Analysis And Implementation Of Stock High Frequency Trading Based On Public Opinion On Weibo

As an on-going development of the modern financial market and information network technology, the means and ways of trading in the securities market are constantly making new changes. The stock market, as the most influential securities market, has shown a lot of characteristics of high frequency trading (HFT). This new trend of development has received a high level of attention from the investors and managers of the securities market as well as the research scholars of the related field. It has become an important new hot topic of the securities market research.Market sentiment, irrational behavior of the investors and noise generated by various events can all influence HFT greatly. This produces a new challenge to traditional financial transactions. In recent years, many researchers find out that there is a strong correlation between the tendency and the spread of market sentiment reflected by public opinion on Weibo and HFT. The internal mechanism and the method of analyzing the public opinion on Weibo are the key point to be explored and researched thoroughly in technical analysis of HFT.This paper selects topic from High Level Academic Research Program of Financial Research Center, Fudan University (No.2012FDFRCGD02). This article tries to construction a new method to analyze HFT based on the public opinion on Weibo, from the point of behavioral finance and affective computing. Starting from the influence factors of HFT, this article first elaborates the essential feature of Chinese stock market, which to a larger extent affected by the news and investor sentiment, based on the analysis of the behavioral finance and the empirical conclusions of related scholars. Then, this paper classifies public opinion caused by all kinds of event messages, and analyzes in depth the systematic mechanism and the process of how the trade behavior is impacted by the generation of the above mentioned public opinion and the spread of sentiment. Base on all of above, this paper researches the method of the sentiment analysis with the public opinion on Weibo.For the problem of text processing Weibo data, we propose an exploratory data analysis method using securities sentiment word dictionary based on natural language processing (NLP) in addition to emoticon sentiment classification. We also propose an analytical framework of affective computing based on the support vector machine (SVM), a machine learning method for the sentiment analysis of the public opinion on Weibo.For the application of technical analysis of HFT, we proposes an HFT analysis model based on the results of affective computing which come from the traditional financial measurement model, combined with sentiment analysis variables and time series analysis of HFT data. In addition, the elements and the framework of this model are analyzed, and an empirical test is performed. This paper also elaborates the framework design of software system, data flow and operation mode of this model, and software implementation.The study of this paper reveals the conduction of stock market sentiment in HFT and the information processing method of opinion on Weibo and provides a new reference for correlation analysis techniques.