BIGDATA: IA: Collaborative Research: Detecting Financial Market Manipulation: An Integrated Data- and Model-Driven Approach

金融股市操纵者可以通过在市场状况上误导投资者来非法获利。例如，在最近的几起事件中，操纵者成功地通过插入订单来欺骗市场，在证券的供应或需求方面欺骗投资者。这种行为随着算法交易的流行而增加。它降低了资本配置的效率，对经济造成了实质性的损害，更严重的是，它威胁到了金融市场的完整性和稳定性。欺骗很难被发现，因为潜在的操作既有合法的目的，也有邪恶的目的。这个项目将应用创新的方法来改进对市场操纵的检测和威慑。该项目将整合数据驱动的方法，包括校准具有正常背景活动的检测器和从增强的时间序列中提取操纵特征，以及基于模型的技术，以基于市场微观结构的战略分析来表征操纵策略。其关键思想是使用模拟和优化来为交易模型生成成功的操纵策略，这些模型是根据可用的市场数据流进行校准的。然后，这些策略将被注入到交易模型中，以产生包括标记操纵活动的增强数据流。通过对活动进行标记，可以应用机器学习技术来提取欺骗活动的特征，这些特征可以用来构建监视和审计算法。该项目制定的方法与市场设计和监管政策指导相结合，有助于减少越来越有能力的市场操纵者带来的威胁。

Financial stock market manipulators can profit illegally by misleading investors about market conditions. For example, in several recent incidents, manipulators successfully spoofed markets by inserting orders that deceived investors about supply or demand for the security. This kind of behavior has increased with the prevalence of algorithmic trading. It imposes substantial harm to the economy, by reducing the efficiency of capital allocation, and more seriously, threatening to compromise the integrity and stability of financial markets. Spoofing is difficult to detect because the underlying actions have legitimate purposes as well as nefarious ones. This project will apply innovative approaches to improve detection and deterrence of market manipulation.The project will integrate data-driven methods, including calibration of detectors with normal background activity and extraction of manipulation signatures from enhanced time series, with model-based techniques for characterizing manipulation strategies based on strategic analysis of market microstructure. The key idea is to use simulation and optimization to generate successful manipulation strategies for trading models calibrated from available market data streams. These strategies will then be injected into the trading models, to produce enhanced data streams that include labeled manipulation activity. Having labeled activity enables the application of machine learning techniques to extract signatures of spoofing activity, which can be used to construct surveillance and audit algorithms. Methods produced in this project in conjunction with guidance on market design and regulation policy can contribute to reducing the threat from increasingly capable market manipulators.