Detecting wash trade in the financial market

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

doi:10.1109/cifer.2014.6924058

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…Wash trading is a market manipulation behavior that has appeared in traditional financial scenarios [22] and is recognized as a financial crime in most countries. Generally speaking, it refers to the repeated trading of assets in order to provide misleading information to the market.…”

Section: Wash Tradingmentioning

confidence: 99%

Financial Crimes in Web3-Empowered Metaverse: Taxonomy, Countermeasures, and Opportunities

Lin

et al. 2023

IEEE Open J. Comput. Soc.

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At present, the concept of metaverse has sparked widespread attention from the public to major industries. With the rapid development of blockchain and Web3 technologies, the decentralized metaverse ecology has attracted a large influx of users and capital. Due to the lack of industry standards and regulatory rules, the Web3-empowered metaverse ecosystem has witnessed a variety of financial crimes, such as scams, code exploit, wash trading, money laundering, and illegal services and shops. To this end, it is especially urgent and critical to summarize and classify the financial security threats on the Web3empowered metaverse in order to maintain the long-term healthy development of its ecology. In this paper, we first outline the background, foundation, and applications of the Web3 metaverse. Then, we provide a comprehensive overview and taxonomy of the security risks and financial crimes that have emerged since the development of the decentralized metaverse. For each financial crime, we focus on three issues: a) existing definitions, b) relevant cases and analysis, and c) existing academic research on this type of crime. Next, from the perspective of academic research and government policy, we summarize the current anti-crime measurements and technologies in the metaverse. Finally, we discuss the opportunities and challenges in behavioral mining and the potential regulation of financial activities in the metaverse. The overview of this paper is expected to help readers better understand the potential security threats in this emerging ecology, and to provide insights and references for financial crime fighting.

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Section: Wash Tradingmentioning

confidence: 99%

Financial Crimes in Web3-Empowered Metaverse: Taxonomy, Countermeasures, and Opportunities

Lin

et al. 2023

IEEE Open J. Comput. Soc.

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“…Given the limit order queues Q b and Q s , the coarse detection can then be formulated as follows. For a large incoming order, examine in the opposite order queue for one or multiple potential matching orders, which are characterized by (3)- (5). The result of the coarse detection comprises all order combinations matched with the incoming order.…”

Section: Problem Formulationmentioning

confidence: 99%

“…where Q contains all opposite orders in the previous δ t periods and L k is the incoming order. Based on the above discussions and the constraints in (5), the function VOL_MATCH (Q t, p , L k ) is defined as follows: given incoming order L k and a set of matched orders Q t, p , find subsets S of the order pairs from Q t, p such that…”

Section: Algorithm 3 Coarse Detectionmentioning

confidence: 99%

“…In the example, in Table VI, the #5 buy order volume (1500 shares) is ∼96.7% matched with all previous sell orders (1450 shares). The price margin δ p is unconstrained in the detection so that any orders following the price matching rules Rules 1 and 2 are scanned for possible matching pairs under the condition in (5).…”

Section: B Determining the Marginal Parametersmentioning

confidence: 99%

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Detecting Wash Trade in Financial Market Using Digraphs and Dynamic Programming

Cao

Coleman

et al. 2016

IEEE Trans. Neural Netw. Learning Syst.

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A wash trade refers to the illegal activities of traders who utilize carefully designed limit orders to manually increase the trading volumes for creating a false impression of an active market. As one of the primary formats of market abuse, a wash trade can be extremely damaging to the proper functioning and integrity of capital markets. The existing work focuses on collusive clique detections based on certain assumptions of trading behaviors. Effective approaches for analyzing and detecting wash trade in a real-life market have yet to be developed. This paper analyzes and conceptualizes the basic structures of the trading collusion in a wash trade by using a directed graph of traders. A novel method is then proposed to detect the potential wash trade activities involved in a financial instrument by first recognizing the suspiciously matched orders and then further identifying the collusions among the traders who submit such orders. Both steps are formulated as a simplified form of the knapsack problem, which can be solved by dynamic programming approaches. The proposed approach is evaluated on seven stock data sets from the NASDAQ and the London Stock Exchange. The experimental results show that the proposed approach can effectively detect all primary wash trade scenarios across the selected data sets.

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