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

Cao, Yi; Li, Yuhua; Coleman, Sonya; Belatreche, Ammar; McGinnity, T.M.

doi:10.1109/smc.2013.514

Cited by 10 publications

(9 citation statements)

References 17 publications

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“…A generic price manipulation tactic is defined as artificially pushing up (or down) the bid (or ask) price of a security and taking advantage of the shifted price so as to make a profit [12]. The deliberately constructed trading order sequences change the market bid (or ask) price and show the trader's manipulative intentions.…”

Section: A Price Manipulation Strategy Characteristicsmentioning

confidence: 99%

Detecting price manipulation in the financial market

Cao

Coleman

et al. 2014

2014 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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Abstract-Market abuse has attracted much attention from financial regulators around the world but it is difficult to fully prevent. One of the reasons is the lack of thoroughly studies of the market abuse strategies and the corresponding effective market abuse approaches. In this paper, the strategies of reported price manipulation cases are analysed as well as the related empirical studies. A transformation is then defined to convert the time-varying financial trading data into pseudostationary time series, where machine learning algorithms can be easily applied to the detection of the price manipulation. The evaluation experiments conducted on four stocks from NASDAQ show a promising improved performance for effectively detecting such manipulation cases.

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Section: A Price Manipulation Strategy Characteristicsmentioning

confidence: 99%

Detecting price manipulation in the financial market

Cao

Coleman

et al. 2014

2014 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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“…For example, Cao et al. () report a sensitivity ranging from 75% up to 90%, without further explanation on how nonmanipulated samples were classified. However, these authors had to synthetically generate abusive patterns and inject them into TAQ data to run the proposed hidden Markov model.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, Cao et al. () develop a Hidden Markov Model with Abnormal States (HMMAS) to detect several trade‐based manipulative strategies, spoofing among them. They used tick data from the LSE and NASDAQ and they report a sensitivity ranging from 75% up to 90%.…”

Section: Related Literaturementioning

confidence: 99%

“…An extension of Cao et al. () is found in Cao et al. (), with the authors adding the framework of adaptiveness for the HMMAS in a real‐time environment.…”

Section: Related Literaturementioning

confidence: 99%

“…Possibly, the most popular of these strategies is spoofing ; an illicit strategy characterized by the use of large non‐bona fide orders which are submitted without the intention of execution, but with the aim of moving the price in favor of the manipulator, who profits from a transaction at the opposite trading side of the spoofed order (Aktas, ). Some solutions to detect spoofing trading have been suggested, mainly based on discriminative models (Díaz‐Solís, Theodoulidis, & Sampaio, ; Öğut, Doğanay, & Aktaş, ) and anomaly detection within the price series (Cao, Li, Coleman, Belatreche, & McGinnity, , ). However, these anomalies are not always a result of market manipulation but rather a product of the intrinsic/extrinsic factors of the financial markets.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Order flow dynamics for prediction of order cancelation and applications to detect market manipulation

2019

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In this work, a methodology is proposed to detect and predict the intention of cancelation of a large order at an optimal event‐time horizon by analyzing real order flow market data. We achieve this by reconstructing the full history of the limit order book and formulate the case as a binary classification supervised learning problem. The results presented in this study suggest that using the information at the microstructure level of the order book is highly efficient for predicting and detecting the cancelation of the large order than using information at the macrostructure level, and that predicting the cancelation is marginally outperformed by the detection case. With this, we make a step forward in identifying potential orders related to price manipulation but the results can be used by institutional traders to anticipate adversary market impact produced by large orders.

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Coarse and fine identification of collusive clique in financial market

Zhai

Cao

Yao

et al. 2017

Expert Systems with Applications

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Collusive transactions refer to the activity whereby traders use carefully-designed trade to illegally ma- nipulate the market. They do this by increasing specific trading volumes, thus creating a false impression that a market is more active than it actually is. The traders involved in the collusive transactions are termed as collusive clique. The collusive clique and its activities can cause substantial damage to the market’s integrity and attract much attention of the regulators around the world in recent years. Much of the current research focused on the detection based on a number of assumptions of how a normal market behaves. There is, clearly, a lack of effective decision-support tools with which to identify poten- tial collusive clique in a real-life setting. The study in this paper examined the structures of the traders in all transactions, and proposed two approaches to detect potential collusive clique with their activi- ties. The first approach targeted on the overall collusive trend of the traders. This is particularly useful when regulators seek a general overview of how traders gather together for their transactions. The sec- ond approach accurately detected the parcel-passing style collusive transactions on the market through analysing the relations of the traders and transacted volumes. The proposed two approaches, on one hand, provided a complete cover for collusive transaction identifications, which can fulfil the different types of requirements of the regulation, i.e. MiFID II, on the other hand, showed a novel application of well-known computational algorithms on solving real and complex financial problem. The proposed two approaches are evaluated using real financial data drawn from the NYSE and CME group. Experimental results suggested that those approaches successfully identified all primary collusive clique scenarios in all selected datasets and thus showed the effectiveness and stableness of the novel application

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

Cited by 10 publications

References 17 publications

Detecting price manipulation in the financial market

Detecting price manipulation in the financial market

Order flow dynamics for prediction of order cancelation and applications to detect market manipulation

Coarse and fine identification of collusive clique in financial market

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