A well-managed financial market of stocks,commodities,derivatives,and bonds is crucial to a country’s economic growth.It provides confidence to investors,which encourages the inflow of cash to ensure good market liqu...A well-managed financial market of stocks,commodities,derivatives,and bonds is crucial to a country’s economic growth.It provides confidence to investors,which encourages the inflow of cash to ensure good market liquidity.However,there will always be a group of traders that aims to manipulate market pricing to negatively influence stock values in their favor.These illegal trading activities are surely prohibited according to the rules and regulations of every country’s stockmarket.It is the role of regulators to detect and prevent any manipulation cases in order to provide a trading platform that is fair and efficient.However,the complexity of manipulation cases has increased significantly,coupled with high trading volumes,which makes the manual observations of such cases by human operators no longer feasible.As a result,many intelligent systems have been developed by researchers all over the world to automatically detect various types of manipulation cases.Therefore,this review paper aims to comprehensively discuss the state-of-theart methods that have been developed to detect and recognize stock market manipulation cases.It also provides a concise definition of manipulation taxonomy,including manipulation types and categories,as well as some of the output of early experimental research.In summary,this paper provides a thorough review of the automated methods for detecting stock market manipulation cases.展开更多
Fraudulent actions of a trader or a group of traders can cause substantial disturbance to the market,both directly influencing the price of an asset or indirectly by misin-forming other market participants.Such behavi...Fraudulent actions of a trader or a group of traders can cause substantial disturbance to the market,both directly influencing the price of an asset or indirectly by misin-forming other market participants.Such behavior can be a source of systemic risk and increasing distrust for the market participants,consequences that call for viable countermeasures.Building on the foundations provided by the extant literature,this study aims to design an agent-based market model capable of reproducing the behavior of the Bitcoin market during the time of an alleged Bitcoin price manipulation that occurred between 2017 and early 2018.The model includes the mechanisms of a limit order book market and several agents associated with different trading strategies,including a fraudulent agent,initialized from empirical data and who performs market manipulation.The model is validated with respect to the Bitcoin price as well as the amount of Bitcoins obtained by the fraudulent agent and the traded volume.Simulation results provide a satisfactory fit to historical data.Several price dips and volume anomalies are explained by the actions of the fraudulent trader,completing the known body of evidence extracted from blockchain activity.The model suggests that the presence of the fraudulent agent was essential to obtain Bitcoin price development in the given time period;without this agent,it would have been very unlikely that the price had reached the heights as it did in late 2017.The insights gained from the model,especially the connection between liquidity and manipulation efficiency,unfold a discussion on how to prevent illicit behavior.展开更多
基金This work was supported in part by the RHB-UKM Endowment Fund through Dana Endowmen RHB-UKM under Grant RHB-UKM-2021-001in part by the Universiti Kebangsaan Malaysia through the Dana Padanan Kolaborasi under Grant DPK-2021-012.
文摘A well-managed financial market of stocks,commodities,derivatives,and bonds is crucial to a country’s economic growth.It provides confidence to investors,which encourages the inflow of cash to ensure good market liquidity.However,there will always be a group of traders that aims to manipulate market pricing to negatively influence stock values in their favor.These illegal trading activities are surely prohibited according to the rules and regulations of every country’s stockmarket.It is the role of regulators to detect and prevent any manipulation cases in order to provide a trading platform that is fair and efficient.However,the complexity of manipulation cases has increased significantly,coupled with high trading volumes,which makes the manual observations of such cases by human operators no longer feasible.As a result,many intelligent systems have been developed by researchers all over the world to automatically detect various types of manipulation cases.Therefore,this review paper aims to comprehensively discuss the state-of-theart methods that have been developed to detect and recognize stock market manipulation cases.It also provides a concise definition of manipulation taxonomy,including manipulation types and categories,as well as some of the output of early experimental research.In summary,this paper provides a thorough review of the automated methods for detecting stock market manipulation cases.
基金provided by Marie Sklodowska-Curie ITN Horizon 2020-funded project INSIGHTS(call H2020-MSCA-ITN-2017,grant agreement n.765710)NWO—Nederlandse Organisatie voor Wetenschappelijk Onderzoek(Award Number:KIVI.2019.006 HUMAINER AI project)。
文摘Fraudulent actions of a trader or a group of traders can cause substantial disturbance to the market,both directly influencing the price of an asset or indirectly by misin-forming other market participants.Such behavior can be a source of systemic risk and increasing distrust for the market participants,consequences that call for viable countermeasures.Building on the foundations provided by the extant literature,this study aims to design an agent-based market model capable of reproducing the behavior of the Bitcoin market during the time of an alleged Bitcoin price manipulation that occurred between 2017 and early 2018.The model includes the mechanisms of a limit order book market and several agents associated with different trading strategies,including a fraudulent agent,initialized from empirical data and who performs market manipulation.The model is validated with respect to the Bitcoin price as well as the amount of Bitcoins obtained by the fraudulent agent and the traded volume.Simulation results provide a satisfactory fit to historical data.Several price dips and volume anomalies are explained by the actions of the fraudulent trader,completing the known body of evidence extracted from blockchain activity.The model suggests that the presence of the fraudulent agent was essential to obtain Bitcoin price development in the given time period;without this agent,it would have been very unlikely that the price had reached the heights as it did in late 2017.The insights gained from the model,especially the connection between liquidity and manipulation efficiency,unfold a discussion on how to prevent illicit behavior.
