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題名 Measuring the consistency of quantitative and qualitative information in financial reports: A designing science approach
作者 金成隆
Chin, Chen-Lung
Chou, Chi-Chun;Chang, C. Janie;Chiang, Wei-Ta
貢獻者 會計系
關鍵詞 Text analytics/mining; K-means; MD&A; disclosure discipline
日期 2018-10
上傳時間 23-Jun-2022 09:50:34 (UTC+8)
摘要 This study uses a design science approach to examine the consistency between quantitative financial ratios and qualitative narrative disclosures in the annual reports. To extract information on the tone of unstructured qualitative textual data, we first use the term frequency/inverse document frequency (TFIDF) text mining technique to classify each company`s narrative disclosure as either “Positive” or “Negative.” For the quantitative information, we use the K-means method to cluster each company`s financial performance data into “Good” or “Poor” groups. Consistency is said to occur when the textual and numerical data form either a “Positive-Good” pair or a “Negative-Poor” pair. The design model is presented in a stepwise fashion and therefore is transparent for evaluation and validation. Our evaluation process demonstrates the feasibility of the design model. The evaluation was conducted using listed semiconductor companies in countries with different levels of market development. The results show that U.S. firms are less likely to exaggerate in their narrative disclosures and are more likely to understate their performance in MD&As compared to companies in other markets such as China and Taiwan.
關聯 Journal of Emerging Technologies in Accounting, 15(2), 93-109
資料類型 article
DOI https://doi.org/10.2308/jeta-52312
dc.contributor 會計系
dc.creator (作者) 金成隆
dc.creator (作者) Chin, Chen-Lung
dc.creator (作者) Chou, Chi-Chun;Chang, C. Janie;Chiang, Wei-Ta
dc.date (日期) 2018-10
dc.date.accessioned 23-Jun-2022 09:50:34 (UTC+8)-
dc.date.available 23-Jun-2022 09:50:34 (UTC+8)-
dc.date.issued (上傳時間) 23-Jun-2022 09:50:34 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/140346-
dc.description.abstract (摘要) This study uses a design science approach to examine the consistency between quantitative financial ratios and qualitative narrative disclosures in the annual reports. To extract information on the tone of unstructured qualitative textual data, we first use the term frequency/inverse document frequency (TFIDF) text mining technique to classify each company`s narrative disclosure as either “Positive” or “Negative.” For the quantitative information, we use the K-means method to cluster each company`s financial performance data into “Good” or “Poor” groups. Consistency is said to occur when the textual and numerical data form either a “Positive-Good” pair or a “Negative-Poor” pair. The design model is presented in a stepwise fashion and therefore is transparent for evaluation and validation. Our evaluation process demonstrates the feasibility of the design model. The evaluation was conducted using listed semiconductor companies in countries with different levels of market development. The results show that U.S. firms are less likely to exaggerate in their narrative disclosures and are more likely to understate their performance in MD&As compared to companies in other markets such as China and Taiwan.
dc.format.extent 547909 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Journal of Emerging Technologies in Accounting, 15(2), 93-109
dc.subject (關鍵詞) Text analytics/mining; K-means; MD&A; disclosure discipline
dc.title (題名) Measuring the consistency of quantitative and qualitative information in financial reports: A designing science approach
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.2308/jeta-52312
dc.doi.uri (DOI) https://doi.org/10.2308/jeta-52312