Throttling hyperactive robots - Message to trade ratios at the Oslo Stock Exchange

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1 Throttling hyperactive robots - Message to trade ratios at the Oslo Stock Exchange Kjell Jørgensen, Johannes Skjeltorp and Bernt Arne Ødegaard February 2014 Abstract We use the introduction of a cost on high message to trade ratios for traders at the Oslo Stock Exchange to investigate the effects on market quality and fragmentation of introduction of such speed bumps to equity trading. The exchange introduced a fee payable by market participants whose orders (messages to the exchange s trade system) exceeded seventy times the number of consummated trades. Market participants quickly adjusted their behavior to avoid paying the extra cost. The overall ratios of messages to trades fell, but common measures of the quality of trading, such as liquidity, transaction costs, and realized volatility, did not deteriorate, they were essentially unchanged. This is a policy intervention where we can match the treated sample (OSE listed stocks) with the same assets traded elsewhere. We can therefore do a diff in diff analysis of liquidity in Oslo compared with liquidity of the same asset traded on other exchanges. Surprisingly, we see that liquidity, as measured by the spread, deteriorated on alternative market places when the tax was introduced, a tax that is only valid for trading at the OSE. The spread is the only liquidity measure for which we observe this difference between the OSE and other markets, for depth and turnover we do not find any differences between other markets and the OSE. JEL Codes: G10, G20 Keywords: High Frequency Trading; Regulation; Message to Trade Ratio; Order to Trade Ratio Jørgensen is at the Norwegian Business School BI and the University of Stavanger, Skjeltorp is at Norges Bank (Norway s Central Bank) and Ødegaard is at the University of Stavanger and the Norwegian School of Economics (NHH). Corresponding author: Bernt Arne Ødegaard (bernt.a.odegaard@uis.no), UiS Business School, University of Stavanger, NO-4036 Stavanger, Norway. The views expressed are those of the authors and should not be interpreted as reflecting those of Norges Bank. We would like to thank Thomas Borchgrevink at the Oslo Stock Exchange for providing us with information and data for the Oslo Stock Exchange.

2 Throttling hyperactive robots - Message to trade ratios at the Oslo Stock Exchange Abstract We use the introduction of a cost on high message to trade ratios for traders at the Oslo Stock Exchange to investigate the effects on market quality and fragmentation of introduction of such speed bumps to equity trading. The exchange introduced a fee payable by market participants whose orders (messages to the exchange s trade system) exceeded seventy times the number of consummated trades. Market participants quickly adjusted their behavior to avoid paying the extra cost. The overall ratios of messages to trades fell, but common measures of the quality of trading, such as liquidity, transaction costs, and realized volatility, did not deteriorate, they were essentially unchanged. This is a policy intervention where we can match the treated sample (OSE listed stocks) with the same assets traded elsewhere. We can therefore do a diff in diff analysis of liquidity in Oslo compared with liquidity of the same asset traded on other exchanges. Surprisingly, we see that liquidity, as measured by the spread, deteriorated on alternative market places when the tax was introduced, a tax that is only valid for trading at the OSE. The spread is the only liquidity measure for which we observe this difference between the OSE and other markets, for depth and turnover we do not find any differences between other markets and the OSE. JEL Codes: G10, G20 Keywords: High Frequency Trading; Regulation; Message to Trade Ratio; Order to Trade Ratio Introduction Algorithmic/High Frequency Trading is the aspect of the current financial market place that seems to be of most concern to financial market participants and regulators. For example, the European Parliament headlined their press release about the updated MiFID regulation Deal to regulate financial markets and products and curb high-frequency trading. 1 Most commentators agree that financial market has become more competitive and liquid together with the increased computerization of the trading process. However, many argue that the increased fragmentation of equity trading, where liquidity is mainly provided by computerized traders, has lead to a fragile system, where the high speed traders have an unfair advantage. As the quote from the European Parliament exemplifies, there is a perceived need to do something about High Frequency Traders (HFT), particularly among politicians and regulators. The evidence from the academic literature, however, is less convincing with respect to the need to curb algorithmic trading, and more specifically HFT. First, technological innovations have lead to the replacement of human traders with computers. Like most industries where automatization has reduced the need for human presence, costs have fallen. Second, the regulation which has encouraged competition among exchanges (Reg NMS in the US, MiFID in Europe.) has resulted in more competitive pricing of trade execution services. Both of these factors should lead to lower costs of financial intermediation, which can be passed through to investors in the form of lower spreads and commissions. Empirical research on algorithmic trading confirm that 1 Press release - European Parliament - Economic and monetary affairs :47. 1

3 objective measures of trading costs and liquidity, such as spreads, have been improving at the same time as the computers have taken over most of the trading. 2 Given these results, technological innovation is not necessarily a problem. To support that claim one need to identify which aspects of the trading system has been significantly affected by algorithmic trading, and examine whether these changes have made equity markets less efficient or more fragile. Any regulatory intervention should primarily be justified by an identified problem brought on by the increased trading speed. In the regulatory debate, there is a large set of proposals of how to regulate HFT. These include transaction taxes (Tobin taxes), minimum resting times for orders, increased tick sizes, market making obligations, circuit breakers, and fees on high order to trade ratios. 3 Not all these proposals are justified by an identified inefficiency, and may not achieve what their proponents claim. Take for example the proposal of a transaction tax. In today s fragmented, but integrated, market place, unless the tax can be enforced equally across markets, the result is likely to repeat the Swedish experience in the mid-eighties, where the introduction of such a tax only led to a migration of trading to London. 4 Even if one managed to enforce a transaction tax across all possible market places, the effect is more likely to throw the baby out with the bathwater in the sense that this may curb all types of high frequency trading, also the type of trading activities that are desirable, such as market making. The Swedish experience shows that the design of such a tax is crucial for it to succeed. In addition, a clear definition of the purpose and target group of the tax is also an important consideration in the design. In this paper we look specifically at one regulatory innovation, pricing of excessive order to trade ratios. Unlike many of the other proposals mentioned, this can be justified by a possible inefficiency. The starting point is the behavior of many high frequency traders, where they will in the course of their trading day continually place, update, and withdraw orders from the exchange s limit order book. For the extreme high frequency traders each such message (order submission/update/withdrawal) may occur at micro- or nano-second intervals, without any intention of execution. These strategies makes the order visible in the order book for such a short time span that only another computer can react to it. A human trader watching the order book can not react before the trading opportunity is gone. The presence of such fleeting trading opportunities makes it necessary for the other participants in the market to do large IT investments to be able to process this information in real time. The same is true for the stock exchange where it needs to invest in improved infrastructure and computing power to be able to process the large amount of message traffic. 5 While these costs represent an economic externality for all non-high frequency traders, there may also be other costs involved. 6 2 See academic surveys by Angel, Harris, and Spatt (2011, 2013), Gomber, Arndt, Lutat, and Uhle (2011) and Jones (2013). 3 See The (UK) Government Office for Science (2012) for a survey of the proposals. 4 See Umlauf (1993). For some more recent evidence, see Colliard and Hoffman (2013). 5 An (in)famous example of such consequences is the US practice of Co-location, where traders are paying the exchange huge fees to place their computers physically next to the exchange s computers, to minimize their reaction time to new information in the exchange s order book. 6 Another example is the attention costs, the need to spend more time watching the screen means less time 2

4 Standard theory of taxation tells us that externalities should be paid for by those imposing the externalities on others. This will also give them the right incentives to change their behavior to reduce the externalities they impose on others. In this case one want to identify the traders which use the exchange s systems excessively, without the intention to trade or provide actionable quotes. To measure this one can compare the number of messages to the exchange s limit order book to the number of trades actually executed. Traders with a very high Message to Trade Ratio 7 can be argued to be the ones imposing an externality on the other traders on the exchange, and should be charged accordingly. The Oslo Stock Exchange designed and introduced a new fee structure in 2012, where members basically are charged a fee if their Message to Trade Ratio is above In this paper we investigate the effects of this regulatory change by looking at what happens to trading in Norwegian equities around the introduction of this fee. The motivation of the research is two-fold. First, by looking at how the participants in the marketplace change their behavior around the introduction of the new fee we provide insights into how new regulation aimed at HFT affect market quality. Second, by analyzing a new tax design tailored to curb speculative HFT activities and retain the liquidity supplying HFTs, the regulation in itself will give valuable input to other regulators and exchanges. As emphasized by SEC chair Mary Jo White, 9 any attempts at regulation should be informed by careful empirical analysis. Our research can therefore give inputs to other regulators considering similar measures. The specific questions we are asking is first, whether this policy intervention seems to achieve its purpose, and second, whether it has any unintended consequences. We start the analysis by examining what happens at the Oslo Stock Exchange in isolation. Is there evidence that traders behavior changes? Is this change in behavior in a desirable direction, and what is the impact on market quality? After examining the effect on OSE in isolation, we widen the scope of the analysis. today s fragmented equity markets traders can quickly move to alternative market places. 10 We therefore also look at what happens to the fragmentation of trading volume, and market quality in OSE listed stocks trading at other trading venues. The fact that most of the stocks listed at OSE are traded in other marketplaces allows us to examine the effect of the policy introduction in Oslo by comparing the change in market quality variables in Oslo with the change in the same variables at the alternative market places in the same stocks. In other words, we perform a diff-in-diff analysis between OSE and other trading venues for the same stocks. Looking only at the OSE, we find that message to trade ratios fell significantly around the introduction of the fee, but without any impact on market quality. Measures of market quality, such as depth, spreads, short term volatility, and trading costs, are not changing. The main for analyzing other news, or watching other stocks. See for example Corwin and Cougenour (2008). 7 Other common terminology for such ratios are Order to Trade Ratio and Order to Executed Ratio. 8 The actual scheme is somewhat involved, as not all messages are counted in this ratio, see the discussion later. 9 Focusing on Fundamentals: The Path to Address Equity Market Structure, Speech by SEC Chair Mary Jo White, Security Traders Association 80th Annual Market Structure Conference, Washington, D.C, Oct. 2, For some academic surveys of the literature on market fragmentation see (US Securities and Exchange Commision, 2013; Gomber, Sagade, Theissen, Weber, and Westheide, 2013). In 3

5 change we see is a fall in turnover, but this may be for two reasons. First, trading may migrate from the OSE to the alternative market places as a result of the new fee, and, second, an economy-wide fall in the interest of trading equities. When we look at alternative market places, and implement the diff-in-diff analysis, we confirm that there is no significant lowering of trading quality at the OSE. The most surprising finding is that spreads widen significantly at the other trading venues. However, depth remains constant both at the OSE and outside OSE. Finally, we compare the change in turnover at the OSE and at the other market places. We see that the decline in turnover we saw at the OSE is also present in the alternative market places, which contradicts a movement of trading interest away from Oslo as a result of the policy change. The structure of the paper is as follows. We start by a discussion of the theoretical and empirical literatures on automated trading. After this we discuss our data sources before we give an overview of trading in Oslo Stock Exchange listed stocks, as well as some descriptives for liquidity at the OSE. Next, we give the details of the regulatory change, before we look at how the policy intervention affect the stock exchange. We do this in two steps. First, in sections 5 and 6, we look at the OSE in isolation, illustrating how participants change their behaviour, and the time series of quality measures at the OSE. In the second step, in section 7 we show how the introduction of the Message to Trade Ratio affects OSE and other market places for the same equities. We finally offer a brief conclusion. 1 Background In this section we discuss the relevant theoretical and empirical background. The backdrop of this research is the technological evolution of trading in financial markets in the last few decades. We can think of this as a two step process. In the first step, trading went from a floor based exchange, where much of the trading was done physically on the exchange, to a centralized electronic limit order system. In the beginning the limit order book coexisted with the physical market place, but the traders soon left the physical exchange. Once the traders were submitting the orders from their own terminals, they found they could also automate this process. The second step is fragmentation of trading, where the automated order submission also decided where to submit, as well as when to submit. This step is a jointly due to technological innovations and the regulatory push towards competition between market places. In the US, Reg NMS, and in Europe, MiFID, both resulted in fragmentation of trading, away from the traditional central market places, to other exchanges (lit markets), and other, alternative market places, such as OTC and Dark pools (dark markets). It is this second step, and the need to continually monitor trading opportunities across different markets, which really has made it necessary to bring in the computer, and brought speed to the forefront of the discussion. We will use the term algorithmic trading to mean direct order submission by computer, where the computer generates the order as part of a broader trading strategy, not as a dumb 4

6 terminal. 11 Algorithmic Trading is in the press often used interchangeably with High Frequency Trading (HFT), particularly when one wants to emphasize the reaction time of the algorithms, but HFT may be better thought of as a subset of Algorithmic Trading, as there are algorithms that do not rely on speed. 12 In our research we focus on the HFT segment of algorithmic traders. HFT is however a heterogeneous group, and it is at times hard to identify which traders are HFT. It may therefore be more fruitful to categorize the HFT traders by the type of trading strategy they follow. Jones (2013) discusses three typical HFT strategies: (a) Market Making, (b) High-frequency relativevalue trading and (c) directional trading on news releases, order flow, or other high frequency signals. book. The first, market making, is to post limit orders on both sides of the electronic limit order The goal of market makers is to earn the bid ask spread, by buying at the bid and selling at the ask. Market makers provide a liquidity service to the market, where they take on inventory as a result of their ask and bids being hit. To minimize their inventory HFT market makers need to continuously monitor the limit order book, and update their bid and ask quotes, reacting to both market conditions and their own inventory. As a result market makers will submit and cancel a large number of orders for each of their transactions. The market maker function has always been part of financial markets, but in todays limit order market it is most likely to be electronic. For one thing it needs to be fast to avoid trading too much with suspected informed traders. Electronic market makers also have lower costs. The second, relative value and arbitrage trading, tries to exploit price differences between similar assets. For example, a common strategy is to monitor differences in the price between an Exchange Traded Fund (ETF) and the value of the basket of shares the ETF contains. If for example the ETF is priced lower than its equivalent basket of stocks, the strategy will involve buying the ETF and shorting its constituent stocks. Another relative value strategy is to buy and sell the same asset traded at different market places. This could for example be to buy a Norwegian Stock in Oslo and simultaneously sell it in Stockholm, exploiting a perceived price difference. Such a strategy need high speed, since it needs both legs of the trade to be executed simultaneously and at unchanged prices from the time the orders were sent. The third example, directional trading, rely on technological innovations in textual analysis of news releases, trying to figure out the expected direction of a change in stock price. example, parsing the headline Oil Spill BP would lead to huge sell orders of BP. For this strategy to be profitable, it is necessary to get in ahead of the crowd. As these examples show, even though they all rely on speed, these are very heterogeneous trading strategies. Some of them are well known strategies that is common to all financial market places. For example, HFT Market Making is just a classical feature of financial market done 11 This follows the European regulation, which defines algorithmic trading in financial instruments as follows. As defined by these rules, such trading takes place where a computer algorithm automatically determines individual parameters of orders, such as whether to initiate the order, the timing, price or quantity A well known example is agency algorithms used by institutional investors to split their large trades into pieces. For 5

7 with computers instead of human traders. Recent academic surveys, such as Foucault (2012) and Jones (2013), argue that the academic literature has not yet agreed on whether there has been a fundamental change in how the markets operate. The speed of trading has been increasing for decades, so is the latest speed increase not just a matter of degree? Or is it really a game changer? 13 The theoretical literature on automated trading is somewhat skeptical. Biais, Foucalt, and Moinas (2013) points to the large fixed costs of gaining a speed advantage. Once one has a speed advantage, it may be used to act on information more quickly than others, leading to adverse selection for all other users of the system. On the other hand, the presence of HFT increases the likelihood of finding counter-parties, which is a gain from trade. So the total effect on market quality is not given. Other theoretical investigations of Algorithmic trading include Jovanovic and Menkveld (2012) and Foucalt, Hombert, and Rosu (2012). The empirical literature is more positive towards algorithmic trading. Most of the empirical studies show that measures of market quality has improved together with the increase in algorithmic trading. A well known example is Hendershott, Jones, and Menkveld (2011) who find that increases in algorithmic trading at the NYSE led to a lowering of spreads. Another example is Boehmer, Fong, and Wu (2012), that looks at a large sample of international exchanges, and find that greater AT intensity improves liquidity and informational efficiency, but increases volatility. They also point out that the improvement is concentrated to the larger and more liquid stocks. For smaller stocks the news is not so good. 14 While some High Frequency Traders are clearly specialized in providing liquidity and earning the bid/ask spread (see e.g. Menkveld (2013)), it is not clear that these traders will continue to do so in times of market stress. A well known example is the US flash crash where the results in e.g. Kirilenko, Kyle, Samadi, and Tuzun (2011) suggest that, while the HFTs initially were supplying liquidity, they eventually became net consumers of liquidity and contributed to the crash. Let us now turn to the regulation we will look at. It is not imposed by regulatory bodies, it is a change in the cost schedule for financial transactions by the exchange. It is a topic that is actually widely discussed, both by the exchanges and their regulators. The concern about it is summarized by Jones (2013) as follows: 15 If excessive messages imposes negative externalities on others, fees are appropriate. But a message tax may act like a transaction tax, reducing share prices, increasing volatility, and worsening liquidity. In other words, this type of fee should be designed carefully, trading off the desire to push the costs over to those generating the externalities, against the risk that the fee will be to broad brush, and discourage all trading, in particular the type of trading that provides liquidity. 13 To quote Chordia, Goyal, Lehmann, and Saar (2013), the editorial in a recent special issue on High Frequency Trading in the Journal of Financial Markets. 14 See also the recent literature surveys by Litzenberger, Castura, and Gorelick (2012), and US Securities and Exchange Commision (2013), and the evidence on HFT strategies in Hagströmer and Nordén (2013). 15 A message tax is also discussed in The (UK) Government Office for Science (2012). 6

8 A similar fee has been introduced in Italy, and has been analyzed by Friederich and Payne (2013). Let us summarize the main findings of their analysis of the Italian experience. We will return to it after we have introduced the Norwegian fees, which differ from the Italian fees along several dimensions. As we will see, this may be behind some differences in the results. The fees were introduced by the Milan Borsa in In summarizing their results, Friederich and Payne (2013) states We find that liquidity deteriorates, whether it is measured by inside spreads or depth away from the best quotes. The same activity is conducted through fewer, larger trades. Smaller stocks in our sample were much more affected by the OTR than large stocks. Finally, consolidated liquidity and activity measures that include exchanges other than the historical one, and to which the OTR constraint did not apply, behave in exactly the same way as single-exchange measures. 2 Data Sources We are using data for equity trading in stocks with a main listing at the Oslo Stock Exchange. We rely on a number of datasets. First, we utilize a microstructure dataset provided by the Market Watch department at the Oslo Stock Exchange. This datasets provides information about all trades and order at the exchange in the period The data comes with separate feeds that contains orders, trades, and the state of the order book (the best five levels) at any time with a change. The data contains a wealth of flags of different types. For example, for each trade there are flags for whether the participating traders are automated (algorithms) or not. This type of information can be used to construct measures of algorithmic participation in the market. The data feed also allow us to distinguish different traders. The data ends in November 2012, when the exchange moved to a new trading system. Second, we have a similar microstructure data set provided by ThomsonReuters, for all European exchanges where stocks with a main listing at the OSE is traded. While this dataset also contains orders, trades and state of the order book, there is less additional information, for example we can not distinguish between traders in this data. This dataset runs through Finally, we utilize data from the Oslo Stock Exchange Information service (OBI), which provide daily price observations, together with information about corporate events, corporate announcements and accounts. In the following analysis we include data for all equities listed at the Oslo Stock Exchange. We only use regular equities, not ETFs and other equity-like instruments. In 2012, the total crossection is 243 equities. We filter out those equities typically removed in asset pricing studies: Low priced stocks (Price below NOK 10), and stocks traded less than 20 days in the year. After filtering our sample contains 131 equities The filtering uses data described in Ødegaard (2014). 7

9 3 Trading in Stocks listed at the Oslo Stock Exchange In this section we give the institutional background. Our data set uses stocks with its main listing at the the Oslo Stock Exchange (OSE) in Norway. Norway is a member of the European Economic Area (EEA), and its equity market is among the 30 largest world equity markets by market capitalization. Notable Norwegian listings include Norsk Hydro, Telenor, and Statoil. At the beginning of the 2000s, OSE was the only market place for most Norwegian stocks, with the exception of a some of the largest companies who had also chosen to list their stock in the US markets. For example, Norsk Hydro had for may years also maintained a listing at NYSE, and the large privatizations of Telenor (The Norwegian state telephone company) and Statoil, the national oil company, were done with simultaneous listings at NASDAQ and the OSE. There were also a few companies maintaining dual listings in London and Stockholm, but the majority of OSE listed firms traded only at the OSE. This has changed in recent years. Like the rest of the worlds equity market places, OSE has seen major fragmentation of trading. Since Norway is a member of the EEA, the MiFID directive also applies to the Norwegian equity markets, meaning that OSE listed stocks can be traded at all European markets. This competition has resulted in the OSE steadily losing market share in trading of OSE-listed stocks, where in particular the large Norwegian stocks also trade in markets like Stockholm, London, Torquise, etc. 3.1 The Oslo Stock Exchange We will start by discussing the Oslo Stock Exchange in isolation, before looking at fragmentation. The OSE is the only regulated marketplace for securities trading in Norway. In figure 1 we show the evolution of the market in the period since [Figure 1 about here.] Since January 1999, the OSE has operated as a fully computerized centralized limit order book system similar to the public limit order book systems in e.g. Paris, Toronto, Stockholm and Hong Kong. 17 Their first trading system was ASTS. The technological solutions used on the exchange has evolved since, for example the exchange early allowed exchange members to facilitate direct order entry by individual traders (Internet trading). In 2002 the OSE introduced the OM trading system SAXESS. To bolster liquidity in smaller shares the OSE in 2004 introduced Designated Market Makers, where the listed firm pays a financial intermediary to maintain a minimum spread. 18 Unlike the other Scandinavian exchanges, the OSE has remained relatively independent, but it has since March 2009 had a strategic strategic partnership with the London Stock Exchange (LSE) to co-operate on marketing and product development. As a part of this agreement, OSE has used trading technology from the LSE since 2010, first using the TradElect system, and 17 For further background on trading at the Oslo Stock Exchange, and the companies on the exchange, see Bøhren and Ødegaard (2001), Næs and Skjeltorp (2006), and Næs, Skjeltorp, and Ødegaard (2011) 18 This was patterned on a similar introduction of DMMs in Stockholm. See Skjeltorp and Ødegaard (2013) for further details. 8

10 then, on November 12, 2012, the OSE moved to the Millennium platform, their second trading system from LSE. 3.2 The limit order book The OSE allows the use of limit orders, market orders, and various customary order specifications. 19 As is normal in most electronic order-driven markets, the order handling rule follows a strict price-time priority. All orders are submitted at prices constrained by the minimum tick size for the respective stocks which is determined by the price level of the stock. 20 The trading day of the OSE comprises two sessions: a pre-trade session and a continuous trading session. 21 During the pre-trade session, brokers can register trades that were executed after the close on the previous day as well as new orders. At the opening auction at the end of the pre-trade session, all orders registered in the order book are automatically matched if the prices are crossing or equal. The quoted opening price is thus the price that clears the market. During the continuous trading session, electronic matching of orders with crossing or equal price generates transactions. Orders without a limit price (market orders) have automatic price priority and are immediately executed at the best available prices. At the OSE, market orders are allowed to walk the book until they are fully executed. Any remaining part left of the market order is removed from the order book. This is different from the treatment of market orders on e.g. the Paris Bourse, where any remaining part of an unfilled order is automatically converted to a limit order at the current quote. The difference implies that market orders on OSE are more aggressive than market orders at the Paris Bourse. On the Paris Bourse, market orders are essentially marketable limit orders. 3.3 The liquidity of the OSE since 2000 To illustrate the longer term evolution of liquidity at the OSE, we plot two common measures of market quality, the relative (closing) spread and the turnover. The relative spread is measured as the difference between the best bid and best offer at the close, in percent of the current price. The turnover is the fraction of the stocks common stock outstanding traded during a time period, such as a quarter of a year. We illustrate these two measures as time series in figures figures 2 and 3. In both cases we plot two versions of the figure, one with an average for the whole exchange, the other with averages of groups sorted by company size. The bid/ask spread seems to follow the business cycle, we clearly see the bursting of the tech bubble early in the period, and the 2007/2008 financial crisis. The picture for turnover is different. It most pronounced feature is the gradual falling off since 2006, which is consistent with the increase of 19 For some early evidence on the working of the OSE order book, see Næs and Skjeltorp (2006). 20 For prices lower than NOK 9.99 (Norwegian kroner) the minimum tick size is NOK 0.01, between NOK 10 and NOK 49.9 the tick size is NOK 0.1, between NOK 50 and NOK the tick size is NOK 0.5 and for prices above NOK 1000 the tick size is NOK The opening hours has changed several times in the period since In 2000 the pre-trade session started at 9:30 and ending with an opening auction at 10:00, and the continuous trading session from 10:00 until the trading closed at 16:00. In 2008 the exchange extended their opening hours to close at 17:30, which increased the overlap with New York. In September 2012 they reversed this to close at 16:30, with a closing auction at 16:25. 9

11 fragmentation of OSE stocks trading, where both other exchanges and dark pools/otc market places have increased their trading of OSE stocks significantly in the period. [Figure 2 about here.] [Figure 3 about here.] 3.4 Movement of trading to other market places than the OSE Trading of stocks with a main listing at the OSE has become increasingly fragmented across various European market places. We will use data from Reuters to give some evidence on the extent of fragmentation. Reuters provide an approximate European equivalent of the US National Best Bid and Offer (NBBO), through what they call their XBO. This is a summary of the trading and quoting of the same stock on public limit order markets (lit markets). We emphasize that this number do not include trading in dark pools and other OTC markets, it is purely trading through market places with posted limit orders. In the data feed of the XBO Reuters provide a complete record of trades. The information on each trade include the price and quantity traded, as well as the exchange where the trade happened. In figure 4 we use this data to illustrate the evolution of fragmentation of trading in Norwegian Stocks. The top figure shows the (kroner) total trading for both the OSE, and the total trading (which includes OSE trading). Trading is the sum of trading throughout each quarter. In the bottom figure we show the proportions of the total trading volume done at the various market places. We see that the proportion of trading at the OSE has been falling throughout this time period. [Figure 4 about here.] It may here be instructive to look in more detail at one example company. Let us look at the largest company at the Oslo Stock Exchange, Statoil. In table 1 we describe where this stock traded in 2012, using the set of exchanges covered by Reuters. We provide the Reuters identifier (RIC) which uniquely identifies the underlying stock and the market place. In 2012 Statoil traded at 22 different trading places in Europe alone. This number does not include trading at American and Asian exchanges, nor trading in Dark Pools and other OTC markets. The biggest trading place amongst these is the OSE (STL.OL), which had about 54% of this trading. The next largest is the LSE (0M2Z.L), with 15%, followed by Stockholm (STLNOK.ST) and Chi-X (STLol.CHI), both with about 10% of this sample. Note that some market places provide unique services, such as STL.DEU, which is also trading at weekends, something that explain the number of trading days of 330 on this exchange. [Table 1 about here.] The Statoil example gives some perspective on the complexity of today s equity markets. Just finding the best bid and ask prices becomes a challenge, if one tries to search all the exchanges where one can potentially trade a stock. In the US equity markets one tries to enforce that the 10

12 NBBO (National Best Bid and Offer) at any point of time contains the best bid and ask prices, and it is the benchmark to judge whether e.g. institutional investors have best execution. But even in the US market there is an ongoing debate about the NBBO, with claims that some market participants observe the NBBO slightly earlier than others, early enough to trade ahead. In Europe there is no such regulated coordinating price. Reuters provide their XBO, but this is just Reuter s estimate of the best bid and ask prices, and has no regulatory role. This example also illustrates why fast communications and computing speed is an important feature of todays market place. There are some HFT firms which has a business model of looking for prices across markets that are out of sync, which they use to generate arbitrage profits. In such a world being the first to get execution is central. 3.5 Message to trade ratios The Message to trade ratio for a given stock is calculated by first counting the number of messages updating the order book, where the message may be all of adding a order to buy/sell, withdrawing an earlier order, changing price/quantity of an existing order, and so. This number is divided by the number of actual trades in the same stock on the same day. Alternative terminology include order to trade ratio and order to executed ratio, but we will use Message to Trade ratio as being more descriptive, since messages also include cancelling and modification of orders. In figure 5 we plot a summary of this number starting in The increase has been large, particularly for the largest stocks on the Exchange. It starts out in 2000 at about five messages (orders/order revisions/cancellations etc) per consummated trade, and ends up at about forty messages to trades for the largest firms on the exchange in For the smaller stocks the increase has been less marked, the quartile of smallest stocks have an order to trade ratio in the order of fifteen. Another interesting observation concerns the different evolution for small vs large stocks. In the early part of the period the message to trade ratio was lowest for the largest stocks. But this reverses around This is also when the high frequency traders entered the OSE in force. One type of HFT trader strategy involves arbitraging between quotes at different exchanges. It is only the larger stocks at the OSE which is quoted at (m)any other exchanges. This is one possible explanation for why it is currently the segment of larger stocks that has the highest message to trade ratios. [Figure 5 about here.] 4 Introduction of the Order to Executed Order Ratio at the OSE The introduction of the measure was announced by the Exchange on May 25, 2012, and seem to have been a surprise to the market. The announcement justified the introduction on efficiency grounds, that excessive orders were imposing indirect costs on all of the market participants. The full text of the press release is shown in figure 6. 11

13 [Figure 6 about here.] In addition to the press release, the OSE also give more details about the actual fees and how the calculation is done. 22 The calculation is done on a monthly basis. The actual fee is NOK 0.05 per order that exceeds the ratio of 70:1 during the month. In the calculation the OSE does not count every order. Specifically, the following activity is not counted: Orders that rest unchanged for more than one second from order entry. Order amendments that improve price, volume, or both. Execute and Eliminate (ENE) and Fill or Kill (FOK) orders What does count Orders residing less than one second, from order insert or the last amendment, before cancelation Order amendments that degrade price, volume, or both, of an order that has resided for less than one second in the trading system. The way executed orders are counted is also specified Orders that result in one or many transactions are counted as one executed order Executed orders, orders that have been involved in one or more trade, but with total executed value of less than NOK 500 will not be counted as an executed order. The specifics of the Norwegian regulation makes it clear that the exchange wants to differentiate between different types of HFT traders. If we relate this back to the typical strategies of HFT traders discussed before, this clearly will be less onerous for market-making like strategies. Market making involve placing orders to buy and sell in the limit order book, hoping to earn the spread. When prices change, these orders are updated. If the market maker maintains the spread, and places new bid and asks centered around a different different price, one of the bid and ask will be price improving, and not be counted. So for market makers maintaining the same spread, only half of the new orders will count towards calculating the OEOR. Similarly, when there is little activity in the market, market makers orders are likely to stay in the order book for longer than one second, and therefore also not count towards the OEOR. A HFT strategy that is more likely to get a high OEOR is the type of relative value strategy we discussed earlier, where the HFT traders jumps at price discrepancies, for example between the order book in Oslo and the order book for the same stock at Chi-X. The HFT strategy is then to send orders to both exchanges at current prices, orders that needs to be filled immediately. Such orders are neither price improving nor long lived, and will all count towards the OEOR. 22 Oslo Børs to Implement Order to Executed Ration, downloadable from the OSE web site (oslobors.no). 12

14 In microstructure terminology, the Norwegian regulation is designed to reward liquidity provision. In that sense it is similar to the payment for order flow used at other exchanges, where those orders judged to provide liquidity will get a cost advantage. The exact implementation of the Norwegian Order to Executed Order Ratio is different from the Italian regulation. The Italian ratio is calculated on a daily basis. 23 The ratio is set at 100:1. For ratios between 100:1 and 500:1 the charge is e0.01. If the ratio is even higher, between 500:1 and 1000:1, the charge is e0.02, and for ratios above 1000:1 the charge is e The fees are capped at e1,000 per firm-day. The Italian regulation is much less designed to differentiate between different types of trading strategies, and has more the flavor of a blanket transaction tax. It is therefore interesting to see whether these differences are large enough to change the results in Friederich and Payne (2013). 5 What happens around the introduction of the tax? Empirically, our analysis will proceed in three steps. We first, in this section, look at the OSE in isolation, and see whether the introduction of the OEOR changes traders behaviour, by looking at the Message to Trade ratio for the exchange, depth, order size, etc, at the time of the introduction of the OEOR. In the next section we look at measures of the quality of trading at the exchange, such as spreads, trading costs and variability of prices, and see if these changes. Finally, we bring in trading of OSE listed stocks outside of Oslo, since this allows a direct test for the effect of the policy intervention, by comparing what happens in Oslo to the alternative market places, markets which did not introduce the fee on message traffic. There are two dates of interest: The announcement of the measure on 24 may, and 1 sep, the day on which the measure was implemented. Market participants thus had three months to change their computer systems to take into account the additional fee. 5.1 Message to trade ratios Let us first look at the the message to trade ratio, which is what the policy change is meant to lower. We expect this to change. We calculate such a ratio for the aggregate trading in a stock, by counting the total number of messages for each stock, and estimating this a a fraction of the total number of trades, to investigate if overall activity falls. This is similar to the number the regulation is designed around, although the tax at ratios above 70 to one is calculated on a trader by trader basis, and not all trades used in this calculation count towards the calculation of the OEOR at the OSE. In figure 7 we show the evolution of daily crossectional averages of message to trade ratios during We show averages for the whole exchange, and for size sorted portfolios. In both pictures the two dates of interest, 24th May and 1st Sep, are indicated by vertical bars. In particular the average across all stocks on the exchange shows that the aggregate message to trade ratio fell from the announcement date to the implementation date, and that the message 23 The following is taken from the discussion in Friederich and Payne (2013). 13

15 to trade ratio declined significantly after the implementation. This visual evidence is confirmed in table 2, where we calculate the average message to trade ratio for the three subperiods, and test for equality. The average fell from 23 before the announcement of the tax, to 19 after the implementation. This difference is highly statistically significant. However, if we look at the portfolios grouped by firm size, we see that the decline is only for the larger half of the stocks, for the smaller half of the stocks there has actually been an increase in the message to trade ratios. Another observations is the the median trade (in square brackets) do not show much change between period 1 and 3, the median Message to Trade Ratio for period 1 is 14, increasing to 14.4 after the introduction of the OEOR. [Figure 7 about here.] [Table 2 about here.] 5.2 Who is trading? A possible effect of the regulation is that traders who are heavy users of automated trading, such as market makers and other HFT traders, lowers their presence at the OSE. In the OSE data, traders are grouped into automated and non-automated. We calculate the fraction of the trades involving automated trading at one or both sides. This fraction is illustrated in figure 8. For the whole exchange we see a slight increase in the fraction of trades involving algorithms in the adjustment period, but this falls again after the introduction of the fee in September. However, this results masks some crossectional differences. Looking at the stocks grouped by firm size, the fraction of automated has actually significantly increased for the quartile of smallest stocks, but has fallen for the larger half of the stocks on the OSE. [Figure 8 about here.] 5.3 Are the traders increasing order size? A possible way for traders to react to the order to trade ratio is to increase the order size. If traders are filling larger orders, they need less messages. To investigate this we look at the average trade size. The results are shown in Table 2 and Figure 9. The average trade size has increased, from 47 thousand to 56 thousand NOK, but note that the change in the median trade is small from 28,025 to 28,809. Crossectionally, there is the interesting difference that the increase in trade size is for the smaller stocks. For the quartile of largest stocks the average trade size has fallen. [Figure 9 about here.] 5.4 Are traders reducing depth? An alternative way for traders to react is to reduce the amount at the best bid and best ask, and instead use orders slightly away from the best prices. Price improvements of these orders 14

16 will not count towards the limit on OEOR. It may therefore be part of a strategy reacting to the introduction of the tax. To investigate this we look at the behavior of the complete order book. A liquid market will not just have willingness to buy and sell at the current best bid and ask, but also willingness to buy and sell larger quantities if the price move (elasticity). We therefore calculate two statistics, first the (kroner) depth at the best bid and asks, and secondly what fraction this volume at the best bid and ask is of total trading interest. To calculate this last we look at the quantities in the order book around the current inner spread, specifically six ticks away from the current best bid and ask prices. 24 To estimate a number comparable across stocks we must avoid basing it on either number of shares or total volume. We therefore normalize by calculating the fraction of total volume which is at the best bid and ask. Take for example a stock with the current best bid of 100 and volume of 100 at that price. If the only other volume in the order book is for 100 shares at 99, the fraction at the best bid is 50%. We calculate this fraction at all points of time with a trade, and take daily averages of these fractions. The results are shown in Table 2 and Figures 10 and 11. We see no sign of withdrawing of liquidity from the inner spread after the introduction of the tax, rather the opposite, in fact. The absolute trading interest in kroner at the inner spread increases slightly, at the same time as the fraction of trading interest resting outside the best bid and ask is increasing. The latter fact is consistent with some traders changing their strategies to keep orders resting slightly outside the spread. But as long as the total depth is not going down, this is good for market quality. [Figure 10 about here.] [Figure 11 about here.] 6 What happens to the quality of trading at OSE? Let us now look at the evolution of measures of trading quality at the OSE. We will consider four different measure, all calculated from the trade by trade data: The relative spread, the Roll estimate of trading costs, realized volatility and finally the stock turnover. The relative spread measures the cost of trading. The spread is the difference between the best bid and best ask, and is the cost one expect to pay when submitting a market order. To make this cost comparable across stocks it is common to measure it as a percentage of the stock price. In our work we want a measure of the prevailing spread during the day as a measure of the cost faced by a random trader. For a given stock, we therefore calculate the inner relative spread, the difference between best bid and best ask as a fraction of the current price, at all times with changes in the order book. As an estimate of the prevailing spread for the stock we take the average of these observations. 24 The tick size is an increasing function of stock price. The exchange sets the tick size based on price intervals. 15

17 In figure 12 we show averages across the stocks on the OSE of this daily relative spread. The figure shows the evolution during 2012 for both the whole exchange (panel A) and size sorted portfolios (panel B). Visually there does not seem to be any major changes in spreads around the introduction. Looking at the numerical estimates, for the whole market the relative spread went slightly up, from to , an increase which is statistically significant. Looking at the portfolios we find different conclusion depending on firm size. For the largest quartile the average spread has fallen slightly, from from to for the largest portfolio, but for quartile 3, the next to largest smallest stocks, the spread increased from to , again statistically significant. But in economic terms, these are small magnitudes. [Figure 12 about here.] The relative spread does however not provide the complete picture. Here we see that the spread is almost unchanged, but we do not know whether this spread is valid for the same number of shares, we need to control for the depth of the market. This information was shown in figures 10 and 11, where we showed the total (NOK) volume at the best bid and ask, and also the trading interest close to the best bid and ask. We did not see any major changes in the depth, and the trading interest just outside the spread actually increased as a fraction of the trading interest at the inner spread. So the spreads we measure are valid for similar amounts as before. [Table 3 about here.] Let us now look at an alternative to the relative spread, the Roll measure of trading costs. The Roll measure uses the bouncing between bid and ask prices to infer an implicit spread. We calculate this using all trades for one stock during a day, and take crossectional averages. Table 3 and Figure 13 shows the resulting averages. For the Roll measure, there are no discernible changes across these subperiods, neither for the aggregate market nor for the size portfolios. [Figure 13 about here.] Another measure of market quality is the variability of prices during the day. A common measure of this is the realized volatility (RV). We calculate RV using the daily trading record for each stock. This daily estimate is then averaged across stocks. Figure 14 shows the evolution of RV. Also for RV there seem to be no increase after Sep 1. There seem to be an increase in variability just after the announcement of the measure on May 24th, but this did not persist till the introduction on Sep 1. This increase in volatility may also be a seasonal phenomenon due to the holiday in July. We saw a similar increase in RV in the summer months of When we calculate the subperiod averages, we actually see that the realized volatility has fallen significantly, for both the market and the three largest portfolios. So by the Realized Volatility metric the message to trade ratio introduction does not seem to have lowered market quality. [Figure 14 about here.] 16

18 The final market quality measure we look at is trading activity, measured by daily turnover. Each day, we aggregate the number of shares traded, and calculate its fraction of shares outstanding. In figure 15 we show crossectional averages of this. Here we see that there seem to be some lowering of turnover after September. We are however not sure that this is due to the introduction of the OEOR. There may be (at least) two explanations of this. One is that there is a decline in the total interest in trading equities, unrelated to the OEOR. This is actually something we see in other markets, a decline in turnover for this period. 25 Another is that this is related to the introduction of the OEOR, caused by movement of trading interest away from the OSE to other exchanges without the tax. To investigate this carefully we also need to look at equity trading outside of the OSE. [Figure 15 about here.] 7 Comparing the effect at OSE to other exchanges When we looked at the OSE in isolation, we do not find any major changes in market quality. To more formally evaluate the effect of this policy intervention we need to compare the effect on the Oslo Stock Exchange with a control group that is not exposed to this policy intervention. The object of interest is the quality of trading at the OSE. Investors wanting to trade stocks listed in Oslo can choose to go elsewhere. If the effect of the policy intervention is to reduce traders use of the OSE, we expect to see improvement in trading quality at the alternative market places. We therefore compare measures of trading quality at the OSE with the same quality measure at other exchanges Before we go to a formal analysis, let us look at an example. Take the trading in Statoil. We earlier illustrated how the 2012 volume of Statoil trading was divided across European exchanges, in table 1. If traders are moving away from Oslo, we expect price discovery to improve on the market places with more trading interest. This will be reflected in liquidity measures, such as the relative spread. Let us therefore look at the evolution of the relative spread for Statoil at the three largest exchanges for Statoil in our sample: OSE, Stockholm and Chi-X. 26 [Figure 16 about here.] In Figure 16 we illustrate daily spreads at these three market places. The results are somewhat surprising. Spreads seem to stay the same at the OSE, but for the other two market places spreads are increasing and more volatile across days. Trading quality for Statoil is deteriorating at market places outside the OSE. Since the introduction of the tax only concerns trading at the OSE, we should expect the opposite, that market places without the tax should be the ones to see the improvements, and that is the test we will perform to evaluate the policy intervention: Comparing market quality at 25 See e.g. the numbers for the US in Angel et al. (2013). 26 Although London is actually larger than both Stockholm and Chi-X in terms of trading volume, there is no limit order book for Statoil at the LSE, so we can not calculate spreads there. 17

19 the OSE with market quality of trading of the same stock in other market places. As the Statoil example showed, there are many available market places, but trading will be concentrated at a few. To make a fair comparison we compare trading at the OSE with trading at the exchange with the largest (in volume terms) trading outside of Oslo. We calculate the percentage change in the average spread from the period before the tax was announced (January-May 2012) to the period after it was implemented (September-Dec 2012), and compare this for stocks on the OSE with the same stock traded on the most active exchange for that stock. Here we can rely on a standard econometric methodology for evaluating policy interventions, which works by comparing observations for which the policy applies, to observations the policy does not apply to. In the econometric literature this is called a diff in diff analysis. 27 Let us use the notation in (Greene, 2012, pg 933). We sant to look at determinants of a liquidity measure, or some other measure of trading quality y it : y it = θ t + X itβ + γc i + u i + ɛ it (1) The liquidity measures y it can be explained by a set of controls X it. In our application this will be variables related to the stock, such as its variability, the size of the underlying company, degree of asymmetric information, and so on. The treatment variable is indicated by the dummy variable C i, equal to one for Oslo after 1 sep. In this formulation u t is some unobserved individual effect. To get rid of this individual effect analysis is done on the differenced version of the regression (1): y it = (θ 1 θ 0 ) + ( X it ) β + γ C i + ɛ it (2) This will remove any individual effects. However, in our application we can simplify this estimation further. We have a matched sample of observations of trading of the same stock in two markets, for example Statoil in Oslo vs Statoil in Stockholm. When we take the difference of the matched observations in equation (2), since the control variables (X it ) are the same for both cases, the control variable falls out of the estimation. We can therefore use γ = [ y ( C = 1)] [ y ( C = 0)], the simple difference in differences estimator, to estimate the effect of the policy intervention. The results for relative spread are summarized in Panel A of Table 4. The complete results are given in Appendix B. Essentially the results confirm that the Statoil example illustrated in Figure 16 is not an isolated one. Relative spreads in Oslo increased 5%, an increase which is not statistically significant, compared to an increase of 30% in spreads on the matched exchange, an increase which is highly statistically significant. This is confirmed with the estimated γ, the difference in differences estimator, reported in the last column. So we soundly reject the hypothesis that trading quality, as measured by the spread, improves more on the exchanges which do not have the tax. The opposite seems to be the case. Quality deteriorates outside the main exchange. 27 E.g. (Angrist and Pischke, 2008, Section 5.2) or (Greene, 2012, Ch 19). 18

20 Let us speculate a bit on why we may get this result. One segment of the HFT industry are using a strategy of arbitraging between prices on different exchanges. This type of trading strategy need to maintain a continuous presence both at the main exchange (OSE) and other exchanges trading the same underlying asset. If it becomes potentially more costly to maintain the link between Oslo and other exchanges, traders on the other exchanges may become more sceptical about maintaining orders in the book at the other exchanges. Withdrawal of HFT traders doing such relative value trading will reduce the liquidity at these other exchanges. Of course we should not just look a the spread. As another measure of trading quality we look at depth, the number of shares available for trading at the best bid and best ask. We find the number of shares (volume) at the best bid and best ask in the order book at each time with a change in the order book. Depth is the sum of the volume at bid and at ask. Our daily measure of depth is the average of these. Note that we do not calculate the depth in the more usual way of multiplying with the prices to get a depth measure in NOK. This is because prices can be in different currencies. For the purposes of our test for differences between Oslo and other exchanges this is unproblematic, since we are matching the stocks before taking differences. In panel B of Table 4 we show the results of the same test for differences between Oslo and the largest alternative exchange, looking at (percentage) change in the depth. The results for depth are different from those for relative spread. None of the tests are significant. So magnitudes of depth do not seem to change, unlike spreads. A third liquidity measure we compare is turnover, the fraction of the stock s outstanding equity traded at a given exchange. In panel C of Table 4 we show the results for a diff in diff analysis of how turnover is changing. Here turnover is falling at both Oslo and the alternative exchanges. This may be due to a secular trend in trading of equities, this decline in not limited to Oslo, for example the US has seen a similar fall in equity trading for the same period. 28 The interesting statistic, however, is the Diff in Diff comparison of the change at the OSE relative to the comparison exchange. Here we see no significant differences, the fall is the same. [Table 4 about here.] The conclusion of our analysis is essentially that nothing much happens when the Oslo Stock Exchange introduces the OEOR. This is remarkebly different from the Italian case analyzed in Friederich and Payne (2013). They find that liquidity significantly worsen when the Milan stock exchange implements the similar regulation. For example, they find a 25% increase in relative spreads, and a 15% decrease in depth 29 Our research design does not allow us to delve deeper into the possible causes of these differences. A possible reason for the differences in the design of the rules, where the Oslo Stock Exchange rules are encouraging liquidity provision to a much larger degree than the Italian rules. 28 See (Angel et al., 2013, pg 4). 29 (Friederich and Payne, 2013, pg 15). 19