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 ([email protected]), 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.
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 - 14-01-2014 - 23:47.
1
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
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 70.8 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. In
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, 2013
10
For some academic surveys of the literature on market fragmentation see (US Securities and Exchange
Commision, 2013; Gomber, Sagade, Theissen, Weber, and Westheide, 2013).
3
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
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.
The first, market making, is to post limit orders on both sides of the electronic limit order
book. 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. For
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.”
http://www.europarl.europa.eu/news/en/news-room/content/20140110IPR32414.
12
A well known example is “agency” algorithms used by institutional investors to split their large trades into
pieces.
5
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
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 2012. 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 1999–2012. 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 2012.
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.16
16
The filtering uses data described in Ødegaard (2014).
7
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 2000.
[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
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).
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 999.5 the tick size is NOK 0.5 and for prices
above NOK 1000 the tick size is NOK 1.
21
The opening hours has changed several times in the period since 2000. 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.
20
9
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
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 2000. 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 2012. 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 2008. 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
[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
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 e0.025.
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 2012. 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
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
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
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 0.0445 to 0.0456, 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 0.0142 to 0.0134 for the largest portfolio, but for
quartile 3, the next to largest smallest stocks, the spread increased from 0.0321 to 0.0333, 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 2011.
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
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).
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.
26
17
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 yit :
yit = θt + X0it β + γCi + ui + it
(1)
The liquidity measures yit can be explained by a set of controls Xit . 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
Ci , equal to one for “Oslo after 1 sep.” In this formulation ut is some unobserved individual
effect. To get rid of this individual effect analysis is done on the differenced version of the
regression (1):
∆yit = (θ1 − θ0 ) + (∆Xit )0 β + γ∆Ci + ∆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 (Xit ) are the same for both
cases, the control variable falls out of the estimation. We can therefore use
γ
b = [∆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
b,
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
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 depth29 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
29
See (Angel et al., 2013, pg 4).
(Friederich and Payne, 2013, pg 15).
19
8
Conclusion
We have investigate the introduction by the Oslo Stock Exchange of a fee specifically targeted at
some High Frequency Traders. The fee was payable by traders whose messages to the exchange’s
limit order book exceeded 70 times the number of actual trades these messages resulted in. Such
a fee has been proposed by several policy makers as a way for the heavy users of the system to
pay some of the costs they are imposing on the system (and other traders).
The fee was introduced at the OSE in September of 2012. In our empirical work we calculate
various measures of trading activity, liquidity and quality of the trading process, and compare
before and after. We find that the average message to trade ratio for the market fell markedly,
and that trade size (number of shares traded) increased. This is consistent with the traders
present at the exchange making changes to their order submission strategies to lower the risk of
paying the fee.
The risk of introducing a fee such as the OEOR is that it negatively affect the quality of
trading at the OSE, which it would if the current market environment makes it necessary to
use strategies which risk paying the fee. We therefore calulate a number of measures for trading
quality, such as depth, spread, trading costs, volatility and turnover.
Overall we find no negative changes in market quality that coincides with the introduction of
the tax. The depth (the kroner trading interest at the best bid and ask prices) was unchanged.
The trading interest in the limit order book away from the best bid actually increased. For the
largest stocks on the exchange the relative spread fell slightly. The spread increased for some of
the smaller stocks, but in both cases the magnitude of the change was small. The Roll measure,
which estimates the cost of trading equity, did not change. Variability, measured by relative
volatility, actually fell slightly. The major change is a fall in trading volume (turnover). The
Oslo Stock Exchange has however been steadily losing volume to other exchanges and dark pools
the last years, so it is by no means certain that this is related to the introduction of the tax.
Since professional traders can easily avoid the OSE tax by moving their trading to other
market places, we would expect trading to increase (and liquidity to improve) on alternative
market places. We investigate this hypothesis using data from Reuters on trading of OSE listed
stocks on other market places. We implement a “diff in diff” analysis, where we compare the
evolution of liquidity (spread) at the OSE with the spread in the same stocks on the largest “lit”
market outside of the OSE. Surprisingly, we find the opposite result. Liquidity deteriorates in
the alternative market places. We speculate that this may be due to a “decoupling” of trading
across exchanges, if HFT traders doing arbitrage between Oslo and other market places fear
paying a cost on the “leg” of their trade that involves the OSE.
Doing the same type of analysis on the policy effect on two other liquidity measures, depth
and turnover, we find no effect on these two variables. The last, turnover, suggests that the
decline in turnover we saw at the OSE is not related to the introduction of the OEOR, since the
turnover fell as much in the alternative market places.
The experience of the Oslo Stock Exchange seem to be very different from the experience
when a similar measure was introduced at the Milan Stock Exchange. In Italy the exchange saw
20
a decline in liquidity around their introduction of an “Order to Trade Ratio.” In Oslo we see
little sign of such a deterioration. This may be due to differences in the design of the measures,
where the Oslo regulation is more designed to encourage liquidity provision, such as HFT market
making strategies.
21
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Competition/fragementation in equities markets: A
literature survey. Working Paper, House of Finance,
Goethe University, November 2013.
William H Greene. Econometric Analysis. Pearson,
seventh edition, 2012.
Björ Hagströmer and Lars Nordén. The diversity of
high frequency traders. Journal of Financial Markets, 16:741–770, 2013.
Terrence Hendershott, Charles M Jones, and Albert J
Menkveld. Does algorithmic trading improve liquidity. Journal of Finance, LXVI(1), February 2011.
Charles M Jones. What do we know about highfrequency trading? Working Paper, Columbia Business School, March 2013.
Boyan Jovanovic and Albert J Menkveld. Middlemen
in limit-order markets. Working Paper, November
2012.
Andrei A Kirilenko, Albert S Kyle, Mehrdad Samadi,
and Tugkan Tuzun. The flash crash: The impact
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Working Paper, SSRN, May 2011.
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trading on market quality. In Annual Review of Financial Economics, volume 4, pages 59–98. Annual
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Albert J Menkveld. High frequency trading and the
new market makers. Journal of Financial Markets,
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Randi Næs and Johannes A Skjeltorp. Order book characteristics and the volume–volatility relation: Empirical evidence from a limit order market. Journal
of Financial Markets, 9:408–432, 2006.
Randi Næs, Johannes Skjeltorp, and Bernt Arne Ødegaard. Stock market liquidity and business cycles.
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Johannes Skjeltorp and Bernt Arne Ødegaard. Why
do listed firms pay for market making in their own
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23
Appendices
A
Variable Definitions
In this appendix we give more detailed definitions of the variables used in the text.
• Depth
The trading interest at the best bid and ask. Either in number of shares, in which case it
is the sum of number of shares at the best bid and ask. It can also be in currency, in which
case it is the number of shares at the best bid times the best bid price plus the number of
shares at the best ask times the best ask price.
• Fraction Automated Traders
For each trade, we check whether either the buyer or seller (or both) is marked by the
exchange as an automated trader. If it is we count this trade as automated. We sum the
number of such trades, and calculate the fraction it is of all trades. In these calculation
we do not weight by the number of shares in a given order.
• Fraction at Inner Spread
At each trade we look at the inner seven levels of the order book (the best bid and best
offer plus six ticks up and down), and calculate the fraction of the total volume at these
seven levels that is at the best bid and ask.
• Message to Trade Ratio
From the order file we count the total number of messages for a particular stock. This is
divided by the number of trades. In these calculation we do not weight by the number of
shares in a given order.
• Realized Volatility
The realized volatility is the second sample moment of the return process over a fixed
interval of a chosen length. (See (Andersen, Bollerslev, and Diebold, 2010)) for a recent
survey of the RV literature.) In our calculation we use one day of trading. To introduce
notation, we collect all price observations {Pt } during the day. Returns are calculated by
splitting the trading day into fifteen minute intervals and use the prevailing price at that
time. The return is the log price difference between these prices.
rt = ln(Pt ) − ln(Pt−1 )
The Realized Volatility is calculated as the sample variance of this returns process.
• Relative Spread
Let P a be the prevailing ask price, and P b the corresponding bid price. The Relative
Spread is the difference between these, divided by the average of the prices
RelSpread =
Pa − Pb
P a +P b
2
In the analysis we have calculated the relative spread for different samples.
– The closing: Calculated based on the best bid and best ask at the close.
24
– During the trading day. Here we use all times with an update of the state of the
limit order book. For each such update we calculate the relative spread using the
currently best bid and best offer. Our estimate of that day’s relative spread is the
sample average of these relative spreads during the day.
• Roll trading cost measure
The Roll measure is an estimate of trading cost that uses the autocovariance induced by
bid/ask bounce to estimate the size of the implicit spread between bid and ask prices. We
collect all
In our calculation we use one day of trading. To introduce notation, we collect all price observations {Pt } for each trade during the day. Let rt be the return between each successive
trade. The Roll spread estimator is
p
ŝ = 2 −cov (rt , rt+1 )
We only use observations where the autocovariance cov (rt , rt+1 ) is negative.
• Trade Size
The size of each trade, either in number of shares, or in currency.
• Turnover
Each day we count the number of stocks traded. This is divided by the number of shares
outstanding to get the daily turnover. To aggregate this for longer time periods we simply
sum the daily turnover, so that for example the monthly turnover is the sum of observed
daily turnovers for that month.
25
B
Details on the “diff in diff” analysis
We provide further details on the analysis summarized in table 4 in the paper.
This table provides detailed results for the analysis summarized in table 4 in the paper. We list the RIC (Reuter Identifier)
for companies on the Oslo Stock Exchange at the left, and a comparison listing on the right. We choose the exchange
outside of Oslo with the largest volume in 2012 to make the comparison. For each stock we show the annual volume (in
number of shares), and the average relative spread for three subperiods: Jan-May20, May21-Aug, Sep-Dec.
RIC
AKER.OL
AKSO.OL
ALGETA.OL
ARCHER.OL
ATEA.OL
AVM.OL
BWO.OL
CEQ.OL
DETNOR.OL
DNO.OL
EMGS.OL
FOE.OL
FRO.OL
GJFS.OL
GOGL.OL
KOA.OL
KVAER.OL
LSG.OL
MHG.OL
NHY.OL
NPRO.OL
NSG.OL
NWC.OL
OPERA.OL
ORK.OL
PGS.OL
PRON.OL
PRSO.OL
QEC.OL
RCL.OL
REC.OL
SBST.OL
SDRL.OL
SEVAN.OL
SNI.OL
SONG.OL
STB.OL
STL.OL
TEL.OL
TGS.OL
TOM.OL
VEI.OL
WRLT.OL
YAR.OL
Oslo Stock Exchange
2012 Annual
volume (in
Relative Spread
mill shares)
Jan-May
May-Aug
Sep-Dec
17.69
0.0047
0.0051
0.0043
356.15
0.0012
0.0015
0.0014
35.09
0.0026
0.0034
0.0039
211.87
0.0067
0.0089
0.0090
47.85
0.0051
0.0068
0.0066
50.36
0.0080
0.0092
0.0082
361.09
0.0062
0.0085
0.0074
32.67
0.0037
0.0055
0.0062
93.27
0.0042
0.0046
0.0029
999.34
0.0023
0.0025
0.0024
461.63
0.0071
0.0072
0.0068
39.53
0.0017
0.0018
0.0016
95.74
0.0044
0.0054
0.0059
138.25
0.0014
0.0016
0.0013
284.38
0.0061
0.0086
0.0063
580.79
0.0089
0.0102
0.0106
221.96
0.0073
0.0067
0.0070
7.43
0.0116
0.0131
0.0094
6187.20
0.0017
0.0014
0.0013
1287.40
0.0008
0.0011
0.0010
195.70
0.0080
0.0084
0.0054
385.71
0.0086
0.0107
0.0106
46.26
0.0060
0.0066
0.0050
103.55
0.0056
0.0058
0.0056
515.78
0.0008
0.0010
0.0008
552.08
0.0012
0.0013
0.0010
82.35
0.0135
0.0136
0.0117
128.55
0.0027
0.0025
0.0023
185.32
0.0096
0.0103
0.0110
180.45
0.0011
0.0015
0.0012
4005.78
0.0031
0.0037
0.0042
64.32
0.0017
0.0020
0.0019
326.32
0.0007
0.0008
0.0007
64.73
0.0151
0.0270
0.0140
11.64
0.0108
0.0127
0.0122
382.65
0.0065
0.0047
0.0050
880.92
0.0015
0.0018
0.0015
1107.28
0.0007
0.0008
0.0008
590.67
0.0010
0.0010
0.0011
133.42
0.0015
0.0016
0.0012
48.48
0.0052
0.0063
0.0059
20.16
0.0087
0.0096
0.0080
50.33
0.0206
0.0381
0.0339
325.56
0.0008
0.0008
0.0007
26
RIC
AKENOK.ST
AKSOo.CHI
ALGETo.CHI
ARCHEo.CHI
ATEANOK.ST
AVM.L
BWOol.BD
CEQol.TQ
DETNORol.TQ
DNOo.CHI
EMGSol.TQ
FOEo.CHI
FROo.CHI
GJFo.CHI
GOGLNOK.ST
KOANOK.ST
KVAERol.TQ
LSGol.TQ
MHGo.CHI
NHYo.CHI
NPROo.CHI
NSGo.CHI
NWCol.BD
OPERAol.TQ
ORKo.CHI
PGSo.CHI
PRONol.TQ
PRSo.CHI
QEC.TO
RCLo.CHI
RECo.CHI
SCHo.CHI
SDRLo.CHI
SEVANo.CHI
SNIol.TQ
SONGo.CHI
STBo.CHI
STLNOK.ST
TELo.CHI
TGSo.CHI
TOMo.CHI
VEIol.TQ
WRLW.L
YARo.CHI
Comparison Exchange
2012 Annual
volume (in
Relative Spread
mill shares)
Jan-May
May-Aug
Sep-Dec
2.06
0.0077
0.0080
0.0069
63.88
0.0017
0.0024
0.0022
3.27
0.0045
0.0056
0.0060
10.27
0.0152
0.0248
0.0316
3.96
0.0069
0.0084
0.0094
315.73
0.0046
0.0051
0.0065
0.29
0.0133
0.0355
0.0286
5.80
0.0065
0.0063
0.0063
2.34
0.0320
0.0192
0.0115
83.36
0.0033
0.0041
0.0039
32.50
0.0159
0.0168
0.0129
9.43
0.0026
0.0034
0.0039
15.94
0.0068
0.0087
0.0083
14.09
0.0020
0.0039
0.0046
37.50
0.0066
0.0088
0.0071
17.33
0.0124
0.0122
0.0121
21.35
0.0107
0.0093
0.0080
0.20
0.0295
0.0264
0.0188
1010.04
0.0021
0.0019
0.0021
262.29
0.0009
0.0014
0.0023
14.99
0.0088
0.0109
0.0089
15.97
0.0125
0.0166
0.0157
0.09
0.0184
0.0133
12.52
0.0106
0.0071
0.0066
120.81
0.0009
0.0017
0.0018
124.55
0.0013
0.0017
0.0017
0.84
0.0415
0.0183
0.0143
35.32
0.0032
0.0033
0.0031
22.44
0.0288
0.0454
0.0430
14.11
0.0024
0.0033
0.0025
330.13
0.0042
0.0053
0.0064
12.57
0.0021
0.0032
0.0033
76.48
0.0009
0.0013
0.0015
0.76
0.0223
0.0405
0.0273
0.18
0.0237
0.0140
0.0134
27.18
0.0064
0.0114
0.0156
141.93
0.0023
0.0035
0.0037
220.56
0.0008
0.0010
0.0011
159.48
0.0011
0.0014
0.0023
37.26
0.0019
0.0026
0.0017
5.15
0.0061
0.0086
0.0104
0.30
0.0171
0.0208
0.0134
8.33
0.0803
0.1064
0.1007
71.99
0.0009
0.0014
0.0020
This table provides detailed results for the analysis summarized in Panel B of Table 4 in the paper. We list the RIC
(Reuter Identifier) for companies on the Oslo Stock Exchange at the left, and a comparison listing on the right. We choose
the exchange outside of Oslo with the largest volume in 2012 to make the comparison. For each stock we show the annual
volume (in number of shares), and the average depth(no shares) for three subperiods: Jan-May20, May21-Aug, Sep-Dec.
RIC
AKER.OL
AKSO.OL
ALGETA.OL
ARCHER.OL
ATEA.OL
AVM.OL
BWO.OL
CEQ.OL
DETNOR.OL
DNO.OL
ECHEM.OL
EMGS.OL
FOE.OL
FRO.OL
GJFS.OL
GOGL.OL
KOA.OL
KVAER.OL
LSG.OL
MHG.OL
NHY.OL
NPRO.OL
NSG.OL
NWC.OL
OPERA.OL
ORK.OL
PGS.OL
PRON.OL
PRSO.OL
QEC.OL
RCL.OL
REC.OL
SBST.OL
SDRL.OL
SEVAN.OL
SNI.OL
SONG.OL
STB.OL
STL.OL
TEL.OL
TGS.OL
TOM.OL
VEI.OL
WRLT.OL
YAR.OL
Oslo Stock Exchange
2012 Annual
volume (in
Depth
mill shares)
Jan-May
May-Aug
17.69
2601
2260
356.15
3373
3103
35.09
1081
917
211.87
28581
17212
47.85
11459
7487
50.36
10346
8882
361.09
15098
12858
32.67
1247
3498
93.27
7900
2730
999.34
14466
11869
941.87
5726280
13413409
461.63
46240
26488
39.53
579
572
95.74
1701
1832
138.25
4133
3479
284.38
25105
22118
580.79
112342
154010
221.96
37992
22259
7.43
1164
1879
6187.20
61068
46108
1287.40
9226
8614
195.70
8177
8012
385.71
14425
16557
46.26
3716
4309
103.55
6770
4938
515.78
4707
4480
552.08
5705
4466
82.35
6466
9761
128.55
1885
1757
185.32
16908
18786
180.45
2988
2032
4005.78
17152
24025
64.32
929
867
326.32
4094
3130
64.73
10960
7361
11.64
2849
2313
382.65
25304
11830
880.92
4320
5753
1107.28
27420
17305
590.67
14686
9753
133.42
1478
1451
48.48
3857
3284
20.16
3058
2605
50.33
14387
12684
325.56
2105
1562
RIC
Sep-Dec
2543
4461
725
11680
7729
8394
27616
3457
1812
13522
10438029
35911
561
2364
3061
27169
153443
39381
1981
42255
8588
9027
13088
8853
8021
4246
4308
309917
1627
19166
1462
40432
796
3300
9808
2456
8245
4244
18316
14537
1637
3957
3794
31373
1717
27
AKENOK.ST
AKSOo.CHI
ALGETo.CHI
ARCHEo.CHI
ATEANOK.ST
AVM.L
BWOol.BD
CEQol.TQ
DETNORol.TQ
DNOo.CHI
ECHEMol.BD
EMGSol.TQ
FOEo.CHI
FROo.CHI
GJFo.CHI
GOGLNOK.ST
KOANOK.ST
KVAERol.TQ
LSGol.TQ
MHGo.CHI
NHYo.CHI
NPROo.CHI
NSGo.CHI
NWCol.BD
OPERAol.TQ
ORKo.CHI
PGSo.CHI
PRONol.TQ
PRSo.CHI
QEC.TO
RCLo.CHI
RECo.CHI
SCHo.CHI
SDRLo.CHI
SEVANo.CHI
SNIol.TQ
SONGo.CHI
STBo.CHI
STLNOK.ST
TELo.CHI
TGSo.CHI
TOMo.CHI
VEIol.TQ
WRLW.L
YARo.CHI
Comparison Exchange
2012 Annual
volume (in
Depth
mill shares)
Jan-May
May-Aug
2.06
1233
1224
63.88
1083
1110
3.27
518
456
10.27
5814
3988
3.96
3656
3261
315.73
7322
7976
0.29
4614
1365
5.80
834
935
2.34
1131
1031
83.36
5966
4009
42.41
680546
1602126
32.50
5317
4292
9.43
374
288
15.94
1460
995
14.09
1250
1214
37.50
23678
19807
17.33
29624
30290
21.35
5861
5458
0.20
467
676
1010.04
20510
14373
262.29
3270
3557
14.99
5061
2144
15.97
3978
2778
0.09
433
12.52
2136
1610
120.81
2220
2340
124.55
2714
1922
0.84
3365
2271
35.32
1601
1289
22.44
21401
18415
14.11
813
746
330.13
7180
7156
12.57
540
525
76.48
1232
1157
0.76
1695
727
0.18
592
281
27.18
3912
3275
141.93
2271
2843
220.56
34280
15088
159.48
5562
4107
37.26
663
614
5.15
1792
1022
0.30
816
510
8.33
31590
26333
71.99
711
705
Sep-Dec
1270
1492
319
2996
3195
18341
2714
791
885
3874
2225218
7229
298
1148
1147
24716
31769
5032
286
13644
3833
2431
4122
160
2014
1834
1667
3235
1257
23174
659
14026
434
1371
1214
345
4243
2088
18579
6365
753
960
570
21885
738
This table provides detailed results for the analysis summarized in Panel C of Table 4 in the paper. We list the RIC
(Reuter Identifier) for companies on the Oslo Stock Exchange at the left, and a comparison listing on the right. We choose
the exchange outside of Oslo with the largest volume in 2012 to make the comparison. For each stock we show the annual
volume (in number of shares), and the average daily turnover for three subperiods: Jan-May20, May21-Aug, Sep-Dec.
RIC
AKER.OL
AKSO.OL
ALGETA.OL
ARCHER.OL
ATEA.OL
AUSS.OL
AVM.OL
BAKKA.OL
BON.OL
BWO.OL
CEQ.OL
CLAVIS.OL
DETNOR.OL
DNO.OL
ECHEM.OL
EKO.OL
EMGS.OL
FOE.OL
FRO.OL
FUNCOM.OL
GOGL.OL
KOA.OL
KVAER.OL
MHG.OL
NHY.OL
NOF.OL
NOR.OL
NPRO.OL
NSG.OL
OPERA.OL
ORK.OL
PGS.OL
PLCS.OL
PRON.OL
QEC.OL
RCL.OL
REC.OL
SDRL.OL
SEVAN.OL
SEVDR.OL
SNI.OL
SONG.OL
STB.OL
STL.OL
TEL.OL
TGS.OL
TOM.OL
VEI.OL
VIZ.OL
YAR.OL
Oslo Stock Exchange
2012 Annual
volume (in
Turnover
mill shares)
Jan-May
May-Aug
17.69
0.00123
0.00074
356.15
0.00685
0.00483
35.09
0.00476
0.00274
211.87
0.00318
0.00205
47.85
0.00245
0.00168
30.52
0.00053
0.00055
50.36
0.00084
0.00147
10.24
0.00095
0.00066
3.03
0.00019
0.00037
361.09
0.00194
0.00095
32.67
0.00154
0.00112
52.54
0.00481
0.00204
93.27
0.00634
0.00393
999.34
0.00425
0.00286
941.87
0.00348
0.00337
5.90
0.00029
0.00111
461.63
0.01018
0.00569
39.53
0.00249
0.00218
95.74
0.00776
0.00264
344.95
0.01168
0.03637
284.38
0.00369
0.00162
580.79
0.74688
0.38288
221.96
0.00457
0.00240
6187.20
0.01494
0.01079
1287.40
0.00317
0.00218
18.47
0.00110
0.00040
259.44
0.00529
0.00200
195.70
0.00144
0.00094
385.71
0.01280
0.00622
103.55
0.00287
0.00329
515.78
0.00243
0.00190
552.08
0.01196
0.00898
1020.15
0.00865
0.00469
82.35
0.00073
0.00072
185.32
0.00473
0.00154
180.45
0.00533
0.00203
4005.78
0.01351
0.00621
326.32
0.00326
0.00294
64.73
0.00738
0.00306
365.22
0.00500
0.00311
11.64
0.00115
0.00040
382.65
0.00595
0.00526
880.92
0.00953
0.00938
1107.28
0.00279
0.00179
590.67
0.00182
0.00133
133.42
0.00571
0.00518
48.48
0.00134
0.00126
20.16
0.00066
0.00055
8.97
0.00077
0.00048
325.56
0.00613
0.00367
RIC
Sep-Dec
0.00088
0.00355
0.00217
0.00152
0.00145
0.00072
0.00078
0.00088
0.00036
0.00330
0.00152
0.01169
0.00662
0.00442
0.00311
0.00067
0.01147
0.00238
0.00362
0.01893
0.00191
0.42437
0.00260
0.00808
0.00195
0.00051
0.00408
0.00220
0.00432
0.00427
0.00159
0.00895
0.01075
0.00184
0.00299
0.00236
0.01228
0.00206
0.00370
0.00463
0.00052
0.01235
0.00438
0.00144
0.00121
0.00444
0.00130
0.00058
0.00063
0.00343
28
AKERNOK.Lp
AKSOo.CHI
ALGETo.CHI
ARCHEo.CHI
ATEANOK.ST
ASTVF.PK
AVM.L
BAKKANOK.STp
BONN.DEU
BWONOK.Lp
0K8K.L
CLAVISol.BD
DETNORol.TQ
DNO.mNOK
ECHEMol.BD
EKO.DEU
EMGSol.TQ
FOEo.CHI
FRO
FUNCOMNOK.STp
GOGLNOK.ST
KOA.mNOK
KVAERNOK.Lp
MHGo.CHI
NHYo.CHI
NFSHF.PK
NORNOK.STp
NPRONOK.Lp
NSGo.CHI
OPERAol.TQ
0FIN.L
PGSo.CHI
PLCSNOK.STp
PRONNOK.STp
QEC.CCP
RCL
RECo.CHI
SDRL.K
SEVANo.CHI
SEVDRNOK.Lp
SNIol.TQ
SONGo.CHI
STBo.CHI
0M2Z.L
0G8C.L
TGSo.CHI
TOMo.CHI
VEIol.TQ
RTZR.DEU
0O7D.L
Comparison Exchange
2012 Annual
volume (in
mill shares)
Jan-May
8.56
0.00208
63.88
0.00111
3.27
0.00033
10.27
0.00017
3.96
0.00019
0.28
0.00001
315.73
0.00627
3.37
0.00198
0.02
0.00001
31.17
0.00051
11.02
0.00448
0.04
0.00001
2.34
0.00016
175.89
0.00076
42.41
0.00017
0.02
0.00001
32.50
0.00053
9.43
0.00056
355.26
0.02953
1.02
0.00009
37.50
0.00048
25.27
0.04702
33.72
0.00152
1010.04
0.00233
262.29
0.00060
4.59
0.00034
2.41
0.00015
138.69
0.01153
15.97
0.00047
12.52
0.00027
297.00
0.00251
124.55
0.00267
10.32
0.00011
2.65
0.00003
33.05
0.00062
635.62
0.01521
330.13
0.00136
576.69
0.00483
0.76
0.00015
8.12
0.00032
0.18
0.00002
27.18
0.00070
141.93
0.00149
306.06
0.00050
345.53
0.00062
37.26
0.00138
5.15
0.00022
0.30
0.00002
0.38
0.00003
94.58
0.00248
Turnover
May-Aug
0.00617
0.00096
0.00031
0.00008
0.00015
0.00001
0.00813
0.00013
0.00001
0.00077
0.00156
0.00001
0.00010
0.00056
0.00022
0.00000
0.00031
0.00058
0.01141
0.00021
0.00023
0.01238
0.00015
0.00194
0.00051
0.00011
0.00009
0.00026
0.00023
0.00050
0.00118
0.00205
0.00022
0.00012
0.00057
0.01003
0.00052
0.00558
0.00004
0.00047
0.00002
0.00034
0.00131
0.00124
0.00206
0.00131
0.00010
0.00001
0.00005
0.00127
Sep-De
0.0000
0.0006
0.0002
0.0000
0.0001
0.0000
0.0047
0.0002
0.0000
0.0004
0.0001
0.0000
0.0003
0.0006
0.0002
0.0000
0.0011
0.0005
0.0103
0.0001
0.0002
0.0222
0.0032
0.0012
0.0003
0.0001
0.0001
0.0005
0.0002
0.0005
0.0000
0.0020
0.0001
0.0002
0.0005
0.0098
0.0008
0.0041
0.0000
0.0003
0.0000
0.0006
0.0009
0.0000
0.0000
0.0016
0.0000
0.0000
0.0000
0.0000
Figure 1 Evolution of the Oslo Stock Market 2000-2013
The figure illustrates the value evolution of the exchange, constructed from daily returns on an equally weighted index of
stocks on the Oslo Stock Exchange. The sample removes stocks with low price and inactive stocks. The source of the index
is Ødegaard (2014).
15
10
5
Index
20
25
Stock Market Index (EW)
2000
2005
2010
Year
29
Figure 2 Relative Spread 2000-2013
Time series of relative bid/ask spread calculated from closing bid and ask observations for 2000-2013. In panel B we do
the same exercise for the stocks on the OSE grouped into four size groups. We remove very illiquid and low priced stocks,
and Windsorize the crossectional averages by removing the 2.5% percent highest and lowest observations. For more details
about the variable see appendix A.
Panel A: Market Average
0.05
0.04
0.02
0.03
Rel B/A Spread
0.06
Relative Bid/Ask Spread
2000
2005
2010
Year
Panel B: Averages Size Portfolios
Relative Bid/Ask Spread
0.08
0.06
0.04
0.02
0.00
Rel B/A Spread
0.10
Small
2
3
Large
2000
2005
2010
Year
30
Figure 3 Turnover 2000-2013
Time series of turnover for 2000-2013. For each share turnover is calculated for each quarter by summing daily turnovers
(number of shares traded during the day divided by number of shares outstanding). We then take crossectional average
each day. In panel B we do the same exercise for the stocks on the OSE grouped into four size groups. We remove very
illiquid and low priced stocks, and Windsorize the crossectional averages by removing the 2.5% percent highest and lowest
observations. For more details about the variable see appendix A.
Panel A: Market Average
0.15
0.10
Turnover
0.20
0.25
Turnover
2000
2005
2010
Year
Panel B: Averages for size-sorted portfolios
0.4
Turnover
0.2
0.1
0.0
Turnover
0.3
Small
2
3
Large
2000
2005
2010
Year
31
Figure 4 Norwegian stocks traded at the OSE and other lit markets
The figures illustrate where Norwegian stocks are trading. The top figure shows the quarterly total (in NOK) traded at the
OSE and the total trading (including the OSE). This total only includes trading at “lit” exchanges, not dark pools. The
bottom figure shows fractions of trading in each market. The bottom line is the fraction of the XBO traded at the OSE.
The other exchanges adds to the total. The fractions sum to one (we leave out some extremely small market places).
Panel A: Total Volume, OSE and all of XBO
Trading Volume − OSE vs whole XBO
1.2e+11
4.0e+10
8.0e+10
Volume
1.6e+11
All
OSE
2009
2010
2011
2012
2013
Time
Panel B: Fraction Volume split across exchanges
0.8
0.7
LSE
BTE
TRQ
CHI
STO
OSE
0.6
Volume
0.9
1.0
Fraction Trading Volume − OSE vs other markets in XBO
2009
2010
2011
Time
32
2012
2013
Figure 5 Message to trade Ratio – from 2000
Averages of message to trade ratios across stocks at the Oslo Stock Exchange. Each day, for each stock, we calculate the
number of messages (items in the order file for that stock.) and divide by the number of trades. We calculate the average
for a given quarter for each stock. We then calculate the crossectional average of these quarterly averages across all stocks
and plot the resulting time series. Panel B shows the same numbers grouped into four size sorted portfolios. We remove
very illiquid and low priced stocks, and Windsorize the crossectional averages by removing the 2.5% percent highest and
lowest observations. For more details about the variable see appendix A.
Panel A: Market Average
15
5
10
Ratio
20
25
Message to Trade Ratios
2000
2002
2004
2006
2008
2010
2012
2010
2012
Year
Panel B: Averages Size Portfolios
Small
2
3
Large
10
20
Ratio
30
40
Message to Trade Ratio
2000
2002
2004
2006
Year
33
2008
Figure 6 Press Release, 25 may 2012, from the Oslo Stock Exchange
With effect from 1 September, Oslo Børs will introduce a fee that will affect
unnecessarily high order activity in the stock market. The purpose of the fee is
to discourage orders that do not contribute to the effective and sound conduct of
stock market trading. Order activity at unnecessarily high levels has the effect
of reducing the transparency of the order picture and so reducing confidence in
the market.
Competition and technological development have played a role in radical changes
in trading behaviour in the stock market over recent years. Increased use of algorithms as a tool for carrying out various kinds of trading strategy has resulted
over time in a steady reduction in the average order size, combined with an increase in the number of order events relative to the number of trades actually
carried out. This creates both direct and indirect costs for all market participants, due in part to greater volumes of data and the requirements this creates
in terms of investment in infrastructure and greater bandwidth.
“Oslo Børs takes the view that high order activity is not in itself necessarily negative for the market, but we are keen to encourage a situation in which all types
of trading contribute to maintaining confidence in the marketplace,” comments
Bente A. Landsnes, President and CEO of Oslo Børs.
“It is in general the case that a market participant does not incur any costs by
inputting a disproportionately high number of orders to the order book, but this
type of activity does cause indirect costs that the whole market has to bear. The
measure we are announcing will help to reduce unnecessary order activity that
does not contribute to improving market quality. This will make the market more
efficient, to the benefit of all its participants,” explains Bente A. Landsnes.
The fee will be linked to an “Order to Executed Order Ratio (OEOR)” of 1:70.
This means that the fee will be charged where the number of orders input relative
to each order carried out exceeds 70. The order activity that will be included in
the calculation of this ratio will principally relate to orders that are cancelled
or amended within one second, and where the change does not contribute to
improved pricing or volume.
Accordingly, orders that remain open in the order book for some time, or which
are updated in a manner that makes a positive contribution to market quality by
reducing the spread between best bid and best offer or by increasing order book
depth will not be included in the calculation of the type of activity that Oslo Børs
wishes to make the subject of the additional fee.
34
Figure 7 Message to trade Ratio – 2012
Day by day average of message to trade ratios across stocks at the Oslo Stock Exchange. Each day, for each stock, we
calculate the number of messages (items in the order file for that stock.) We then calculate the crossectional average of
these across all stocks and plot the resulting time series. Panel B shows the same numbers grouped into four size sorted
portfolios. We remove very illiquid and low priced stocks, and Windsorize the crossectional averages by removing the 2.5%
percent highest and lowest observations. For more details about the variable see appendix A. In both pictures 24th May
and 1st Sep are indicated by vertical bars. The horizontal lines in the figure in panel A are subperiod means. The size
quartile is indicated by different point marks.
Panel A: Market Average
25
15
20
Ratio
30
35
Message to Trade Ratio
jan.
mars
mai
juli
sep.
nov.
sep.
nov.
Time
Panel B: Averages Size Portfolios
40
60
80
Small
2
3
Large
20
Ratio
100
120
Message to Trade Ratio
jan.
mars
mai
juli
Year
35
Figure 8 Fraction automated – 2012
We calculate the fraction of trades during a day where automated traders are involved on either the buy or sell side. This
is calculated for each stock. The pictures show crossectional averages across stocks of these. In panel B we show similar
averages grouped by firm size. The size quartile is indicated by different point marks.We remove very illiquid and low
priced stocks, and Windsorize the crossectional averages by removing the 2.5% percent highest and lowest observations.
For more details about the variable see appendix A. In both pictures 24th May and 1st Sep are indicated by vertical bars.
The horizontal lines in the figure in panel A are subperiod means.
Panel A: Market Average
0.80
0.70
0.75
Frac
0.85
0.90
Fraction of trades involving automated traders
jan.
mars
mai
juli
sep.
nov.
Time
Panel B: Averages Size Portfolios
Small
2
3
Large
0.7
0.6
0.5
Frac
0.8
0.9
1.0
Fraction of trades involving automated traders
jan.
mars
mai
juli
Year
36
sep.
nov.
Figure 9 Average trade size – 2012
We calculate the average trade size (in NOK) by taking the daily total trading volume in NOK and dividing by the number
of trades during the day. In panel B we show similar averages grouped by firm size. We remove very illiquid and low priced
stocks, and Windsorize the crossectional averages by removing the 2.5% percent highest and lowest observations. For more
details about the variable see appendix A. In both pictures 24th May and 1st Sep are indicated by vertical bars. The
horizontal lines in the figure in panel A are subperiod means. The size quartile is indicated by different point marks.
Panel A: Market Average
100000
50000
Trade Size
150000
200000
Average Trade Size (NOK)
jan.
mars
mai
juli
sep.
nov.
sep.
nov.
Time
Panel B: Averages Size Portfolios
2e+05
Small
2
3
Large
0e+00
Trade Size
4e+05
Average Trade Size
jan.
mars
mai
juli
Year
37
Figure 10 Average depth – 2012
We calculate the average depth (in NOK). We sum the volume outstanding at the best bid and best ask, and multiply
this sum with the mid price. In panel B we show similar averages grouped by firm size. We remove very illiquid and low
priced stocks, and Windsorize the crossectional averages by removing the 2.5% percent highest and lowest observations.
For more details about the variable see appendix A. In both pictures 24th May and 1st Sep are indicated by vertical bars.
The horizontal lines in the figure in panel A are subperiod means. The size quartile is indicated by different point marks.
Panel A: Market Average
350000
250000
Depth
450000
Depth
jan.
mars
mai
juli
sep.
nov.
juli
sep.
nov.
Time
Panel B: Averages Size Portfolios
4e+05
6e+05
Small
2
3
Large
2e+05
Depth
8e+05
1e+06
Depth
jan.
mars
mai
Year
38
Figure 11 Fraction of trading interest at best bid and ask – 2012
We look at the total trading interest six ticks out from the best bid and aks, and calculate the fraction of this interest which
is at the inner spread (the best bid and asks). In panel B we show similar averages grouped by firm size. We remove very
illiquid and low priced stocks, and Windsorize the crossectional averages by removing the 2.5% percent highest and lowest
observations. For more details about the variable see appendix A. In both pictures 24th May and 1st Sep are indicated by
vertical bars. The horizontal lines in the figure in panel A are subperiod means. The size quartile is indicated by different
point marks.
Panel A: Market Average
0.48
0.44
Fraction
0.52
Fraction at Inner Spread
jan.
mars
mai
juli
sep.
nov.
sep.
nov.
Time
Panel B: Averages Size Portfolios
0.4
0.6
Small
2
3
Large
0.2
Fraction
0.8
Fraction at Inner Spread
jan.
mars
mai
juli
Year
39
Figure 12 Relative spread evolution in 2012
Day by day average of relative spreads across stocks at the Oslo Stock Exchange. Each day, for each stock, we calculate
the average of all relative spreads observed during the day. (The spread is re-estimated each time there is a change in the
order book.) We then calculate the crossectional average of these across all stocks and plot the resulting time series. In
Panel B we show similar averages for stocks grouped by firm size into four portfolios. We remove very illiquid and low
priced stocks, and Windsorize the crossectional averages by removing the 2.5% percent highest and lowest observations.
For more details about the variable see appendix A. In both pictures 24th May and 1st Sep are indicated by vertical bars.
The horizontal lines in the figure in panel A are subperiod means. The size quartile is indicated by different point marks.
Panel A: Market Average
0.046
0.042
0.038
Rel B/A Spread
0.050
Relative Bid/Ask Spread
jan.
mars
mai
juli
sep.
nov.
sep.
nov.
Year
Panel B: Averages Size Portfolios
0.04
0.06
Small
2
3
Large
0.02
Rel B/A Spread
0.08
Relative Bid/Ask Spread
jan.
mars
mai
juli
Year
40
Figure 13 Roll measure of trading costs
The pictures shows daily averages of the Roll measure of trading costs. For each day and each stock, we use all trades during
the day to calculate the Roll measure. This is then averaged across the whole exchange (Panel A) and for four portfolios
sorted by size (Panel B). We remove very illiquid and low priced stocks, and Windsorize the crossectional averages by
removing the 2.5% percent highest and lowest observations. For more details about the variable see appendix A. In both
pictures 24th May and 1st Sep are indicated by vertical bars. The horizontal lines in the figure in panel A are subperiod
means. The size quartile is indicated by different point marks.
Panel A: Market Average
0.004
0.002
Roll
0.006
Roll
jan.
mars
mai
juli
sep.
nov.
juli
sep.
nov.
Time
Panel B: Averages Size Portfolios
0.004
0.008
Small
2
3
Large
0.000
Roll
0.012
Roll
jan.
mars
mai
Year
41
Figure 14 Realized Volatility – 2012
The pictures shows daily averages of Realized Volatility. For each day and each stock, we use all trades during the day to
calculate the Realized Volatility. This is then averaged across the whole exchange (Panel A) and for four portfolios sorted
by size (Panel B). We remove very illiquid and low priced stocks, and Windsorize the crossectional averages by removing
the 2.5% percent highest and lowest observations. For more details about the variable see appendix A. In both pictures
24th May and 1st Sep are indicated by vertical bars. The horizontal lines in the figure in panel A are subperiod means.
The size quartile is indicated by different point marks.
Panel A: Market Average
0.008
0.004
0.006
Vol
0.010
0.012
Realized Volatility
jan.
mars
mai
juli
sep.
nov.
sep.
nov.
Time
Panel B: Averages Size Portfolios
0.015
Small
2
3
Large
0.005
Vol
0.025
Realized Volatility
jan.
mars
mai
juli
Year
42
Figure 15 Daily Turnover – 2012
The pictures shows crossectional averages of Daily Turnover. For each day and each stock, we use all trades during the
day to calculate the Turnover (Total number of shares traded divided by the number of shares outstanding.) This is then
averaged across the whole exchange (Panel A) and for four portfolios sorted by size (Panel B). We remove very illiquid and
low priced stocks, and Windsorize the crossectional averages by removing the 2.5% percent highest and lowest observations.
For more details about the variable see appendix A. In both pictures 24th May and 1st Sep are indicated by vertical bars.
The horizontal lines in the figure in panel A are subperiod means. The size quartile is indicated by different point marks.
Panel A: Market Average
0.0015
0.0010
Turnover
0.0020
Daily Turnover
jan.
mars
mai
juli
sep.
nov.
jan.
sep.
nov.
jan.
Time
Panel B: Averages Size Portfolios
0.002
0.004
Small
2
3
Large
0.000
Turnover
0.006
Daily Turnover
jan.
mars
mai
juli
Year
43
Figure 16 Relative Spreads for Statoil on different exchanges, 2012
The figures plots averages of daily spreads for Statoil stock at three different exchanges: OSE, Stockholm and Chi-X. The
spreads are calculated from the best bid and ask prices at all times with a change in the order book, and averaged across
the day. Data from Reuters. For ease of comparison we we use the same y axes in the plots. This has truncated some
extreme observations at Chi-X.
Panel A: OSE (STL.OL)
0.0012
0.0008
RelSpread
0.0016
Statoil Relative Spread 2012, OSE
jan.
mars
mai
juli
sep.
nov.
jan.
nov.
jan.
nov.
jan.
Time
Panel B: Stockholm (STLNOK.ST)
0.0012
0.0008
RelSpread
0.0016
Statoil Relative Spread 2012, Stockholm
jan.
mars
mai
juli
sep.
Time
Panel C: Chi-X (STLo.CHI)
0.0012
0.0008
RelSpread
0.0016
Statoil Relative Spread 2012, Chi−X
jan.
mars
mai
juli
Time
44
sep.
Table 1 Distribution of trading in Statoil in 2012.
The table shows the distribution of trading of Statoil stock. At each exchange, indicated by the RIC code, we list the total
number of shares traded during the year, the average and median traded each day, and the number of trading days.
RIC
STL.OL
0M2Z.L
STL.BE
STL.D
STL.DE
STL.DEU
STL.F
STL.MU
STL.SG
STL.TG
STLEUR.DEp
STLEUR.STp
STLNOK.DEp
STLNOK.PAp
STLNOK.ST
STLNOK.STp
STLo.BS
STLo.CHI
STLol.BD
STLol.HFT
STLol.TQ
STOHF.PK
Annual
Volume
(mill)
1107.28
306.06
0.12
0.02
6.55
15.30
6.35
0.73
1.52
2.52
1.81
0.00
2.30
18.21
220.56
3.08
93.51
205.72
5.58
0.06
63.15
2.55
Daily volume
(thousands)
Average Median
4411.5
4081.1
1219.3
24.6
1.0
0.5
0.3
0.2
25.8
19.8
46.4
45.2
25.0
23.4
3.0
1.8
6.1
4.6
9.9
8.7
7.5
1.0
0.3
0.1
22.3
12.4
85.1
28.8
878.7
826.6
14.6
2.8
372.5
346.0
819.6
777.7
22.2
14.2
0.6
0.3
251.6
224.8
14.2
0.8
45
No days
with
trades
251
251
118
76
254
330
254
246
251
254
242
13
103
214
251
211
251
251
251
108
251
180
Fraction
of
total(%)
53.7
14.8
0.0
0.0
0.3
0.7
0.3
0.0
0.1
0.1
0.1
0.0
0.1
0.9
10.7
0.1
4.5
10.0
0.3
0.0
3.1
0.1
Table 2 Measures of traders behavior across subperiods of 2012
Summary statistics for measures of trading behaviour across three subperiod, whole market and for (firm) size quartiles.
We calculate the averages of daily observations over three subperiods; 1: 1jan2012–24may2012, 2: 25may2012–31aug2012,
3: 1sep2012–24nov2012.Column 1 specify the measure. Column 2 what portfolio this is calculated for. Columns 3-6 show
the subperiod averages. The final three columns give results for t-tests of difference of means of the various subperiods.
The means are Windsorized at 2.5%. Numbers in Square Brackets are medians. The numbers in parenthesis are p-values.
For more details about the variable see appendix A.
Periods
Measure
Whole Exchange
Message
to
Trade
Ratio
Size 1 (small)
Size 2
Size 3
Size 4 (large)
Whole Exchange
Fraction
Automated
Size 1 (small)
Size 2
Size 3
Size 4 (large)
Whole Exchange
Average
Trade
Size
(NOK)
Size 1 (small)
Size 2
Size 3
Size 4 (large)
Whole Exchange
Depth
(NOK)
Size 1 (small)
Size 2
Size 3
Size 4 (large)
Whole Exchange
Fraction
at
Inner
Spread
Size 1 (small)
Size 2
Size 3
Size 4 (large)
1
23.46
[23.18]
14.46
[14.62]
15.66
[15.60]
32.59
[29.45]
28.61
[28.52]
0.78
[0.78]
0.69
[0.69]
0.76
[0.76]
0.80
[0.80]
0.88
[0.89]
47289
[44625]
35763
[28637]
52035
[46021]
39487
[34508]
42249
[41680]
341425
[335545]
132305
[128584]
285375
[267711]
305736
[309762]
579617
[587076]
0.48
[0.48]
0.73
[0.73]
0.64
[0.63]
0.38
[0.38]
0.17
[0.17]
Means
2
22.55
[21.87]
15.09
[14.93]
20.23
[19.39]
22.39
[20.01]
23.90
[23.40]
0.79
[0.79]
0.73
[0.72]
0.77
[0.78]
0.80
[0.81]
0.88
[0.88]
48258
[43172]
34647
[25870]
41281
[34732]
42302
[35108]
38996
[38151]
304077
[297863]
128812
[120789]
204947
[200387]
308053
[289543]
505502
[497021]
0.51
[0.50]
0.75
[0.75]
0.68
[0.68]
0.41
[0.41]
0.19
[0.18]
46
3
19.25
[19.12]
15.85
[15.48]
18.69
[17.61]
17.70
[17.21]
21.14
[20.89]
0.77
[0.77]
0.73
[0.74]
0.74
[0.74]
0.76
[0.76]
0.86
[0.86]
56744
[48876]
48796
[35650]
62054
[47558]
46437
[43113]
38895
[37654]
371562
[363868]
138612
[132766]
248643
[241267]
395236
[401597]
599680
[576808]
0.46
[0.46]
0.71
[0.71]
0.61
[0.61]
0.35
[0.35]
0.18
[0.18]
Test
1 vs 2
1.25
(0.21)
-1.87
(0.06)
-5.61
(0.00)
5.40
(0.00)
5.41
(0.00)
-1.62
(0.11)
-2.82
(0.00)
-0.63
(0.53)
-0.92
(0.36)
1.02
(0.31)
-0.30
(0.76)
0.21
(0.84)
2.16
(0.03)
-0.84
(0.40)
3.68
(0.00)
5.04
(0.00)
0.66
(0.51)
6.48
(0.00)
-0.20
(0.84)
4.30
(0.00)
-8.84
(0.00)
-4.12
(0.00)
-6.42
(0.00)
-7.10
(0.00)
-7.06
(0.00)
for equality
2 vs 3 1 vs 3
4.65
7.69
(0.00) (0.00)
-1.64
-3.27
(0.10) (0.00)
1.47
-3.54
(0.14) (0.00)
3.66
8.89
(0.00) (0.00)
3.91
9.60
(0.00) (0.00)
3.10
2.00
(0.00) (0.05)
0.17
-2.31
(0.86) (0.02)
2.05
1.82
(0.04) (0.07)
5.59
5.37
(0.00) (0.00)
2.52
4.02
(0.01) (0.00)
-2.06
-2.85
(0.04) (0.00)
-1.84
-1.99
(0.07) (0.05)
-2.10
-0.95
(0.04) (0.34)
-1.12
-2.30
(0.26) (0.02)
0.10
3.80
(0.92) (0.00)
-7.71
-3.68
(0.00) (0.00)
-1.75
-1.37
(0.08) (0.17)
-4.22
2.69
(0.00) (0.01)
-5.77
-6.60
(0.00) (0.00)
-5.23
-1.08
(0.00) (0.28)
15.00
6.96
(0.00) (0.00)
5.97
2.93
(0.00) (0.00)
10.38
4.43
(0.00) (0.00)
12.00
5.84
(0.00) (0.00)
1.29
-5.42
(0.20) (0.00)
Table 3 The quality measures across subperiods of 2012
For four measures of trading quality: Daily average inner relative spread, Roll cost measure, Realized volatility and Daily
turnover, we calculate the averages of daily observations over three subperiods; 1: 1jan2012–24may2012, 2: 25may2012–
31aug2012, 3: 1sep2012–24nov2012.Column 1 specify the measure. Column 2 what portfolio this is calculated for. Columns
3-6 show the subperiod averages. The final three columns give results for t-tests of difference of means of the various
subperiods. Numbers in Square Brackets are medians. The numbers in parenthesis are p-values. We remove very illiquid
and low priced stocks, and Windsorize the crossectional averages by 2.5%. For more details about the variables see
appendix A.
Periods
Measure
Whole Exchange
Relative
Spread
Size 1 (small)
Size 2
Size 3
Size 4 (large)
Whole Exchange
Roll
Size 1 (small)
Size 2
Size 3
Size 4 (large)
Whole Exchange
Realized
Volatility
Size 1 (small)
Size 2
Size 3
Size 4 (large)
Whole Exchange
Turnover
Size 1 (small)
Size 2
Size 3
Size 4 (large)
1
0.0445
[0.0442]
0.0735
[0.0729]
0.0590
[0.0581]
0.0321
[0.0320]
0.0142
[0.0138]
0.0022
[0.0020]
0.0037
[0.0036]
0.0026
[0.0024]
0.0010
[0.0009]
0.0008
[0.0005]
0.0045
[0.0042]
0.0058
[0.0058]
0.0046
[0.0045]
0.0035
[0.0033]
0.0036
[0.0028]
0.00151
[0.00149]
0.00046
[0.00037]
0.00102
[0.00095]
0.00086
[0.00082]
0.00345
[0.00341]
Means
2
0.0466
[0.0470]
0.0792
[0.0793]
0.0600
[0.0595]
0.0341
[0.0343]
0.0145
[0.0145]
0.0023
[0.0022]
0.0044
[0.0043]
0.0026
[0.0024]
0.0012
[0.0010]
0.0007
[0.0005]
0.0044
[0.0041]
0.0056
[0.0054]
0.0047
[0.0044]
0.0035
[0.0035]
0.0033
[0.0029]
0.00120
[0.00119]
0.00034
[0.00029]
0.00065
[0.00060]
0.00073
[0.00072]
0.00268
[0.00258]
47
3
0.0456
[0.0455]
0.0735
[0.0733]
0.0629
[0.0629]
0.0333
[0.0331]
0.0134
[0.0134]
0.0023
[0.0021]
0.0038
[0.0036]
0.0025
[0.0025]
0.0011
[0.0009]
0.0009
[0.0006]
0.0038
[0.0037]
0.0049
[0.0047]
0.0040
[0.0040]
0.0030
[0.0030]
0.0028
[0.0023]
0.00120
[0.00115]
0.00047
[0.00037]
0.00105
[0.00091]
0.00071
[0.00072]
0.00222
[0.00218]
Test
1 vs 2
-4.93
(0.00)
-5.85
(0.00)
-0.93
(0.35)
-5.04
(0.00)
-1.27
(0.20)
-1.21
(0.23)
-2.96
(0.00)
0.04
(0.97)
-2.48
(0.01)
0.32
(0.75)
0.38
(0.70)
0.86
(0.39)
-0.72
(0.47)
0.16
(0.87)
0.60
(0.55)
6.77
(0.00)
3.48
(0.00)
6.55
(0.00)
3.17
(0.00)
6.53
(0.00)
for equality
2 vs 3 1 vs 3
2.25
-2.54
(0.02) (0.01)
5.29
0.02
(0.00) (0.98)
-2.27
-3.07
(0.02) (0.00)
2.07
-2.71
(0.04) (0.01)
5.82
3.93
(0.00) (0.00)
0.07
-0.84
(0.94) (0.40)
2.72
-0.17
(0.01) (0.87)
0.49
0.54
(0.62) (0.59)
0.55
-1.08
(0.58) (0.28)
-0.50
-0.29
(0.62) (0.77)
3.82
4.08
(0.00) (0.00)
3.41
4.25
(0.00) (0.00)
3.94
4.69
(0.00) (0.00)
5.61
5.46
(0.00) (0.00)
1.17
1.47
(0.24) (0.14)
-0.15
7.41
(0.88) (0.00)
-2.78
-0.06
(0.01) (0.95)
-3.96
-0.21
(0.00) (0.83)
0.42
4.00
(0.67) (0.00)
3.97
13.02
(0.00) (0.00)
Table 4 Changes in Oslo versus a comparison exchange (“Diff in diff”).
The tables shows the percentage increase in the liquidity measure from the period before the tax (Jan-May20 2012) to the
period after its implementation (Sep-Dec 2012).
The comparison is done for stocks at the Oslo Stock Exchange (Oslo) and a comparison exchange which list the same stock.
The exchange chosen is that with the highest volume of that stock traded. The last column (Diff in Diff) is the difference
between the two previous differences, where we calculate the difference for each matched stock.
The comparison is done for relative spread in Panel A, for Depth in Panel B and for Turnover in Panel C.
Panel A: Change in Relative Spread
Average change in Spread(%)
Median change in Spread(%)
Oslo
5.06
(0.1410)
1.33
γ
b (Diff if diff)
-25.36
(0.0020)
-14.22
Comparison
30.43
(0.0002)
25.44
Panel B: Change in Depth
Average Change in Depth(%)
Median Change in Depth(%)
Oslo
113.1
(0.273)
-3.1
Comparison
-3.8
(0.620)
-14.1
γ
b (Diff in Diff)
116.9
(0.258)
6.3
Panel C: Change in Turnover
Average Change in Turnover(%)
Median Change in Turnover(%)
Oslo
-5.64678
(0.461)
-22.56970
48
Comparison
-5.89261
(0.727)
-28.35079
γ
b(Diff in Diff)
0.24583
(0.986)
0.17948