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

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

A hidden Markov model with abnormal states for detecting stock price manipulation

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