Detecting wash trade in financial market using digraphs and dynamic programming

Wash trade refers to the illegal activities of traders who utilise 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, wash trade can be extremely damaging to the proper functioning and integrity of capital markets. Existing work focuses on collusive clique detections based on certain assumptions of trading behaviours. Effective approaches for analysing and detecting wash trade in a real-life market have yet to be developed. This paper analyses and conceptualises 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 datasets from NASDAQ and the London Stock Exchange. Experimental results show that the proposed approach can effectively detect all primary wash trade scenarios across the selected datasets

Dodd-Frank and the Spoofing Prohibition in Commodities Markets

The Dodd-Frank Act amended the Commodity Exchange Act and adopted an explicit prohibition regarding activity commonly known as spoofing in commodities markets. This Note argues that the spoofing prohibition is a necessary step towards improved market discipline and price integrity in the relevant commodities markets. It fills an important gap in the CEA in relation to an elusive form of price manipulation activity by providing an explicit statutory authority on which regulators and market operators may rely in policing suspect trading strategies falling under the spoofing umbrella. Congress’ explicit denouncement of spoofing as an illegal act has ramifications not only for traders, but also for brokers and market makers. In the past, when courts have considered the issue of secondary liability of brokers regarding manipulative activity of their customers in the context of wash sales, they have determined the CEA’s explicit prohibition of wash sales and the relatively easier identification of wash sales activity as important factors that may potentially increase the secondary liability risk of derivatives brokers. Applying the same analogy to spoofing, greater public awareness and the increasing visibility of spoofing activity (resulting from improvements in the monitoring systems of regulators and market operators) will provide strong incentives for market participants to adapt to changing norms. However, areas of concern, such as risk of selective enforcement and inconsistencies among the applicable market rules, will pose challenges in the spoofing prohibition’s implementation. Therefore, regulators must seek cooperation with relevant market operators to encourage structural reform and self-regulatory measures, such as implementation of appropriate structural safeguards into the trading infrastructure

The Essential Role of Securities Regulation

This Article posits that the essential role of securities regulation is to create a competitive market for sophisticated professional investors and analysts (information traders). The Article advances two related theses-one descriptive and the other normative. Descriptively, the Article demonstrates that securities regulation is specifically designed to facilitate and protect the work of information traders. Securities regulation may be divided into three broad categories: (i) disclosure duties; (ii) restrictions on fraud and manipulation; and (iii) restrictions on insider trading-each of which contributes to the creation of a vibrant market for information traders. Disclosure duties reduce information traders\u27 costs of searching and gathering information. Restrictions on fraud and manipulation lower information traders\u27 cost of verifying the credibility of information, and thus enhance information traders\u27 ability to make accurate predictions. Finally, restrictions on insider trading protect information traders from competition from insiders that would undermine information traders\u27 ability to recoup their investment in information. Normatively, the Article shows that information traders can best underwrite efficient and liquid capital markets, and, hence, it is this group that securities regulation should strive to protect. Our account has important implications for several policy debates. First, our account supports the system of mandatory disclosure. We show that, although market forces may provide management with an adequate incentive to disclose at the initial public offering (IPO) stage, they cannot be relied on to effect optimal disclosure thereafter. Second, our analysis categorically rejects calls to limit disclosure duties to hard information and self-dealing by management. Third, our analysis supports the use of the fraud-on-the-market presumption in all fraud cases even when markets are inefficient. Fourth, our analysis suggests that in cases involving corporate misstatements, the appropriate standard of care should, in principle, be negligence, not fraud

The Essential Role of Securities Regulation

This Article posits that the essential role of securities regulation is to create a competitive market for sophisticated professional investors and analysts (information traders). The Article advances two related theses-one descriptive and the other normative. Descriptively, the Article demonstrates that securities regulation is specifically designed to facilitate and protect the work of information traders. Securities regulation may be divided into three broad categories: (i) disclosure duties; (ii) restrictions on fraud and manipulation; and (iii) restrictions on insider trading-each of which contributes to the creation of a vibrant market for information traders. Disclosure duties reduce information traders\u27 costs of searching and gathering information. Restrictions on fraud and manipulation lower information traders\u27 cost of verifying the credibility of information, and thus enhance information traders\u27 ability to make accurate predictions. Finally, restrictions on insider trading protect information traders from competition from insiders that would undermine information traders\u27 ability to recoup their investment in information. Normatively, the Article shows that information traders can best underwrite efficient and liquid capital markets, and, hence, it is this group that securities regulation should strive to protect. Our account has important implications for several policy debates. First, our account supports the system of mandatory disclosure. We show that, although market forces may provide management with an adequate incentive to disclose at the initial public offering (IPO) stage, they cannot be relied on to effect optimal disclosure thereafter. Second, our analysis categorically rejects calls to limit disclosure duties to hard information and self-dealing by management. Third, our analysis supports the use of the fraud-on-the-market presumption in all fraud cases even when markets are inefficient. Fourth, our analysis suggests that in cases involving corporate misstatements, the appropriate standard of care should, in principle, be negligence, not fraud

Coarse and fine identification of collusive clique in financial market

Collusive transactions refer to the activity whereby traders use carefully-designed trade to illegally manipulate 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 potential 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 activities. 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 second approach accurately detected the parcel-passing style collusive transactions on the market through analyzing 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 fulfill 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

Exchange trading rules, surveillance and insider trading : [draft 15 oct 2013]

We examine the impact of stock exchange trading rules and surveillance on the frequency and severity of suspected insider trading cases in 22 stock exchanges around the world over the period January 2003 through June 2011. Using new indices for market manipulation, insider trading, and broker-agency conflict based on the specific provisions of the trading rules of each stock exchange, along with surveillance to detect non-compliance with such rules, we show that more detailed exchange trading rules and surveillance over time and across markets significantly reduce the number of cases, but increase the profits per case

Crypto Wash Trading: Direct vs. Indirect Estimation

Recent studies using indirect statistical methods estimate that around 70% of traded value on centralized crypto exchanges like Binance, can be characterized as wash trading. This paper turns to NFT markets, where transaction transparency, including analysis of roundtrip trades and common wallet activities, allows for more accurate direct estimation methods to be applied. We find roughly 30% of NFT volume and between 45-95% of traded value, involve wash trading. More importantly, our approach enables a critical evaluation of common indirect estimation methods used in the literature. We find major differences in their effectiveness; some failing entirely. Roundedness filters, like those used in Cong et al. (2023), emerge as the most accurate. In fact, the two approaches can be closely aligned via hyper-parameter optimization if direct data is available

AI-powered Fraud Detection in Decentralized Finance: A Project Life Cycle Perspective

In recent years, blockchain technology has introduced decentralized finance (DeFi) as an alternative to traditional financial systems. DeFi aims to create a transparent and efficient financial ecosystem using smart contracts and emerging decentralized applications. However, the growing popularity of DeFi has made it a target for fraudulent activities, resulting in losses of billions of dollars due to various types of frauds. To address these issues, researchers have explored the potential of artificial intelligence (AI) approaches to detect such fraudulent activities. Yet, there is a lack of a systematic survey to organize and summarize those existing works and to identify the future research opportunities. In this survey, we provide a systematic taxonomy of various frauds in the DeFi ecosystem, categorized by the different stages of a DeFi project's life cycle: project development, introduction, growth, maturity, and decline. This taxonomy is based on our finding: many frauds have strong correlations in the stage of the DeFi project. According to the taxonomy, we review existing AI-powered detection methods, including statistical modeling, natural language processing and other machine learning techniques, etc. We find that fraud detection in different stages employs distinct types of methods and observe the commendable performance of tree-based and graph-related models in tackling fraud detection tasks. By analyzing the challenges and trends, we present the findings to provide proactive suggestion and guide future research in DeFi fraud detection. We believe that this survey is able to support researchers, practitioners, and regulators in establishing a secure and trustworthy DeFi ecosystem.Comment: 38 pages, update reference

Prohibited Floor Trading Activities Under the Commodity Exchange Act

In algorithmic graph theory, a classic open question is to determine the complexity of the Maximum Independent Set problem on Pt -free graphs, that is, on graphs not containing any induced path on t vertices. So far, polynomial-time algorithms are known only for t≤5 (Lokshtanov et al., in: Proceedings of the twenty-fifth annual ACM-SIAM symposium on discrete algorithms, SODA 2014, Portland, OR, USA, January 5–7, 2014, pp 570–581, 2014), and an algorithm for t=6 announced recently (Grzesik et al. in Polynomial-time algorithm for maximum weight independent set on P6 -free graphs. CoRR, arXiv:1707.05491, 2017). Here we study the existence of subexponential-time algorithms for the problem: we show that for any t≥1 , there is an algorithm for Maximum Independent Set on Pt -free graphs whose running time is subexponential in the number of vertices. Even for the weighted version MWIS, the problem is solvable in 2O(tnlogn√) time on Pt -free graphs. For approximation of MIS in broom-free graphs, a similar time bound is proved. Scattered Set is the generalization of Maximum Independent Set where the vertices of the solution are required to be at distance at least d from each other. We give a complete characterization of those graphs H for which d-Scattered Set on H-free graphs can be solved in time subexponential in the size of the input (that is, in the number of vertices plus the number of edges): If every component of H is a path, then d-Scattered Set on H-free graphs with n vertices and m edges can be solved in time 2O(|V(H)|n+m√log(n+m)) , even if d is part of the input. Otherwise, assuming the Exponential-Time Hypothesis (ETH), there is no 2o(n+m) -time algorithm for d-Scattered Set for any fixed d≥3 on H-free graphs with n-vertices and m-edges