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.

財務報表中的主觀性詞彙往往影響市場中的參與者對於報導公司價值和獲利能力衡量的決策判斷。因此，公司的管理階層往往有高度的動機小心謹慎的選擇用詞以隱藏負面的消息而宣揚正面的消息。然而使用人工方式從文字量極大的財務報表挖掘有用的資訊往往不可行，因此本研究採用人工智慧方法驗證美國財務報表中的主觀性多字詞 (subjective MWEs) 和公司的財務狀況是否具有關聯性。多字詞模型往往比傳統的單字詞模型更能掌握句子中的語意情境，因此本研究應用條件隨機域模型 (conditional random field) 辨識多字詞形式的意見樣式。另外，本研究的實證結果發現一些跡象可以印證一般人對於財務報表的文字揭露往往與真實的財務數字存在有落差的印象；更發現在負向的盈餘變化情況下，公司管理階層通常輕描淡寫當下的短拙卻堅定地承諾璀璨的未來。 / Subjective assertions in financial statements influence the judgments of market participants when they assess the value and profitability of the reporting corporations. Hence, the managements of corporations may attempt to conceal the negative and to accentuate the positive with "prudent" wording. To excavate this accounting phenomenon hidden behind financial statements, we designed an artificial intelligence based strategy to investigate the linkage between financial status measured by annual earnings and subjective multi-word expressions (MWEs). We applied the conditional random field (CRF) models to identify opinion patterns in the form of MWEs, and our approach outperformed previous work employing unigram models. Moreover, our novel algorithms take the lead to discover the evidences that support the common belief that there are inconsistencies between the implications of the written statements and the reality indicated by the figures in the financial statements. Unexpected negative earnings are often accompanied by ambiguous and mild statements and sometimes by promises of glorious future.

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.

本研究針對國內1996至2003年68筆受證期局強制重編財報事件為研究對象，採個案與實證兼具之研究方法，探討財報強制重編之成因、外顯徵兆與經濟後果。 就財報強制重編之成因而言，本研究發現盈餘管理誘因亦適用於財報強制重編之情形；公司治理機制中控制權與所有權偏離程度的縮小、外部審計品質的提升與關係人交易之透明與簡單化，皆可以降低財報強制重編之機率。就財報強制重編之外顯徵兆，則發現會計師出具無保留以外之意見與損益品質的下降，為公司錯誤報導財務報表之重要指標。就財報強制重編之經濟後果，實證顯示財報強制重編與財務困難間具有顯著的關聯性。此外，本研究並發現財報重編影響的報表數愈多、金額幅度愈大、涉及業外非核心盈餘與投資損益之重編時，公司發生財務困難的可能性將顯著增加。 / This research employs both case study and empirical approaches to investigate the causes, demeanors and economic consequences of a sample of firms that were enforced by the Securities and future Bureau to restate their financial statements over the period of year 1996 to year 2003. The analysis of the causes of mandatory restatements shows that the motivations of earnings management also apply to the context of financial restatements. The firms with smaller deviation of control rights from the cash flow rights, higher audit quality and more transparency in related-party transactions help alleviate the probability of restatements. The results also indicate that unclean audit opinions and decrease in quality of earnings are important demeanors of misstatement of financial statements. In addition, firms are enforced to restate their financial statements are found to be significantly related to the occurrence of financial difficulties in the future. The findings show that the severer the materiality of the restatement in terms of the length of period, dollars, and the nature of the restatements, the higher the possibility of firms will experience financial difficulties.

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.

第一章說明本研究之目的、研究範圍與限制、研究方法並扼要說明論文各章節彙要。 第二章財務報表分析與財務比率：（一）財務報表分析之意義及功能：報表分析乃是 將所應用之分析工具資訊。其功能可就內部與外部分論之。（二）說明報表分析種類 與常見之分析工具（三）分析工具之一──財務比率之發展歷史之介紹（四）探討財 務比率分析之特性。 第三章財務比率之類型：就財務比率分析之目的，將其分為四類型：（一）安全性評 估之比率（二）衡量獲利能力之比率（三）測驗財務槓桿之比率（四）衡量經營成效 之比率。並各就其常見且具重要性之比率分別探討其意義。 第四章財務比率之應用：先就如何有效應用財務比率，探討分析的原則與可能的問題 ，並就比率之實際應用，分管理階層、投資者、債權人等說明之。 第五章財務比率與預測功能：（一）說明財務比率曾應用於企業預測之種類並將重點 置於企業破產預測之功能上（二）探討破產企業其財比率可能具有之特性（三）相關 學者之實證研究介紹與討論。第六章結論與建議。

當今社會經濟資源的流通體系中，證券市場是十分重要之機制，當投資人於證券市場將其資金挹注於經營體質良好之公司，所產生的連動效果將有助於整體經濟的蓬勃發展。被投資公司之財務報表以及財務報表衍生的各類分析，是投資人決策過程中極為重要的參考資訊，亦是被投資公司檢視自我財務狀況及經營成果的主要依據，從而產生由會計師進行查核簽證等需求，期藉由會計師之獨立性及專業審核，合理確認財務報表是否允當表達。 　然而近年來國內外接連發生多起重大財務舞弊案件，廣大投資人及債權人直接蒙受其害，財務報表編製者、公司高階主管與簽證會計師之專業能力和職業道德備受質疑，尤其是位居專家立場，為財務報表之允當表達與否背書的會計師，更是成為眾矢之的，主管機關亦即刻就證券交易法、會計師法等相關法令進行修正，加重會計師法律責任成為報章雜誌一再出現的主題。 　　關於財務報表真實性之確保，會計師之簽證職能雖被賦予極高的期待，惟財務報表供應鍊整體尚包括公司管理階層、監察人、準則制定者、主管機關等，倘能就所有環節進行個別審視檢討，進而就供應鍊整體加以綜合規劃，對於建立證券市場健全的資訊公開體系，當能提供更完整而根本之助益。 　　本文首先說明財務報表於證券市場資訊公開體系中之定位，包括財務報表之主要內容與功能，以及進行編製、查核簽證時所應遵循之法令規範；次則就財務報表不實之意義及案例、法院判決進行探討，並介紹相關研究報告之主要內容；接著說明證券市場監理法制對於財務報表不實之防制體系，包括自律與他律機制對於確保財務資訊真實性之規範，其中由於會計師簽證係證券市場監理法制中，確保財務資訊真實性之最基本要求與第一道防線，因而另以會計師為法律責任探討主體，下分行政責任、對投資人以及委託查核客戶之民事責任、刑事責任、對受查公司追究法律責任之可行性等，依序說明現行規範及新近修法概況；最後關於財務報表內容存在不實情事時，現行法令中已納入規範之責任主體，包括有價證券發行人、證券商、發行人之職員、主辦會計人員、會計師及其他專門技術人員等，就其所應負之行政、民事、刑事責任本文以表格方式整理列示，期就財務報表不實法律責任之現行法令構成體系，作一整體檢視，並試加探討建議。

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.

本研究以台灣上市櫃公司及分析師投資報告為研究對象，探討會計師忙碌程度與審計品質之關聯性，以及產生學習或互斥效果。審計品質以裁決性應計項目、財務報表重編及迎合或擊敗分析師預期三種特性進行分析。 研究結果發現，會計師忙碌程度與裁決性應計項目、財務報表重編及迎合或擊敗分析師預期皆呈現顯著負相關。此結果表示，會計師忙碌程度愈高，受查公司管理當局進行盈餘管理之可能性愈低。本研究藉此結果推論，會計師忙碌程度愈高，透過累積查核經驗，促進知識改善，產生正向的學習效果，進而提高審計品質。 / This thesis examines the association between the busyness of auditors at partner level and audit quality and whether the association stems from learning effect or crowding-out effect, by using the listed firms’ data and the analysts’ reports in Taiwan. I use the following three measures of audit quality to examine my hypotheses: discretionary accruals, financial restatement, and meet or beat analyst forecast. The empirical results indicate that firms experience lower discretionary accruals, are less likely to restate financial statements and have lower likelihood of meeting or beating analysts’ expectations when auditors are busier. The results are consistent with the notion that auditor busyness is positively related to audit quality. Taken together, the findings provide strong evidence in favor for learning effect rather than crowding-out effect of auditor busyness. Further analyses reveal that the results remain unchanged, regardless of lead auditor busyness or concurring auditor busyness.

本文主要針對因安隆公司所造成的會計醜聞事件對美國資本市場及投資大眾之影響，因此為解決公司治理及重建投資人之信心，美國國會迅速通過「沙賓法案」。由於沙賓法案對在美國的公開發行公司有重大的影響，尤其是第404條管理階層對內部控制之評估影響最大，故以沙賓法案第404條來探討其對金融業的影響及執行細節。 本論文分為五個章節 第一章，緒論為研究動機、研究目的、研究架構、研究方法 第二章，沙賓法案之產生背景、目的、內容及美國會計監督委員會之成立。 第三章，主要說明沙賓法案第404條相關內部控制之要求，及金融業如何藉由COSO的內部控制架構來證明其財務報表內部控制制度(ICOFR)之有效性及自我評估之要求。 第四章，沙賓法案對金融業所造成之影響及應採取之步驟。 第五章，結論與建議