Marking the Close analysis in Thai Bond Market Surveillance using association rules

Mongkolnavin, Janjao; Tirapat, Sunti

doi:10.1016/j.eswa.2008.10.073

Cited by 22 publications

(14 citation statements)

References 3 publications

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“…A very simple technique, association rule, has been proposed for detecting the closing price manipulation in the Thai Bond Market [16]. The approach was based on the assumption that the trading time of a trader should be random regardless of the traders.…”

Section: B Price Manipulation Detectionmentioning

confidence: 99%

Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection

Cao

Coleman

et al. 2015

IEEE Trans. Neural Netw. Learning Syst.

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Abstract-Price manipulation refers to the activities of those traders who utilise carefully designed trading behaviours to manually push up or down the underlying equity prices for making profits. With increasing volumes and frequency of trading, price manipulation can be extremely damaging to the proper functioning and integrity of capital markets. Existing work focuses on either empirical studies of market abuse cases or analysis of particular manipulation types based on certain assumptions. Effective approaches for analysing and detecting price manipulation in real-time are yet to be developed. This paper proposes a novel approach, called Adaptive Hidden Markov Model with anomaly States (AHMMAS) for modelling and detecting price manipulation activities. Together with wavelet transformations and gradients as the feature extraction methods, the AHMMAS model caters for price manipulation detection and basic manipulation type recognition. The evaluation experiments conducted on seven stock tick data from NASDAQ and the London Stock Exchange and ten simulated stock prices by stochastic differential equation show that the proposed AHMMAS model can effectively detect price manipulation patterns and outperforms the selected benchmark models.

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Section: B Price Manipulation Detectionmentioning

confidence: 99%

Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection

Cao

Coleman

et al. 2015

IEEE Trans. Neural Netw. Learning Syst.

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“…Evaluations and comparisons of different techniques have been presented [6], [13], but the studies lack any reliable quantitative analysis of the relationship between the manipulation tactic and the relevant features like return, trading volume, liquidity and volatility. An association rule, has been proposed to detect closing price manipulation in the Thai Bond Market [11]. The approach is based on the assumption that trading time should be random regardless of the trader.…”

Section: B Price Manipulation Detectionmentioning

confidence: 99%

“…The approach is based on the assumption that trading time should be random regardless of the trader. It is stated as a supplementary to the existing surveillance system within the Thai Bond Market for identifying a specific type of trade based manipulation [11] rather than a general detection approach. A market close "ramping" detection algorithm developed by Smarts Group International (a surveillance technique provider), and applied by NASDAQ OMX is described in [2].…”

Section: B Price Manipulation Detectionmentioning

confidence: 99%

“…Trade based market abuse is generally characterized by price manipulation, where the manipulation targets equity bid/ask prices only [3]. Most existing literature consists of empirical studies of the phenomenon and the market response to the manipulation [6], [10], [11], [13]. Very few address an analysis of the features of different price manipulation strategies [2] and corresponding detection approaches.…”

Section: Introductionmentioning

confidence: 99%

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A Hidden Markov Model with Abnormal States for Detecting Stock Price Manipulation

Cao

Coleman

et al. 2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Abstract-Price manipulation refers to the act of using illegal trading behaviour to manually change an equity price with the aim of making profits. With increasing volumes of trading, price manipulation can be extremely damaging to the proper functioning and integrity of capital markets. Effective approaches for analysing and real-time detection of price manipulation are yet to be developed. This paper proposes a novel approach, called Hidden Markov Model with Abnormal States (HMMAS), which models and detects price manipulation activities. Together with the wavelet decomposition for features extraction and Gaussian Mixture Model for Probability Density Function (PDF) construction, the HMMAS model detects price manipulation and identifies the type of the detected manipulation. Evaluation experiments of the model were conducted on six stock tick data from NASDAQ and London Stock Exchange (LSE). The results showed that the proposed HMMAS model can effectively detect price manipulation patterns.

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“…They showed that data mining techniques (Artificial neural network and support vector machine) were superior methods for detecting stock price manipulation than multivariate techniques (logistic regression and discriminant analysis). According to J. Mongkolnavin and S. Tirapat [6], association rules were applied to detect mark-the-close in intraday trades from the Thai Bond Market Association. Price variation in the market and behavior of investors were integrated to analyze warning signals in real time.…”

Section: Literature Reviewmentioning

confidence: 99%

Stock Price Manipulation Detection Based on Mathematical Models

Leangarun¹,

Tangamchit²,

Thajchayapong³

2016

IJTEF

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Abstract-We investigated two popular scenarios of stock price manipulations: pump-and-dump and spoof trading. Pump-and-dump is a procedure to buy a stock and push its price up. Then, the manipulator dumps all of the stock he holds to make a profit. Spoof trading is a procedure to trick other investors that a stock should be bought or sold at the manipulated price. We proposed mathematical definitions based on level 2 data for both scenarios, and used them to generate a training set consisting of buy/sell orders in an order book of 10 depths. Order cancellations, which are important indicators for price manipulation, are also visible in this level 2 data. In this paper, we considered a challenging scenario where we attempted to use less-detailed level 1 data to detect manipulations even though using level 2 data is more accurate. First, we implemented feedforward neural network models that have level 1 data, containing less-detailed information (no information about order cancellation), but is more accessible to investors as an input. The neural network model achieved 88.28% accuracy for detecting pump-and-dump but it failed to model spoof trading effectively. Therefore, we further investigate the two-dimensional Gaussian model and show that it can detect spoof trading using level 2 data as input.Index Terms-Stock price manipulation, pump-and-dump, spoof trading, neural network. I. INTRODUCTIONStock market gathers participations from all kinds of investors. Millions of buy/sell orders enters the market every day. Stock price fluctuates due to several factors, mainly from the profit that the company can make. However, there are some investors who attempt to get benefits from the stock market using irregular trade behaviors that affect the stock price. Some of these attempts are illegal. The control of these irregular trade behaviors is difficult due to the large amount of trade data.Automatic computer algorithms for detecting price manipulation are the solution to this problem. It can scan large amount of price data and spot manipulations in a short time. Price manipulation can be divided into three categories: trade-based, information-based, and action-based. This research discusses a mathematical model that classifies trade-based price manipulations from normal trades in stock markets. Two types of manipulations are investigated: pump-and-dump and spoof trading. Pump-and-dump is an action of buying stock, making the price to go higher, and then selling to others for a profit. Spoof trading is an action of sending passive orders in large volume to trick others that the stock should be sold at that price. After the manipulators secure enough benefits from that artificial price, they cancel their passive orders. These actions allow the manipulators to sell their stock at a price higher than usual.The effectiveness of manipulation detection depends on how much the information we have. We rely on using the price data that buyers and sellers sent to the market. The trade data can be classified into two levels. Level 1...

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Marking the Close analysis in Thai Bond Market Surveillance using association rules

Cited by 22 publications

References 3 publications

Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection

Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection

A Hidden Markov Model with Abnormal States for Detecting Stock Price Manipulation

Stock Price Manipulation Detection Based on Mathematical Models

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