SEBI’s QUEST TO CURB FRONT-RUNNING IN INDIAN STOCK MARKET – A COMPARATIVE STUDY WITH USA, UK, & EU By- Ujjawal Anand, Aditi Manya & Prof. S.P. Srivastava
SEBI’s QUEST TO CURB FRONT-RUNNING IN INDIAN STOCK MARKET – A
COMPARATIVE STUDY WITH USA, UK, & EU
Authored
By- Ujjawal Anand,
Aditi Manya & Prof. S.P. Srivastava
Abstract
With the advent of computer
technology in the late 1990s, financial market in India saw a new kind of
trading emerge –Algorithmic Trading (AT). Today, algorithmic trading in India
stands at around 50 per cent of the daily trading volume at both NSE and BSE.
But, with this swift growth in the usage of algo – trading, financial market
regulators saw the advent of manipulative trade practices like 'front –
running' too in the Indian Capital Market. Front-running is described as
utilizing the information on a big, incoming order to take a trading decision
which can put the trader in a better position than others in respect of that
incoming order. Set forth plainly, realizing that a big order is approaching,
front-running comprises purchasing and quickly relisting that specific stock at
a greater price, before the order is executed. Several studies report an
overall improvement in market quality in the era of algorithmic trading or high
frequency trading (HFT) that there has been significant improvement in virtually
all aspects of market quality over time but there have been various occasions
on which traders with High Frequency Technology have essentially preyed over
the non-algo traders. The prevalent regulatory measures appear not to have even
an idea of how High Frequency Trading works. In this regard, the U.K. Financial
Services Authority commented that regulators are riding bicycles to chase down
the high-frequency trading Ferrari. The SEBI (Prohibition of Fraudulent and
Unfair Trade Practices relation to Securities Market) Regulations, 2003 along
with measures proposed by SEBI in its 2016 Discussion paper titled
“Strengthening of the Regulatory Framework for Algorithmic Trading and
Co-location” are though good, not enough to curb the front-running. And
hence, improvements are still required.
1. Introduction
The research work is a critical
analysis of the regulation of algorithmic trading in India with special focus
on the issue of front running and the role of Securities and Exchange Board of
India (SEBI) in controlling the menace of front-running.
It tries to analyse critically the
measures that have been adopted till date by SEBI to control the problem of
front-running, analyse the effectiveness of those measures, and will try to put
forward suggestions and recommendations to improve the scenario wherever
required.
To this end, a study has been done to
understand how the practice of front-running used by High-Frequency Traders is
problematic. Also, the research work will look into the measures adopted by
SEBI to regulate the high-frequency trading in India.
At the end, an attempt will be made
to give some viable suggestions and recommendations for the betterment of the
algorithmic trading regulatory framework in India.
For this research work, it is
important to understand some basic elements of algorithmic trading. These
include algorithm[1],
Algorithmic Trading (AT)[2],
High Frequency Trading (HFT)[3],
Front Running[4]
and Co-location[5].
2. Importance of the Study
Algorithmic trading in India across
the cash and derivatives market as a percentage of total turn-over has
increased up to more than 50% in 2022 from merely 9.26% (average) in 2010. But,
in the past few years, we have seen a tremendous growth in fraudulent and
manipulative trade practices. This calls for an urgent attention to it. The
growing malpractices in the Indian stock market have not only impacted the
market dynamics where the small traders are being exploited by institutional
and other High-Frequency Traders, but, if left unaddressed, it can have major
economic impact for our country by resulting in a scenario where the small
investors may leave the market altogether to move to market of other countries.
3. Review of the Literature
High-frequency
trading, stock volatility, and intraday crashes (2022)[6] The
study examines the effect of high-frequency trading (HFT) on the price
volatility of listed stocks and shows that under stable market conditions,
greater HFT intensity is associated with decreased stock price volatility.
Vivek Rajvanshi, Samit Paul (2022)[7] The study shows that the front-runners can achieve
5%–7% returns within a week around the event day. Lagged cumulative abnormal
returns, change in volume and change in delivery explain the abnormal returns.
The results are robust after controlling for Bullish and Bearish Periods.
Aritra
Pan, Arun Kumar Misra (2021)[8]
The researchers explore determinants of bid-ask spread significantly for
low-frequency datasets in many developed markets. The study found a positive
relation between market–capitalization and spread, supporting the theory that a
higher trading volume cannot decrease the bid-ask spread.
Mousumi
Bhattacharya, Sharad Nath Bhattacharya, Sumit Kumar Jha (2021)[9]
This article
examines variations in illiquidity in the Indian stock market, using intraday
data. Panel regression reveals prevalent day-of-the-week, month, and holiday
effects in illiquidity across industries, especially during exogenous shock
periods. Illiquidity fluctuations are higher during the second and third quarters.
The study suggests that the impact of illiquidity is more severe during periods
of extreme high and low returns.
Dubey, R.K., Babu, A.S., Jha (2021)[10] This study takes a step in the direction to
decriminalize algorithmic trading and give AT it’s due towards improvement in
market quality. This study uses direct identification of AT from Indian Stock
Market (National Stock Exchange, NSE) and uses Order-to-Trade Ratio (OTR) as a
measure of AT efficiency and paves the way for regulators and traders to come forward
and appreciate the positive impact of AT on market quality.
Ernawati & Herlambang (2020) The study demonstrates a positive illiquidity-return
relationship. It shows that decreased liquidity in markets due to front-running
and other manipulative practices results in investors leaving markets which
decreases liquidity and in turn decreases the returns from market.
4. Aims and Objectives of the Study
(i) Analysis of the viability and finding
the pros and cons of the measures suggested by SEBI to control the menace of
front-running in its 2016 discussion paper.
(ii) Bringing out the issues involved and
determining the liability of SEBI in addressing the problem of front-running.
(iii)
Offering viable suggestions for improvement of
trading mechanism of the stock market, so as to strengthen the mechanism for
investor protection.
5. Hypothesis
The measures proposed by SEBI in its
2016 Discussion paper titled “Strengthening of the Regulatory Framework for
Algorithmic Trading and Co-location” for tackling front-running are though
good, not enough to curb the front-running. And hence, improvements are
still required.
6. Research Methodology
This research work has been prepared
by using the ‘doctrinal’ research methodology. The researcher has gone through
existing legislation and the SEBI discussion paper of 2016 titled
“Strengthening of the Regulatory Framework for Algorithmic Trading and
Co-location” as the primary source and different existing literature as a
secondary source.
7. Meaning of Front-running
Front-running is described as
utilizing the information on a big, incoming order to take a trading decision
which can put the trader in a better position than others in respect of that
incoming order. Set forth plainly, realizing that a big order is approaching,
front-running comprises purchasing and quickly relisting that specific stock at
a greater price, before the order is executed. It is one of the most prevalent
frauds of the capital market, and is made possible with the help of 'High
Frequency Trading'. The two principal methods by which HFT firms obtain a
speed, and thus informational advantage, is by utilizing direct data feeds and
co-location[11].
8. Why Front – running is
problematic?
Smart utilization of information ( like direct data
feeds ) and superior machinery acquired by specific individuals employing High
Frequency Trading and co-location services have pepped up a two-layered
arrangement which works so that the advantaged class is capitalizing and is
essentially hunting the ones with not all the top tier resources, employing
practices like 'front – running'. This effectively creates a 2 – layered
pattern of 'haves' and 'have-nots'. Consequently, the traders start to lose
their confidence in market, and in some not-so-exceptional cases, they even
quit the market.[12]
Disappeared reliance of traders in the potential to trade at foreseeable
prices, diminishes their zest to trade. This in after-effect results in
illiquidity. Illiquidity depreciates the securities on trade and makes
collection of capital inconvenient.[13]
This is very much evident in the United States of America. As for illustration,
the normal figure of firms opening up to the world per annum was 530 from
1990-2000.[14] This
figure has fallen since 2001 to 125 generally, a fall of more than 400%.[15]
This impacted economy instantly.
Contrary claim is that any person who
wishes to pay for these services can get benefitted equally.[16]
Regardless, this claim is illogical simply taking into account the fact that
these are high-priced facilities which are more suited to High Frequency
Traders.
9. SEBI’s attempts to curb the menace
of Front-Running
SEBI, in line with the efforts of
other global securities market regulators, has taken various steps to frame
regulatory guidelines for Algo trading. The following circulars have been
issued by SEBI to regulate Algo trading:
i.
Circular
No. CIR/MRD/DP/09/2012 dated March 30, 2012, on ‘Broad Guidelines on
Algorithmic Trading’, inter alia, advised Stock Exchanges to ensure that
certain checks are in place while permitting Algo trading.[17]
ii.
Circular
No. CIR/MRD/DP/16/2013 dated May 21, 2013, inter alia, advised Stock Exchanges
to ensure that trading members that provide the facility of Algo trading shall
subject their system to a system audit every six months in order to ensure that
the requirements prescribed by SEBI/ Stock Exchanges with regard to Algo
trading are effectively implemented.[18]
iii.
Circular
No. CIR/MRD/DP/07/2015 dated May 13, 2015, inter alia, advised Stock Exchanges
to ensure fair and equitable access to their co-location facility.[19]
iv.
Circular
No. CIR/HO/MRD/DP/CIR/P/2016/129 dated December 1, 2016, inter alia, allowed
direct connectivity between two co-location facilities of Stock Exchanges.[20]
Discussion Paper on Algorithmic
Trading and Co-location
In order to level the playing field
for both algo and manual traders, and to give rid off to the manual traders of
their difficulties, on August 5, 2016, SEBI issued a discussion paper inviting
the inputs of market participants on the suggested mechanisms and the
requirement of any further mechanisms for the purpose of constraining the
problem of front-running.
Taking into consideration the
responses, the underneath measures were suggested:[21]
i.
Stock
Exchanges may be advised to introduce Shared Co-location Services.
ii.
Tick-by-Tick
data feed may be provided to all trading members free of cost subject to
trading members creating the necessary infrastructure for receiving and processing
it. Further, stock exchanges may also be advised to increase the depth of
snapshot of 5 best bid and ask quotes, in consultation with trading members.
iii.
Algo
orders placed within ±0.75% of the LTP may be exempted from the framework for
imposing penalty for high Order-to-Trade Ratio (OTR). Further, the OTR
framework may also be extended to orders placed in the equity cash segment and
orders placed under LES.
iv.
Stock
Exchanges may be advised to allot a unique identifier for each algo.
v.
Stock
Exchanges may be advised to publish additional details regarding the latency
observed within Exchange trading infrastructure. Further, Exchanges may also be
advised to publish a reference latency between a reference rack in the
co-located facility and the core router of the Exchange.
In view of the above, it is proposed
that Exchanges may be allowed to provide a simulated market environment for
testing of software including algos. Such a facility may be made available over
and beyond the current framework of mock trading. After assessing the
robustness of the facility, the decision to phase out with monthly mock trading
may be taken in consultation with the appropriate technical committees of SEBI.
10. Analysis of the measures proposed by
SEBI to curb front–running, on the basis of final inputs received by it on its
2016 Discussion Paper “Strengthening of the Regulatory Framework for
Algorithmic Trading and Co-location”
i. Minimum Resting Time for Orders
It is the timeframe between the getting of an order by
the Stock Exchange and its correction or cancellation from that point. By and
large, the resting time ensures that the speed at which markets work is
managed. As indicated by the proposition, orders got before the pass of a
specific time-span would not be permitted, in order to hold under control the
'fleeting' or 'vanishing' liquidity.[22]
ii. Frequent Batch Auctions (Periodic Call Auctions)
This is an option put forward to the as of now in use
'continuous matching system', under which continuous matching of purchase and
sell orders occur. The suggested option collects the orders for a determined
time frame (around 100 milliseconds), and afterward toward the finish of such
time-spans, match the orders. The hope is that the proposition, whenever
actualized, will stop the latency edge of co-located traders.[23]
iii. Speed Bumps in order processing
This mechanism includes utilization of randomized
order processing deferral of milliseconds. Investors engaged with HFT don't
feel it to be ideal because of evident reasons.[24]
iv. Randomization of orders received over a period of
time
Under this mechanism, orders are revised with
randomised time-priority first, and are then transmitted for matching.
v. Maximum order message-to-trade
ratio requirement
It requires that for every set of orders sent; at
least one must be executed.
vi. Separate queue system.
The requests will be time marked and
transmitted through 2 order queues (1 for co-location orders and 1 for
non-co-location orders) to the order book applying the round robin technique.
Round Robin is an algorithm which is commonly utilized for process and network
planning.[25]
vii. Review of Tick-by-Tick Data Feed
The TBT data feeds give particulars
relating to on a real-time basis. Tick by tick data is required by the traders
doing HFT, to exploit the market developments at the soonest. The suggestion is
to give organized data to all the traders at the expiry of prescribed
time-spans.
Benefits
- Lessening of traffic due to cutting out of
surplus cancellation and resubmission orders.
- Better Price Discovery.
- Reduction in the benefit of latency to
co-location HFT users.
- Elimination of informational advantage.
- Prevention of manipulative practices like
spoofing and layering.
Losses
- Less liquidity can shoot up transaction costs.
- Transparency in real-time price updates and
price-discovery may get decreased.
- Investors from Indian markets can get driven away
to foreign markets.
- Cross market arbitrage may get decreased.
- Higher unpredictability can result into adverse
selection costs.
11. LEGISLATIONS AND MEASURES
REGULATING HFT IN USA, UK & EU
11.1.
UNITED STATES OF AMERICA (U.S.A.)
With the
objective to provide more openness to the market, the Securities and Exchange
Commission introduced the regulation of ATS firstly on April 21, 1999. It
consisted of systems called proprietary trading systems, broker-dealer trading
systems, and ECN. As per Arnuk and Saluzzi (2012), Regulation ATS “required
alternative trading systems that trade five percent or more of the volume in
NMS securities to be tied with a registered market in order to advertise the
best priced orders in those national market system securities advertised in
their systems (including institutional orders) into the public quote stream.”
(Arnuk and Saluzzi, 2012, p.69)[26]
In
January, 2010, the SEC requested remarks on the effect of HFT systems on the
quality and impartiality of markets. After the Flash Crash, the SEC and CFTC
shaped a joint committee to introduce directions on developing administrative
worries in May 2010. In February 2011, they conveyed a report with
administrative reaction proposals to the Flash Crash. The upcoming years, the
SEC put into effect many regulations and frameworks expected at confining the
antagonistic conduct of HFTs. These incorporate Market Information Data
Analytics System (MIDAS), the Consolidated Audit Trail (CAT), Regulation System
Compliance and Integrity (Reg SCI), Large Trader Reporting Rule, rule 15c3-5 to
forbid HFT firms from accepting raw access and institution of new circuit
breakers (Shorter and Miller, 2014).[27]
One of
the most significant initiatives from the SEC is MIDAS which was started in
2012. Its motivation was to assemble trading information from markets in the
U.S. It approves the SEC to survey in subtleties the episode which was
impractical years ago. This framework permitted to gather freely accessible
data. Furthermore, another regulation called CAT was voted in 2012 which
included that all enlisted stock exchanges and Self-Regulatory Organizations
keep an audit trail of trading activity. This framework permitted to gather
private data.[28]
On March
7, 2013, the Chairman of the SEC, Elisse Walter declared "a new regulation
targeting technological challenges facing US markets." Regulation SCI has
been made as an immediate result of later and recorded occasions identified
with technological breakdowns in the market. This guideline isn't explicitly
focusing on HFT. In fact, HFT isn't referenced once in the 104 pages long
proposed regulation yet adopts a wide strategy to address all software related
issues in the U.S. market. In spite of the fact that HFT may be the inception
of the whole technology debate in the U.S., it has been stirred recently by
episodes with technology breakdowns. We can give such models as Black Monday,
Knight Capital and BATS which have made the overall population extremely
mindful of the outcomes of programming blunders in trading systems.[29]
11.2.
UNITED KINGDOM (U.K.)
TABB
Group, a research and strategy advisory firm assesses that high-frequency
trading makes up roughly 36 percent of UK stock-market transactions (Credit
Suisse, 2012). A 2010 report by the London Stock Exchange Group demonstrated
HFT made up 33 percent of order-book executions by number of trades and 32
percent by value traded (London Stock Exchange Group, 2010). Government
authorities in Europe initially called for limitations intended to encourage
the "termination" of HFT in 2012 in light of the 2012 US "flash
crash" and breakdown of Knight Capital because of a flawed algorithm
(Ross, Fitzgibbon and Mathiason, 2012). Latest fines forced on Panther
Energy Trading LLC by the UK's Financial Conduct Authority (FCA), the U.S.
Commodities Futures Trading Commission, and the Chicago Mercantile Exchange for
purposely tampering products markets have prompted increased calls to
"check" HFT in the UK (Wessing, 2013; Jones and McCrank, 2013).[30]
The
UK Treasury, which appointed an examination on HFT, does not favour the
elimination of HFT in Europe. The report (Foresight 2012) found that technology
is a significant part of financial market innovation and new market services;
that HFT improves liquidity, decreases expenses of transactions, and increases
the efficiency of market pricing; and that there is no immediate proof that HFT
enhances volatility. The report inferred that "it is profoundly
needed" that any new strategies or market guideline protect the advantages
of HFT, and any new arrangements or guidelines should be evidence-based and
think about the related dangers and advantages. The report proposed the
supervision of algo trading and the building up of safety apparatus. In light
of the report, the UK Treasury expressed it accepted the advantages of HFT
exceeded the present dangers.
Nonetheless,
promptly following the July 2013 fines of Panther Energy Trading LLC, the
Business, Innovation and Skills Committee of the UK House of Commons reacted by
proposing the enforcement of an FTT on high-frequency trading of publicly
listed stocks. As indicated by Taxation (2013a), "the Committee wanted a
financial transaction tax to be set '. .
at a level which is the normal benefit made on a high-frequency trade in
the UK'" (para. 3).[31]
Nevertheless, the
House of Commons Business, Innovation and Skills Committee Third Report of
Session 2013–14 from July 16, 2013 (House of Commons, 2013), concentrated on
HFT as an issue of short-termism that should have been settled. Backing for a
HFT transaction tax rose in this work. These endeavors are pushing ahead in
spite of the declaration of economist and professor John Kay, chair of the
Review of UK Equity Markets and Long-Term Decision Making, whom the committee
employed to examine UK financial markets wanting to affirm their negative view
of HFT and uphold their suggested financial transaction tax. Kay affirmed
before the Business, Innovation and Skills Select Committee on February 5,
2013, that "the existence of high-frequency trading isn't something that
one could say is strong of long-term decision making in British business, [but]
I closed soon that it isn't the chief issue and difficulty" (p. Ev 14).
Truth be told, Kay's report (2012) made no proposals for high-frequency
trading, to a limited extent since he discovered HFTs don't claim an enormous
extent of British stocks.[32]
On
the other hand, EBS, a private foreign-exchange platform claimed by ICAP PLC,
is at present executing one market solution for the worries about HFT in the
UK. EBS intends to change the manner by which orders are handled with an end
goal to remove the benefits of HFTs. Before the new rule, EBS had a first-in,
first-out processing routine inside which the first firm to submit its order
would be the first to have it cleared. This procedure, used by most stock exchanges,
gives those with the fastest technology an edge in order handling. The changed
framework will arrange orders into groups positioned by who was first to place
in the order. A representative for EBS told the BBC that the inspiration for
change was "tied in with making a more trustworthy marketplace" (BBC,
2013; Iosebashvili, 2013). Be that as it may, it seems, by all accounts, to be
intended to address the public relations issue related with HFT in the UK as
opposed to a market disappointment. It is not yet clear whether the race to be
among the first instead of the first will have any effect on easing back HFT,
as there still stay solid motivations to assemble data rapidly. On the off
chance that extra forceful guideline is enacted all the while, it might be hard
to decide the effect of any individual change.[33]
11.3.
EUROPEAN UNION (E.U.)
The
Markets in Financial Instruments Directive (MiFID) 1 was actualized in 2007
(Bréhier, 2013). Its aim was to reinforce and modernize the international
financial framework, including advancing rivalry between Stock Exchange. Be
that as it may, in 2012 it was at that point beginning to be updated because of
the financial crisis that uncovered shortcomings in the straightforwardness of
financial markets. MiFID 1 was European Markets' Regulation ATS. (Gregoriou,
2015).[34]
On
December 2010, The European Commission's MiFID Review Consultation record
(European Commission, 2010) set forth many regulatory recommendations on
algorithmic trading and HFT. MiFID II, which applies to all Member States
inside the EU, was passed in 2014. Guidelines from MiFID II manage the
development of HFT and also algorithmic exchanging (AT). The European
Securities and Market Authority (ESMA) are making a plain contrast among AT and
HFT to make the guideline progressively proper and to help the market members
to apply the rules. MiFID II expresses that any individual who is managing HFT
falls under the rules and regulations set out by MiFID II (Gregoriou, 2015). It
additionally expresses that all HFT firms need to enlist, need to keep records
pretty much all orders placed, dropped and executed, and that all records
should be accessible for the specialists upon request to ensure that the proper
guidelines are followed (Gregoriou, 2015).[35]
MiFID II
desire was to build up a transparent financial system. It incorporated the
first EU-based administrative checks on HFT exercises, including the necessity
to introduce circuit breakers, prerequisite to get approval by controllers on
trading algorithms and the commitments to store all trade orders. (Shorter and
Miller, 2014)[36]
12.
Conclusion and Suggestions
Conclusion
The Discussion Paper analyses
concerns identifying with market quality emerging from HFT and looks to address
these issues. While it is understood that it is just a starting point in the
exercise of updating SEBI's regulatory framework, it doesn't include a lot of
significant worth and isn't considerably more than a general emphasis of
measures and components, presently under the thought at other places. Its key
failings are by virtue of the fact that it doesn't contain particulars of the
proposals; or give subtleties of the nature, degree and range of the issues
dealt by Indian markets, and isn't grounded on India centred experimental proof
(as we can understand by their criticisms done by BSE and NSE). Furthermore, it
doesn't give an execution plan to any of its proposals. This has brought about
an at first sight assessment of these recommendations in vacuum.
Also, the measures, albeit
acceptable, comprises of genuine dangers like winding down off traders, which
can additionally bring about diminished liquidity and expanded bid-ask.
Concerns like adverse selection, execution faults, shortage of mirror-orders
matching, cross-market arbitrage, etc. also need to be addressed. Additionally,
the BSE and NSE are exceptionally disparaging of certain measures like creation
of separate queues for algo and non-algo traders. Batch-auctions may be a good
measure. Batch-auctions and randomization would amplify the pre-execution order
exposure and essentially lessen, if not wipe out, the speed-advantages enjoyed
by algorithmic traders and accordingly discredit the importance of co-location.
However, the revised market structure should be carefully conceived through and
upheld by robust strong examination.
From
the comparative study, we can understand that although we have adopted some of
the very good measures to control the front-running, we are still short of the
global standards.
Taking into consideration every one
of these contradictions, it is concluded that the hypothesis stands true and
it's essential to invest some more amounts of energy in drawing measures, as an
experimentation strategy can have serious consequences on the market, and
henceforth, the economy all in all.
Suggestions
Given the issues pointed above, more
profound control of SEBI might be basic in regulating HFT. In the event that
SEBI wishes to assign regulatory powers to the exchanges, it ought to require
earlier approval of SEBI to rules encircled by the exchanges before they are
executed. It is suggested that SEBI may consider mandating exchanges to look
for approval from SEBI before such exchanges execute any new regulations. On
the other hand, SEBI may consider a different licensing system for High
Frequency Traders, which would force constant 'fit and proper' criteria to be
kept up by licensees.
[1] An algorithm is a set of ordered
instructions or commands used to carry out a particular work in a pre-defined
manner. A trading strategy is basically a plan or a set of rules which are
defined to conduct the process of buying and selling while trading in order to
achieve a particular outcome like increasing profitability, better execution,
etc.[1]
[2] Algorithmic trading is trading by
using computer programmes, which follow a defined set of instructions for doing
a trade so as to generate fast and frequent profits.
[3] High Frequency Trading (HFT) is a
subset of Algo-Trading. It is done within time-frames of nano & milli
seconds. Some HFT strategies depend on price discrepancies & make profit
taking their advantage. Others function by forecasting movements on the basis
of trends, using Machine Learning & Artificial Intelligence.
[4] Front-running is described as
utilizing the information on a big, incoming order to take a trading decision
which can put the trader in a better position than others in respect of that incoming
order. The two principal methods by which HFT firms obtain a speed, and thus
informational advantage, is by utilizing direct data feeds and co-location.
[5] A co-location is a data centre
facility in the exchange premises where the exchange’s servers are on the same
network. It is used to rent space to trading firms to locate their servers and
other computing hardware. Co-location facility provides the power, bandwidth,
IP address and cooling systems. Space is generally rented in terms of racks and
units. Co-location helps in reducing the latency by minimizing the travel time
between your server and the exchange’s matching engine.
[6] The Quarterly Review of Economics
and Finance Volume 84, May 2022, Pages 337-344
[7]
What’s hidden behind bulk deals? A study on Indian stock market, Managerial Finance, ISSN:
0307-4358
[8] A comprehensive study on bid-ask
spread and its determinants in India, Cogent Economics & Finance, 9:1, DOI:
10.1080/23322039.2021.1898735
[9] Does time-varying illiquidity
matter for the Indian stock market? Evidence from high-frequency data,
Australian Journal of Management, Volume: 47 issue: 2, page(s): 251-272.
[10] Algorithmic Trading Efficiency and
its Impact on Market-Quality. Asia-Pac Financ Markets (2021).
[11] Purba Mukerji, Christine Chung,
Timothy Walsh and Bo Xiong, The Impact of Algorithmic Trading in a Simulated
Asset Market, 12, Journal of Risk and Financial Management, 68 (2019).
[12] See, e.g., Computerized
Trading: What Should the Rules of the Road Be?:
Hearing
Before the Subcomm. on Sec., Ins. and Inv. of the S. Comm. on Banking
and
Urban Affairs,
112th Cong. 20 (2012) (statement of David Lauer)
[13] Id.
[14] Id.
[15] Id.
[16] See, e.g., Holly A.
Bell, High Frequency Trading: Do Regulators Need to
Control
this Tool of Informationally Efficient Markets?, CATO INSTITUTE
POLICY
ANALYSIS (July 22, 2013)
[18] Id.
[19] Id.
[20] Id.
[22]. CA
Aseema Dake Kulkarni, High Frequency
Trading: Review of Regulatory Initiatives in the Indian Capital Market, International Journal of Money, Banking and Finance,
IJMBF/Volume 7/ Issue 3 /July-December 2018.
[23] CA
Aseema Dake Kulkarni, High Frequency
Trading: Review of Regulatory Initiatives in the Indian Capital Market, International Journal of Money, Banking and Finance,
IJMBF/Volume 7/ Issue 3 /July-December 2018.
[24] Id.
[26] Holly A. Bell and Harrison
Searles, An analysis of Global HFT regulation, motivations, market failures,
and alternative outcomes; Working Paper, Mercatus Center, George Mason
University, No. 14-11, April 2014
[27] Holly A. Bell and Harrison
Searles, An analysis of Global HFT regulation, motivations, market failures,
and alternative outcomes; Working Paper, Mercatus Center, George Mason
University, No. 14-11, April 2014
[28] Id.
[29] Id.
[30] Holly A. Bell and Harrison
Searles, An analysis of Global HFT regulation, motivations, market failures,
and alternative outcomes; Working Paper, Mercatus Center, George Mason
University, No. 14-11, April 2014
[31] Id.
[32] Id.
[33] Id.
[34] Holly A. Bell and Harrison
Searles, An analysis of Global HFT regulation, motivations, market failures,
and alternative outcomes; Working Paper, Mercatus Center, George Mason
University, No. 14-11, April 2014
[35] Id.
[36] Id.