Abstract
The objective of this study is to develop an algorithm to support a decision-making process in stock investment through opinion mining and graph-based semi-supervised learning. For this purpose, this research addresses the following core processes: (1) filtering fake information, (2) assessing credit risk and detecting risk signals, and (3) predicting future occurrences of credit events through sentiment analysis, word2vec, and graph-based semi-supervised learning. First, financial data, including news, texts in social network services, and financial statements, were collected. Among these data, fake information such as rumors and fake news was filtered by author analysis and a rule-based approach. Second, credit risk was assessed by opinion mining and sentiment analysis for both social data and news in the form of a sentiment score and the trend of documents for each stock. A signal for a credit event was then detected by the degree of assessed risk. Consequently, the possibility of credit events such as delisting and bankruptcy in the near future was forecast based on the risk signal using logistic regression. This research illustrated the real case of a company to validate the applicability of the proposed approach. The results of this study can help investors monitor a large amount of historically accumulated data and detect hidden signals of risk events ahead of time.
Highlights
A global economic crisis arose from the subprime mortgage crisis of 2008, resulting in corporate bankruptcies or delistings from securities markets amid a shrinking national economy [1]
Since uncertainties in the shipping industry can affect the variability of firms, the financial crisis could be on the rise
In summary, this paper suggests a new algorithm to support decision-making in stock investment by detecting early signals and predicting the occurrence probability of credit events through opinion mining and logistic regression models
Summary
A global economic crisis arose from the subprime mortgage crisis of 2008, resulting in corporate bankruptcies or delistings from securities markets amid a shrinking national economy [1]. Such an economic crisis is usually caused by an accumulation of small events leading to a potentially great impact [2]. If these small events can be recognized and caught beforehand, a severely damaging national or global crisis may be prevented by treating them ahead of time. Monitoring and prevention based on early detection have been emphasized in recent times as important for
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