JeremyTan_120058974_FYP_Final

BSc (Hons) Investment and Financial Risk Management
Cass Business School, City University London
Does Computer-based Trading Harm Market Liquidity?
JEREMY TAN DAOJIE
ABSTRACT
Computer-based trading has been increasingly prevalent in the financial markets over the
past decade and is forecasted to increase even further to 2022. Such a significant change in
the market microstructure warrants a vital understanding of how such activity impacts
underlying market liquidity. This paper provides additional insight amidst the growing
literature on this issue with a focus on the U.K. equity market. In particular, this study
examines the period before and after the Millennium System upgrade on the London Stock
Exchange, using this exogenous influence on CBT activity as an instrument to establish
causality between CBT and market liquidity. A proxy is constructed to measure for CBT
activity and characteristics of liquidity that include breadth, depth, and tightness were
measured. The study found surprising results with regard to the instrument. Following the
upgrade, the level of CBT activity decreased. The analysis also provides evidence of
statistically significant relationships between the level of CBT activity and majority of the
liquidity measures, with the exception of tightness for large cap stocks. However, despite this
significance, the actual impact that CBT activity has on all the respective measures are so
minuscule that it renders the relationship economically insignificant.
Supervisor: Prof. RICHARD PAYNE
Submission Date: 8th April 2016
“I certify that I have complied with the guidelines on plagiarism outlined in the Course
Handbook in the production of this dissertation and that it is my own, unaided work.”
Signature:

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ACKNOWLEDGEMENTS
I would like to express my deepest appreciation to my academic supervisor Prof. Richard
Payne, whose knowledge and expertise in this field greatly aided in my own better understanding
of how the financial markets operate. For helping me obtain the data that made this research
possible, advising me on the appropriate measures to construct, and giving me feedback on the
interpretation of the results. Without his guidance and persistent support, this dissertation would
not have been possible.
I would also like to express my gratitude to Mr. Basel Bangun, whose fluent competency in
multiple programming languages taught a beginner like me how to think like a programmer,
motivating and guiding me to develop the most efficient solutions to solve various problems.
Without his consultation and patience, this dissertation too would not have been possible.
I would also like to thank my family at Euston church, whose unyielding care and support
that they have shown me, has often encouraged me and help gravitate my priorities back to the
right path. Without their intentional actions of love, my final year at university would have not
been this joyful.
Importantly, I would like to thank my parents, for who made it possible that I even have the
precious opportunity to be able to pursue an education overseas. Without their unconditional love,
none of this would have been possible.
Above all, the utmost reverence to the Heavenly Father, for His divine intervention made
this academic endeavor possible.

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Table of Contents
INTRODUCTION 4
1. LITERATURE REVIEW 7
1.1 HOW HAS MARKET LIQUIDITY CHANGED OVER THE YEARS 7
1.2 MEASURING FOR CBT ACTIVITY AND ITS EFFECTS ON MARKET LIQUIDITY 8
1.3 HOW THIS PAPER SUPPLEMENTS THE CURRENT LITERATURE 10
2. METHODOLOGY & DATA 11
2.1 DATA SAMPLE, PERIOD AND COMPANIES 11
2.2 SEVERAL ISSUES 11
2.3 ADOPTED MEASURES 12
A. CBT PROXY 12
B. LIQUIDITY MEASURES 14
C. CONTROL VARIABLE 16
3. DATA FINDINGS 18
3.1 TIME-SERIES PLOTS 19
A. CBT PROXY 19
B. LIQUIDITY MEASURES 19
4. EMPIRICAL ANALYSIS 21
4.1 JUSTIFICATION OF PANEL DATA REGRESSION MODEL 21
4.2 MILLENNIUM SYSTEM UPGRADE AND ITS EFFECTS ON THE LEVEL OF CBT ACTIVITY 22
4.3 CBT ACTIVITY AND ITS IMPACT ON MARKET LIQUIDITY 24
5. CONCLUSION AND RECOMMENDATIONS 28
APPENDIX 32

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Introduction
Persistent technological advancements and significant reductions in the cost of computing power
have ushered in an era of innovation in the global financial markets. The use of automation in the
financial markets has witnessed a sharp increase, with institutions switching to a more digital
approach of monitoring and executing their trade process. This transition has largely been
motivated by lower costs and more effective strategy implementation, and subsequently led to the
advent of computer-based trading (CBT). However, the shift towards a more electronic market
has been met with both support and criticism.
Over the past decade, the usage of CBT by market participants has experienced a dramatic
increase, particularly in the equities market (Cliff et al., 2011). A shift towards more
electronically driven stock exchanges and changes in legislative regulations such as National
Market System (NMS) in the United States (U.S.), and the Markets in Financial Instruments
Directive (MiFID) in Europe have played a role in fostering part of this growth (Linton, 2012). As
a result, the market structure of the global financial markets has undergone substantial change
with alternative trading venues such as BATS in Chicago and Chi-X in London being created to
serve the needs of different clientele (Gresse, 2011). In the U.S., computerization in the financial
markets emerged in the mid 1970s with the introduction of the Designated Order Turnaround
(DOT) system on the New York Stock Exchange (NYSE) (McGowan, 2010). A study conducted by
the Securities Exchange Commission and Boston Consulting Group (2011), estimated that in
2010, high frequency trading (HFT) had approximately accounted for 56% and 38% of all equity
trading volume in the U.S. and Europe respectively. For the similar year, this figure was
estimated to be 30% in the United Kingdom (U.K.) (Furse et al., 2011, p. 19).
It is first important to differentiate between the commonly used terms: CBT, HFT, and
algorithmic trading (AT). Furse et al. (2011) does this clearly by defining CBT as the electronic
trading system itself of which different strategies such as AT and HFT are subsumed under (p.
20). Though various definitions for AT and HFT exist, both are generally differentiated by the
frequency of which orders are executed, the latter being the one of a higher frequency with
positions being closed out by the end of the trading day (Furse et al., 2011, pp. 165-166). In the

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pursuit of measuring the collective effects of CBT, this paper will take the similarly broad
definition of the term and refer to CBT as one that is inclusive of both AT and HFT activity.
As technology advances and the cost of computing power continues to decline, the prevalence
of CBT in the global financial markets is forecasted to increase. With key developments in
technologies such as cloud computing and artificial intelligence, Cliff et al. (2011) foresees a
future where major trading exchanges are threatened by clusters of automated trading systems
by the year 2022. Such a drastic change in the market microstructure of the financial markets
has caused an overwhelming consensus from academics, practitioners, and regulators to better
understand the implications of CBT (McGowan, 2011; Furse et al., 2011; SEC, 2010). This would
allow for more informed policies when regulators seek to manage the overall risk in the market.
Understanding the effects of CBT is crucial as there is much controversy as to whether is it
actually harmful or beneficial to the financial markets (McGowan, 2011; Vuorenmaa, 2013).
Infamous events such as the “Flash Crash” that occurred in 2010 have had many market
participants pin majority of the blame on CBT activity (Cliff & Northrop, 2012). Though after
thorough investigation both academics and regulators have concluded that CBT activity did not
trigger the crash, they concurred that it did exacerbate the poor liquidity and high volatility
experienced during the event (Kirilenko et al., 2011; CTFC/SEC, 2010). On the other hand, under
normal market conditions, a couple of academic studies have shown that CBT activity has the
potential to increase both market liquidity and price efficiency (McGowan, 2011; Vuorenmaa,
2013). With such a divide in the general consensus, this paper will investigate the implications
that CBT activity has with regard to market liquidity, so as to supplement the current academic
literature to aid regulators in policy making.
On 14 February 2011, the London Stock Exchange (LSE) upgraded its U.K. cash equity
markets to MillenniumIT’s multi-asset class, ultra low-latency platform, the Millennium
Exchange (LSE, 2015, p. 6). This upgrade from the exchange’s previous electronic trading
platform, TradElect, provided even lower and sub-millisecond latency. Such low latency would
entail faster response times and creates an environment that fosters increased CBT activity. By
utilizing this change in the infrastructure, this paper will analyze the effects that CBT activity
has on market liquidity during both pre and post the Millennium System upgrade.

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This paper investigates the empirical relationship between CBT activity and market liquidity
in the financial markets, in particular, the U.K. equities market. The level of CBT activity is
measured using a proxy. This proxy is constructed by using a normalized measure of LSE tick-
time electronic message traffic. The message traffic data is normalized by taking it over the
number of corresponding executed trades. This normalized measure provides the benefit of
capturing both computerized trades and human trades. Market liquidity is measured by looking
at three key aspects; these include, breadth, depth, and tightness.
The study begins by examining the changes in the level of CBT activity and the measures of
market liquidity for the sample period of a year from 1st July 2010 to 30th June 2011. The findings
show that the level of CBT activity has surprisingly declined over the year. As CBT declined,
market breadth and depth do not seem to have changed significantly except for a slight increase
in breadth for smaller cap stocks and a slight increase in depth for large cap stocks. Tightness on
the other hand has declined slightly over the period for all stocks. To establish causality, this
study introduces an exogenous event that affects the level of CBT activity in all stocks. This
instrument is the upgrade of the LSE’s electronic trading platform to the Millennium System, an
improvement that drastically lowers latency on the exchange. Such an upgrade would be more
accommodating to CBT activity and should typically entail an increase in the level of
computerized trades.
Surprisingly, the empirical results show that the Millennium System upgrade, instead of
leading to an increase in the level of CBT activity rather led to a decline across most stocks. The
results also show that CBT is significantly associated with all three measures of market liquidity
for most samples. However, despite the statistical significance, the absolute effect that CBT has
on the three measures of market liquidity is so minuscule that the impact that CBT has on
market liquidity is economically insignificant. Unless the level of CBT activity increases vastly,
the results show that CBT does not have a significant effect on market liquidity.
The paper adopts the following structure. Section 1 presents the literature review of related
studies that have been conducted within this field. Section 2 discusses the methodology this paper
has adapted to measure the level of CBT activity and the various key liquidity measures. Section
3 provides the findings from the data collected for the sample period. Section 4 empirically

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examines the data, discusses and interprets the results. Section 5 concludes and gives
recommendations.
1. Literature Review
1.1 How has market liquidity changed over the years
Following the evolution of trading exchange platforms from open outcry to a more electronically
driven platform, this transition would have significant impacts on various aspects of market
liquidity. Given the recent surge of CBT activity in the financial markets, several papers have
documented the changes in market liquidity in various equity markets over past periods. These
papers acknowledge the growth of CBT activity in their respective exchanges over their sample
period but do not directly measure for such activity. Instead, they measure various aspects of
market liquidity over the period of interest and document the corresponding changes in these
measures over time.
With regard to the U.S. equity markets, Castura, Litzenberger, Gorelick, and Dwivedi (2010)
examined bid-ask spreads, available liquidity, and variance ratios in Russell 1000 and 2000
stocks over the period of 2006 to 2010. The paper further partitioned their sample of stocks into
groups belonging either to NASDAQ-listed or NYSE-listed. NASDAQ-listed stocks traded
primarily on automated, electronic exchanges while NYSE-listed stocks were in the process of
transiting from trading manually to electronic. The study found an overall improvement in
market liquidity measures with a reduction in bid-ask spreads, and greater available liquidity
and price efficiency. Angel, Harris, and Spatt (2011) similarly investigated S&P 500 and Russell
2000 stocks that were NASDAQ-listed or NYSE-listed over the period of 2003 to 2010. The study
produced evidence that overall market liquidity has improved dramatically. There have been
significant reductions in execution speeds, bid-ask spreads, and commissions while market depth
has increased. Furthermore, as electronic trading grew more prevalent, the paper found that
competition among exchanges intensified along with smaller average trade sizes, increased
quoted traffic, and greater available liquidity.

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With regard to the U.K. equity markets, Linton (2011) surveyed the FTSE All-share index
over the period of 1997 to 2011. The paper measured various market quality indicators including
returns, volatility, liquidity, and price efficiency. In contrast to the U.S. equity markets, the study
found that overall market quality for U.K. stocks have seen only little improvement over the last
decade.
1.2 Measuring for CBT activity and its effects on market liquidity
Given that the phenomenon of CBT activity is relatively new, the collective amount of academic
research in this field is fairly low. However, this area has certainly gained the attention of
regulators, practitioners, and academics with the Government Office for Science in the U.K.
commissioning a Foresight project to explore the implications of computer trading in the financial
markets (Furse et al., 2011).
Measuring CBT activity directly can be tricky. As such, most studies adopt one of the two
following approaches. Researchers will be able to measure CBT activity directly if they are given
access to the data of traders that have identified themselves as AT or HFT traders, enabling them
to analyze how the respective trading strategies that these participants adopt affect the market of
which they reside in. If such access were unavailable, the next best option would be to construct a
proxy to measure the level of CBT activity in a market. The most commonly used method so far
by practitioners and academics is to observe the level of electronic message traffic in the market
(Hendershott et al., 2011). Both methods however are disadvantaged in their own way and can be
argued to not be an accurate measure of CBT activity in certain cases. An essential component of
examining the implications of CBT activity on market liquidity would be to establish causality.
This is conducted by most studies by introducing an exogenous event that is known to influence
the level of CBT activity in the market of interest. Such an event would include the introduction
or an upgrade in infrastructure of the electronic trading platform. It is also important to note that
some studies measure for either AT or HFT exclusively, and only in some cases collectively such
as the approach that is adopted in this paper.
With regard to the U.S. market, studies that constructed proxy measures for CBT activity
include Hendershott, Jones, and Menkveld (2011), Hasbrouck and Saar (2011), and Chaboud,

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Chiquoine, and Hjalmarsson (2009). On the other hand, Brogaard (2010) measured for HFT
activity directly by looking at the strategies of 26 traders that engaged primarily in HFT.
Hendershott, Jones, and Menkveld (2011), measured for AT by considering the level of electronic
message traffic on the NYSE and established causality with the gradual introduction of the auto-
quote system in 2003. The paper presented evidence that AT improved liquidity and the
informativeness of quotes with narrower spreads, and reduced adverse selection and trade-
related price discovery, particularly for large stocks. Hasbrouck and Saar (2011) measured for
HFT by identifying “strategic runs” which consists of a combination of order submissions,
cancellations, and executions on order-level NASDAQ data. Analyzing the largest 500 NASDAQ-
listed shares over two distinct periods in 2007 and 2008, the paper found evidence that increased
HFT activity corresponded with lower short-term volatility, tighter spreads, and larger market
depth. Chaboud, Chiquoine, and Hjalmarsson (2009) observed for AT in the Electronic Broking
Services (EBS) exchange rate market using minute by minute trading data for three major
currency pairs over the period of 2006 and 2007. The study found similar evidence that increased
AT activity did not increase volatility, but instead lowers volatility. Brogaard (2009) studied a
sample of 120 NASDAQ stocks over the period or 2008 to 2010. The study found that its sample of
26 HFT traders played an integral part of the price discovery process and price efficiency, and
similarly reduced volatility.
With regard to the European market, Friederich and Payne (2011) measured the usage of key
words such as “Algorithmic Trading” and “High Frequency Trading” in the U.K. press as a proxy
to distinguish the period over 2005 to 2009 where CBT activity started to grow in prevalence. The
paper does not establish causality but rather measures aspects of market quality over this period
on electronic exchanges such as the London Stock Exchange, Chi-X, BATS, and Turquoise for
stocks listed on the FTSE 100 and FTSE Small Cap. The study found that increasing CBT
activity corresponded with narrower spreads, greater market depth, and higher number of trades
particularly for large cap stocks. Small cap stocks have had no significant change in its average
trade size, which suggests that CBT activity is not actively involved in trading these types of
stocks. Small cap stocks have also seen little improvement in liquidity over the sample period.
Brogaard, Hendershott, Hunt, and Ysusi (2014) were able to measure for HFT activity directly

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through the use of data provided by the Financial Services Authority (FSA). They also measured
for the execution costs of institutional investors from the Abel Noser data set. The study was
focused on the U.K. equity market and utilized four technology upgrades in the LSE’s old
TradElect system that resulted in progressively lower latency with each upgrade. They
established causality between HFT activity and execution costs using this instrument and found
no clear evidence that HFT activity impacts institutional execution costs. Menkveld (2012)
analyzed the trading details of a new HFT trader in Dutch stocks on the Euronext and Chi-X over
the period of 2007 to 2008. The paper found that spreads narrowed by 30% for the Dutch stocks
as compared to Belgian stocks that were not traded by the HFT trader.
The collective evidence from the current academic literature indicates that CBT activity does
in fact have a positive impact on various aspects of market liquidity. Most studies find that
higher CBT activity resulted in narrower spreads and greater market depth, with this effect
being more pronounced in large cap stocks. Furthermore, contrary to the impression that CBT
activity might exacerbate market conditions during times of stress, some studies concurred that
volatility is instead lower with the presence of CBT.
1.3 How this paper supplements the current literature
Although significant strides have been made in this field, more collaboration is needed with
regard to studies concerning other major exchanges that are gradually shifting towards more
electronic-based trading. This would give further insight as to whether the effects of CBT are
generalized or localized. A good example would be with the LSE. As majority of the current
literature have been primarily focused on U.S. equity markets, additional research into the
impacts of CBT activity on the U.K. equity market would aid in painting a more complete picture.
At present, studies pertaining to the U.K. equity market have been met with somewhat
conflicting findings. Furthermore, no academic study has measured for CBT activity in the U.K.
equity market, established causality and drew an association to changes in market liquidity for
U.K. stocks. This paper will attempt to fill this gap by addressing these exact issues and
hopefully assist in shedding more light on this topic with regard to the LSE.

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2. Methodology & Data
2.1 Data Sample, Period and Companies
To be able to adequately conduct the analysis required of this research, level 2 market data from
the LSE was collected. This data includes both tick-time message traffic quote data and its
corresponding trade information. The message traffic quote data was collected for up to the first
five levels of depth for both bid and ask quote sizes and prices.1 The trade information data
provided the successful trades executed at each respective tick-time message level.2
This data was collected over a 1-year period from 1st July 2010 to 30th June 2011, constituting
of 252 trading days. With the Millennium Upgrade going live on the 14th February 2011, this
divides the sample period into an approximate 7-month window period for the pre-Millennium
upgrade and an approximate 5-month window period for the post-Millennium upgrade.
Data for a total of 48 stocks was collected for the study.3 The collection of stocks was selected
from constituents of the FTSE 100 and FTSE 250 index. The prior represents large capitalization
stocks while the latter represents relatively smaller capitalization stocks. The largest stocks from
each of the ten Global Industry Classification Standard (GICS) were selected from both indices to
ensure that the sample composition would be at best a good representation of the index itself. The
sample of stocks was further sorted into quartiles based on market capitalization for means of
robustness checks that will be elaborated on in the later sections.
2.2 Several Issues
It is crucial to highlight several issues that were pertinent to the data collected before progressing
further. With regards to the data sample, there is an instance of 16 consecutive lines for the stock
PMTLq.L, which had several empty fields for the corresponding message traffic lines. This posed
an issue with the initial code that was written to analyze the data as it threw up an error upon
reaching the erroneous lines. This problem was resolved by modifying the data analysis code to
1 Refer to Sample Data Structure 1 for an excerpt of the message traffic quote data structure.
2 Refer to Sample Data Structure 2 for an excerpt of the trade information data structure.
3 Refer to Table 7 for the list of stocks selected. This list also provides information on the stocks’ GICS,
proportion of the index, market capitalization, and its respective quartile group. This information was
procured on the 10 November 2015.

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fill in these respective empty fields with a zero value and proceeding with the calculations as
originally set out. Because these empty fields were probably the result of an error, the best value
to fill such fields would be with the value of zero. The second issue was that 2 out of the 48 stocks
that were selected did not have data that fulfilled the full 252 trading day period.4 These stocks
were removed from the sample in order to maintain a balanced panel for constructing panel data
regressions. The stock sample was thus reduced from 48 to 46 companies for the empirical
analysis. Finally, the data was also contaminated with a degree of negative level 1 bid-ask
spreads for various message traffic lines throughout the entire data sample. This resulted in an
overall negative level 1 bid-ask spread across a range of stocks for particular days during the
preliminary data analysis. This issue was resolved by modifying the data analysis code to
completely omit all data fields that were present on each respective message line that consisted of
a negative level 1 bid-ask spread.
2.3 Adopted Measures
A. CBT Proxy
Given that the access to the trading data of firms that have identified themselves as ones who
undertake AT or HFT trading strategies is unavailable. Level 2 market data collected for the
purpose of this study also did not include the counterparty identities as other datasets such as
the Financial Services Authority (FSA) Sabre II data set used in Brogaard, Hendershott, Hunt,
and Ysusi (2014) would have provided. Thus, this paper will utilize the alternative approach of
measuring the level of CBT activity through a proxy. The method adopted in this paper is similar
to the one implemented by Hendershott, Charles, and Menkveld (2011), in the sense that a
normalized measure of the rate of electronic message traffic is used as a proxy for the level of
CBT activity taking place in the market.
CBT activity stems from the rapid submission and cancellation of market and/or limit orders
as such market participants scan the order flow to avoid adverse selection (Biais, Foucault, and
4 BETF.L and HWDN.L were the stocks that were removed from the sample. BETF.L was only listed on 22
Oct 2010 and thus did not satisfy the sample period of this study. HWDN.L had missing data possibly due to
a data collection glitch.

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Moinas, 2011; Bernales, 2013). Furthermore, Malinova, Park, and Riordan (2013) discovered that
the amount of electronic message traffic in the Toronto Stock Exchange (TSX) dropped by 30% in
April 2012, when the Investment Industry Regulatory Organization of Canada (IIROC) decided to
change the regulatory fee structure by introducing per-message fees, essentially increasing the
costs of conducting computerized-based trades on the exchange. Therefore, one can typically
expect a higher amount of electronic message traffic to correspond with high levels of CBT
activity within a market.
With respect to the tick-time message traffic data collected on the LSE, every submission and
cancellation of an order is captured by the system and displayed as a new message line shown in
the sample data. Likewise for the trade information data, every new successive trade is captured
and displayed as a new message line. Thus, the total number of message traffic for stock i on day
t can be calculated by totaling the number of ! message lines for that particular day and is
represented as "#$$%&#'()*. The same is true when calculating the total number of successful
trades made for stock i on day t and is represented as +,%-#'()*.
The proposed proxy measure for CBT activity used in this paper is constructed by observing
the amount of message traffic per unit of executed trade. Hence, for the +).
day in stock /, the
CBT proxy measure, 012 4,567(), is defined as:
012 4,567() =
"#$$%&#'()*
9
*:;
+,%-#'()*
9
*:;
(1)
Measuring the rate of electronic message traffic in isolation, in this case, the numerator in
equation (1) might not be the best measure for CBT activity, as this might only capture an
increase in the level of trading as opposed to the actual nature of the participants executing the
trades. By normalizing the rate of message traffic as shown in equation (1), this measure
accounts for the factor of human traders within the market. Human traders typically send in a
single order for each executed trade. In contrast to this, CBT algorithms send vastly more orders
for each executed trade. Therefore, if a market is completely void of CBT activity and only human

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participants were present, one would expect this measure to give a figure of 1. If however, there
exists CBT activity in the market, this figure would be larger.
B. Liquidity Measures
There is no single and universally accepted measure that is able to accurately determine a
market’s degree of liquidity (Baker, 1996). Rather, a market’s liquidity tends to exhibit five key
characteristics, which include (i) depth, (ii) immediacy, (iii) breadth, (iv) tightness, and (v)
resiliency (Sarr and Lybek, 2002). This paper will be focusing particularly on the characteristics
of breadth, depth, and tightness, as the type of data collected is most appropriate for estimating
such measures.
Sarr and Lybek (2002) define breadth as “orders that are both numerous and large in volume
with minimal impact on prices”. In pursuit of measuring the level of this attribute, this paper will
look at the average breadth of both the level 1 bid and ask orders. This is done by taking the
arithmetic average of the total level 1 bid and ask orders for each respective day. Expressing this
figure as a proportion of the average daily volume traded for each stock over the entire sample
period then normalizes the result. The average daily volume traded for each stock is calculated by
summing the all executed trades within the sample period for each stock, and dividing that figure
by 252 (as the sample period is 252 trading days). This normalization allows for comparison
between the stocks included in the research sample. However, the limitation of this measure of
breadth is that it accounts only for the volume but not the impact on prices as volume changes.
For the +).
day in stock /, the breadth measure, 1,#%-+ℎ(), is defined as:
1,#%-+ℎ() =
=1?$/@#()* + =1%$/@#()*
9
*:; "#$$%&#'()*
9
*:;
BC2(
(2)
where =1?$/@#()* + =1%$/@#()* is the total amount of all summed level 1 bid and ask quote sizes
for day t, "#$$%&#'()* is the total number of messages for the day t, and BC2( is the average daily
volume traded over the sample period for stock i.

15
Sarr and Lybek (2002) define depth as “the existence of abundant orders, either actual or
easily uncovered of potential buyers and sellers, both above and below the price at which a security
now trades”. In pursuit of measuring the level of this attribute, this paper will look at all five
levels of both bid and ask orders collected in the data. It is important to highlight that this
measure accounts also for breadth but to keep simplicity in the notation, the measure will be
referred to as depth. This is done so by calculating the average quote size for all levels of both bid
and ask orders on a per level basis for each day and normalizing that figure by expressing it as a
proportion of the average daily volume traded for the stock. This entails first summing up the
quote sizes for all five levels of both bid and ask and dividing that figure by 10 (as there are 5
levels of bid and ask each). So the average level quote size for each message line,
BD#,%&# '#D#' EF5+# $/@#()* is given by:
BD#,%&# '#D#' EF5+# $/@#()* =
?$/@#G
H
G:; + %$/@#G
H
G:;
10
(3)
where ?$/@#G and %$/@#G are the corresponding bid and ask quote sizes for the ').
level respectively.
Then by calculating the total figure for that day, dividing it by the number of message lines,
and finally expressing it as a proportion of the average daily volume traded for the corresponding
stock. So the final measure, C#J+ℎ(), is defined as:
C#J+ℎ() =
BD#,%&# '#D#' EF5+# $/@#()*
9
*:; "#$$%&#'()*
9
*:;
BC2(
(4)
where BD#,%&# '#D#' EF5+# $/@#()* is the summed total of all averages of ! message lines from
equation (3) for stock i on day t, "#$$%&#'()* is the total number of messages for the day t, and
BC2( is the average daily volume traded over the sample period for stock i.
The benefit of this measure is that it contains information for both breadth and depth
attributes. A limitation however, is that it does not account for the full level of market depth
available for the stock as it only looks at the first five levels.

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According to Sarr and Lybek (2002), tightness is the level of transaction costs, typically the
spread between bid and ask prices. A market is generally considered more liquid when bid-ask
spreads are low, or when the market is “tight” (Gabrielsen et al., p. 20, 2011). In pursuit of
measuring the level of this attribute, this paper will take a time weighted measure of the level 1
bid-ask spread expressed as a proportion of the level 1 bid-ask mid-price for each respective day.
This is done so by calculating the time elapsed for each spread, which is the time a bid-ask spread
is prevailing until a subsequent new message traffic (new message line), and summing all figures
up to obtain the time-weighted tightness measure of the day for the corresponding stock. In
addition, pertaining to the issue of negative bid-ask spreads as introduced in the prior section, it
is important to state that the time elapsed for message lines with negative bid-ask spreads were
added to the previous message line with a positive bid-ask spread. Therefore the total time
elapsed for each day remains the same apart from the fact that all other information within a
negative bid-ask spread message line is omitted. So, the tightness measure, 2/&ℎ+K#$$(), is defined
as:
2/&ℎ+K#$$() =
+)*L; − +)*
2()
×
%$O4()* − ?/-4()*
0.5(%$O4()* + ?/-4()*)
9
*:;
(5)
where +)* is the time in milliseconds for the !).
message line, 2() is the total time elapsed for stock /
on day +, and %$O4()* and ?/-4()* are the level 1 ask and bid prices respectively.
C. Control Variable
Volatility is a factor that is expected to play a significant role in driving market characteristics
such as the level of CBT activity and the various liquidity measures. One would typically expect
higher levels of volatility in the market to be associated with higher levels of CBT activity. Higher
levels of volatility would also have an impact on the bid-ask spreads. To control for this factor in
the data analysis regressions, a measure for volatility is estimated from the data.
This daily measure of volatility is estimated by observing the variance of the level 1 bid-ask
mid-price. The variance formula is given by:

17
T%, U() = (U()* − V)W
9
*:W
(6)
where T%, U() is the daily variance, U()* is the percentage change of level 1 bid-ask price mid-
price for message line j, and V is the average intra-day percentage change of the level 1 bid-ask
mid-price.
With respect to V, it is safe to assume that the average intra-day percentage change between
message lines for each day will be relatively small, and thus can assumed to be zero. In this case,
equation (6) can thus be redefined as:
T5'() = (ln =1Z/-()* − ln =1Z/-()*[;)W
9
*:W
(7)
where T5'() is the control variable for volatility, and (ln =1Z/-()* − ln =1Z/-()*[;) represents the
percentage change in the level 1 bid-ask price midpoint for message line j.
2.4 Millennium Exchange System Upgrade
This section provides an overview of the instrument that this paper uses that will help establish
causality between the level of CBT activity and the implications that it has on the market
liquidity measures.
A change in the LSE market microstructure is appropriate for achieving the goal stated
above as such a change typically has an exogenous effect on the level of CBT activity in the
market. A couple of other academic studies have used a similar approach in their research on this
field. These include the staggered introduction of the Autoquote system for the NYSE in 2003
which was used by Hendershott, Jones, and Menkveld (2011), and the multiple technology
upgrades to the TradElect electronic trading platform for the LSE over the period of 2007 to 2010
which was used by Broggard, Hendershott, Hunt, and Ysusi (2014).

18
Technological improvements in the electronic trading system typically have a welcoming
effect on CBT activity as such upgrades might improve the rate at which algorithms are able to
pick up and analyze information from market prices. Such upgrades also help reduce the latency
of the electronic trading platform, which in turn would be more accommodating for CBT activity.
As such, one should typically expect the level of CBT activity to increase significantly post the
instrument implementation date.
The LSE migrated from its old TradElect system to the Millennium Exchange system on
February 14, 2011. This upgrade drastically reduced the latency of the trading platform by 96%, a
decrease in average latency of 3ms to 0.113ms (Brogaard et al., p.357, 2014). It is important to
note that these changes in the network speed are in milliseconds and such changes will have a
direct impact on CBT activity while only marginally affecting human-based trades (Ibid, p.356).
Therefore, it would be reasonable to conclude that the use of the Millennium Exchange upgrade
would be an appropriate instrument for achieving this paper’s goal of establishing causality.
3. Data Findings
This section provides a time-series plot of the respective measures used in this paper to illustrate
how these individual measures have varied over the sample period. The red line in each plot
represents the arithmetic mean value for the sample. The blue line represents the best-fit linear
regression for the mean values (red line) to indicate the trend of the data. The green dotted line
indicates the date on which the Millennium System upgrade was implemented in the LSE. The
faded grey lines represent the respective data for the stocks included in the stated sample. It is
also important to highlight that for all plots, the y-axis was cut short accordingly so as to zoom in
on the mean value plot of the sample to make it adequate for visual analysis. The sample data
was analyzed using the Python programming language and subsequently manipulated in
Microsoft Excel. The Python script is provided in the Appendix for reference (See Data Analysis
1).

19
3.1 Time-Series Plots
A. CBT Proxy
The time-series plot for 012 4,567() showed the most surprising findings. For both FTSE 100 and
FTSE 250 stocks in our sample, the data showed that the level of CBT activity has a downward
trend over the sample period (See Figure. 1). Furthermore there is no visually significant increase
in CBT activity in the period post the Millennium System upgrade, which was initially expected.
The downward trend is however more pronounced for larger cap stocks as compared to smaller
cap stocks.
[insert Figure 1]
Further investigation was conducted to ensure the robustness of the data findings. The entire
sample of stocks was split into quartiles with Q1 and Q2 representing the upper and lower 50% of
the FTSE 100 stocks based on market capitalization respectively, and likewise with Q3 and Q4
for the FTSE 250 stocks. The individual time-series plot of 012 4,567() for each quartile is
depicted in Figure 2. The findings show similar results. However, the downward trend of CBT
activity is less pronounced in Q3 and more so in Q4.
[insert Figure 2]
B. Liquidity Measures
This section provides the respective time-series plots of the proposed liquidity measures for the
sample FTSE 100 and FTSE 250 stocks.
Figure 3 illustrates the time-series plot of 1,#%-+ℎ(). A visual analysis of the data shows that
1,#%-+ℎ() remained relatively constant throughout the sample period for large cap stocks.
However, for smaller cap stocks, the measure has a downward trend over the sample period. This
indicates that the average best volume sizes for both bid and ask quotes have generally been
decreasing over the sample period. Finally, there is no significant change in the trend for both

20
sample indices after the Millennium System upgrade, implying that the change in the market
microstructure might not have had any significant effects on market breadth.
[insert Figure 3]
Figure 4 illustrates the time-series plot of C#J+ℎ(). A visual analysis of the data shows that
C#J+ℎ() has an upward trend for FTSE 100 stocks, while it remained relatively constant over the
sample period for FTSE 250 stocks. This indicates that on average the volume of bid and ask
quote sizes for the first five levels in the market book has not changed significantly for the sample
FTSE 100 stocks over the sample period. However, this average has experienced a decreasing
trend for the sample FTSE 250 stocks over the sample period. Finally, the introduction of the
Millennium System upgrade here also does not seem to effect any significant change in trend for
both sample indices, implying that the instrument might not have had any significant effects on
market depth.
[insert Figure 4]
Figure 5 illustrates the time-series plot of 2/&ℎ+K#$$(). A visual analysis of the data shows
that 2/&ℎ+K#$$() has a downward trend for both sample indices over the sample period. This
indicates that bid-ask spreads have narrowed for both larger and relatively smaller cap stocks,
providing lower transaction costs for investors over the sample period. Again, the Millennium
Upgrade does not seem to effect any significant change on the trend for both sample indices,
implying that the instrument might not have had any significant change on spreads.
[insert Figure 5]

21
4. Empirical Analysis
The results from the econometric analysis performed on the data sample are presented in this
section. The econometrics package used for this study’s analysis is the open-source software R,
and the written script is provided in the Appendix for reference (See Data Analysis 2).
4.1 Justification of Panel Data Regression Model
Given that the data collected for this research contains dimensions of both cross-sectional and
time-series data; = 46 stocks collected over a period of 2 = 252 trading days, the most
appropriate type of regression to run for the sample would be a panel data regression. A pooled
data regression is obviously ruled out, as each stock will have its own idiosyncratic level of CBT
activity and thus such a model would not be the most efficient. This paper proposes to use the
fixed-effects model to control for a potential omitted variable bias in our regression, as opposed to
a random-effects model. For purposes of justification, a Hausman test was performed to
determine which model was statistically preferred.
Both fixed-effects and random-effects model were constructed for all intended regressions for
the FTSE 100 and FTSE 250 stocks. These include regressions for the measures 012 4,567(),
1,#%-+ℎ() , C#J+ℎ() , 2/&ℎ+K#$$() . The details of these regressions will be expanded on in the
following sections, as the purpose of this initial segment is to present the justifications for the
final model type used to perform the regression analysis in this study. The Hausman test was
conducted using the plm package in R, and the results are tabulated in Table 1.
[insert Table 1]
The Hausman test concluded that a random-effects model be preferably used for the FTSE
100 sample of stocks while a fixed-effects model be used for the FTSE 250 sample of stocks. The
results for each all the intended regressions are also provided for both model types in Table 2.
[insert Table 2]

22
It can be seen that the results provided for both fixed-effects and random-effects models for
the FTSE 100 sample are almost identical. Therefore, in pursuit of adopting a conservative
stance, this paper will use the fixed-effects models for all samples for the remaining analysis in
pursuit of controlling for a potential omitted variable bias. As seen from the almost identical
results between the fixed and random effects model from Table 2, it should not pose a major
concern with regard to the data analysis if a fixed-effects model is used over the random effects
model for the FTSE 100 sample.
4.2 Millennium System Upgrade and its Effects on the Level of CBT Activity
As seen in the time-series plot in Figures 1 and 2, this study’s instrument (Millennium System
Upgrade) does not seem to significantly increase CBT activity. This is now formally tested
through an econometric regression that takes the following form:
012 4,567() = `( + aZC() + bT5'() + c() (8)
where 012 4,567() is the relevant dependent variable, for example, the number of electronic
messages per successfully executed trade, `( is the fixed-effect of preceding CBT activity for the
corresponding stock, ZC() is the Millennium Upgrade dummy variable taking the value of 0 for
date values before 14 February 2011 and 1 for date value equal to and after this break point, and
T5'() is the control variable representing the volatility of each stock. Separate regressions for the
respective quartiles were also ran as a robustness check.
[insert Table 3]
Table 3 reports the slope coefficients for equation (8) for both the sample indices and the
quartiles. The results are extremely surprising. It is found that the Millennium Upgrade is
instead associated with a decrease in the level of CBT activity for both sample indices, contrary to
the typical type of exogenous influence that such upgrades have on preceding CBT activity, which
increases activity rather than decreases. The slope coefficient of -8.101 on the instrument dummy

23
variable implies that the Millennium Upgrade decreases CBT activity by an average of
approximately 8 messages per executed trade for FTSE 100 stocks. The coefficient of -12.241
implies the same interpretation, only a more pronounced effect for smaller cap stocks residing in
the FTSE 250. Results for the control variables are mixed. With higher volatility resulting in a
decrease in CBT activity for FTSE 100 stocks while the same scenario results in an increase in
CBT activity for FTSE 250 stocks, though not by a large amount. Furthermore, the results for the
quartile regressions echo similar results. The coefficient for the instrument dummy variable is
negative and statistically significant for all quartiles. As a form of a robustness check, double
clustered standard errors are run for all the regressions and reported in the lower half of Table 3.
The results are virtually identical to the original models.
As this result is massively unexpected, further investigation was conducted to find out if the
data was different for individual stocks in the sample, or if it was the cause of several stocks that
could possibly be influencing the entire sample (though it would be highly unlikely). Individual
linear regressions identical to equation (8) were constructed for 11 out of the 46 stocks in the
sample. Included in this subset are 7 FTSE 100 stocks, while the remaining 4 belong to the FTSE
250. The results are reported in Table 4.
[insert Table 4]
The results again illustrate a similar conclusion with 9 out of the 11 stocks displaying
statistically significant negative slope coefficients for the instrument dummy variable. BWY stock
does have a positive slope coefficient but is not statistically significant and hence does not have
much influence on the results. After collectively considering the analysis conducted, the results
appear to be robust and point to the conclusion that post the Millennium System upgrade, the
level of CBT activity did generally decrease for all stocks in the sample.
This result is inconsistent with the findings from similar instruments studied by Hendershott,
Jones, and Menkveld (2011) and Broggard, Hendershott, Hunt, and Ysusi (2014). Hendershott,
Jones, and Menkveld (2011) found that the staggered implementation of the autoquote system in
the NYSE led to an increase in message traffic of by approximately 50% for stocks, which had the

24
autoquote phased in. The instrument used in Broggard, Hendershott, Hunt, and Ysusi (2014) is
identical to the one used in this paper and their study found that for 2 out of the 4 system
upgrades in their sample, the decrease in system latency led to an increase in HFT activity. Their
paper looked at the largest 250 stocks of the U.K. equity market (FTSE 100 stocks plus the top
150 stocks from the FTSE 250), similar to the sample stocks in this paper.
At this juncture, it is important to highlight that the latter result also indicates that
improvements in the exchange system’s latency does not always lead to an increase in CBT
activity. This might provide some supporting evidence to the results found in this paper. The two
system upgrades Broggard, Hendershott, Hunt, and Ysusi (2014) found that did not significantly
affect the level of HFT activity were the TradElect 3 and TradElect 4.1 upgrades. These upgrades
were also the ones that reduced latency but a larger proportion as compared to the other 2 (45%
and 26% latency reduction respectively). This counterintuitive result seems to suggest that there
might be other underlying factors that contribute to the increase in the level of HFT activity
apart from just latency improvements. Perhaps the changes in exchange rules and regulations
surrounding algorithmic trading may either positively or negatively affect the level of CBT
activity despite a significant improvement in the exchange’s latency.
Despite these findings, the instrument is still valid for use of establishing causality between
CBT activity and market liquidity as the upgrade did have an exogenous influence on the level of
CBT activity that is statistically significant. Thus, the remainder of the analysis is still proceeded
as planned.
4.3 CBT Activity and its Impact on Market Liquidity
As seen in the time-series plot in Figures 3, 4 and 5, the graphical evidence indicates varying
observations for the various liquidity measures. Some seem to remain relatively constant
throughout the entire sample period while the rest seem to take a slight downward trend, with
the exception of C#J+ℎ() for the FTSE 100 sample. To establish the relationship between CBT
activity and changes in the various liquidity measures, the following econometric regression is
estimated:

25
=/EF/-/+7 Z#%$F,#() = `( + a012 4,567() + bT5'() + c() (9)
where =/EF/-/+7 Z#%$F,#() is the relevant dependent variable, that would include, 1,#%-+ℎ() ,
C#J+ℎ(), 2/&ℎ+K#$$(), `( is the stock fixed effect, 012 4,567() is the corresponding measure of CBT,
T5'() is the corresponding control variable representing the volatility of each stock. Again,
separate regressions for the respective quartiles and separate models incorporating double
clustered standard errors were also ran as a robustness check.
[insert Table 5]
Table 5 reports the slope coefficients for equation (9) for the sample indices of all three
liquidity measures. The results are mostly statistically significant and show somewhat conflicting
signs to the current findings in the academic literature for the slope coefficient, a. However, the
absolute values of the coefficients are so minuscule that it renders any contribution of CBT
activity to the various liquidity measures, economically insignificant. This is elaborated in more
detail for each respective liquidity measure.
[insert Table 6]
Table 6 reports the slope coefficients for equation (9) according to the respective quartiles for
all three liquidity measures. The results echo the ones from the main sample indices and do not
show anything new. This further ensures that the analysis conducted on the data is robust.
With regard to breadth, each unit increase in 012 4,567() leads to a decrease of 0.001% in
1,#%-+ℎ() for stocks in both the FTSE 100 and 250 samples as depicted in Table 5. When spilt
into quartiles, the results are relatively similar as depicted in Table 6. With the exception of Q1
not being statistically significant, for the remaining quartiles, each unit increase in 012 4,567()
lead to a similar decreases in magnitude when compared to the sample indices. Furthermore,
results for the regressions with double clustered standard errors show virtually identical results
as with the primary regressions. However, it is important to highlight that although the

26
012 4,567() coefficients are significant for both samples, the absolute value of the coefficients are
so minuscule that any change in CBT activity would have an economically insignificant impact on
market breadth. For example, a fairly high increase in 012 4,567() by a 100 units would lead to a
corresponding increase in 1,#%-+ℎ() of only 0.1%.
With regard to depth, the 012 4,567() coefficient is positive for FTSE 100 and negative for
FTSE 250 as depicted in Table 5. Both are statistically significant at the 1% significance level and
still remain statistically significant at least at the 5% significance level with double clustered
standard errors. However, the coefficient values for both sample indices are infinitesimal, such
that each unit increase in 012 4,567() essentially has no impact on market depth, and is thus
economically insignificant. When split into quartiles, the results are relatively similar as depicted
in Table 6. The 012 4,567() coefficient only takes a positive value for Q1, while it is negative for
the remaining three quartiles. All quartiles are also statistically significant. However, this result
changes in the model with double clustered standard errors, with only Q1 and Q4 remaining
significant. Despite the significance, the absolute coefficient values for the quartiles still remain
infinitesimal; rendering any impact that CBT activity has on market depth, economically
insignificant.
With regard to tightness, the 012 4,567() coefficient is positive for both sample indices but
only statistically significant for the FTSE 250 as depicted in Table 5. The T5'() coefficient is
significant and positive for both sample indices indicating that higher levels of volatility are
associated with higher spreads, and thus higher transaction costs. The results for the models
with double clustered standard errors are similar with the coefficient still remaining significant
for the FTSE 250. However, the coefficient values for both sample indices are again so minuscule
that each unit increase in 012 4,567() essentially has a negligible effect on market tightness. For
FTSE 100 the coefficient value is 0, indicating that CBT activity has no impact on tightness for
large cap stocks. For the FTSE 250 sample, each unit increase in 012 4,567() will lead to an
increase in spreads of 0.001%. When split into quartiles, it can be seen that the 012 4,567()
coefficient is only statistically significant for the Q4 sample while is insignificant for the other
quartiles. This indicates that CBT activity only affects spreads for the smallest stocks in the
overall sample. Furthermore, the quartile models with double clustered standard errors do not

27
show a different result with respect to the 012 4,567() coefficient. Despite the statistical
significance for the FTSE 250 sample, the absolute coefficient values for the quartiles are
likewise, so minuscule that any change in 012 4,567() has an economically insignificant impact
on market tightness.
Overall, the empirical analysis of the data suggests that 012 4,567() coefficient is
statistically significant with majority the various liquidity measures estimated in this study, with
the exception of bid-ask spreads for large cap stocks. However, the absolute values of the
coefficients are so minuscule that any change in the levels of CBT activity will have negligible
effects on the measures of liquidity, rendering it economically insignificant. As such, the analysis
suggests that CBT activity does not have a significant economical impact on market liquidity.
This result is not entirely congruent with key findings within the academic literature of this field.
With respect to the U.S. equity market, Hendershott, Jones, and Menkveld (2011) found that AT
activity in fact lowered the costs of trading as quoted and effective spreads narrow for large-cap
stocks after the introduction of the autoquote. A possible reason as to why the findings in this
paper differ could be attributed to the difference in markets. Regulation imposed on CBT activity
differs in both U.S. and the U.K., and as such might limit any potential benefits that such activity
could have on the financial market of interest. However, with respect to the U.K. equity market,
findings within the literature are slightly more supportive. Broggard, Hendershott, Hunt, and
Ysusi (2014), whom similarly used latency changes in the LSE as an instrument, did not manage
to find any evidence that HFT activity was responsible for a decline in execution costs. Utilizing
the system upgrades, they find no statistically significant relationship between HFT activity and
execution costs. This result supports this study’s finding of statistical insignificance between CBT
activity and large cap stocks.

28
5. Conclusion and Recommendations
In an increasingly information-driven economy, technological advancements enable market
participants to trade more efficiently and effectively. Computer algorithms are able to process
and react to new information at speeds that is impossible for a human trader. As major market
participants vie for the best prices in the market, it can be expected that computer-based trading
will be more prevalent in the financial markets in the foreseeable future. Such a change in the
market microstructure warrants an important understanding of such a structural change that
could impact the stakeholders of financial markets.
This study shows that the level of CBT activity has surprisingly declined over the period of a
year from July 2010 to June 2011. Furthermore, breadth and depth of the U.K. equity market do
not seem to have changed much, while bid-ask spreads have generally declined slightly for both
large and smaller-cap stocks.
What is most startling is the finding that the Millennium System upgrade is associated with
a decline in the level of CBT activity for stocks in the LSE. There are a several possible reasons
for this unusual phenomenon. First, an improvement in an exchange’s system latency does not
always necessarily entail that the subsequent level of CBT activity will increase. Indeed, lower
latency would foster an environment that is more accommodating to CBT participants, but there
could be other underlying factors that motivate these participants to engage less in trading, or
engage elsewhere completely. This leads to the second reason. Multilateral Trading Facilities
(MTFs) are alternative trading venues where participants can trade electronically. These include
Chi-X Europe, BATS, and Turquoise.5 Some participants might prefer to trade on such venues as
MTFs might provide even lower latencies and overall transaction costs as compared to primary
trading venues like the LSE. Finally, the sample period pertinent to this study might be period
where CBT activity had already been saturated or was at full capacity, and would not have had
experienced further growth until further technology strides were made such that it became cost-
effective for further expansion.
5 Chi-X and BATS have since merged in February 2011. Previously both MTFs, the merged entity BATS Chi-
X Europe was given Recognized Investment Exchange (RIE) status from the Financial Services Conduct
Authority (FCA) in May 2013. Turquoise was acquired by the LSE Group on 21 December 2009.

29
This study also found that the level of CBT activity has an economically insignificant effect
on market liquidity, namely measures such as breadth, depth and tightness. However, it might
not be reasonable to immediately conclude that CBT activity does not have any impact on market
liquidity. As stated previously, it could be due to the fact that the level of CBT activity in the
sample period is already relatively high, such that subsequent changes in the level of CBT
activity would be considered relatively small. Thus, if liquidity in the financial market benefits
from a significant change in CBT activity, rather than a relatively moderate increase or decrease,
then the approach used in this study would not have been able to detect it.
Understanding how changes in the market microstructure influences market liquidity is
important and relevant for regulators to know how to structure policies so as to lessen the
potential negative implications such changes have. It will also help them to be more equipped in
preventing extreme events such as ones similar to the Flash Crash of 2010 from occurring in the
future. Within the confines of this study’s research question, it is concluded from the empirical
analysis that CBT activity though statistically significant, has economically insignificant effects
on market liquidity for U.K. equities. This suggests that there is no need for stricter regulation of
CBT activity in the financial markets since CBT activity does not seem to have any sort of
implication on the market’s liquidity.
However, it is crucial to state the limitations of this study. It would not be comprehensive to
test the hypothesis by simply looking at the Millennium System upgrade on the LSE in isolation.
Further research is therefore warranted to complement this study before reasonable conclusions
can be made regarding the implications of CBT activity on market liquidity. In particular, CBT
activity in MTFs over the same period should be investigated as well, to examine how the level of
CBT activity in these alternative venues compared with that of the LSE. It would also be useful
to investigate the period over which the LSE Group implemented the Millennium System
upgrade in the Turquoise MTF as a trial period before its official introduction on the LSE.

30
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32
Appendix
Figure 1: Level of CBT activity over sample period
The left plot shows the plot for the FTSE 100 sample and the right plot shows the plot for the
FTSE 250 sample. Both plots show the daily average level of CBT activity measured using
012 4,567(). The y-axis takes the value of the number of message traffic per unit of successfully
executed trade. The x-axis is the date, which is the sample period as stated in section 2.1.
Figure 2: Level of CBT activity over sample period (Quartiles)
The top plots show the plots for quartiles 1 and 2, split from the FTSE 100 sample. The bottom
plots show the plots for quartiles 3 and 4, split from the FTSE sample. All plots show the daily
average level of CBT activity measured using 012 4,567(). The y-axis takes the value of the
number of message traffic per unit of successfully executed trade. The x-axis is the date, which is
the sample period as stated in section 2.1.

33
Figure 3: Level of market breadth over sample period
Both plots show the daily average level of level 1 market breadth measured using 1,#%-+ℎ(). The
y-axis takes the value of percentages expressed in decimals (e.g. 0.002 = 0.2%). The x-axis is the
date, which is the sample period as stated in section 2.1.
Figure 4: Level of market depth over the sample period
Both plots show the daily average level breadth for the first five levels of depth in the market
measured using C#J+ℎ(). The y-axis takes the value of percentages expressed in decimals (e.g.
0.002 = 0.2%). The x-axis is the date, which is the sample period as stated in section 2.1.

34
Figure 5: Level of market tightness over the sample period
Both plots show the daily average level tightness for level 1 market orders measured using
2/&ℎ+K#$$(). The y-axis takes the value of percentages expressed in decimals (e.g. 0.002 = 0.2%).
The x-axis is the date, which is the sample period as stated in section 2.1.

35
Table 1
Hausman Test
This table provides the results for the Hausman test that was conducted on the both types of panel data regressions, Fixed-Effects and Random-Effects,
to determine which model was more statistically appropriate for the sample indices. CBT Proxy*+ represents the regression that was run for the
Millennium Upgrade’s impact on CBT activity and is defined by the following econometric regression:
,-. /012345 = 74 + 9:;45 + <=1>45 + ?45
The regression for the liquidity measures include are denoted by Breadth*+ , Depth*+ , and Tightness*+ , and is specified by the following econometric
regression:
KLMNLOLP3 :QRSN0Q45 = 74 + 9,-. /012345 + <=1>45 + ?45
The null hypothesis of the Hausman test is to use the Random-Effects model. The alternative hypothesis is to use the Fixed-Effects model.

FTSE 100 FTSE 250
CBT Proxy*+ Breadth*+ Depth*+ Tightness*+ CBT Proxy*+ Breadth*+ Depth*+ Tightness*+
p-value 0.9736 0.9392 0.9977 0.6206 0.02942** 0.0007255*** 0.03847** 2.2e-16***
chisq 0.053468 0.12538 0.0046512 0.9541 7.0519 14.457 6.5155 122.03
df 2 2 2 2 2 2 2 2
Result Use Random-Effects Use Fixed-Effects
Indicator of statistical significant as the respective levels: * p<1%, ** p<5%, *** p<1%. If result is statistically significant, then reject the null hypothesis of
using Random-Effects model, and accept alternative hypothesis of using Fixed-Effects model.

36
Table 2
Results for Fixed-Effects vs. Random-Effects Model
This table provides the results for the coefficient values for both the CBT proxy regression and the Liquidity Measure regression shown in Table 1. The
table provides a comparison of the coefficient values for both the fixed-effects model and the random-effects model when used to construct the necessary
regressions. The standard errors are shown in the parentheses.
CBT Proxy*+ Breadth*+ Depth*+ Tightness*+
FTSE 100 FTSE 250 FTSE 100 FTSE 250 FTSE 100 FTSE 250 FTSE 100 FTSE 250
Fixed Effects
βMD*+ -8.101*** -12.241***
(0.38) (1.31)
CBT Proxy*+ -0.00001*** -0.00001*** 0.00000*** -0.00000*** 0 0.00001***
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
γVol*+ -9.131* 2.536*** 0.00004 -0.0001 -0.0002 -0.0001* 0.093*** 0.0003***
(5.34) (0.90) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
Obs. 5,040 6,552 5,040 6,552 5,040 6,552 5,040 6,552
R2 0.084 0.015 0.005 0.002 0.003 0.003 0.292 0.013
Adjuste
d R2
0.084 0.014 0.005 0.002 0.003 0.003 0.29 0.013
F
Statisti
c
230.482*** (df
= 2; 5018)
48.177*** (df =
2; 6524)
12.898*** (df =
2; 5018)
5.971*** (df =
2; 6524)
8.030*** (df =
2; 5018)
10.588*** (df =
2; 6524)
1,032.947*** (df
= 2; 5018)
43.612*** (df =
2; 6524)
Random Effects
βMD*+ -8.101*** -12.235***
(0.38) (1.32)
CBT Proxy*+ -0.00001*** -0.00001*** 0.00000*** -0.00000*** 0 0.00001***
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
γVol*+ -9.100* 2.715*** 0.00004 -0.0001 -0.0002 -0.0001* 0.093*** 0.0004***
(5.34) (0.90) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
Interce
pt
30.429*** 42.395*** 0.003*** 0.008*** 0.003*** 0.006*** 0.001*** 0.002***

37
(3.38) (4.05) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
Obs. 5,040 6,552 5,040 6,552 5,040 6,552 5,040 6,552
R2 0.084 0.015 0.005 0.002 0.003 0.003 0.291 0.019
Adj. R2 0.084 0.015 0.005 0.002 0.003 0.003 0.291 0.019
F Stat. 230.320*** (df
= 2; 5037)
48.446*** (df =
2; 6549)
12.952*** (df =
2; 5037)
5.274*** (df =
2; 6549)
8.042*** (df =
2; 5037)
10.246*** (df =
2; 6549)
1,034.026*** (df
= 2; 5037)
61.910*** (df =
2; 6549)
Indicator of statistical significant as the respective levels: * p<1%, ** p<5%, *** p<1%
< Table 2 continued >

38
Table 3
Millennium Upgrade Impact on CBT activity
This table provides the results for the regression performed on the CBT proxy measure, CBT Proxy*+ . The CBT proxy was regressed against the
instrumental variable, which is the point after which the Millennium Upgrade was implemented on the London Stock Exchange. The instrumental
variable is a dummy variable that takes a value of 1 for dates that are equal to and after 14th February 2011, and 0 otherwise. The control variable for
volatility is included in the regression. A fixed-effects model was used to construct the panel data regression for the sample indices and the quartiles. As
a robustness check, a model with double clustered standard errors was performed for each regression to determine the validity of the coefficient results.
The general specification for the econometric regression is:
,-. /012345 = 74 + 9:;45 + <=1>45 + ?45
where :;45 is the instrument dummy variable, =1>45 is the control variable for volatility, and 74 is the fixed effects dummy. The standard errors are
provided in the parentheses.
FTSE100 FTSE250 Q1 Q2 Q3 Q4
β -8.101*** -12.241*** -9.640*** -6.565*** -4.983*** -20.597***
(0.38) (1.31) (0.57) (0.50) (1.11) (2.52)
γ -9.131* 2.536*** -10.017 -6.526 99.389*** 2.278*
(5.34) (0.90) (6.98) (8.56) (14.94) (1.18)
With Double Clustered Standard Errors
β -8.101*** -12.241*** -9.640*** -6.565*** -4.983*** -20.597***
(0.34) (1.15) (0.52) (0.42) (1.03) (2.16)
γ -9.13 2.536* -10.017 -6.526 99.389** 2.278*
(8.54) (1.35) (15.00) (12.06) (47.14) (1.16)
Obs. 5,040 6,552 2,520 2,520 3,528 3,024
R2 0.858 0.496 0.883 0.804 0.657 0.43
Adj. R2 0.857 0.493 0.882 0.803 0.655 0.428
Residual
SE
13.041 (df = 5018) 51.303 (df = 6524) 13.880 (df = 2508) 12.104 (df = 2508) 31.849 (df = 3512) 66.907 (df = 3010)
F Stat. 1,374.241*** (df = 22;
5018)
228.883*** (df = 28;
6524)
1,571.744*** (df = 12;
2508)
859.561*** (df = 12;
2508)
420.208*** (df = 16;
3512)
162.464*** (df = 14;
3010)

39
Table 4
Millennium Upgrade Impact on CBT activity for Individual Stocks
This table provides the results for the individual regressions performed on 11 chosen stocks for the purposes of further investigation into the robustness
of the data. A similar regression to the one performed in Table 3 was run but this time instead of a panel data regression, an OLS regression was
conducted for each individual stock.
ARM AZN BAES BARC BATS BGC BLT BOK BP BVIC BWY
β -12.478*** -0.822 -2.922*** -8.895*** -5.675*** -36.600*** -11.754*** -13.108*** -18.227*** -5.008*** 1.387
(1.59) (1.01) (0.87) (1.17) (0.73) (6.28) (1.09) (2.83) (2.18) (1.77) (1.22)
γ -14.262 294.439 -4,471.285** -12.478 -3,920.61 56.565 -623.606 31.951 30.148 245.360**
*
25.707**
(29.82) (8,300.78) (2,176.59) (20.47) (4,176.05) (63.40) (3,527.38) (35.71) (38.68) (21.42) (11.96)
Intercept 28.973*** 28.022*** 25.509*** 28.691*** 27.206*** 77.326*** 37.093*** 28.781*** 41.576*** 25.491*** 25.712**
*
(0.97) (0.98) (0.73) (0.71) (0.69) (3.89) (1.26) (1.73) (1.33) (1.08) (0.75)
Obs. 252 252 252 252 252 252 252 252 252 252 252
R2 0.202 0.003 0.055 0.192 0.196 0.123 0.333 0.081 0.219 0.367 0.023
Adj. R2 0.196 -0.005 0.048 0.185 0.189 0.116 0.328 0.074 0.213 0.362 0.015
Residual SE (df =
249)
12.149 7.533 6.618 8.962 5.479 48.224 8.054 21.744 16.688 13.529 9.342
F Stat. (df = 2;
249)
31.507*** 0.358 7.263*** 29.572*** 30.271*** 17.509*** 62.206*** 11.001*** 34.952*** 72.145*** 2.936*

40
Table 5
Impact of CBT activity on Market Liquidity
This table provides the results for the regression performed on the three liquidity measures, Breadth*+, Depth*+, Tightness*+. Each measure was regressed
against the CBT proxy, CBT Proxy*+. A control variable for volatility, γVol*+, was included in the regression. A fixed-effects model was used to construct the
panel data regression for both sample indices, FTSE 100 and FTSE 250. As a robustness check, a model with double clustered standard errors was
performed for each regression to determine the validity of the coefficient results. The general specification for the econometric regression is:
KLMNLOLP3 :QRSN0Q45 = 74 + 9,-. /012345 + <=1>45 + ?45
where KLMNLOLP3 :QRSN0Q45 is the relevant dependent variable liquidity measure, ,-. /012345 is the CBT proxy, =1>45 is the control variable for volatility,
and 74 is the fixed effects dummy. The standard errors are provided in parentheses.
Breadth*+ Depth*+ Tightness*+
FTSE 100 FTSE 250 FTSE 100 FTSE 250 FTSE 100 FTSE 250
β -0.00001*** -0.00001*** 0.00000*** -0.00000*** 0 0.00001***
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
γ 0.00004 -0.0001 -0.0002 -0.0001* 0.093*** 0.0003***
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
β -0.00001*** -0.00001*** 0.00000** -0.00000*** 0 0.00001**
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
γ 0.00004 -0.0001 -0.0002 -0.0001* 0.09 0.0003
(0.00) (0.00) (0.00) (0.00) (0.07) (0.00)
Obs. 5,040 6,552 5,040 6,552 5,040 6,552
R2 0.951 0.616 0.951 0.893 0.307 0.397
Adj. R2 0.951 0.614 0.95 0.893 0.304 0.395
Residual
SE
0.001 (df = 5018) 0.007 (df = 6524) 0.001 (df = 5018) 0.002 (df = 6524) 0.005 (df = 5018) 0.004 (df = 6524)
F Stat. 4,435.425*** (df =
22; 5018)
373.943*** (df = 28;
6524)
4,388.486*** (df =
22; 5018)
1,948.015*** (df =
28; 6524)
101.067*** (df = 22;
5018)
153.603*** (df = 28;
6524)

41
Table 6
Impact of CBT activity on Market Liquidity (Quartiles)
This table provides the results for the same regression as performed in Table 5, but for the samples when they were split into quartiles. Q1 and Q2
represent the top 50% and bottom 50% of the FTSE 100 index. Q3 and A4 represent the top 50% and bottom 50% of the FTSE 250 index. The
specification for the econometric regression takes a similar form as to the one performed in Table 5.
Breadth*+ Depth*+ Tightness*+
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
β
0 -
0.00001**
*
-
0.00002**
*
-
0.00000**
*
0.00001**
*
-
0.00000**
*
-0.00000** -
0.00000**
*
-0.00001 0 0 0.00001**
*
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
γ 0 0.0002 0.002 -0.0001 -0.0003 -0.0001 0.0003 -0.0001** 0.104*** 0.072*** 0.025*** 0.0003***
(0.00) (0.00) (0.01) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
β
0 -0.00001 -
0.00002**
*
-
0.00000**
*
0.00001**
*
0 0 -
0.00000**
*
-0.00001 0 0 0.00001*
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
γ 0 0.0002 0.002 -0.0001 -0.0003 -0.0001 0.0003 -0.0001* 0.10 0.072 0.025* 0.0003
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.11) (0.33) (0.01) (0.00)
Obs. 2,520 2,520 3,528 3,024 2,520 2,520 3,528 3,024 2,520 2,520 3,528 3,024
R2 0.956 0.948 0.536 0.865 0.954 0.949 0.898 0.884 0.546 0.128 0.505 0.389
Adj.
R2
0.956 0.948 0.533 0.865 0.954 0.949 0.898 0.884 0.543 0.124 0.503 0.386
Resi
dual
SE
0.001 (df =
2508)
0.001 (df =
2508)
0.010 (df
= 3512)
0.003 (df =
3010)
0.001 (df =
2508)
0.001 (df =
2508)
0.002 (df =
3512)
0.002 (df =
3010)
0.004 (df
= 2508)
0.006 (df
= 2508)
0.002 (df
= 3512)
0.005 (df
= 3010)
F
Stat.
4,583.574*
** (df = 12;
2508)
3,833.492*
** (df = 12;
2508)
253.052**
* (df = 16;
3512)
1,383.367*
** (df = 14;
3010)
4,323.082*
** (df = 12;
2508)
3,874.435*
** (df = 12;
2508)
1,941.756*
** (df = 16;
3512)
1,640.784*
** (df = 14;
3010)
250.934**
* (df = 12;
2508)
30.744**
* (df =
12; 2508)
224.011**
* (df = 16;
3512)
136.601**
* (df = 14;
3010)

42
Table 7
List of Sample Companies
This table provides the list of all 48 stocks that were selected from the FTSE 100 and FTSE 250
index. The data was collected on the 10th November 2015 and thus all information reflects this
date. The FTSE 100 constituents make up a total of 51.69% of the FTSE 100 index. The FTSE
250 constituents make up a total of 22.87% of the FTSE 250 index. Market Cap is measured in
the thousandth US dollar. The two companies highlighted in red are removed from the final
testing sample, as the data collected did not fulfill the 252 trading day period.

# Ticker Category Company Name % Index Market Cap Quartile
1 HSBA.L Financials HSBC Holdings PLC 6.33 F.100 84,537.10 Q1
2 BATS.L Consumer
Goods
British American Tobacco
PLC
4.35 F.100 71,478.42 Q1
3 GSK.L Healthcare GlaxoSmithKline PLC 4.14 F.100 66,097.70 Q1
4 BP.L Oil & Gas BP PLC 4.33 F.100 61,040.28 Q1
5 RDSa.L Oil & Gas Royal Dutch Shell PLC 4.08 F.100 60,319.33 Q1
6 VOD.L Telecommu
nications
Vodafone Group PLC 3.71 F.100 55,896.73 Q1
7 AZN.L Healthcare AstraZeneca PLC 3.27 F.100 51,663.99 Q1
8 DGE.L Consumer
Goods
Diageo PLC 2.91 F.100 46,890.25 Q1
9 RB.L Consumer
Goods
Reckitt Benckiser Group
PLC
2.44 F.100 46,540.96 Q1
10 LLOY.L Financials Lloyds Banking Group PLC 2.78 F.100 44,451.56 Q1
11 NG.L Utilities National Grid PLC 2.13 F.100 35,721.12 Q2
12 RIO.L Basic
Materials
Rio Tinto PLC 1.76 F.100 26,326.90 Q2
13 BARC.L Financials Barclays PLC 2.40 F.100 26,325.84 Q2
14 CPG.L Consumer
Services
Compass Group PLC 1.08 F.100 20,265.62 Q2
15 WPP.L Consumer
Services
WPP PLC 1.19 F.100 19,076.00 Q2
16 BAES.L Industrials BAE Systems PLC 0.86 F.100 15,891.19 Q2
17 CRH.L Industrials CRH PLC 0.90 F.100 14,545.23 Q2
18 BLT.L Basic
Materials
BHP Billiton PLC 1.24 F.100 14,458.19 Q2
19 TSCO.L Consumer
Services
Tesco PLC 0.89 F.100 14,347.09 Q2
20 ARM.L Technology ARM Holdings PLC 0.91 F.100 13,231.46 Q2
21 DCC.L Industrials DCC PLC 1.45 F.250 5,049.00 Q3
22 PFG.L Financials Provident Financial PLC 1.53 F.250 4,754.00 Q3
23 INF.L Consumer
Services
Informa PLC 1.12 F.250 4,370.62 Q3
24 REX.L Industrials Rexam PLC 1.12 F.250 4,282.00 Q3
25 CRDA.
L
Basic
Materials
Croda International PLC 1.11 F.250 4,014.39 Q3
26 RMV.L Consumer
Services
Rightmove PLC 1.04 F.250 3,645.03 Q3
27 WMH.L Consumer
Services
William Hill PLC 0.87 F.250 3,492.58 Q3
28 SMDS.
L
Industrials DS Smith PLC 1.11 F.250 3,482.71 Q3
29 PNN.L Utilities Pennon Group PLC 0.95 F.250 3,370.56 Q3

43
30 AML.L Financials Amlin PLC 0.97 F.250 3,370.00 Q3
31 INCH.L Consumer
Services
Inchcape PLC 1.01 F.250 3,136.70 Q3
32 SGRO.L Financials Segro PLC 0.91 F.250 3,120.26 Q3
33 MCRO.
L
Technology Micro Focus International
PLC
0.59 F.250 3,046.90 Q3
34 SMT.L Financials Scottish Mortgage
Investment Trust PLC
1.01 F.250 3,045.56 Q4
35 BWY.L Consumer
Goods
Bellway PLC 0.88 F.250 3,025.05 Q4
36 RTO.L Industrials Rentokil Initial PLC 0.81 F.250 2,964.84 Q4
37 BOK.L Consumer
Services
Booker Group PLC 0.88 F.250 2,906.13 Q4
38 COB.L Industrials Cobham PLC 0.90 F.250 2,837.33 Q4
39 PMTLq
.L
Basic
Materials
Polymetal International
PLC
0.44 F.250 2,723.27 Q4
40 PFC.L Oil & Gas Petrofac Ltd 0.58 F.250 2,684.28 Q4
41 TATE.L Consumer
Goods
Tate & Lyle PLC 0.84 F.250 2,645.10 Q4
42 TCY.L Technology Telecity Group PLC 0.69 F.250 2,600.00 Q4
43 BGC.L Healthcare BTG PLC 0.58 F.250 2,315.01 Q4
44 WG.L Oil & Gas John Wood Group PLC 0.61 F.250 2,304.00 Q4
45 TALK.L Telecommu
nications
TalkTalk Telecom Group
PLC
0.33 F.250 2,075.60 Q4
46 BVIC.L Consumer
Goods
Britvic PLC 0.51 F.250 1,785.98 Q4
47 HWDN.
L
Industrials Howden Joinery Group PLC 0.90 F.250 - -
48 BETF.L Consumer
Services
Betfair Group PLC 0.79 F.250 - -
< Table 7 continued >

44
Sample Data Structure 1: Level 2 Market Data File: Message Traffic and Bid-Order Summary
The first line in blue is the header line of the CSV file. #RIC denotes the stock ticker. Date[L] denotes the date. Time[L] denotes the time down to the
millisecond. Type denotes the type of data collected. L#-BidPrice, BidSize, AskPrice, AskSize denotes the corresponding bid and ask prices and quote
sizes for the respective levels of the market order book beginning at level 1.
#RIC,Date[L],Time[L],Type,L1-BidPrice,L1-BidSize,L1-AskPrice,L1-AskSize,L2-BidPrice,L2-BidSize,L2-AskPrice,L2-
AskSize,L3-BidPrice,L3-BidSize,L3-AskPrice,L3-AskSize,L4-BidPrice,L4-BidSize,L4-AskPrice,L4-AskSize,L5-BidPrice,L5-
BidSize,L5-AskPrice,L5-AskSize
HSBA.L,20100701,08:05:00.178,Market
Depth,608.1,5086,608.8,898,608,1573,608.9,828,607.9,13774,609,17239,607.8,4367,609.1,5972,607.6,1200,609.2,6444
HSBA.L,20100701,08:05:01.023,Market
Depth,608.1,5086,608.8,898,608,1573,608.9,828,607.9,13774,609,17239,607.8,4367,609.1,5972,607.7,2022,609.2,6444
HSBA.L,20100701,08:05:01.023,Market
Depth,608.9,5086,608.5,898,608,1573,608.9,828,607.9,13774,609,17239,607.8,4367,609.1,5972,607.7,2022,609.2,6444
HSBA.L,20100701,08:05:01.996,Market
Depth,608.1,5086,608.8,898,608,1573,608.9,828,607.9,11774,609,17239,607.8,4367,609.1,5972,607.7,2022,609.2,6444
HSBA.L,20100701,08:05:01.997,Market
Depth,608.1,5086,608.8,898,608,3573,608.9,828,607.9,11774,609,17239,607.8,4367,609.1,5972,607.7,2022,609.2,6444
HSBA.L,20100701,08:05:01.997,Market
Depth,608.1,5086,608.8,898,608,3573,608.9,828,607.9,11774,609,17239,607.8,4367,609.1,5972,607.7,2022,609.2,6444
.
.
Sample Data Structure 2: Level 2 Market Data File: Trade Summary
The first line in blue is the header line of the CSV file. #RIC denotes the stock ticker. Date[L] denotes the date. Time[L] denotes the time down to the
millisecond. Type denotes the type of data collected. Price denotes the price of which the trade was executed at. Vo denotes the number of shares that
were traded.
#RIC,Date[L],Time[L],Type,Price,Vo
HSBA.L,20100701,08:05:34.224,Trade,608.8352,5000
HSBA.L,20100701,08:05:37.730,Trade,608.8,1124
HSBA.L,20100701,08:05:37.730,Trade,608.8,828
HSBA.L,20100701,08:05:37.750,Trade,608.8,1961
HSBA.L,20100701,08:05:37.788,Trade,608.8,511
HSBA.L,20100701,08:05:41.625,Trade,608.8,2494
HSBA.L,20100701,08:05:47.615,Trade,608.4,839
HSBA.L,20100701,08:05:48.112,Trade,608.4,1400
.
.

45
Data Analysis 1: Python Source Code
Provided below is the entire code written in the Python programming language that was used to perform the handling of the data collected. An
electronic version of the file can be provided upon request for easier reference.
1 import csv
2 import math
3 from datetime import datetime, timedelta
4
5 # definition for bid files
6 class bidsummary:
7 def __init__( self, epic, ymd, nblines, l1minbid, l1maxbid, l1bidsize, l1minask, l1maxask, l1asksize,
8 l2minbid, l2maxbid, l2bidsize,
l2minask, l2maxask, l2asksize,
12 mintime, maxtime, deltatime,
weightedbas, weightedbasabs, volmeasure, volmeasure2):
13 self.epic = epic
14 self.ymd = ymd
15 self.nblines = nblines
16 self.l1minbid = l1minbid
17 self.l1maxbid = l1maxbid
18 self.l1bidsize = l1bidsize
19 self.l1minask = l1minask
20 self.l1maxask = l1maxask
21 self.l1asksize = l1asksize

46
46 self.mintime = mintime
47 self.maxtime = maxtime
48 self.deltatime = deltatime
49 self.weightedbas = weightedbas
50 self.weightedbasabs = weightedbasabs
51 self.volmeasure = volmeasure
52 self.volmeasure2 = volmeasure2
53
54 # definition for trade file
55 class tradesummary:
56 def __init__( self, epic, ymd, nbtrade, price, sumvol):
57 self.epic = epic
58 self.ymd = ymd
59 self.nbtrade = nbtrade
60 self.price = price
61 self.sumvol = sumvol
62
63 # int conversion with default to 0
64 def intx(somestr):
65 try:
66 return int(somestr)
67 except Exception:
68 return int(0)
69 pass
70

47
71 # float conversion with default to 0
72 def floatx(somestr):
73 try:
74 return float(somestr)
76 return float(0)
77 pass
78
79 # time conversion with default to 00:00:00
80 def strptimex(somestr):
81 thetimeformat = '%H:%M:%S.%f'
82 try:
83 return datetime.strptime(somestr, thetimeformat)
85 return datetime.strptime('00:00:00.000', thetimeformat)
86 pass
87
88 # add/update Bid summary with new row
89 def AddBidSummaryMap( TheBidSummaryMap, TheBidSummaryObject ) :
90 epicymdkey = TheBidSummaryObject.epic+str(TheBidSummaryObject.ymd)
91 if epicymdkey in TheBidSummaryMap.keys():
92 TheBidSummaryMap[epicymdkey].nblines += 1
93 TheBidSummaryMap[epicymdkey].l1minbid = min(TheBidSummaryMap[epicymdkey].l1minbid,
TheBidSummaryObject.l1minbid)
94 TheBidSummaryMap[epicymdkey].l1maxbid = max(TheBidSummaryMap[epicymdkey].l1maxbid,
TheBidSummaryObject.l1maxbid)
95 TheBidSummaryMap[epicymdkey].l1bidsize += TheBidSummaryObject.l1bidsize
96 TheBidSummaryMap[epicymdkey].l1minask = min(TheBidSummaryMap[epicymdkey].l1minask,
TheBidSummaryObject.l1minask)
97 TheBidSummaryMap[epicymdkey].l1maxask = max(TheBidSummaryMap[epicymdkey].l1maxask,
TheBidSummaryObject.l1maxask)
98 TheBidSummaryMap[epicymdkey].l1asksize += TheBidSummaryObject.l1asksize

48
123 TheBidSummaryMap[epicymdkey].mintime = min(TheBidSummaryMap[epicymdkey].mintime,
TheBidSummaryObject.mintime)
124 TheBidSummaryMap[epicymdkey].maxtime = max(TheBidSummaryMap[epicymdkey].maxtime,
TheBidSummaryObject.maxtime)
125 TheBidSummaryMap[epicymdkey].deltatime = (TheBidSummaryMap[epicymdkey].maxtime -
TheBidSummaryMap[epicymdkey].mintime).total_seconds()*1000
126 TheBidSummaryMap[epicymdkey].weightedbas += TheBidSummaryObject.weightedbas
127 TheBidSummaryMap[epicymdkey].weightedbasabs += TheBidSummaryObject.weightedbasabs

49
128 TheBidSummaryMap[epicymdkey].volmeasure += TheBidSummaryObject.volmeasure
129 TheBidSummaryMap[epicymdkey].volmeasure2 += TheBidSummaryObject.volmeasure2
130 else:
131 TheBidSummaryMap[epicymdkey] = TheBidSummaryObject
132
133 # insert/update only Bid summary with new row
134 def SetBidSummaryMap( TheBidSummaryMap, TheBidSummaryObject ) :
136 TheBidSummaryMap[epicymdkey] = TheBidSummaryObject
137
138 # Check if new row key already exist in the Bid summary
139 def IsExistBidSummaryMap( TheBidSummaryMap, TheBidSummaryObject ) :
141 return epicymdkey in TheBidSummaryMap.keys()
142
143 # Get row information if exist in the bid summary
144 def GetBidSummaryMap( TheBidSummaryMap, TheBidSummaryObject ) :
146 if (epicymdkey in TheBidSummaryMap.keys()):
147 return TheBidSummaryMap[epicymdkey]
148 else:
149 return None
150
151 # add/update trade summary with new row
152 def AddTradeSummaryMap( TheTradeSummaryMap, TheTradeSummaryObject ) :
153 epicymdkey = TheTradeSummaryObject.epic+str(TheTradeSummaryObject.ymd)
154 if epicymdkey in TheTradeSummaryMap.keys():
155 TheTradeSummaryMap[epicymdkey].nbtrade += 1
156 TheTradeSummaryMap[epicymdkey].sumvol += TheTradeSummaryObject.sumvol
157 else:
158 TheTradeSummaryMap[epicymdkey] = TheTradeSummaryObject
159
160 def ProcessBidFile(TheBidFile, TheBidSummaryMap):
161 print ('Processing ' + TheBidFile)
162 with open(TheBidFile, 'rt') as infile:
163 reader = csv.DictReader(infile, delimiter = ",", lineterminator = "n")
164 rowcounter = 0.0
165
166 # mapping the csv data points to their corresponding data type
167 for row in reader:

50
168 rowcounter += 1
169 if rowcounter % 100000 == 0:
170 print (str(rowcounter) + ' processed')
171
172 try:
173 parsed = [str(row['#RIC']), # row 0
174 intx(row['Date[L]']), # row 1
175 strptimex(row['Time[L]']), # row 2
176 str(row['Type']), # row 3
177 floatx(row['L1-BidPrice']), # row 4
178 floatx(row['L1-BidSize']), # row 5
179 floatx(row['L1-AskPrice']), # row 6
180 floatx(row['L1-AskSize']), # row 7
196 floatx(row['L5-AskSize'])] # row 23
197
198 tempbidsummary = bidsummary(parsed[0], parsed[1], 1, parsed[4], parsed[4],
parsed[5], parsed[6], parsed[6], parsed[7],
199
parsed[8], parsed[8], parsed[9], parsed[10], parsed[10], parsed[11], parsed[12], parsed[12], parsed[13],
200
parsed[14], parsed[14], parsed[15], parsed[16], parsed[16], parsed[17], parsed[18], parsed[18], parsed[19],
201
parsed[20], parsed[20], parsed[21], parsed[22], parsed[22], parsed[23], parsed[2], parsed[2], 0, 0, 0, 0, 0)
202
203 # ignore message line with negative bid-ask spreads

51
204 if (tempbidsummary.l1minask-tempbidsummary.l1minbid < 0):
205 continue
206
207 # calculate weightedbas/volatility measure
208 if IsExistBidSummaryMap(PrevBidSummaryMap, tempbidsummary):
209 prevbidsummary = GetBidSummaryMap(PrevBidSummaryMap, tempbidsummary)
210 # for weighted bas
211 if (prevbidsummary.l1minask + prevbidsummary.l1minbid ==0):
212 tempbidsummary.weightedbas = 0
213 else:
214 tempbidsummary.weightedbas = (((tempbidsummary.mintime -
prevbidsummary.mintime).total_seconds()*1000.0)
215 *
((prevbidsummary.l1minask - prevbidsummary.l1minbid)
216
/ (0.5*(prevbidsummary.l1minask + prevbidsummary.l1minbid))))
217 # calculation for volatility measure
218 if ((prevbidsummary.l1minask - prevbidsummary.l1minbid == 0) or
(tempbidsummary.l1minask - tempbidsummary.l1minbid == 0)):
219 tempbidsummary.volmeasure = 0
220 tempbidsummary.volmeasure2 = 0
221 else:
222 tempbidsummary.volmeasure = (math.log((tempbidsummary.l1minask
+ tempbidsummary.l1minbid)/2) - math.log((prevbidsummary.l1minask + prevbidsummary.l1minbid)/2)) ** 2
223 tempbidsummary.volmeasure2 = tempbidsummary.volmeasure *
((tempbidsummary.mintime - prevbidsummary.mintime).total_seconds()*1000.0)
224 tempbidsummary.weightedbasabs = abs(tempbidsummary.weightedbas)
225
226
227
228
229 # remember this as previous bid summary
230 SetBidSummaryMap(PrevBidSummaryMap, tempbidsummary)
231
232 # add this to the map
233 AddBidSummaryMap(TheBidSummaryMap, tempbidsummary)
234
235
236 except Exception as inst:
237 print("Exception on line " + str(rowcounter) + ": " + str(inst))

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