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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
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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
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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: [email protected]
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.
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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