Harris MAD

7/31/2019 Harris MAD

1/53Electronic copy available at: http://ssrn.com/abstract=1890928

Trade-Based Manipulation and Market Efficiency after the Introduction of Real-Time

Surveillance: A Cross-Market Comparison

Michael J. AitkenChair of Capital Markets Technologies

University of New South Wales

Australian School of [email protected]

Frederick H. deB. HarrisJohn B. McKinnon Professor of Economics and Finance

Wake Forest University Schools of [email protected]

Shan JiResearch Manager

Capital Markets Cooperative Research Centre, and s

University of New South [email protected]

Abstract

We develop a testable hypothesis that trade-based manipulation as proxied by the incidence oframping alerts raises execution costs on 34 security markets worldwide 2000-2005. The alternativehypothesis is that ramping alerts represent information arrivals that are delayed, unmasked asrumors, or proven false. Using observational error components to represent the presence of amanipulator or the arrival of information in a random effects model, we show that spreads arepositively related to the incidence of ramping alerts across 7 of 10 liquidity deciles. The magnitude

is economically significant; cutting ramping manipulation by half reduces the effective spread 31,39, and 59 basis points in the middle liquidity deciles worldwide. In addition, we identify thedeterminants of manipulation by estimating a simultaneous equations structural model of rampingalert incidence, spreads, and the probability of deploying real-time surveillance (RTS). Directmarket access, RTS procedures, regulations requiring surveillance, and a commitment toenforcement reduces trade-based manipulation, assuring better market integrity and enhancingmarket efficiency. Closing auctions and circuit breakers are associated with increased manipulationbut lower spreads, posing an integrity-efficiency tradeoff

Keywords: Market manipulation, ramping, market integrity, surveillance, spreads

JEL Classification: G28 (Financial Institutions & Services, Government Policy & Regulation)

This Draft: June 2011Acknowledgements: We wish to thank the Security Industry Research Centre of Asia-Pacific(SIRCA) and the Capital Markets Cooperative Research Centre (CMCRC) in Sydney for data andfinancial support, respectively. Kumar Venkataraman, Peter Swan, Laurent Fournier at NYSEEuronext Paris Research, Frank Hathaway at NASDAQ, Wei-man Liu, and especially Tom Smithprovided invaluable feedback and advice.


2/53Electronic copy available at: http://ssrn.com/abstract=1890928

2

Trade-Based Manipulation and Market Efficiency:

A Cross-Market Comparison

Abstract

We develop a testable hypothesis that trade-based manipulation as proxied by the

incidence of ramping alerts raises execution costs on 34 security markets worldwide2000-2005. The alternative hypothesis is that ramping alerts represent informationarrivals that are delayed, unmasked as rumors, or proven false. Using observational errorcomponents to represent the presence of a manipulator or the arrival of information in arandom effects model, we show that spreads are positively related to the incidence oframping alerts across 7 of 10 liquidity deciles. The magnitude is economicallysignificant; cutting ramping manipulation by half reduces the effective spread 31, 39, and59 basis points in the middle liquidity deciles worldwide. In addition, we identify thedeterminants of manipulation by estimating a simultaneous equations structural model oframping alert incidence, spreads, and the probability of deploying real-time surveillance(RTS). Direct market access, RTS procedures, regulations requiring surveillance, and a

commitment to enforcement reduces trade-based manipulation, assuring better marketintegrity and enhancing market efficiency. Closing auctions and circuit breakers areassociated with increased manipulation but lower spreads, posing an integrity-efficiencytradeoff

Keywords: Market manipulation, ramping, market integrity, surveillance, spreads

JEL Classification: G28 (Financial Institutions & Services, Government Policy &

Regulation)

1. Introduction

Not all market volatility is natural, deriving from fundamentals. Instead, some volatility isinduced by adopting poor market design that fails to achieve the twin goals of market integrity

and market efficiency. Trade-based manipulation, one of the three major prohibited trading

behaviors, threatens market integrity and imposes induced volatility. With the VIX reaching

record levels in 2008-2009, major security exchanges, regulators, and self-regulatory

organizations began to declare their commitment to market integrity (fairness) and efficiency as

twin pillars of market quality.

For example, the International Organization of Securities Commissions is unequivocal

about fairness and efficiency as equally important objectives of security regulation and marketdesign. The Financial Services Authority in the UK identifies its goal as being to promote

efficient, orderly, and fair markets. The Securities and Exchange Commission seeks to protect

investors, maintain fair, orderly, and efficient markets, and facilitate capital formation. The

Ontario Securities Commission undertakes to foster fair and efficient capital markets.

NASDAQs website states


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NASDAQ is among the worlds most regulated stock markets,

employing sophisticated surveillance systemsto protect investors and

provide a fair and competitive trading environment... fostering

innovative technologies that] continue to build the most efficient

trading environmentto the benefit of all market participants and

investors.

Although significant resources have been invested in automated real-time surveillance

systems to detect prohibited trading behaviors, little is known about how the introduction of these

new systems affects market quality. Still less is known about how a violation of market integrity

affects market efficiency. Primarily this reflects the extreme difficulty of estimating proxies for

the incidence of manipulation, insider trading, front running, and other types of prohibited trading

behaviors. In this paper, our analysis focuses on trade-based manipulation, a type of trading

designed to fool other traders into believing that undisclosed information is arriving in themarketplace. This study covers some 90 million security-day observations between 2000 and

2005, which comprise all the listed securities on 34 exchanges worldwide processed by one of the

worlds leading market surveillance companies.1

We first employ a random effects model to analyze the possible presence of a manipulator

as well as the possible arrival of information, both of which are observed with error in the market

place. In this error components framework, we investigate whether a reduction in trade-based

manipulation (higher market integrity) is associated with tighter spreads (higher market

efficiency). We find that reducing the incidence of manipulation by half lowers relative spreads

31 to 59 basis points in the middle liquidity deciles 4, 5, 6 and 7 worldwide where spreads are

2.56% to 7.52%, so approximately 10%.

We then estimate the cross-sectional determinants of trade-based manipulation within a

system of three simultaneous structural equations. The first uses ramping manipulation alert

incidence as a dependent variable to proxy violations of market integrity; the second uses relative

spreads as a dependent variable to proxy market efficiency; and the third uses the probability of

real time surveillance as a dependent variable illustrative of a pertinent market design change.

With this second set of structural equation methods, we again show that halving the incidence of

trade-based manipulation lowers spreads by an economically significant 23 to 26 basis points.

Our structural equation estimates of the determinants of market integrity (lower alert incidence)

and market efficiency (lower spreads) yield insights into market design changes that can improve

market quality.

1 The company is SMARTSGroup, a NASDAQ-OMX company (see www.smartsgroup.com).


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Figure 1 identifies proxy metrics for market efficiency and market integrity as the twin

pillars of market quality -- e.g., spreads and other measures of transaction costs for market

efficiency and manipulation incidence, insider trading incidence, or front running incidence for

market integrity (Aitken and Harris 2011). We then hypothesize a role for each of the following

elements of security market structure: Regulation, Trading and Surveillance Technology, Security

Market Infrastructure, Information, Instruments, and Market Participants.

The paper proceeds as follows. Section 1 examines the theory of profitable manipulation

of security markets. Section 2 develops hypotheses, and section 3 discusses the data and

measurement of the proxy variables. Section 4 presents and discusses the results of estimating

the random effects/error components model. Section 5 discusses integrity-efficiency hypotheses

for each element of the market quality framework. Section 6 presents a simultaneous structural

equations model of the integrity-efficiency-regulation relationship, and section 7 discusses those

results. A summary and conclusion is provided.

1.2. Theory of Trade-Based Manipulation: An Overview

Allen and Gale (1992) define trade-based manipulation as a trader attempting to

manipulate a stock price simply by buying and then selling (or vice versa), without releasing any

false information or taking any other publicly observable action designed to alter the securitys

value.2 Traditional full-information financial theory asserts that such speculation stabilizes prices

because manipulators like all rational speculators buy when the prices are low and sell when the

prices are high. In contrast, with incomplete and asymmetric information, Hart and Kreps (1986)

show that speculation can destabilize prices and increase volatility because uninformed traders

can not distinguish between the rational speculators and strategic insiders with private

information.

As a result of this pooling equilibrium, profitable manipulation can exist under quite

general conditions of expected utility maximization and rational expectations by manipulators,

strategic insiders, market makers, and noise traders pursuing a momentum strategy (Allen and

Gale 1992, Allen et. al. 2006). Even without momentum traders, Aggarwal and Wu (2006) show

that if information seekers cannot distinguish between a manipulator and an informed trader

acting strategically, trade-based manipulation can be profitable. A strategic insider can make

unprofitable initial trades against the direction of his information, then set in motion a price trend

2 Trade-based manipulation is thereby distinguishable from other failures of market integrity like tradingahead of information anouncements (Bhattacharya and Daouk 2002) or the spreading of false rumours (VanBommel 2008).


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among partially informed followers, and thereafter profitably unwind his position against still less

informed market makers and other liquidity providers (Chakraborty and Yilmaz 2004, 2007).

With a manipulator present in these nested information environments, the efficient market

no longer serves as an aggregator of equilibrium price information alone. Instead, security price

trends may represent induced volatility rather than the arrival of new information. Whenever

markets begin to trend, quasi-market makers who were content to earn the spread in mean-

reverting regimes will choose instead to go flat. This reduction in liquidity will widen the BBO

quoted spread. Moreover, with reduced non-execution risk because of the higher volatility,

Foucaults (1999) theory of optimal order placement implies that liquidity suppliers will also

submit limit orders less aggressively. Aitken, Almeida, Harris and McInish (2007) confirm

empirically that in electronic markets liquidity suppliers will then layer orders further from the

BBO thereby raising the effective bid-ask spread for completing larger trades.

1.3. Prior Empirical Findings on Manipulation and Our Contribution

Given this theoretical link between trade-based manipulation, induced volatility, and

spreads, what does prior empirical research show about the effect of laws, regulations and

surveillance designed to prevent manipulation? When a securities market is laden with

manipulators, investors choose to invest elsewhere. Cumming, Johan, and Li (2009) establish

that trading activity decreases if exchanges fail to adopt surveillance procedures and regulations

that assure market integrity. A number of other empirical studies of particular events in particular

exchanges confirm that manipulation also increases volatility (e.g., Stoll and Whaley (1987,

1991), Chamberlain, Chueng and Kuan (1989), and Chiou, et al (2007). However, there has been

no direct test of the relationship between manipulation and spreads across groups of securities

and exchanges. In this paper, we first estimate a random effects model of the error components

relationship between trade-based manipulation and quoted or effective spreads across 34

exchanges 2000-2005.

We then investigate the determinants of ramping alert incidence with a cross-sectional

simultaneous equations model of AI, spreads, and the probability of adopting real time-time

surveillance (RTS) in 2005, a year for which we have particularly extensive regulatory rule

information. Factors that affect market quality (trading regulations, market technology, market

design infrastructure, market participants) are measured across exchanges and then related to alert

incidence, spreads, and Prob(RTS). The data is across all listed stocks and aggregated to the

exchange-specific liquidity decile as a unit of analysis to mirror market surveillance in practice.


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We find that the incidence of our proxy for ramping manipulation, ramping alerts, is

reduced by a particular market design choice (no auction at the close), a particular technology in

combination with real-time surveillance (direct market access), and market integrity regulations

more generally but not by a regulation specifically prohibiting ramping manipulations. In the

market efficiency equation, spreads are reduced by circuit breakers, closing auctions in

combination with real-time surveillance, and a more extensive set of security regulations

addressing any aspect of market integrity. Spreads are tighter the lower the incidence of ramping

manipulation alerts in highly liquid and moderately liquid securities, supporting our principal

hypothesis. Regulations prohibiting shorting markedly increase spreads across all liquidity

deciles. Real-time surveillance is more likely to be adopted the lower the volatility, the smaller

the foreign direct investment, the more immediate the execution with DMA, the fewer the

competing integrity objectives, and the greater the risk of market manipulation as proxied by

ramping alert incidence in highly liquid securities. Circuit breakers, a specific regulation

prohibiting ramping, and the codification of violations of integrity in civil law-based security

regulations all raise Prob(RTS).

The paper proceeds as follows: after documenting the forms and evidence of trade-based

manipulation, section 2 explains the random effects methodology, develops our testable

hypothesis, distinguishes a competing alternative, and specifies the empirical models we estimate.

Section 3 describes the data, our measurement of ramping alert incidence, and the distributional

properties of AI and spreads worldwide, concluding with several data-driven limitations of our

research design. Section 4 presents and discusses the empirical results of the error components

model. Section 5 addresses the factors that drive trade-based manipulation including an

elaoration of our framework for pursuing market integrity research. Section 6 develops a cross-

sectional simultaneous structural equations model of manipulation alert incidence, spreads, and

the probability of deployment of real-time surveillance for all listed securities on 24 exchanges in

2005. Section 7 presents the maximum likelihood systems estimation results for these AI, Spr,

and Prob(RTS) structural equations. A summary and conclusion are provided.

2. Hypothesis Development2.1.1. The Myriad Forms of Market ManipulationSecurity market manipulation exists in a wide variety of forms ramping, wash trades, layering

the order book, churning, cornering, squeezing, bait and switch, and other intentionally

misleading orders and trades.3 No matter the form, successful market manipulation temporarily

3 An Appendix describing each of these types of manipulation in some detail is available from the authors.


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dislocates a securitys price. The overall aim is to drive the price in the direction beneficial to the

manipulator who then liquidates his holdings or covers his short position at a price better than the

implicit efficient price in a full-information equilibrium.

2.1.2. Ramping Manipulation

Ramping manipulation is the focus of the present research because this is the only form of trade-

based manipulation for which the requisite data are publicly available. Statistically rare price

movements (e.g., less than = .005) that revert the following day may indicate market

manipulation to artificially inflate or deflate the price of a security. ARamping manipulation

normally involves two surveillance alerts:Marking the Close and then Reversal at the Start of the

next Trading Day. Marking the Close refers to the practice of executing purchase or sale orders at

or near the close of the trading session in order to raise or lower artificially the closing price, the

bid, or the offer. In one contextRamping is also referred to as Painting the Tape when a fund

manager manipulates a securitys closing price at the end of the evaluation period. This

manipulators purpose is often to reduce margin or net capital requirements for enhancing profit

and loss, or to influence the mark-to-market calculations mandated by regulatory authorities for

credit authorization or reporting purposes if holding a large position in derivative contracts.

Figure 2, Panel A illustrates a Marking the Close alert incident screen developed by

Smarts Group International for assisting regulators and brokers in detecting trade-based

manipulation. Trades at the ask are shown in red, and trades at the bid are shown in green. The

size of the trade is indicated by the diameter of the circle. Blue circles represent off-market trades

that can be negotiated at prices away from the continuous auction price beyond a minimum size.

Trading volume is shown in the bar diagrams colored yellow. Such a significant price increase at

the end of the day (as depicted) normally reflects anticipated positive information arrivals.

If, however, these price trends are reversed the next morning, a ramping incident alert is

triggered, as illustrated in Figure 1, panel B. The particular 21% appreciation observed in the last

15 minutes of trading in panel B occurs in less than of 1% of the trading days for this stock.

An Australian Securities Exchange (ASX) case involving the tracking stock on the Standard &

Poors ASX 200 Index illustrates the behavior underlying Ramping Alerts. On Friday, 29 June

2001 between 4 and 4:15 p.m., this stock increased 45.5 points (approximately 30%) following

the closing auction. The last trading day of the financial year almost always pushes share prices

somewhat higher on the ASX, but on 29 June 2001 the All Ordinaries Index rose by only 2%, and

the ASX became concerned that market manipulation may have been involved in the tracking

stock. By market open on the following Monday, this unusual increase was reversed.

Specifically, on 2 July, the index tracking stock fell by 54 points (28%), as the ramping buyers


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(believed to be fund managers and derivative players) dumped the index tracking stock and

withdrew.

This paper will use such Ramping Alerts as a proxy for trade-based securities market

manipulation. Of course, such price reversals may also be explained by information

announcements that are quickly reversed, unmasked as rumors, or proven false. Our data

collection and empirical testing with random effects/error components modeling, complemented

by the estimation of a simultaneous structural equations framework for the model, are designed to

distinguish between these two competing hypotheses.

2.1.3. Other evidence of closing price manipulationsFlexison and Pelli (1998) and Hillion and Souminen (2004) find that brokers manipulate

the closing price of a stock preceding large agency trades in order to improve their customers

impression of execution quality. Carhart, Kaniel, Kusto and Reed (2002), and Bernhardt and

Davies (2005) show that mutual funds paint the tape by manipulating closing prices at the endof evaluation periods to improve fund performance against a closing VWAP benchmark.

Stoll and Whaley (1987), and Chamberlain, Chueng and Kuan (1989) find empirical

evidence in the North American markets that on the expiration day of index futures/option

contracts, the price mean-reversals are significantly higher than month-ends or quarter-ends

without index futures/options expiration. Stoll and Whaley (1991) point out that the change of

settlement procedure to use the next days opening price in the New York Futures Exchange and

New York Stock Exchange would shift expiration day timing but not affect this motivation to

manipulate closing prices. In our phraseology, the suggested market design change would notaffect the market integrity outcome.

2.2.1. The Market Integrity-Efficiency Null HypothesisWe focus not on the relationship between manipulation and informational efficiency but

rather on manipulation and execution costs. Across the 34 exchanges we study worldwide, we

can measure two widely-used and accepted relative spreads:

(1) the cost of a round trip transaction at the best bid and offer relative to the quote midpoint(the quoted spread), and

(2) the average cost beyond the quote midpoint to complete all trades relative to the quotemidpoint (the effective spread).

By maintained hypothesis, market manipulation increases price volatility. Foucault (1999)

develops a theory of order placement relating volatility to effective spreads. Order placement

strategy consists of two components, order type and order aggressiveness. Traders choose from

market orders and limit orders. When non-execution risk is high, traders employ market orders to

gain immediate execution. When picking-off risk is high, limit orders are preferable. Order


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aggressiveness refers to how close the limit order price is to the prevailing best bid or offer

(BBO) when the order is entered or amended.

Foucault theorizes that traders place limit orders rather than market orders to reduce their

picking-off risk at the cost of higher execution risk. When execution risk is also particularly high,

liquidity demanders are under pressure to trade immediately upon arrival and are therefore

willing to place market orders at less favorable prices. This induces liquidity suppliers to post

less aggressive limit orders (farther away from the BBO) in order to take advantage of the

impatient traders. The implication is that effective bid-ask spreads (volume-weighting the trade

prices that walk up and down the book) will rise as volatility increases. If the volatility of

security markets is indeed higher in the face of market manipulation, the effective spread measure

of market efficiency will be adversely affected.

Aitken, Almeida, Harris and McInish (2007) provide empirical evidence supporting the

implications of Foucaults theory. They hypothesize that hedge funds and proprietary trading

desks tend to have short-lived information about valuation and/or the state of the market. As a

result, these classes of traders face higher costs of non-execution and lower picking-off risk than

insurance companies and mutual funds. They hypothesize that the order placement strategy of

proprietary trading desks and hedge funds will therefore be more aggressive than insurance

companies and mutual funds. Using a large sample of trading desk instructions, they show that

insurance companies and mutual funds do tend to be less aggressive order placers, ceteris paribus.

Our testable hypothesis is that trade-based manipulation also reduces order

aggressiveness. Specifically, the null hypothesis of our research is

: A higher incidence of trade-based manipulation (as proxied by ramping alert

incidence AI) is associated with wider quoted and effective spreads, ceteris paribus

where AI is the number of alerts per security day triggered by an algorithm implemented by the

worlds leading surveillance technology firm, Smarts Group International.4

Mean reversion of prices the next morning following a statistically extraordinary price

increase (or decrease) marking the close represents a decline in execution risk. By walking up or

down the book and then quickly liquidating or covering open positions before other traders can

react, manipulators induce greater price volatility. Foucaults theory of optimal order placement

predicts liquidity suppliers will therefore reduce their order aggressiveness. Limit orders for

larger volume would be spread farther from the BBO which shows up as a wider volume-

4 At present, the SMARTS Market Integrity Platform is deployed in 40+ national exchanges and regulatorsand 150+ brokers across 35 countries. See www.smartsgroup.com.


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weighted effective spread. Quoted spreads for trivial depth on electronic markets may remain

unchanged as technical transaction costs are not affected directly.

Nevertheless, if a particular security exchange were to exhibit repeated manipulations, we

would expect quoted spreads to widen as well. All rational traders seek quasi-price protection in

rigged markets. Quasi-liquidity suppliers and market makers go flat when information

uncertainty develops. If information announcements are not involved, manipulation is suspected,

and liquidity suppliers again rush to reduce their exposure, raising even the quoted spread. This

is a disreputation effect of failing to assure market integrity.

2.2.2. Alternative hypothesis

The alternative hypothesis is that ramping alerts represent not market manipulations but

rather information arrivals that are retracted, unmasked as rumors, or proven false. Such

information arrivals and the subsequent informational uncertainty must be distinguished from a

perception of rigged markets. Consequently, under the alternative hypothesis, such information

arrivals that are quickly reversed would lead to no persistent change in quoted spreads, and

controlling for induced volatility, no change in effective spreads either. Ramping alert incidence

(AI) if mistakenly capturing information arrivals that are quickly reversed would be unrelated to

long-term average spreads.

In contrast, confirmedinformation arrivals (both positive and negative) trigger herding

and other information-based trading that cause market prices to trend (rather than mean revert).

Markets that trend do exhibit increased spreads as quasi market-makers go flat and liquidity

traders protect themselves against picking off risk. But not so with information arrivals that are

quickly reversed. So, it is important to collect the data and design a test in such a way as to

distinguish this alternative hypothesis that information events can cause prices to ramp and then

mean revert. In this paper, we screen for information announcements the day prior to the ramping

event, and exclude alerts on that basis.

2.3.A random effects/error components model for market integrity researchBoth information arrivals and market manipulations are inherently unobservable variables,

subject a priori to observational errors. Moreover, since spreads in round trip transactions, andthe spread to complete larger trades, are substantive costs of engaging in market manipulations,

we also expect reverse causality from relative spreads to manipulation alert incidence (AI).

Hence, our prior is that relative spreads and AI are endogenously determined. For both of these

reasons, we first utilize a Random Effects Model to test the reduced-form correlation between


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Ramping Alert Incidence and bid/ask spreads. Thereafter, we develop a simultaneous equations

model of the determinants of AI and spreads to investigate causality in the structural equations.

In light of the lognormality of the cross-sectional spread indicated by the descriptive

statistics (and illustrated in Figure 3), we hypothesize that the theoretical relation between relative

effective spread and ramping alert incidence is

( 1 )

and transform for estimation to the regression relation

( 1 )where

= the relative spread of market i at time t

= the number of Ramping Alerts per security day in market i in period t

= an observational error on information arrivals at time t

= an observational error on Ramping Alert detection of a manipulator in market i

= a residual error term

Specification of the Random Effects Model is based on priors about the error

components. That is, we expect spreads to reflect both the observational error on

information arrivals at time t, as well as the observational error on manipualtor detection in

market i. If is independent of and (i.e., if Cov( ) = 0 and Cov( ) =

0), then and are not jointly dependent on random observational errors in detectingeither informational arrivals over time or market manipulations across securities. In that event,

the correlation between relative spreads and manipulation alert incidence can be estimated

directly without bias using Da Silvas autoregressive model:

(2)

which assumes a mixed variance-component moving average for the error structure of order k

determined by minimizing the RMSE.

However, when a Hausman m specification test rejects ( : Cov( ) = 0 and/or

), we conclude that (2) would be a misspecification because Ramping

Alert incidence is itself stochastic. In that event, we introduce fixed effect dummy variables to

control for the systematic effects of observational errors in measuring the arrival of information

over timeDtand the presence of a manipulator across securitiesDi:

(3)


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The exchange-specific dummy variables Di capture idiosyncratic surveillance, detection,

prosecution, or enforcement reasons why manipulation may be observed only with error. The

time-series dummy variables Dt control for idiosyncratic reasons in each time period why

information may arrive but be observed only with error.

2.3.1. Cov( ) Illustrated:The following examples illustrate various possible observational errors for the arrival of

information in a time period, relevant to testing :(1) The price reversal detected by a Ramping Alert could result from an information arrival

followed by the rush of liquidity appearing thereafter in resilient limit order books. Or

alternatively, it could result from the ramp-and-dump trading behaviour of a closing price

manipulator. Because in both cases a Ramping Alert would be triggered, the null

hypothesis of zero systematic relationship (i.e., zero covariance) between information

arrivals and Ramping Alert incidence over time would in this example be accepted;

(2) In opposition to (1), if reinforcing positive (or negative) news is announced overnightwhen the Marking the Close Alert is triggered, there will be no price reversal the next

morning. As the market continues to trend in response to fundamentals, this stops the

Ramping Alert. In such an event, the null hypothesis of zero covariance between

information arrivals and Ramping Alert incidence over time would be rejected;

(3) What we have been labelling an information event could also have been a non-event. Forexample, a pure white noise announcement by a small listed company could be out of

traders sight and therefore doesnt trigger a Ramping Alert. In such a scenario, the null

hypothesis of zero covariance between information arrivals and Ramping Alert incidence

over time would again be accepted.

2.3.2. Cov( ) Illustrated:The following examples illustrate various possible observational errors for the presence of a

manipulator in the market relevant to testing .

(1) When closing price manipulation is truly present for a security from a market and aRamping Alert is triggered for that security, the null hypothesis of zero covariance

between Ramping Alert incidence and manipulation detection error across securities

markets would be accepted;

(2) When closing price manipulation is truly present for a security, but due to insufficientmonitoring, few momentum traders or arb traders emulate the manipulators trading

activities, the manipulation strategy therefore fails and no Ramping Alert is triggered. In


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such a scenario, the null hypothesis of zero covariance between Ramping Alert incidence

and manipulation detection error across securities markets would be rejected;

(3) The alert detection surveillance process could also generate pure white noise. Forexample, an arbitrary time period before closing (e.g., 15 minutes) is used in all markets

to detect closing price manipulation (AI). But the manipulation could occur at other

randomly chosen time distances from closing. In such a case, the null hypothesis of zero

covariance between manipulation detection error and Ramping Alert incidence over 15

minutes would again be accepted.

3. Data and measurementThe data for this research is obtained from the Reuters database maintained by the

Securities Industry Research Centre of Asia-Pacific (SIRCA). This Reuters database contains

intra-day trade and quote data since 2000 for more than 200 exchanges worldwide. We analyze

the entire sample of listed securities in all ten liquidity deciles from each of 34 of the largest

securities markets from January 2000 to December 2005. Table 1 shows the range of ramping

alert incidence across the exchanges studied (from 0.08 x 10-3 ramping events per day in Korea

and 0.32 x 10-3 in NASDAQ to 1.8x10-3 per day in Taiwan and Bombay). The data is collected

for us at the individual security-day level. With 1,769 mean listings on each of 34 exchanges

over six years for 250 trading days, we draw upon almost 90 million security-days. Because

ramping manipulation alerts are so rare at the individual security-day level (on average less than

one every 1000 days), we had the security-day observations aggregated to the month, quarter and

year level. This confidential and proprietary raw data was further aggregated into liquidity-

deciles on each of the 34 markets for calculation of our manipulation incidence proxy--ramping

alerts. It is this liquidity-decile data that was released to us.

Liquidity deciles are determined by dividing the total number of securities in each market

into 10 groups, based on their monthly trading turnover. Security market microstructure

phenomena of interest are known to be very non-homogeneous across thinly-traded versus the

most liquid stocks, and this non-homogeneity emerges in our models. Initially, we ran separate

random effects models and grouped regressions for the ten separate liquidity deciles. Subsequent

Chow test showed adjacent deciles 1,2,3 and 4,5,6,7 and 8,9,10 were homogenous and could be

estimated and reported together.5

5Relative liquidity is what really matters to trade-based manipulation incidence and detection. The leastliquid stocks are much the same everywhere, and trade under much the same conditions everywhere. Themost liquid stocks are somewhat less homogeneous across exchanges. But still the first liquidity decilestocks in New York are much more akin on spreads and trading behavior to the first decile stocks in


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3.2. Descriptive statistics3.2.2. Ramping alert incidence (AI)

Table 1 shows the mean annual alert incidence of daily manipulation in each of the 34 exchanges.

The grand mean of means is 0.88x10-3, meaning slightly less than one event each day per 1000

listed securities. The median exchange in our sample lists 1,110 securities, so 1 per day in themedian exchange. AI ranges from 0.08 in Korea, 0.28 in Hong Kong, and 0.32 in NASDAQ to

1.48 in Portugal, 1.76 in Bombay, and 1.8 in Taiwan. Table 2, Panel A presents the mean annual

alert incidence of ramping manipulation by liquidity decile. Across these 34 markets,

manipulation alert incidence is monotonically decreasing from 1.24 and 1.08 ramping alerts per

day in thickly-traded deciles 1 and 2 to 0.48 and 0.52 in thinly-traded deciles 9 and 10. In the

least liquid securities because of the paucity of market participants, detection of manipulation

strategies by surveillance authorities is much more likely. On the other hand, in the most liquid

securities, the capital required to ramp a security is too extensive. Therefore, we would expectmanipulations to be highest frequency in the moderate liquidity deciles 3, 4, 5, and 6 where the

incidence of ramping alerts is in fact a consistently high 0.92 to 0.88 per thousand security-days.

3.2.2. SpreadsThe descriptive statistics for the average spreads across the 34 exchanges over the 6 year

period (2000-2005) are presented in Table 3. It can be seen that the average Quoted Spreads and

Effective Spreads are demonstrably non-normal. For example, the effective spread (ES) has

mean ( ) of 6.64% and standard deviation ( ) of 0.1383 with skewness

of 5.1899 and kurtosis of 28.7423. After a natural log transform, we observe the distribution of

to be approximately normal ( = -3.4645 and = 1.2172) with

skewness of -0.1480 and kurtosis of 0.9826. The same is also observed for quoted spread. Figure

3 presents the histogram for the two spread measures before and after the natural log transform.

Using the properties of the lognormal distribution and assuming exact log-normality for our

transformed observations, an estimator of would be = =

6.56%. This mean estimate differs from of 6.64% because our sample differs slightly from

an exact lognormal distribution.

Singapore than the first decile stocks in New York (or Singapore) are to the ninth or tenth decile stocks inNew York (or Singapore). Table 2 Panel B makes this point. It shows NYSE decile 10 effective/quotedspreads 11/21 times greater than NYSE decile 1 effective/quoted spreads. Similarly, Singapore decile 10effective/quoted spreads are 21/39 times greater than Singapore decile 1 effective/quoted spreads. Butdecile 1 effective/quoted spreads in Singapore (82/86 b.p.) are only 5 to 6 times greater than in New York(16 /14 b.p.). So, as we confirmed in our Chow testing, whilst there is some lack of homogeneity acrossexchanges, the greater concern in grouping data for separate analysis is across the liquidity deciles.


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Table 2, Panel B shows that the Quoted Spread varies enormously across the ten deciles

from 0.73% for the most liquid decile to 20.98% for the least liquid decile. Effective Spreads

vary from 2.19% to 13.56%. In general, the three thinnest-trading deciles exhibit quoted spreads

that are an order of magnitude wider than the three most liquid deciles. These descriptive

statistics suggest Deciles 4, 5, 6 and 7 should be grouped as a separate class of transactions,

separate from the thickest and thinnest-trading deciles, and we perform Chow tests on the

estimated single equation models to confirm whether or not to do so. The pooling of the data is

rejected. Consequently, we report below grouped regressions for thickly-traded (1,2,3),

moderately liquid (4,5,6,7), and thinly-traded (8,9,10) subsets of the ten liquidity deciles.

3.4. Limitations of the error components research design

The higher manipulation alert incidence at the top of Table 2, Panel A could be caused by

alert detection errors especially in thickly-traded deciles 1, 2 and 3. In fact, much post-

surveillance workflow is designed to validate false positive alerts that can be triggered by a

variety of legitimate reasons not involving manipulation. Exchanges therefore refer to

regulators only a subset of all the alerts they receive from surveillance. This proportion is

highly proprietary and kept very confidential for obvious reasons. To decipher which

alerts should be referred to regulators for investigation, the exchanges use broker ID and

position data to separate legitimate trade-based ramping events typically involving partial

fills of client instructions from trade-based ramping manipulation. We have based our

research only on publicly available data. We do not have access to the audit trail data, so

we cannot separate this first type of false positives from the ramping events that will be

referred for investigation.

Second, information events that do not involve announcements often lead to

overnight mean reversion. An example would be the informational uncertainty triggered

by unanticipated disclosures or rumors at the end of the trading day. After-hours

clarifications or rebuttals by the company would then lead to overnight mean reversion.

Surveillance providers use real-time scans of the Reuters and other similar databases to

isolate these information-based alerts and hone their algorithms to identify just trade-

based manipulation. Still, these types of information events clearly trigger price volatility

which could, if it were extreme enough statistically, be mischaracterized as ramping

manipulation.


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The problem of false positives in ramping alerts is known to be highly correlated with

trading volume. The higher incidence of false positives in more liquid securities probably

explains the highest mean AI statistics in deciles 1 and 2. However, it is important to realize that

this false positives data collection issue on AI in fact biases our findings against(not in favor of)

our testable hypothesis of a positive spread-AI relationship. When mean reversion occurs quickly

as an equilibrating response to unannounced information shocks in resilient limit order books,

liquidity providers tend to retighten the spreads. Decreased perceived picking-off risk in flat as

opposed to trending markets then results in lower spreads, the opposite of our hypothesized

positive relationship between SPR and AI. Hence, false positives in our AI data collection would

increase the chances of falsifying our hypothesis, not the reverse.

A second limitation of error components modeling as an initial approach to market

integrity research is that we learn nothing about the structural equation relationships. Spreads and

AI are simultaneously determined. Higher AI definitionally raises volatility, and volatility

reduces order aggressiveness raising spreads. Turning things around, quoted spreads are an

execution cost of market manipulation, especially in mid and thin-liquidity deciles; worldwide,

round-trip transaction costs may run several hundred basis points. So, higher quoted spreads

reduce the incidence of manipulation, ceteris paribus.

Controling for exogenous volatility in estimating structural equations later in the paper,

our manipulation (misintegrity-inefficiency) hypothesis would be that higher AI results in still

less order aggressiveness because the induced volatility from ramping adds to any natural

volatility, thereby reducing non-execution risk, raising spreads still further. Note that the two

cannot be spuriously correlated through exogenous volatility, since the spreads-exogenous

volatility relationship is a priori positive, whilst the basic AI-exogenous volatility relationship is a

priori negative. Specifically, an increase in exogenous volatility makes more difficult a

manipulators task of fooling the uninformed into spotting a trend when none exists.

Simultaneous structural equation estimation is required to sort out these SPR-volatility and AI-

volatility relationships, and we undertake that research in sections 6 and 7 below.

4. Empirical results of the error components model

Table 4, Panel A shows that across all liquidity deciles, effective spreads averaging 664 basis

points across our 34 exchanges are decreased by 11.92% (i.e., e0.1126 1) or about 79 basis points

when ramping alert incidence is cut in half. These elasticity parameter estimates from the Full

Random Effects Model are unbiased by the possible covariance between AI and the observational

errors (since the Hausman m specification test is insignificant) but they explain only about 5% of


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the variation in spreads. With full fixed effects introduced for n-1 exchanges and years in the

bottom row of Panel A), the model can explain 81.7% of the variation in spreads with F=18.52.

The SPR-AI relationship remains positive and significant at 0.01. Again a halving of ramping

alerts is estimated to reduce spreads by 11.14% or 74 basis points.6

Table 4, Panel B shows that even the relationship between AI and quoted spreads for

doing trivial size at the BBO is positive, though weaker (as expected). A halving of alert

incidence decreases quoted spreads averaging 699 basis points across our 34 exchanges by 4.00%

(i.e., e0.0432 1) or 28 basis points. Here, the Hausman m test indicates the Full Random Effects

estimate of the SPR-AI relationship is biased by joint interdependence attributable to fixed effects

across exchanges and over time. Controling for exchange-specific dummy variables (31 of 33 of

which are significant) and controlling for annual dummy variables (4 of 5 of which are signifi-

cant), the model explains 94.3% of the variation in spreads with F=71.3, significant at 0.01, and

the AI elasticity parameter (i.e., e0.0392 1) remains positive and significant.

Chow tests indicate however that these pooled estimations across all ten liquidity deciles

cannot be validly pooled (F = 10.09 with p-value less than 0.01) -- i.e., that the All Deciles results

are masking enormous heterogeneity in the SPR-AI relationship across thickly versus thinly-

traded stocks. In disaggregated results by decile in Table 5, Panel A, 7 of 10 liquidity deciles

exhibit a statistically significant effect of halving ramping alert incidence, reducing effective

spreads 31 to 59 basis points. Almost all the moderately liquid deciles have substantial estimated

elasticities of effective spreads with respect to alert incidence: i.e., (e0.0384 1) or 3.9% x 790 b.p.

of mean spread in decile 4 = 31 b.p., (e0.0720

1) or 7.5% x 523 b.p. of mean spread in decile 6 =

39 b.p., and (e0.0786 1) 8.2% x 721 b.p. in decile 7 = 59 b.p.. In sum, assuring that ramping

manipulation is halved provides a very substantial gain of market efficiency in completing larger

trades.7

4.1. Discussion of error components results

4.1.1. Parameter magnitudes across the deciles

The fact that in decile 1 (and also decile 3), no significant correlation is found between

effective spreads and alert incidence is consistent with the expectation that manipulations are

more costly and difficult to implement in highly liquid securities. The elasticity of spreads with

respect to alert incidence in the moderately-liquid deciles is much larger, as expected. The range

6 Allowing for a moving average of error components, the AI parameter estimate declines to a 7.44% (i.e.,e0.0718 1) decrease in spreads or about 49 basis points when AI is cut in half.

7 In quoted spreads, reported in Table 5, Panel B, the estimated elasticities are generally smaller but againare statistically significant in 7 or 10 liquidity deciles.


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of basis point impacts on effective spreads is 7.5 to 8.18% (39 b.p. to 59 b.p.). This compares to

effective spreads of 72 b.p. for the index-listed securities in the most efficient markets worldwide

during our time period. In decile 10, a halving of ramping alert incidence decreases a much

larger effective spread averaging 1356 basis points by only 3.4 percent (i.e., e0.0333 1). Indeed,

most thinly-traded deciles worldwide exhibit these substantially lower elasticities. This finding

can be explained by the fact that detection of manipulation activities is too likely in the least

liquid deciles, which effectively deters manipulators.

4.1.2. Traders involved in capturing intraday profit

To understand further the roles of various market participants potentially caught up in a

manipulators ramping transactions, we describe below the traders involved in capturing intraday

profit. Closing price manipulations must be distinguished from legging patterns that arise in the

normal functioning of quote-driven or order-driven markets. Legging refers to one side of the

book changing while the other side does not (usually as large orders walk the book). In mean-

reverting, stationary price sequences, most traders desire to get flat when a legging pattern

develops. In non-stationary price sequences, however, trading profits are available in legging

patterns by shorting one side of the market and inventorying the other. For that reason, intraday

traders with sufficient access to order flow data to detect when a market begins to trend, often

desire to participate in the trend. Manipulators know this and attempt to mimic the other natural

players involved in legging patterns.

Legging patterns arise from the buy-side trading of momentum traders, basket traders,

and value-traders operating with no discretion as to timing. And on the sell side, legging patterns

arise from the trading activities of specialists, arbitrageurs, day traders, and fair-weather market

makers. In the Market Participant Appendix, we describe the behavior of each of these types of

traders in turn.

5. Cross-sectional determinants of trade-based manipulation

Although the correlations between AI and spreads using observational error components

methods are indeed compelling, and the magnitudes are economically significant, the

determinants of ramping incidence and the causal relations remain unknown. To identify the

potential drivers of trade-based manipulation, we return to the framework for market quality

research in Figure 1. In the next several subsections, we hypothesize a role in the integrity-

efficiency relationship for each of the following elements of security market structure:

Regulation, Trading and Surveillance Technology, Security Market Infrastructure, Information,

Instruments, and Market Participants. Because the present study focuses on trade-based


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manipulation rather than insider trading or false rumor dissemination, the topic of information

generation and disclosure (Pagano and Roell 1996, Lang and Lundholm 1996, and Oved 2002) is

excluded from our research. Hypotheses for all the other elements on the left-hand-side of Figure

1 are discussed below.

5.1. Regulation

Bhattacharya and Daouk (2002) confirm a negative relationship between the cost of equity capital

and the enforcement of insider trading laws across 108 countries. Cumming and Sofia (2008)

investigate the number of trading regulations with corresponding surveillance technology to

monitor alerts and the existence of a trading regulation specifically against ramping across 25

exchanges. They find that comprehensive rules prohibiting trade-based manipulation generate

higher turnover and larger market cap.

We hypothesize that security exchanges with regulations specifically against ramping

(Variable name: RampReg) are expected to have fewer ramping manipulations. A larger number

of trading regulations (Variable name: Regs) that have corresponding surveillance alerts (e.g.,

against trading ahead of price-sensitive announcements, front running, or other broker-client

conflicts of interest) signify an atmosphere of more aggressive surveillance and enforcement and

should lower all types of manipulation including ramping manipulation. Hence, we hypothesize a

negative relationship between AI and RampReg, and between AI and Regs. The indirect effect on

market efficiency of this improved market integrity should be tighter spreads, ceteris paribus.

5.2.Trading and surveillance technologyThe recent revolution in internet-based order filing has provided investors access to a real-time

and centralized order book with an expedited channel for order submission. The latest

development on some exchanges (e.g., the London Stock Exchange, Direct Edge etc) is the effort

to launch an Enhanced Liquidity Provider Program (ELP), which offers subscribed traders an

integrated view of both displayed and dark pool order books. Historically, traders had to seek

executions in either the displayed market or a single "dark pool". ELP now offers a

comprehensive solution for traders looking to aggregate liquidity of all types before

implementing particular execution strategies.8

To keep up with more sophisticated trading, real-time surveillance (RTS) technology has

also been gradually replacing traditional T+N market surveillance or transaction log books. For

example, SMARTS, the leading real-time market surveillance platform from Smarts Group

8 According to Reuters (2008), Direct Edge's ELP Program broke the 100 million shares traded/per daymark on 15th July 2008 while the overall trading volume was 1.23 billion shares on that same day.


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International Ltd., has been deployed by more than 50 national securities exchanges and

regulators around the world. But there has been no prior research studying the relationship

between RTS technology and market quality. We will use the deployment of the SMARTS

surveillance platform as a proxy for experience with Real Time Surveillance (Variable name:

RTS) technology. In cross-section we expect exchanges more vulnerable to trade-based

manipulations to adopt RTS. Over time RTS should help reduce the incidence of market

manipulation.

Another technology advance is Direct Market Access (DMA) defined as electronic

facilities which allow brokers to offer clients direct access to the exchange trading system through

the brokers infrastructure without manual intervention by the broker. DMA facilitates

algorithmic trading and makes market manipulation more difficult. To be successful, market

manipulators must avoid signature footprints and exit faster than counterparty hedge funds or

proprietary trading desks who often adopt algorithmic trading with computer bots. Hal Varian

refers to such datarati as firm[s] whose business hinges on making smart, daring

choicesgleaned from algorithmic spelunking and executed with the confidence that comes from

really doing the math. Such businesses are difficult to mousetrap on the wrong side of VWAP,

to mislead into chasing false trends, or to manipulate at the close. DMA we predict will be

inversely related to ramping alert incidence.

5.3. Security market infrastructure

The following dimensions of market infrastructure are expected to impact market integrity: (1)

the presence of a closing call auction, (2) volatility defined as the standard deviation of daily

returns, (3) market liquidity defined as the market turnover, and (4) the technical transaction costs

measured by quoted spreads. Many securities exchanges have introduced closing call auctions

(Variable name: CallAucDum) to improve market quality but have achieved mixed results

(Pagano and Schwartz 2003, Comerton-Forde and Rydges 2006, etc). In our context, by allowing

traders to unwind their intraday positions and go flat overnight if so desired, closing call auctions

should reduce the exposure to manipulation resulting in tighter spreads as AI declines. In the

cross-section, the presence of a closing call auction tends to be associated with higher alert

incidence, as closing calls are often used to address concerns about manipulation at the close.

Higher volatility (Variable name: Vol) leads to less aggressive order placement as non-

execution risk declines for any given picking off risk (Foucault 1999). Thus, time-series

increases in volatility result in wider spreads. Cross-sectional increases in price volatility make it

more difficult for manipulators to convince uninformed traders that a trend exists when it does

not, decreasing the likelihood of attempted ramping manipulations. On the other hand, more


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volatile price environments can increase the likelihood of ramping manipulations since less

ramping alerts are triggered and legal safe harbors widen, lowering the probability of detection

and enforcement. The effect of Vol on AI is therefore indeterminate a priori.

Securities that are highly liquid normally have big market capitalization and are therefore

difficult to manipulate due to the higher total costs involved to entice momentum traders to chase

a false trend. Hence, we expect higher market liquidity (Variable name: Turnover) to be

associated with a lower number of alerts.

Quoted spreads (Variable name: QSpr), as a measure of the percentage cost of a round

trip transaction at the BBO, is directly proportional to the technical transactional costs involved in

manipulations. It is expected that higher quoted spreads will therefore cause lower market

manipulation and alert incidence, ceteris paribus. Turning the question around, higher AI implies

more volatility which lowers non-execution risk, raising spreads. When non-execution risk is

high (for any given picking off risk), liquidity traders prefer market orders or limit orders close to

the BBO to gain immediate execution. When non-execution risk declines or picking-off risk

rises, limit orders away from the BBO are preferred. Foucaults (1999) theory predicts that when

price volatility increases (perhaps because of manipulation) and non-execution risk consequently

declines (for any distance from the BBO), liquidity traders will tend to place less aggressive limit

orders in order to reduce picking off risk, which results in wider effective spreads.

5.4. Market participants

Several emerging markets have encouraged foreign capital investment in their equity

markets hoping that overseas hedge funds and proprietary trading desks would boost

liquidity and tend to stabilize the financial market. But research on several financial

crises in emerging markets has tied those foreign investors to excessive volatilities or

bubbles. Chiou et al. (2007) emphasizes two related findings. The first is that extremely

low transaction costs and fast adjustment of order placement (with cancellations and

pinging searches for hidden liquidity) create greater chances for the informed to

manipulate the market and take advantage of the uninformed. This reasoning implies

quoted spreads would be a negative determinant of alert incidence, consistent with ourresults in Table 4, Panel B. Chiou also finds that informed foreign direct investors have a

greater incentive to manipulate the market because they are beyond the reach of national

security market regulators. Using the United Nations data on foreign direct investment

(FDI) as a percentage of Gross Fixed Investment, we predict a negative correlation with

the adoption of sophisticated surveillance technology (RTS).


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6. A simultaneous structural equations model for market quality research6.1.Research designTo study the cross-sectional determinants of trade-based manipulation we decided to focus not on

heterogeneous enforcement actions in highly divergent regulatory regimes across exchanges but

rather on the comparable data that is publicly-available worldwide, ramping alert incidence data.

As a maintained hypothesis we assume that trade-based manipulation more generally can be well

represented by this ramping alert proxy.9

The empirical model structure is a simultaneous set of three structural equations

describing ramping alert incidence (AI), the quoted spread (QSpr), and the deployment of real-

time surveillance (RTS) systems or more generally, a change in market design:

Market Integrity Eqn: AI = f ( , , Control variables, Fixed effects) (I)

Market Efficiency Eqn: QSpr = g ( , , Control variables, Fixed effects) ( II )

Market Design Change Eqn: Prob(RTS )= h ( , , Control variables, Fixed effects) ( III )

The empirical specifications prove to be highly non-linear with lognormal transformations,

interaction terms, and a probit equation. Nevertheless, because each of the endogenous variables

could in principle affect the others, we assure the order condition for identification by excluding

from each equation two control variables (3 endogenous-1) present elsewhere in the system. In

each equation, the excluded variables are control variables found to be insignificant in

preliminary single-equation estimations of the focal equation but highly significant in the other

two structural equations. In addition, we test for and thereafter incorporate into the model,

exchange-specific fixed effects to address the idiosyncratic institutional features prevalent

throughout the sample of 24 security markets for which we could obtain data on detailed

surveillance regulatory procedures in 2005.

We hypothesize the following regression relation between the level of ramping

manipulation or conversely the level of integrity of an exchange and the measures for Regulation,

Technology, Security Market Infrastructure, and Market Participants discussed above:

(I) AIi = + 1 i + 2CloseAuci + 3DMAi + 4DMAi * i + 5PrLimi +

6ShortProhibi + 7RampRegi + 8Regsi + 9Voli + 10EngComLawi + 11EngComLawi

* i + 12 i + Fixed Effects i + ,

and similarly for the quoted spread (our measure of round-trip transaction costs),

9Regulators and exchange officials investigate nine other forms of securities market manipulation usingproprietary databases (Ji 2009).


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(II) QSPRi = + 13 i + 14Voli + 15CloseAuci + 16CloseAuci * i + 17PrLimi

+ 18ShortProhibi + 19ShortProhibi * i + 20RampRegi + 21Regsi

+ 22Turnoveri + 23EngComLawi + 24EngComLawi * i +

25 i + Fixed Effects i +

where

AIi= Mean number of daily ramping alerts per security in market i,

QSPRi = Mean quoted spread measuring the round-trip transaction costs at the BBO forsecurities in market i at time t,

CloseAuci = Dummy variable for the existence of a Closing Call Auction in market i,

DMAi = Dummy variable for the existence of Direct Market Access in market i,

PrLimi = Dummy variable for the existence of circuit breakers in market i,

ShortProhi = Dummy variable for prohibitions against shorting in market i,

RampRegi = Dummy variable for the existence of a Ramping Regulation in market i,

Regsi = Dummy variable for the existence of a Ramping Regulation in market i,

Vol i = Mean standard deviation of daily returns in market i (a potentially endogenous

variable),

EngComLawi = Dummy variable for an English common law origin of law (as opposedto French, German or Scandanavian civil law),

Prob(RTSi) = Probability of deployment of a Real Time Surveillance system in market i,

Fixed effectsi = exchange-specific dummy variables to capture idiosyncratic institutionalfactors,10

,

, = residual error terms (e.g., a false positive alert or a negotiated fee for block

execution).

In deciding where (in which securities on which exchanges) and when to execute,

ramping manipulators consider the round-trip technical transaction costs (the quoted bid-ask

spread), any requisite fees and commissions, the trading volume required to elicit a desired price

impact, and the frequency and severity of civil and criminal penalties. A securitys baseline price

volatility (i.e., unramped) often determines the likelihood of detection by surveillance officials as

well as the availability of legal safe harbors that reduce the probability of indictment andconviction. Real-time surveillance (RTS) tends to be deployed in exchanges that perceive a

greater vulnerability to manipulation and at least initially therefore often experience the higher

volatility that accompanies more manipulation alerts.

10 We retain in each specification all exchange dummies found to be significant at < 0.01.


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6.2. Simultaneity

Given the potential endogeneity of Prob(RTS) and price volatility (Vol), Hausman-Wu

specification tests are conducted to determine whether simultaneity issues significantly bias the

estimates from the above OLS model of alert incidence. We estimate RTS and VOL as

instrumental variables with a two-stage least squares (2SLS) procedure and then test for

parameter equivalence between the IV and OLS estimates.

Although Stoll and Whaley (1987, 1991), Chamberlain, Chueng and Kuan (1989), and Chiou, et

al (2007) all find evidence that price volatility is higherduring the time period of manipulation

within a security market, we find little evidence that higher AI increases volatility across

securities or exchange-specific liquidity deciles. Specifically, volatility proves exogenous in the

cross-sectional AI equation we study (Hausman-Wu 2 test 2.09 with fixed effects for 1 d.o.f.

yields = 0.143).11 Volatility is therefore specified in raw data form as a control variable.

Similarly, we predict the decision to deploy real-time surveillance using the following

specification:

(III) Prob(RTS)i = + 26 i + 27CloseAuci + 28DMAi + 29PrLimi + 30RampRegi +31Regsi + 32Voli + 33FDIi + 34EngComLawi + Fixed Effects i +

where = Foreign direct investment/Gross fixed capital to the country of market i,and

othervariables are as defined previously. Prob(RTS) proves to be endogenous in the structural

equations model I, II, III, as expected.

6.3. Other econometric issuesWe find in the Probit analysis of the decision to deploy real-time surveillance that the

determinants of alert incidence influence the decision by an exchange as to whether to adopt RTS.

That is, whether to have an RTS capability in Hong Kong depends upon the presence of order

filling with Direct Market Access (DMA) or the ability to unwind positions in a closing auction

(CloseAuc). The desire to assure market integrity by reducing the vulnerability to manipulators

surely increases the adoptions of RTS, ceteris paribus, but the CloseAuc and DMA mechanisms

may serve as complements to or substitutes for RTS.

And the combination of RTS and DMA or RTS and CloseAuc would then have a ratherdifferent impact on spreads and manipulation alert incidence than DMA or CloseAuc taken alone.

Consequently, the parametric effects of market design changes in those exchanges that adopt RTS

may differ from those that do not. In the estimation of the three structural equations, we therefore

explored the possible impact of full interaction terms between the deployment of RTS and all the

11 In contrast, in a time-series cross-sectional model structure, volatility would be endogenous a priori.


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r.h.s. variables using ML estimation of a Heckman-style selectivity bias model.The results were

negative; no selection bias is present in this sample.

Beyond potential simultaneity and selectivity bias, there are several other econometric

issues to resolve: 1) the pooling of trading and surveillance data across subgroups of liquidity

deciles, 2) the relevance of exchange-specific fixed effects, and 3) the likely cross-equation

correlation of the error terms ,

, and the consequent need for maximum likelihood

systems estimation. We address each of these issues below.

6.4. Descriptive statistics in 2005

We again employ the entire universe of trades and quotes for all listed securities in the Reuters

database maintained by the Securities Industry Research Centre of Asia-Pacific (SIRCA). The

Trading Regulations Database from Cumming and Johan (2008) covers 25 security exchanges, 24

of which overlap with Reuters. Because panel data analysis of regulatory time-series is beyond

the scope of the present research, our analysis of the determinants of trade-based manipulation

addresses the single year 2005 on these 24 exchanges. We aggregate the data to obtain a unit of

analysis that is an exchange-specific liquidity decile based on monthly turnover. We term this

unit of analysis an exchange decile for short.12

The descriptive statistics for the following variables are presented in Table 6 below.

Annual Average Quoted Spread in 2005 (QSPR), Annual Average Standard Deviation of Logarithmic Daily Return in 2005 (Vol), Annual Average Turnover in 2005, Number of Trading Regulations that are surveillance monitored in 2005 (Regs).

It can be seen that except for the number of trading regulations that are surveillance monitored

(Regs), all the other 3 variables are demonstrably non-normal as in the 2000-2005 sample

reported earlier. For example, the quoted spread has a mean of 5.36%,13 and a standard deviation

of 0.0956 with skewness of 3.4166 and kurtosis of 13.1504. After a natural log transform, we

observe the distribution of to be approximately normal ( = -3.9911 and

12 To check for any aggregation bias, we introduced fixed effects for the liquidity deciles themselves,omitting decile seven. Not surprisingly, because a continuous measure of turnover itself is a right-hand-side variable in our models, the results were qualitatively identical.

13 A quoted spread of 536 b.p. at the mean conveys how much this sample of 24 securities exchangesdiffers from the lowest execution costs worldwide. Using the universe of all ten liquidity deciles, Aitken,Cook, Harris, and McInish (2009) report 61 b.p. for NYSE, 257 b.p. for ASX, 283 b.p. for TSE, 303 b.p.for NASDAQ, 371 for Euronext, and 381 for Xetra in matched samples.


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= 1.4776) with skewness of 0.2425 and kurtosis of -0.6729. The same is also observed for

AI, Vol and Turnover.

In estimating I, the pooling of thickly-traded stocks in liquidity deciles 1, 2, and 3, the

moderately-liquid stocks in deciles 4-7, and the thinly-traded stocks in deciles 8,9, and 10 is

rejected by a Wald test (F yielding < 0.01). Estimation of the quoted spread and real-

time surveillance equations II and III yields the same result. Consequently, we perform separate

estimations for these 3 subsets of the ten liquidity deciles throughout our subsequent analysis.

6.5.Probability of deployment of real-time surveillanceSurveillance of financial markets has a long history. The reasons why are both obvious and

subtle. The assurance of market integrity typically requires an aggressive surveillance regime in

tandem with regulatory enforcement against those who conduct prohibited practices. In addition,

however, as self-regulatory organizations (SROs), many exchanges have more extensive

obligations to monitor trading, detect manipulative behavior, and punish violators than might

exist in an industry like insurance that operates under detailed and continuous regulatory review

and approval. Assuring integrity becomes therefore a possible source of competitive advantage

across trading venues because the incidence of manipulation, trading ahead of price sensitive

announcements, front running and the like varies more across SROs than it would in a totally

mandated regulatory environment.

Real-time surveillance (RTS) has grown more sophisticated in the last decade concurrent

with the growth of electronic (and especially algorithmic electronic) trading. Today, lower

latency and an explosion of trade executions barely imaginable a few years ago, necessitate real-

time mechanisms for capturing and processing surveillance data. RTS systems have become a

more prevalent response to heightened SRO obligations and are now deployed in 10 of the 24

exchanges we study.

6.5.1. HypothesesA priori, we expect the deployment of real-time surveillance systems to increase with an

instrumental variable for greater alert incidence (AI), with greater vulnerability to ramping

manipulation necessitating specific ramping regulations (RampReg) or circuit breakers (PrLim),and with French, German, and Scandanavian-origin laws. These civil-code-based legal and

regulatory environments delineate security market transaction standards, violations of those

standards, and the associated penalties. English common law-based security regulatory

environments (EngComLaw) are beset with the constant evolution of safe harbors that make

enforcement more problematic and the return on surveillance effort less certain. Closing call


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auctions (CloseAuc) and direct market access (DMA) technology may complement the presence

of RTS and be positively associated with the probability of deployment. Alternatively, CloseAuc

and DMA as well as foreign direct investment (FDI) especially by hedge funds may offer

potential substitute mechanisms that reduce the need for real-time surveillance by making

attempted manipulation of security markets less likely to suceed. Finally, we expect RTS to

decline when higher return volatility (Vol) makes prosecuting and convicting manipulators more

difficult.

6.5.2. Empirical Results for Prob(RTS)

Table 7 reports our PROBIT analysis of real-time surveillance system deployment in

pooled and decile grouped regressions. Alert incidence is positively related to the adoption of

RTS, indicating a perceived vulnerability to manipulation that RTS can help mitigate. The

grouped regressions reveal that this regulatory responsiveness happens as a result of alerts

experienced in the middle liquidity deciles 4,5,6,7 where we hypothesized attempted

manipulation would be most successful. Closing call auctions (CloseAuc) in moderately liquid

and thinly-traded deciles appear to substitute in part for real-time surveillance. By removing the

broker intermediary, direct market access (DMA) facilitates manipulation, requiring an expanded

capability by exchange officials to monitor these situations as they evolve. But perhaps because

DMA markedly enhances quick responses by counterparties to any attempted manipulation,

DMA is negatively related to the deployment of real time surveillance, ceteris paribus. Circuit

breakers (PrLim) and specific regulations against ramping (RampReg) in the most liquid and

moderately-liquid deciles are indicative of perceived vulnerability to manipulation indicating

exchanges are more likely to deploy RTS.

RTS assists in separating the true and false positives in a surveillance program. Ceteris

paribus, the number of integrity regulations (REG) necessitating RTS for effective enforcement is

unrelated to the deployment of RTS among the most liquid securities. And in the moderately-

liquid and least liquid securities, fewer integrity regulations addressing other violations like

insider trading and front running focus attention on RTS as a means to enhance indictment and

conviction for ramping manipulation, increasing the likelihood of deployment. Increased price

volatility (Vol) decreases RTS presumably because of more extensive legal safe harbors. A

higher percentage of foreign direct investment (FDI) appears to reduce the profitability of ramp-

and-dump manipulation schemes, so RTS deployment to combat it decreases. Overall 2 out of 23

exchanges have significant idiosyncratic effects; Shenzen trading characteristics make RTS

deployment particularly likely, and Xetra trading characteristics make RTS deployment

particularly less likely.


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7. Systems Estimation of the Structural EquationsHaving established that the probability of deployment of RTS is itself related to alert incidence,

and the determinants of spreads, we use maximum likelihood to estimate a simultaneous system

involving a probiti.e., three equations I, II, and III characterizing integrity (AI), efficiency

(QSpr), and the likelihood of RTS (Prob(RTS)). The estimation encompasses all the listed

securities across the 24 exchanges in 2005, the only year for which we have detailed surveillance

regulation data. As throughout our previous work, all ten exchange-deciles cannot be validly

pooled and are instead grouped for estimation into thickly-traded deciles 1, 2, 3, moderately-

liquid deciles 4, 5, 6, 7, and thinly-traded deciles 8, 9, 10.

A priori, as we hypothesized in explaining our research design in section 6.1 and 6.2,

each endogenous variable could affect all the others. One exception emerges from our results;

quoted spreads do not affect Prob(RTS). The order condition for identification is satisfied by

excluding Turnover and FDI from the AI equation, FDI and DMA from the QSpr equation, and

ShortProhibit and Turnover from the Prob(RTS) equation. In each case, these identifying

instrumental variables in the cross-section were insignificant at = 0.05 in the equation from

which they were omitted.

7.1. Integrity Equation (AI)

Table 8 Panel A reports on the determinants of alert incidence across the three decile subgroups.

As a transaction cost of attempted ramping manipulations, quoted spreads (QSR) reduce AI

significantly only in thinly-traded securities, ceteris paribus. Across all deciles, we find that the

presence of a closing call auction (CloseAuc) is associated with an increase in the incidence of

ramping alerts. Trade-based manipulation increases when a manipulator can use closing auctions

to unwind their ramping exposures. The policy implication for security market quality depends in

addition on whether or not closing auctions increase market efficiency by decreasing quoted

spreads.14 High speed execution on direct access (DMA) lines results in fewer ramping alerts and

increased market integrity when real-time surveillance (RTS) is present. Our interpretation is that

counterparties are able to employ DMA to set preventative algorithms in place that render ramp-

and-dump manipulation unprofitable.

Controling for this interaction effect between DMA and Prob(RTS), DMA itself is

positive and significant as a determinant of AI in all liquidity deciles. Circuit breakers

(PriceLimits) are similarly associated with an exchange regulators perception of vulnerability to

manipulation, and they prove to be positively associated with AI in highly liquid and thinly-

14 As we shall see in the next subsection, they do.


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traded stocks. Finally, in thickly and thinly-traded deciles, the endogenous instrument for the

probability of deploying RTS is on net negatively related to alert incidence when DMA and

English common law-origin of the security regulations are present. More detailed and extensive

mandates for authorized security trading and specified penalties for particular infractions tends to

emerge from civil law origin legal codes originating in France, Germany and Scandanavia. Civil

law origin therefore enhances market integrity whilst English common law (EngComLaw) origin

of law increases alert incidence in all deciles. The presence of RTS reverses this adverse effect

on market integrity of English common law-based safe harbors for manipulators, thereby

reducing what would otherwise be increased AI. Finally, two exchanges have statistically

significant fixed effects: Shenzen has institutional features that imply reduced manipulation, and

Indonesia has just the reverse.

Prohibiting short positions (ShortProhib) does not significantly reduce AI, thereby

improving market integrity. In one specification on the middle-liquidity deciles, ShortProhib

actually worsens integrity by raising AI. Hence, like price limits, this market design decision

should hinge on its efficiency consequences i.e., the effect on spreads analyzed below.

None of the regressors is correlated with one another beyond 0.45 with one exception.

The variable Regs is highly collinear with RampReg (i.e., partial correlation = 0.69) as evidenced

by almost triple digit variance inflation factors. So, in a second set of specifications we

eliminated Regs to see whether RampReg would emerge significant. In the second row of the

middle liquidity deciles, we report that, even alone, RampReg remains insignificant at 90%.

In the thinly-traded stocks in deciles 8,9 and 10, we find that the extraordinarily high

quoted spreads (QSPR) decrease the incidence of ramping alerts. Manipulation necessitates a

substantial proportion of trading volume, so it is in these illiquid securities where a few

momentum traders can be enough to allow manipulators to execute a ramp-and-dump strategy.

Such momentum trading is very much discouraged by high transaction costs for a round trip. We

also find in these thin liquidity deciles that the more numerous the regulations assuring market

integrity (Regs), the lower the ramping alert incidence. Our interpretation is that Regs is a proxy

for a more aggressive security regulation environment. Potential manipulators appear convinced

that the exchange will pursue to conviction violators, all of whom are relatively easily detected.

One exchange (Xetra) has a significant negative AI dummy variable effect, indicative of

idiosyncratic higher market integrity in their illiquid securities.

7.2. Efficiency equation (QSpr)

Although our primary focus remains on alert incidence and real time surveillance, Table

8, Panel B displays results for the determinants of time-weighted quoted spreads. We control for


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volatility (Vol) which reduces non-execution risk and can signal an increase in picking off risk,

thereby raising the optimal quoted spread in moderately-liquid and thinly-traded deciles, and for

Turnover which consistently lowers spreads across all deciles. The endogenous instrument for

alert incidence itself (AI) increases quoted spreads in thickly-traded and moderately liquid

securities. Only in the least liquid, thinnest trading deciles does this positive association between

ramping manipulation incidence and inefficiency break down, consistent with our conjecture that

ease of detection deters most if not all attempted manipulation in the thinnest deciles. In further

support of this connection between (mis)integrity and diminished efficiency, fewer integrity

regulations (Regs) designed to be enforced through surveillance of some kind increases the

quoted spread in all decile subgroups.

In all deciles, closing call auctions (CloseAuc) and circuit breakers (PrLimits) lower

quoted spreads, as expected. The efficiency policy implications are clear; but both of these

market design changes result in an increased incidence of ramping manipulation, resulting in an

integrity-efficiency tradeoff. In all decile subgroups, English common law (EngComLaw) origins

of law increases alert incidence, and we would expect it therefore to raise spreads. It does so.

Our interpretation is that the safe harbor contests of the English common law impose additional

transaction costs relative to the precise definitions of violations and penalties under civil-code-

based security law and regulation. A regulation specifically prohibiting ramping (RampReg)

indicates in cross-section the perceived vulnerability to ramping manipulation which, as we have

seen, is associated with higher quoted spreads. Prohibition of short positions (ShortProhib) takes

pessimists out of the market, for any given mean expectation of fundamental value, and lowers

the ask of those who remain, increasing the spread. So, recalling the insignificant coefficient on

ShortProhib in the AI equation, prohibiting shorts reduces efficiency with no offsetting increase

in market integrity.15

As to the endogenous instrument for Prob(RTS), the own effect on spreads is positive and

significant in the most liquid and moderately liquid deciles. But when one takes into account

three interaction terms

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