U.S. stocks during European trading hours, along with other 12 European countries in the same market4. Before March 09,

2009, stocks traded in NAE were traded only on its home exchange, while, after March

2009 these stocks are traded not only on the home exchange but also traded on NAE. In addition, NAE is fully integrated with NYSE- Euronext systems. Existing NYSE

Euronext members can trade on the same platform simply by extending their

3 Current literature on multi-market trading focuses on either the market quality of traded stocks or the distribution of trading volume among competing markets (Pagano 1989; Chowdry and Nanda 1991; Baruch, Karolyi and Lemmon 2007; Menkveld 2008; Moulton and Wei 2009; Halling, Moulton and Panayides 2013). However, to date, our knowledge lacks any empirical evidence of how commonality in liquidity is affected by multi-market trading or changes in stock market design. 4 Section 3 provides, in more details, the institutional details of NYSE Arca Europe market. 3 membership5. Such a unique setting enables us to also investigate whether the indirect access given to NYSE and Euronext can add a new source of commonality to NAE traded stocks. Our results suggest that commonality in liquidity of NAE traded stocks is not affected by the indirect access of NYSE-Euronext traders.

Prior research suggests that market-specific factors are possible sources of commonality in liquidity. For example Chordia, Roll and Subrahmanyam (2001) and

Brockman, Chung and Perignon (2009) show that unemployment and GDP announcements have significant effects on commonality in liquidity. In addition, Karoyli,

Lee and van Dijk (2012) find that commonality in liquidity is high during periods of high market volatility or large market decline6. One of the key advantages of using NAE setting is that it combines stocks from different countries that do not share the same macro-economic and other market-specific factors. This means that we cannot simply extrapolate the reported commonality to market specific factors7. However, around 90% of stocks traded in NAE are from Europe, which might reflect a regional source of commonality, other than trading on NAE (Brockman, Chung and Perignon, 2009)8. To rule out this possibility, we construct a control sample of firms that is also listed in the respective home stock exchange but not traded in NYSE Arca Europe market. Our results

5 NYSE, Euronext and NYSE Arca Europe are connected through the universal trading platform. In February 2008 NYSE Euronext began a two-year program to decommission of four platforms and create Universal Trading Platform (UTP) to support all of its markets. The ultimate goal of such project is that NYSE-Euronext, at the end, will have global network for both European and US customers. Appendices A and B show the integration process as well as the relationship between the three connected market NYSE, Euronext and NYSE Arca Europe. 6 The findings of Chordia, Roll and Subrahmanyam (2001), Brockman, Chung and Perignon (2009) and Karoyli, Lee and van Dijk (2012) are consistent with Bunnermeier and Pederson (2009) provide a model that links an asset‘s market liquidity and traders‘ funding constraints (i.e., the ease with which they can obtain funding). They predict that large market declines or high volatility would affect liquidity provision and hence increase commonality in liquidity. 7 In addition, we cannot attribute changes in liquidity commonality to factors such as common language, trading times, legal system and others because these factors were already in place before our main event. 8 Brockman, Chung and Perignon, (2009) document a regional co-movement in European stocks; however, they did not explain the source behind it. 4 confirm that only stocks traded in NAE show a significant liquidity co-movement with

NAE aggregate market liquidity.

Another possible explanation proposes that commonality in liquidity is primarily driven by correlated trading behavior within groups of traders and the prevalent increase in basket trading. Kamara, Lou and Sadka (2008) and Karoyli, Lee and van Dijk (2012) provide empirical evidence that the increase in commonality in liquidity can be attributed to the correlated trading decisions of institutional traders in the U.S. and international stock market respectively. Consistent with these results, Corwin and Lipson (2011) also show that commonality in liquidity is driven by program and institutional traders. In addition, the model of Gorton and Pennacchi (1993) predicts that equity basket trading increases the commonality in liquidity for the constitute stocks in the basket, but reduces liquidity commonality for individually traded stocks. Kamara, Lou and Sadka (2008) confirmed this prediction and find that index traders have increased the commonality in liquidity in the US market.

Our results are consistent with the correlated trading behavior and basket trading explanation. Using our experimental setting, we show that trading a basket of equity stocks that are combined from 13 different countries, NAE market, add a new source of commonality in liquidity, in addition to the home market liquidity co-movement. These results support the prediction of Gorton and Pennacchi (1993) that basket trading increase commonality in liquidity. Moreover, our results can also be attributed to the correlated trading decisions of specific groups of traders (Kamara, Lou, and Sadka, 2008; Koch,

Ruenzi, and Starks, 2009; Corwin and Lipson, 2011; Karoyli, Lee and Van Dijk, 2012).

Although, NAE stocks do not share the same home market, location, macro-economic or

5 other market specific factors, they do share the same investors‘ pool. This existence of a common investors‘ pool suggests that commonality in liquidity in NAE might be driven by the correlated trading decisions of specific groups of traders9.

Our analysis is closely related to Brockman, Chung and Perignon (2009) and

Karoyli, Lee and Van Dijk, (2012). Brockman, Chung and Perignon (2009) conduct a comprehensive study of commonality in liquidity using data from 47 stock exchanges.

They find that firm-level changes in liquidity are significantly influenced by exchange- level changes across most of the world‘s stock exchange10. Karoyli, Lee and Van Dijk,

(2012) examine the main drivers of commonality in liquidity in the international stock market. They document that correlated trading behavior of international and institutional investors and common investor sentiment are the main drivers of commonality in liquidity. Although, both studies investigate the commonality in liquidity in the international stock market, none of them have studied the effect of multi-market trading on commonality in liquidity. In this paper we extend the empirical model of Chordia,

Roll, and Subrahmanyam (2000) to show that multi-market trading can add a new source of commonality in liquidity.

Studying the determinants of commonality in liquidity is important for various reasons. Systematic liquidity risk is priced i.e., stocks whose returns are more sensitive to fluctuations in aggregate liquidity earn higher returns than stocks that exhibit lower sensitivity (Brennan and Subrahmanyam, 1996; Pastor and Stambaugh, 2003; Acharya

9 We do not have data that will allow us to directly test the correlated trading behavior and determine the specific group of investors that drive commonality in liquidity. 10 Brockman, Chung and Perignon (2009) find evidence of a distinct global component in commonality in liquidity. They further show that both developed and emerging markets are susceptible to global commonality, although developed markets are more sensitive to liquidity spillover effects than are emerging markets. However, they do not test the multi-market trading environment or explain the source behind the global commonality component. 6 and Pedersen, 2005; Chen, 2005; Sadka, 2006). The commonality in liquidity the various firms are exposed to has important implications on the optimal diversification strategy and portfolio choices of investors (Longstaff, 2001 and 2005; Amihud, Mendelson, and

Wood 1990). Moreover, variations in systematic and total liquidity volatility affect the ability of arbitrageurs and other traders to exploit and eliminate ‗‗mispricing‘‘ (see, e.g.,

Kamara, 1988; Amihud and Mendelson, 1991; Pontiff, 1996; Korajczyk and Sadka,

2004; Sadka and Scherbina, 2007).

2. Related Literature

The liquidity of a stock and how it evolves over time are of important concern to many investors. Most of the early liquidity studies analyze firm-specific determinants of liquidity. This line of research has shown that variations in price, volume, and volatility explain much of the cross-sectional of stock market liquidity (Benston and Hagerman,

1974; Stoll, 1978; Barclay and Smith, 1988; Brockman and Chung, 1999). Liquidity is more than just an attribute that belongs to a single asset. According to Amihud and

Mendelson, 1986; Brennan and Subrahmanyam, 1996; Liu, 2006, investors prefer assets that are either liquid or assets that are not exposed to systematic liquidity risk. Other studies find that a stock‘s exposure to systematic liquidity risk and whether its liquidity dries up at inopportune times matter for investors (e.g., Pastor and Stambaugh, 2003;

Acharya and Pedersen, 2005; Sadka, 2006; Korajczyk and Sadka, 2008; Lee, 2011).

It is now widely accepted that the liquidity changes over time and these time variations are governed by a significant common component in the liquidity across assets.

Chordia, Roll, and Subrahmanyam 2000; Hasbrouck and Seppi 2001; Huberman and

Halka, 2001; Kamara, Lou and Sadka, 2008; Corwin and Lipson 2011; demonstrate that

7 liquidity has a common systematic factor in US market. In addition, there is also number of papers that document the existence of commonality in liquidity in the international context (Domowitz, Hansch and Wang, 2005; Brockman, Chung and Perignon, 2009;

Karoyli, Lee and van Dijk, 2012). Commonality in liquidity gains in importance because; systematic liquidity variation is a priced source of risk. Amihud and Mendelson, (1986)

Brennan and Subrahmanyam, (1996) Pastor and Stambaugh (2003), Acharya and

Pedersen (2005), Chen (2005), and Sadka (2006) provide evidence of a premium for systematic liquidity risk (Stocks whose returns are more sensitive to fluctuations in aggregate liquidity earn higher returns than stocks that exhibit lower sensitivity).

There are several reasons why the evolution of systematic liquidity across firms is an interesting topic of financial research. First, the divergence of liquidity in the cross section of firms has important implications for the ability to diversify return volatility and aggregate liquidity shocks across firms. Longstaff (2001 and 2005) show that asset illiquidity has a significant effect on the optimal portfolio choices of investors leading them to abandon diversification strategy. Second, it has implications for the efficient functioning of financial markets: Amihud, Mendelson, and Wood (1990) find that sudden unanticipated declines in liquidity have played a key role in the stock market crash of

October 1987. Third, variations in (systematic and total) liquidity volatility affect the ability of arbitrageurs and derivative traders to exploit and eliminate ‗‗mispricing‘‘ (see, e.g., Kamara, 1988; Amihud and Mendelson, 1991; Pontiff, 1996; Mitchell and Pulvino,

2001; Lesmond, Schill, and Zhou, 2004; Korajczyk and Sadka, 2004; Sadka and

Scherbina, 2007). Last, since liquidity is associated with the price discovery process and, can thus affect the systematic and idiosyncratic volatility of stock returns (O‘Hara, 2003),

8 our study may also have implications for the recently documented pricing of idiosyncratic return volatility (Goyal and Santa-Clara, 2003; Ghysels, Santa-Clara, and Valkanov,

2006; Ang, Hodrick, Xing, and Zhang, 2006).

Several alternative explanations for commonality in liquidity have been proposed in literature. First, commonality in liquidity could be driven by style investing. Barberis and Shleifer (2003) develop a model of asset prices where some investors categorize assets into different styles, moving funds between style categories based on relative performance. This type of style investing leads to common factors in the order flow of securities within the same style and reduces the correlations between stocks in different styles. Barberis and Shleifer suggest that style investing provides a reasonable explanation for many empirical findings related to commonality. A second explanation is based on habitat investing, as described in Lee, Shleifer, and Thaler (1991). They argue that some securities maybe held by only particular subsets of investors. As the sentiment or risk preferences of these investors change, their resulting trades may lead to common factors in the order flow of the securities they hold.

Notably, style and habitat-based explanations suggest that common factors reflect correlated trading decisions within specific groups of traders. Kamara, Lou, and Sadka

(2008) provide evidence that the increase in commonality in liquidity among U.S. large cap stocks in particular over the past 25 years can be attributed to the increasing importance of institutional and index related trading for these stocks, consistent with

Gorton and Pennacchi‘s (1993) result that basket trading increases commonality. Koch,

Ruenzi, and Starks (2009) also show that stocks with higher mutual fund ownership and stocks owned by mutual funds with high turnover or funds that experience liquidity

9 shocks exhibit greater commonality in liquidity. The intuition is that growing institutional ownership may give rise to correlated trading across stocks, which, in turn, creates common buying or selling pressure and thus higher levels of common variation in liquidity. Consistent with the previous arguments Corwin and Lipson (2011) find that program trades and other institutional trades are the primary drivers of commonality in order flow. Their results suggest that commonality is driven by the correlated trading decisions of professional traders, as executed through program trades, and not by correlated trading among retail traders.

On the other hand, Previous literature suggests that commonality in liquidity may be driven by funding constraints of financial intermediaries. For example, Brunnermeier and Pederson (2009) predict that large market declines or high volatility adversely affect the funding liquidity of financial intermediaries that act as liquidity suppliers of financial markets. As a consequence these intermediaries reduce the provision of liquidity across many securities, which results in a decrease in market liquidity and increase in commonality in liquidity. Coughenour and Saad (2004) argue that common market makers, induce common liquidity movements because they share the same firm share capital and information, the manner which they provide liquidity is likely to be correlated. Their evidence indicates that individual stock liquidity co-varies with specialist portfolio liquidity, unique from information causing the co-variation with market liquidity. However, liquidity provision is not limited to market-makers, as investors on both the buy and sell sides can choose to either provide liquidity in the form of limit orders or take liquidity in the form of market orders. As a result, commonality in liquidity may be driven by both the direction of trade and the type of order decision. This

10 idea is formalized by Domowitz, Hansch, and Wang (2005); they show that return commonality is driven by order flow while liquidity commonality is driven by order type.

3. Institutional details

Stock exchanges are now increasingly changing their business model and restructuring themselves across the world due to the simultaneous convergence of a number of powerful developments. The most notable of these developments has been the:

(i) rapid advancement and innovation in technology that has facilitated trading access and speed; (ii) growing market competition and integration; (iii) regulatory changes that enhance competition and transparency of the stock markets such as RegNMS, RegATS in the US, and MiFID I and MiFID II in Europe.

Together these developments have eroded the significance of physical national stock exchanges and their trading floors. The market pressure to cut costs and become more competitive have forced stock exchanges, across the globe, to think more in strategic moves to expand its activities into different geographical markets and to provide their customers with range of financial products. Consequently, exchanges are now reinventing their business strategy, through stock market exchange mergers and consolidation to enhance its competitive position. In this paper, we furnish a better understanding of how multimarket trading and changes in stock design can affect the systemic liquidity risk. In this paper we use the introduction of the Multi-lateral Trading

Facility (MTF) NYSE Arca Europe (NAE) in 2009 and the indirect access of NYSE and

Euronext through the Universal Trading Platform (UTP) as exogenous events to examine the dynamics of commonality in liquidity.

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3.1. NYSE and Euronext Merger

The New York Stock Exchange‘s (NYSE‘s) $11billion acquisition of Euronext gave the group control of several major markets in Europe and US, supported by four underlying trading platform: Arca (for Arca stocks and Arca derivatives) and traditional

NYSE order book in the US, the Nouveau Systeme de Cotation (NSC) supporting the

Euronext markets and Liffe Connect for the derivative market. In February 2008 NYSE

Euronext began a two-year program to decommission these four platforms and create

Universal Trading Platform (UTP) to support all of its markets. The ultimate goal of such project is that NYSE-Euronext, at the end, will have global network for both European and US customers. This means that from one single connection, customers will be able to access all NYSE Euronext trading and market data services. Appendix B figure 1 shows the consolidation process of NYSE-Euronext group.

Developing one platform that is sufficiently flexible to suit a different exchange's global needs is an audacious plan. As for the construction of the UTP, Euronext may have been NYSE's biggest single corporate transaction, but it was only part of a strategy to build scale and technology capabilities through acquisition. A few weeks after the closure of the Euronext deal in April 2007, NYSE acquired TransactTools, a provider of enterprise messaging, in mid-2008, it acquired Wombat Financial Software, a market data distribution technology provider, all of this in addition to acquiring Archipelago, in 2006, for handling high-volume trading. According to Exchange officials, the UTP brings together the fruits of all of those acquisitions to present customers with a more competitive global offering. The result of that best-of-breed approach is a platform that can delivers latency of 150-400 microseconds, with sub-millisecond round-trip times

12 available to customers in co-location with the search engine. The UTP Capacity is able to handle 100,000 orders per second. Appendix (B) figure 2 shows the key components of the universal trading platform.

3.2. Introduction of NYSE-Arca Europe

NYSE-Euronext decides to roll out the UTP to Europe first because competition is developing faster in that market and they need to have the best platform to compete for market share. As UTP rolls out across the Atlantic to the US markets, the European division of NYSE Euronext focused on extending the use of UTP and introduces a new major venture ―NYSE Arca Europe‖. NYSE Arca Europe is a more traditional Multi

Trading Facility (MTF) that aims to extend the exchange's European reach beyond

Euronext-listed securities to compete with pan-European MTFs. According to Roland

Bellegarde, NYSE- Euronext group executive vice president and head of European execution for the cash markets, connectivity will be the unique selling point for NYSE

Arca Europe. The conjecture of bringing a complete set of connectivity is to provide all existing customers, who are already connected to the regulated market, with a new connection to NYSE Arca Europe using the same platform. NYSE Arca Europe will have limbs in multiple jurisdictions. Powered by the UTP in Paris, it will be regulated in the

Netherlands with staff and customers in London. The key strategy of adding the new venture, NYSE Arca Europe is to leverage on existing technology and connectivity, in addition to finding ways of providing innovation and competitive advantages.

NYSE Arca Europe extends the trading scope of NYSE-Euronext group of regulated markets by adding blue-chip stocks from Austria, Czech Republic, Denmark,

Finland, Germany, Hungary, Ireland, Italy, Norway, Spain, Sweden, Switzerland, United

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Kingdom and United States. NYSE Arca Europe is fully integrated with NYSE Euronext systems; it is accessible through the SFTI global network. Existing NYSE Euronext members can trade on the same platform simply by extending their membership.

The impacts of such stock exchange restructuring are largely unknown. There are many aspects of interest in such an analysis, both economic and regulatory issues which affect investors, firms, financial intermediaries and the overall economy. Thus, any profound study of the effects of stock exchange merger is bound to be selective and incomplete in its coverage. We find the introduction of NYSE-Arca Europe event is a great opportunity to investigate how the changes commonality in liquidity resulted from trading in NAE and sharing the same trading system with NYSE and Euronext.

4. Data and Methodology

We compile data from Thomson Reuters DataStream; we collect daily total return index (RI), the trading volume (VO; expressed as thousands of shares), the daily-adjusted price (P; in local currency), Number of shares outstanding (NOSH), and Market value

(MV) for individual stocks from their home stock exchange. We use the instrument list available at NYSE website to determine stocks traded in NYSE Arca Europe11. Our sample starts with 1009 firms from 15 European and American stock exchanges. We exclude securities from our sample if the exchange code in NYSE Arca Europe instrument list does not match the exchange code in DataStream database12. We end up

11 NYSE Arca Europe instrument list is available at the following link: https://europeanequities.nyx.com/markets/nyse-arca-europe. The data set includes Security name, Country Issuer, Country Code, Currency, Primary Market and International Securities Identification Number (ISIN). 12 Karolyi, lee and Dijk (2012) exclude depository receipts (DRs), real estate investment trusts (REITs), preferred stocks, investment funds and other stocks with special features. Robustness confirms are not affected by removing stock with special features such as REITs. Following Karolyi, lee and Dijk (2012) we also include dead stocks in the sample to limit the effect of survivorship bias. 14 with a final sample of 995 securities from 15 different exchanges. Our sample covers the period of January 2005 till December 2012.

Following Ince and Porter (2006) who call for caution in handling data errors in

Datastream, we adopt the following screening. We discard daily observations with trading volume that are greater than number of shares . We set daily return to missing if the value of the total return index is below 0.01. In addition, we also discard stock –day observations if:

( ) ( ) Equation (1)

Where and are the stock return of firm i in day t and t-1, respectively, and at least one is greater than or equal 300%. Moreover, we winsorize the daily observations in top or the bottom 1% to remove the effect of extreme values.

4.1. Commonality in Liquidity

We use Amihud (2002) liquidity measure as our main proxy of stock market liquidity. Empirical research evidence from the U.S indicates that this measure is strongly positively related to microstructure estimates of illiquidity, including the bid-ask spread, price impact and fixed trading costs. Goyenko, Holden and Trzcinka (2009) investigate to what extent different liquidity measures capture the high frequency measures of transaction costs based on U.S data. The Amihud performs well relative to other proxies.

In addition Hasbrouck (2009) reports that Amihud is the most strongly correlated measure with TAQ high frequency price impact coefficients. For international context,

Lesmond (2005) show that Amihud measure has a high correlation with bid-ask spread in

23 emerging markets. An important advantage of Amihud illiquidity that we can calculate based on daily data. Karoyli, Lee and Van Dijk (2012) use the Amihud to

15 investigate the time series variation in commonality in the liquidity of individuals stocks around the world.

Many empirical studies rely on the Amihud liquidity measure to capture systematic liquidity risk and even commonality in liquidity among stocks. Acharya and

Pedersen (2005) employ the measure in their investigation of the role of liquidity risk in asset prices. Watanabe and Watanabe (2008) use Amihud liquidity to uncover time variation in liquidity betas and the liquidity risk premium. Avramov, Chordia and Goyal

(2006) use it in their analysis of the relation between liquidity and short-run stock return reversals.

We follow Karoyli, Lee and Van Dijk (2012) to calculate Amihud liquidity measure. We add a constant to the Amihud measure and take logs, to reduce the impact of outliers. We also multiply the result by -1 to arrive at a variable that is increasing in the liquidity of individual stocks. We calculate Amihud illiquidity measure using the following equation.

Equation (2)

where is the Amihud liquidity proxy, is the return in local currency,

is the price in local currency, and is the trading volume. The subscript i is for the trader security, d is the domestic market and t is day t.

In addition, we construct a daily turnover measure for stock i on day t, using the following equation.

Equation (3)

where and are the daily turnover and trading volume stock i on the domestic market d and day t. is the number of shares outstanding at the 16 beginning of the year y. We measure turnover in logs to account for non-stationarity. A similar approach has been used by, among others, Karoyli, Lee and Van Dijk (2012) and

Griffin, Nardari and Stulz (2007). We winsorize the daily observations in top or the bottom 1% to remove the effect of extreme values.

In this study we investigate the effect of the introduction of NYSE Arca Europe

Multi-Trading facility and sharing the Universal Trading platform with big established markets such as NYSE and Euronext on the dynamics and changes commonality in liquidity. Our analysis is divided into two main empirical tests. In the first test we examine the relative change of commonality in liquidity for home versus NYSE Arca

Europe markets. The test aims to examine whether commonality in liquidity has a natural boundary at the home exchange frontier, or whether it spills over onto others exchanges.

Before March 09, 2009, stocks traded in NAE were traded only on its home market.

The introduction of NYSE Arca Europe a secondary market is a great opportunity to investigate the changes in the dynamic of commonality in liquidity. The stocks traded in NAE are from 13 different countries that do not share the same market characteristics; this cross-sectional variation helps rule out the effect macro-economic or other market specific sources of commonality. In addition, changes commonality in liquidity cannot be traced to factors such as common language, common trading times, common legal system and others because these factors were already in place before the event. However, we suggest that stock market integration and multimarket trading might provide a possible source of illiquidity risk that is not been studied before.

This line of inquiry is highly connected to Brockman, Chung and Perignon (2009) that suggests that there is a global component in the total commonality in liquidity in

17 addition to the local commonality.13 In order to test the commonality in liquidity, we follow Chordia, Roll and Subrahmanyam (2000) methodology and the extension provided by Brockman, Chung and Perignon (2009) to test the commonality in multi- market setting. Daily proportional changes in an individual stock's Amihud liquidity measure are regressed in time series on proportional changes in the liquidity for all stocks traded in NYSE Arca Europe Market as well as all stock traded in the home market. We use the following firm-by firm time series regression to test the commonality in liquidity in each sample exchange.

h

Equation (4)

Where is the proportional change in liquidity measured by Amihud liquidity or turnover of firm i in day t. is the return volatility, measured by the change in squared return, of firm i in day t. is the daily return of the home market. is the equal weighted average of each corresponding liquidity measure for all firms trading on the home market. Following prior studies (e.g., Chordia, Roll and Subrahmanyam 2000; Brockman, Chung and

Perignon 2009; Karolyi, lee and Dijk 2012) we exclude firm i in the computation of the innovation in the Home market liquidity (aggregate change in market liquidity).

13 Brockman, Chung and Perignon (2009) examine the relative impact of the local and global components of commonality in liquidity of individual firms. We extend equation (5) to investigate the commonality in liquidity for stock traded in NAE. 18

is the equal weighted change in liquidity calculated the for all firms listed in NYSE Arca Europe. In calculating proportional change in NYSE

Arca Europe aggregate market liquidity index, we exclude all firms listed in (NAE) from the home market to avoid double counting of firms. h is a dummy variable for the date of NYSE Arca Europe market start trading using UTP, that takes the value 0 for all dates before UTP and 1 afterwards.

is the interaction terms between the UTP

Dummy variable and the contemporaneous proportional change in liquidity index for

NYSE Arca Europe. is the interaction term between the UTP Dummy variable and the contemporaneous proportional change in liquidity for the home market.

Following Chordia, Roll and Subrahmanyam (2000) and Brockman, Chung and

Perignon (2009), we include one lead and one lag of the home and NYSE Arca Europe market average liquidity plus the contemptuous, leading and lagged home market return and the contemptuous change in individual stocks. The lead and lags are deigned to capture any lagged adjustment in commonality, while the market return is intended to remove spurious dependence induced by an association between returns and liquidity measures. Finally the squared stock return is included to account for stock market volatility.

4.1.1. Control Sample

It is important to note that different trends in commonality in liquidity across stocks may of course be unrelated to being traded in multimarket setting or the market integration events. In other words, it need to be quantified whether these events are

19 directly associated with any possible changes in the dynamic of commonality in liquidity.

To investigate this issue we construct a control sample of firms that is also listed in the home stock exchange but not traded in NYSE Arca Europe market. This approach allows us to rule out the possibility that the observed changes in commonality in liquidity are due to macro-economic variables or other unobserved trends. For example, if the integration and multimarket setting events are truly influential, one should expect that

NAE traded firms to react more strongly to the events than other home exchange firms that are not traded in NYSE Arca Europe. The control sample is constructed based on the nearest average of Amihud liquidity measure for the period before the launch of NYSE

Arca Europe on March 9 of 200914. Table one present descriptive statistics for both the original and control sample.

4.2. Direct Vs. Indirect Access

In the second test, we examine whether firms that are traded in NAE, which also share the Universal Trading Platform with NYSE and Euronext markets would co-move with these big established markets. NYSE and Euronext traders have indirect access to

NAE stocks through the same trading platform, where they just need to extend their membership to NAE market. With an increasingly changing stock exchange industry, this question is very important to investigate the effect of indirect access or using the same trading platform on the systemic risk profile and commonality in liquidity. One advantage of the structure of NYSE Arca Europe market is that it has both the direct and indirect access facility to trade NAE stocks. Hence it can enhance our understanding of the mechanism that can really change the systemic risk profile of the firm. To answer this

14 We also tried matching sample based on the nearest average of Amihud liquidity measure and the Market value at the year of the event 2009 and our results are the same. 20 question, we add separately to equation ( ) equal weighted average of each daily changes

Amihud liquidity measure for all firms trading on NYSE and Euronext. We perform firm- by-firm regressions as follows:

h

Equation (5)

In the above equation we add to equation (5) a new layer of markets that have indirect access through the Universal trading platform to trade NAE stocks. Where

is the equal weighted average of each Amihud liquidity measure for all firms trading on either NYSE or Euronext. In addition we also add

, which is the interaction term of

UTP Dummy variable and the contemporaneous proportional change in liquidity index for either NYSE or Euronext. The indirect access interaction term is our main interest to understand the effect of indirect access on the dynamics of the commonality in liquidity for NAE traded stocks. In addition it rules out the possibility that the changes in commonality of liquidity is not due to multimarket trading setting in NAE but due to indirect access of other big established markets such as NYSE and Euronext.

4.3. Firm size and commonality in liquidity.

21

Chordia, Roll and Subrahmanyam (2000) documents that smaller stocks are less sensitive to market-wide shocks in spreads in the US market. On the other hand,

Brockman Chung and Perignon global commonality in liquidity contrast sharply the results of the US market. They find that second-smallest firm size quintile have the highest sensitivity to commonality in liquidity. We participate in this debate by investigating the relation between firm size and commonality in liquidity in NYSE Arca

Europe. For each exchange, we divide NYSE Arca Europe traded stocks into 10 size- deciles based on the market value in, the year of the event, 2009.

5. Empirical Results

5.1. Descriptive statistics

Table 1 presents descriptive statistics for stocks traded on NYSE Arca Europe and respective statistics of the control sample. Our sample consists of 995 firms from 15 different stock exchanges. We report, for each sample exchange, the name, country, number of firms traded in NAE, the average of Amihud (2002) liquidity measure,

Proportional change in Amihud, daily turnover and proportionate change in daily turnover. All averages are calculated over the period of January 2005 till December 2012.

By construction, Amihud liquidity measure is negative with greater value indicating a greater liquidity. It is important to note that the direct comparison of level of Amihud liquidity measure is not possible because of the differences in currency units across countries. However, for the regression analysis, we have used the proportionate changes in Amihud to measure the co-movement in liquidity that makes the comparison possible.

The number of actively traded stocks varies from a low of 8 stocks from Prague stock exchange to a high of 387 stocks from London stock exchange. We combine

22 exchanges that have less than 20 stocks traded in NAE in the others category. It includes

18 firms from NASDAQ, 17 firms from Dublin stock exchange, 15 firms Budapest stock exchange and 8 firms from Prague stock exchange. To account for other factors that might affect stock market commonality, we create a control sample based on our variable of interest, proportionate change in Amihud liquidity measure. To make the sample of stocks traded and untraded in NAE more comparable, we match them based on exchange, and nearest average of proportionate change in Amihud (2002). The average proportionate change in Amihud is calculated based on the period before the launch of

NAE, from January 2005 to March 2009.

Insert Table 1

5.2. Commonality in liquidity

The trading of shares of the same firm in multiple markets has become common over time and expanded very fast over last few years in Europe. As more shares of the same firm are traded in multiple markets, it is increasingly important for traders, policymakers, market operators and issuers to understand the possible consequences of the Multi-trading decision. The main purpose of this paper is to help market participants to understand how changes in market design can affect liquidity co-variation, shed more light on diversification challenges that face stock market participants and enable them to bear systematic illiquidity risk with greater efficiency. Previous literature provides evidence of a premium for such a systematic liquidity risk (Amihud and Mendelson,

1986; Brennan and Subrahmanyam, 1996; Pastor and Stambaugh, 2003; Acharya and

Pedersen, 2005; Chen 2005; and Sadka 2006).

In this section we present the regression analysis for the commonality in liquidity for stocks traded in NAE. We extend the empirical model of Chordia, Roll and

23 subrahmanyam (2000) to investigate the commonality in liquidity in a multi-trading environment as discussed in section (4). To test our main hypothesis, daily proportional changes in an individual stock's Amihud liquidity measure are regressed in time series on proportional changes in the liquidity for all stocks traded in NYSE Arca Europe Market as well as all stock traded in the home market. Each column shows the cross-sectional averages of time series slope coefficients with t-statistics in parentheses for each sample exchange. Our main variable of interest is the interaction term between the NYSE Arca

Europe of the concurrent change in equal weighted average of Amihud liquidity measure and the Universal Trading Platform (UTP) dummy variable. A positive and significant coefficients would mean that NAE market liquidity exert a substantial influence on firm f‘s liquidity.

Our results show that the interaction term is positive and significant for the whole sample as well as exchange-by-exchange regressions except for Milano stock exchange.

These findings provide a strong support for the postulation that; changes in an individual firm‘s liquidity are significantly influenced by a common liquidity factor in NAE market.

These results confirm that commonality in liquidity does not have natural boundary at the home exchange frontier. It can spill over onto others exchanges due to sharing the same base of investors. Our results cannot be attributed to market-specific characteristics or macro-economic variables; because stocks traded in NAE are combined from 13 different countries that do not share the same market characteristics. However, NAE stocks only share the same investors‘ pool. The results are consistent with style and habitat-based explanations. It suggests that commonality in liquidity is driven by the correlated trading decisions of specific groups of traders (Kamara, Lou, and Sadka, 2008; Koch, Ruenzi,

24 and Starks, 2009; Corwin and Lipson, 2011; Karoyli, Lee and Van Dijk, 2012). In addition, the results are consistent with Gorton and Pennacchi‘s (1993) result that basket trading increases commonality.

On the other hand, the Home-UTP interaction term has a negative sign in all sample exchanges, except Madrid and Swiss stock exchange, but the cross section coefficients are not statistically significant. As a result, we could not conclude that the co- movement of NAE stocks with the home market is affected by multi-market trading environment. These results are of a great importance, it shows that the increase in co- movement between individual firms and NAE liquidity index is not offset by a decrease in the co-movement in the home market. Or in other words, it shows that we have a new source of systematic liquidity risk coming from the multi market trading of NAE stocks.

Table 2 also reveals that the coefficients of concurrent change in liquidity of the home market are positive for all exchanges, except others, and statistically significant for most of the investigated markets. These results are in agreement with Brockman, Chung and Perignon (2009), Karoyli, Lee and Van Dijk (2012) that commonality in liquidity is an international phenomenon and not limited to the US market. Also, we have found that the coefficients of concurrent change in liquidity of the NAE market are positive for all sample exchanges. The coefficients are statistically significant for 8 exchanges of our sample.

Insert Table 2

To investigate whether our results are directly associated with multi-market trading or market integration events, we create a control sample that has similar characteristics before the launch of NAE market and also listed in the same home

25 exchange. Table 3 shows the results for the control sample. We find that the coefficient

NAE interaction term is statistically insignificant for the whole sample and only two markets show a statistically positive co-movement with the NAE market. Although,

London stock exchange, 387 firms traded in NAE, is one of the two markets that show positive and significant coefficients, the coefficient of the whole sample is still insignificant. Our results are strong enough to rule out the possibility that the commonality in liquidity is driven by any market specific or macro-economic variables.

These results are in line with our main hypothesis that liquidity in commonality is mainly driven by correlated trading behavior among market participants.

Insert Table 3

To confirm our results, we follow Karoyli, Lee and Van Dijk, (2012) to use the turnover to test the commonality in liquidity. Firm by firm Proportionate Changes in daily turnover are regressed, in time series, on proportional changes in the liquidity for all stocks traded in NYSE Arca Europe Market as well as all stock traded in the home market. Table 4 reported the results for NAE original stocks, while Table 5 reports the control sample results. We find that only the original sample shows positive and significant coefficients for the NAE interaction term for most of the investigated exchanges. However we could not conclude that the whole sample co-move with the

NAE market, it is clear that whole sample is biased toward the negative and insignificant coefficient of London stock exchange. The coefficients of interaction term in the control sample show mixed results. Only one NYSE stocks shows a positive and statistically significant coefficient. Hence we can conclude that only stocks traded in NAE co-move with the aggregate liquidity co-movement of the whole market, but there is no evidence

26 that other stocks traded on the same home market co-move with aggregate liquidity co- movement of NAE market. These results confirm the findings of table 2 and 3. It shows that correlated trading behavior can increase the co-movement in liquidity.

Insert Table 4

Insert Table 5

5.3. Direct Vs. Indirect Access.

One of the key advantages our paper is the investigation of the effect of market integration on commonality in liquidity, through sharing the same trading platform and extending market access to traders from other well established markets. According to

NYSE-Euornext officials, connectivity will be a unique selling point to NYSE Arca

Europe. They aim to provide NYSE-Euronext existing customers, who already connected to the regulated market, with a new connection to NYSE Arca Europe using the same platform, Universal Trading Platform. NAE provides a direct access to NAE stocks to

NAE subscribed customers, in addition to indirect access to customers of NYSE-Euonext regulated markets. Such a unique structure of NAE provides us with a unique opportunity to investigate the effect of market design changes on the commonality in liquidity.

To test the difference between direct and indirect access of NAE traders, we use equation 5 that extend our original model, to account for both direct and indirect access in the same model. In investigating the indirect access we separate NYSE traders from

Euronext traders, as each one of the markets has its own pool traders. Our results in table

6, shows that the coefficients of Euronext interaction term are mostly negative. However, the negative sign of the coefficients imply that NAE stocks and Euronext are substitutes to each other; we could not conclude that there is a negative co-movement between NAE

27 and Euronext because the reported coefficients are not statistically significant. Table 7 reveals the same trend for NYSE. The coefficients of the NYSE interaction term are mixed and there is no evidence that NYSE Arca Europe stocks co-move with the NYSE aggregate liquidity co-movement. In both table 6 and 7, the coefficients of NAE interaction term are not affected by adding market with indirect access. Hence, our results reveal that neither extending the membership with other markets nor the using the same trading platform can affect the systematic liquidity risk. These results are in favor of the style and habitat-based explanation of commonality in liquidity. It suggests that commonality in liquidity is affected only if we have the same set of traders and is not driven by sharing the platform of extending trading membership with other markets.

Insert Table 6

Insert Table 7

5.4. Commonality in liquidity by firm size.

In Table 8, we examine the relationship between firm size and commonality in liquidity. We form portfolio deciles on an exchange-by-exchange basis (i.e., deciles are first formed separately within each exchange and then combined, pooling all firms in the same decile across exchanges). Prior literature yields contradicting explanations. One stream of literature suggests that, smaller stocks are less sensitive to market-wide shocks in liquidity. Chordia, Roll and Subrahmanyam (2000), documents that firm co-movement with the market wide liquidity increase with firm size. They attribute the increased co- movement of large firms to the greater prevalence of institutional herd trading in larger firms. Kamara, Lou and Sadka (2008), find that the sensitivity of the stock‘s liquidity to market liquidity has increased significantly for large-cap firms, but decreased

28 significantly for small-cap firms over time. They also show that this trend can be explained by the patterns of institutional ownership over their sample period. Another stream of literature argues that small-cap firms have the highest co-movement with the market wide liquidity shocks. Brockman Chung and Perignon (2009) contrast sharply the results of the US market. They find that second-smallest firm size quintile have the highest sensitivity to commonality in liquidity.

In table 8 we show that firm size play an important role in determining the commonality in liquidity. Only five deciles have shown positive and statistically significant coefficients for NAE interaction term. NAE interaction term has increased significantly with firm size. We find that the largest decile has the highest coefficient of

NAE interaction term (0.215) and the smallest decile has the lowest coefficient of (-

0.311). Our results are consistent with the finding of the US market, reported by Chordia,

Roll and Subrahmanyam (2000) and Kamara, Lou and Sadka (2008). It shows that co- movement is more prevalent to large firms than small firms. However, we do not have trader level data to investigate the reason behind this co-movement trend. We expect that these results are associated with the increased preferences of large stocks trading.

Insert Table 8

29

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32

Table 1 Descriptive statistics

Table 1 presents descriptive statistics for stocks traded in NYSE Arca Europe (NAE). For each sample exchange we report, the name, country, number of firms traded in NAE, the average of Amihud (2002) and proportionate change in Amihud liquidity measure, average daily turnover and proportionate change in turnover for both original and control sample over the period of January 2005 to December 2012. The heading (control) is added to differentiate the descriptive of the control sample from the original sample. We match the control sample based on the nearest Amihud liquidity measure for the period before the launch of NYSE Arca Europe on March 9 of 2009. The number of unique stocks traded in NYSE Arca Europe is collected from NYSE-Euronext website. The screening procedures applied in the selection of the sample are described in Section 4. Amihud Liquidity measure for individual stocks is the average of the daily Amihud (2002) measures—computed as the absolute stock return divided by local currency trading volume. The Amihud measure reported in table 1 is multiplied by -1,000. Turnover is the average of the ratio of daily volume over the number of shares outstanding for individual stocks. Turnover reported here is multiplied by 1,000. Following Karolyi lee and Dijk (2012) we add a constant and take the log of Amihud liquidity measure and daily turnover to reduce the effect of outliers as explained in equations (2 and 3). We exclude firms that have different primary exchange in NYSE Arca Europe instrument list and DataStream. Others category combines all exchanges that have less than 20 stocks traded in NAE. It includes 18 firms from NASDAQ, 17 firms from Dublin stock exchange, 15 firms Budapest stock exchange and 8 firms from Prague stock exchange. Finally we reported the statistics of the whole sample under All category.

Amihud # Stocks Amihud Amihud Daily Home DataStream Liquidity Amihud Daily Turnover Home Exchange traded in Liquidity Liquidity Turnover Country Code Measure Liquidity Turnover Turnover (Control) NAE Measure Measure (Control) (Control) Measure (Control) Copenhagen stock exchange Denmark CSE 23 -0.0168 -0.6607 1.318 1.615 5.7666 3.3144 0.4443 0.8221 Frankfurt stock exchange Germany FRA 100 -0.5239 -2.2572 3.0735 3.7324 0.2806 0.2647 0.689 9.4513 Helsinki stock exchange Finland HEL 25 -0.0026 -0.1217 1.6868 2.8789 7.5592 2.116 0.2068 1.2512 London stock exchange UK LON 387 -0.0011 -0.0215 2.6899 7.2619 4.7019 3.5338 1.1827 7.1505 Madrid stock exchange Spain MAD 60 -0.0262 -1.1082 1.7622 2.1207 6.138 3.0927 0.2898 1.1121 Milano stock exchange Italy MIL 110 -0.0691 -0.5303 1.8019 2.3566 4.6621 3.6877 0.6234 0.8269 New York stock exchange USA NYS 90 -0.0001 -0.0009 2.1379 2.1696 10.1026 13.9612 0.0695 0.1086 Oslo stock exchange Norway OSL 26 -0.0038 -0.1096 1.2973 1.8791 11.1443 5.0592 0.2299 1.0112 Stockholm stock exchange Sweden OME 39 -0.0002 -0.0386 1.1593 1.3588 8.1812 5.6896 0.1663 0.4703 Swiss stock exchange Switzerland SWX 51 -0.0019 -0.0495 1.3754 1.8513 6.7673 2.6771 0.1641 0.5608 Vienna stock exchange Austria WBO 26 -0.0252 -1.4198 2.4868 3.624 2.8488 4.3652 0.3512 1.6578 Others -- -- 58 -0.0697 -0.6498 7.6873 9.4904 7.1291 10.1785 1.4219 2.6008 All -- -- 995 -0.0675 -0.4398 2.5332 5.0775 5.453 4.7546 0.7329 3.8622

33

Table 2 Commonality in Liquidity (Amihud) in NYSE Arca Europe

Daily proportional changes in an individual stock's Amihud liquidity measure are regressed in time series on proportional changes in the liquidity for all stocks traded in NYSE Arca Europe Market as well as all stock traded in the home market as explained in equation (4). The change in Amihud liquidity measure of stock f is regressed on concurrent, lag, and lead changes in the NYSE Arca Europe market portfolio (where the market excludes stock f and all stocks from the home market) and the concurrent, lag, and lead changes in the Home market portfolio (where the market excludes stock f). ‗Concurrent', ‗Lag', and ‗Lead' refer, respectively, to the same, previous, and next trading day observations of market liquidity. Each column shows the cross-sectional averages of time series slope coefficients with t-statistics in parentheses for each sample exchange. The DataStream exchange code is presented in table 1. is the equal-weighted average daily return of the home market, , is the stock f return volatility, measured by the change in squared return, of firm stock f in day t. is a dummy variable for the date in which the NYSE Arca Europe‘s firms start trading using the UTP, that takes the value 0 for all dates before UTP and 1 afterwards. is the interaction terms between the UTP dummy variable and the concurrent proportional change in liquidity of NYSE Arca Europe market index. is the interaction terms between the UTP dummy variable and the concurrent proportional change in liquidity of Home market index.

Variable CSE FRA HEL LON MAD MIL NYS OME OSL SWX WBO Others ALL Intercept 1.370*** 2.431*** 1.828*** 3.404*** 1.709*** 1.559*** 2.127*** 0.891*** 1.464*** 1.154*** 2.473*** 9.526** 2.813*** (7.78) (3.22) (15.84) (5.67) (14.52) (5.82) (3.26) (13.39) (4.55) (14.65) (5.01) (2.65) (8.81)

-0.024** -0.054* -0.014 -0.010** -0.013 -0.008 -0.206* -0.007 0.001 0.001 -0.022 -0.115 -0.038***

(-2.22) (-1.93) (-1.51) (-2.44) (-1.35) (-0.48) (-1.86) (-0.70) (0.08) (0.05) (-0.93) (-0.94) (-2.98)

0.006 0.287*** 0.044** 0.005 0.049*** 0.088 0.402*** 0.079*** 0.013** 0.093*** 0.224 -0.391* 0.076***

(0.52) (2.97) (2.54) (0.50) (3.49) (0.87) (7.42) (7.68) (2.06) (4.24) (1.18) (-1.91) (3.70)

0.003 0.003 -0.019*** -0.000 -0.028*** -0.008 -0.168*** -0.011 0.018 -0.036*** -0.010 0.130 -0.014

(0.31) (0.11) (-3.28) (-0.04) (-3.05) (-0.31) (-3.55) (-1.28) (1.18) (-3.45) (-0.41) (0.78) (-1.27)

-0.080*** -0.127*** -0.103*** 0.065 -0.044* -0.072*** -0.054 -0.053*** -0.048* -0.050*** -0.118** 0.213 -0.006

(-5.03) (-4.58) (-8.70) (0.39) (-1.97) (-3.64) (-1.18) (-6.43) (-1.94) (-4.10) (-2.66) (0.62) (-0.09)

0.086*** 0.098*** 0.132*** 0.154*** 0.128*** 0.217 0.018 0.085*** 0.054** 0.151*** 0.056 0.307 0.139***

(3.53) (2.86) (6.39) (2.85) (6.29) (1.33) (1.25) (8.28) (2.15) (9.33) (0.89) (1.09) (4.37)

-0.052** -0.073* -0.063*** -0.053 -0.035* -0.098** -0.023 -0.038*** -0.048*** -0.050*** -0.095** -0.050 -0.057

(-2.43) (-1.74) (-3.94) (-0.61) (-1.90) (-2.00) (-0.72) (-4.83) (-4.26) (-2.90) (-2.20) (-0.15) (-1.45)

5.718 -1.593 2.689 25.821* 5.009* 8.591 -0.240 2.254 1.809 -4.479 -35.393* -39.317 8.315

(1.05) (-0.26) (0.75) (1.75) (1.75) (0.92) (-0.09) (1.41) (0.64) (-1.27) (-1.92) (-1.14) (1.34)

-15.657** 4.729 -11.564*** 17.638 -14.218*** -21.570** 6.181 -11.625*** -13.901*** -8.920 15.924 -13.604 2.451

(-2.55) (0.52) (-3.00) (0.78) (-3.65) (-2.04) (1.12) (-4.33) (-4.59) (-1.42) (0.84) (-0.23) (0.26)

34

-4.639 -1.498 0.292 -17.225 -2.520 -1.066 2.325 -3.576* 1.308 -5.931 -4.954 -28.377 -9.030

(-1.34) (-0.18) (0.09) (-1.49) (-0.81) (-0.29) (1.06) (-1.83) (0.33) (-1.48) (-0.49) (-0.40) (-1.53)

490.541*** 1095.80*** 568.643*** 983.501*** 951.100*** 828.779*** 858.628*** 585.606*** 302.647*** 799.447*** 1010.24*** 1611.47*** 932.080*** (7.01) (4.99) (10.18) (11.78) (11.21) (8.82) (10.51) (11.26) (4.39) (8.02) (3.76) (2.87) (17.95)

0.051 0.245 -0.682*** -1.548*** -0.396*** 0.016 0.062 -0.175* -0.528 -0.405*** -0.580 -7.863** -1.072*** (0.33) (0.61) (-6.86) (-3.44) (-3.16) (0.17) (0.10) (-1.73) (-1.28) (-4.32) (-0.81) (-2.05) (-3.85)

0.171*** 0.160*** 0.273*** 0.174*** 0.109*** -0.001 0.242* 0.254*** 0.248*** 0.161*** 0.404** 2.570* 0.291***

(3.45) (3.14) (8.02) (2.80) (2.83) (-0.00) (1.68) (7.89) (4.93) (5.00) (2.28) (1.73) (3.44)

-0.008 -0.063 -0.022 -0.006 0.056** -0.023 -0.144 -0.023 -0.003 0.037 -0.187 -0.075 -0.030

(-0.49) (-0.75) (-1.02) (-0.65) (2.45) (-0.22) (-0.47) (-1.32) (-0.21) (1.45) (-0.96) (-0.43) (-0.90)

Adjusted R2 0.054 0.022 0.046 0.038 0.045 0.059 0.046 0.084 0.054 0.065 0.033 0.032 0.044

35

Table 3 Commonality in Liquidity (Amihud) in the control sample

This table measures changes in commonality in liquidity for the control sample. The control sample is matched based on the nearest average of Amihud liquidity measure for the period before the launch of NYSE Arca Europe on March 9 of 2009. Daily proportional changes in an individual stock's Amihud liquidity measure are regressed in time series on proportional changes in the liquidity for all stocks traded in NYSE Arca Europe Market as well as all stock traded in the home market as explained in equation (4). For each exchange, the change in Amihud liquidity measure of stock f is regressed on concurrent, lag, and lead changes in the NYSE Arca Europe market portfolio (where the market excludes stock f and all stocks from the home market) and the concurrent, lag, and lead changes in the Home market portfolio (where the market excludes stock f). ‗Concurrent', ‗Lag', and ‗Lead' refer, respectively, to the same, previous, and next trading day observations of market liquidity. Each column shows the cross-sectional averages of time series slope coefficients with t-statistics in parentheses. The DataStream exchange code is presented in table 1. is the equal-weighted average daily return of the home market, , is the stock f return volatility, measured by the change in squared return, of firm stock f in day t. is a dummy variable for the date in which the NYSE Arca Europe‘s firms start trading using the UTP, that takes the value 0 for all dates before UTP and 1 afterwards. is the interaction terms between the UTP dummy variable and the concurrent proportional change in liquidity of NYSE Arca Europe market index. is the interaction terms between the UTP dummy variable and the concurrent proportional change in liquidity of Home market index.

Variable CSE FRA HEL LON MAD MIL NYS OME OSL SWX WBO Others ALL Intercept 2.458** 3.157*** 2.606*** 5.343*** 2.074*** 1.201*** 0.928*** 1.252*** 2.366** 1.447** 0.474 3.554 5.482*** (2.79) (3.94) (4.24) (6.05) (8.68) (2.91) (3.50) (6.69) (2.69) (2.09) (0.44) (0.60) (6.05)

-0.042 -0.088 0.000 0.012 0.069 0.046 0.009 -0.010 -0.005 0.630 0.055 0.019 0.065 (-1.46) (-1.14) (0.01) (0.30) (0.81) (1.24) (0.09) (-1.01) (-0.13) (1.07) (1.24) (0.28) (1.47)

-0.009 0.203** 0.076 -0.011 -0.001 0.459** 0.602*** 0.092*** -0.023 0.167 0.515* 0.160** 0.123 (-0.22) (2.02) (1.18) (-0.11) (-0.04) (2.39) (11.35) (5.62) (-0.68) (1.15) (1.93) (2.33) (1.12)

0.011 -0.011 -0.045** 0.150* -0.016 0.011 -0.098*** -0.043*** -0.066 -0.145 0.306 0.001 -0.035 (0.34) (-0.20) (-2.26) (1.70) (-0.64) (0.14) (-2.84) (-4.09) (-1.55) (-1.02) (1.46) (0.02) (-0.44)

-0.014 -0.111* 0.101 0.106 -0.070 -0.061 0.020 -0.034** -0.046 -0.098 -0.118 0.842 -0.080 (-0.13) (-1.71) (1.08) (0.20) (-1.49) (-1.05) (0.99) (-2.19) (-1.02) (-1.48) (-0.99) (0.59) (-0.34)

-0.049 -0.061 0.212** -0.341 0.105** 0.059 -0.009 0.084*** 0.051 -0.028 0.066 3.851 -0.276 (-0.56) (-0.82) (2.34) (-1.24) (2.07) (1.49) (-0.21) (4.12) (1.13) (-0.31) (0.28) (1.18) (-1.11)

-0.144** 0.126 -0.083* -0.063 -0.041 0.051 0.021 -0.024* 0.209 -0.068** 0.108 0.696 0.029 (-2.53) (1.54) (-2.00) (-0.29) (-0.89) (1.06) (1.02) (-1.93) (1.49) (-2.24) (0.78) (0.53) (0.16)

-32.120 10.416 -16.922 -43.892 -36.422 5.203 -2.943 10.353** 34.425 -10.135 27.123 66.472 15.414 (-1.16) (0.69) (-0.63) (-0.64) (-1.11) (0.27) (-1.21) (2.05) (1.58) (-0.91) (0.64) (0.35) (0.45)

-14.894 -4.405 -24.546 -261.47 -3.481 -36.469*** 1.797 -11.437** -20.282* -51.223 -4.615 82.840 -158.64* (-0.66) (-0.31) (-1.53) (-1.19) (-0.29) (-2.75) (0.51) (-2.16) (-1.74) (-1.32) (-0.21) (0.77) (-1.83) 36

-4.038 -2.484 13.907 -119.13 14.287 24.545 -0.238 -4.317 6.743 -8.751 34.071* -82.762 -33.289 (-0.32) (-0.15) (1.40) (-1.37) (0.92) (0.89) (-0.10) (-1.20) (0.40) (-1.02) (1.78) (-0.70) (-0.90) 1181.56** 774.396** 988.726*** 873.838*** 172.426 721.891*** 640.769*** 529.404*** 362.885*** 216.231*** 3131.75*** 13256.3 1704.68*** * (2.17) (7.73) (5.69) (0.79) (4.00) (8.90) (6.72) (5.85) (4.63) (3.87) (1.06) (4.19) (3.09) -0.981 3.111 -0.160 -1.813 0.750 1.075** -0.430* -0.245 -1.023 -0.072 1.619 2.937 -0.489 (-1.29) (1.49) (-0.24) (-0.87) (0.99) (2.43) (-1.94) (-1.55) (-1.33) (-0.22) (1.68) (0.64) (-0.46)

0.133 -0.345 0.003 0.857* 0.055 0.003 0.063 0.182*** 0.418 0.000 0.569 -2.685 0.424 (1.45) (-1.23) (0.04) (1.65) (0.39) (0.04) (0.92) (3.85) (1.63) (0.00) (1.32) (-0.74) (1.23)

0.225 -0.154 0.000 0.374 0.076 -0.404** 0.060 -0.069*** 0.070* 0.050 -0.522** 0.025 0.079 (1.12) (-0.50) (0.00) (1.00) (0.68) (-2.05) (0.91) (-3.36) (1.90) (0.66) (-2.27) (0.20) (0.54) Adjusted R2 0.040 0.073 0.024 0.078 0.055 0.034 0.047 0.046 0.049 0.104 0.148 0.032 0.068

37

Table 4 Commonality in Liquidity (Turnover) in NYSE Arca Europe

Daily proportional changes in an individual stock's Turnover liquidity measure are regressed in time series on proportional changes in the liquidity for all stocks traded in NYSE Arca Europe Market as well as all stock traded in the home market as explained in equation (4). For each exchange, the change in Turnover liquidity measure of stock f is regressed on concurrent, lag, and lead changes in the NYSE Arca Europe market portfolio (where the market excludes stock f and all stocks from the home market) and the concurrent, lag, and lead changes in the Home market portfolio (where the market excludes stock f). ‗Concurrent', ‗Lag', and ‗Lead' refer, respectively, to the same, previous, and next trading day observations of market liquidity. Each column shows the cross-sectional averages of time series slope coefficients with t-statistics in parentheses. The DataStream exchange code is presented in table 1. is the equal-weighted average daily return of the home market, , is the stock f return volatility, measured by the change in squared return, of firm stock f in day t. is a dummy variable for the date in which the NYSE Arca Europe‘s firms start trading using the UTP, that takes the value 0 for all dates before UTP and 1 afterwards. is the interaction terms between the UTP dummy variable and the concurrent proportional change in liquidity of NYSE Arca Europe market index. is the interaction terms between the UTP dummy variable and the concurrent proportional change in liquidity of Home market index.

Variable CSE FRA HEL LON MAD MIL NYS OME OSL SWX WBO Others ALL

Intercept 0.561 0.274** 0.130*** 2.278*** 0.162*** 1.635 0.008** 0.081*** 0.270* 0.082*** 0.001 1.479*** 1.217*** (1.32) (2.61) (4.19) (3.29) (4.35) (1.04) (2.36) (4.37) (2.02) (7.30) (0.00) (3.39) (3.74)

-0.022 -0.003 -0.000 -0.010 -0.004 -0.101 -0.051*** -0.015*** 0.008 -0.020** 0.027 -0.000 -0.022**

(-1.24) (-0.77) (-0.11) (-0.86) (-0.27) (-1.38) (-18.44) (-8.76) (0.87) (-2.25) (0.80) (-0.03) (-2.26)

0.001 0.201*** 0.013** -0.011 0.212*** -0.217 0.293*** 0.041*** 0.004 0.149*** 0.394 -0.039 0.050

(0.02) (15.15) (2.29) (-0.44) (8.28) (-0.38) (45.74) (10.77) (0.68) (10.32) (1.41) (-1.09) (0.76)

-0.009*** -0.003 -0.001 -0.005 0.001 -0.192 -0.077*** -0.009*** 0.003 -0.026*** -0.026 0.026 -0.032*

(-4.91) (-0.95) (-1.13) (-0.58) (0.08) (-1.18) (-19.36) (-5.97) (0.77) (-4.38) (-1.60) (0.66) (-1.71)

0.154 -0.005 0.003 -0.319 -0.020*** -0.157 -0.005*** 0.003 0.044 -0.015** -0.015 -0.191** -0.151*

(1.14) (-0.22) (0.18) (-1.60) (-2.92) (-1.06) (-4.06) (0.79) (0.97) (-2.44) (-0.71) (-2.28) (-1.89)

0.106*** 0.046** 0.107*** 0.278** 0.076*** -0.014 0.024*** 0.105*** 0.050 0.068*** 0.082*** 0.301 0.150***

(9.32) (2.46) (13.08) (2.55) (6.81) (-0.16) (10.00) (17.65) (0.80) (8.66) (5.64) (1.40) (3.33)

-0.135 -0.013 -0.004 -0.075 -0.041*** -0.191 -0.006*** 0.003 0.042 -0.038*** -0.048** -0.019 -0.061

(-1.32) (-0.51) (-0.23) (-0.75) (-5.71) (-1.13) (-5.43) (0.42) (0.66) (-7.39) (-2.27) (-0.22) (-1.40)

-22.666 1.812 -0.656 -0.321 0.931* 27.652 -0.761*** -0.142 -2.777 -0.004 1.929* 12.962* 3.285

(-1.04) (0.72) (-0.75) (-0.03) (1.93) (0.96) (-11.56) (-0.42) (-0.91) (-0.01) (1.74) (1.77) (0.63)

38

7.467 -8.690*** -1.671** 24.025 -1.749** 11.205 -0.752*** -2.802*** 1.133 -3.016*** -0.041 -1.616 9.385

(0.72) (-3.36) (-2.64) (1.57) (-2.32) (1.45) (-9.11) (-4.64) (0.70) (-4.23) (-0.01) (-0.15) (1.54)

10.603 -3.290** -0.460 20.333 1.401* -0.761 -0.207*** 0.811** 4.307 0.729 -1.448 -15.117 7.178

(1.30) (-2.46) (-0.52) (1.12) (1.76) (-0.69) (-3.66) (2.03) (1.25) (1.53) (-0.85) (-1.63) (1.01)

140.783*** 235.122*** 132.548*** 245.287*** 139.962*** 234.868*** 76.046*** 113.044*** 57.348*** 143.518*** 96.856*** 240.933*** 196.287*** (4.01) (11.54) (7.36) (5.52) (10.19) (6.33) (11.29) (10.47) (6.55) (5.64) (3.12) (3.89) (10.64)

-0.413 0.505*** -0.085*** -1.357** -0.058 -0.738 -0.025*** -0.017 -0.292 -0.080*** 0.060 -0.629 -0.622**

(-1.19) (3.25) (-4.10) (-2.31) (-1.67) (-0.76) (-6.12) (-1.16) (-1.30) (-4.78) (0.31) (-1.31) (-2.43)

0.127*** 0.058 0.082*** -0.089 0.083*** 0.074** 0.020*** 0.056*** 0.114 0.110*** 0.150*** 0.895 0.013

(5.82) (1.28) (4.59) (-0.32) (6.66) (2.41) (5.90) (6.58) (1.55) (10.82) (5.66) (1.28) (0.21)

0.006 -0.195*** -0.007 0.018 -0.120*** 0.270 0.116*** -0.029*** 0.005 -0.032** -0.262 0.026 0.052

(0.28) (-14.59) (-1.32) (0.71) (-5.25) (0.52) (10.21) (-5.68) (0.79) (-2.01) (-0.94) (0.59) (0.45) Adjusted R2 0.100 0.057 0.094 0.051 0.072 0.078 0.146 0.099 0.076 0.119 0.067 0.070 0.074

39

Table 5 Commonality in Liquidity (Turnover) in the control sample

This table measures changes in commonality in liquidity for the control sample. The control sample is matched based on the nearest average of Turnover liquidity measure for the period before the launch of NYSE Arca Europe on March 9 of 2009. Daily proportional changes in an individual stock's Turnover liquidity measure are regressed in time series on proportional changes in the liquidity for all stocks traded in NYSE Arca Europe Market as well as all stock traded in the home market as explained in equation (4). For each exchange, the change in Turnover liquidity measure of stock f is regressed on concurrent, lag, and lead changes in the NYSE Arca Europe market portfolio (where the market excludes stock f and all stocks from the home market) and the concurrent, lag, and lead changes in the Home market portfolio (where the market excludes stock f). ‗Concurrent', ‗Lag', and ‗Lead' refer, respectively, to the same, previous, and next trading day observations of market liquidity. Each column shows the cross-sectional averages of time series slope coefficients with t- statistics in parentheses. The DataStream exchange code is presented in table 1. is the equal-weighted average daily return of the home market, , is the stock f return volatility, measured by the change in squared return, of firm stock f in day t. is a dummy variable for the date in which the NYSE Arca Europe‘s firms start trading using the UTP, that takes the value 0 for all dates before UTP and 1 afterwards. is the interaction terms between the UTP dummy variable and the concurrent proportional change in liquidity of NYSE Arca Europe market index. is the interaction terms between the UTP dummy variable and the concurrent proportional change in liquidity of Home market index.

Variable CSE FRA HEL LON MAD MIL NYS OME OSL SWX WBO Others ALL Intercept 0.342*** 0.736*** 0.675*** 7.005*** 0.236* 0.312*** 0.031*** 0.322*** 1.286** 0.365** -0.869 3.749*** 3.062*** (3.63) (5.41) (3.40) (5.32) (1.78) (4.49) (3.49) (7.22) (2.70) (2.16) (-0.52) (4.36) (5.91)

0.022 -0.024 0.025 -0.005 0.115 -0.018 -0.017 -0.012* -0.018*** -0.050 0.390 -0.064*** 0.004

(1.52) (-0.63) (1.40) (-0.08) (0.77) (-0.90) (-1.39) (-1.71) (-4.04) (-1.41) (1.22) (-2.70) (0.17)

0.074*** 0.111 0.127* -0.005 0.383*** 0.450*** 0.272*** 0.069*** 0.026 0.185** 2.904 0.141*** 0.216***

(3.06) (1.52) (1.81) (-0.08) (3.80) (10.66) (20.51) (5.49) (1.39) (2.22) (1.23) (4.39) (3.03)

0.179 -0.039 -0.005 -0.002 0.042 -0.039** -0.085*** -0.008 0.004 -0.037 -0.086 -0.004 -0.016

(1.11) (-1.00) (-0.42) (-0.02) (0.56) (-2.13) (-13.91) (-1.03) (0.26) (-1.23) (-0.55) (-0.19) (-0.37)

-0.039 0.010 0.072 0.175 0.071 -0.014 0.000 -0.003 0.000 0.086 -0.258 -0.474* 0.045

(-0.98) (0.15) (0.91) (0.22) (0.99) (-1.17) (0.17) (-0.22) (0.01) (0.68) (-1.40) (-1.86) (0.15)

0.096*** 0.190 0.238*** 1.797 0.043 0.061*** 0.036*** 0.109*** -0.178 0.064** -0.230 0.847 0.767*

(3.55) (1.21) (4.27) (1.61) (1.02) (3.46) (3.23) (8.98) (-1.02) (2.46) (-1.46) (1.27) (1.79)

-0.074 -0.067*** 0.061 0.881 -0.037 -0.007 -0.005** 0.006 -0.062** 0.023 -0.234* -0.193 0.310

(-0.95) (-2.74) (0.74) (1.07) (-0.81) (-0.33) (-2.15) (0.66) (-2.21) (0.51) (-1.88) (-0.75) (0.98)

-12.315 7.841** -3.858 7.585 10.634 2.620 -0.934*** 0.156 -19.638 5.241 9.972 -31.796** 2.616

(-0.91) (2.08) (-0.65) (0.08) (1.10) (1.56) (-7.48) (0.07) (-1.33) (1.33) (1.42) (-2.22) (0.07)

6.172 -11.156* 4.103 27.528 1.778 5.835*** -0.307** 3.082* 25.610 -11.249* 36.919 39.447* 13.633

(0.72) (-1.76) (0.55) (0.32) (0.33) (2.98) (-2.36) (1.85) (1.62) (-1.74) (1.43) (1.70) (0.42) 22.467 -4.010 -1.831 -98.347 4.247 -2.207 -0.120 0.065 9.368 5.358 -12.233 36.863 -35.553 40

(0.97) (-1.62) (-0.33) (-0.52) (0.59) (-1.26) (-0.96) (0.05) (1.14) (0.79) (-1.33) (1.62) (-0.49)

224.051** 186.917*** 476.480*** 720.933*** 281.148*** 235.909*** 52.468*** 144.015*** 109.617*** 617.454*** 244.640* 422.449*** 429.774*** (2.83) (6.73) (2.83) (6.43) (6.09) (10.61) (11.75) (7.29) (4.98) (5.31) (1.95) (3.25) (9.51) -0.418 0.469** -0.164 -0.467 0.839* 0.230*** -0.008 0.061 0.050 0.089 2.164 -1.940*** -0.097 (-0.62) (2.48) (-0.77) (-0.28) (2.00) (2.78) (-1.17) (1.05) (0.12) (0.70) (1.32) (-2.80) (-0.15)

0.348 -0.064 -0.095 0.049 -0.255 -0.097 0.091** -0.046*** -0.023 -0.049 -2.949 0.086 -0.084

(1.04) (-0.81) (-1.36) (0.49) (-1.33) (-1.50) (2.56) (-3.56) (-1.48) (-0.48) (-1.26) (0.79) (-1.07)

-0.105 -0.107 0.430 1.533 0.058 0.018 0.000 0.024 0.428 0.049 0.460* 1.780 0.708

(-0.71) (-0.64) (1.17) (0.66) (0.40) (0.52) (0.01) (0.71) (1.26) (0.50) (1.73) (1.31) (0.80) Adjusted R2 0.069 0.083 0.044 0.041 0.058 0.057 0.107 0.070 0.052 0.099 0.064 0.072 0.062

41

Table 6 Direct Vs. Indirect Access (Euronext) to NYSE Arca Europe

This table examines the effect having direct versus indirect access to NYSE Arca Europe. Euronext members can extend their membership to trade NYSE Arca Europe stocks through the same trading platform-Universal Trading System. Daily proportional changes in an individual stock's Amihud liquidity measure are regressed in time series on proportional changes in the liquidity for all stocks traded in NYSE Arca Europe Market, all stocks traded in the home market as well as all stocks traded in Euronext, as explained in equation (5). For each exchange, the change in Amihud liquidity measure of stock f is regressed on concurrent, lag, and lead changes in the NYSE Arca Europe market portfolio (where the market excludes stock f and all stocks from the home market) and the concurrent, lag, and lead changes in the Home market portfolio (where the market excludes stock f). ‗Concurrent', ‗Lag', and ‗Lead' refer, respectively, to the same, previous, and next trading day observations of market liquidity. Each column shows the cross-sectional averages of time series slope coefficients with t-statistics in parentheses. The DataStream exchange code is presented in table 1. is the equal-weighted average daily return of the home market, , is the stock f return volatility, measured by the change in squared return, of firm stock f in day t. is a dummy variable for the date in which the NYSE Arca Europe‘s firms start trading using the UTP, that takes the value 0 for all dates before UTP and 1 afterwards. is the interaction terms between the UTP dummy variable and the concurrent proportional change in liquidity of NYSE Arca Europe market index. is the interaction terms between the UTP dummy variable and the concurrent proportional change in liquidity of Home market index.

Variable CSE FRA HEL LON MAD MIL NYS OME OSL SWX WBO Others ALL Intercept 1.395*** 2.333*** 1.798*** 2.987*** 1.697*** 1.538*** 2.153*** 0.867*** 1.058*** 1.148*** 2.532*** 9.795** 2.643*** (7.31) (2.95) (15.21) (6.76) (14.42) (5.16) (3.28) (12.69) (8.48) (14.43) (5.07) (2.63) (9.34)

-0.024** -0.060** -0.013 -0.011** -0.012 -0.007 -0.207* -0.006 0.002 0.002 -0.021 -0.110 -0.038***

(-2.24) (-2.00) (-1.47) (-2.36) (-1.25) (-0.43) (-1.88) (-0.61) (0.26) (0.17) (-0.91) (-0.90) (-2.98)

0.006 0.272*** 0.044** 0.006 0.049*** 0.087 0.398*** 0.077*** 0.012 0.092*** 0.225 -0.386* 0.075***

(0.50) (2.76) (2.52) (0.62) (3.46) (0.87) (7.25) (7.50) (1.70) (4.20) (1.18) (-1.88) (3.63)

0.003 0.005 -0.019*** 0.001 -0.027*** -0.009 -0.170*** -0.011 0.020 -0.035*** -0.008 0.131 -0.013

(0.30) (0.16) (-3.24) (0.12) (-2.99) (-0.32) (-3.54) (-1.26) (1.22) (-3.41) (-0.33) (0.79) (-1.23)

-0.004 -0.009 -0.003 0.023 -0.003* -0.005** -0.002 -0.002** -0.005*** -0.002 -0.009*** 0.012 0.007

(-1.44) (-1.61) (-1.60) (1.42) (-1.81) (-2.17) (-1.25) (-2.28) (-3.04) (-1.18) (-4.02) (0.51) (1.10)

0.000 0.069** 0.012** 0.067 0.009 0.014 0.003 0.010*** 0.101 0.006 -0.013 -0.139* 0.032

(0.04) (2.29) (2.42) (1.15) (1.42) (1.54) (0.41) (2.77) (1.01) (1.67) (-1.01) (-1.74) (1.35)

-0.006* -0.012* -0.003** 0.011 -0.005** -0.005* -0.002 -0.004** -0.004 -0.006* -0.008 0.020 0.002

(-1.76) (-1.86) (-2.42) (0.90) (-2.63) (-1.90) (-0.62) (-2.26) (-1.39) (-1.75) (-1.64) (0.59) (0.42)

-0.076*** -0.126*** -0.103*** 0.064 -0.044* -0.071*** -0.054 -0.052*** -0.049* -0.050*** -0.107** 0.235 -0.004

(-5.10) (-4.49) (-8.64) (0.37) (-1.98) (-3.81) (-1.16) (-6.17) (-1.93) (-4.07) (-2.57) (0.69) (-0.06)

0.087*** 0.093*** 0.132*** 0.154*** 0.129*** 0.217 0.018 0.085*** 0.048 0.151*** 0.061 0.361 0.141***

(3.62) (2.79) (6.39) (2.96) (6.25) (1.32) (1.29) (8.25) (1.58) (9.38) (0.96) (1.25) (4.48)

-0.050** -0.066 -0.062*** -0.050 -0.033* -0.096* -0.026 -0.036*** -0.046*** -0.049*** -0.090* -0.052 -0.054

42

(-2.25) (-1.63) (-3.81) (-0.55) (-1.79) (-1.97) (-0.79) (-4.43) (-4.12) (-2.81) (-2.00) (-0.15) (-1.35)

6.075 -0.573 3.097 28.690** 5.087* 8.198 -0.296 2.316 1.615 -4.452 -35.349* -37.811 9.590

(1.10) (-0.09) (0.85) (1.99) (1.76) (0.88) (-0.11) (1.44) (0.59) (-1.25) (-1.91) (-1.09) (1.58)

-16.136** 4.347 -11.941*** 19.150 -14.492*** -21.791** 6.330 -12.202*** -14.282*** -9.209 13.994 -15.660 2.750

(-2.66) (0.47) (-3.10) (0.83) (-3.69) (-2.08) (1.13) (-4.58) (-4.90) (-1.47) (0.78) (-0.26) (0.28)

-4.188 0.947 0.804 -17.764 -2.018 -0.949 2.337 -2.964 1.096 -5.379 -5.447 -30.318 -8.990

(-1.21) (0.12) (0.24) (-1.57) (-0.65) (-0.26) (1.03) (-1.51) (0.29) (-1.35) (-0.54) (-0.42) (-1.54)

489.910*** 1101.39*** 568.772*** 976.195*** 950.231*** 828.051*** 857.367*** 584.157*** 301.407*** 798.898*** 1002.94*** 1607.24*** 929.003*** (7.02) (4.97) (10.15) (11.79) (11.22) (8.81) (10.53) (11.25) (4.43) (8.00) (3.80) (2.87) (17.95)

0.043 0.401 -0.639*** -1.316*** -0.361*** 0.053 0.104 -0.157 -0.119 -0.388*** -0.726 -8.104** -0.958*** (0.30) (0.99) (-5.75) (-3.19) (-2.99) (0.55) (0.15) (-1.50) (-1.11) (-3.92) (-0.88) (-2.04) (-3.48)

0.008 -0.042** -0.008 -0.055 -0.003 -0.005 -0.004 -0.001 -0.091 -0.002 0.054 0.058 -0.025

(0.69) (-2.05) (-1.11) (-0.90) (-0.55) (-0.60) (-0.46) (-0.30) (-0.90) (-0.35) (1.09) (0.70) (-1.02)

0.162*** 0.155*** 0.269*** 0.174*** 0.103*** -0.006 0.245* 0.248*** 0.244*** 0.157*** 0.378** 2.545* 0.287***

(3.17) (2.96) (8.08) (2.98) (2.68) (-0.04) (1.68) (7.73) (4.68) (4.93) (2.35) (1.70) (3.38)

-0.008 -0.057 -0.023 -0.007 0.054** -0.024 -0.159 -0.022 -0.002 0.036 -0.190 -0.066 -0.031

(-0.46) (-0.67) (-1.03) (-0.74) (2.35) (-0.23) (-0.51) (-1.23) (-0.17) (1.41) (-0.97) (-0.38) (-0.90)

Adjusted R2 0.055 0.024 0.048 0.043 0.046 0.062 0.047 0.087 0.058 0.067 0.034 0.038 0.047

43

Table 7 Direct Vs. Indirect (NYSE) Access to NYSE Arca Europe

This table examines the effect having direct versus indirect access to NYSE Arca Europe. NYSE members can extend their membership to trade NYSE Arca Europe stocks through the same trading platform-Universal Trading System. Daily proportional changes in an individual stock's Amihud liquidity measure are regressed in time series on proportional changes in the liquidity for all stocks traded in NYSE Arca Europe Market, all stocks traded in the home market as well as all stocks traded in Euronext, as explained in equation (5). For each exchange, the change in Amihud liquidity measure of stock f is regressed on concurrent, lag, and lead changes in the NYSE Arca Europe market portfolio (where the market excludes stock f and all stocks from the home market) and the concurrent, lag, and lead changes in the Home market portfolio (where the market excludes stock f). ‗Concurrent', ‗Lag', and ‗Lead' refer, respectively, to the same, previous, and next trading day observations of market liquidity. Each column shows the cross-sectional averages of time series slope coefficients with t-statistics in parentheses. The DataStream exchange code is presented in table 1. is the equal-weighted average daily return of the home market, , is the stock f return volatility, measured by the change in squared return, of firm stock f in day t. is a dummy variable for the date in which the NYSE Arca Europe‘s firms start trading using the UTP, that takes the value 0 for all dates before UTP and 1 afterwards. is the interaction terms between the UTP dummy variable and the concurrent proportional change in liquidity of NYSE Arca Europe market index. is the interaction terms between the UTP dummy variable and the concurrent proportional change in liquidity of Home market index.

Variable CSE FRA HEL LON MAD MIL NYS OME OSL SWX WBO Others ALL Intercept 1.118*** 2.336*** 1.643*** 3.368*** 1.456*** 1.375*** 2.252*** 0.760*** 1.331*** 1.053*** 2.028*** 10.244** 2.766*** (5.03) (2.82) (9.62) (3.91) (11.55) (4.22) (3.11) (10.00) (3.31) (10.44) (4.09) (2.43) (6.60)

-0.021* -0.032 -0.013 -0.010** -0.006 -0.023 -0.262* -0.002 0.001 0.003 -0.023 -0.103 -0.041**

(-1.75) (-1.10) (-1.34) (-2.15) (-0.45) (-1.23) (-1.67) (-0.24) (0.08) (0.20) (-0.87) (-1.09) (-2.57)

-0.002 0.298*** 0.040** 0.008 0.035** 0.084 0.087 0.075*** 0.012* 0.085*** 0.228 -0.435** 0.045

(-0.14) (3.00) (2.09) (0.72) (2.25) (0.79) (0.36) (6.41) (1.84) (3.51) (1.13) (-2.04) (1.49)

0.001 -0.006 -0.014** 0.001 -0.028*** -0.008 -0.169*** -0.013* 0.020 -0.036*** -0.007 0.131 -0.014

(0.08) (-0.19) (-2.21) (0.13) (-3.16) (-0.23) (-3.23) (-1.71) (1.13) (-3.38) (-0.24) (0.88) (-1.33)

0.020 -0.028 -0.001 -0.037 -0.013 0.037 0.041 -0.007 -0.008 -0.012 0.027 -0.297* -0.025

(0.74) (-0.63) (-0.14) (-1.12) (-1.20) (1.10) (1.15) (-1.02) (-0.85) (-1.55) (0.98) (-1.68) (-1.47)

0.056 0.044 0.092* -0.065 0.148*** 0.055 0.277 0.052*** 0.053* 0.085*** 0.253** 0.193 0.048

(1.50) (1.03) (1.79) (-0.37) (3.37) (1.16) (1.34) (3.83) (1.89) (4.42) (2.34) (0.53) (0.65)

0.025 -0.055 -0.017* -0.007 -0.002 -0.012 -0.012 -0.005 0.003 -0.027*** -0.058* -0.109 -0.020

(1.18) (-1.55) (-1.77) (-0.26) (-0.12) (-1.17) (-1.66) (-0.56) (0.27) (-2.90) (-1.84) (-0.79) (-1.42)

-0.073*** -0.130*** -0.109*** 0.078 -0.056*** -0.069*** -0.054 -0.050*** -0.021 -0.043*** -0.106** 0.400 0.010

(-5.11) (-3.73) (-6.94) (0.42) (-3.33) (-2.76) (-1.18) (-5.34) (-0.63) (-3.32) (-2.52) (1.07) (0.13)

0.074*** 0.108*** 0.127*** 0.188*** 0.118*** 0.260 0.019 0.073*** 0.046 0.135*** 0.027 0.239 0.152***

(3.88) (2.91) (5.40) (3.56) (5.13) (1.42) (1.39) (7.05) (1.46) (8.17) (0.40) (0.78) (4.53) -0.047* -0.058 -0.048*** -0.038 -0.020 -0.099* -0.020 -0.017** -0.039*** -0.038* -0.090** -0.098 -0.048 44

(-1.96) (-1.35) (-2.97) (-0.37) (-1.02) (-1.73) (-0.63) (-2.19) (-3.03) (-1.91) (-2.32) (-0.24) (-1.04)

5.793 0.955 4.444 23.342 5.142 7.442 -0.078 2.251 2.183 -4.984 -34.407* -22.293 8.425

(1.08) (0.14) (1.16) (1.50) (1.56) (0.69) (-0.03) (1.45) (0.76) (-1.23) (-1.75) (-0.71) (1.30)

-13.927** 3.254 -10.509** 16.474 -14.073*** -17.388 6.679 -12.064*** -12.525*** -9.538 15.745 -11.754 2.512

(-2.15) (0.31) (-2.52) (0.68) (-4.09) (-1.53) (1.10) (-4.32) (-4.08) (-1.36) (0.80) (-0.19) (0.25)

0.382 -2.835 2.294 -25.509 -0.462 -0.604 2.374 -1.664 3.762 -3.307 7.615 -27.008 -11.378

(0.08) (-0.32) (0.68) (-1.61) (-0.13) (-0.17) (1.01) (-0.85) (0.90) (-0.81) (0.54) (-0.36) (-1.55) 500.315*** 1054.22*** 557.597*** 970.892*** 936.765*** 828.444*** 869.116*** 572.240*** 292.027*** 798.716*** 1068.31*** 1550.66*** 920.519*** (6.30) (5.06) (10.50) (11.39) (10.53) (8.95) (9.77) (10.84) (4.33) (7.42) (3.79) (2.86) (17.88) 0.117 0.300 -0.532*** -1.540** -0.222* 0.212 0.076 -0.269** -0.608 -0.254** -0.190 -7.007* -0.968*** (0.68) (0.61) (-3.46) (-2.17) (-1.69) (1.58) (0.10) (-2.24) (-1.23) (-2.26) (-0.26) (-1.72) (-2.69)

-0.036 0.077 -0.060 0.145 -0.080* -0.032 -0.141 0.112** 0.015 -0.041 -0.163 -0.544 0.011

(-0.90) (0.90) (-1.06) (0.80) (-1.73) (-0.63) (-0.54) (2.41) (0.29) (-1.55) (-1.21) (-1.35) (0.14)

0.184*** 0.103* 0.255*** 0.094 0.094** -0.074 0.224* 0.193*** 0.253*** 0.132*** 0.389* 2.579 0.239***

(3.63) (1.76) (5.68) (1.33) (2.33) (-0.39) (1.82) (5.54) (4.38) (4.08) (1.92) (1.60) (2.60)

0.004 -0.085 -0.014 -0.009 0.054** -0.027 -0.002 -0.033* -0.003 0.050* -0.198 -0.010 -0.017

(0.24) (-0.90) (-0.61) (-0.81) (2.51) (-0.25) (-0.00) (-1.72) (-0.20) (1.81) (-0.97) (-0.06) (-0.31) Adjusted R2 0.062 0.032 0.051 0.046 0.050 0.065 0.048 0.108 0.066 0.070 0.037 0.037 0.051

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Table 8 NYSE Arca Europe commonality in liquidity by size deciles

This table shows the effect of firm size in commonality in liquidity. We sort all stocks traded in NYSE Arca Europe into 10 deciles based on the market value on the year launching NYSE Arca Europe. Daily proportional changes in an individual stock's Amihud liquidity measure are regressed in time series on proportional changes in the liquidity for all stocks traded in NYSE Arca Europe Market as well as all stock traded in the home market as explained in equation (4). For each exchange, the change in Amihud liquidity measure of stock f is regressed on concurrent, lag, and lead changes in the NYSE Arca Europe market portfolio (where the market excludes stock f and all stocks from the home market) and the concurrent, lag, and lead changes in the Home market portfolio (where the market excludes stock f). ‗Concurrent', ‗Lag', and ‗Lead' refer, respectively, to the same, previous, and next trading day observations of market liquidity. Each column shows the cross-sectional averages of time series slope coefficients with t-statistics in parentheses. The DataStream exchange code is presented in table 1. is the equal-weighted average daily return of the home market, , is the stock f return volatility, measured by the change in squared return, of firm stock f in day t. is a dummy variable for the date in which the NYSE Arca Europe‘s firms start trading using the UTP, that takes the value 0 for all dates before UTP and 1 afterwards. is the interaction terms between the UTP dummy variable and the concurrent proportional change in liquidity of NYSE Arca Europe market index. is the interaction terms between the UTP dummy variable and the concurrent proportional change in liquidity of Home market index.

Decile 1 Decile 10 Variable Decile 2 Decile 3 Decile 4 Decile 5 Decile 6 Decile 7 Decile 8 Decile 9 (Smallest) (Largest) Intercept 0.903 4.209* 3.294*** 1.838*** 2.027*** 1.547** 2.091*** 1.684*** 1.610*** 2.066*** (0.24) (1.84) (4.37) (8.16) (7.69) (2.45) (7.51) (11.63) (11.70) (5.10)

-0.062*** -0.196 -0.001 -0.022** -0.041** 0.042 -0.017 -0.028** 0.004 -0.065

(-2.60) (-0.98) (-0.06) (-2.13) (-2.29) (0.70) (-1.34) (-2.24) (0.30) (-1.43)

0.201** -0.458 0.022 0.059 0.041 0.077* -0.045 0.098*** 0.075*** 0.062*

(2.15) (-0.94) (0.54) (1.02) (1.14) (1.87) (-0.37) (4.85) (4.10) (1.90)

-0.034 0.017 -0.012 -0.027** -0.008 -0.056*** -0.062 -0.041*** -0.030** 0.028

(-0.73) (0.64) (-0.57) (-2.41) (-0.64) (-3.38) (-1.55) (-3.24) (-2.18) (0.44)

1.430 -0.199 0.053 -0.008 -0.082** 0.099 -0.122*** -0.048** -0.068*** -0.131**

(0.86) (-0.92) (0.79) (-0.20) (-1.98) (0.60) (-2.98) (-2.35) (-2.82) (-2.10)

0.657 0.544*** 0.207** 0.425 0.190*** 0.119*** 0.414 0.120*** 0.113*** 0.050

(1.39) (2.87) (2.30) (1.46) (3.52) (4.39) (1.56) (5.16) (4.34) (0.87)

-0.070 0.015 -0.167* -0.066** 0.035 -0.124** -0.176** -0.070*** -0.060*** -0.043

(-0.68) (0.06) (-1.92) (-2.06) (0.56) (-2.07) (-2.00) (-5.13) (-3.71) (-1.29)

46

53.435 -19.447 37.740*** -0.763 0.535 53.752 9.316 4.648 -3.612 -1.418

(1.39) (-0.78) (2.85) (-0.12) (0.07) (1.11) (0.47) (1.44) (-0.54) (-0.19)

182.742 27.552 -23.870 -1.959 -5.518 2.285 16.399 -7.652 -12.914***

(1.45) (0.84) (-1.10) (-0.17) (-0.71) (0.12) (0.68) (-1.46) (-2.83) (-0.43)

-122.80 8.185 19.086 -11.101 2.440 0.461 0.787 -1.239 4.572 -36.957*

(-0.96) (0.25) (1.26) (-1.52) (0.40) (0.07) (0.12) (-0.24) (0.71) (-1.69)

1945.56*** 2236.01*** 2608.64*** 1416.25*** 1556.17*** 1496.89*** 1132.60*** 1370.51*** 1359.42*** 1145.45*** (3.36) (5.27) (4.43) (12.45) (12.54) (10.92) (5.70) (12.76) (11.40) (11.03)

0.526 -0.287 -0.311 -0.451 -0.343* -0.002 -0.152 -0.253*** -0.151 -0.452*** (0.79) (-1.49) (-1.42) (-1.61) (-1.94) (-0.02) (-0.57) (-3.85) (-0.45) (-3.01)

-0.311 -0.017 0.019 0.059 0.096** 0.077*** -0.096 0.104*** 0.142** 0.215***

(-0.66) (-0.29) (0.22) (0.21) (2.37) (3.20) (-0.35) (4.20) (2.46) (4.61)

-0.099 0.038 0.008 0.021 0.002 -0.014 0.147 0.038** 0.033 0.027

(-1.08) (1.07) (0.39) (0.53) (0.04) (-0.60) (1.31) (2.26) (1.17) (0.78)

Adjusted R2 0.062 0.077 0.084 0.084 0.082 0.089 0.082 0.080 0.083 0.067

47

Appendix A

Figure 1: Consolidation Process in NYSE-Euronext

Figure1 shows the consolidation process in NYSE-Euronext group. Panel (A), shows the replacement of Arca, traditional Hybrid in the US in addition to Nouveau Systeme de Cotation (NSC) in Europe by a single Universal equity platform. Derivative trading platforms from US and Europe are also replaced by a single universal platform for all derivative trading. In Panel (B), we show the consolidation of networks. The consolidation of network carried over in two steps, the first consolidate European networks into one single network SFTI Europe, US networks into SFTI Americas and extend the network to give Asian investors access to NYSE- Euronext group markets. In the second step, the three SFTI networks are connected to each other through one big global SFTI network. Finally in Panel (C), we show the consolidation of data centers, or Liquidity centers, from 10 data centers into 4. The Basildon liquidity center, in UK, is responsible providing a colocation trading facility for all NYSE-Euronext‘s European markets. However Mahwah liquidity center located in New Jersey, USA is charged for handling US markets.

Panel A: Consolidation of Platform Cash Equities Trading Platform before the Derivative Trading Platform before the merger merger

NSC Hybrid Arca OX Liffe Connect

One Universal Trading Platform for cash Equities and another for Derivative

after the merger

Global Universal Universal Equities Electronic Trading Place Derivatives

48

Panel B: Consolidation of Networks

NYSE Networks Euronext Networks

ARCA Net SFTI BCNN Liffe Net

NYSE-Euronext Networks after the merger

SFTI Americas SFTI Europe SFTI Asia

Global SFTI

Panel C: Consolidation of data (Liquidity) centers Ten data centers before the merger

9 10 1 2 3 4 5 6 7 8

4 Giant liquidity centers (after the merger)

Mahwah Basildon Toronto Tokyo Brazil NJ Essex (Expected 2013)

Source: Based on NYSE-Euronext website

49

Figure 2: The Universal Trading Platform Figure 2 shows the construction of Universal Trading Platform. Customers can access UTP through the SFTI network or through and extranet that is connected to the SFTI network. The SFTI network connects customers to the matching engine for both cash and derivative markets. In the middle layer between customers and Market matching engine, the TransactTools provides the customer with a Common Customer Gateway (CCG) for electronic trading of all NYSE-Euronext group markets including NYSE Arca Europe. In addition Wombat works as high performance data distributors to all market participants.

Source: NYSE-Euronext website

50

Appendix B

This table explains the relationship between stock traded in NYSE Arca Europe (NAE) and NYSE and Euronext markets. Stocks listed in NYSE Arca NYSE listed Euronext Notes Europe Firms Listed Firms

NYSE Arca Europe: is a Multi Trading Facility (MTF). MTF: is A trading system that facilitates the 1000 stocks from European Are not Are not exchange of financial instruments between multiple countries Traded on the same Traded in Traded in parties. Multilateral trading facilities allow eligible Trading NYSE Arca Europe Market. NYSE Arca NYSE Arca contract participants to gather and transfer a variety of In addition each stock is also Europe Europe securities. Traders will usually submit orders traded in its home market Market. Market. electronically, where a matching software engine is used to pair buyers with sellers.

Use Use Trading Use Universal Trading Universal Universal system Platform Trading Trading Platform Platform

Use SFTI Use SFTI Network Use SFTI Network Network Network

Benefits to NYSE-Euronext members  An efficient solution fully integrated into NYSE Yes, using the same trading Euronext systems. Can NYSE- system as NYSE and  Ultra-low latency, powered by robust, proven cutting Euronext Euronext. However, they edge technology. Yes Yes  Fee predictability with a pricing structure that delivers members have to expand their very competitive fees. trade? membership to be able to  Easy connectivity for existing members through trade NAE stocks existing trading access.  Full market transparency, leveraging on the depth of the central order book.

51

Yes, Need to Yes, Need to Can other pay pay trades other Yes, Need to pay membership membership than NYSE- membership fees either for fees either for fees either for Euronext NAE market only or all other Euronext NYSE market members‘ markets market only only or all trade? or all other other markets markets

Blue chips stocks from 13 European markets via a central limit order book. NYSE Arca Europe also includes access to the 100 most liquid U.S. equities; featuring 90 NYSE listed Paris, companies and 18 other NYSE Amsterdam, Coverage listings to create the first Market Brussels and truly transatlantic trading Lisbon platform. For the first time ever, trading firms can now trade U.S. securities on a European platform during European trading hours.

Source: https://europeanequities.nyx.com/en/markets/nyse-arca-europe/key-benefits

52