臺灣博碩士論文加值系統

確認公司是否能持續經營對於投資者和股東來說是一個重要的議題。在會計和審計領域，持續經營是一個眾所周知的概念，其用來衡量公司是否有足夠的資源得以永續經驗。然而，在當今複雜的商業環境難以評估公司的財務狀況。為了改善這個問題，有一些研究人員提出了新的方法來協助審計過程。大多數這些研究都是提出單一模型，並應用從財務報表收集的數據來驗證其方法。然而，仍有改進的空間，例如缺乏靈活性，普遍性和時間效率。為了解決這些問題，在本研究中，我們引進一個稱為集成方法的框架，並採用財務新聞作為分析數據的來源。集成框架的特徵之一是，如果新的方法效能比較好的話，較差的方法可以很容易的被替換。此外，財務新聞是一個重要的訊息來源，特別是考量到初上市公司缺乏年度報告的問題。本研究應用文字探勘技術來取出隱藏在財務新聞中的訊息，並將文件內容轉換為可於實驗中使用的數字格式。在研究一中，應用隨機森林方法來實現集成方法的概念。由實驗結果可得知，隨機森林方法在準確率，ROC面積，Kappa值，型II誤差，精確度和回憶率方面皆優於基準方法。此外，在研究二中獲得的實驗結果顯示，文字探勘技術對持續經營之預測表現良好。財務新聞是一個有用的參考來源，過去尚未有研究應用其於分析非新上市公司或是新上市公司在發布年度報告之前的持續經營狀況。

Ascertainment of the going-concern status of a company is a critical issue for investors and stockholders. In the Accounting and Auditing domain, the going-concern is a well-known concept used to measure whether a company has the resources to operate indefinitely or not. However, it is difficult to evaluate a company’s financial condition in today’s complicated business environment. To make this easier, some researchers have proposed new methods to assist in the auditing process. The majority of these studies have proposed single models, applying numerical data gathered from financial statements to verify their methodology. However, shortcomings remain such as a lack of flexibility, generalizability and time efficiency. In order to address these issues, in this study, we introduce a framework called the ensemble method and adopt financial news as source of data source. One of the characteristics of the ensemble framework is that a weaker algorithm can be easily replaced by another if it is better. In addition, financial news is an important source of information, especially given the issue of the lack of annual reports for a new to market company. Text mining techniques are applied to capture messages hidden in financial news, and convert the textual data to a numerical format for implementation in the experiments. In study one, the random forest method is applied to implement the concepts of the ensemble method. The experimental results show that the random forest method outperforms the baseline methods in terms of accuracy rate, ROC area, kappa value, type II error, precision and recall rate. In addition, the experimental results obtained in study two reflect that text mining techniques perform well for going-concern prediction. Financial news is a useful data source for analyzing the going-concern status of a company before the issue of an annual report or for a new to market company, where such reports do not yet exist.

論文審定書 i
中文摘要 ii
ABSTRACT iii
Chapter 1 Introduction 1
1.1 Research background 1
1.2 Research motivation 1
1.3 Research objectives 6
Chapter 2 Literature Review 8
2.1 Going-concern prediction literature 8
2.2 Literature comparison 11
Chapter 3 Ensemble Framework based on Structured Data 15
3.1 Datasets 15
3.2 Variables 16
3.3 Ensemble method 19
3.4 Random forest 20
3.4.1 Decision Tree 22
3.5 Prediction methods 24
3.5.1 Random forest 24
3.5.2 Baseline methods 24
3.5.3 Evaluation method 26
3.5.3.1 Prediction accuracy and Type II error rate 27
3.5.3.2 Kappa statistic value 28
3.5.3.3 Receiver Operating Characteristic Curve 29
3.5.3.4 Precision and recall rates 31
3.5.3.5 F-measure 31
3.6 Imbalanced data 32
Chapter 4 Ensemble Framework based on Un-structured Data 34
4.1 Financial news articles 35
4.2 Text mining 37
4.3 Textual representation 44
4.3.1 Bag-of-words Method 44
4.3.2 Term weighting 45
4.3.3 Latent Topic Analysis 48
4.4 Clustering algorithms 51
4.4.1 K-nearest neighbor 51
4.4.2 K-means 53
4.4.3 Self-organizing map 54
4.5 Configuration of the un-structured data framework 56
4.5.1 Data source 56
4.5.2 Model Construction and Evaluation Methods 57
Chapter 5 Performance Evaluation of the Structured Model 63
5.1 Experimental results for pre-subprime mortgage crisis 63
5.1.1 Experimental results for the original dataset 64
5.1.2 Performance comparison using datasets with different proportion 68
5.2 Experimental results for the post-subprime mortgage crisis 75
5.2.1 Experimental results for the original dataset 75
5.2.2 Performance comparison using datasets with different proportions 81
5.3 Experimental results for the full dataset 87
5.3.1 Experiment results with the original dataset 88
5.3.2 Performance comparison on datasets with different proportions 91
5.4 Discussion of Type II error risk and summary 97
5.5 Verification of generalizability on a Taiwan dataset 100
5.6 Performance evaluation from the viewpoint of a time series 101
5.6.1 Performance comparison based on a one pair one combination 103
5.6.2 Performance comparison based on the two pair one combination 108
Chapter 6 Performance Evaluation of the Un-structured Model 115
6.1 Experimental results for scenario one 116
6.1.1 Prediction performance of the TFIDF method 116
6.1.2 Prediction performance of LDA method 125
6.1.3 Performance comparison of the two methods 132
6.1.4 Prediction performance of the TFIDF method after feature extraction 138
6.2 Experimental results of scenario two 145
6.2.1 Prediction performance of the LDA method 145
Chapter 7 Conclusions 161
References 167

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