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研究生:安玲瓏
研究生(外文):ATTHAPORN LIENGTHIRAPHAN
論文名稱:彙整語意分析與非平行決策平面於盈餘管理之預測
論文名稱(外文):Integration of Linguistic Cues and Non-Parallel Decision Surface for Earnings Management Forecasting
指導教授:徐銘甫徐銘甫引用關係
指導教授(外文):HSU, MING-FU
口試委員:盧文民葉清江
口試委員(外文):LU, WEN-MINYEH, CHING-CHIANG
口試日期:2020-06-23
學位類別:碩士
校院名稱:中國文化大學
系所名稱:全球商務碩士學位學程碩士班
學門:商業及管理學門
學類:一般商業學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:52
中文關鍵詞:盈餘管理文字信息非並行超平面支持向量機神經網絡決策樹
外文關鍵詞:Earnings ManagementTextual InformationNon-parallel Hyperplane Support Vector MachineNeural NetworkDecision Tree
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本文旨在開發一種預警模型,以提前發現盈餘管理水平。由於盈餘管理,會造成不透明性,從而降低了利益相關者評估公司實際業績的能力,也破壞了資本市場的運作。研究人員已經廣泛研究了通過操縱公司的會計應計利潤進行的盈餘管理,但是,這些研究很少關注盈餘操縱的程度,而是探索了特定因素與盈餘管理之間的相關性。盈餘管理的計算水平與Dechow等人一致。 (1995)誰執行了改良的瓊斯模型。文本數據是從年度報告中的管理討論和分析(MD&A)中提取的。由於年度報告中披露的信息包括許多專業術語和特定註釋,因此可讀性水平的日益下降已經對公司年度報告的傳遞功能產生了負面影響。有目的的是,這項研究能夠激勵公司使用更清晰,更易混淆的語言來提高年度報告的清晰度和可理解性。數據收集自2017年至2018年的《台灣經濟日報》數據庫(TEJ),主要針對集成電路(IC)製造商。然後將分析的數據輸入到人工智能(AI)技術中,以構建用於盈利管理預測的模型。通過提出一種非並行超平面支持向量機(NHSVM),它將傳統SVM的大QPP問題分解為兩個小QPP問題。為了增強研究結果,本研究還以提出的模型為基準,並將其與其他兩個模型(決策樹(DT)和神經網絡(NN))進行比較。結果表明,在所有評估標準下,該模型均優於其他兩個模型。為了防止結果僅因巧合而被遮蓋,我們進行了統計檢驗。經實際案例檢驗的模型是盈利管理預測的有希望的替代方法。
This paper is conducted to develop an early warning model to detect the level of earnings management in advance. Due to earnings management could create opacity which reduces the stakeholders’ ability to assess the real performance of the firm and also break down a functioning of capital markets. Earnings management through the manipulation of firm’s accounting accruals has been extensively investigated by researchers, however, these studies barely focus on a degree of earnings manipulating but rather explored the correlation between a specific factors and earnings management. The level of earnings management calculation is in accordance with Dechow et al. (1995) who performed modified Jones model. The textual data is extracted from management discussion and analysis (MD&A) in annual reports. The increasing deterioration in levels of readability has negatively affected the transmission function of firm annual reports due to the information disclosed in annual reports includes many professional terms and specific notes. Purposefully, this study is able to stimulate corporates to improve the clarity and understandability of their annual report by using language that is clearer and less convoluted. The data are collected from Taiwan Economic Journal databank (TEJ) ranging from 2017 to 2018 by targeting Integrated Circuit (IC) manufacture. The analyzed data are then fed into artificial intelligence (AI) technique to construct the model for earning management forecasting. By proposing, a non-parallel hyperplane support vector machine (NHSVM) that decomposes the conventional SVM’s big QPP problem into two small QPP problems. To robust the research findings, this study also takes the proposed model as a benchmark and compares it with the other two models which are decision tree (DT), and neural network (NN). The result shows that the proposed model outperforms the other two models under all assessing criteria. To prevent the results just happed by coincidence, the statistical test is conducted. The model, examined by real cases, is a promising alternative for earning management forecasting.
CONTENTS

ABSTRACT ii
LIST OF TABLES vii
LIST OF FIGURES viii
CHAPTER ONE INTRODUCTION 1
1.1 Research Background and Motivations 1
1.2 Research Purposes 5
1.3 Organization of the Dissertation 6
CHAPTER TWO LITERATURE REVIEW 7
2.1 Earnings Management 7
2.2 Earnings Management Measurement 8
2.2.1 The Modified Jones model 9
2.3 Readability 10
2.3.1 Measure of Readability 12
2.3.2 Readability Measure for Chinese 14
2.4 Neural Network 15
2.5 Decision Tree 17
2.6 Confusion Matrix 18
2.7 Non-Parallel Hyperplane Support Vector Machine 19
CHAPTER THREE METHODOLOGY 21
3.1 Research Sample 21
3.2 Data Collection Methods 23
3.2.1 Numerical Data 23
3.2.2 Textual Data 24
3.3 Data Processing 24
3.3.1 Textual Data Processing 24
3.3.2 Numerical Data Processing 27
3.4 Earnings Management Forecasting Model Construction 28
CHAPTER FOUR RESULTS AND DATA ANALYSIS 29
4.1 Descriptive Statistical Analysis Results 29
4.2 The Proposed Model: Earnings Management Forecasting 30
CHAPTER FIVE CONCLUSION 33
5.1 Research Findings 33
5.2 Research Contribution 34
5.3 Research Limitations and Suggestions 34
REFERENCE 36

REFERENCE

Ajina, A., & Habib, A. (2017). Examining the relationship between earning management and market liquidity. Research in International Business and Finance, 42, 1164-1172. DOI: 10.1016/j.ribaf.2017.07.054
Asay, H., Libby, R., & Rennekamp, K. (2018). Firm performance, reporting goals, and language choices in narrative disclosures. Journal of Accounting and Economics, 65(2-3), 380-398. DOI: 10.1016/j.jacceco.2018.02.002
Ayers, B., Jiang, J., & Yeung, P. (2006). Discretionary Accruals and Earnings Management: An Analysis of Pseudo Earnings Targets. The Accounting Review, 81(3), 617-652. DOI: 10.2308/accr.2006.81.3.617
Bartov, E., Gul, F. A., & Tsui, J. (2000). Discretionary-accruals models and audit qualifications. Journal of Accounting and Economics, 30, 421–452.
Bergstresser, D., & Philippon, T. (2006). CEO incentives and earnings management. Journal of Financial Economics, 80(3), 511-529. DOI: 10.1016/j.jfineco.2004.10.011
Bloomfield, R. (2002). The 'Incomplete Revelation Hypothesis' and Financial Reporting. SSRN Electronic Journal. DOI: 10.2139/ssrn.312671
Bonsall, S., Miller, B., 2017. The impact of narrative financial disclosure complexity on bond ratings and the cost of debt capital. Rev.Account.Stud. DOI 10.1007/s11142-017-9388-0
Bonsall, S., Leone, A., Miller, B., & Rennekamp, K. (2017). A plain English measure of financial reporting readability. Journal of Accounting and Economics, 63(2-3), 329-357. DOI: 10.1016/j.jacceco.2017.03.002
Bose, I. (2006). Deciding the financial health of dot-coms using rough sets. Information & Management, 43(7), pp.835-846. DOI: 10.1016/j.im.2006.08.001
Cavalli-Sforza, V., Saddiki, H., & Nassiri, N. (2018). Arabic Readability Research: Current State and Future Directions. Procedia Computer Science, 142, 38-49. DOI: 10.1016/j.procs.2018.10.459
Chang, W.-J., Chou, L.-T., & Lin, H.-W. (2003). Consecutive changes in insider holdings and earnings management. The International Journal of Accounting Studies, 37, 53–83.
Chowdhury, A., Mollah, S., & Al Farooque, O. (2018). Insider-trading, discretionary accruals and information asymmetry. The British Accounting Review, 50(4), 341-363. https://doi.org/10.1016/j.bar.2017.08.005
Dechow, P., & Sloan, R. (1991). Executive incentives and the horizon problem. Journal of Accounting and Economics, 14, 51–89.
Dechow, P., Sloan, R., & Sweeney, A. (1995). Detecting earning management. The Accounting Review, 70(April), 193–225.
Dechow, P., Hutton, A., Kim, J., & Sloan, R. (2011). Detecting Earnings Management: A New Approach. SSRN Electronic Journal. DOI: 10.2139/ssrn.1735168
Deng, X., Liu, Q., Deng, Y., & Mahadevan, S. (2016). An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Information Sciences, 340-341, 250-261. https://doi.org/10.1016/j.ins.2016.01.033
De Souza, J., Rissatti, J., Rover, S., & Borba, J. (2019). The linguistic complexities of narrative accounting disclosure on financial statements: An analysis based on readability characteristics. Research in International Business and Finance, 48, 59-74. DOI: 10.1016/j.ribaf.2018.12.008
Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple Classifier Systems. No. 1857 in Lecture Notes in Computer Science (pp. 1–15). Berlin, Heidelberg: Springer. DOI: 10.1007/3- 540-45014-9_1.
Embong, Z., & Hosseini, L. (2018). Analyst Forecast Accuracy and Earnings Management. Asian Journal of Accounting and Governance, 10, 97-108. DOI: 10.17576/ajag-2018-10-09
Fayed, M., Elhadary, M., Ait Abderrahmane, H., & Zakher, B. (2019). The ability of forecasting flapping frequency of flexible filament by artificial neural network. Alexandria Engineering Journal, 58(4), 1367-1374. https://doi.org/10.1016/j.aej.2019.11.007
Feng, M., Ge, W., Luo, S., & Shevlin, T. (2011). Why do CFOs become involved in material? Accounting manipulations? Journal of Accounting and Economics, 51(21-36). DOI.org/10.1016/j.jacceco.2010.09.005
Flesch, R. (1948). A new readability yardstick. Journal of Applied Psychology, 32(3), 221-233. DOI: 10.1037/h0057532. (1949). the Art of Readable Writing. New York, Harper.
Frankel, R., Johnson, M., & Nelson, K. (2002). The Relation between Auditors' Fees for Non-Audit Services and Earnings Management. SSRN Electronic Journal. DOI: 10.2139/ssrn.296557
Ghazali, A., Shafie, N., & Sanusi, Z. (2015). Earnings Management: An Analysis of Opportunistic Behaviour, Monitoring Mechanism and Financial Distress. Procedia Economics And Finance, 28, 190-201. https://doi.org/10.1016/s2212-5671(15)01100-4
Gunning, R., 1952. The Technique of Clear Writing. McGraw-Hill International Book Co., New York, NY.
Habib, A., & Hasan, M. (2018). Business strategies and annual report readability. Accounting & Finance. DOI: 10.1111/acfi.12380
Healy, P. M. (1985). The effect of bonus schemes on accounting decisions. Journal of
Accounting and Economics, 7(1), 85-107.
Healy, P., & Wahlen, J. (1999). A Review of the Earnings Management Literature and its Implications for Standard Setting. SSRN Electronic Journal. DOI: 10.2139/ssrn.156445
Heo, S., & Lee, J. (2019). Parallel neural networks for improved nonlinear principal component analysis. Computers & Chemical Engineering, 127, 1-10. https://doi.org/10.1016/j.compchemeng.2019.05.011
Hribar, P., & Jenkins, N. (2004). The Effect of Accounting Restatements on Earnings Revisions and the Estimated Cost of Capital. Review of Accounting Studies, 9(2/3), 337-356. DOI: 10.1023/b:rast.0000028194.11371.42
Huang, C.-L., Chung, C. K., Hui, N., Lin, Y.-C., Seih, Y.-T., Lam, B. C. P., Chen, W.-C., Bond, M. H., & Pennebaker, J. W. (2012). The development of the Chinese linguistic inquiry and word count dictionary. Chinese Journal of Psychology, 54(2), 185–201.
Islam, M., Ali, R., & Ahmad, Z. (2011). Is Modified Jones Model Effective in Detecting Earnings Management? Evidence from A Developing Economy. International Journal of Economics and Finance, 3(2). DOI: 10.5539/ijef.v3n2p116
Jones, J. (1991). Earnings Management during Import Relief Investigations. Journal of Accounting Research, 29(2), 193. DOI: 10.2307/2491047
Kothari, S., Leone, A., & Wasley, C. (2005). Performance matched discretionary accrual measures. Journal of Accounting and Economics, 39(1), 163-197. DOI: 10.1016/j.jacceco.2004.11.002
Kumar, G. (2014). Determinants of Readability of Financial Reports of U.S.-Listed Asian Companies. Asian Journal of Finance & Accounting, 6(2), 1. DOI: 10.5296/ajfa.v6i2.5695
Lan, T., Hu, H., Jiang, C., Yang, G., & Zhao, Z. (2020). A comparative study of decision tree, random forest, and convolutional neural network for spread-F identification. Advances in Space Research, 65(8), 2052-2061. DOI: 10.1016/j.asr.2020.01.036
Lehavy, R., Li, F., & Merkley, K. (2011). The Effect of Annual Report Readability on Analyst Following and the Properties of Their Earnings Forecasts. The Accounting Review, 86(3), 1087-1115. DOI: 10.2308/accr.00000043
Lewis, M. P. (2009). Ethnologue: Languages of the world (Sixteenth Ed.). Dallas: SIL International.
Li, F. (2008). Annual report readability, current earnings, and earnings persistence. Journal of Accounting and Economics, 45(2-3), 221-247. DOI: 10.1016/j.jacceco.2008.02.003
Lin, K. (2006). The impact of tax holidays on earnings management: An empirical study of corporate reporting behavior in a developing-economy framework. The International Journal of Accounting, 41(2), 163-175. DOI: 10.1016/j.intacc.2006.04.006
Loughran, T., & Mcdonald, B. (2014). Measuring Readability in Financial Disclosures. The Journal of Finance, 69(4), 1643-1671. DOI: 10.1111/jofi.12162
Meng, X., Zhang, P., Xu, Y., & Xie, H. (2020). Construction of decision tree based on C4.5 algorithm for online voltage stability assessment. International Journal of Electrical Power & Energy Systems, 118, 105793. DOI: 10.1016/j.ijepes.2019.105793
Needles Jr. B.E.,Powers M., Senyigit Y.B. (2018). Earnings Management: A Review of Selected Cases. MED - Journal of Accounting Institute.
Oztekin, A., Delen, D., Turkyilmaz, A., & Zaim, S. (2013). A machine learning-based usability evaluation method for eLearning systems. Decision Support Systems, 56, 63-73. DOI: 10.1016/j.dss.2013.05.003
Rockness, H., & Rockness, J. (2005). Legislated Ethics: From Enron to Sarbanes-Oxley, the Impact on Corporate America. Journal of Business Ethics, 57(1), 31–54. DOI: 10.1007/s10551-004-3819-0
Roychowdhury, S. (2006). Earnings management through real activities manipulation.
Journal of Accounting and Economics, 42(3), 335-370.
Ruppert, D. (2004). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Journal of the American Statistical Association, 99(466), pp.567-567.
Ruuska, S., Hämäläinen, W., Kajava, S., Mughal, M., Matilainen, P., & Mononen, J. (2018). Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle. Behavioural Processes, 148, 56-62. https://doi.org/10.1016/j.beproc.2018.01.004
Schipper, K., 1989. Commentary on earnings management. Accounting Horizons 3, 91-102.
Securities and Exchange Commission, 1998b. A Plain English Handbook: How to Create Clear SEC Disclosure. SEC Office of Investor Education and Assistance Washington, District of Columbia 〈http://www.sec.gov/pdf/handbook.pdf〉.
SEC. (December 29, 2003). Interpretation, Commission Guidance Regarding Management’s Discussion and Analysis of Financial Condition and Results of Operations.” https://www.sec.gov/rules/interp/33-8350.htm
Shao, Y., Chen, W., & Deng, N. (2014). Nonparallel hyperplane support vector machine for binary classification problems. Information Sciences, 263, 22-35. https://doi.org/10.1016/j.ins.2013.11.003
Shiue, M., & Lin, C. (2004). The Study of Earnings Management Detecting Models: A Case of Firms in Financial Distress, 5(1), 105-130.
Smith, M., & Taffler, R. (1992). Readability and Understandability: Different Measures of the Textual Complexity of Accounting Narrative. Accounting, Auditing & Accountability Journal, 5(4). DOI: 10.1108/09513579210019549
Sun, J., Li, H., Chang, P. and Huang, Q. (2015). Dynamic credit scoring using B & B with incremental-SVM-ensemble. Kybernetes, 44(4), pp.518-535. DOI: 10.1108/k-02-2014-0036
Sung, Y., Chang, T., Lin, W., Hsieh, K., & Chang, K. (2015). CRIE: An automated analyzer for Chinese texts. Behavior Research Methods, 48(4), 1238-1251. DOI: 10.3758/s13428-015-0649-1
Tan, H., Ying Wang, E., & Zhou, B. (2014). When the Use of Positive Language Backfires: The Joint Effect of Tone, Readability, and Investor Sophistication on Earnings Judgments. Journal of Accounting Research, 52(1), 273-302. DOI: 10.1111/1475-679x.12039
Tian, Y., Ju, X., & Qi, Z. (2013). Efficient sparse nonparallel support vector machines for classification. Neural Computing and Applications, 24(5), 1089-1099. https://doi.org/10.1007/s00521-012-1331-5
Tian, Y., & Qi, Z. (2014). Review on: Twin Support Vector Machines. Annals of Data Science, 1(2), 253-277. https://doi.org/10.1007/s40745-014-0018-4
Tsai, C., & Chiou, Y. (2009). Earnings management prediction: A pilot study of combining neural networks and decision trees. Expert Systems with Applications, 36(3), 7183-7191. https://doi.org/10.1016/j.eswa.2008.09.025
Wang, Y., Chen, Y. & Wang, J. 2015. Management earnings forecasts and analyst forecasts: Evidence from mandatory disclosure system. China Journal of Accounting Research 8(2): 133-146.
West, D. (2000). Neural network credit scoring models. Computers & Operations Research, 27(11-12), pp.1131-1152. DOI: 10.1016/s0305-0548(99)00149-5
Xiong, Y. (2006). Earnings management and its measurement: A theoretical perspective. Journal of American Academy of Business, 9(1), 214–219.
Xu, J., Zhang, Y., & Miao, D. (2020). Three-way confusion matrix for classification: A measure driven view. Information Sciences, 507, 772-794. https://doi.org/10.1016/j.ins.2019.06.064
Yoon, S., Miller, G., & Jiraporn, P. (2006). Earnings Management Vehicles for Korean Firms. Journal of International Financial Management and Accounting, 17(2), 85-109. DOI: 10.1111/j.1467-646x.2006.00122.x
Yu, Q., Du, B., & Sun, Q. (2006). Earnings management at rights issues thresholds—Evidence from China. Journal of Banking & Finance, 30(12), 3453-3468. DOI: 10.1016/j.jbankfin.2006.01.011
Zhou, L., Si, Y. and Fujita, H. (2017). Predicting the listing statuses of Chinese-listed companies using decision trees combined with an improved filter feature selection method. Knowledge - Based Systems, 128, pp.93-101. DOI: 10.1016/j.knosys.2017.05.003

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