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研究生: 黃裕培
Huang, Yu-Pei
論文名稱: 應用深度信念網路演算法於公司財務困境預測模型建構之研究
Devising a Model to Predict Financial Distress Based on the Deep Belief Network Algorithm
指導教授: 顏盟峯
Yen, Meng-Feng
學位類別: 碩士
Master
系所名稱: 管理學院 - 高階管理碩士在職專班(EMBA)
Executive Master of Business Administration (EMBA)
論文出版年: 2017
畢業學年度: 105
語文別: 英文
論文頁數: 32
中文關鍵詞: 深度信念網路機器學習支持向量機財務困境預測
外文關鍵詞: Deep belief network, Machine learning, Support vector machine, Financial distress prediction
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  • 近年來,以機器學習(machine learning)等人工智慧(AI)技術應用於會計資訊系統日益被學者所重視。由會計資訊系統所產生之財務四大報表內容,可瞭解一間公司的財務和運作狀況。因此從財務報表所產生的各項財務數據,可用於預測一間公司是否將面臨財務危機(financial distress)。本研究提出以16個選擇的財務變數結合「深度信念網路」(deep beleief network)和「支持向量機」(support vector machine)機器學習演算法,以建立「財務危機預測模型」。並以台灣經濟新報資料庫(Taiwan Economic Journal)提供之32家在2010年到2016年間發生財務危機之公司及32家對照公司之財務資料,驗證預測模型之準確度。本研究提出之模型使用「深度信念網路」從16個選擇的財務變數資料萃取出3個內蘊之特徵參數,並藉由「支持向量機」區別財務危機公司和財務健全公司。本研究之實驗結果證實該模型具備準確之預測能力,使用交叉驗證(cross-validation)法計算出,當使用財務危機發生前兩季財報數據作為輸入,此「財務危機預測模型」的預測準確度最高可達89%,而其型一誤差和型二誤差分別為4.7%和6.2%。

    The use of new artificial intelligent (AI) techniques, such as machine learning (ML) in the accounting domains, have unleashed great potential for researchers to improve accounting information systems (AIS). An automatic AIS reports financial statements from the supporting documents. The basic four financial statements provide information about the results of operations and financial position of an enterprise. As a result, those financial statements could be used to establish a diagnosis model for financial distress prediction (FDP). This study proposes an FDP model based on two ML approaches. Sixteen selected financial variables are calculated from financial statements to establish an FDP model using the deep belief network (DBN) algorithm coupled with the support vector machine (SVM) algorithm. Thrity-two distressed and thirty-two non-distressed companies (as matching samples) companies are selected from the Taiwan Economic Journal (TEJ) database, spanning the 2010-to-2016 sample period, to construct the FDP model. Three latent features of the financial data are extracted from 16 selected ratios by DBN and then divided into validation and training sets for SVM classification model construction. The constructed model is further used for prediction and evaluated by cross-validation. Our empirical results demonstrate that the proposed model could accurately predict the financial distress of a company. When using the previous two consecutive quarters of financial data before any event of distress, the prediction accuracy of the model could reach around 89% with the type I error of 4.7% and the type II error of 6.2%.

    ABSTRACT (CHINESE) I ABSTRACT II ACKNOWLEDGES III CONTENT IV LIST OF TABLES V LIST OF FIGURES VI ABBREVIATIONS VII Chapter 1 INTRODUCTION 1 1.1 BACKGROUND 1 1.2 ORGANIZATION OF THE THESIS 4 Chapter 2 LITERATURE REVIEW 5 Chapter 3 METHODOLOGY 7 3.1 PREDICATION MODEL 7 3.2 DEEP LEARNING FOR FEATURE EXTRACTION 11 3.2.1 BELIEF NETWORK 11 3.2.2 RESTRICTED BOLTZMANN MACHINE 12 3.2.3 DEEP BELIEF NETWORK 14 3.3 SUPPORT VECTOR MACHINE 17 Chapter 4 EMPIRICAL RESULTS 21 4.1 FEATURE EXTRACTION RESULTS 21 4.2 CLASSIFICATION RESULTS 26 Chapter 5 CONCLUSION 29 REFERENCES 30

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