||Due to the explosion of the information technology, decision makers or market participants can easily access to the information from the Internet in order to form their investment decision. Among all of the information, the financial news can reflect the publics’ opinions and provide immediate information about the corporate’s operating situation. In comparison with the traditional accounting-based ratio analysis, the textual information derived from financial news can provide market participants much more instant, relevant and valuable cues related to the corporate operation status. It is very essential information for decision-makers to adjust their strategies. |
To deal with the large amount of textual information, this study proposes a novel decision-making architecture. Latent Dirichlet allocation (LDA), one of the topic modelling techniques, is implemented to extract the useful topics that are related to corporate operating status from large amount of textual information. The extracted topics are matched with the corpus to determine the indicators, namely “Intensity of Business Performance-corpus index (IBPCI)”, that can transform the textual information to numerical ratios.
To examine the effectiveness of the IBPCI indicators, the experimental designs in this study was divided into two scenarios: (1) SVM and (2) Tobit regression. According to the experimental results, the IBPCI indicators not only can enhance the model’s forecasting performance (SVM and Tobit regression) as well as facilitate the model’s explanation ability. The decision makers can take this model as a roadmap to modify their investment strategies as well as to maximize their personnel wealth.