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NSYSU

1. Jhang, Ya-ting. A forecasting model to predict business performance trend by combining textual information and financial ratios.

Degree: Master, Information Management, 2017, NSYSU

The annual report is a complete financial report of the company. It contains textual and financial information such as balance sheet, operating conditions and financial status to help investors have better understanding of the companyâs operating status and the future policy. Compared with traditional analysis method based on financial ratios, the textual information derived from annual reports can supply much more immediate and helpful clues related with the companyâs operating status and future direction. Therefore, textual contents are very necessary information for investors to make decisions. In the previous studies, we found that few researchers employed textual information to predict the business performance trend. Most of them estimated corporate performance only with financial ratios. Therefore, this study combines textual information and financial ratios to predict business performance trend. To analyze the textual information of annual reports, we examine the explanatory contents extracted from annul reports to obtain the text information. The number of variables are reduced by exploratory factors analysis (EFA) into more accurate variables. Afterwards, we adopt Synthetic Minority Over-sampling Technique (SMOTE) to address imbalanced data problem. To examine the performance of combing textual information and financial ratios, we apply three classifiers including Naïve Bayes, SVM, logistic regression. According to the results of experiment, the textual information can strengthen the modelâs forecasting performance. The investors and shareholders can take this model to support them managing their investment strategies. Advisors/Committee Members: Yi-Ling Lin (chair), Te-Min Chang (committee member), Ming-Fu Hsu (chair).

Subjects/Keywords: Support vector machine; Logistic regression; Business performance trend; Naïve Bayes; Forecasting models

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APA (6th Edition):

Jhang, Y. (2017). A forecasting model to predict business performance trend by combining textual information and financial ratios. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708117-200339

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Jhang, Ya-ting. “A forecasting model to predict business performance trend by combining textual information and financial ratios.” 2017. Thesis, NSYSU. Accessed January 23, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708117-200339.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Jhang, Ya-ting. “A forecasting model to predict business performance trend by combining textual information and financial ratios.” 2017. Web. 23 Jan 2020.

Vancouver:

Jhang Y. A forecasting model to predict business performance trend by combining textual information and financial ratios. [Internet] [Thesis]. NSYSU; 2017. [cited 2020 Jan 23]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708117-200339.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Jhang Y. A forecasting model to predict business performance trend by combining textual information and financial ratios. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708117-200339

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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