Trading networks, abnormal motifs and stock manipulation

We study trade-based manipulation of stock prices from the perspective of complex trading networks constructed by using detailed information of trades. A stock trading network consists of nodes and directed links, where every trader is a node and a link is formed from one trader to the other if the former sells shares to the latter. Specifically, three abnormal network motifs are investigated, which are found to be formed by a few traders, implying potential intention of price manipulation. We further investigate the dynamics of volatility, trading volume, average trade size and turnover around the transactions associated with the abnormal motifs for large, medium and small trades. It is found that these variables peak at the abnormal events and exhibit a power-law accumulation in the pre-event time period and a power-law relaxation in the post-event period. We also find that the cumulative excess returns are significantly positive after buyer-initiated suspicious trades and exhibit a mild price reversal after seller-initiated suspicious trades. These findings can be better understood in favour of price manipulation. Our work sheds new lights into the detection of price manipulation resorting to the abnormal motifs of complex trading networks.

[1]  Foort Hamelink,et al.  Systematic Patterns Before and After Large Price Changes: Evidence from High Frequency Data from the Paris Bourse , 1999 .

[2]  Tālis J. Putniņš,et al.  Measuring Closing Price Manipulation , 2009 .

[3]  G. Krishna,et al.  Agglomerative clustering using the concept of mutual nearest neighbourhood , 1978, Pattern Recognit..

[4]  Andrew K. Prevost,et al.  Block Trade Price Asymmetry and Changes in Depth: Evidence from the Australian Stock Exchange , 2002 .

[5]  P. Mahoney,et al.  The stock pools and the Securities Exchange Act 1 I thank two anonymous referees, the editor, Willia , 1999 .

[6]  Andreas Geyer-Schulz,et al.  On the Analysis of Asymmetric Directed Communication Structures in Electronic Election Markets , 2006 .

[7]  Karl Felixson,et al.  Day end returns--stock price manipulation , 1999 .

[8]  J. Kertész,et al.  Studies of the limit order book around large price changes , 2009, 0901.0495.

[9]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[10]  J. Tseng,et al.  Experimental evidence for the interplay between individual wealth and transaction network , 2010, 1001.3731.

[11]  Zhi-Qiang Jiang,et al.  Detrended fluctuation analysis of intertrade durations , 2008, 0806.2444.

[12]  M. Ammann,et al.  Intraday characteristics of stock price crashes , 2009 .

[13]  Robert W. Holthausen,et al.  Large-block transactions, the speed of response, and temporary and permanent stock-price effects , 1990 .

[14]  Large price changes on small scales , 2004, cond-mat/0401055.

[15]  Shuigeng Zhou,et al.  Characteristics of real futures trading networks , 2010, 1004.4402.

[16]  F. Lillo,et al.  Specialization and herding behavior of trading firms in a financial market , 2007, 0707.0385.

[17]  Jan Schröder,et al.  On the Analysis of Irregular Stock Market Trading Behavior , 2007, GfKl.

[18]  Fabrizio Lillo,et al.  Market reaction to a bid-ask spread change: a power-law relaxation dynamics. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Zhi-Qiang Jiang,et al.  Complex stock trading network among investors , 2010, 1003.2459.

[20]  Tālis J. Putniņš,et al.  Market Manipulation: A Survey , 2011 .

[21]  Xueqi Cheng,et al.  Statistical properties of trading activity in Chinese stock market , 2010 .

[22]  A. Frino,et al.  The Determinants of the Price Impact of Block Trades: Further Evidence , 2007 .

[23]  J. Kertész,et al.  Random matrix approach to the dynamics of stock inventory variations , 2012, 1201.0433.

[24]  D. Cumming,et al.  Global Market Surveillance , 2007 .

[25]  Jari Saramäki,et al.  Temporal motifs in time-dependent networks , 2011, ArXiv.

[26]  J. Bouchaud,et al.  Stock price jumps: news and volume play a minor role , 2008, 0803.1769.

[27]  Xueqi Cheng,et al.  Distinguishing manipulated stocks via trading network analysis , 2011, 1110.2260.

[28]  S. Shen-Orr,et al.  Networks Network Motifs : Simple Building Blocks of Complex , 2002 .

[29]  M. Fleming,et al.  What Moves the Bond Market? , 1997 .

[30]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.

[31]  Huawei Shen,et al.  Degree-Strength Correlation Reveals Anomalous Trading Behavior , 2012, PloS one.

[32]  Ramazan Aktas,et al.  Detecting stock-price manipulation in an emerging market: The case of Turkey , 2009, Expert Syst. Appl..

[33]  Wayne E. Baker,et al.  The Social Structure of a National Securities Market , 1984, American Journal of Sociology.

[34]  Jie-Jun Tseng,et al.  Network topology of an experimental futures exchange , 2007, 0705.2551.

[35]  János Kertész,et al.  Short-term market reaction after extreme price changes of liquid stocks , 2004, cond-mat/0406696.

[36]  Nikolaus Hautsch,et al.  Quantifying High-Frequency Market Reactions to Real-Time News Sentiment Announcements , 2009 .

[37]  Pierre Hillion,et al.  The manipulation of closing prices , 2004 .

[38]  Franklin Allen,et al.  Stock-Price Manipulation , 1992 .

[39]  Jie-Jun Tseng,et al.  Statistical properties of agent-based models in markets with continuous double auction mechanism , 2010, 1002.0917.

[40]  Md. Nazrul Islam,et al.  An approach to improve collusion set detection using MCL algorithm , 2009, 2009 12th International Conference on Computers and Information Technology.

[41]  Shuigeng Zhou,et al.  Detecting potential collusive cliques in futures markets based on trading behaviors from real data , 2012, Neurocomputing.

[42]  Jianping Mei,et al.  Market Manipulation: A Comprehensive Study of Stock Pools , 2004 .

[43]  János Kertész,et al.  Order flow dynamics around extreme price changes on an emerging stock market , 2010, 1003.0168.

[44]  Babis Theodoulidis,et al.  Analysis of stock market manipulations using knowledge discovery techniques applied to intraday trade prices , 2011, Expert Syst. Appl..

[45]  Fabrizio Lillo,et al.  Identification of clusters of investors from their real trading activity in a financial market , 2011, ArXiv.

[46]  Wei-Xing Zhou,et al.  Universal price impact functions of individual trades in an order-driven market , 2007, 0708.3198.

[47]  Jie-Jun Tseng,et al.  Emergence of Scale-Free Networks in Markets , 2009, Adv. Complex Syst..

[48]  S. Shen-Orr,et al.  Superfamilies of Evolved and Designed Networks , 2004, Science.

[49]  Dennis J. Lasser,et al.  Electronic Limit Order Book and Order Submission Choice Around Macroeconomic News , 2009 .

[50]  Girish Keshav Palshikar,et al.  Collusion set detection using graph clustering , 2008, Data Mining and Knowledge Discovery.

[51]  M. Fleming,et al.  Price Formation and Liquidity in the U.S. Treasury Market: The Response to Public Information , 1999 .

[52]  G. Gemmill.  Transparency and Liquidity: A Study of Block Trades on the London Stock Exchange under Different Publication Rules , 1996 .

[53]  Ray A. Jarvis,et al.  Clustering Using a Similarity Measure Based on Shared Near Neighbors , 1973, IEEE Transactions on Computers.