Narrow Results

In the era of big data, how to obtain useful knowledge from online news and utilize it as an important basis to make investment decision has become the hotspot of industrial and academic research. At present, there have been research and practice on explicit knowledge acquisition from news, but tacit knowledge acquisition is still under exploration. Based on the general mechanism of domain knowledge, knowledge reasoning, and knowledge discovery, this paper constructs a framework for discovering tacit knowledge from news and applying the knowledge to stock forecasting. The concrete work is as follows: First, according to the characteristics of financial field and the conceptual cube, the conceptual structure of industry–company–product is constructed, and the framework of domain ontology is put forward. Second, with the construction of financial field ontology, the financial news knowledge management framework is proposed. Besides, with the application of attributes in ontology and domain rules extracted from news text, the knowledge reasoning mechanism of financial news is constructed to achieve financial news knowledge discovery. Finally, news knowledge that reflects important information about stock changes is integrated into the traditional stock price forecasting model and the newly proposed model performs well in the empirical analysis of polyester industry.

This paper aims at issues of Goldman Sachs’ stock forecasting in a short time by using the time series analysis based on four models: AR, MA, ARMA, and ARMA-GARCH models and chooses the optimal model. In this paper, after selecting the sample data and preprocessing data, the regression evaluation index is used to analyze the preliminary models. After that, use the Sequence Stationarity Test and ADF Test to test the series’ stationarity, and analyze the solution of the ARMA model to conclude the formula. The regression evaluation parameters are then compared to the initial models. Later, by selecting from Gaussian distribution, student t distribution, and biased student t distribution, the solution of the AGMA-GARCH model is analyzed. By constructing ARMA and GARCH models, the short-term forecast stock price results are valid and feasible. It concludes that the Arma-GARCH model greatly improves the accuracy of stock forecasting.