TRADE-BASED MANIPULATION IN FINANCIAL MARKETS

A THESIS SUBMITTED TO

THE GRADUATE SCHOOL OF SOCIAL SCIENCES

OF

MIDDLE EAST TECHNICAL UNIVERSITY

BY

SERKAN İMİŞİKER

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR

THE DEGREE OF DOCTOR OF PHILOSOPHY

IN

THE DEPARTMENT OF ECONOMICS

AUGUST 2013

Approval of the Graduate School of Social Sciences

Prof. Dr. Meliha Altunışık

Director

I certify that this thesis satisfies all the requirements as a thesis for the degree of

Doctor of Philosophy.

Prof.Dr. Erdal Özmen

Head of Department

This is to certify that we have read this thesis and that in our opinion it is fully

adequate, in scope and quality, as a thesis for the degree of Doctor of Philosophy.

Assist. Prof. Dr. Esma Gaygısız Lajunen

Supervisor

Examining Committee Members

Assoc. Prof. Dr. Işıl Erol (METU, ECON)

Assist. Prof. Dr. Esma Gaygısız Lajunen (METU, ECON)

Assist. Prof. Dr. Nil İpek Şirikçi (METU, ECON)

Assist. Prof. Dr. Yeliz Yolcu Okur (METU, IAM)

Assoc. Prof. Dr. Bedri Kamil Onur Taş (UET, ECON)

iii

I hereby declare that all information in this document has been obtained and

presented in accordance with academic rules and ethical conduct. I also declare

that, as required by these rules and conduct, I have fully cited and referenced

all material and results that are not original to this work.

Name, Last name: Serkan İmişiker

Signature :

iv

ABSTRACT

TRADE-BASED MANIPULATION IN FINANCIAL MARKETS

İmişiker, Serkan

Ph.D., Department of Economics

Supervisor: Assist. Prof. Dr. Esma Gaygısız Lajunen

August 2013, 73 pages

This study implements the cost element to the theoretical model of stock market

manipulation. For this purpose, Aggarwal and Wu's (2006) model of a stock price

manipulation is followed and it is assumed that the number of active information

seekers for a potentially manipulated stock is determined by the informed trader,

either a truthful party or a manipulator, with some cost. This extension to the original

model brings out that a successful trade-based manipulation scheme can only be

observed whenever the cost factor for introducing active information seekers into the

market is sufficiently low. The recent study also empirically investigates which firms

are more susceptible to successful manipulation. For this purpose, a unique data set

consisting of manipulation cases from 1998–2006 from the Istanbul Stock Exchange

(ISE) were collected and firm-specific variables are used to explain these

manipulations. Probit regression results show that small firms, firms with less free

float rate and a higher leverage ratio are more prone to stock price manipulation.

Dynamic probit analysis concludes that the probability of manipulation of a stock is

significantly higher for stocks that have been previously manipulated.

Keywords: Manipulation, Stock Market, Firm Characteristics, Probit Regression

v

ÖZ

FİNANSAL PİYASALARDA İŞLEM BAZLI MANİPÜLASYON

İmişiker, Serkan

Doktora, İktisat Bölümü

Tez Yöneticisi: Yrd. Doç. Dr. Esma Gaygısız Lajunen

Ağustos 2013, 73 sayfa

Bu çalışma hisse senetlerinde manipülasyona ilişkin teorik modele maliyet

unsurunu eklemektedir. Modelde potansiyel olarak manipüle edilebilecek bir hisse

senedinin piyasasındaki enformasyon arayan aktif yatırımcıların sayısının belli bir

maliyet ödemek karşılığında enformasyon sahibi yatırımcı tarafından belirlendiği

varsayılmıştır. Modele getirilen bu eklenti sonrasında ortaya çıkan bulgular başarılı

bir işlem bazlı manipülasyonun, enformasyon arayan aktif yatırımcıları piyasaya

çekmek için gereken maliyet faktörünün yeterince düşük olması durumunda

görülebileceğini ortaya koymaktadır. Bu çalışma ayrıca hangi şirketlerin hisse

senetlerinin manipülasyonuna daha yatkın olduğunu ampirik olarak incelemektedir.

Bu amaçla İstanbul Menkul Kıymetler Borsası’nda (İMKB) 1998-2006 yıllarında

yapılan işlemlerle ilgili tespit edilen manipülasyon olaylarına ilişkin bir veri seti

oluşturulmuş ve manipülasyon vakalarının görülmesi ihtimali şirketlere özel bazı

değişkenlerle açıklanmaya çalışılmıştır. Probit regresyonuna ait sonuçlar küçük

şirketlerin, halka açıklık oranı düşük olan şirketlerin ve yüksek kaldıraç oranına

sahip olan şirketlerin hisse senedi manipülasyonuna daha yatkın olduğunu

göstermektedir. Ayrıca dinamik probit analizi ise herhangi bir hisse senedinin

manipüle edilme ihtimalinin daha önce manipüle edilmiş olması durumunda önemli

düzeyde daha fazla olduğunu ortaya koymaktadır.

Anahtar Kelimeler: Manipülasyon, Hisse Senedi Piyasası, Şirket Özellikleri, Probit

Regresyonu

vi

To my wife, Şeyma and my son, Mustafa İzzet

vii

ACKNOWLEDGMENTS

I would like to express my deepest gratitude to my supervisor Assist. Prof.

Dr. Esma Gaygısız Lajunen for her patient guidance and continuous support. I would

also like to thank Assoc. Prof. Dr. Bedri Kamil Onur Taş for his help especially in

doing the empirical part of this study.

I also thank to the other members of the thesis committee, Assoc. Prof. Dr.

Işıl Erol, Assist. Prof. Dr. Nil İpek Şirikçi and Assist. Prof. Dr. Yeliz Yolcu Okur for

their constructive comments.

I would also like to extend my special thanks to Ahmet Alper Aycan and

Atilla Bektaş for their help in the process of collecting the data. I would also like to

thank Mustafa Çağatay, Alaattin Ecer, Abdurrahman Çarkacıoğlu, İbrahim Kumsal

and Bekir Emre Haykır for their valuable support and comments.

Finally, I wish to thank my family for their support and encouragement

throughout my study.

viii

TABLE OF CONTENTS

PLAGIARISM ....................................................................................................... iii

ABSTRACT ........................................................................................................... iv

ÖZ ............................................................................................................................ v

DEDICATION. ...................................................................................................... vi

ACKNOWLEDGMENTS ..................................................................................... vii

TABLE OF CONTENTS ..................................................................................... viii

LIST OF TABLES .................................................................................................. x

LIST OF FIGURES ................................................................................................ xi

LIST OF ABBREVIATIONS ............................................................................... xii

CHAPTER

1. INTRODUCTION ................................................................................... 1

2. MODEL OF COSTLY TRADE-BASED MANIPULATION .............. 10

2.1 Introduction ...................................................................................... 10

2.2 Market with a Truthful Informed Party ........................................... 14

2.3 Market with a Manipulator .............................................................. 18

3. EMPIRICAL ANALYSIS ..................................................................... 23

3.1 Introduction ...................................................................................... 23

3.2 Data and Methodology .................................................................... 27

3.2.1 Data ....................................................................................... 27

3.2.2 Stock Manipulation in the ISE .............................................. 29

3.2.3 Methodology ......................................................................... 31

3.3 How does the CMBT Detect Manipulation? ................................... 33

3.4 Firm-specific Variables and Manipulation ...................................... 35

3.5 Empirical Results and Policy Implications ...................................... 36

4. CONCLUSION ...................................................................................... 41

REFERENCES ...................................................................................................... 43

APPENDICES

A. SECOND ORDER ANALYSIS ............................................................ 46

ix

B. ROBUSTNESS ANALYSIS .................................................................. 49

C. EQUILIBRIUM ANALYSIS ................................................................. 52

D. TURKISH SUMMARY… ..................................................................... 55

E. CURRICULUM VITAE…..................................................................... 71

F. TEZ FOTOKOPİSİ İZİN FORMU… .................................................... 73

x

LIST OF TABLES

TABLES

Table 3.1: Summary Statistics of Variables .......................................................... 29

Table 3.2: Distribution of Manipulations across Sectors and Years ..................... 30

Table 3.3: Random Effects Probit Regression Results ......................................... 37

Table 3.4: Dynamic Probit regressions with lagged values of the dep. variable .. 38

Table D.1: Robustness Analysis of Random Effects Probit Regression Results .. 49

Table D.2: Robustness Analysis of Dynamic Probit regressions with lagged

values of the dep. var. ............................................................................................ 50

Table D.3: Robustness Analysis of Dynamic Probit regressions with dummy

variable of manip. any year ................................................................................... 51

xi

LIST OF FIGURES

FIGURES

Figure 3.1: Monthly Values of ISE-100 Index between January 1998 and December

2006 ........................................................................................................................ 31

xii

LIST OF ABBREVIATIONS

CMBT: Capital Markets Board of Turkey

CML: Capital Market Law

IMF: International Monetary Fund

ISE: Istanbul Stock Exchange

IOSCO: The International Organization of Securities Commissions

MiFID: Markets in Financial Instruments Directive

OTC: Over the counter

PairGain: Pair Gain Technologies, Inc.

SEC: Securities Exchange Commision

US: United States

1

CHAPTER 1

INTRODUCTION

Manipulation in securities markets can be described as using fraudulent

practices in order to deceive investors through artificially altering normal functioning

of the market and effecting the securities prices. Usually, this behavior is conducted

by manipulators to extract profits at the expense of other investors. Stock market

manipulation harms public confidence in capital markets through distorting the fair

pricing of securities by creating artificial prices. Mainly for this reason manipulation

is strictly forbidden in most legislative systems.

La Porta et al. (2006) and Jackson and Roe (2009) argued that the securities

regulation has a major impact on the development of stock markets. Cumming et al.

(2011) analyzed the trading rules for various stock exchanges of 42 countries and

concluded that having more detailed and precise rules for prohibiting fraudulent

practices has a significant effect on the liquidity of the market. Capital market

regulators are enhancing their legal structure framework for decades and the Markets

in Financial Instruments Directive (MiFID) is a notable example at this progress for

the European exchanges.

Furthermore, regulators are increasing their enforcement efforts in order to

cope with manipulators as well as amplifying international cooperation with each

other. Jackson and Roe (2009) used the securities regulators' resources as a measure

of public enforcement and revealed a significant correlation with the financial market

development. Detection of manipulation is investigated in the accounting literature.

Several studies like Beneish (1999) and Wuerges and Borba (2010) analyze detection

of earnings manipulation. Beneish (1999) constructs an M-score composed of eight

accounting ratios that capture financial statement distortions. Wuerges and Borba

2

(2010) conduct a probit analysis to examine accounting fraud in US companies.

These studies mostly focus on the detection of financial statement fraud after the

statement is manipulated. In other words, they construct an index which can be used

to analyze whether the financial statement of a firm is manipulated or not.

Several studies empirically examine the effect of manipulation on stock

prices. Aggarwal and Wu (2006) investigate the price and volume effects of past

manipulation cases which are prosecuted by the Securities Exchange Commission

(SEC). They find that manipulation leads to a rise in volatility, liquidity and returns

of the stocks. In general, prices rise in the mean time of the manipulation scheme but

drop after the end of the manipulation period. Theoretical studies like Goldstein and

Guembel (2008) display the harmful effect on the allocation role of prices on the

financial markets.

Allen and Gale (1992) classified manipulation schemes into three parts,

namely, action-based, information-based and trade-based manipulation. Action-based

manipulation involves actions, other than spreading false rumors or trading that can

change the value of a security. Bagnoli and Lipman (1996) investigate take-over bids

as a mean of action-based manipulation. In their set-up, a manipulator, who is also an

existing shareholder of the company, can earn some profit by making an unserious

take-over bid and selling her shares at an elevated price level as a result of the take-

over bid. Bagnoli and Lipman (1996, p. 124-125) also give an example to this

method:

...in 1988, T Boone Pickens' Mesa Limited Partnership announced the

acquisition of 3.8% of the stock of Homestake Mining Company. After a

stock price increase of $4 per share, Pickens liquidated his position. The

Securities and Exchange Commission (SEC) alleged that Pickens'

activities constituted stock price manipulation, and in an out-of-court

settlement, Pickens agreed to disgorge $2.3 million in profits.

Take-over bid is only an example in a set of alternative actions of a

manipulator. Allen and Gale (1992, p. 504) give Harlem Railway case as another real

life example of action-based manipulation:

At the beginning of 1863, Commodore Cornelius Vanderbilt bought

stock in the Harlem Railway at around $8 to $9 a share. He took an

3

interest in running the company and its stock price advanced to $50 per

share. In April 1863, the New York City Council passed an ordinance

allowing the Harlem Railway to build a streetcar system the length of

Broadway and, as a result, the stock price went to $75. Members of the

council then conspired to sell the stock short, repeal the ordinance, and

thus force the price down. However, Vanderbilt discovered the plot and

managed to buy the entire stock of the company in secret. When the

members of the council tried to cover their short positions after the repeal

of the ordinance, they discovered that none of the stock could be

purchased. Vanderbilt forced them to settle at $179 per share.

Information-based manipulation based on dissemination of false or

misleading information about a security through various types of communication

channels in order to mislead the price of that security. In this case, manipulators

spread rumors about a stock through different types of channels such as newspapers,

online stock message forums, emails etc. in order to direct the market prices to

desired direction and make profit out of this movement. Leinweber and Madhavan

(2001) reports that the new communication technologies makes it much more easy to

spread rumors anonymously, duplicate them in a very short period of time and

distribute with a very low cost. Leinweber and Madhavan (2001, p. 5) give an

example to information-based manipulation on the stock of Pair Gain Technologies,

Inc. (PairGain):

In April 1999, an employee of PairGain posted a message on a Yahoo!

bulletin board alleging that PairGain had agreed to be acquired. The

message included a hyperlink to the supposed source of the rumor, a

Bloomberg news announcement…

...The announcement was a fake, as was the Bloomberg page, which was

complete with phony advertisements. PairGain’s stock price soared on

the announcement. An investigation by the SEC led to a guilty plea by

the employee, who received five years’ probation.

On the other hand, trade-based manipulation refers to the case that

manipulator only buys and sells the stock in order to mislead normal investors. At

first, it may seem implausible to manipulate a stock by only buying and selling the

stock but there is some academic evidence about the possibility of a trade-based

manipulative scheme. Allen and Gale (1992) show in their theoretical framework

that an uninformed manipulator could profit by mimicking the behaviors of an

informed trader with the help of information asymmetries. In a similar framework,

4

Aggarwal and Wu (2006) demonstrate that a manipulator can make positive profits

by mimicking and the probability of a successful manipulation depends positively to

the number of information seekers in the market. Jarrow (1992) shows that a

profitable manipulation is possible whenever a manipulator can achieve a price

momentum in the market and trade accordingly.

Lee et al. (2013) identify another type of manipulation named as

microstructure-based manipulation which can also be categorized as a subcategory of

trade-based manipulation. They show that the unique microstructure of the Korea

Exchange provides an opportunity for manipulators to use spoofing orders, that have

very small probability of being executed and are given for misleading other

investors. They report that a manipulator can earn extra profits of 67-83 basis points

in a very short period of time.

Besides these academic evidences, market regulators and supervisors'

litigation announcements are full of trade-based manipulation cases. Chapter 3 of this

study empirically investigates which firms are more susceptible to successful trade-

based manipulation. For this purpose, a unique data set consists of 306 trade-based

manipulation cases between 1998 and 2006 from the Istanbul Stock Exchange (ISE),

is collected by using the Capital Markets Board of Turkey's (CMBT) litigation

releases through weekly bulletins. Aggarwal and Wu (2006) also depicts that a

developed country's capital market is not an exception in terms of the number of

manipulation cases and their data set consists of 142 manipulation cases for the US

market between 1990-2001. Their data set includes not only the trade-based

manipulation cases but nevertheless is an evidence of a significant number of trade-

based manipulation.

Computerized exchange systems and outstanding developments in

communication systems facilitated the securities trades in the last decades. This

development precedes to sophisticated trade-based manipulation schemes.

Manipulators can easily control hundreds of accounts simultaneously and use these

as a mean of manipulation by the help of internet. As a result, detection and

investigation of the trade-based manipulation cases become difficult for supervisors.

5

So, using supervisory resources in an efficient way has become much more valuable

over time.

As the means of manipulative schemes are continuously evolving over time

since manipulators are trying to avoid being caught, they use some special tools in

order to mimic the buying and selling behaviors of the informed large traders for

accomplishing successful schemes of manipulation.

The International Organization of Securities Commissions (IOSCO) (2000)

specified some manipulative methods that are commonly used by manipulators such

as:

• Wash sales

• Painting the tape

• Improper matched orders

• Advancing the bid

• Pumping and dumping

• Marking the close

• Corner

• Squeeze

• Dissemination of false or misleading market information

IOSCO's (2000, p. 5) definition for wash sales is “Improper transactions in

which there are no genuine change in actual ownership of the security or derivative

contract”. It is hard to find any economic explanation for a rational investor to be a

part of a wash sale especially if canceling an existing order is a valid option for the

investor. In a wash sale, there is no actual change in ownership but on the other hand

the involving investor exposes to transaction costs like commission to the

intermediaries.

6

One purpose of manipulators to use wash sales would be artificially

increasing the daily volume of a stock in order to mislead investors by the

appearance of a liquid market. Many investors may value this increase in the

liquidity as a positive indicator for the relevant stock. In another set up, a

manipulator initially gives a number of passive selling orders for the stock and then

matches her own orders by giving binding buy orders. Thereby, manipulator can

increase the price of the stock successively whereas there is no genuine change in the

ownership of the shares. But in most of the cases, the other investors can not

recognize this fact and perceive this movement as a regular rise in the stock price.

Many types of investors, including intra-day traders, can be vulnerable to this

manipulative method. In addition, detecting and investigating this manipulative

scheme can be very difficult whenever it is conducted by a manipulation network.

Manipulators can open and control various accounts on behalf of different investors

and make trades across these accounts to conduct wash sales.

Painting the tape is defined as “Engaging in a series of transactions that are

reported on a public display facility to give the impression of activity or price

movement in a security” by IOSCO (2000, p. 5). Spoofing orders can be given as an

example for this method of trade-based manipulation. A manipulator can give buying

or selling orders at a limit price that is well below or above the market price, called

spoofing order, so the execution probability of that order is very low. But whenever

the microstructure of the exchange does not let the other investors to notice that this

artificial imbalance in the order book is set by the spoofing orders, investors can

trade the stock based on this information, which is the case noted by Lee et al. (2013)

for the Korean Exchange. Only the investors that are monitoring the trading sessions

through public displays become susceptible to this kind of manipulation whereas the

others who does not follow the intra-day order book movements are not vulnerable to

the spoofing order manipulation.

Marking the close can be described as trading at the very end of the trading

session to alter the closing price for the securities. Some of the investors can use

technical analysis tools as an integral part of their decision process which attribute a

7

considerable importance to the closing price of securities. By marking the close,

manipulators can affect the decisions of the normal investors. Comerton-Forde and

Putnins (2011) studied closing price manipulation cases. They constructed an index

of probability and intensity of closing price manipulation by using a sample of

manipulation cases prosecuted by US and Canadian prosecutors. They argue that

returns, spreads, trading frequencies and return reversals can be used to distinguish

the manipulated closing prices from normal trading behavior.

Many exchanges around the world have implemented closing auction

mechanisms to cope with marking the close manipulation schemes. At the closing

auction, both buying and selling orders of the traders are collected for a given period

and then these orders are executed at one price that is usually determined by the

maximization of the trading volume at the auction.

In corner and squeeze schemes manipulator controls a substantial portion of

the demand side of a security and by doing so either forces the investors holding an

opposite position in that security to trade at a higher price or creates an artificial price

by the help of shortage in the supply side of the security. Investors with a short

position in the market are quite susceptible to this kind of manipulation. Allen, Litov

and Mei (2006) examined stock market and commodity market corners from 1863–

1980. They asserted that large investors and insiders have market power that may let

them to manipulate prices and these manipulations with corners lead to increases in

volatility. Merrick, Naik and Yadav (2005) investigated manipulation cases with a

squeeze on the bond futures market.

Pumping and dumping manipulation scheme can be described as buying a

stock at increasingly higher prices with insistence, usually generating a price

momentum in the same direction and then sell them at higher prices. Mei et al.

(2004) showed that an uninformed manipulator could use investors’ behavioral

biases in order to profit by using pump and dump strategies.

IOSCO’s findings consist of the joint efforts of many capital market

regulators across the world. So, we can assert that these means of manipulations are

themes of successful manipulations in various exchange markets. The principle

8

purpose of the trade-based manipulation methods (wash sales, painting the tape,

marking the close, etc.) is to draw attention of normal investors to a particular stock

and mislead their decision on buying or selling this stock. As an example, an

artificially inflated daily volume of a stock, by using wash sales, may grab the

attention of an investor who uses technical analysis indicators that are utilizing

volume hikes as a positive sign. So, the trade-based manipulation activity usually

targets to increase the number of investors that trades a particular stock in the desired

direction.

A successful manipulation scheme usually contains more than one

manipulative method. Almost all of these manipulative means do have some costs for

the manipulator. By doing wash sales, a manipulator buys and sells the same stock

without changing the real ownership of these stocks for the sake of artificially

creating an appearance of an active trading environment in order to direct the

attention of some information seekers to this stock. This scheme of wash sales

creates transaction costs for the manipulator. Even if the execution probability of a

spoofing order is extremely low, whenever an investor gives a large market order,

spoofing orders of the manipulator can be matched with this market order and this

impose a cost on the manipulator. Thus, these manipulative methods increase the

participation of normal investors to the market in the desired direction by bearing

some cost.

The cost element of these manipulative methods is not much analyzed in the

literature and this study implements the cost element to the theoretical model of stock

market manipulation. For this purpose, Aggarwal and Wu's (2006) model of a stock

price manipulation is followed and assumed that the number of active information

seekers for a potentially manipulated stock is determined by informed trader, with

some cost. This extension to the original model brings out that a successful trade-

based manipulation scheme can only be observed whenever the value of cost factor

for motivating information seekers to participate into the market is sufficiently low.

The study, then, empirically investigates which firms are more susceptible to

successful manipulation. For this purpose, a unique data set consisting of

manipulation cases from 1998–2006 from the Istanbul Stock Exchange (ISE) was

9

collected and firm-specific variables are used to explain these manipulations. Probit

regression results show that small firms, firms with less free float rate and a higher

leverage ratio are more prone to stock price manipulation. Dynamic probit analysis

concludes that the probability of manipulation of a stock is significantly higher for

stocks that have been previously manipulated.

The study is organized as follows: the second chapter depicts the model of

costly trade-based manipulation the third part empirically investigates the type of

firms that are more prone to trade-based manipulation and the fourth chapter

concludes the study with policy implications of the results.

10

CHAPTER 2

MODEL OF COSTLY TRADE-BASED

MANIPULATION

2.1 Introduction

There are three types of investors in the model. Namely, the informed trader

(I), information seekers (S) and uninformed traders (U). The first type of investor is

the informed trader and she knows whether the future value of the relevant stock will

be high (𝑉𝐻) or low (𝑉𝐿), as an insider. If the informed trader knows that the future

value of the stock will be high then she can buy the stock, in which case we call her

truthful (superscripted T). On the other hand, if she knows that the future value will

be low and prefers to buy the stock anyway then we call her a manipulator

(superscripted M)1.

The second type of investors are information seekers. Information seekers do

not know future stock prices, since they are not insiders, but they try to extract

information about the future values by observing past and present prices and traded

quantities. In this study, distinctively from the existing studies similar to Aggarwal

and Wu (2006), we assume that in a stock market there are potential information

seekers and an informed trader independent of her type, whether she is truthful or a

manipulator, can attract these traders into a market for a certain stock by bearing a

certain cost. In this way, instead of taking the number of information seekers as an

exogenous parameter as in Aggarwal and Wu (2006), we make it an endogenous

1 As pointed out by Aggarwal and Wu (2006), the cases where the informed trader uses her

information of low future value by selling her existing stocks or short selling the stock are precluded.

Therefore, only the case where she uses this information by manipulating the stock is considered.

11

parameter. In this context, we can differentiate between potential information seekers

and active information seekers: in a stock market potential information seekers are

the traders observing many different stocks but they trade a stock if they are attracted

to that stock and active information seekers are the ones who are attracted to a certain

stock with active trading intentions.

An informed trader attracts information seekers to trade a certain stock if it is

profitable to bear the cost of attracting activities. The informed trader, by dealing

with various types of costly trade-based activities, can increase the number of active

information seekers. Whenever a potential information seeker becomes interested in

the stock, she becomes an active information seeker, then, she will observe the same

information set that is available to the other active information seekers.

It is supposed that the number of active information seekers for the stock, 𝑁

(superscripted 𝐴𝑖, 𝑖 ∈ 𝑁), is determined by the informed trader. We assume that the

total cost function of attracting potential information seekers is continuous,

differentiable and convex:

𝐶(𝑁) = 𝑐𝑁2 (1)

where 𝑐 > 0 is the cost factor.

The informed trader can determine the number of active information seekers

by choosing her total cost. One can argue that in this set up, both types of the

informed trader, either truthful or manipulative, can apply a scheme in order to

determine the number of active information seekers. But the truthful informed trader

can always wait until the realization of the future value of the stock. On the other

hand, a rational manipulator does not choose to wait until the future value of the

stock is announced, which is 𝑉𝐿.

Active information seekers are simply searching for the information about the

future value of the stock. These investors do not have any information about the type

of the informed trader in the market. They only know past prices and traded

quantities and the present total number of active information seekers in the market of

that particular stock.

12

The third type of investors are composed of a continuum of uninformed

traders (superscripted U). These investors are simply providing liquidity to the

market and forming a supply curve as:

𝑃(𝑄) = 𝑎 + 𝑏𝑄 (2)

where 𝑃 is the market price and 𝑄 is the quantity supplied and 𝑏 > 0 is the slope of

the supply curve. Initially, all of the supply is belongs to the uninformed traders

initially and the informed trader does not hold any stock2. Since the price of the

shares cannot exceed 𝑉𝐻 even if all the shares are demanded by other investors, the

total outstanding shares are assumed to be:

𝑉𝐻 − 𝑎

𝑏 (3)

The informed party is the manipulator with probability 𝛾 and the truthful

trader with probability 𝛿. This information is common knowledge.

Initially, uninformed traders hold all outstanding shares. The informed party

chooses to enter or not to the market. By definition the informed trader is called as

the truthful trader whenever the future value of the stock is VH and the manipulator

when the future value of the stock is 𝑉𝐿. The probability of having 𝑉𝐻 as the future

value of the stock is 𝛿. The informed party does not choose to enter the market with

probability 1 − 𝛾 − 𝛿. In that case the future value of the stock will be 𝑉𝐿.

It can be assumed that initial price of 𝑎 is the expected value of the future

cash flows:

𝑎 = 𝛿𝑉𝐻 + (1 − 𝛿)𝑉𝐿 (4)

Sequence of the game is as follows:

2 In this study only the trade-based manipulation is considered and the case where informed trader has

some initial position in the stock and tries to spread rumors and false information about the stock is

precluded, which was categorized as information-based manipulation by Allen and Gale (1992).

13

Stage 1: The informed trader is in the market and she chooses 𝐶, the total

cost of attracting potential information seekers to actively participate in the market,

and the quantity to buy from the uninformed traders.

Stage 2: Each active information seeker observes Stage 1 stock price, the

quantity demanded by the informed trader and the total number of active information

seekers (𝑁) in the market that are attracted by the informed trader at the first stage3

but does not know the type of the informed trader. Each active information seeker

believes with probability 𝛾

𝛾+𝛿 the informed trader is a manipulator and with

probability 𝛿

𝛾+𝛿 the informed trader is truthful. Uninformed traders provide supply to

the market by selling shares according to the supply curve of equation (2) and at this

stage, using Stage 1 observations each active information seeker chooses the amount

of their purchases strategically by taking the other information seekers' purchases of

shares as given. There is a Cournot type of a game between active information

seekers at this stage.

After the dynamic game, composed as Stage 1 and Stage 2 games, the actual

value of the stock is announced.

We assume that it is not profitable for the informed trader to hold the shares

until the end of the dynamic game. As in Aggarwal and Wu (2006), we introduce a

cost of 𝑘 > 0 of holding shares until the end of the game. Then at the end of the

game if the stock value is 𝑉𝐻 the payoff for the informed trader will be 𝑉𝐻 − 𝑘 . In

addition, we introduce the condition 𝑉𝐻 − 𝑘 − 𝑎 > 0 (as in Aggarwal and Wu

(2006)), so that the truthful informed trader (who knows that the future value of the

stock will be 𝑉𝐻) chooses to buy shares at Stage 1. Naturally the manipulator needs

to sell her shares at Stage 2.

3 Since active information seekers know the total number of the investors with their own type, then,

this would imply that they also observe the manipulative scheme in the market. In this case, one can

argue that the capital market supervisor can also observe the manipulative scheme after Stage 1.

Enforcement and prosecution of the deceitful activities depend upon the legal framework of the

particular jurisdiction. Manipulative schemes and their implementation by the manipulators largely

evolved over time. By the help of electronic trading, the manipulators can use many accounts

simultaneously in order to accomplish manipulative schemes and it becomes more difficult to

prosecute these highly sophisticated schemes and to penalize the manipulators.

14

Similar to the Aggarwal and Wu's (2006) methodology, two cases are

considered where in the first case the informed party is truthful and in the other case

she can be either a truthful trader or a manipulator.

2.2 Market with a Truthful Informed Party

In this case there is a truthful informed party at the market who knows that

the future value of the stock will be VH. The truthful informed trader simultaneously

purchases shares from the uninformed traders and spend 𝑐𝑁𝑇2 in order to call 𝑁𝑇

number of active information seekers to the market at Stage 1 by correctly

anticipating the Stage 2 equilibrium price at which she will sell her shares.

Stage 2 Solution with a Truthful Informed Party

At this stage there are 𝑁𝑇 ∈ ℕ active information seekers with the aggregate

demand

𝑄2𝐴 = 𝑞2

𝐴1 + ⋯ , 𝑞2𝐴𝑖−1 + 𝑞2

𝐴𝑖 + 𝑞2𝐴𝑖+1 + ⋯ + 𝑞2

𝐴𝑁𝑇 (5)

where 𝑞2𝐴𝑖 is the demand of active information seeker 𝑖 at Stage 2. Since the truthful

informed party sells her shares at Stage 2 active information seeker 𝑖’s payoff

function takes the form

𝜋𝐴𝑖 (𝑞2𝐴1 , … ,𝑞

2𝐴𝑖−1 ,𝑞

2𝐴𝑖 ,𝑞

2

𝐴𝑖+1 , … ,𝑞2

𝐴𝑁𝑇) = (𝑉𝐻 − 𝑃2)𝑞2𝐴𝑖

= [𝑉𝐻 − (𝑎 + 𝑏 (𝑞2𝐴1 + ⋯ + 𝑞2

𝐴𝑖−1 + 𝑞2𝐴𝑖 + 𝑞2

𝐴𝑖+1 + ⋯ + 𝑞2

𝐴𝑁𝑇))] 𝑞2𝐴𝑖 (6)

Active information seeker 𝑖 takes the other active information seekers share

purchases, [𝑞2𝐴1 , … ,𝑞2

𝐴𝑖−1 ,𝑞2𝐴𝑖+1 , … , 𝑞2

𝐴𝑁𝑇], as given and maximizes her payoff by

solving the problem:

max𝑞2

𝐴𝑖𝜋𝐴𝑖 (𝑞2

𝐴1 , … , 𝑞2𝐴𝑖−1 , 𝑞2

𝐴𝑖 , 𝑞2𝐴𝑖+1 , … , 𝑞2

𝐴𝑁𝑇) (7)

15

The solution to this problem gives the best response function of active

information seeker 𝑖:4

𝑞2𝐴𝑖∗ = 𝑅𝐴𝑖 (𝑞2

𝐴1 , … , 𝑞2𝐴𝑖−1 ,𝑞2

𝐴𝑖+1 , … ,𝑞2

𝐴𝑁𝑇)

=[𝑉𝐻 − 𝑎 − 𝑏(𝑞2

𝐴1 + ⋯ + 𝑞2𝐴𝑖−1 + 𝑞2

𝐴𝑖+1 + ⋯ + 𝑞2

𝐴𝑁𝑇)]

2𝑏 (8)

for all 𝑖 = 1, … ,𝑁𝑇. In a symmetric equilibrium we have

𝑞2𝐴1∗

= ⋯ = 𝑞2𝐴𝑖−1∗

= 𝑞2𝐴𝑖∗ = 𝑞2

𝐴𝑖+1∗= ⋯ = 𝑞2

𝐴𝑁𝑇∗= 𝑞2

𝐴∗ (9)

resulting in

𝑞2𝐴∗ =

𝑉𝐻 − 𝑎

(𝑁𝑇 + 1)𝑏 (10)

The aggregate demand for 𝑁𝑇 active information seekers is

𝑄2𝐴∗ = 𝑁𝑇𝑞2

𝐴∗ =𝑁𝑇(𝑉𝐻 − 𝑎)

(𝑁𝑇 + 1)𝑏 (11)

Using the supply curve of (2), stock price at Stage 2 is

𝑃2∗(𝑁𝑇) = 𝑉𝐿 + 𝛿(𝑉𝐻 − 𝑉𝐿) +

(1 − 𝛿)(𝑉𝐻 − 𝑉𝐿)𝑁𝑇

𝑁𝑇 + 1 (12)

As the number of active information seekers approaches to infinity, 𝑁𝑇 → ∞,

the aggregate demand converges to the total outstanding shares and the price

converges to the fundamental value VH at Stage 2, so, more active information

seekers drive the market into efficiency.

Stage 1 Solution with a Truthful Informed Party

The truthful informed party purchases shares at Stage 1 and then sells those

shares at Stage 2. At Stage 1, the informed party chooses the amount of shares to

purchase and determines the number of active information seekers, 𝑁𝑇, by deciding

how much to spend for attracting potential information seekers.

4 Further equilibrium analysis is given at Appendix C.

16

Important Remark: At Stage 1, 𝑁𝑇 is treated as a continuous variable to be

able to have a continuous and differentiable objective function for the informed

trader, although at Stage 2 it is assumed to be an integer. The examples of the

continuous treatment of discrete variables, as in the problems we face, exist in the

following studies with relevant justifications: Mankiw and Whinston (1986), Seade

(1980) and Novshek (1980).

When we ignore the integer constraint on 𝑁𝑇 and treat it as a continuous

variable as explained in the remark, we have a continuous and differentiable total

cost function and it is assumed to be a convex function: 𝐶(𝑁𝑇) = 𝑐𝑁𝑇2,𝑐 > 0.

The informed trader correctly anticipates the subgame equilibrium price of

Stage 2, 𝑃2∗(𝑁𝑇). At Stage 1, the informed trader simultaneously decides how many

active information seekers, 𝑁𝑇, to attract to the market for the relevant stock and the

number of shares, 𝑞1,𝑇, to buy from the uninformed traders at price 𝑃1 = 𝑎 + 𝑏𝑞1,𝑇 to

be to sold at correctly anticipated Stage 2 equilibrium price, 𝑃2∗(𝑁𝑇). This leads to

the following payoff function:

𝜋𝑇(𝑞1,𝑇 , 𝑁𝑇) = [𝑃2∗(𝑁𝑇) − 𝑝1(𝑞1,𝑇)]𝑞1,𝑇 − 𝐶(𝑁𝑇)

= [𝑁𝑇𝑉𝐻 + 𝑎

𝑁𝑇 + 1− (𝑎 + 𝑏𝑞1,𝑇)] 𝑞1,𝑇 − 𝑐𝑁𝑇

2 (13)

The aim of the informed trader to solve the following problem:

max𝑞1,𝑇,𝑁𝑇

𝜋𝑇(𝑞1,𝑇 , 𝑁𝑇) (14)

Details about the concavity of the objective function is given at Appendix A.

Taking the first-order conditions yield the optimal number of active information

seekers

𝑁𝑇∗ = (

1

4𝑏𝑐)

1

3

(1 − 𝛿)2

3(𝑉𝐻 − 𝑉𝐿)2

3 − 1 (15)

and the optimal amount of shares

17

𝑞1,𝑇∗ =

(1 − 𝛿)(𝑉𝐻 − 𝑉𝐿) − [4𝑏𝑐(1 − 𝛿)(𝑉𝐻 − 𝑉𝐿)]1

3

2𝑏 (16)

In order to restrict our attention to integer realizations of 𝑁𝑇∗ it is assumed

that the parameters 𝑉𝐻, 𝑉𝐿, 𝛿, 𝑏 and 𝑐 only take values which make the optimal

number of active information seekers at (15), 𝑁𝑇∗, an integer.

These constitute the subgame perfect outcome of the game resulting in the

Stage 1 market price

𝑃1∗ = 𝑉𝐿 +

(𝑉𝐻 − 𝑉𝐿)(1 + 𝛿)

2−

[4𝑏𝑐(1 − 𝛿)(𝑉𝐻 − 𝑉𝐿)]1

3

2 (17)

and the Stage 2 market price

𝑃2∗ = 𝑉𝐻 − [4𝑏𝑐(1 − 𝛿)(𝑉𝐻 − 𝑉𝐿)]

1

3 (18)

associated with the truthful party's profit

𝜋𝑇∗ =

[(1 − 𝛿)(𝑉𝐻 − 𝑉𝐿) − [4𝑏𝑐(1 − 𝛿)(𝑉𝐻 − 𝑉𝐿)]1

3]2

4𝑏 (19)

With these outcomes the following condition ensures that the truthful party

would want to sell the shares at Stage 2 rather than waiting the end of the two stages

of the game:

𝑃2∗ = 𝑉𝐻 − [4𝑏𝑐(1 − 𝛿)(𝑉𝐻 − 𝑉𝐿)]

1

3 ≥ 𝑉𝐻 − 𝑘 (20)

This condition can be reexpressed as follows:

𝑘 ≥ [4𝑏𝑐(1 − 𝛿)(𝑉𝐻 − 𝑉𝐿)]1

3 (21)

This inequality reveals that a high level of 𝑘 could be the only reason for the

truthful party to sell the shares at Stage 2. As 𝑏, 𝑐 and (𝑉𝐻 − 𝑉𝐿) increase or 𝛿

decreases the truthful trader would have better reasons to wait until the end of the

game if 𝑘 is not discouragingly high.

18

2.3 Market with a Manipulator

Now assume that the informed trader can be a truthful party with probability

𝛿 and a manipulator with a probability 𝛾 respectively. With probability 1 − 𝛿 − 𝛾 the

informed trader does not enter into the market. In this set up, there can be both

pooling and separating equilibria. At the pooling equilibrium it is assumed that the

both the truthful party and the manipulator purchases the same quantity of shares at

Stage 1 and provoke same number of information seekers. In this conjectured

equilibrium the posterior belief of information seekers that informed trader is a

manipulator is

𝛽 =𝛾

𝛾 + 𝛿 (22)

Stage 2 Solution with a Manipulator

If there exists a pooling equilibrium with Stage 1 outcome [𝑞1 ,̂ �̂�] then

equilibrium beliefs of information seekers are:

[𝑃𝑟𝑜𝑏([𝑞1,𝑀, 𝑁𝑀]|[𝑞1 ,̂ �̂�]) = 𝛽, 𝑃𝑟𝑜𝑏([𝑞1,𝑇 , 𝑁𝑇]|[𝑞1 ,̂ �̂�]) = 1 − 𝛽] (23)

With these beliefs active information seeker 𝑖’s payoff function

becomes:

𝜋𝐴𝑖(𝑞2𝐴1 , … , 𝑞2

𝐴𝑖−1 , 𝑞2𝐴𝑖 , 𝑞2

𝐴𝑖+1 , … , 𝑞2𝐴𝑁) =

{(𝑉𝐿 − 𝑃2)𝑞2

𝐴𝑖 𝑤𝑖𝑡ℎ 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝛽

(𝑉𝐻 − 𝑃2)𝑞2𝐴𝑖 𝑤𝑖𝑡ℎ 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 1 − 𝛽

} (24)

where

𝑃2 = 𝑎 + 𝑏 (𝑞2𝐴1 + ⋯ + 𝑞2

𝐴𝑖−1 + 𝑞2𝐴𝑖 + 𝑞2

𝐴𝑖+1 + ⋯ + 𝑞2

𝐴�̂�) (25)

The expected payoff function of active information seeker 𝑖 is:

𝐸𝜋𝐴𝑖 (𝑞2𝐴1 , … , 𝑞2

𝐴𝑖−1 , 𝑞2𝐴𝑖 , 𝑞2

𝐴𝑖+1 , … , 𝑞2

𝐴�̂�) = (1 − 𝛽)(𝑉𝐻 − 𝑃2)𝑞2𝐴𝑖 + 𝛽(𝑉𝐿 − 𝑃2)𝑞2

𝐴𝑖

19

= [𝑉𝐻 − 𝛽(𝑉𝐻 − 𝑉𝐿) − 𝑎 − 𝑏 (𝑞2𝐴1 + ⋯ + 𝑞2

𝐴𝑖−1 + 𝑞2𝐴𝑖 + 𝑞2

𝐴𝑖+1 + ⋯ + 𝑞2

𝐴�̂�)] 𝑞2𝐴𝑖 (26)

Active information seeker 𝑖 takes the other active information seekers share

purchases, [𝑞2𝐴1 , … ,𝑞2

𝐴𝑖−1 ,𝑞2𝐴𝑖+1 , … , 𝑞2

𝐴�̂�], as given and maximizes her payoff by

solving the problem:

max𝑞2

𝐴𝑖𝐸𝜋𝐴𝑖 (𝑞2

𝐴1 , … , 𝑞2𝐴𝑖−1 ,𝑞2

𝐴𝑖 ,𝑞2𝐴𝑖+1 , … ,𝑞2

𝐴�̂�) (27)

The solution to this problem gives the best response function of active

information seeker 𝑖:

𝑞2𝐴𝑖∗ = 𝑅𝐴𝑖 (𝑞2

𝐴1 , … , 𝑞2𝐴𝑖−1 ,𝑞2

𝐴𝑖+1 , … , 𝑞2

𝐴�̂�; 𝛽([𝑞1,𝑀, 𝑁𝑀]|[𝑞1 ,̂ �̂�]))

=[𝑉𝐻 − 𝛽(𝑉𝐻 − 𝑉𝐿) − 𝑎 − 𝑏(𝑞2

𝐴1 + ⋯ + 𝑞2𝐴𝑖−1 + 𝑞2

𝐴𝑖+1 + ⋯ + 𝑞2

𝐴�̂�)]

2𝑏 (28)

for all 𝑖 = 1, … , �̂�. In a symmetric equilibrium we have

𝑞2𝐴1∗

= ⋯ = 𝑞2𝐴𝑖−1∗

= 𝑞2𝐴𝑖∗ = 𝑞2

𝐴𝑖+1∗= ⋯ = 𝑞2

𝐴�̂�∗= 𝑞2

𝐴∗ (29)

resulting in

𝑞2𝐴∗ =

𝑉𝐻 − 𝛽(𝑉𝐻 − 𝑉𝐿) − 𝑎

(�̂� + 1)𝑏 (30)

The aggregate demand for �̂� ∈ ℕ active information seekers is

𝑄2𝐴∗ = �̂�𝑞2

𝐴∗ =�̂�[𝑉𝐻 − 𝛽(𝑉𝐻 − 𝑉𝐿) − 𝑎]

(�̂� + 1)𝑏 (31)

Using the supply curve of (2), stock price at Stage 2 is

𝑃2∗(�̂�) = 𝑎 + 𝑏𝑄2

𝐴∗ =�̂�[𝑉𝐻 − 𝛽(𝑉𝐻 − 𝑉𝐿)] + 𝑎

�̂� + 1 (32)

20

As the number of active information seekers approaches to infinity, �̂� → ∞,

the price converges to the expected value (1 − 𝛽)𝑉𝐻 + 𝛽𝑉𝐿 at Stage 2.

Each active information seekers' expected profits are

𝜋𝐴𝑖∗ =1

𝑏[(1 − 𝛽)𝑉𝐻 + 𝛽𝑉𝐿 − 𝑎

�̂� + 1]

2

(33)

Stage 1 Solution with a Manipulator

Important Remark: At Stage 1, 𝑁 is treated as a continuous variable to be

able to have a continuous and differentiable objective function for the informed

trader, although at Stage 2 it is assumed to be an integer. The examples of the

continuous treatment of discrete variables, as in the problems we face, exist in the

following studies with relevant justifications: Mankiw and Whinston (1986), Seade

(1980) and Novshek (1980).

Informed Trader's Decision Making Problem

The payoff function of the informed trader 𝑗 = 𝑇,𝑀 is:

𝜋𝑗(𝑞1,𝑗, 𝑁𝑗) = [𝑃2∗(𝑁𝑗) − 𝑃1(𝑞1,𝑗)]𝑞1,𝑗 − 𝑐𝑁𝑗

2 (34)

where

𝑃2∗(𝑁𝑗) = 𝑎 + 𝑏𝑄2

𝐴∗ =𝑁𝑗[𝑉𝐻 − 𝛽(𝑉𝐻 − 𝑉𝐿)] + 𝑎

𝑁𝑗 + 1 (35)

is Stage 2 equilibrium price correctly anticipated by the informed trader 𝑗 = 𝑇,𝑀 and

𝑁𝑗 = �̂� for all 𝑗 = 𝑇,𝑀.

The informed trader 𝑗’s problem is

max𝑞1,𝑗,𝑁𝑗

𝜋𝑗(𝑞1,𝑗, 𝑁𝑗) (36)

Details about the concavity of the objective function is given at Appendix A.

Taking first-order conditions yields optimal number of active information seekers

that will be called by informed trader 𝑗

21

�̂� = (1

4𝑏𝑐)

1

3

[𝛿1 − 𝛿 − 𝛾

𝛿 + 𝛾(𝑉𝐻 − 𝑉𝐿)]

2

3

− 1 (37)

𝑗’s optimal amount of shares

�̂�1,𝑗 =𝛿

1−𝛿−𝛾

𝛿+𝛾(𝑉𝐻 − 𝑉𝐿) − [4𝑏𝑐𝛿

1−𝛿−𝛾

𝛿+𝛾(𝑉𝐻 − 𝑉𝐿)]

1

3

2𝑏 (38)

the equilibrium price

�̂�1 = 𝑉𝐿 +1

2𝛿 (1 +

1

𝛿 + 𝛾) (𝑉𝐻 − 𝑉𝐿) −

1

2[4𝑏𝑐𝛿

1 − 𝛿 − 𝛾

𝛿 + 𝛾(𝑉𝐻 − 𝑉𝐿)]

1

3

(39)

and 𝑗’s optimal profits

�̂�𝑗 =1

4𝑏[𝛿

1 − 𝛿 − 𝛾

𝛿 + 𝛾(𝑉𝐻 − 𝑉𝐿) − [4𝑏𝑐𝛿

1 − 𝛿 − 𝛾

𝛿 + 𝛾(𝑉𝐻 − 𝑉𝐿)]

1

3

]

2

(40)

Stage 2 equilibrium price is

�̂�2 = 𝑉𝐿 +𝛿

𝛿 + 𝛾(𝑉𝐻 − 𝑉𝐿) − [4𝑏𝑐𝛿

1 − 𝛿 − 𝛾

𝛿 + 𝛾(𝑉𝐻 − 𝑉𝐿)]

1

3

(41)

In order to restrict our attention to integer realizations of �̂� it is assumed that

the parameters 𝑉𝐻, 𝑉𝐿, 𝛿, 𝛾, 𝑏 and 𝑐 only take values which make the optimal number

of active information seekers at (37), �̂�, an integer.

With these outcomes the following condition ensures that the truthful party

would want to sell the shares at Stage 2 rather than waiting the end of the two stages

of the game:

�̂�2 = 𝑉𝐿 +𝛿

𝛿 + 𝛾(𝑉𝐻 − 𝑉𝐿) − [4𝑏𝑐𝛿

1 − 𝛿 − 𝛾

𝛿 + 𝛾(𝑉𝐻 − 𝑉𝐿)]

1

3

≥ 𝑉𝐻 − 𝑘 (42)

This condition can be reexpressed as follows:

22

𝑘 ≥𝛾

𝛿 + 𝛾(𝑉𝐻 − 𝑉𝐿) + [4𝑏𝑐𝛿

1 − 𝛿 − 𝛾

𝛿 + 𝛾(𝑉𝐻 − 𝑉𝐿)]

1

3

(43)

This inequality reveals that a high level of 𝑘 could be the only reason for the

truthful party to sell the shares at Stage 2. As 𝑏, 𝑐 and (𝑉𝐻 − 𝑉𝐿) increase or 𝛿

decreases the truthful trader would have better reasons to wait until the end of the

game if 𝑘 is not discouragingly high.

In addition to the incentive compatibility condition, out-of equilibrium beliefs

for information seekers needs to be specified. In order to guarantee the existence of

the pooling equilibrium described above we assume that each active information

seeker's belief is

𝜇(𝑉𝐿|[𝑞1, 𝑁]) = {1 𝑓𝑜𝑟 [𝑞1, 𝑁] ≠ [�̂�1, �̂�]

𝛽 𝑓𝑜𝑟 [𝑞1, 𝑁] = [�̂�1, �̂�]} (44)

These results show that the cost factor for introducing active information

seekers is an important factor in terms of the occurrence of a fraudulent scheme in

the market. For only sufficiently low levels of 𝑐, 𝑏 and (𝑉𝐻 − 𝑉𝐿) and high levels of

𝑘 and 𝛿, the informed investor conducts manipulative actions in order to pull

information seekers into the market.

In the next chapter, it is checked whether the manipulators have actually

valued the cost of manipulative activities or not by using all litigation

announcements of the Capital Markets Board of Turkey (CMBT) dealing with the

stock market manipulations during 1998–2006.

23

CHAPTER 3

EMPIRICAL ANALYSIS

3.1 Introduction

Stock market manipulation harms public confidence in capital markets

through distorting the fair pricing of securities by creating artificial prices. Mainly

for this reason manipulation is strictly forbidden in most legislative systems. For

decades, capital market regulators have been increasing their enforcement efforts in

order to cope with manipulators as well as amplifying international cooperation with

each other. In this chapter, firm-specific factors that make a stock more susceptible to

manipulation are investigated. In other words, it is identified which stocks are more

likely to be manipulated by looking at the previous incidents of manipulation that

were detected by the market supervisor. A unique data set of individual manipulation

cases is constructed by analyzing the Capital Markets Board of Turkey's (CMBT)

releases for the period 1998–2006. Panel dynamic probit regression analysis is

conducted in order to identify the firm-specific and market-specific factors which

affect the probability that a specific stock will be manipulated.

Several studies empirically examine the effect of manipulation on stock

prices. Aggarwal and Wu (2006) investigate the price and volume effects of past

manipulation cases which are prosecuted by the Securities Exchange Commission

(SEC). They find that manipulation leads to a rise in volatility, liquidity and returns

of the stocks. In general, prices rise in the mean time of the manipulation scheme but

drop after the end of the manipulation period. Theoretical studies like Goldstein and

Guembel (2008) display the harmful effect on the allocation role of prices on the

financial markets.

24

On the other hand, means of manipulative schemes are continuously evolving

over time since manipulators are trying to avoid being caught. Allen and Gale (1992)

showed in their theoretical framework that an uninformed manipulator could profit

by mimicking the behaviors of an informed trader with the help of information

asymmetries. But in reality manipulators do not only mimic the buy and sell

behaviors of informed large traders but they also use some special tools to

accomplish successful schemes of manipulation.

A successful manipulation scheme usually contains more than one

manipulative method. Almost all of these manipulative means do have some costs for

the manipulator. By doing wash sales, a manipulator buys and sells the same stock

without changing the real ownership of these stocks for the sake of artificially

creating an appearance of an active trading environment in order to direct attention of

some information seekers to this stock. This scheme of wash sales creates transaction

costs for the manipulator. Likewise, pumping and dumping, and cornering or

squeezing the market have similar kinds of costs.

Cost characteristics of manipulative methods may differ for different stocks

depending on the firm-specific characteristics. It may be less expensive to

manipulate smaller firms’ stocks or stocks with lower free float than the others since

a manipulator needs much less effort to artificially create an appearance of an active

market or corner the market. Aggarwal and Wu (2006) report that most manipulation

cases occur in inefficient markets in their data set, such as the OTC Bulletin Board

and the Pink Sheet. Jiang et al. (2005) broadly studied the well-known stock pools of

the 1920s and their results also support the idea that regulatory enforcement should

focus on illiquid segments of the market. In a recent study, Lee et al. (2013) find that

stocks with less market capitalization, lower stock price, higher return volatility and

lower managerial transparency are more vulnerable to spoofing order manipulation.

These findings indicate that firm-specific characteristics and market characteristics

should be studied empirically as conducted in this study.

This study focuses on firm-specific factors that can be used to identify similar

characteristics of stocks that are more likely to be manipulated. Analyses consist of

25

all of the trade-based manipulation cases that are identified by the CMBT. For this

purpose, all litigation announcements dealing with the stock market manipulations

during 1998–2006 of CMBT are collected by reading all the releases of the CMBT

for that period. Similar to Aggarwal and Wu (2006), data set of this study is

restricted by the enforcement power of the regulator over manipulation detection and

there may be some other prosperous manipulation affairs that were not caught by the

CMBT. But considering the state of the art detection and enforcement techniques of

the regulatory bodies, it is quite reasonable to use this data to determine some

common characteristics of incidents of successful manipulation.

This study uses the trade-based manipulation cases from the Istanbul Stock

Exchange (ISE) for the following reasons. First, ISE is a developing market which

has a suitable environment for manipulators. Cumming et al. (2011) identify trading

rule indices for 42 stock exchanges and ISE's scores are all zero (minimum) for all

categories of Price Manipulation Index, Volume Manipulation Index, Spoofing

Index, False Disclosure Index, Market Manipulation Index, Insider Trading Index

and Broker-Agency Index. Likewise, there are 306 incidents of trade-based stock

market manipulation in the analyzed period that makes our data set quite rich

compared to the size of previous studies5. On the other hand, the market

capitalization of ISE is 162.4 billion US dollars at the end of 2006 which is similar to

the Tel Aviv, Irish, Warsaw, Jakarta and Santiago Stock Exchanges. Finally, the

CMBT is a well-established regulatory and supervisory body that allocates

significant resources for monitoring and detecting potential manipulation incidents

on ISE. Thus, the bulletins of the CMBT provide us a reliable source for identifying

manipulated stocks.

The main empirical analysis regresses incidents of trade-based manipulation

cases on the firm-specific variables of market capitalization, free float rate,

profitability, leverage ratio and current ratio. Changes of the ISE index over time are

also used as a control variable in dynamic probit model. Market capitalization of a

5 Aggarwal and Wu (2006) present that there are 142 stock market manipulation cases pursued by the SEC from January 1990 to October 2001. Comerton-Forde and Putnins (2011) analyze the closing price manipulation cases prosecuted in the US and Canada for the January 1, 1997 - January 1, 2009 period and they identify 184 instances of manipulation.

26

firm is expected to negatively affect the probability of manipulation since it is much

more costly to manipulate a large market of a stock than a smaller one. As expected,

and consistent with the findings of Aggarwal and Wu (2006), Jiang et al. (2005), and

Lee et al. (2013), our results support the view that larger firms are less likely to be

manipulated. Also, it is found that firms with a higher free float rate seem to be

significantly less manipulated. This result was also expected due to the increasing

transaction costs of manipulators. Another probable reason for this result may be the

fact that stocks on the market do exhibit more executive power over the firm with

higher free float so the manipulators may face much more resistance from the current

managers of the relevant firm.

Profitability, leverage ratio and current ratio of the firm are widely accepted

as important indicators of financial performance. Regression results exhibit that only

the leverage ratio has a significant and positive effect on the possibility of

manipulation. This infers that stocks of firms with a higher level of external

financing of assets are more likely to face incidents of manipulation.

On the other hand, the ISE index does not exhibit any significant effect on the

probability of manipulation of stocks. Aggarwal and Wu (2006) indicated that a

larger set of information seekers improves market efficiency but also increases the

possibility of manipulation. Since booming periods of stock indexes lead to higher

number of investors, it can be expected some positive effect of index changes to the

probability of manipulation. This effect does not seem to be significant enough.

Besides examining firm-specific variables, the dynamic probit analysis

contributes to the literature by showing that if a firm’s stock is manipulated before,

then the probability of re-occurrence of manipulation is significantly higher in later

years, and much greater for the successive year. As far as we know, this phenomenon

has never been analyzed in the stock manipulation literature. This finding indicates

that micro-structural modeling of stock market manipulation should take into account

previous manipulation incidents.

As mentioned above, the literature presents limited evidence that most of the

manipulation cases are observed in the illiquid segments of stock exchanges.

27

However, there are no studies that empirically investigate the common characteristics

of the manipulated stocks with trade-based manipulation methods in general to best

of our knowledge. The main contribution of the study is that it empirically analyzes

the factors that determine the probability of trade-based manipulation of an

individual stock by using firm specific variables. Previous studies either descriptively

present some common characteristics of the firms that are manipulated or identify

them for some specific trade-manipulation tools such as spoofing orders. The second

contribution of this study is the investigation of financial indicators as determinants

of the likelihood of manipulation. The other contribution is implementing a dynamic

approach to examine the probability of manipulation using whether a stock is

manipulated before as an explanatory variable. To sum up, this study provides

empirical facts that can be used by securities regulators in order to categorize stocks

with respect to their manipulation probabilities and allocate their enforcement

resources accordingly.

The results of this study have policy implications and lead to new

perspectives for regulatory bodies of capital markets. Regulators and exchanges

decide on quotation standards of publicly held companies. Using the factors that are

identified in this study, they may separate stocks into different segments according to

their likelihoods to be manipulated. They may implement different trading rules for

some segments of the market like using call auction instead of continuous auction.

Also, they can allocate resources and more of their enforcement power into the

segments that have a high probability of being manipulated. These segments may

include firms with smaller market capitalization, less free float rates, higher leverage

ratio and that have already been manipulated.

3.2 Data and Methodology

3.2.1 Data

A data set of stock market manipulation cases is constructed by analyzing

CMBT litigation releases from 1998–2006. The frequency of the data set is annual to

be able to use reliable data from year-end balance sheets. All of the CMBT's weekly

28

bulletins between 1998 and mid-2010 are read and manipulation cases identified by

the CMBT according to the 47/A-2 article of Capital Market Law (CML) are

collected6. This article of CML defines criminals of stock manipulation as:

Real entities, the authorized persons of legal entities and those acting

together with them all which trade capital market instruments in order to

artificially affect their demand and supply, to give the impression of

existence of active market, to hold the prices at the same level, to

increase or decrease the prices.

Then a database of all these manipulation cases and firm specific

characteristics is constructed by using year-end balance sheets7. The variables can be

described as follows:

1. Manipulation (M): Dummy variable that is one if the stock is manipulated

in that year8.

2. Free Float Rate (FR): the portion of the market capitalization available for

sale.

3. Market Capitalization (MC): the share price times the number of shares

outstanding at the year end.

4. Return on Equity Ratio (RE): Net profit divided by shareholder's equity.

5. Leverage Ratio (LR): Total value of debt divided by total assets.

6. Current Ratio (CR): Current assets divided by current liabilities.

7. ISE Stock Index (SI): Year-end percentage change of the ISE index.

8. Sector dummy variables (Sec1-Sec13): The stocks are grouped in 13

different sectors. These sectors are: food, textile, paper, chemicals, stone,

metal, metal goods, energy, technology, tourism, consumer products,

financial and other.

6 The reason of we only use the manipulation cases until 2006 is that in the year of 2010, CMBT was still announcing new manipulation litigations for the years later than 2007. 7 Banks and insurance firms are excluded since the balance sheet items of these sectors are quite different from the others. It is not possible to construct comparable measures for most of the variables for banking and insurance sectors. These sectors can be analyzed in a separate study. 8 There are some manipulation cases which are started in a year and continued in the consecutive year. In that case, manipulation variable takes 1 only in the starting year, because it can be thought that the manipulation decision was taken at that year.

29

Table 3.1 presents the summary statistics of all the variables used in the

empirical analysis. 359 stocks are investigated for the 1998-2006 period and 306

manipulation cases are identified by the CMBT9.

Table 3.1: Summary Statistics of Variables

Variable # of Obs. Mean St. Dev. Min Max

Manipulation Dummy Among 2725 possible cases there are 306 manipulations.

Market to Book Ratio 2543 2.34 7.7 0 268.07

Free Float Ratio 2319 0.36 0.23 0.01 1

Market Cap. (Billion TL) 2472 0.25 0.9 0.0001 15.73

Return on Equity Ratio 2725 0.13 3.82 -71.38 131.08

Leverage 2725 35.28 961.11 0.0001 32219.98

Current Ratio 2722 43.44 664.41 0.00002 30723.94

ISE Stock Index 2725 68.11 151.6 -37.95 485.42

3.2.2 Stock Manipulation in the ISE

During the 1998-2006 period 306 trade-based manipulation cases are

determined by the CMBT. The data set is limited by the condition that all of the

possible cases (2725 stock*year) are investigated by the CMBT. Table II displays the

distribution of manipulation cases among sectors and years.

Table 3.2 shows that the textile sector had the maximum number of

manipulation cases whereas the stocks in the consumer sector were not detected as

being manipulated at all for that period.

9 359 stocks are investigated for the 1998-2006 period. (Banking and insurance stocks are not included.) 329 of them have enough data for explanatory variables. Market to book ratio is not used in regression analyses.

30

Table 3.2: Distribution of Manipulations across Sectors and Years

Sector Num. of

Man. Cases

Number of

Stocks

Year Num. of

Man. Cases

Food 46 36 1998 42

Textile 60 41 1999 38

Paper 21 18 2000 57

Chem 24 26 2001 38

Stone 16 32 2002 36

Metal 13 17 2003 47

Metal Good 28 36 2004 12

Energy 4 8 2005 20

Tech 8 11 2006 16

Tourism 13 7 Total 306

Consumer 0 11

Financial 50 83

Other 23 33

Total 306 359

Monthly price movements in the sample period of 1998-2006 for the ISE-100

index can be seen at Graph I. Turkey experienced a very destructive earthquake in

August 1999. After that disaster, ISE-100 index soared very rapidly from the level of

5,018 at the end of August 1999 to 19,206 at the end of April 2000. Stand-by

agreement and the exchange-rate based stabilization program between the Turkish

government and the IMF played an important role on the economic expectations and

this stock market boom at that period. This positive outlook on the stock market

continued until the financial crises of 2000 and 2001. CMB's detection of 57

manipulation cases for the year of 2000, which is the maximum number of

31

occurrences across the sample period, coincides with this highly volatile economic

conjuncture10.

Figure 3.1: Monthly Values of ISE-100 Index between January 1998 and December

2006.

3.2.3 Methodology

The main objective of this study is to identify firm and stock-specific

characteristics that affect the probability that the stock of that firm will be

manipulated. Among ratios and balance sheet items, another significant feature of a

stock is whether it was manipulated before. In an econometric sense, this calls for

using the lag value of the dependent dummy variable of being manipulated as an

explanatory variable in the regression equations. As stated by Stewart (2006) a

dynamic probit model might have an “initial conditions” problem which renders the

standard random effects probit estimator inconsistent.

As presented by Miranda (2007) three methods of estimation have been

suggested for analysis of dynamic probit models: Heckman (1981), Orme (1997),

and Wooldridge (2005). Miranda (2007) conducts simulations to examine the

10 Detailed information about the IMF program and the 2000-2001 financial crises of Turkey can be seen at Cizre and Yeldan (2005), Çapoğlu (2004), Gökkent et al. (2003) and Ozkan (2005).

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000Ja

n-9

8

Ap

r-9

8

Jul-

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Oct

-98

Jan

-99

Ap

r-9

9

Jul-

99

Oct

-99

Jan

-00

Ap

r-0

0

Jul-

00

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-00

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-01

Ap

r-0

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Jul-

01

Oct

-01

Jan

-02

Ap

r-0

2

Jul-

02

Oct

-02

Jan

-03

Ap

r-0

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Jul-

03

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-03

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r-0

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Jul-

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r-0

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-06

32

performance of these three alternative methods. Miranda (2007, s. 20) concludes

that: “Heckman’s method delivers estimators that are hardly subject to bias and that

are estimated with high precision. The methods suggested by Wooldridge and Orme

... deliver estimators that can be subject to substantial bias and low precision”.

Stewart (2006) compares Heckman's method with estimating a standard panel probit

regression with the lagged value of the binary dependent variable as an explanatory

variable. His simulation results show that the standard probit regression imposes a

bias on the coefficient of the dynamic variable. In this study, Miranda (2007) and

Stewart (2006) are followed and the methodology proposed by Heckman (1981) for

the estimation of dynamic regression is implemented.

First, the following unbalanced panel probit regression equation is estimated11

without dynamic effects using maximum likelihood.

𝑀𝑖,𝑡 = 𝛽0 + 𝛽1𝐹𝑅𝑖,𝑡 + 𝛽2𝑀𝐶𝑖,𝑡 + 𝛽3𝑅𝑖,𝑡 + 𝛽4𝐿𝑄𝑖,𝑡 + 𝛽5𝐶𝑅𝑖,𝑡 + 𝛽6𝑆𝐼𝑡 + ∑ 𝑆𝑒𝑐(𝑘)

13

𝑘=1

+ 휀𝑖,𝑡 (45)

Since probit coefficients are not easily interpretable, the effects of one-unit

changes in regressors on the probability of manipulation (expressed in percentage

points) are also reported, evaluated at the mean of the data.

Using the Heckman (1981) methodology the following dynamic probit

regression equation is estimated to investigate whether a stock that has been

manipulated before is more likely to be manipulated. All the explanatory variables of

equation (45) are also used.

𝑀𝑖,𝑡 = 𝛽0 + 𝛽1𝑀𝑖,𝑡−1(𝑀𝑖,𝑡−1𝑎𝑛𝑦𝑦𝑒𝑎𝑟

) + 𝛽2𝐹𝑅𝑖,𝑡 + 𝛽3𝑀𝐶𝑖,𝑡 + 𝛽4𝑅𝑖,𝑡 + 𝛽5𝐿𝑄𝑖,𝑡 + 𝛽6𝐶𝑅𝑖,𝑡

+ 𝛽7𝑆𝐼𝑡 + ∑ 𝑆𝑒𝑐(𝑘)

13

𝑘=1

+ 휀𝑖,𝑡 (46)

𝑀𝑖,𝑡−1 is the dummy variable that gets the value of one if the stock has been

manipulated in the previous year and 𝑀𝑖,𝑡−1𝑎𝑛𝑦𝑦𝑒𝑎𝑟

is the dummy variable that gets the

11 Panel logit regression presents similar results.

33

value of one if the stock has been manipulated in any of the previous years. Thus, the

coefficient 𝛽1 of equation (46) gauges the effect of the stock being manipulated

before (either the previous year or in any of the previous years) on the probability

that the stock is manipulated in the current year. As displayed in Miranda (2007) and

Arulampalam and Stewart (2009), Heckman (1981) suggests estimating the dynamic

probit equation and the equation for the initial conditions simultaneously as a system.

The system can be displayed as the following

𝑦𝑖,𝑡∗ = 𝑥𝑖,𝑡𝛽 + 𝛾𝑦𝑖,𝑡−1 + 𝜃𝑖𝛼𝑡 + 휀𝑖,𝑡 (47)

𝑦𝑖,0∗ = 𝑧𝑖𝜆 + 𝛿𝑢𝑖 + 휀𝑖,0 (48)

where 𝑧𝑖 is a vector of exogenous covariates that is expected to include 𝑥𝑖,0.

Equations (47) and (48) are estimated using maximum likelihood12.

3.3 How does the CMBT Detect Manipulation?

The dependent variable of this empirical analysis is the dummy variable that

is one if the stock is manipulated in that year. To be able to assess the validity and

robustness of the empirical results we present the mechanism used by the ISE and

CMBT to detect manipulation. The mechanism can be described as the following:

1. CMBT-ISE Observation System is used to identify suspicious trades and

behavior. CMBT-ISE Observation System uses different algorithms13 to

a. Electronically watch all the orders and trades in terms of the

market, stocks, brokers and investors,

b. electronically analyze the relationship between orders and trades

by end of day, all day and specific periods of time,

c. identify suspicious and extraordinary behaviors that might be part

of a manipulation scheme,

d. and produce warning signs.

12 The likelihood function and details of the estimation procedure are displayed in Arulampalam and Stewart (2009). 13 These algorithms are developed by the ISE and CMBT and they are confidential.

34

2. The warning signs are observed by the ISE. The trade, order and price

behavior of suspected investors are examined in detail by the ISE experts.

3. If the ISE experts are convinced that there might be a manipulation

scheme the information about the stocks and investors are transferred to

the CMBT.

4. The detailed analysis of the possible manipulation is conducted by the

CMBT experts. CMBT may release litigation and report that in the

weekly bulletins.

To sum up, the ISE and CMBT experts analyze the order, trade and price

behavior using different algorithms to detect manipulation.

One can argue that we are only able to observe and analyze stock market

manipulation cases that were detected and prosecuted. In other words, there are

potentially many cases of manipulation that are never prosecuted. This might impose

a bias and affect the interpretation of the regression results. Possible cases can be

described as the following:

1. One of the extreme cases can be the case that all stocks are manipulated

and CMBT can detect only some of these manipulations. In this case, the

probit regression results do not analyze the likelihood of manipulation but

identify the factors that CMBT uses to detect manipulation.

2. Another extreme case is the case that none of the stocks are manipulated

but CMBT mistakenly identify these stocks being manipulated. In this

case, the probit regression results present the factors that lead CMBT to

make mistakes.

3. A more probable case is the case that some instances of manipulation is

undetected by the CMBT. In this case, the probit regression analysis

identifies the factors that affect the joint probability that a stock is

manipulated and detected by the CMBT.

Since the first two cases are extreme cases; it is reasonable to focus on the

third case and analyze whether this case might occur and distort the empirical results

of this study. First of all, as explained above ISE and CMBT uses trade, order and

35

price information to detect manipulations. In other words the variables used by the

ISE and CMBT to detect manipulated stocks are different from the variables

investigated in this study. Second, the detected and undetected manipulation cases

are a random subset of all occurrences. The ISE and CMBT experts do not focus on a

specific subset of stocks or time period. All of the stocks and time periods are

examined both electronically and manually. So, it is very unlikely that there is a

systematic bias about detection of manipulation. Thus, it can be argued that the

estimated coefficients and error terms are unbiased. Aggarwal and Wu (2006) and

Comerton-Forde and Putnins (2011) use the same methodology implemented in this

study and use the publications by the authorities to identify manipulation cases.

3.4 Firm-specific Variables and Manipulation

The main purpose of this study is to identify firm-specific characteristics that

affect the probability that a stock is manipulated. Cost characteristics of manipulative

methods may differ for different stocks depending on the firm-specific

characteristics. It may be less expensive to manipulate firms with lower market

capitalization or stocks with lower free float than the others since a manipulator

needs much less effort to artificially create an appearance of an active market or

corner the market. Traded stocks on the market do exhibit more executive power

over the firm with higher free float rate so the manipulators may face much more

resistance from the current managers of the relevant firm.

Profitability, leverage ratio and current ratio of the firm are widely accepted

as important indicators of financial performance of a firm. These financial indicators

of the firms set out the trading environment of the relevant stocks. So, they also

affect the characteristics of the investors that prefer to become a shareholder of the

relevant firm. As a result these variables describe an environment which might be

suitable for manipulation.

36

3.5 Empirical Results and Policy Implications

In this section, the results of the panel probit and dynamic panel probit

regressions are presented. Since dynamic probit regression calls for an alternative

methodology the results of the dynamic regression are presented in a separate table,

Table 3.4.

Table 3.3 presents the random effects probit regression to examine the firm

specific determinants of manipulation. There are two regression specifications: with

and without sector dummy variables as explanatory variables. In both of the

regression specifications free float ratio, market capitalization14 and leverage

variables have significant coefficients. The effects of free float ratio and market

capitalization are negative where as leverage ratio positively affects the probability

of being manipulated.

A comparison of the marginal effects of these variables indicates that a one

percent increase in the FR decreases the probability of being manipulated by 0.13

percent. One unit increase in MC causes a decrease in manipulation probability by

0.19 percent. Finally, one unit increase in LR induces a 0.04 percent increase in the

probability that the stock is manipulated.

Table 3.4 analyzes the dynamic relationship, in other words whether a stock

is more likely to be manipulated if it has been manipulated before. Table 3.4 presents

the dynamic probit results calculated using the Heckman (1981) methodology.

14 One might think that free floating market capitalization might be a significant determinant of manipulation. An alternative regression specification is also implemented with the interaction variable of free float ratio and market capitalization instead of market capitalization. The results do not change in this specification and free float ratio is still significant.

37

Table 3.3: Random Effects Probit Regression Results15

Dependent Variable: Dummy 1 if stock in manipulated in that year

Regression Specification

Variable (1) (2)

Coefficient Marginal

Effect Coefficient Marginal

Effect

Free Float Ratio -0.80

(3.55)**

-0.13

(3.50)**

-0.76

(3.19)**

-0.13

(3.18)**

Market Capitalization -1.12

(4.61)**

-0.19

(4.38)**

-0.95

(4.02)**

-0.16

(3.9)**

Return on Equity Ratio 0.01

(0.91)

-0.002

(0.91)

-0.01

(0.91)

-0.002

(0.91)

Leverage 0.24

(2.94)**

0.04

(2.94)**

0.22

(2.68)**

0.04

(2.69)**

Current Ratio -0.00

(0.34)

-0.00

(0.34)

-0.00

(0.36)

-0.00

(0.36)

ISE Stock Index 0.00

(0.93)

0.00

(0.93)

0.00

(1.02)

0.00

(1.02)

Constant -1.03

(9.30)** -1.01

(5.18)**

Log Likelihood -778.69 -765.36

Number of Observations 2225 2225

Number of Stocks 329 329

15 Absolute value of z statistics in parentheses. Sector dummy variables also used as control variables in regression specification (46). The coefficients of sector dummy variables are not displayed. * significant at 5%; ** significant at 1%.

38

Table 3.4: Dynamic Probit regressions with lagged values of the dep. variable16

Dependent Variable: Dummy 1 if stock in manipulated in that year

Regression Specification

Variable Unbalanced Balanced

(1) (2) (3) (4)

Manip Lag 0.50

(5.22)**

0.51

(5.11)**

Manip any year 0.35

(4.50)**

0.31

(3.80)**

Free Float Ratio -0.56

(3.09)**

-0.63

(3.44)**

-0.58

(3.04)**

-0.65

(3.41)**

Market Capitalization -0.97

(4.63)**

-0.94

(4.54)**

-0.90

(4.28)**

-0.90

(4.29)**

Return on Equity Ratio -0.01

(0.86)

-0.01

(0.82)

-0.01

(0.78)

-0.01

(0.78)

Leverage 0.18

(2.69)**

0.19

(2.83)**

0.23

(3.14)**

0.24

(3.29)**

Current Ratio 0.00

(0.56)

-0.00

(0.55)

-0.00

(0.31)

-0.00

(0.30)

ISE Stock Index 0.00

(0.85)

0.00

(1.70)

0.00

(0.67)

0.00

(1.38)

Constant -1.04

(11.22)**

-1.11

(11.23)**

-1.06

(10.93)**

-1.11

(10.72)*

* Log Likelihood -694.1 -697.12 -626.71 -632.08

Number of Observations 2013 2013 1807 1807

16 Absolute value of z statistics in parentheses. Sector dummy variables also used as control variables. The coefficients of sector dummy variables are not displayed. * significant at 5%; ** significant at 1%.

39

Table 3.4 displays regression results for both balanced and unbalanced panel

data. Unbalanced panel includes all the observations in the data set. The balanced

data set includes the stocks that have observations for the whole time period, 1998-

200617. The dynamic relationship is defined in two different ways:

- whether the stock is manipulated one year before

- whether the stock is manipulated in any of the previous years

Dynamic panel results indicate that the dynamic relationship is a significant

factor of probability of manipulation. The coefficient of the lagged manipulation

dummy variable is positive and significant in all of the regression specifications.

Thus, it can be asserted that a stock is more likely to be manipulated if that stock has

been manipulated the year before or in any of the previous years. Similar to the

results of Table 3.3, FR, MC and LR are important determinants of the probability of

manipulation. Interestingly the effect of the manipulation of a stock one year before

is substantially higher and more significant than the effect of manipulation in any of

the previous years18.

These results of the dynamic panel regression are surprising since Aggarwal

and Wu (2006) indicate that an increase in the probability of informed trader to be a

manipulator will decrease the possibility of pooling between the truthful trader and

the manipulator. How can it be possible to have multiple cases of successful trade-

based manipulation in consecutive years since the investors realized the existence of

a fraudulent scheme in a way at the first place? One explanation would be, there is a

substantial number of information seeker entry each year and the newcomers do not

17 Stewart (2006) argues that the Heckman (1981) methodology present reliable results for a balanced panel data set. Thus, two separate regression specifications are analyzed with balanced and unbalanced panel date sets to examine the robustness of the dynamic regression analysis. 18 Accounting practices used by publicly-traded companies changed after the year of 2003. First, inflation accounting was implemented, then the firms started to use international financial reporting standards. These practices might have some effects on financial ratios. For that reason, whether these results are affected by this transition is investigated using several methods. First, the regressions using the data set before 2003 are conducted. Then, an interaction variable is introduced to gauge the changes in the coefficients of balance sheet variables after 2002. All of the interaction variables are insignificant. These analysis show that the findings in Table 3.3 and 3.4 are not affected by the implementation of the new accounting rules. The tables that display additional regression analysis are presented at Appendix B.

40

recognize or value the importance of previous manipulation schemes. Another

explanation, which also leads to a potential future research, would be that there is

another kind of investor, can be called as co-movers, and these investors try to make

some positive profits by, following the informed investor, buying the shares at the

same time with the informed trader but then hoping to sell these shares to some other

co-movers or information seekers before the sale of informed trader. So, co-movers

may hope to have profit even if the informed trader in the market is a manipulator. In

order to analyze, co-movers' behavior we need another model of trade-based

manipulation.

41

CHAPTER 4

CONCLUSION

One of the main duties of capital market regulators is to identify and prevent

manipulation in the securities markets. Regulators are increasing their enforcement

efforts in order to cope with manipulators as well as amplifying international

cooperation with each other. Stock market manipulation harms the public confidence

in the capital markets and decreases participation, especially by small investors.

The cost element of these manipulative methods is not much analyzed in the

literature and this study, first implements the cost element to the theoretical model of

stock market manipulation. It is assumed that the number of active information

seekers for a potentially manipulated stock is determined by the informed trader,

either truthful or manipulative, with some cost This study theoretically shows that for

sufficiently low levels of cost factor of introducing active information seekers into

the market, the informed trader may engage in fraudulent schemes of trade-based

manipulation and mislead other investors. The possibility of pooling between the

truthful party and the manipulator becomes more probable whenever the

manipulation cost factor decreases or the probability of having a truthful party and

waiting cost of the informed trader for the announcement of the actual value of the

stock increases.

Cost characteristics of manipulative methods may differ for different stocks

depending on the firm-specific characteristics. It may be less expensive to

manipulate smaller firms’ stocks or stocks with lower free float than the others since

a manipulator needs much less effort to artificially create an appearance of an active

market in order to provoke other investors, especially the information seekers to

42

participate in to the market. Other studies report that most manipulation cases occur

in illiquid and inefficient markets.

This study also empirically investigates which firms are more susceptible to

successful trade-based manipulation. For this purpose, a unique data set consisting of

manipulation cases from 1998–2006 from the Istanbul Stock Exchange (ISE) was

collected and firm-specific variables are used to explain these manipulations. Probit

regression results show that small firms, firms with less free float rate and a higher

leverage ratio are more prone to stock price manipulation. Surprisingly, dynamic

probit analysis presents that the probability of manipulation of a stock is significantly

higher for stocks that have been previously manipulated, which may lead to a future

research.

The findings of this study have significant policy implications. Regulators

can use these factors to identify stocks that are more likely to be manipulated and

allocate more resources to closely monitor those stocks. Alternatively, they may

implement different trading rules for these stocks in order to prevent them from

being manipulated. This will result in better use of the regulator’s limited resources

and increase the ability of the regulators to prevent manipulation.

Market regulators have limited resources for monitoring manipulation cases.

It is even harder for the regulators to identify manipulation cases before the act of

manipulation is completed. The empirical results suggest that regulators can separate

stocks into groups according to their probabilities of being manipulated. These

probabilities can be calculated using lagged values of manipulation, free float rate,

market capitalization and leverage ratio variables. Then the regulator can allocate its

enforcement power more to inspect the stocks with higher probabilities of

manipulation. This will increase the ability of the regulators to prevent manipulation.

Many exchanges around the world implement a call auction mechanism for

the closing session of stocks in order to avoid closing price manipulation. As an

alternative regulators may use periodic call auctions rather than continuous trading

for the stocks with illiquid market, low free float rate, high leverage ratio or the

stocks which are already manipulated.

43

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46

APPENDICES

APPENDIX A

SECOND ORDER ANALYSIS

1. Market with a Truthful Informed Party

Now, consider the second order condition of the truthful informed party's

maximization problem at (14). Replacing (13) and taking derivatives yield the

Hessian matrix:

𝐷2𝐹(𝑞1, 𝑁) =

[ −2𝑏

𝑉𝐻 − 𝑎

(𝑁 + 1)2

𝑉𝐻 − 𝑎

(𝑁 + 1)2

−2𝑞1(𝑉𝐻 − 𝑎)

(𝑁 + 1)3− 2𝑐

]

(49)

In order to see if the objective function is strictly concave we check for

negative definiteness of the Hessian matrix, which is to check:

𝑑𝑒𝑡(𝐹11) < 0, 𝑑𝑒𝑡(𝐹22) < 0 𝑎𝑛𝑑 𝑑𝑒𝑡𝐷2𝐹(𝑞1, 𝑁) (50)

Then, 𝑑𝑒𝑡(𝐹11) = −2𝑏 < 0 since 𝑏 > 0, 𝑑𝑒𝑡(𝐹22) =−2𝑞1(𝑉𝐻−𝑎)

(𝑁+1)3− 2𝑐 < 0

since 𝑞1, 𝑉𝐻 − 𝑎, c and 𝑁 > 0.

𝑑𝑒𝑡𝐷2𝐹(𝑞1, 𝑁) =𝑉𝐻 − 𝑎

(𝑁 + 1)3(4𝑏𝑞1 −

𝑉𝐻 − 𝑎

𝑁 + 1) + 4𝑏𝑐 (51)

Replacing 𝑞1 with 𝑞1,𝑇∗ in equation (16) and using (4) yields:

𝑑𝑒𝑡𝐷2𝐹(𝑞1𝑇∗, 𝑁∗) =

(𝑉𝐻 − 𝑎)2

(𝑁 + 1)3[2 − (32𝑏𝑐)

1

3(𝑉𝐻 − 𝑎)−2

3 −1

𝑁 + 1] + 4𝑏𝑐 (52)

47

Second term on the right hand side of (52) is positive. (𝑉𝐻−𝑎)2

(𝑁+1)3 is positive for

∀𝑁 ∈ ℕ. So, we need to check whether [2 − (32𝑏𝑐)1

3(𝑉𝐻 − 𝑎)−2

3 −1

𝑁+1] is positive

or not. Thus, whenever [2 − (32𝑏𝑐)1

3(𝑉𝐻 − 𝑎)−2

3 −1

𝑁+1] > 0 the objective function

is strictly concave.

2. Market with Manipulator

Now, consider the second order condition of the informed party's

maximization problem at (36). Replacing (2) and (35) into (34) and taking

derivatives yield the Hessian matrix:

𝐷2𝐹(𝑞1, 𝑁) =

[ −2𝑏

(1 − 𝛽)𝑉𝐻 + 𝛽𝑉𝐿 − 𝑎

(𝑁 + 1)2

(1 − 𝛽)𝑉𝐻 + 𝛽𝑉𝐿 − 𝑎

(𝑁 + 1)2

−2𝑞1[(1 − 𝛽)𝑉𝐻 + 𝛽𝑉𝐿 − 𝑎]

(𝑁 + 1)3− 2𝑐

]

(53)

In order to see if the objective function is strictly concave we check for

negative definiteness of the Hessian matrix, which is to check the conditions at (50).

Then, 𝑑𝑒𝑡(𝐹11) = −2𝑏 < 0 since 𝑏 > 0, 𝑑𝑒𝑡(𝐹22) =−2𝑞1[(1−𝛽)𝑉𝐻+𝛽𝑉𝐿−𝑎]

(𝑁+1)3− 2𝑐 < 0

since 𝑞1, (1 − 𝛽)𝑉𝐻 + 𝛽𝑉𝐿 − 𝑎, 𝑐 and 𝑁 > 0.

𝑑𝑒𝑡𝐷2𝐹(𝑞1, 𝑁)

=(1 − 𝛽)𝑉𝐻 + 𝛽𝑉𝐿 − 𝑎

(𝑁 + 1)3(4𝑏𝑞1 −

(1 − 𝛽)𝑉𝐻 + 𝛽𝑉𝐿 − 𝑎

𝑁 + 1) + 4𝑏𝑐 (54)

Replacing 𝑞1 with �̂�1,𝑗 =𝛿

1−𝛿−𝛾

𝛿+𝛾(𝑉𝐻−𝑉𝐿)−[4𝑏𝑐𝛿

1−𝛿−𝛾

𝛿+𝛾(𝑉𝐻−𝑉𝐿)]

13

2𝑏 in equation (38)

yields:

48

𝑑𝑒𝑡𝐷2𝐹(�̂�1,𝑗, 𝑁)

= (1 − 𝛿 −𝛾

𝛾 + 𝛿) (𝑉𝐻 − 𝑉𝐿) [2𝛿

1 − 𝛿 − 𝛾

𝛿 + 𝛾(𝑉𝐻 − 𝑉𝐿)

− [32𝑏𝑐𝛿1 − 𝛿 − 𝛾

𝛿 + 𝛾(𝑉𝐻 − 𝑉𝐿)]

1

3

−(1 − 𝛽)𝑉𝐻 + 𝛽𝑉𝐿 − 𝑎

𝑁 + 1]

+ 4𝑏𝑐 (55)

Second term on the right hand side of (55) is positive for 𝑐, 𝑏 > 0. So, if

(1 − 𝛿 −𝛾

𝛾+𝛿) (𝑉𝐻 − 𝑉𝐿) [2𝛿

1−𝛿−𝛾

𝛿+𝛾(𝑉𝐻 − 𝑉𝐿) − [32𝑏𝑐𝛿

1−𝛿−𝛾

𝛿+𝛾(𝑉𝐻 − 𝑉𝐿)]

1

3−

(1−𝛽)𝑉𝐻+𝛽𝑉𝐿−𝑎

𝑁+1] > 0, then, the objective function is strictly concave.

49

APPENDIX B

ROBUSTNESS ANALYSIS

Table D.1: Robustness Analysis of Random Effects Probit Regression Results

Dependent Variable: Dummy 1 if stock in manipulated in that year

Regression Specification

Variable (1) (2) (3)

Free Float Ratio -0.96

(2.74)**

-0.76

(3.20)**

-0.64

(2.63)**

Market Capitalization -0.82

(2.56)*

-1.00

(4.16)**

-0.93

(3.95)**

Return on Equity Ratio -0.04

(1.55) -0.04

(1.56)

ROE Interaction 0.06

(1.80)

Leverage 0.21

(2.09)* 0.26

(2.93)**

Leverage Interaction -0.16

(1.55)

Current Ratio -0.000

(0.25) -0.00

(0.19)

Current Ratio Interaction -0.00

(1.52)

ISE Stock Index 0.000

(0.27)

0.00

(0.99)

0.00

(0.66)

Constant -0.67

(2.75)**

-0.88

(4.65)**

-1.02

(5.26)**

Log Likelihood -475.31 -769.81 -759.77

Number of Observations 1164 2228 2228

Description 1998-2002 data

period

without balance

sheet var.

2002

interaction

var.

50

Table D.2: Robustness Analysis of Dynamic Probit regressions with lagged values of the

dep. var.

Dependent Variable: Dummy 1 if stock in manipulated in that year

Manip Lag

Variable (1) (2) (3)

Manip Lag 0.50

(3.90)**

0.53

(5.35)**

0.51

(5.05)**

Free Float Ratio -0.65

(2.37)*

-0.63

(3.41)**

-0.48

(2.47)*

Market Capitalization -0.72

(2.62)**

-0.94

(4.45)**

-0.91

(4.27)**

Return on Equity Ratio -0.06

(1.23)

-0.07

(1.28)

ROE Interaction 0.09

(1.55)

Leverage 0.18

(2.02)*

0.26

(2.93)**

Leverage Interaction -0.10

(0.96)

Current Ratio -0.00

(0.19)

-0.00

(0.09)

Current Ratio Interaction -0.00

(1.25)

ISE Stock Index -0.00

(0.28)

0.00

(0.60)

0.00

(0.37)

Constant -0.89

(7.02)**

-0.92

(10.61)**

-1.06

(10.90)**

Log Likelihood -362.81 -632.36 -621.48

Number of Observations 865 1810 1807

Description 1998-2002 data

period

without balance

sheet var.

2002

interaction

var.

51

Table D.3: Robustness Analysis of Dynamic Probit regressions with dummy variable of

manip. any year

Dependent Variable: Dummy 1 if stock in manipulated in that year

Manip any year

Variable (1) (2) (3)

Manip any year 0.43

(3.82)**

0.32

(4.00)**

0.33

(3.91)**

Free Float Ratio -0.66

(2.42)*

-0.70

(3.80)**

-0.54

(2.74)**

Market Capitalization -0.66

(2.46)*

-0.94

(4.46)**

-0.89

(4.21)**

Return on Equity Ratio -0.06

(1.20)

-0.08

(1.41)

ROE Interaction 0.10

(1.66)

Leverage 0.18

(2.01)*

0.30

(3.48)**

Leverage Interaction -0.19

(1.69)

Current Ratio -0.00

(0.20)

-0.00

(0.07)

Current Ratio Interaction -0.00

(1.22)

ISE Stock Index 0.00

(0.36)

0.00

(1.34)

0.00

(0.92)

Constant -0.95

(7.21)**

-0.97

(10.25)**

-1.12

(10.74)**

Log Likelihood -362.96 -638.17 -626.15

Number of Observations 865 1810 1807

Description 1998-2002 data

period

without balance

sheet var.

2002

interaction

var.

52

APPENDIX C

EQUILIBRIUM ANALYSIS

In the case of there is only a truthful informed trader in the market, whenever

the truthful party enters to the market by purchasing shares at Stage 1 of the game,

then each active information seeker’s belief about the informed trader is a truthful

one is 𝜇𝑖(𝑇) = 1. In this case, all active information seekers know that the future

value of each share is 𝑉𝐻. Then, at Stage 2, each active information seeker observed

the total purchases of the informed trader, 𝑞1,𝑇, and the total number of active

information seekers, 𝑁𝑇, that are motivated by the informed traderat Stage 1. They

compete each other in order to purchase shares and their optimization problem is:

max𝑞2

𝐴𝑖𝑉𝐻𝑞2

𝐴𝑖 − [𝑎 + 𝑏 (∑𝑞2𝐴𝑖

𝑖∈𝑁

)] 𝑞2𝐴𝑖 (56)

First-order condition for the active information seeker 𝑖:

𝑉𝐻 − 𝑎 − 𝑏 ∑ 𝑞2

𝐴𝑗

𝑗∈𝑁∖{𝑖}

− 2𝑏𝑞2𝐴𝑖∗ = 0 (57)

So, the active information seeker i’s best response, while competing against

other 𝑁𝑇 − 1 number of active information seekers, is to purchase

𝑞2𝐴𝑖∗(𝑞2

𝐴1∗, … , 𝑞2

𝐴𝑖−1∗ , 𝑞2

𝐴𝑖+1∗ , … , 𝑞2

𝐴𝑁𝑇∗) =

𝑉𝐻 − 𝑎 − 𝑏 ∑ 𝑞2

𝐴𝑗𝑗∈𝑁∖{𝑖}

2𝑏 (58)

where 𝑖 = 1,…… ,𝑁𝑇 and j = 1, . . , i − 1, i + 1, . . , 𝑁𝑇.

We are interested in a symmetric equilibrium where

𝑞2𝐴1∗

= 𝑞2𝐴2∗

=. . . = 𝑞2𝐴𝑖∗ = ⋯ = 𝑞2

𝐴𝑁𝑇∗= 𝑞2

𝐴∗ (59)

and hence

53

𝑞2𝐴𝑖∗ = 𝑞2

𝐴∗ 𝑎𝑛𝑑 ∑ 𝑞2

𝐴𝑗∗

𝑗∈𝑁∖{𝑖}

= (𝑁𝑇 − 1)𝑞2𝐴∗ (60)

which yields

𝑞2 𝐴∗ =

𝑉𝐻 − 𝑎

(𝑁𝑇 + 1)𝑏 (61)

At the second stage of the game, each active information seeker has two

alternatives. One is to enter the market by purchasing shares and the other is not to

enter the market. The latter strategy brings zero profit for the active information

seeker while the expected profit for each active information seeker from the former

strategy is (1 −𝑁𝑇

𝑁𝑇+1)

(𝑉𝐻−𝑎)2

(𝑁+1)𝑏, which is derived by using (6), (9) and (10). Since

𝑉𝐻 > 𝑎, the best response of each active information seeker is to buy some positive

amount of shares, 𝑞2𝐴∗ =

𝑉𝐻−𝑎

(𝑁𝑇+1)𝑏, at Stage 2 and this brings some positive expected

profit for each active information seeker. Thus, purchasing 𝑞2𝐴∗ =

𝑉𝐻−𝑎

(𝑁𝑇+1)𝑏 number of

shares at Stage 2 is the optimal strategy for each active information seeker given

their belief of the informed trader is a truthful one is 𝜇𝑖(𝑇) = 1 and given the

informed trader’s strategy of purchasing 𝑞1𝑇 number of shares and calling 𝑁𝑇 number

of active information seekers at Stage 1 of the game.

Also, the informed trader’s strategy of purchasing 𝑞1,𝑇∗ =

(1−𝛿)(𝑉𝐻−𝑉𝐿)−[4𝑏𝑐(1−𝛿)(𝑉𝐻−𝑉𝐿)]13

2𝑏 and calling 𝑁𝑇

∗ = (1

4𝑏𝑐)

1

3(1 − 𝛿)

2

3(𝑉𝐻 − 𝑉𝐿)2

3 − 1

number of active information seekers into the market at Stage 1 and then selling

these shares at Stage 2 is already shown to be the optimal strategy of the informed

party for sufficiently low levels of 𝑐, 𝑏, and (𝑉𝐻 − 𝑉𝐿), and sufficiently high levels of

𝑘 and 𝛿, considering the optimal strategy of each active information seeker at Stage

2.

Thus, buying 𝑞1,𝑇∗ shares and calling 𝑁𝑇

∗ active information seekers at Stage 1,

then, selling 𝑞1,𝑇∗ shares for the truthful party, and buying 𝑞2

𝐴∗ shares at Stage 2 for

each 𝑁𝑇∗ active information seekers, with the belief of the informed trader is a

54

truthful one is 𝜇𝑖(𝑇) = 1, is a perfect Bayesian equilibrium for sufficiently low

levels of 𝑐, 𝑏, and (𝑉𝐻 − 𝑉𝐿), and sufficiently high levels of 𝑘 and 𝛿.

55

APPENDIX D

TURKISH SUMMARY

Finansal piyasalarda işlenen suçlar yatırımcıların güvenini sarsarak fonların

ekonomi içerisinde etkin bir şekilde dağılmasına engel teşkil etmektedir. Bu nedenle

söz konusu suçlar birçok ülkenin hukuki yapısı içerisinde yasaklanmış ve bunlara

ilişkin önemli cezalar belirlenmiştir.

Finansal piyasalarda işlenen önde gelen suçlardan biri olan menkul kıymet

manipülasyonu, yatırımcıları kandırabilmek amacıyla yapılan ve yapay olarak

piyasaların normal işleyişini bozacak ve menkul kıymetlerin fiyatlarını etkileyecek

hileli işlemler kullanılması olarak tarif edilebilir. Manipülasyon genelde bundan

etkilenen diğer yatırımcıların aleyhine ancak manipülatörlerin lehine kar elde etmek

amacıyla yapılmaktadır. Bu nedenle de diğer finansal piyasa suçlarında da olduğu

gibi birçok ülkede sert bir şekilde yasaklanmıştır.

La Porta ve ark. (2006) ve Jackson ve Roe (2009) menkul kıymet piyasalarına

ilişkin düzenlemelerin hisse senedi piyasalarının gelişimi hususunda büyük bir

öneme sahip olduğunu vurgulamaktadır. Cumming ve ark. (2011) ise 42 ülke

borsasında uygulanmakta olan işlem kurallarını inceleyerek, hileli işlemlere karşı

geliştirilmiş daha ayrıntılı ve daha kesin kuralların piyasa likiditesi üzerinde önemli

bir etkisinin olduğu sonucuna varmıştır. Sermaye piyasası otoriteleri de bu

doğrultuda hukuki alt yapılarını güçlendirmek adına gerekli çalışmaları uzun bir

süredir yürütmeye devam etmektedir. Avrupa borsaları için hazırlanmış olan

Finansal Araçlar Piyasaları Direktifi (MiFID) bu konuda atılmış adımlara örnek

olarak verilebilir.

Öte yandan, sermaye piyasaları düzenleyicileri manipülasyonla mücadele

etmek adına bir yanda denetim kapasitelerini artırırken diğer yandan kendi

aralarındaki uluslararası işbirliğini artırmaktadır. Jackson ve Roe (2009) sermaye

piyasası düzenleyicilerinin kaynaklarını kamu denetiminin bir ölçütü olarak ele alıp

56

bunun da finansal piyasaların gelişimiyle arasında önemli düzeyde bir korelasyon

bulunduğunu tespit etmiştir.

Akademik literatürdeki bazı çalışmalar ise manipülasyonun hisse senedi

fiyatları üzerindeki etkisini incelemiştir. Aggarwal ve Wu (2006) Menkul Kıymetler

Borsası Komisyonu’nun (SEC) piyasasında yapılan işlemlerden dolayı suç

duyurusunda bulunulmuş hisse senetlerinde meydana gelen fiyat ve hacim

gelişmelerini incelemiş ve çalışmalarının sonucunda manipülasyonun volatiliteyi,

likiditeyi ve kazançları artırıcı bir etkisinin görüldüğünü vurgulamıştır. Ortaya çıkan

bulgulara göre genel olarak manipülasyonun devam ettiği süreçte hisse senedinin

fiyatı yükselmekte fakat manipülasyonun bitmesinin ardından ise fiyatlarda düşüş

görülmektedir.

Allen ve Gale (1992) hisse senetleri piyasasında görülen manipülasyonları üç

ana kategoriye ayırmıştır. Bunlar; aksiyon bazlı manipülasyon, enformasyon bazlı

manipülasyon ve aynı zamanda bu çalışmanın da konusu olan işlem bazlı

manipülasyondur.

Bu kategorilerden ilki olan aksiyon bazlı manipülasyonda bunu yapan

yatırımcılar hisse senedini alıp satmanın yanı sıra söz konusu şirketin değerini

etkileyebilecek devralma teklifi gibi aksiyonlarda bulunmaktadırlar. Bagnoli ve

Lipman (1996) devralma tekliflerini aksiyon bazlı manipülasyon aracı olarak

incelemiştir. Kurmuş oldukları yapıda, halihazırda söz konusu şirketin bir ortağı olan

manipülatör öncelikle ciddi olmayan bir devralma teklifini şirket için vererek şirketin

hisse senetlerinin fiyatlarının piyasada yükselmesini sağlamaktadır. Bunun ardından

ise yükselen fiyatlardan elinde bulunan hisse senetlerini satarak kar elde etmektedir.

Devralma tekliflerinin kullanılması aksiyon bazlı manipülasyonun sadece bir çeşidini

oluşturmaktadır. Pek çok farklı manipülatif aksiyon kullanılarak da öncelikle hisse

senedinin değeri değiştirilerek ardından kar elde edilebilecektir. Allen ve Gale (1992)

Harlem Demiryolları’nda 1863 yılında başlayan manipülatif hareketleri aksiyon bazlı

manipülasyona bir örnek olarak göstermiştir. Bu örnek olayda New York Şehir

Konseyi, Harlem Demiryolları’nın Broadway’de tramvay sistemi yapmasına karar

vermiş bunun neticesinde hisse senedinin fiyatı 75 dolara kadar yükselmiştir. Ancak

57

bunun ardından Konsey üyeleri hisse senedini açığa satıp bir yandan da daha önce

almış oldukları kararı geri çekerek hisse senedinin fiyatını düşürmeye çalışmışlardır.

Manipülasyonun bir başka çeşidi olan enformasyon bazlı manipülasyon ise

herhangi bir menkul kıymet hakkında çeşitli iletişim araçları kullanarak gerçek dışı

bilgiler yayarak söz konusu kıymetin değerini hileli bir şekilde yönlendirmek olarak

tarif olunabilir. Bu durumda manipülatörler hisse senedi ile ilgili olarak gazete, dergi

ve internet gibi ortamlarda çeşitli söylentiler yayarak piyasada oluşan fiyatları

istedikleri doğrultuda yönlendirmekte ve bu hareketlerden kar elde etmeye

çalışmaktadırlar. Leinweber ve Madhavan (2001) yeni iletişim araçlarının söylenti ve

gerçek dışı haberleri yaymayı kolaylaştırdığını ve bu haberlerin kısa sürede

çoğaltılmasını daha düşük maliyetli hale getirdiğini bildirmektedir. Ayrıca, bu tip

manipülasyona ilişkin olarak 1999 yılının Nisan ayında meydana gelen bir olayı

örnek vermektedirler. Buna göre Pair Gain Technologies, Inc. şirketinin bir çalışanı

Yahoo! Bülten tahtasına şirketin satın alınması yönünde bir anlaşma yapıldığına

ilişkin bir mesaj bırakmıştır. Mesajda ayrıca, kaynak olarak Bloomberg’de yer alan

bir habere link bulunmaktadır. Böyle bir açıklama olmadığı gibi link verilen haber de

sahtedir. Oysa ki, bu haber sonrasında şirketin hisse senedinin fiyatları artış

kaydetmiştir. Konuyu inceleyen SEC’nin soruşturması sonucu ilgili çalışan ceza

almıştır.

Manipülasyonun bir diğer çeşidi olan ve aynı zamanda bu çalışmanın da

konusunu oluşturan işlem bazlı manipülasyon da ise görünüşte manipülatör hisse

senedini sadece alıp satıyor görünmektedir. İlk bakışta bir hisse senedinin sadece

alınıp satılmak yoluyla manipüle edilmesi güç görünebilir. Genellikle görülen durum

ise manipülatörlerin meydana gelen yapay işlemler sonucunda piyasada aktif bir

görünüm oluşmasını sağlaması ve bu yolla fiyatların istedikleri yönde ilerlemesini

başarabilmesidir. Akademik literatürde bir manipülatörün bu yolla kar elde

edebileceğine ilişkin bulgulara rastlanmaktadır.

Allen ve Gale (1992) kurdukları teorik model sayesinde iyi enformasyon

sahibi olmayan bir manipülatörün yatırımcılar arasındaki enformasyon

asimetrilerinden faydalanarak sadece enformasyon sahibi olan bir yatırımcının

58

davranışlarını piyasada taklit etmek yoluyla kar elde edebileceğini göstermiştir.

Benzer bir çerçevede, Aggarwal ve Wu (2006) da manipülatörün iyi enformasyon

sahibi yatırımcıyı taklit ederek enformasyon arayanların aleyhine olacak şekilde kar

elde edebildiğini ve başarılı bir manipülasyon gerçekleştirebilme olasılığının

piyasada yer alan enformasyon arayan yatırımcı sayısıyla doğru orantılı bir ilişkiye

sahip bulunduğunu ortaya koymaktadır.

Lee ve ark. (2013) ise işlem bazlı manipülasyonun bir alt çeşidi olarak

nitelendirilebilecek piyasanın mikro yapısından kaynaklanan manipülasyonu

incelemiştir. Söz konusu çalışmalarında Kore Borsası’nın kendine özgü yapısının

manipülatörlerin hileli işlemler kullanarak kar elde edebilmelerine olanak sağladığını

dile getirmişlerdir. Buna göre manipülatörler gerçekleşme ihtimali oldukça düşük

ancak önemli düzeylerde alım ya da satım emri verebilmekte ancak Kore

Borasası’nın kendine özgü kamuyu bilgilendirme yapısından ötürü diğer yatırımcılar

bu emirlerin fiyat ayrımını ve dolayısıyla gerçekleşme ihtimali çok düşük olan

emirler olabildiğini ayırt edememektedir. Bu durumda iletilen emirler en son

gerçekleşen fiyatlardan bir hayli uzak olsa bile yakın olabilecek emirlerin

oluşturabileceği izlenimini doğurmakta, bu da diğer yatırımcıların ilgili piyasada

verecekleri alım satım kararlarını etkileyebilmektedir. Söz konsuu emirlerin

gerçekleşme ihtimali oldukça düşük olduğundan dolayı manipülatörler fazlaca da bir

maliyet üstlenmeden fiyatları istedikleri yönde hareket ettirebilmektedirler.

Örnek verecek olursak bir manipülatör herhangi bir hisse senedinde önemli

düzeylerde alım yaptıktan sonra, mevcut fiyattan çok daha düşük bir seviyeden

büyük miktarlarda alım emri verebilir. Piyasanın mikro yapısından dolayı verilen bu

gerçekleşme ihtimali düşük olan emirleri normal yatırımcılar diğer alım emirlerinden

ayırt edemeyecek şekilde görünce bu durumda hisse senedine yönelik yoğun bir talep

olduğunu düşünerek alım yönünde karar verebilecektir. Bu durum ise hisse senedinin

fiyatını daha yukarılara taşıyabilecek ve manipulatör ise yükselen fiyatlardan elinde

bulunan payları satarak kar elde edebilecektir. Lee ve ark. (2013) manipülatörlerin bu

metodu kullanarak 67-83 baz puan fazladan kar elde edebildiği bulgusuna ulaşmıştır.

59

Bu akademik bulguların yanısıra sermaye piyasası otoritelerinin yaptıkları suç

duyurularının birçoğu işlem bazlı manipülasyon örnekleriyle doludur. Aggarwal ve

Wu (2006) yapmış olduğu çalışmada gelişmiş bir piyasa olan Amerikan sermaye

piyasalarının da bu hususta bir istisna olmadığını göstermektedir. Çalışmalarında yer

alan veri seti Amerikan piyasası için 1990-2001 yılları arasındaki döneme ilişkin

olarak denetleyici otorite olan SEC tarafından yapılan 142 adet manipülasyon

vakasını içermektedir. Veri setleri sadece işlem bazlı manipülasyonlardan

oluşmamaktadır.

Son dönemde borsalar için elektronik işlem platformlarının oluşturulması ve

iletişim sistemlerinde hızlı gelişmeler menkul kıymet işlemlerinin büyük oranda

kolaylaşmasını sağlamıştır. Öte yandan bu gelişmeler son derece karmaşık işlem

bazlı manipülasyon yöntemlerinin kullanılmasına da zemin hazırlamıştır.

Manipülatörler, internet aracılığıyla işlem yaparak veya haberleşerek yüzlerce hesabı

aynı anda kontrol ederek manipülatif davranışlar sergileyebilmektedir. Bunun bir

sonucu olarak ise işlem bazlı manipülasyonun denetleyici otoriteler tarafından tespit

edilip soruşturulması güçleşmektedir. Bu nedenle de denetleyici kurumların elinde

bulunan kaynakların etkin ve verimli kullanılması zaman geçtikçe daha büyük önem

taşımaktadır.

Manipülatörlerin yakalanmamak adına vermiş oldukları çabalar neticesinde

kullandıkları manipulatif araçlar zaman içerisinde değişime uğramaktadır.

Manipülatörler yapay olarak piyasada aktif bir görünüm oluşturmak adına bir çok

özel manipülatif araç kullanmaktadır.

Çeşitli ülkelerin sermaye piyasası otoritelerini bir araya getirmekte olan

Uluslararası Menkul Kıymet Komisyonları Organizayonu (IOSCO) (2000),

manipülatörler tarafından yaygın bir şekilde kullanılan manipülatif araçlar arasında

şunlar yer vermektedir:

• Kendinden kendine alım satım yapmak (Wash sales)

• Tahtayı boyamak (Painting the tape)

• Uygunsuz karşılaşan emirler (Improper matched orders)

60

• Teklifi yükseltme (Advancing the bid)

• Şişirme ve indirme (Pumping and dumping)

• Kapanış fiyatını belirleme (Marking the close)

• Köşeye sıkıştırma (Corner)

• Sıkma (Squeeze)

• Yanlış veya yanıltıcı piyasa enformasyonu yayma

Kendinden kendine işlemler hisse senetlerinin sahipliğinde herhangi bir

değişime neden olmayan, alan ve satan tarafın aynı olduğu işlemlerdir. Bu

manipülasyon yönteminin manipülatörler tarafından kullanılan başlıca işlem bazlı

manipülasyon araçları arasında bulunduğu söylenebilir. Bu tip bir işlemin

gerçekleştirilmesi için ekonomik bir rasyonel bulmak, özellikle de piyasada verilen

emirlerin iptal edilmesi mümkünse, oldukça güç görünmektedir. Kendinden kendine

işlemlerde payların sahipliğinde gerçek değişim olmamakla birlikte bunu yapan

yatırımcının işleme aracılık yapan finansal kuruluşlara aracılık maliyeti ödemesi

gerebilmektedir. Bu açıdan bakıldığında sonuç olarak kendinden kendine işlemler

kendi başına yapan yatırımcıya kar ettirmekten ziyade tam tersine zarar ettiren

işlemler olmaktadır.

Bu tip işlemlerin olası gerekçelerinden biri hisse senedinin işlem hacmini

yapay olarak artırarak likit bir piyasa izlenimi oluşturmak olabilir. Bu sayede

manipülatör likiditeye önem veren yatırımcıların ilgisini çekecek ayrıca herhangi bir

hisse senedinin piyasasında normalde görülenin çok üzerinde bir işlem hacmi

oluştuğunu gören yatırımcılar bu hisse senedinin piyasasında olağanüstü bir

gelişmenin olabileceğini düşünerek söz konusu şirketle ilgilenmeye

başlayabilecektir. Bu durumda bir manipülatör önce herhangi bir hisse senedinin

piyasasında alım yönlü işlemler yaparak bir portföy oluşturduktan sonra yükselen

fiyat seviyelerinde kendinden kendine işlemler yaparak diğer yatırımcıların ilgisini

çekip elinde bulunan payları da bu yatırımcılara satarak kar elde edebilmektedir.

61

Benzer bir durumda ise bir manipülatör herhangi hisse senedinin piyasasında

öncelikle pasif satış emirleri verdikten sonra, bekleyen bu emirleri yine kendi verdiği

alım emirleriyle kısa bir süre zarfında eşleştirip kendiden kendine işlemler yaparak

piyasada yapay olarak yukarı yönlü bir fiyat hareketi olduğu izlenimini vererek diğer

yatırımcıların alım kararlarını etkileyebilmektedir. Her iki durumda da normal

yatırımcılar gerçekleşen işlemlerin hem alım hem de satım tarafında aynı

yatırımcının olduğunu fark edemediğinden dolayı alım satım kararları bundan

etkilenebilmektedir.

Bu işlemlerle ilgili olarak akla gelen bir düzenleme bu tip işlemlerin

yapılmasını yasaklamak olabilir. Ancak günümüz teknolojilerini kullanarak

manipülatörler onlarca farklı kişi adına açılmış olan hesapları kolay bir şekilde

kontrol edebilmekte bu hesaplar üzerinden görünüşte kendinden kendine işlem

olmayan ancak esas itibariyle bir manipülasyon ağı içerisinde bu özellikleri taşıyan

işlemleri gerçekleştirebilmektedirler.

Güniçi işlem yapan yatıırmcılar başta olmak üzere birçok yatırımcı bu tip bir

manipülasyondan etkilenebilecektir. Öte yandan, kendinden kendine işlem

manipülasyonunu tespit etmek ve soruşturmak oldukça zordur. Manipülasyonun tek

bir yatırımcı değil de bir manipülasyon ağı tarafından gerçekleştirildiği durumlarda

yapılan işlemlerin kendinden kendine işlem niteliğine sahip olduğunu göstermek

denetleyici otorite açısından güç olabilecektir.

Bir diğer manipülasyon aracı olan tahtayı boyama metodu ise geniş kitlelerin

işlemleri takip edebildiği platformlarda görülebilecek ve ilgili menkul kıymette

yoğun bir aktivite veya fiyat değişimi izlenimi verebilecek hareketler

gerçekleştirmek olarak tarif edilebilir. Daha önce de bahsi geçen gerçekleşmesi

düşük ihtimalli aldatıcı emirler vermek yoluyla manipülasyon yapmak bu araca bir

örnek olarak gösterilebilir. Bu tip emirler kamuoyu tarafından takip edilebilecek olan

ekranlardan görülebilmekte ve ilgili borsanın mikro yapısının müsait olması

durumunda normal yatırımcılar bu emirleri normal emirlerden ayırt edemeyerek

etkilenebilmektedirler. Bu tip bir manipülasyondan özellikle güniçi borsa işlemlerini

canlı olarak derinlikli terminallerden takip eden yatırımcılar etkilenebilmektedir.

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Kapanış fiyatını belirleme manipülasyon yöntemi ise adından da

anlaşılabileceği üzere manipülatörün hisse senedinin kapanış fiyatını belirlemek

amacıyla tam da gün sonunda işlem yapması olarak tarif edilebilir. Bazı yatırımcılar

yatırım kararlarını verme aşamasında teknik analiz metotlarını yoğun bir şekilde

kullanabilmektedir. Bu metotların bir çoğunda hisse senetlerinin kapanış fiyatı alım

ve satım sinyalleri üreten önde gelen indikatörlerin türetilmesinde büyük öneme

sahip bulunmaktadır. Manipülatör hisse senetlerinin kapanış fiyatlarını belirleyerek

özellikle teknik analiz kullanmakta olan yatırımcılar olmak üzere kapanış fiyatını

önemli bir gösterge olarak değerlendirek birçok kişinin alım satım kararını

etkileyebilmektedir. Comerton-Forde ve Putnins (2011) kapanış fiyatı

manipülasyonu üzerine yapmış oldukları araştırmada kapanış fiyatı

manipülasyonunun ihtimali ve yoğunluğu üzerine Amarika ve Kanada’nın sermaye

piyasası otoritelerinin yapmış oldukları suç duyuruları verilerini kullanarak bir

endeks geliştirmiştir. Çalışmanın neticesinde getiri, alım satım aralığı (spread), alım

satım frekansı ve getiri dönüşlerinin kapanış manipülasyonunu tespit etme hususunda

kullanılabileceği sonucuna ulaşılmaktadır.

Dünyanın birçok ülkesinde yer alan menkul kıymet borsaları ise kapanış

manipülasyonuna engel olarak kapanış fiyatlarının daha adaletli bir şekilde

belirlenebilmesi amacıyla kapanış seansı düzenleme uygulamasına geçmiş

bulunmaktadır. Bu sayede herhangi bir hisse senedinin günsonu fiyatı gerçekleşen

son işlemin fiyatı olarak belirlenmemektedir. Bu sistemde genel olarak günsonuna

kısa bir süre kala hisse snetlerinin işlem gördüğü devamlı müzayede sistemi sona

erdirilmekte ve belli bir periyot boyunca kapanış seansına ilişkin yatırımcıların alım

ve satım emirleri toplanmaktadır. Bunun ardından ise toplanan emirler birleştirilerek

kapanış fiyatı ortaya çıkmaktadır.

Bir diğer manipülasyon aracı olan köşeye sıkıştırma ve sıkma yöntemlerinde

ise genel itibariyle manipülatör piyasadaki talep tarafının önemli bir kısmını kontrol

etmekte ve bu şekilde tam tersi pozisyon alan yatırımcıları daha yüksek fiyatlardan

işlem yapmasını sağlamakta veya arz tarafındaki kıtlıktan istifade ederek piyasada

yapay bir fiyatın oluşmasını sağlamaktadır. Bu tip bir uygulamaya piyasada açık

pozisyonda yakalanan yatırımcılar bundan yoğun bir şekilde etkilenme riskiyle karşı

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karşıya kalabilmektedir. Allen, Litov ve Mei (2006) 1863-1980 yılları arasındaki

hisse senedi ve emtia piyasası köşeye sıkıştırmalarını incelemiş ve büyük

yatırımcıların ve içeriden öğrenenlerin kendilerine fiyatları manipüle etme imkanı

veren bir piyasa gücü olduğunu ve bu köşeye sıkıştırma tipi manipülasyonların

volatiliteyi artırdığını ortaya koymuştur. Merrick, Naik ve Yadav (2005) ise tahvil

piyasasında sıkma yöntemiyle yapılan manipülasyonları incelemiştir.

Şişirme ve indirme tipi manipülasyonda ise manipülatör herhangi bir menkul

kıymeti öncelikle yükselen fiyatlardan sürekli olarak almaya devam etmekte, bu

hareket genellikle hisse senedinin fiyatında bir momentum oluşturmakta ve oluşan

yüksek fiyatlardan elindeki payları satarak kar elde edebilmektedir. Mei ve ark.

(2004) enformasyon sahibi olmayan bir manipülatörün şişirme ve indirme

metodundan faydalanarak kar elde edebilmek adına yatırımcıların davranışsal

yanılgılarını kullanabildiğini göstermiştir.

IOSCO tarafında ortaya konan bu ve benzeri manipülatif yöntemler değişik

ülkelerin sermaye piyasası otoritelerinin ortak çalışması olarak ortaya çıkmış

bulunmaktadır. Dolayısıyla bu metotların birçok menkul kıymet borsasında meydana

gelen başarılı manipülasyonlarda kullanılan yaygın araçlar olduğu iddia edilebilir.

Kendinden kendine işlem, tahtayı boyama, kapanış fiyatını belirleme gibi öne

çıkan işlem bazlı manipülasyon yöntemlerinin temel amacı normal yatırımcıların

ilgisini söz konusu olan hisse senedine çekerek bu yaıtımcıların alım satım

kararlarını etkilemektir. Dolayısıyla işlem bazlı manipülasyon faaliyeti genel olarak

belirli bir hisse senedinin piyasasında manipülatörün arzu ettiği yönde alım satım

kararı verecek yatırımcı sayısını artırmayı hedeflemektedir.

Başarılı bir manipülasyon uygulaması genellikle birden fazla manipülatif

yöntemi içermektedir ve bu manipülatif yöntemlerin ise kendine özgü maliyetleri

bulunmaktadır. Manipülatör kendinden kendine işlemler yaparak aracı kurumlara

fazladan aracılık komisyonu ödemek zorunda kalabilirken, gerçekleşme ihtimali

düşük olan aldatıcı emirler veren bir manipülatör ise bu emirlerin başka bir yatırımcı

tarafından karşılanması durumunda gerçekleşecek olan işlemlerden ötürü ciddi bir

maliyete katlanmak durumunda kalabilecektir. Dolayısıyla, piyasada enformasyon

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arayışı içerisinde olan diğer yatırımcıların dikkatini çekerek bu yatırımcıların alım

satım kararlarını etkilemek amacıyla yapılan işlem bazlı manipülasyonun

manipülatöre belli bir maliyeti bulunmaktadır.

Akademik literatürde manipülatörlerin ödemesi gereken bu maliyet unsurunu

inceleyen fazlaca çalışma bulunmamaktadır. Bu çalışma hisse senetlerinde

manipülasyona ilişkin teorik modele maliyet unsurunu eklemektedir. Bu amaçla

Aggarwal ve Wu (2006) tarafından kullanılan manipülasyon modeli takip edilmiş ve

potansiyel olarak manipüle edilebilecek bir hisse senedinin piyasasındaki

enformasyon arayan aktif yatırımcıların sayısının belli bir maliyet ödemek

karşılığında enformasyon sahibi yatırımcı tarafından belirlendiği varsayılmıştır.

Modele getirilen bu eklenti sonrasında ortaya çıkan bulgular başarılı bir işlem bazlı

manipülasyonun yalnızca her bir enformasyon arayan aktif yatırımcıyı piyasaya

çekmek için gereken maliyet faktörünün yeteri kadar düşük olması durumunda

olabileceğini göstermektedir. Bu çalışma ayrıca hangi şirketlerin hisse senetlerinin

manipülasyonuna daha yatkın olduğunu ampirik olarak incelemektedir. Bu amaçla

İstanbul Menkul Kıymetler Borsası’nda (İMKB) 1998-2006 yıllarında yapılan

işlemlerle ilgili tespit edilen manipülasyon olaylarına ilişkin bir veri seti

oluşturulmuş ve manipülasyon vakalarının görülmesi ihtimali şirketlere özel bazı

değişkenlerle açıklanmaya çalışılmıştır. Probit regresyonuna ait sonuçlar küçük

şirketlerin, halka açıklık oranı düşük olan şirketlerin ve yüksek kaldıraç oranına

sahip olan şirketlerin hisse senedi manipülasyonuna daha yatkın olduğunu

göstermektedir. Ayrıca dinamik probit analizi ise herhangi bir hisse senedinin

manipüle edilme ihtimalinin daha önce manipüle edilmiş olması durumunda önemli

düzeyde daha fazla olduğunu ortaya koymaktadır.

Manipülasyona ilişkin maliyetlerin teorik olarak incelendiği ve çalışmanın

ikinci bölümünde yer alan modelde üç tip yatırımcı bulunmaktadır. Bunlar;

enformasyon sahibi yatırımcı (I), enformasyon arayan yatırımcı (S) ve enformasyon

sahibi olmayan yatırımcıdır (U). Enformasyon sahibi olan yatırımcı hisse senedinin

ilerideki değerinin yüksek (𝑉𝐻) mi yoksa düşük (𝑉𝐿) mü olacağı bilgisine sahip

bulunmaktadır. Eğer hisse senedinin ilerideki değerinin yüksek olacağını bilerek

piyasaya giriyorsa bu tip enformasyon sahibi yatırımcıya doğrucu (T) denmektedir.

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Öte yandan, bu tip bir yatırımcı hisse senedinin ilerideki değerinin düşük

olabileceğini bilmesine rağmen payları satın alarak piyasaya giriyorsa bu durumda

kendisine manipülatör (M) denmektedir.

Piyasaya potansiyel olarak girebilecek enformasyon arayan yatırımcıların

bulunduğu varsayılmıştır. Bu yatırımcılar hisse senetlerinin gelecekteki değerinin

yüksek mi yoksa düşük mü olacağı bilgisini güniçi gerçekleşen ve izlenebilen

hareketlerin yanısıra günsonunda oluşan fiyat ve miktar gelişmelerini takip ederek

elde etmeye çalışmaktadır. Tipi ne olursa olsun enformasyon sahibi olan yatırımcı ise

işlem bazlı manipülasyon yöntemlerini kullanarak enformasyon arayan yatırımcıları

piyasaya çekebilmektedir. Enformasyon sahibi yatırımcı birden fazla manipülasyon

yöntemini kullanabilmekte ve her enformasyon arayan yatırımcının manipulatif

yöntemlere karşı hassasiyeti farklı olabilmektedir. Ancak herhangi bir enformasyon

arayan yatırımcının ilgisi herhangi bir hisse senedine yöneldiği anda söz konusu

yatırımcı enformasyon arayan aktif bir yatırımcı durumuna gelmekte ve diğer

enformasyon arayan aktif yatırımcılarla aynı veri setine sahip bulunmaktadır.

Enformasyon sahibi yatırımcı bu koşullarda daha fazla manipülatif aktivite

sergileyerek piyasada söz konusu hisse senedi piyasasına giren enformasyon arayan

aktif yatırımcı sayısını artırabilmektedir. Bu durumda, enformasyon arayan

yatırımcıların değişik manipülatif yöntemlere karşı hassasiyetinin farklı olduğu

ancak amaç ve tercihlerinin ise simetrik olduğu kabul edilmiştir.

Modeli basitleştirmek adına herhangi bir hisse senedinin piyasasına ilgi

duyarak giren enformasyon arayan aktif yatırımcı sayısının, N (𝐴𝑖, 𝑖 ∈ 𝑁), tamamen

enformasyon sahibi yatırımcı tarafından belirlendiği, dolayısıyla modelin içerisinde

belirlenen içsel bir değişken olduğu kabul edilmiştir. Enformasyon sahibi

yatırımcının herbir enformasyon arayan aktif yatırımcıyı piyasaya çekebilmek bir

maliyet üstlendiği kabul edilmiştir. Enformasyon sahibi yatırımcı söz konusu maliyet

fonksiyonunun yapısını bilerek, toplam harcayacağı manipülatif işlemlerin maliyeti

olan C’nin düzeyine ve dolayısıyla piyasadaki enformasyon arayan aktif yatırımcı

sayısına karar vermektedir. Modelin bu yapısına göre gerek doğrucu gerekse

manipülatör tipte olan enformasyon sahibi yatırımcılar manipülatif işlemler

yapabilmektedir. Ancak doğrucu tipte olan bir enformasyon sahibi yatırımcı hisse

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senedinin ilerideki değerinin açıklanmasını bekleyebilmektedir ve dolayısıyla kar

elde edebilmek için manipülatif işlemler yapmasına gerek bulunmamaktadır. Öte

yandan, her rasyonel manipülatör tipinde olan enformasyon sahibi yatırımcının ise

hisse senedinin gerçek değeri açıklanmadan elindeki payları satması gerekmektedir.

Herbir enformasyon arayan aktif yatırımcı hisse senedinin piyasasında oluşan

fiyat ve hacim gelişmelerinin yanısıra enformasyon arayan aktif yatırımcıların

toplam sayısını görmekte ancak enformasyon sahibi yatırımcının hangi tipte

olduğunu bilmemektedir.

Enformasyon sahibi olmayan yatırımcıların devamlı olarak bütün bir havuz

teşkil ettiği varsayılmıştır. Bu yatırımcıların temel rolü piyasaya likidite

sağlamalarıdır. Bu likiditeyi de lineer bir arz eğrisine uygun bir şekilde

vermektedirler.

Bahsi geçen 3 çeşit yatırımcı arasında geçen ve 3 zamandan oluşan söz

konusu oyunun zamanlaması ise şöyledir:

- 0 zamanında tüm hisse senetleri enformasyon sahibi olmayan yatırımcıların

elinde bulunmaktadır.

- 1 zamanında öncelikle enformasyon sahibi yatırımcı piyasaya girip girmeme

kararı almaktadır. Enformasyon sahibi yatırımcı 𝛾 ihtimalle manipülatör

olarak, 𝛿 ihtimalle doğrucu olarak girmekte, 1 − 𝛾 − 𝛿 ihtimalle ise piyasaya

girmemektedir. Enformasyon sahibi olan yatırımcı girmeye karar verdiyse

alacağı payları enformasyon sahibi olmayan yatırımcıdan almakta,

harcayacağı toplam manipülatif işlemlerin maliyetine de yine bu zamanda

karar vermektedir.

- 2 zamanında ise eğer enformasyon sahibi yatırımcı 1 zamanında manipülatif

işlem yapmaya karar verdi ise enformasyon sahibi yatırımcı tarafından

gerçekleştirilen davranışı gören bazı enformasyon arayan potansiyel

yatırımcılar piyasaya giriş yapmakta ve bu enformasyon arayan aktif

yatırımcılar almak istedikleri hisse senedi miktarına karar vermektedir. Yine

bu zamanda, enformasyon sahibi yatırımcı da alım yapmaya devam

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edebilmekte ya da elinde bulunan hisse senetlerini gelen enformasyon arayan

aktif yatırımcılara satabilmektedir.

- 3 zamanında ise hisse senedinin gerçek değeri ilan edilmektedir.

Aggarwal ve Wu’nun (2006) modelinde olduğu üzere enformasyon sahibi

yatırımcının elindeki hisse senetlerini 3 zamanına kadar tutmayı pek istemediği ve

elindeki hisse senetlerini bu zamana kadar tutmanın toplam k kadar bir maliyetinin

bulunduğu varsayılmıştır. Kendileri bu varsayımım 3 zamanının çok uzun vade

olması veya enformasyon sahibi olan yatırımcının potansiyel bir içeriden öğrenen

olarak riskten kaçınan bir yatırımcı olabileceği durumuyla açıklamışlardır.

Modelin sonuçları değerlendirilecek olursa, enformasyon arayan aktif

yatırımcıları piyasaya çekmenin maliyet faktörünün ve hisse senedinin gerçek

değerinin alabileceği yüksek ve düşük değerler arasındaki farkın düşmesi ve

enformasyon sahibi yatırımcının doğrucu olma ihtimalinin ve enformasyon sahibi

yatırımcı için 3. döneme kadar beklemenin maliyetinin artması ise piyasada

manipülasyon olma ihtimalini artırmaktadır. Bu koşullar altında enformasyon sahibi

yatırımcı, ister doğrucu isterse manipülatör olsun, 1 zamanında alım yapıp aynı

zamanda bir miktar enformasyon arayan aktif yatırımcıyı piyasaya çekmekte, 2

zamanında ise elinde bulunan hisse senetlerinin tümünü gelen enformasyon arayan

aktif yatırımcılara satarak kar elde edebilmektedir.

Bu sonuçlar şu ana kadar literatürde pek incelenmemiş olan manipülatif işlem

maliyetlerinin piyasada manipülasyon görülmesi üzerinde etkili olabileceğini ortaya

koymaktadır. Enformasyon arayan aktif yatırımcıları piyasaya çekmenin maliyetinin

düşük olduğu hisse senetlerinde manipülasyon daha çok görülebilecektir.

Bu doğrultuda çalışmanın üçüncü bölümünde işlem bazlı manipülasyonun

daha çok hangi şirketlerde görüldüğü ampirik olarak incelenmiştir. Bu konuda

şimdiye kadar yapılmış olan akademik çalışmalar oldukça sınırlı görünmektedir.

Aggarwal ve Wu (2006) çalışmalarında manipülasyonun daha ziyade verimliliği

düşük OTC Bulletin Board gibi piyasalarda görüldüğünü söylemektedir. Jiang ve

ark. (2005) ise 1920’lerin hisse senedi havuzlarıyla ilgili yaptıkları çalışmalarında

denetleyici kaynakların daha ziyade likit olmayan piyasalarda yoğunlaşması

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gerektiğini bildirmektedir. Lee ve ark. (2013) ise gerçekleşme ihtimali düşük aldatıcı

emirlerin üzerine yoğunlaşmış bulunan çalışmalarında bu tip manipülatif emirlerin

daha ziyade düşük piyasa kapitilizasyonuve hisse fiyatı olan, daha yüksek getiri

volatilitesi ve daha düşük yönetsel şeffaflık gösteren şirketlerde görüldüğünü ampirik

olarak ortaya koymaktadır.

Bu çalışmada ise genel olarak işlem bazlı manipülasyonların tümüne

yoğunlaşılarak bu tip manipülasyonların hangi şirketlerde görüldüğü incelenmiştir.

Bu amaçla Sermaye Piyasası Kurulu’nun, 1998 ile 2006 yılları arasında İstanbul

Menkul Kıymetler Borsası’nda işlendiğini tespit ederek hakkında suç duyurusunda

bulunduğu manipülasyon olaylarına ilişkin bir veri tabanı oluşturulmuştur. Bu

verilerin toplanabilmesi için 1998 yılı başından 2010 yılı ortasına kadar Sermaye

Piyasası Kurulu tarafından yayımlanan tüm haftalık bültenler taranarak Sermaye

Piyasası Kanunu’nun 47/A-2 maddesi uyarınca piyasasında işlem bazlı

manipülasyon gerçekleştiği tespit edilen şirketler ve manipülasyonun gerçekleştiği

belirtilen tarihler çıkartılmıştır.

Buna göre İstanbul Menkul Kıymetler Borsası’nda söz konusu dönemde

toplam 306 adet manipülasyon tespit edildiği bulgusuna ulaşılmıştır. Yıllar itibariyle

bakıldığında en çok 2000 yılında, sektörler itibariyle bakıldığında ise en çok tekstil

sektöründe işlem bazlı manipülasyonun tespit edildiği anlaşılmaktadır.

Çalışmada şirketlerde manipülasyon görülme olasılığı üzerinde şirketlere

özgü bazı özelliklerin etkisi probit analizi yapılarak incelenmiştir. Banka ve sigorta

sektöründe yer alan şirketler ise diğer şirketlerin bilanço yapılarından farklı mali

tablo yapısına sahip bulunduklarından dolayı dışarıda tutulmuştur. Kullanılan

açıklayıcı değişkenler ise şirketin halka açıklık oranı, piyasa kapitilizasyonu,

sermaye karlılığı, kaldıraç rasyosu, cari oran, İMKB-100 endeksinin değişimi, ve

sektör kukla değişkenleridir.

Bunlardan piyasa kapitilizasyonu, şirketin tüm hisse senetlerinin değeri

toplamından oluşarak şirketin büyüklüğünü ölçmek amacıyla kullanılmaktadır.

Büyük şirketlerin kurumsal yapılarından, bu şirketlerle ilgilenen yatırımcı havuzuna

kadar görece küçük şirketlere göre farklı yapıları bulunabilmektedir. Dolayısıyla

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büyük bir şirketin hisse senetlerini manipüle etmek görece daha maliyetli

olabilecektir. Halka açıklık oranı ise bir şirketin tüm hisse senetlerinden yüzde

kaçının borsada alınıp satılmakta olduğunu göstermektedir. Halka açıklık oranı

arttıkça şirketlerin yönetsel açıdan daha önemli bir kısmı borsada el

değiştirebilmektedir. Dolayısıyla halka açıklık oranı yüksek şirketlerin manipüle

edilmesi görece daha güç olabilecektir. Sermaye karlılığı, kaldıraç rasyosu ve cari

oran ise genel olarak şirketin finansal durumunu ve performansını ölçme amacıyla

kullanılan değişkenlerdir. Bağımlı değişken olarak ise şirketin o yıl içerisinde

manipüle edilmesi durumunda bir aksi takdirde sıfır değerini alan bir kukla değişken

kullanılmıştır. Çalışma yıllık veriler kullanılarak yapılmıştır.

Panel analizinde ortaya çıkan sonuçlara göre halka açıklık oranı ve piyasa

kapitilizasyonunun manipüle edilme ihtimali üzerinde önemli düzeyde negatif etkisi

görülürken kaldıraç oranının ise tam tersi yönde önemli bir etkisi görülmektedir.

Buna göre beklenildiği üzere daha küçük bir kısmı halka arz edilmiş ya da daha

küçük bir piyasa büyüklüğüne sahip olan şirketler görece daha fazla bir ihtimalle

manipüle edilmektedirler. Bu sonuçlar, manipülasyona ilişkin maliyetlerin önemli

olduğunu ortaya koyan ve ikinci bölümde yer alan modelin ardından bunu

destekleyen ampirik bulgular ortaya koymaktadır.

Bunun yanısıra çalışmada bir de dinamik probit panel regresyon metodu

kullanılmış ve bahsi geçen açıklayıcı değişkenlerin yanısıra şirketin hisse senetlerinin

bir önceki yıl manipüle edilmesi durumunda veya daha önceki yılların herhangi

birinde manipüle edilmesi durumunda bir değerini alan birer kukla değişken daha

analize dahil edilmiştir. Buna ilişkin sonuçlar oldukça ilginç bir bulguyu ortaya

koymaktadır. Buna göre biryıl önce veya daha önceki yılların herhangi birinde

manipüle edilmiş olan hisse senetlerinin tekrar manipüle edilme ihtimali daha önce

manipüle edilmemiş olanlara nazaran önemli düzeyde fazladır. Oysa ki gerek bu

çalışmada gerekse Aggarwal ve Wu’nun (2006) yapmış olduğu çalışmada yer alan

teorik modellerde bir hisse senedinde enformasyon sahibi olan yatırımcının

manipülatör olma ihtimalinin daha fazla olması hisse senedinin piyasasında

manipülasyon ihtimalini düşürmektedir. Dolayısıyla dinamik modelde ortaya çıkan

değerler teorik çalışmanın öngördüğü sonuçlarla pek bağdaşmamaktadır.

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Bunun olası nedenleri arasında piyasaya her yıl giriş yapan enformasyon

arayıcıların sayısının oldukça yüksek olması ve yeni gelen yatırımcıların piyasada

işlem yapan enformasyon sahibi yatırımcının manipülatör olma ihtimaline ilişkin

inançlarının farklı olmasından kaynaklanabilir. Bir başka açıklama ise bazı

yatırımcıların manipülasyon olması durumunda bile manipülatörün davranışlarını

takip ederek önce alım yapıp daha sonra ise manipülatörden önce satış yaparak kar

elde edebileceğini düşünmesi olabilir. Bu ikinci açıklamanın geçerli olması

durumunda daha önce bahsi geçmeyen farklı bir yatırımcı tipinin daha modellenmesi

gerekmektedir. Bu tip yatırımcılara takip ediciler denebilecektir. Böyle bir modelin

incelenmesi ileride yeni bir çalışmanın konusunu teşkil edebilecektir.

Ortaya çıkan bu bulguların sermaye piyasasını düzenleyen ve denetleyen

otoriteler açısından da önemli olabilecek sonuçları bulunmaktadır. Sermaye piyasası

otoriteleri borsalarda hisse senetlerinin işlem görme kurallarını da

belirleyebilmektedirler. Buna göre manipülasyonun görülme potansiyeli yüksek olan

hisse senetlerine devamlı müzayede sistemi yerine emirlerin toplulaştırıldığı gün içi

seanslar düzenleyerek bazı manipülatif metotların kullanımına engel olabileceklerdir.

71

APPENDIX E

CURRICULUM VITAE

PERSONAL INFORMATION

Surname, Name: İmişiker, Serkan

Nationality: Turkish (TC)

Date and Place of Birth: 22 September 1979, Bursa

Marital Status: Married

Phone: +90 216 542 31 20

Fax: +90 312 507 56 40

Email: serkan.imisiker@tcmb.gov.tr

EDUCATION

Degree Institution Year of Graduation

MA Bilkent University, Economics 2004

BA Bilkent University, Economics 2000

High School Bursa Boys High School, Bursa 1997

WORK EXPERIENCE

Year Place Enrollment

2013-Present Central Bank of the Republic of

Turkey

Deputy Director General

2011-2013 Central Bank of the Republic of

Turkey

Chief of Staff

2006-2011 Capital Markets Board of Turkey Expert

2003-2006 Capital Markets Board of Turkey Assistant Expert

2001-2003 Bilkent University Teaching Assistant

2000-2001 University of Arizona Teaching Assistant

FOREIGN LANGUAGES

Advanced English.

72

PUBLICATIONS

1. İmişiker, S., Özlale, Ü. (2008). “Assessing Selectivity and Market Timing

Performance of Mutual Funds for an Emerging Market: The Case of Turkey”,

Emerging Markets Finance and Trade, 44(2), 87-99.

2. Imisiker, S., and Tas, B. K. O. (2013). “Which firms are more prone to stock

market manipulation?”, Emerging Markets Review, 16, 119–130.

AWARDS AND HONORS

Full Graduate Scholarship, Bilkent University 2001 – 2003

Full Graduate Scholarship, University of Arizona 2000 – 2001

Full Undergraduate Scholarship, Bilkent University 1997 – 2000

73

APPENDIX F

TEZ FOTOKOPİSİ İZİN FORMU

ENSTİTÜ

Fen Bilimleri Enstitüsü

Sosyal Bilimler Enstitüsü

Uygulamalı Matematik Enstitüsü

Enformatik Enstitüsü

Deniz Bilimleri Enstitüsü

YAZARIN

Soyadı : İmişiker

Adı : Serkan

Bölümü : İktisat

TEZİN ADI (İngilizce) : Trade-Based Manipulation in Financial Markets

TEZİN TÜRÜ : Yüksek Lisans Doktora

1. Tezimin tamamından kaynak gösterilmek şartıyla fotokopi alınabilir.

2. Tezimin içindekiler sayfası, özet, indeks sayfalarından ve/veya bir

bölümünden kaynak gösterilmek şartıyla fotokopi alınabilir.

3. Tezimden bir bir (1) yıl süreyle fotokopi alınamaz.

TEZİN KÜTÜPHANEYE TESLİM TARİHİ:

X

X

X