臺灣博碩士論文加值系統

年度報告是公司在一個會計年度結束後製作的財務會計報告，內容含有資產負債表、營運與財務狀況等等資訊，有助於投資者和股東更了解該年度公司的財務狀況以及未來政策的運行方向。我們相信相較於財務報表的財務數據，年度報告的解釋性文字資訊能夠帶來更多有價值的資訊，例如最近年度的投資政策、虧損或獲利的主要原因、風險管理評估分析事項，以及未來一年投資計畫，而公司的財務報表則需要符合特定的會計準則規定，可能會造成財務資訊無法呈現最真實的狀況。另外一方面，財務數據是總結過去公司的表現，資料具有時間上的延遲性，若是投資者僅以財務資訊作為投資的參考依據，會產生時效性的問題，無法了解公司未來走向並做下更有效的決定。而文字資訊具有即時性，除了說明公司當下的狀況，也包含未來公司如何發展的資訊內涵，故文字資訊能夠彌補財務資訊在時效性上的缺點。
在以往的相關研究中，我們發覺很少有研究將文字資訊應用在預測企業的績效表現，大部分只採用財務資訊來衡量績效，因此，本研究結合文字資訊和財務指標來預測公司績效成長的能力。在本研究中，我們將實驗可分成三個步驟來說明，首先，為了深入分析與了解年度報表的文字資訊，本研究利用年度報表中含有未來策略資訊內涵的解釋性內容來進行分析。再來我們使用探索性因素分析，將過多的變數結合成核心因素之後，採用SMOTE過採樣算法來進一步處理不平衡資料問題，最後再用分類器來得到結果。
為了更加檢驗文字資訊結合財務指標的預測能力，本論文使用三種分類器來進行檢驗，實驗的結果發現，在單純貝氏分類(Naïve Bayes)、支援向量機(SVM)和邏輯式迴歸(Logistic regression)中，文字資訊結合財務指標的預測能力都比純用財務指標或純用文字資訊的預測能力都還要好，這說明若能在財務資訊的基礎上再搭配上文字資訊的輔助，能夠幫助投資者和股東對公司績效成長有更好的判斷。

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 company’s 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 company’s 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 model’s forecasting performance. The investors and shareholders can take this model to support them managing their investment strategies.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
List of Figures vii
List of Tables viii
Chapter 1. Introduction 1
Chapter 2. Literature review 5
2.1 Textual information in finance domains 5
2.2 Processing of textual information 6
2.3 The measurement of business performance trend 8
2.4 Financial variables in finance domain 10
2.5 Variable reduction 13
2.6 Imbalance data preprocessing 14
2.7 The application of text mining 16
Chapter 3. Methodology 23
Step1. Appropriate indicator selection 24
Step2. Dimension reduction 28
Step3. Imbalance data manipulation 29
Step4. Model generation 30
Chapter 4. Experiment results and analyses 32
4.1 Experiment Design 32
4.2 Experiment I: Step 2. Dimension reduction 35
4.3 Experiment II: Step 3. Imbalanced data manipulation 39
4.4 Experiment III: Step 4. Model generation 40
Chapter 5. Conclusion 44
5.1 Summary 44
5.2 Future work 46
REFERENCE 47

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