TRADE-BASED MANIPULATION IN FINANCIAL MARKETS A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF SOCIAL SCIENCES OF MIDDLE EAST TECHNICAL UNIVERSITY

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1 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

2 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. Examining Committee Members Assoc. Prof. Dr. Işıl Erol Assist. Prof. Dr. Esma Gaygısız Lajunen Supervisor (METU, ECON) Assist. Prof. Dr. Esma Gaygısız Lajunen (METU, ECON) Assist. Prof. Dr. Nil İpek Şirikçi Assist. Prof. Dr. Yeliz Yolcu Okur (METU, ECON) (METU, IAM) Assoc. Prof. Dr. Bedri Kamil Onur Taş (UET, ECON)

3 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 : iii

4 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 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 iv

5 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) 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 v

6 To my wife, Şeyma and my son, Mustafa İzzet vi

7 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. vii

8 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 MODEL OF COSTLY TRADE-BASED MANIPULATION Introduction Market with a Truthful Informed Party Market with a Manipulator EMPIRICAL ANALYSIS Introduction Data and Methodology Data Stock Manipulation in the ISE Methodology How does the CMBT Detect Manipulation? Firm-specific Variables and Manipulation Empirical Results and Policy Implications CONCLUSION REFERENCES APPENDICES A. SECOND ORDER ANALYSIS viii

9 B. ROBUSTNESS ANALYSIS C. EQUILIBRIUM ANALYSIS D. TURKISH SUMMARY E. CURRICULUM VITAE F. TEZ FOTOKOPİSİ İZİN FORMU ix

10 LIST OF TABLES TABLES Table 3.1: Summary Statistics of Variables Table 3.2: Distribution of Manipulations across Sectors and Years Table 3.3: Random Effects Probit Regression Results 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 Table D.3: Robustness Analysis of Dynamic Probit regressions with dummy variable of manip. any year x

11 LIST OF FIGURES FIGURES Figure 3.1: Monthly Values of ISE-100 Index between January 1998 and December xi

12 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 xii

13 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 1

14 (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 takeover bid. Bagnoli and Lipman (1996, p ) 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 2

15 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, s 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, 3

16 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 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 tradebased 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 Their data set includes not only the trade-based manipulation cases but nevertheless is an evidence of a significant number of tradebased 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. 4

17 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. 5

18 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 6

19 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 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 7

20 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 tradebased 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 from the Istanbul Stock Exchange (ISE) was 8

21 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. 9

22 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 (V H ) or low (V L ), 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. 10

23 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 A i, 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) = cn 2 (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 V L. 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. 11

24 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 stock 2. Since the price of the shares cannot exceed V H even if all the shares are demanded by other investors, the total outstanding shares are assumed to be: V H 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 V H and the manipulator when the future value of the stock is V L. The probability of having V H 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 V L. It can be assumed that initial price of a is the expected value of the future cash flows: a = δv H + (1 δ)v L (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). 12

25 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 stage 3 but does not know the type of the informed trader. Each active information seeker believes with probability probability δ γ+δ γ γ+δ the informed trader is a manipulator and with 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 V H the payoff for the informed trader will be V H k. In addition, we introduce the condition V H 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 V H ) 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. 13

26 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 V H. The truthful informed trader simultaneously 2 purchases shares from the uninformed traders and spend cn T in order to call N T 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 N T N active information seekers with the aggregate demand Q 2 A = q 2 A 1 +, q 2 A i 1 + q 2 A i + q 2 A i q 2 A NT (5) where q 2 A i 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 π A i (q 2 A 1,, q 2 A i 1, q 2 A i, q 2 A i+1,, q 2 A NT ) = (V H P 2 )q 2 A i = [V H (a + b (q 2 A q 2 A i 1 + q 2 A i + q 2 A i q 2 A NT ))] q 2 A i (6) Active information seeker i takes the other active information seekers share purchases, [q 2 A 1,, q 2 A i 1, q 2 A i+1,, q 2 A NT ], as given and maximizes her payoff by solving the problem: max π A A i (q 1 A A 2,, q i 1 A 2, q i A 2, q i+1 A NT 2,, q q i 2 ) (7) 2 14

27 The solution to this problem gives the best response function of active information seeker i: 4 q 2 A i = R A i (q 2 A 1,, q 2 A i 1, q 2 A i+1,, q 2 A NT ) = [V A H a b(q 1 A q i 1 A 2 + q i+1 A NT q 2 )] 2b (8) for all i = 1,, N T. In a symmetric equilibrium we have resulting in q 2 A 1 = = q2 A i 1 = q2 A i = q2 A i+1 = = q2 A NT = q2 A (9) q 2 A = V H a (N T + 1)b (10) The aggregate demand for N T active information seekers is Q 2 A = N T q 2 A = N T(V H a) (N T + 1)b (11) Using the supply curve of (2), stock price at Stage 2 is P 2 (N T ) = V L + δ(v H V L ) + (1 δ)(v H V L )N T N T + 1 (12) As the number of active information seekers approaches to infinity, N T, the aggregate demand converges to the total outstanding shares and the price converges to the fundamental value V H 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, N T, by deciding how much to spend for attracting potential information seekers. 4 Further equilibrium analysis is given at Appendix C. 15

28 Important Remark: At Stage 1, N T 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 N T 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(N T ) = cn T 2,c > 0. The informed trader correctly anticipates the subgame equilibrium price of Stage 2, P 2 (N T ). At Stage 1, the informed trader simultaneously decides how many active information seekers, N T, to attract to the market for the relevant stock and the number of shares, q 1,T, to buy from the uninformed traders at price P 1 = a + bq 1,T to be to sold at correctly anticipated Stage 2 equilibrium price, P 2 (N T ). This leads to the following payoff function: π T (q 1,T, N T ) = [P 2 (N T ) p 1 (q 1,T )]q 1,T C(N T ) = [ N TV H + a N T + 1 (a + bq 1,T)] q 1,T cn T 2 (13) The aim of the informed trader to solve the following problem: max π T (q 1,T, N T ) (14) q 1,T,N T 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 N T = ( 1 1 4bc ) 3 2 (1 δ) 3(V H V L ) (15) and the optimal amount of shares 16

29 q 1,T = (1 δ)(v H V L ) [4bc(1 δ)(v H V L )] 1 3 2b (16) In order to restrict our attention to integer realizations of N T it is assumed that the parameters V H, V L, δ, b and c only take values which make the optimal number of active information seekers at (15), N T, an integer. These constitute the subgame perfect outcome of the game resulting in the Stage 1 market price P 1 = V L + (V H V L )(1 + δ) 2 [4bc(1 δ)(v H V L )] (17) and the Stage 2 market price P 2 = V H [4bc(1 δ)(v H V L )] 1 3 (18) associated with the truthful party's profit π T = [(1 δ)(v H V L ) [4bc(1 δ)(v H V L )] 1 2 3] 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: P 2 = V H [4bc(1 δ)(v H V L )] 1 3 V H k (20) This condition can be reexpressed as follows: k [4bc(1 δ)(v H V L )] 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 (V H V L ) 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. 17

30 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 [q 1, N ] then equilibrium beliefs of information seekers are: [Prob([q 1,M, N M ] [q 1, N ]) = β, Prob([q 1,T, N T ] [q 1, N ]) = 1 β] (23) becomes: With these beliefs active information seeker i s payoff function π A i(q 2 A 1,, q 2 A i 1, q 2 A i, q 2 A i+1,, q 2 A N ) = { (V L P 2 )q 2 A i with probability β (V H P 2 )q 2 A i with probability 1 β } (24) where P 2 = a + b (q 2 A q 2 A i 1 + q 2 A i + q 2 A i q 2 A N ) (25) The expected payoff function of active information seeker i is: Eπ A i (q 2 A 1,, q 2 A i 1, q 2 A i, q 2 A i+1,, q 2 A N ) = (1 β)(v H P 2 )q 2 A i + β(v L P 2 )q 2 A i 18

31 = [V H β(v H V L ) a b (q 2 A q 2 A i 1 + q 2 A i + q 2 A i q 2 A N )] q 2 A i (26) Active information seeker i takes the other active information seekers share purchases, [q 2 A 1,, q 2 A i 1, q 2 A i+1,, q 2 A N ], as given and maximizes her payoff by solving the problem: max Eπ A A i (q 1 A A 2,, q i 1 A 2, q i A 2, q i+1 A 2,, q N q i 2 ) (27) 2 The solution to this problem gives the best response function of active information seeker i: A q i 2 = R A A i (q 1 A 2,, q i 1 A 2, q i+1 A 2,, q N 2 ; β([q 1,M, N M ] [q 1, N ])) = [V A H β(v H V L ) a b(q 1 A q i 1 A 2 + q i+1 A q N 2 )] 2b (28) for all i = 1,, N. In a symmetric equilibrium we have resulting in q 2 A 1 = = q2 A i 1 = q2 A i = q2 A i+1 = = q2 A N = q2 A (29) q 2 A = V H β(v H V L ) a (N + 1)b (30) The aggregate demand for N N active information seekers is Q 2 A = N q 2 A = N [V H β(v H V L ) a] (N + 1)b (31) Using the supply curve of (2), stock price at Stage 2 is P 2 (N ) = a + bq 2 A = N [V H β(v H V L )] + a N + 1 (32) 19

32 As the number of active information seekers approaches to infinity, N, the price converges to the expected value (1 β)v H + βv L at Stage 2. Each active information seekers' expected profits are π Ai = 1 b [(1 β)v 2 H + βv L a ] N + 1 (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: π j (q 1,j, N j ) = [P 2 2 (N j ) P 1 (q 1,j )]q 1,j cn j (34) where P 2 (N j ) = a + bq 2 A = N j[v H β(v H V L )] + a N j + 1 (35) is Stage 2 equilibrium price correctly anticipated by the informed trader j = T, M and N j = N for all j = T, M. The informed trader j s problem is max π j (q 1,j, N j ) (36) q 1,j,N j 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 20

33 j s optimal amount of shares N = ( 1 1 4bc ) 3 1 δ γ [δ δ + γ 2 3 (V H V L )] 1 (37) q 1,j = δ 1 δ γ (V δ+γ H V L ) [4bcδ 1 δ γ 1 (V 3 δ+γ H V L )] 2b (38) the equilibrium price P 1 = V L δ (1 + 2 δ + γ ) (V H V L ) 1 1 δ γ [4bcδ 2 δ + γ (V H V L )] 1 3 (39) and j s optimal profits π j = 1 4b 1 δ γ [δ (V δ + γ H V L ) [4bcδ 1 δ γ δ + γ (V H V L )] ] (40) Stage 2 equilibrium price is P 2 = V L + δ δ + γ (V H V L ) [4bcδ 1 δ γ δ + γ (V H V L )] 1 3 (41) In order to restrict our attention to integer realizations of N it is assumed that the parameters V H, V L, δ, γ, b and c only take values which make the optimal number of active information seekers at (37), N, 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: P 2 = V L + δ δ + γ (V H V L ) [4bcδ 1 δ γ δ + γ This condition can be reexpressed as follows: 1 3 (V H V L )] VH k (42) 21

34 k γ δ + γ (V H V L ) + [4bcδ 1 δ γ δ + γ (V H V L )] 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 (V H V L ) 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(v L [q 1, N]) = { 1 for [q 1, N] [q 1, N ] β for [q 1, N] = [q 1, N ] } (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 (V H V L ) 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

35 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 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. 23

36 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 24

37 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 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 studies 5. On the other hand, the market capitalization of ISE is 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 Comerton-Forde and Putnins (2011) analyze the closing price manipulation cases prosecuted in the US and Canada for the January 1, January 1, 2009 period and they identify 184 instances of manipulation. 25