臺灣博碩士論文加值系統

全球化趨勢下，各國政經或疫情等因素致使經濟瞬息萬變，各類型企業面臨更多的經營風險與發展危機，因此能時刻掌握企業的財務狀況以確保目前狀態與預測未來走向堪為首要之重。若能有效掌握財務狀況，將有助於會計師或查核人員及早發現企業是否有繼續經營的疑慮，並進一步藉由查核人員的專業判斷，做出正確的意見判斷。
本研究採用深度學習(Deep Learning)中的長短期記憶網絡(Long Short-Term Memory; LSTM)方法預測繼續經營疑慮，該方法是基於多層架構並回饋歷史資料進行學習分析，也許更適合處理複雜且龐大的資料，對於有時續性的財務比率資訊可預測性更高。本文同時比較過去文獻較常使用機器學習(Machine Learning; ML)的決策樹(Decision Tree; DT)及支援向量機(Support Vector Machine; SVM)演算法，且建構驗證組以2018第一季當作基期分別以t期至t+4期作驗證組預測。
研究結果評比方面，本研究採用混淆矩陣(Confusion Matrix)方式找出分類結果之優劣，並結合分類模型的各種評估指標，準確率(Accuracy Rate)、精確率(Precision Rate)、召回率(Recall Rate)及F1分數(F1-score)。實證結果顯示，LSTM方法對於評估指標的優越性(準確率、精確率和F1分數)表現優於DT及SVM模型，並證實LSTM方法在訓練組及測試組都有最佳的預測結果：在訓練組中，334筆季報被出具繼續經營疑慮的樣本的中有332筆預測成功；測試組中，有98筆被出具繼續經營疑慮的樣本中有96筆預測成功。

Under the trend of globalization, various countries’ politics, economies, or epidemics have caused rapid economic changes, and various types of enterprises are facing more business risks and development crises. Therefore, it is important to keep abreast of the enterprise's financial status to ensure the current status and predict the future direction. If the auditor can effectively understand the client's financial situation, it will help to make correct judgments as soon as possible on issues the going concerned opinion.
This research uses the Long Short-Term Memory method in Deep Learning to predict continued business doubts. This method is based on a multi-layer architecture and feeds back historical data for learning and analysis. It may be more suitable for processing complex and huge data is more predictable for sometimes continuous financial ratio information. Compared with the past literature, Machine Learning’s Decision Tree and Support Vector Machine are more commonly used. The construction verification team uses the first quarter of 2018 as the base period, respectively. From t period to t+4 period, the prediction of the validation group was made.
As for the evaluation of research results, this study uses the confusion matrix method to compare the classification results of different models and combines the various evaluation indicators of the classification model with accuracy, accuracy, recall, and F1- score. The empirical results show that the superiority (Accuracy Rate, Precision Rate, and F1-score) of the LSTM method is better than the DT and SVM models, and it is confirmed that the LSTM method has the best prediction results in the training group and the test group: In the group, 334 of the 334 quarterly reports that were issued with continued business doubts, 332 predicted success. In the test group, 96 out of 98 samples with continued business doubts were predicted to succeed.

摘要 i
ABSTRACT ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第三節 研究流程 4
第二章 文獻探討 5
第一節 繼續經營疑慮分類相關文獻 5
第二節 遞歸神經網絡 8
第三節 長短期記憶網絡 9
第四節 LSTM 運用在會計領域預測模型之探討 11
第三章 研究方法 13
第一節 研究架構 13
第二節 研究變數 15
第三節 樣本選取與資料來源 16
第四節 資料探勘預測之模型架構 18
一、資料探勘定義 18
二、DT 19
三、SVM 20
第五節 深度學習LSTM 之模型架構 21
第六節 分類模型績效評估 22
第四章 實驗設計與結果分析 24
第一節 實驗環境 24
第二節 研究規劃與設計 25
第三節 模型參數設置及預測結果 29
一、 DT 分析 29
二、 SVM 分析 31
三、 LSTM 分析 33
第四節 預測結果比較 35
第五章 結論與建議 39
第一節 研究結論與研究貢獻 39
參考文獻 40

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