基于突发事件的股票风险预测方法研究

发布时间：2018-05-20 18:00

本文选题：风险预测 + 主题模型　；参考：《哈尔滨工业大学》2013年硕士论文

【摘要】：2008年金融海啸发生后，金融风险问题开始日渐引起管理机构及学者的重视。近年来，随着社交网络的飞速发展，突发性金融信息会在短时间内迅速传递至每一个用户，而用户会多渠道、多次转发相同信息，更会加剧突发事件所蕴含金融风险的影响。突发事件在短时间内即会对相关金融资产价格造成较强扰动，，因此迫切需要研究面向突发事件的金融风险预测方法。然而，传统方法在突发事件发生的情况下并不能有效的预测突发事件带来的金融风险。原因在于传统的方法如宏观模型法、时间序列等方法仅针对金融资产价格变化，或其基本面影响因子进行预测研究，而这些方法已经被证明在突发事件带来的强扰动下是无效的。因此，需要研究能够快速提炼突发事件所蕴含风险的建模方法。已有研究表明，金融文本信息与股票价格变化存在关联关系，在此基础上，本文提出通过学习突发事件的主题的分布与股票风险变化之间的关联关系，来对股票风险进行预测。本文的主要研究工作包括：首先，针对突发新闻与股票对应关系的缺失问题，本文采用基于高斯分布对突发新闻与股票异常波动的共现关系进行过滤，从而识别突发事件与股票的多对多关系；其次，针对突发事件所蕴含金融风险的估值量化问题，本文首先使用无监督的主题聚类模型，对风险主题事件进行聚类，利用事件与股票的多对多关系，建立随机游走模型，识别并量化主题事件对应的股票金融风险；最后，针对主题聚类模型未能考虑股票价格变化对模型的反馈影响问题，本文提出建立有监督的主题聚类模型，模型学习时同时考虑股票价格变化及新闻主题分布的耦合关系，最后利用训练好的模型来估计股票的金融风险。实验结果表明在突发事件发生的情况下，通过本文所提出的两种主题聚类的方法对股票的金融风险进行预测时，能够较传统方法获得更为准确的预测结果。
[Abstract]:After the 2008 financial tsunami, the financial risk problem began to attract the attention of regulators and scholars. In recent years, with the rapid development of social networks, the sudden financial information will be transmitted to every user in a short time, and the users will transmit the same information through multiple channels, which will aggravate the impact of financial risks contained in emergencies. Sudden events will cause strong disturbances to the prices of related financial assets in a short period of time, so it is urgent to study the financial risk forecasting methods for emergencies. However, the traditional method can not effectively predict the financial risks brought by emergencies. The reason is that the traditional methods, such as macroscopic model method, time series method and so on, are only used to predict the change of financial asset price or its fundamental influencing factors. These methods have been proved to be ineffective under the strong disturbance caused by the emergency. Therefore, it is necessary to study modeling methods that can quickly extract the risks involved in emergencies. Previous studies have shown that financial text information has a correlation with stock price changes. On this basis, this paper proposes to predict the stock risk by learning the correlation relationship between the distribution of the subject of unexpected events and the change of stock risk. The main research work of this paper includes: First of all, aiming at the lack of corresponding relation between emergency news and stock, this paper uses Gao Si distribution to filter the co-occurrence relationship between unexpected news and stock abnormal fluctuation, so as to identify the many-to-many relationship between unexpected news and stock. Secondly, aiming at the quantification of financial risk involved in unexpected events, this paper first uses the unsupervised thematic clustering model to cluster the risk thematic events, and establishes a random walk model by using the many-to-many relationships between events and stocks. Identifying and quantifying the stock financial risk corresponding to the subject event; Finally, in view of the problem that the topic clustering model fails to consider the feedback effect of stock price change on the model, this paper proposes a supervised thematic clustering model, in which the coupling relationship between stock price change and news topic distribution is taken into account in model learning. Finally, the trained model is used to estimate the financial risk of stocks. The experimental results show that when the financial risk of stocks is predicted by the two thematic clustering methods proposed in this paper, it can obtain more accurate results than the traditional methods.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F830.91;F224

【共引文献】