politically motivated taxes in financial markets: the case of the french financial transaction tax
TRANSCRIPT
J Financ Serv ResDOI 10.1007/s10693-013-0189-8
Politically Motivated Taxes in Financial Markets:The Case of the French Financial Transaction Tax
Stephan Meyer · Martin Wagener · Christof Weinhardt
Received: 4 June 2013 / Revised: 3 December 2013 / Accepted: 5 December 2013© Springer Science+Business Media New York 2014
Abstract This paper studies the effects of the introduction of the French financial trans-action tax in August 2012. With the tax, the French government aims to generate revenuesfor financing the burdens of the financial crisis and to curb short-term trading. We find thatthe financial transaction tax has a strong impact on trading intensity and liquidity supplierbehavior. Trading volume decreases by about one-fifth compared to the pre-event period.While liquidity suppliers reduce the number of quote and price updates and post less vol-ume at best prices, there is no evidence that spreads increase. Our results suggest that policymakers need to be well aware of the links between tax design and investor behavior, beforeintroducing a financial transaction tax.
Keywords Financial transaction tax · Trading intensity · Market liquidity
1 Introduction
European policy makers are asking for a fair contribution of the financial sector for financ-ing the burdens of the financial crisis in 2008. One possibility may be the taxation of finan-cial transactions. Plans for the introduction of a financial transaction tax in the European
S. Meyer (�)Research Group Financial Market Innovation, Karlsruhe Institute of Technology,Englerstr. 14, 76133 Karlsruhe, Germanye-mail: [email protected]
M. WagenerBoerse Stuttgart Holding GmbH, Boersenstrasse 4, 70174 Stuttgart, Germanye-mail: [email protected]
C. WeinhardtInstitute of Information Systems and Marketing, Karlsruhe Institute of Technology,Englerstr. 14, 76133 Karlsruhe, Germanye-mail: [email protected]
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Union (EU) have a strong support in the European Parliament.1 The aim of a tax on transac-tions in financial instruments and derivatives is to generate revenue and to curb short-termtrading. In France, a financial transaction tax is already in place. It came into effect onAugust 1, 2012. This paper gives an overview over the French regulation and providesevidence on its impact on trading intensity and liquidity.
The EU failed to reach an agreement on the introduction of a financial transactiontax from all 27 member states in June 2012. While its introduction was strongly sup-ported by Germany, France, and other countries, the United Kingdom, Sweden, and theNetherlands were against the proposition. However, the EU treaty allows its introductionunder the “enhanced cooperation procedure” when at least nine EU member states cometogether in agreement. Following their plans, eleven states formally asked the EuropeanCommission to prepare a tax proposal in October 2012. The European Commission pro-posed its plan in February 2013 and started a detailed discussion how to apply the tax.2 ThePresident of the European Commission, Jose Manuel Barroso, emphasizes that its introduc-tion “is about fairness: we need to ensure the costs of the crisis are shared by the financialsector instead of shouldered by ordinary citizens”.3
The revenue raising potential and the corrective function of financial transaction taxeshave been discussed for many years. Both theoretical and empirical studies on the effect ofsuch taxes generally find that increasing transaction costs tend to have negative effects ontrading volume, liquidity, and some measures of market efficiency.4 The microstructure ofa market, however, plays an important role how these effects materialize. In globalized con-nected financial markets, investors may react differently to the introduction of a financialtransaction tax compared to previously studied cases. Overall, it remains unclear to whichextent the results of previous studies can be transferred to the present case.5 The introduc-tion of the French financial transaction tax provides an ideal opportunity to analyze theintroduction of a financial transaction tax in a modern financial market. In this context, theaim of this paper is to provide insights into possible effects for countries planning their owntax.
This paper studies the effect of the French financial transaction tax on trading inten-sity and liquidity on Euronext Paris, the main market, and Chi-X, a multilateral tradingfacility (MTF). Together both trading venues account for almost 90 % of exchange tradedvolume in French blue-chips.6 To account for potential market-wide fluctuations during ourobservation period between June and September 2012, we use a control sample of FTSE 100
1The European Parliament voted in favor of a financial transaction tax on May 23, 2012. See EuropeanParliament, “Parliament adopts ambitious approach on financial transaction tax” (Ref. 20120523IPR45627),May 23, 2012 and “Commissioner Semeta welcomes European Parliament vote on financial transaction tax”(Ref. MEMO/13/652), July 3, 2012.2The introduction of a financial transaction tax is supported by Austria, Belgium, Estonia, France,Germany, Greece, Italy, Portugal, Slovakia, Slovenia, and Spain. See, for example, European CommissionPress Release, “Taxing financial transactions - making it work”, February 14, 2013.3See The Guardian, “European financial transaction tax moves step closer”, October 23, 2012.4See Metheson (2011) for a review of these studies.5Two examples of interacting forces in modern financial markets are competition between trading venuesintroduced by the Markets in Financial Instruments Directive (MiFID) in the EU and the rise of algorithmicand high-frequency trading (HFT). See, for example, Riordan et al. (2011) and Hendershott and Riordan(2012), respectively, for a study of these effects.6See http://fragmentation.fidessa.com.
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stocks. We find that the trading intensity in the French stocks affected by the financial trans-action tax decreases strongly compared to the pre-event period (before August 1, 2012), theaverage daily trading volume per stock drops by about 18 % on Euronext Paris and 26 % onChi-X and the number of trades falls by 19 % and 14 %, respectively.
The results on liquidity are ambiguous. While there is no evidence that spreads increase,the quoted volume at best prices in the order book decreases by about 21 % on Euronext and56 % on Chi-X relative to pre-event period. The quoted order book volume at the top threelevels of the order book decreases slightly stronger on both trading venues. In line with theseresults, we find evidence that liquidity suppliers adjust their behavior and reduce the numberof quote and price updates. Overall, our analyses show that the financial transaction tax hasa strong impact on trading in the affected stocks and investors obviously take the increasedtransaction costs into account. For example, anecdotal evidence suggests that institutionalinvestors escape the tax by trading Contracts for Difference (CFD).7 Retail investors withoutaccess to these instruments can only choose between a taxable transaction or no transactionat all.
The remainder of the paper is structured as follows. Section 2 presents details onthe French regulation and summarizes previous literature on financial transaction taxes.Section 3 provides details on our data and the sample selection. Section 4 introduces tradingintensity and liquidity measures. Section 5 presents the results of our analyses and Section 6concludes.
2 Financial transaction taxes
2.1 Details on the french financial transaction tax
The French financial transaction tax was adopted by the French Parliament in March 2012.8
Two other new taxes on high-frequency trading (HFT) and on naked credit default swaptransactions are also part of the French bill.9 These three taxes came into effect on August 1,2012. The first deferred tax-collection date was November 9, 2012.
The French financial transaction tax applies to the transfer for consideration of the own-ership of equity instruments or assimilated instruments issued by a French firm, having amarket capitalization larger than one billion Euros on January 1, 2012. A list of affectedfirms will be updated by the French authorities on a yearly basis. Equity instruments aredefined as shares and other securities that could give access to capital or voting rights.The effective tax rate is 0.2 % of the acquisition value. Sales transactions are not taxed.The tax is payable by the financial intermediary (e.g. investment service provider, broker)that executed the order or negotiated the transaction for its own account. In the case that a
7See Bloomberg, “Hollande transaction tax drives investor quest for loopholes”, July 24, 2012.8The French financial transaction tax is included in Article 5 of the Amended Budget Act 2012-354.9According to the Securities and Exchange Commission (2010), HFT “typically is used to refer to profes-sional traders acting in a proprietary capacity that engage in strategies that generate a large number of tradeson a daily basis”. A purchase of a French stock may be subject to the financial transaction tax and the HFTtax. Since our data does not allow to distinguish trades from HFTs and non-HFTs, we are not able to disen-tangle the effects of both taxes. However, the HFT tax rate is significantly lower (0.01 % of the aquisationvalue) than the rate of the financial transaction tax (0.2 %), does only apply to firms located in France, andis only triggered when the cancellation rate of orders exceeds a certain threshold. Therefore, we believe thatthe financial transaction tax is the contributing factor to the patterns that we find in our data.
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transaction does not involve a financial intermediary, the purchaser’s custodian is the liableparty. It applies irrespectively of the location of the purchaser or financial intermediary andis payable on the first day of the month after the month of the acquisition.
In the scope of the French financial transaction tax are shares of any kind (e.g. ordinaryshares, preference shares), the physical exercise of a derivative, and preferential subscrip-tion rights.10 The tax applies when transactions in these instruments are settled, i.e. at thetime of legal ownership transfer. Therefore, only the daily net balance of all sales and pur-chases of one investor is taxed. There are a number of exemptions from the French financialtransaction tax. For example, transactions carried out by clearing houses and central secu-rities depositories, primary market transactions, market maker activities, and transactionsunder liquidity contracts11 are not taxed.
The idea of a French financial transaction tax was unveiled by the former French presi-dent Nikolas Sarkozy. His successor Francois Hollande implemented the proposal and alsodoubled the tax rate to 0.2 %. The new Socialist government argues that the tax reducesspeculation and forces banks and financial institutions to pay for the governmental helpduring the financial crisis in 2008. The French government expects revenues at 170 millionEuros in 2012 and at 500 million in each of the following years.
2.2 Related literature
In the literature there are a number of studies, both theoretical and empirical, on the impactof financial transaction taxes on financial markets and investor behavior.12 The idea of afinancial transaction tax goes back to Keynes (1936). He proposed a tax on stock transac-tions to curb short-term speculation and thus to reduce the cost of capital for firms. Similarly,Tobin (1978) argues for a tax on currency transactions. Proponents argue that a financialtransaction tax would reduce long-term deviations of prices from fundamental values (i.e.the probability of bubbles) and short-term excess market volatility. Moreover, a tax has beenclaimed to reduce the diversion of resources into the financial sector, limit socially undesir-able transactions, and raise revenues to finance social initiatives (e.g. Darvas and Weizsacker2011, Schulmeister et al. 2008, and Summers and Summers 1989).
Opponents of a financial transaction tax emphasize the relationship between transactioncosts and market efficiency. They argue that increasing transaction costs may reduce liquid-ity and slow down price discovery. These effects can lead to lower asset prices and lowerreturn savings (e.g. Kupiec 1996, Jones and Seguin 1997, Schwert and Seguin 1993 andBaltagi et al. 2006). Liu and Zhu (2009) find that volatility increased at Tokyo StockExchange after the introduction of higher explicit fees in 1999.
Several studies have analyzed Sweden’s experience with financial transaction taxes dur-ing the 1980s. Umlauf (1993) and Cambell and Froot (1994) provide evidence that a largefraction of trading migrated overseas or moved off-exchange and that the tax has led toincreased volatility. Since any tax gives investors an incentive to change their behavior, thedesign of a tax is an important factor.
10For example, financial contracts such as Contracts for Difference (CFD) should be out of scope.11Financial service providers offer continuously bid and ask prices, acting on behalf of the stock issuer. Givensuch kind of liquidity provision agreement, purchases of the financial service provider are exempt from theFrench financial transaction tax.12See Metheson (2011) for a detailed review of the previous literature.
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The Swedish tax is a good example that a lot of details in the nature, size, and imple-mentation of a financial transaction tax may play a role. The tax was levied on the usageof registered Swedish brokerage services; private transactions involving equity instrumentsof Swedish firms were free of taxation. Therefore, it was relatively easy to avoid the taxby trading Swedish stocks abroad or OTC. This experience has reinforced the skepticismof many countries against a financial transaction tax. It is difficult to implement unlessa substantial number of countries is willing to introduce the tax. Any potential loopholemay cause investors to change the location of trade or to substitute instruments which havesimilar pay-off structures to those whose transactions are taxed.
The introduction of the French financial transaction tax is a unique policy experiment. Incontrast to previous studies, the French transaction tax was introduced in a fully electronicmarket. In this market environment, trading strategies, for instance used by HTFs, maydiffer from previously dominant approaches. Colliard and Hoffmann (2013) also study theintroduction of the French financial transaction tax, focusing on the tax impact on HFTs onEuronext Paris. Their findings generally support our empirical results, using a control groupof Dutch stocks instead of FTSE 100 constituents used in this paper. In our paper, we alsoanalyze Chi-X and thus, provide a complete picture of the tax effects for the main market,Euronext Paris, and the largest multilateral trading facility (MTF).
3 Data, sample selection, and matched sample
3.1 Data sources
Our analyses are based on two data sets, retrieved from the Thomson Reuters DataScopeTick History archive through the Securities Industry Research Center of Asia Pacific(SIRCA).13 The first one contains French stocks that are subject to the introduction ofthe French financial transaction tax and the second data set includes a control sample ofFTSE 100 stocks. Data on the EUR/GBP currency pair and the EURO STOXX 50 vola-tility index is also provided by SIRCA. Data on daily market capitalization for all stocks isretrieved from Thomson Reuters Datastream. We analyze the impact of the French financialtransaction tax by looking at a 4 months window surrounding its introduction on August 1,2012. The pre-event window spans the time period between June 6, 2012 and July 31, 2012(40 trading days) and the post-event window between August 2, 2012 and September 27,2012 (40 trading days). We also analyze a shorter horizon of ±1 month before and after theevent date. We exclude August 27, 2012 because of UK bank holidays.
To analyze the impact of the French financial transaction tax, we focus on trading onEuronext Paris and Chi-X for the treatment group of French stocks. Euronext Paris is theregulated market where the French stocks are listed. Chi-X is a multilateral trading facil-ity (MTF). For our control group, we use stocks that are part of the British FTSE 100index14 and traded on the London Stock Exchange (LSE). Our data contains trade prices,volumes, the best bid/ask including associated volumes, and order book information at thetop three levels of the order book for each trading venue. We restrict our analyzes to con-tinuous trading between 8:01 a.m. and 4:29 p.m. (GMT) and use specific data qualifiers to
13We thank SIRCA for providing access to its data archive, http://www.sirca.org.au/.14The FTSE 100 index is a share index of blue-chips with the highest market capitalization listed on the LSE.
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exclude cross-reported trades and intra-day auctions. Time stamps are provided up to mil-liseconds. A single market order trading against more than one limit order in the order bookproduces multiple data entries in the raw data. For each trading venue, we combine all buy(sell) orders in one stock that are reported in the same millisecond and treat the resultingaggregation as a single trade.
3.2 Sample selection and matched sample
The French authorities have published a list of stocks and their American depositaryreceipts (ADR)/global depositary receipts (GDR) that are affected by the French financialtransaction tax. The list contains 109 firms headquartered in France and whose market capi-talization exceeds 1 billion Euros as of January 1, 2012. Since, transactions in ADRs/GDRsare not taxable until the end of 2012, we focus on the common stocks of these 109 firms.We require that stocks are tradeable on both trading venues Euronext Paris and Chi-X.15 Weexclude firms with stock splits and missing data on SIRCA or Datastream. These selectioncriteria leave us with 94 firms. FTSE 100 constituents in our control group are identifiedusing Thomson Reuters Instrument Codes (RIC), a unique identifier. We require that firmsare part of the FTSE 100 over the whole observation period and exclude firms with stocksplits. Finally, we obtain 99 firms traded on the LSE.16
Following Davies and Kim (2009), we construct a one-to-one matching of French andFTSE 100 stocks according to market capitalization and share price. A FTSE 100 stock j isthe best match to a French stock i if it minimizes the following matchingErrori,j :
matchingErrori,j = 1
2
( |MCi − MCj |1/2 × (MCi + MCj)
+ |pi − pj |1/2 × (pi + pj )
), (1)
where MC denotes the market capitalization of a stock and p its average price on June 1,2012. We use EUR/GBP exchange rates to convert share prices of FTSE 100 stocks in Euros.We calculate all matching combinations of French and FTSE 100 stocks and choose thosematched stock pairs that minimize the sum of all matching errors across the sample. Finally,we obtain a sample of 94 French stocks and their matches. Table 1 reports descriptivestatistics on market capitalization, share price, and matching error for our matched sam-ple. Share prices of FTSE 100 stocks are generally lower as their French matches, resultingin a relatively high matching error compared to other studies (e.g. Huang and Stoll 1996,Venkataraman 2001).
A high matching error may blur existing effects of the French financial transaction tax.To check the robustness of the overall sample results, we also run analyses separately foreach market capitalization tercile.
4 Trading intensity and liquidity measures
We use different measures to analyze the impact of the French financial transaction taxon trading intensity and liquidity. The most common liquidity measure is the spread. The
15Initially, we also analyzed trading on the Frankfurt Stock Exchange. Therefore, we also require that stocksare tradeable on this exchange. Two stocks are affected by this filter criteria. However, French stocks are nottraded actively in Frankfurt. Therefore, we do not discuss the results.16A list of all stocks in our treatment group and control sample is available from the authors upon request.
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Table 1 Matched Sample: Matching Quality. We construct a matched sample of French stocks in the treat-ment group and FTSE 100 stocks in our control sample. Following (Davies and Kim 2009), we use aone-to-one matching on market capitalization and daily average prices (as of 1-Jan-2012). For each Frenchstock, we search a stock in the control group, minimizing the total matching error of all matched couples. Weuse EUR/GBP exchange rates to convert daily prices of FTSE 100 stocks in Euros. Our final sample consistsof 94 stock pairs. This table reports the average market capitalization in thousands of Euros, the daily averageprice in Euros, and the matching error. We report each measure for three categories of market capitalizationof the French stocks and the total sample. Standard deviations are reported in brackets
Market MCAP (kEUR) Price (EUR)
Capitalization French FTSE 100 French FTSE 100 Matching
stocks stocks stocks stocks error
High 23,779.76 22,564.41 59.07 16.24 53.56 %
(18,595.37) (17,413.40) (46.44) (10.09) (38.49 %)
Medium 4,892.92 5,007.23 34.46 14.20 42.01 %
(1,311.22) (1,660.78) (27.60) (13.49) (40.15 %)
Low 1,840.74 28,827.12 34.58 9.43 105.21 %
(682.73) (48,634.27) (26.27) (9.23) (41.15 %)
Total sample 10,082.52 18,906.26 42.62 13.25 67.33 %
(14,392.45) (31,461.99) (36.14) (11.32) (48.30 %)
spread measures ex-ante observable liquidity in the order book and is defined as the lowestprice at which someone is willing to sell (best ask) and the highest price at which someoneis willing to buy (best bid). The wider the spread, the less liquid is a stock. We analyze bothmeasures the spread in Euros (GB pence) and expressed in basis points of the prevailingmidpoint. Let Aski,t (Bidi,t ) be the best ask (bid) at time t for stock i, then the relativequoted half spread (qspreadi,t) in basis points is defined as:
qspreadi,t = Aski,t − Bidi,t
Midi,t × 2× 10, 000, (2)
where Midi,t denotes the midpoint of the best bid and ask. We compute different variationsof the quoted spread. First, we calculate the quoted spread for every price and volume updateand execution in our data set. Second, we calculate quoted spreads at the time of executionand third, we provide a time weighted measure. The latter reflects the availability of liquiditythroughout the trading day.
The effective spread (espreadi,t ) measures the execution costs that a liquidity demanderhas to pay. It is defined as the difference between the execution price and the midpoint ofthe best bid and ask at the time of execution. This measure also captures the costs that arisewhen the trade size of an incoming market order is larger than the available order bookvolume at the best bid or ask, depending on the trade direction. The effective spread in basispoints is computed as:
espreadi,t = Di,t × Pi,t − Midi,t
Midi,t
× 10, 000, (3)
where Pi,t denotes the execution price and Di,t is the trade direction, which equals +1 for abuy order and -1 for a sell order. To estimate the trade direction, we use the Lee and Ready(1991) standard algorithm as proposed by Bessembinder (2003).
The change in liquidity provider revenues is measured by decomposing the spread alongits different components, the realized spread (rspreadi,t ) and the price impact (pimpacti,t ).
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The realized spread reflects the transitory component of the spread. Assuming that liquidityproviders are able to close their positions 5 minutes after the trade, the realized spread inbasis points is defined as:
rspreadi,t = Di,t × Pi,t − Midi,t+5
Midi,t
× 10, 000. (4)
The permanent component of the spread reflects the portion of the spread that arises dueto the presence of informed liquidity demanders. The five-minute price impact (pimpacti,t )in basis points is defined as:
pimpacti,t = Di,t × Midi,t+5 − Midi,t
Midi,t
× 10, 000. (5)
The half quoted depth (depthx,i,t ) measures available order book volume and iscalculated as:
depthx,i,t =X∑
x=1
(Bx,i,t + Ax,i,t )/2, (6)
where Bi,t is the corresponding volume at the bid and Ai,t at the ask. X = {1, 3} char-acterizes different order book levels. depth1,i,t is the average half quoted volume at thebest bid and ask and depth3,i,t incorporates the available depth at the top three levels ofthe order book. All liquidity measures are winsorized at the 1.0 % and 99.0 % level toaccount for potential extreme values through technical data recording errors. We aggregateall measures per day and per stock for each trading venue. Trading intensity measures arethe average daily number of trades per stock, the average order size, the average number ofquote changes per minute (price and quantity updates), and the average number of midpointchanges per minute.
5 Results
5.1 Descriptive statistics
For each stock in our treatment group and each match, we calculate a number of tradingintensity and liquidity measures (see Section 4). Figure 1 depicts the daily variation of theaverage trading volume and the average quoted spread per stock for our sample of stockstraded on Euronext Paris, Chi-X, and the LSE. On each trading venue, the trading volumestays relatively stable in June and July, drops in August, and recovers at the end of thesummer holidays in September. Another reason for the decrease in trading volume in Augustmay be market tensions and investor uncertainty in connection with the European sovereigndebt crisis.17 Overall, it appears that investor activity falls stronger with the introductionof the French financial transaction tax (August 1, 2012) in our treatment group of Frenchstocks compared to our control sample of FTSE 100 stocks.
In terms of quoted spreads, the LSE is the most liquid market in our data sample. Onthe LSE, quoted spreads are on average about 5 bps per stock over the observation period,7 bps on Euronext Paris, and 11 bps on Chi-X.18 Quoted spreads increase slightly on all
17On September 6, 2012, the European Central Bank (ECB) announced an unlimited support for all euro areacountries and calmed financial markets.18The standard deviation of quoted spreads on Chi-X is much higher compared to Euronext Paris and theLSE. We find no evidence that this pattern is driven by a small number of stocks only.
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Fig. 1 Development over Time: Volume and Quoted Spread. These figures plot the trading volume in mil-lions of Euros and British Pounds (upper figure) and quoted spreads in basis points (lower figure) for ourtreatment and control group per day and per stock. The gaps in the lines mark our event date (1-Aug-2012)
three trading venues at the beginning of August, but return to their pre-event level after afew days. Overall, it seems that the introduction of the financial transaction tax does notinfluence quoted spreads of the affected French stocks negatively.
Table 2 reports descriptive statistics for our sample with respect to the periods before andafter the introduction of the French financial transaction tax for Euronext Paris, Chi-X, andthe LSE. Descriptive statistics on trading volume and the number of executed trades show
J Financ Serv Res
Tabl
e2
Des
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ntro
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pute
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Vol
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10,7
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258
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337)
(42,
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(27,
813)
(14,
844)
(37,
833)
(33,
817)
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kets
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39.5
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40.3
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(10.
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Tra
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1,73
42,
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2,00
11,
363
1,73
11,
782
−370
−631
−219
(1,9
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(2,4
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(1,8
01)
(1,7
26)
(1,9
58)
(1,9
31)
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8,43
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614
4,43
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10,7
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14)
(5,2
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(5,7
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(2,8
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(5,9
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(6,5
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(57.
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(18.
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(13.
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(19.
49)
(13.
20)
(11.
16)
Spre
ad(E
UR
;GB
penc
e)0.
138
0.07
21.
107
0.12
30.
069
1.10
8−0
.015
−0.0
030.
001
(0.4
67)
(0.1
29)
(1.0
53)
(0.4
10)
(0.1
34)
(1.0
57)
Spre
adti
me
(EU
R;G
Bpe
nce)
0.13
70.
056
1.08
90.
123
0.05
91.
085
−0.0
140.
003
−0.0
03
(0.4
96)
(0.0
85)
(1.0
83)
(0.4
27)
(0.1
06)
(1.1
08)
Quo
ted
spre
ad(b
ps)
11.5
617.
326
5.43
59.
837
6.49
55.
187
−1.7
25−0
.831
−0.2
48
(19.
777)
(6.9
27)
(2.1
42)
(16.
455)
(7.0
46)
(2.0
23)
J Financ Serv Res
Tabl
e2
(con
tinu
ed)
Bef
ore
Aug
ust1
,201
2A
fter
Aug
ust1
,201
2D
iffe
renc
e:po
st-
pre-
even
t
Chi
-XE
uron
ext
LSE
Chi
-XE
uron
ext
LSE
Chi
-XE
uron
ext
LSE
Quo
ted
spre
adti
me
(bps
)11
.023
6.04
35.
267
9.57
45.
688
4.99
8−1
.448
−0.3
55−0
.268
(20.
519)
(4.6
19)
(2.0
66)
(16.
747)
(5.4
12)
(1.9
51)
Quo
ted
spre
adtr
ade
(bps
)6.
813
4.78
93.
986
6.40
24.
471
3.92
7−0
.411
−0.3
18−0
.059
(12.
989)
(3.5
66)
(1.4
64)
(11.
226)
(4.1
94)
(1.4
73)
Eff
ectiv
eSp
read
(bps
)6.
739
4.56
73.
848
6.30
74.
317
3.74
0−0
.433
−0.2
49−0
.108
(12.
834)
(3.4
59)
(1.4
25)
(11.
062)
(4.1
86)
(1.4
13)
Rea
lize
dsp
read
5(b
ps)
2.83
40.
293
0.70
95.
243
0.41
00.
888
2.40
90.
117
0.17
9
(103
.123
)(2
.216
)(3
.147
)(1
52.7
00)
(3.2
86)
(1.0
28)
Pric
eim
pact
5(b
ps)
4.11
84.
308
3.20
91.
383
3.93
82.
922
−2.7
35−0
.371
−0.2
87
(102
.508
)(2
.806
)(2
.839
)(1
52.5
36)
(2.7
73)
(1.3
46)
Dep
th1
(EU
R;G
BP)
10,0
9925
,968
41,2
579,
583
27,2
1646
,904
−516
1,24
85,
647
(10,
254)
(22,
707)
(32,
620)
(9,2
21)
(23,
699)
(36,
354)
Dep
th1
tim
e(E
UR
;GB
P)10
,466
27,7
3539
,400
10,0
9728
,136
45,0
20−3
6940
15,
620
(10,
683)
(24,
615)
(30,
970)
(9,6
87)
(24,
765)
(35,
352)
Dep
th3
(EU
R;G
BP)
40,7
4787
,213
206,
232
36,8
1889
,628
230,
926
−3,9
282,
415
24,6
94
(55,
991)
(96,
232)
(171
,685
)(4
7,01
6)(1
00,2
51)
(187
,593
)
J Financ Serv Res
the expected negative sign on Euronext Paris and Chi-X. The average trading volume in theFrench stocks that are affected by the financial transaction tax decreases by 4,917 kEUR(1,953 kEUR) per day and per stock on Euronext Paris (Chi-X) compared to the pre-eventperiod. Trading volume on the LSE stays roughly the same. The average number of tradesdecreases stronger on Euronext Paris and Chi-X for the treatment group compared to ourcontrol sample of FTSE 100 stocks, 631 trades per day and per stock on Euronext Paris, 370on Chi-X, and 219 on the LSE. The average number of quote and price changes per minutedecreases on all three trading venues.
Spread measures indicate that liquidity increases on all three trading venues betweenthe two observation periods. On Chi-X, quoted spreads decrease by 1.73 bps, 0.83 bps onEuronext Paris, and 0.25 bps on the LSE.19 It seems that irrespectively of the new tax onfinancial transactions, the stocks in our treatment group become more liquid compared to thestocks in the control group. However, available order book volume, another liquidity mea-sure, paints a different picture. In contrast to the LSE, where Depth1 and Depth3 increaseover 10 % relative to the pre-event period, available order book volume increases less strongon Euronext Paris and even falls on Chi-X.
5.2 Panel regression
Our descriptive statistics and figures suggest that the French financial transaction tax hada significant impact on trading intensity and available order book volume of the affectedstocks. However, other factors may impact the changes over time. To control for market-wide fluctuations, we employ a difference in difference panel regression analysis. Wefurther include a number of control variables. For each measure, we run the followingregression:
�V ariablesi,t = α + β1Taxt + β2VSTOXXt +∑j
βjControlsj,i,t + ui,t (7)
where �V ariablesi,t is the realization of the trading intensity or liquidity measure of thetreatment group for stock i and day t less the realization of the measure for its match. T axt
is the variable of interest that is 0 before the introduction of the French financial transactiontax on August 1, 2012 and 1 after the event. V ST OXXt is the EURO STOXX 50 volatilityindex and Controlsj,i,t are daily stock level control variables for the French firm and itsmatch: the log of market capitalizations and the log of daily average prices. We includefirm and day dummy variables and use Thompson (2011) robust standard errors, which arerobust to both cross-sectional correlation and idiosyncratic time-series persistence.
The regression results presented in Table 3 confirm the descriptive statistics presented inthe previous subsection. For brevity, we display only the estimates for the coefficient β1 onthe tax dummy and we omit the estimates for the constant as well as all other control vari-ables. We estimate two different models for both trading venues Euronext Paris and Chi-X,one with the EURO STOXX 50 volatility index only (Model I) and one with additional con-trol variables (Model II). To illustrate the effects of the French financial transaction tax, wecalculate its impact on our different trading intensity and liquidity measures as a percentage
19We observe slightly higher values for quoted spreads at trade compared to effective spreads. This result canbe explained through inside-the-spread executions, i.e. marketable orders are executed against completelyhidden orders in the order book, and data reporting errors. Over the sample period, on average 8.6 % of alltrades on Euronext Paris, 2.9 % on Chi-X, and 8.3 % on the LSE are executed inside the individual orderbook’s bid-ask spread.
J Financ Serv Res
Tabl
e3
Reg
ress
ion:
Impa
ctof
the
Fren
chFi
nanc
ialT
rans
acti
onTa
xon
Tra
ding
Inte
nsit
yan
dL
iqui
dity
.Thi
sta
ble
pres
ents
the
resu
lts
from
our
regr
essi
onon
the
impa
ctof
the
intr
oduc
tion
ofth
eFr
ench
fina
ncia
ltra
nsac
tion
tax
ontr
adin
gin
tens
ity
and
liqu
idit
y.T
hesa
mpl
eco
ntai
ns40
trad
ing
days
prio
rto
the
even
tdat
e(A
ugus
t1,
2012
)an
d40
trad
ing
days
afte
rwar
ds.
We
use
EU
R/G
BP
exch
ange
rate
sto
conv
ert
all
vari
able
sin
Eur
os,
whe
rene
cess
ary.
Dep
ende
ntva
riab
les
are
trea
tmen
tgr
oup
min
usco
ntro
lgr
oup
valu
efo
rth
etr
adin
gin
tens
ity
and
liqu
idit
ym
easu
res
expl
aine
din
Tabl
e2;
the
vari
able
ofin
tere
stis
the
even
tdu
mm
y,T
axt,t
hati
s0
befo
reA
ugus
t1,
2012
and
1ot
herw
ise.
The
esti
mat
edeq
uati
onis
:�V
aria
ble
s i,t
=α
+β
1Ta
x t+
β2V
STO
XX
t+
∑ jβ
jC
ontr
ols
j,i,t
+u
i,t,
whe
reV
ST
OX
Xt
isth
eE
UR
OST
OX
X50
vola
tili
tyin
dex
atda
yt
and
Contr
ols
j,i,t
are
dail
yst
ock
leve
lico
ntro
lvar
iabl
esfo
rth
efi
rman
dit
sm
atch
:the
log
ofm
arke
tcap
ital
izat
ions
and
the
log
ofda
ily
aver
age
pric
es.W
ein
clud
efi
rman
dda
ydu
mm
yva
riab
les
and
use
(Tho
mps
on20
11)
robu
stst
anda
rder
rors
.t-s
tati
stic
sar
epr
esen
ted
belo
wth
ere
gres
sion
esti
mat
es.T
heim
pact
ofth
eFr
ench
fina
ncia
ltra
nsac
tion
tax
isth
ees
tim
ated
coef
fici
entr
elat
ive
toth
eav
erag
eva
lue
ofth
epr
e-ev
entp
erio
d.W
efu
rthe
rte
stdi
ffer
ence
sfo
rea
chst
ock
pair
sepa
rate
lyus
ing
(New
eyan
dW
est1
987)
stan
dard
erro
rs.#
Stoc
ksA
ffec
ted
isth
enu
mbe
rof
Fren
chst
ocks
for
whi
chw
efi
ndco
effi
cien
tsw
ith
the
expe
cted
sign
atth
e10
%si
gnif
ican
cele
vel.
Not
e:To
esti
mat
e(T
hom
pson
2011
)st
anda
rder
rors
,we
use
the
SAS
code
prov
ided
byJo
elH
asbr
ouck
onhi
sw
ebsi
te.D
ueto
com
puta
tion
alre
ason
s,w
edo
noto
btai
nes
tim
ates
for
allc
oeff
icie
nts.
The
refo
re,w
eal
soru
nal
lmod
els
usin
g(W
hite
1980
)st
anda
rder
rors
and
repo
rtth
eco
rres
pond
ing
t-st
atis
tics
,fla
gged
wit
h#
Mod
elI
Mod
elII
Stoc
k-by
-Sto
ck
Est
imat
esE
stim
ates
Impa
ct#S
tock
saf
fect
ed
Chi
-XE
uron
ext
Chi
-XE
uron
ext
Chi
-XE
uron
ext
Chi
-XE
uron
ext
Vol
ume
(kE
UR
)−3
,216
**−5
,821
***
−2,8
04**
−5,9
35**
*−2
6.1
%−1
7.6
%23
33−2
.44
−4.0
2−2
.03
−3.7
6
Tra
deco
unt
−236
***
−447
***
−243
***
−452
***
−14.
0%
−19.
2%
2238
−3.5
5−4
.83
−3.2
8−4
.49
Tra
desi
ze(E
UR
)−1
,051
***
−828
**−9
60**
*−8
36**
−21.
7%
−8.1
%46
40−3
.24
−2.4
5−2
.80
−2.3
6Q
uote
upda
te(#
/min
)−3
.02*
**−1
3.99
***
−3.0
0*−1
5.59
***
−4.6
%−1
1.1
%25
34−2
.54
−4.0
2−1
.71
−3.7
8
Pric
eup
date
(#/m
in)
−3.4
2***
−2.9
5***
−3.5
1***
−2.9
8***
−14.
8%
−15.
9%
5653
−3.7
0−3
.83
−3.6
9−3
.81
Spre
ad(E
UR
)−0
.01
0.01
0.03
0.05
***
18.6
%66
.8%
2425
−0.4
7#
1.19
#1.
664.
78
Spre
adti
me
(EU
R)
−0.0
10.
010.
050.
05**
*35
.1%
87.1
%26
29−0
.84
#1.
21#
4.78
3.58
#Q
uote
dsp
read
(bps
)−1
.54*
**−0
.13*
**−1
.54*
**−0
.08
−13.
3%
−1.1
%35
30−3
.84
−5.2
7−2
.83
−0.6
7
J Financ Serv Res
Tabl
e3
(con
tinu
ed)
Mod
elI
Mod
elII
Stoc
k-by
-Sto
ck
Est
imat
esE
stim
ates
Impa
ct#S
tock
saf
fect
ed
Chi
-XE
uron
ext
Chi
-XE
uron
ext
Chi
-XE
uron
ext
Chi
-XE
uron
ext
Quo
ted
spre
adT
ime
(bps
)−1
.46*
**0.
18**
−1.4
6**
0.22
**−1
3.3
%3.
7%
3030
−2.7
62.
46−2
.24
2.00
Quo
ted
spre
adtr
ade
(bps
)−0
.48*
*−0
.07
−0.4
9−0
.03
−7.2
%−0
.7%
2938
−2.1
2−0
.85
−1.4
9−0
.34
Eff
ectiv
esp
read
(bps
)−0
.46*
*0.
04−0
.46
0.07
−6.9
%1.
6%
2634
−2.1
40.
50−1
.47
0.72
Rea
lize
dsp
read
5(b
ps)
1.57
0.08
1.56
0.08
55.1
%27
.7%
2511
0.91
0.46
0.91
0.44
Pric
eim
pact
5(b
ps)
−1.9
3−0
.05
−1.9
3−0
.01
−47.
0%
−0.3
%11
19
−0.9
6−0
.27
−0.9
7−0
.07
Dep
th1
(EU
R)
−6,2
33**
*−5
,548
***
−5,6
12**
*−5
,352
***
−55.
6%
−20.
6%
6749
−8.6
9−7
.34
#−8
.58
−9.9
4
Dep
th1
tim
e(E
UR
)−6
,426
***
−6,5
14**
*−5
,869
***
−6,3
87**
*−5
6.1
%−2
3.0
%63
50
−40.
05−3
8.38
−8.8
2−9
.89
Dep
th3
(EU
R)
−28,
532*
**−2
4,91
8***
−24,
497*
**−2
2,33
4***
−60.
1%
−25.
6%
6860
−38.
12−6
.14
−6.6
1−6
.78
Con
trol
vari
able
s
VST
OX
Xx
xx
xx
x
MC
AP
xx
Pric
ex
x
***
deno
tes
sign
ific
ance
atth
e1
%le
vel,
**at
the
5%
leve
l,an
d*
atth
e10
%le
vel
J Financ Serv Res
Fig. 2 Trading Intensity: Volume and Number of Trades. These figures plot the differences in trading volumein millions of Euros (upper figure) and the number of trades (lower figure) between our treatment and controlgroup per day and per stock. The gaps in the lines mark our event date (1-Aug-2012)
of the average pre-event value. The results of both regression models are similar. Therefore,we focus on Model II in the following.20
For trading volume, the estimate on the tax dummy suggests a decline of 17.6 % onEuronext Paris and 26.1 % on Chi-X after the introduction of the financial transaction tax.
20The subsequently discussed results are robust to a shorter observation period. Further information isavailable from the authors upon request.
J Financ Serv Res
Fig. 3 Trading Intensity: Trade Size and Quote Update. These figures plot the differences in trade sizes inEuros (upper figure) and the number of quote updates per minute (lower figure) between our treatment andcontrol group per day and per stock. The gaps in the lines mark our event date (1-Aug-2012)
Figure 2 depicts the daily time variation of trading volume differences between the treatmentgroup and the control sample, confirming the mark decline in trading volume. The numberof trades declines by 19.2 % on Euronext Paris and 14.0 % on Chi-X in the post-eventperiod. We find that the number of quote updates falls by 4.6 % on Chi-X and 11.1 % onEuronext Paris and price updates by about 15 % on both trading venues (Figs. 3, 4, 5 and 6).
J Financ Serv Res
Fig. 4 Trading Intensity: Price Update. This figure plots the difference in the number of price updates perminute between our treatment and control group per day and per stock. The gap in the line marks our eventdate (1-Aug-2012)
Our results are robust for different possible influences. First, the presented results arenot driven by changes in EUR/GBP exchange rates that we use to convert trading vol-ume in FTSE 100 stocks into Euros. Compared against the British Pound, the averageEuro exchange rate was only 0.5 % higher in the post-event period than in the pre-eventperiod. Second, our results seem to be robust for different control group stocks. Colliard andHoffmann 2013 also study the introduction of the French financial transaction tax, usinga sample of Dutch stocks to control for market-wide fluctuations. They report a decline intrading volume of 32 % on Euronext Paris in August and 8 % in September relative to thepre-event period. Our findings on trading intensity are also similar to the introduction offinancial transaction taxes in other markets. For example, Baltagi et al. (2006) show for a0.2 % increase of China’s financial transaction tax in 1997 that trading volume decreases byabout one-third.
Some of our trading intensity measures exhibit a decreasing trend over the sample period,e.g. order book depth (see Fig. 7). To control for the potential influence on our results, weuse robust Thompson (2011) clustered standard errors and day dummy variables (see Eq. 7).We further run additional regression analyses, including a time trend variable. Overall, theregression estimates on the tax dummy show the expected sign. However, the magnitude andsignificance of the effect that we find in the data, decreases considerably for some variablessuch as Depth1 and Depth3.21 A continuous adaption of traders to the new regulatory envi-ronment could result to some extent in a false assignment of the tax impact to the time trend.For all trading intensity and liquidity variables, the plots show clear evidence for the effectof the introduction of the financial transaction tax, supported by our regression results.
21The detailed regression results are available from the authors upon request.
J Financ Serv Res
Fig. 5 Liquidity: Quoted Spread and Effective Spread. These figures plot the differences in quoted spreads(upper figure) and effective spreads in basis points (lower figure) between our treatment and control groupper day and per stock. The gaps in the lines mark our event date (1-Aug-2012)
Our results on liquidity are ambiguous. While the estimate on the tax dummy has apositive sign for absolute spread measures, it is mainly negative for quoted spreads. Forquoted spreads, the estimate on the tax dummy is only significant for Chi-X. However,quoted spreads are significantly higher on Chi-X compared to Euronext Paris over the pre-event period and thus should have a natural tendency to improve. In summary, we find no
J Financ Serv Res
Fig. 6 Liquidity: Realized Spread and Price Impact. These figures plot the differences in realized spreads(upper figure) and price impacts in basis points (lower figure) between our treatment and control group perday and per stock. The gaps in the lines mark our event date (1-Aug-2012)
evidence that spreads are negatively affected by the introduction of the French financialtransaction tax.
However, order book volume, another measure of liquidity, decreases sharply in the post-event period. For quoted volume at best prices (Depth1), the estimates on the tax dummysuggest a decline of 20.6 % on Euronext Paris and 55.6 % on Chi-X. Quoted volume atthe top three levels of the order book decreases slightly stronger, 25.6 % on Euronext Parisand 60.1 % on Chi-X. It seems that investors circumvent higher implicit transaction costs
J Financ Serv Res
Fig. 7 Liquidity: Depth1 and Depth3. These figures plot the differences in half quoted depth at the best bidand ask (upper figure) and half quoted depth at the top three levels of the order book in Euros (lower figure)between our treatment and control group per day and per stock. The gaps in the lines mark our event date(1-Aug-2012)
by reducing their average trade size. Since the tax dummy on effective spreads is neithersignificantly positive for Euronext Paris nor Chi-X, our results suggest that liquidity takersdo not pay higher implicit transaction costs in the post-event period. However, splittingorders results in additional explicit transaction costs (e.g. brokerage commissions, exchangefees), which may be greater than savings in implicit costs.
J Financ Serv Res
Table 4 Regression: Impact of the French Financial Transaction Tax − Small and Large Stocks. This tablepresents the results from our regression on the impact of the introduction of the French financial transactiontax on trading intensity and liquidity for different market capitalization terciles. The sample contains 40trading days prior to the event date (August 1, 2012) and 40 trading days afterwards. The regression isspecified as in Table 3. We run regressions separately for each market capitalization category (High, Medium,Low). We use robust (Thompson 2011) standard errors and present t-statistics below the regression estimates.The impact of the French financial transaction tax is the estimated coefficient relative to the average valueof the pre-event period. We further test differences for each stock pair separately using (Newey and West1987) standard errors. #Stocks Affected is the number of French stocks for which we find coefficients withthe expected sign at the 10 % significance level. Note: To estimate (Thompson 2011) standard errors, we usethe SAS code provided by Joel Hasbrouck on his website. Due to computational reasons, we do not obtainestimates for all coefficients. Therefore, we also run all models using (White 1980) standard errors and reportthe corresponding t-statistics, flagged with #
Model II Stock-by-Stock
Estimates Impact #Stocks affected
Chi-X Euronext Chi-X Euronext Chi-X Euronext
Panel A: high MCAP
Volume (kEUR) −7,172*** −12,628*** −28.4 % −20.0 % 11 14
−2.68 −3.29
Trade count −429*** −780*** −13.0 % −18.4 % 8 19
−3.53 −4.07
Trade size (EUR) −1,400*** −980** −19.5 % −7.1 % 17 11
−3.03 −2.00
Quote update (# / min) −4.65*** −25.90*** −0.3 % −3.4 % 10 13
−2.89 −2.75
Price update (# / min) −2.46 −1.52 −0.1 % −0.0 % 13 14
−1.53 −1.07
Spread (EUR) 0.06** 0.06** 43.4 % 81.6 % 13 12
2.71 2.26
Spread time (EUR) 0.03 0.04* 21.2 % 80.8 % 12 13
1.39 1.90
Quoted spread (bps) −0.03 0.25*** −0.4 % 7.0 % 2 2
−0.07 # 3.72
Quoted spread time (bps) −0.31 0.29* −4.7 % 8.7 % 5 4
−0.75 1.67
Quoted spread trade (bps) −0.02 0.15 −0.4 % 5.3 % 4 6
−0.06 # 1.11
Effective spread (bps) 0.06 0.21* 1.5 % 7.7 % 3 4
0.24 # 1.67
Realized spread 5 (bps) −6.52 0.02 −389.6 % 18.3 % 10 4
−1.44 0.12
Price impact 5 (bps) 6.70 0.18* 249.2 % 6.6 % 1 2
1.48 1.79
Depth (EUR) −11,707*** −10,874*** −62.5 % −22.8 % 29 22
−11.71 −19.87
Depth time (EUR) −12,326*** −13,066*** −63.3 % −25.6 % 28 21
−10.06 −10.48
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Table 4 (continued)
Model II Stock-by-Stock
Estimates Impact #Stocks affected
Chi-X Euronext Chi-X Euronext Chi-X Euronext
Depth3 (EUR) −57,141*** −48,638*** −67.6 % −28.3 % 27 22
−9.35 −7.57
Volume (kEUR) −853 −2,896** −16.5 % −21.0 % 5 11
−1.02 −2.21
Trade count −229** −343*** −17.8 % −19.2 % 10 13
−2.52 −3.19
Trade size (EUR) −368 −508 −10.1 % −7.5 % 12 13
−1.15 −1.35
Quote update (# / min) −3.71* −15.99*** −0.2 % −1.0 % 9 12
−1.90 −3.70
Price update (# / min) −2.95*** −2.60*** −0.1 % −0.0 % 22 21
−2.61 −2.93
Spread (EUR) 0.09 0.09 169.2 % 224.1 % 5 6
1.42 1.53
Spread time (EUR) 0.10** 0.10** 222.8 % 296.6 % 7 8
2.01 2.11
Quoted spread (bps) −0.56*** −0.29 −7.3 % −4.8 % 14 12
−3.10 −1.51
Quoted spread time (bps) −0.30 −0.07 −4.1 % −1.3 % 10 12
−1.04 # −0.47
Quoted spread trade (bps) −0.10 −0.25** −2.2 % −6.0 % 12 15
−0.60 # −2.00
Effective spread (bps) −0.10 −0.17 −2.3 % −4.2 % 10 13
−0.65 # −1.50
Realized spread 5 (bps) 0.83 −0.21 2,224.5 % −4,141.2 % 10 1
0.70 −0.53
Price impact 5 (bps) −1.01 0.04 −22.1 % 1.0 % 4 5
−0.85 0.12
Depth (EUR) −2,154*** −3,437*** −31.8 % −17.2 % 15 13
−2.58 −3.81
Depth time (EUR) −1,623* −3,451*** −22.8 % −16.3 % 12 14
−1.95 −3.79
Depth3 (EUR) −5,206 −9,504** −23.1 % −16.8 % 17 14
−1.21 −2.33
Panel C: low MCAP
Volume (kEUR) −9 −2,005 −0.4 % −29.6 % 7 8
−0.00 −0.82
Trade count −90 −274 −14.1 % −25.1 % 4 6
−0.52 −1.37
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Table 4 (continued)
Model II Stock-by-Stock
Estimates Impact #Stocks affected
Chi-X Euronext Chi-X Euronext Chi-X Euronext
Trade size (EUR) −1,058** −1,013** −42.5 % −21.2 % 17 16
−2.17 −2.01
Quote update (# /min) −0.04 −7.15 −0.0 % −0.2 % 6 9
−0.01 −1.15
Price update (# /min) −5.69*** −5.02*** −0.1 % −0.1 % 21 18
−3.59 −3.50
Spread (EUR) −0.02 0.02* −9.6 % 23.1 % 6 7
−1.02 1.89
Spread time (EUR) −0.03 0.03* −12.5 % 33.2 % 7 8
−1.09 1.84
Quoted spread (bps) −3.05** −0.05 −15.1 % −0.4 % 19 16
−2.41 −0.13
Quoted spread time (bps) −2.84* 0.48 −14.9 % 5.1 % 15 14
−1.76 1.38
Quoted spread trade (bps) −0.79 0.04 −6.9 % 0.6 % 13 17
−0.92 0.15
Effective spread (bps) −0.79 0.22 −7.0 % 3.1 % 13 17
−0.97 0.74
Realized spread 5 (bps) 10.68*** 0.50 160.2 % 66.6 % 5 6
4.05 1.55
Price impact 5 (bps) −11.24*** −0.29 −223.2 % −4.7 % 6 12
−3.11 −1.05
Depth (EUR) −3,027*** −2,380*** −61.2 % −22.0 % 24 19
−7.45 −3.60
Depth time (EUR) −3,966*** −3,567*** −79.6 % −31.0 % 24 18
−6.38 −4.74
Depth3 (EUR) −12,059*** −11,526*** −76.2 % −32.9 % 24 24
−3.02 −2.76
Control variables
VSTOXX x x x x
MCAP x x
Share Price x x
*** denotes significance at the 1 % level, ** at the 5 % level, and * at the 10 % level
Looking on realized spreads, we do not find significant estimates on the tax dummy.However, the estimates for Euronext Paris and Chi-X have a positive sign, indicating thatliquidity suppliers demand a higher compensation for supplying liquidity after the introduc-tion of the French financial transaction tax. Taken together with the results on the update
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frequency of quotes and prices as well as the available order book volume, this findingsuggests that they reduce their willingness to post additional volume on a given quote.Irrespectively of the market maker exemption in the French regulation, liquidity suppliersdemand a compensation to take the risk of paying the tax. There is the potential risk thatless market maker activity leads to a lower level of market liquidity. Since liquidity affectsexpected returns of investors (e.g. Amihud and Mendelson 1986), the financial transactiontax may lead to higher cost of capital for French firms on the long run.
The effects of the French financial transaction tax on trading intensity and available orderbook volume, that we find in our data, are generally more pronounced for Chi-X than forEuronext Paris. One explanation may be that Chi-X is thought to be an HFT-friendly envi-ronment (e.g. Menkveld 2013), offering a low latency trading infrastructure and attractivefee schemes to investors. However, since HFTs normally monitor their inventory positionsintensively and are particularly eager to avoid overnight positions, their exposure to theFrench financial transaction tax should be limited.22 The introduction of the French HFT-tax may be a second explanation. But it only applies to firms located in France, which couldrelocate their business beyond the border. Colliard and Hoffmann (2013) provide furtherinsights on changes in low latency trading activity after the policy changes in France (e.g.cancellation time of orders). However, future research has to further evaluate the asymmet-ric effects of the taxes on both trading venues, as the data sets of Colliard and Hoffmann(2013) and ours do not allow to distinguish transactions of HFTs and non-HFTs.
5.3 Differences between small and large stocks
To gain more insights into the effects of the financial transaction tax on trading intensity andliquidity, we analyze three different market capitalization categories of French stocks thatare affected by the tax. We run regressions as defined in Eq. 7 separately for each tercile.Table 4 presents the regression estimates for each category.
The effects of the policy change are more pronounced for relatively large stocks. Forexample, the trading volume decreases by 20.0 % on Euronext Paris and 28.4 % on Chi-Xfor the highest market capitalization category, i.e. 2 percentage points stronger comparedto the overall sample. Overall, our results seems to be robust with respect to market cap-italization, showing no contradicting signs between large and small stocks. However, thesignificance and magnitude of changes in trading intensity and liquidity measures varybetween subsamples. There are two potential explanations: (i) HFTs, that are thought tobe more active in larger stocks, are particularly affected by the new regulation and (ii) therelatively high matching error for smaller stocks, due to the generally lower stock priceof FTSE 100 stocks compared to their French matches, may influence the results in thisspecific category.23
6 Summary and policy implications
Our study on the French market shows that the introduction of the financial transaction taxhad a significant impact on investor behavior. The tax was introduced on August 1, 2012 and
22Only the daily net balance of all sales and purchases of one investor is taxed. See Section 2.23Note that the reported patterns are not driven by our matching procedure, since similar effects have beenpresented by related academic and professional studies on the French financial transaction tax (e.g. Colliardand Hoffmann (2013), The Economist, “Skimming the froth”, December 15, 2012).
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is imposed on French stock purchases within the country and beyond the border. Anothernew tax on high-frequency trading (HFT) was also part of the bill. Both taxes increasetransaction costs and may therefore have similar effects on trading intensity and liquidity.Since our data does not allow to distinguish trades from HFTs and non-HFTs, we are notable to disentangle the effects of both taxes. However, the HFT tax rate is significantlylower than the rate of the financial transaction tax. Therefore, we believe that the latter isthe contributing factor to the patterns that we find in our data.
We study trading intensity and liquidity measures for a sample of French stocks thatare affected by the financial transaction tax. To evaluate the impact of the French financialtransaction tax, we analyze quote and transaction data of the main market, Euronext Paris,and the largest multilateral trading facility (MTF), Chi-X, before and after the introductionof the tax, controlling for market-wide fluctuations and other stock specific factors. Tradingintensity measured in terms of total trading volume, the number of trades, and the frequencyof quote and price updates decreases between 14.0 % and 26.1 % relative to the periodwithout the tax. While there is no empirical evidence that spreads increase under the newtax, the quoted order book volume at best prices decreases by 20.6 % on Euronext Paris and55.6 % on Chi-X. Results on the quoted depth at the top three levels of the order book aresimilar.
Some measures, e.g. quoted depth, indicate that the level of market quality in Frenchstocks on both markets Euronext Paris and Chi-X is lower under the newly introduced tax.There is the potential risk that when market quality deteriorates and price discovery slowsdown, financial activity falls and thus the cost of capital for firms increases. This trend mayalso weaken the real economy. The taxation of financial transactions is only one possibilityto share the burdens of the financial crisis, which is one of the major political aims beyondthe introduction of the tax in France.
Policy makers should be well aware that a financial transaction tax will increase transac-tion costs for all types of investors alike. Depending on the design of the tax, retail investorswill also pay the full tax rate, for example on their transactions for retirement saving plans.We believe that there are more efficient tax measures than a financial transaction tax to askfor a fair contribution of the financial industry. Metheson (2011), for example, suggests tointroduce a tax on balance-sheet debt such as the financial sector contribution (FSC) or abroadened VAT on fee-based financial services.
The first experience with the French financial transaction tax shows that such a type oftax must be broad-based to avoid loopholes. The tax design must minimize the potentialrisk that professional investors simply trade instruments in which their activities will not betaxed. For example, transactions in substitutes for stocks that can have similar pay-off struc-ture, such as futures, options, and other derivatives, have to be taxed. To reduce the risk ofavoidance, a financial transaction tax should be levied on a multinational basis. Executionson all types of trading venues and OTC should be subject to such a tax. Irrespectively ofthe market maker exemption in the French regulation, our results show that liquidity suppli-ers reduce their activity. Since market making is vital for the function of financial markets,policy makers should exempt market maker activities from taxation.
Countries that plan to introduce a financial transaction tax should design a tax accord-ingly and closely monitor its effects on the market for financial services. The EuropeanCommission proposal for a European transaction tax under the “enhanced cooperation pro-cedure”, released on February 14, 2013, only includes some of the aforementioned points.While it defines a broad-based taxation of financial transactions, market makers do not ben-efit from a tax exemption. The ongoing discussions and consultations, how to apply the tax,
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will show whether the pan-European financial transaction tax will become a cautious ormore ambitious approach.
Acknowledgments Financial support from Boerse Stuttgart is gratefully acknowledged. The viewsexpressed here are those of the authors and do not necessarily represent the views of the Boerse Stuttgart. Wethank an anonymous referee for valuable feedback.
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