基于量化特征的价格操纵行为监测模型研究

价格操纵通常不包括明显的非法行为(诸如散布金融谣言和控制股权的供需),而是通过看似合法的报单、撤单等交易行为来实现.本文提出了一种基于隐马尔可夫模型的市场价格操纵监测模型系统:首先,基于三类典型的市场价格操纵实例,分析市场价格操纵行为模式的内在特性,利用小波变换和梯度分析作为特征抽取工具,抽取关键特征模式,量化操纵行为的特征模式,通过隐含状态转换机制完整描述市场操纵行为的各种情况组合,解决"异常检测"进行价格操纵监测时不能确定异常行为的具体类型及概率密度函数问题;其次,为提高模型对非平稳性金融数据的适应性,模型增加了一个自适应机制来进行校准,自动跟踪金融时间序列的统计特性的变化,提出基于隐马尔可夫模型的市场价格操纵行为监测模型;最后,模型利用纳斯达克和伦敦股票交易所的真实交易数据及模拟数据对模型的有效性、精确性和稳定性进行实验验证,结果显示无论是使用真实数据还是随机模拟的数据集,本文提出的基于隐马尔可夫模型的监测模型性能均显著高于市场中常见的三类基准模型,为理论研究和实际操作提供了一个完备的验证渠道.

A study of price manipulation behaviours surveillance based on quantized features

Price manipulation usually does not contain explicitillegitimate activities (i.e. financial rumour spreading, equity supply or demand squeezing), instead, includes submission and cancellation of limit orders, which appear to be normal trading behaviours. This paper proposes a Hidden Markov Model based system for detecting price manipulation behaviours in the capital markets. This paper starts from a thorough study of three primary types of price manipulation strategies, from which the intrinsic patterns of the manipulation is extracted through features extraction module, composed of wavelet transformation and gradient method. The extracted features are modelled by Hidden Markov Model, where the intentions of the trading are distinguished, quantitated and designated through the hidden states, which generate the variables that can be directly observed from the market. To overcome the non-stationary nature of the financial data, an adaptive mechanism is proposed for adaptively updating the model. Experimental evaluations for the new proposed system are conducted based on real financial data from NASDAQ and London Stock Exchanges as well as the simulated stock prices. Evaluations show that the proposed system stably outperforms the selected bench market models.