本研究旨在由傳統財務指標及公司治理角度分析近年來我國因財務報表舞弊遭一審判決有罪之公司發生的舞弊警訊(Red Flags)，建立財務報表舞弊預警模型，作為會計師執行舞弊偵測時的參考，降低因查核舞弊所產生的審計失敗，並可作為主管機關實質審查選案標準及投資人選擇投資標的之參考。 本文以民國85年至95年因財務報表舞弊遭法院一審判決有罪之27家公司為樣本，分別使用財務及公司治理變數建立財務報表舞弊預警模型，再以Cascade Logistic迴歸分析結合財務及公司治理模型，檢視Cascadev Logistic模型的正確判別率。實證結果顯示，財務變數中舞弊當期應付帳款成長率、舞弊當期存貨成長率、應收帳款收現天數對前期同一比率與財務報表舞弊呈顯著正相關；舞弊當期資產報酬率對前期同一比率、舞弊當期銷貨成長率與財務報表舞弊呈顯著負相關。公司治理變數中董監持股、控制持股、外部個人監事席次與財務報表舞弊顯著負相關；財報重編次數呈顯著正相關，良善的公司治理機制與財務舞弊具負向關連性。而整合的Cascade Logistic模型，財務及公司治理變數均與財務報表舞弊呈顯著相關，且均具增額解釋。 / Presented are a profile of a sample of fraud companies in Taiwan from 1996 to 2006, their financial and corporate governance characteristics, suggested fincial and corporate governance logistic models for detecting fraud and a final combine cascade logistic model. The results suggest both systematic relationships between the probability of fraud and financial and corporate governance characteristic variables. This evidence is consistent with the usefulness of accounting data in detecting fraud and the effectiveness of proper corporate governance in protecting fraud happening. Because the cascade logistic model correctly identifies approximately 90% of the companies involved or not invleved in fraud, the model can be a useful screening device for auditors to detect fraud, low down the propobility of audit failure and the cost of lawsuit due to fraud.

由於財務報表舞弊往往足以震撼投資大眾，造成資本市場重大損失，各國監管單位無不盡力降低此事件之爆發，以維護資本市場秩序、保障投資人，是以本研究欲瞭解影響中國大陸上市公司舞弊之因素為何，以及如何建立舞弊預測模型提供財務報表使用者作為參考之用。本文利用2007年至2014年受懲罰之上市公司為研究對象，採Logistic迴歸進行實證分析，結果顯示裁決性收入與Z"-Score對於財務報表舞弊無顯著相關，相反的獨立董事比例、是否具ST壓力、存貨週轉率、應收帳款週轉率、主營業務利潤率與財務報表舞弊具顯著關係，另外利用迴歸結果中顯著變數建立財務報表舞弊模型，發現整體正確率為53.31％。 / Due to the severe impacts caused by fraudulent financial reporting, securities regulatory commissions in most countries put much emphasis on maintaining the order of the capital markets and protecting the investors’ interests. In order to realize the factors of financial statement fraud, especially for China listed companies, and build the detecting model for the financial statements users, I select some listed companies punished by the government during the period 2007-2014 as the samples in this dissertation. Then, I use logistic regression model to test which variables are significant to fraudulent financial reporting, and the results show that the discretionary revenue and Z"-Score do not have impact on it. On the contrary, the percentage of independent directors, pressure from avoiding being “ST”, inventory turnover, accounts receivable turnover, and percentage of income from main operation are significantly relevant to fraudulent financial reporting. Moreover, when including these significant variables in the detecting model, the accuracy of the model can up to 53.31 percent.

Financial reporting fraud leads to not only significant investment risks for external stockholders, but also financial crises for the capital market. Although the issue of fraudulent financial reporting has drawn much attention, relevant research is much less than issues of predicting financial distress or bankruptcy. Furthermore, one purpose of exploring the financial reporting fraud with various forms is to obtain a better understand of the corporate through investigating its financial and corporate governance indicators. This study addresses the challenge with proposing an approach with the following four phases: (1) to identify a set of financial and corporate governance indicators that are significantly correlated with the financial reporting fraud; (2) to use the Growing Hierarchical Self-Organizing Map (GHSOM) to cluster the normal and fraud listed corporate data; (3) to extract knowledge about the financial reporting fraud through observing the hierarchical relationship displayed in the trained GHSOM; and (4) to make the justification of the extracted knowledge. The proposed approach is feasible because researchers claim that the GHSOM can discover the hidden hierarchical relationship from data with high dimensionality.

財務危機模型的研究一般納入財務正常公司與財務危機公司兩者當樣本，探討區分危機與正常公司的因素，本研究則進一步以財務危機公司為樣本，探討在財務危機公司中區分舞弊公司與正常經營公司的基本因素。 本研究從財務危機公司中，分出財務舞弊公司與正常經營公司，因此研究樣本包含發生舞弊的財務危機公司與正常經營而發生財務危機的公司。研究變數則從文獻篩選23個財務解釋變數，以及13個公司治理解釋變數，運用羅吉斯迴歸法進行實證，結果顯示3個財務變數和1個公司治理變數在區分財務危機公司中的財務舞弊公司與正常經營公司有顯著的區別能力，公司治理變數的董監事持股比率尤其顯著。 / Financial distress prediction is usually based on both financial distressed firms and non-distressed firms. Based on financial distressed firms, this study further investigates the factors distinguishing financial fraud firms from non-fraud firms. The sample includes fraud and no-fraud firms while both are financial distressed. Twenty-three financial and thirteen corporate governance variables are surveyed from literature. The empirical result of logit regression shows that three financial variables and one corporate governance variable are significant factors in distinguishing fraud from no-fraud firms in distressed companies. Especially, the percentage of holding stocks of board of directors is the most significant variable.

由於各國地雷股事件層出不窮，致使投資人財富遭受巨大損失，若是能事先察覺地雷股的存在，便能使投資人財富有更大的保障。本研究以四家國內外大型的舞弊個案－安隆、世界通訊、博達、力霸作為研究樣本，透過四家公司之財務資訊深入剖析各個案公司之舞弊手法。本研究歸納整理出21個預警指標，以作為未來投資人的評估基礎，以發現危機的早期徵兆，能及早避開地雷股。 研究發現即便是有進行窗飾財務報表的財務危機公司，仍能透過財務報導資訊中發現其端倪，四家個案公司在獲利性指標、流動性指標、安全性指標都有出現至少一項的紅旗警訊。研究結果顯示在下列指標上有較多家公司同時符合：(1) 獲利性指標。當資產報酬率以及股東權益報酬率過低或逐年下滑；(2)流動性指標。現金流量比率過低或逐年下滑；(3)安全性指標。借款依存度過高或逐年增加，以及流動比率過低或逐年下滑。 / Does financial reporting information itself provide early insightful information in detecting financial distress? Window dressing in financial reporting casts doubtful questions on this issue. As with investors usually taking a look at individual firm’s financial reporting, this study utilizes case study with four cases to address this fundamental role of financial reporting. Among the four fraud cases investigated, two are from the United States and the other two are Taiwan companies, including Enron, WorldCom, Procomp, and Rebar. This study sorts out 21 warning indices to evaluate each company’s financial condition and find out the signals for financial distress. All of these four cases investigated have at least one red flag signaled in profitability, liquidity and leverage. The most prominent indices in these three dimensions include (1) Profitability---ROE or ROA decreases in trend annully, (2) Liquidity---low cash flow ratio or decreasing in trend annually and low current ratio or decreasing in trend annually, and (3) Leverage---high debt to equity ratio or increasing in trend annually.

增長層級式自我組織映射網路(GHSOM)屬於一種非監督式類神經網路，為自我組織映射網路(SOM)的延伸，擅長於對樣本分群，以輔助分析樣本族群裡的共同特徵，並且可以透過族群間存在的空間關係假設來建立分類器，進而辨別出異常的資料。 因此本研究提出一個創新的對偶方法(即為一個建立決策支援系統架構的方法)分別對舞弊與非舞弊樣本分群，首先兩類別之群組會被配對，即辨識某一特定無弊群體的非舞弊群體對照組，針對這些配對族群，套用基於不同空間假設所設立的分類規則以檢測舞弊與非舞弊群體中是否有存在某種程度的空間關係，此外並對於舞弊樣本的分群結果加入特徵萃取機制。分類績效最好的分類規則會被用來偵測受測樣本是否有舞弊的嫌疑，萃取機制的結果則會用來標示有舞弊嫌疑之受測樣本的舞弊行為特徵以及相關的輸入變數，以做為後續的決策輔助。 更明確地說，本研究分別透過非舞弊樣本與舞弊樣本建立一個非舞弊GHSOM樹以及舞弊GHSOM樹，且針對每一對GHSOM群組建立分類規則，其相應的非舞弊/舞弊為中心規則會適應性地依循決策者的風險偏好最佳化調整規則界線，整體而言較優的規則會被決定為分類規則。非舞弊為中心的規則象徵絕大多數的舞弊樣本傾向分布於非舞弊樣本的周圍，而舞弊為中心的規則象徵絕大多數的非舞弊樣本傾向分布於舞弊樣本的周圍。 此外本研究加入了一個特徵萃取機制來發掘舞弊樣本分群結果中各群組之樣本資料的共同特質，其包含輸入變數的特徵以及舞弊行為模式，這些資訊將能輔助決策者(如資本提供者)評估受測樣本的誠實性，輔助決策者從分析結果裡做出更進一步的分析來達到審慎的信用決策。 本研究將所提出的方法套用至財報舞弊領域(屬於財務舞弊偵測的子領域)進行實證，實驗結果證實樣本之間存在特定的空間關係，且相較於其他方法如SVM、SOM+LDA和GHSOM+LDA皆具有更佳的分類績效。因此顯示本研究所提出的機制可輔助驗證財務相關數據的可靠性。此外，根據SOM的特質，即任何受測樣本歸類到某特定族群時，該族群訓練樣本的舞弊行為特徵將可以代表此受測樣本的特徵推論。這樣的原則可以用來協助判斷受測樣本的可靠性，並可供持續累積成一個舞弊知識庫，做為進一步分析以及制定相關信用決策的參考。本研究所提出之基於對偶方法的決策支援系統架構可以被套用到其他使用財務數據為資料來源的財務舞弊偵測情境中，作為輔助決策的基礎。 / The Growing Hierarchical Self-Organizing Map (GHSOM) is extended from the Self-Organizing Map (SOM). The GHSOM’s unsupervised learning nature such as the adaptive group size as well as the hierarchy structure renders its availability to discover the statistical salient features from the clustered groups, and could be used to set up a classifier for distinguishing abnormal data from regular ones based on spatial relationships between them. Therefore, this study utilizes the advantage of the GHSOM and pioneers a novel dual approach (i.e., a proposal of a DSS architecture) with two GHSOMs, which starts from identifying the counterparts within the clustered groups. Then, the classification rules are formed based on a certain spatial hypothesis, and a feature extraction mechanism is applied to extract features from the fraud clustered groups. The dominant classification rule is adapted to identify suspected samples, and the results of feature extraction mechanism are used to pinpoint their relevant input variables and potential fraud activities for further decision aid. Specifically, for the financial fraud detection (FFD) domain, a non-fraud (fraud) GHSOM tree is constructed via clustering the non-fraud (fraud) samples, and a non-fraud-central (fraud-central) rule is then tuned via inputting all the training samples to determine the optimal discrimination boundary within each leaf node of the non-fraud (fraud) GHSOM tree. The optimization renders an adjustable and effective rule for classifying fraud and non-fraud samples. Following the implementation of the DSS architecture based on the proposed dual approach, the decision makers can objectively set their weightings of type I and type II errors. The classification rule that dominates another is adopted for analyzing samples. The dominance of the non-fraud-central rule leads to an implication that most of fraud samples cluster around the non-fraud counterpart, meanwhile the dominance of fraud-central rule leads to an implication that most of non-fraud samples cluster around the fraud counterpart. Besides, a feature extraction mechanism is developed to uncover the regularity of input variables and fraud categories based on the training samples of each leaf node of a fraud GHSOM tree. The feature extraction mechanism involves extracting the variable features and fraud patterns to explore the characteristics of fraud samples within the same leaf node. Thus can help decision makers such as the capital providers evaluate the integrity of the investigated samples, and facilitate further analysis to reach prudent credit decisions. The experimental results of detecting fraudulent financial reporting (FFR), a sub-field of FFD, confirm the spatial relationship among fraud and non-fraud samples. The outcomes given by the implemented DSS architecture based on the proposed dual approach have better classification performance than the SVM, SOM+LDA, GHSOM+LDA, SOM, BPNN and DT methods, and therefore show its applicability to evaluate the reliability of the financial numbers based decisions. Besides, following the SOM theories, the extracted relevant input variables and the fraud categories from the GHSOM are applicable to all samples classified into the same leaf nodes. This principle makes that the extracted pre-warning signal can be applied to assess the reliability of the investigated samples and to form a knowledge base for further analysis to reach a prudent decision. The DSS architecture based on the proposed dual approach could be applied to other FFD scenarios that rely on financial numbers as a basis for decision making.