1、中文 4300 字 ， 2400 英文单词， 1.3万英文字符 出处： Ishibashi K, Iwasaki T, Otomasa S, et al. Model selection for financial statement analysis: Variable selection with data mining technique J. Procedia Computer Science, 2016, 96(C):1681-1690. 英 文： Model selection for financial statement analysis: Variable selection
2、 with data mining technique Ken Ishibashia, Takuya Iwasakia, Shota Otomasaa and Katsutoshi Yadaa Abstract The purpose of this study is to verify the effectiveness of a data-driven approach for financial statement analysis. In the area of accounting, variable selection for construction of models to p
3、redict firms earnings based on financial statement data has been addressed from perspectives of corporate valuation theory, etc., but there has not been enough verification based on data mining techniques. In this paper, an attempt was made to verify the applicability of variable selection for the c
4、onstruction of an earnings prediction model by using recent data mining techniques. From analysis results, a method that considers the interaction among variables and the redundancy of model could be effective for financial statement data. Keywords: Financial statement analysis; earnings prediction
5、model; model selection; variable selection; data mining 1. Introduction Recent advancement in information and communication technology is dramatically improving computational speeds. Under the circumstances, researchers have addressed studies focused on big data accumulated in various areas. Data mi
6、ning techniques play an important role in data-driven analysis and modeling. Various methods related to data mining have been developed until now, and software such as SPSS and Weka has been developed to enable us to use them easily. However, for these applications, we generally need to select a method appropriate to data. The purpose of this study is to verify the effectiveness of a data-driven approach for the financial statement analysis. In the area of accounting, Ou and Penman (1989
