Abstract
Financial text-based risk prediction is an important subset for financial analysis. Through automatic analysis of public financial comments, fundamentals on current financial expectations can be evaluated. A deep learning method for financial risk prediction based on sentiment classification is proposed in this paper. The proposed method consists of two steps. Firstly, the abstract of the financial message is extracted according to the seq2seq model. During the extraction process, the seq2seq model can cope with the situation of different input message lengths. After the abstraction, invalid information in the financial messages can be effectively filtered, thus accelerating the subsequent sentiment classification step. The sentiment classification step is performed through the GRU model according to the abstracted texts. The proposed method has the following advantages: (1) it can handle financial messages of different lengths; (2) it can filter out the invalid information of financial messages; (3) because the extracted abstract is more refined, it can speed up the subsequent sentiment classification step; and (4) it has better sentiment classification accuracy. The proposed method in this paper is then verified through financial message dataset from the financial social network StockTwits. By comparing the classification performances, it can be seen that compared with the classical SVM and LSTM methods, the proposed method in this paper can improve the accuracy of sentiment classification by 5.57% and 2.58%, respectively.
Highlights
Despite having new tools and algorithms, financial market analysis is still a complex subject. e main purpose for financial market analysis is to assist investors in making decisions by analyzing the price fluctuations of the financial products
Sequence length of 40, the recognition rate is increased by 2.43%
After adopting GRU instead of LSTM, the training time of the model has reduced by about 48%
Summary
Despite having new tools and algorithms, financial market analysis is still a complex subject. e main purpose for financial market analysis is to assist investors in making decisions by analyzing the price fluctuations of the financial products. Financial risk analysis is a subtopic for financial market analysis, which can be adopted to predict the future price trends of financial products, and it is a hot topic of current research. With the in-depth application of machine learning in financial analysis in recent years, an effective financial analysis method has become more and more popular; that is, by mining important features from financial messages, such as financial news [7, 8], financial comments [9, 10], and social networks [11, 12], the sentiment tendency for a large number of users or authors can be evaluated, thereby capable of predicting financial prices. E mentioned method has the factors in fundamental analysis and has the advantages of automatic technical analysis, which has been a research hotspot in recent years With the in-depth application of machine learning in financial analysis in recent years, an effective financial analysis method has become more and more popular; that is, by mining important features from financial messages, such as financial news [7, 8], financial comments [9, 10], and social networks [11, 12], the sentiment tendency for a large number of users or authors can be evaluated, thereby capable of predicting financial prices. e mentioned method has the factors in fundamental analysis and has the advantages of automatic technical analysis, which has been a research hotspot in recent years
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