| Reference: | 中文文獻
吳昱萱，2018，股價波動與財務預警-數據分析觀點，國立政治大學會計研究所碩士論文。
英文文獻
Altman, E. I. (1968), Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.
Beaver, W. H. (1966), Financial ratios as predictors of failure. Journal of Accounting Research, 71-111.
Brazel, J. F., Jones, K. L., & Zimbelman, M. F. (2009), Using Nonfinancial Measures to Assess Fraud Risk. Journal of Accounting Research, 47(5), 1135-1166.
Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2010), Data mining for credit card fraud: A comparative study. Decision Support Systems, 50, 602-613.
Carlos, S. C., & Begoña, G. N. (2016), The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending. Decision Support Systems, 89, 113-122.
Carneiro, N., Figueira, G., & Costa, M. (2017), A data mining based system for credit-card fraud detection in e-tail. Decision Support Systems, 95, 91-101.
Chen, Y. J., Wu, C.H., Chen, Y.M., Li, H.Y., & Chen, H.K. (2017), Enhancement of fraud detection for narratives in annual reports. International Journal of Accounting Information Systems, 26, 32-45.
Churyk, N. T., Lee, C. C., & Clinton, B. G. (2009), Early detection of fraud: Evidence from restatements. Advances in Accounting Behavioral Research, 12, 25-40.
Dalnial, H., Kamaluddin, A., Sanusi, Z. M., & Khairuddin, K. S. (2014), Detecting fraudulent financial reporting through financial statement analysis. Journal of Advanced Management Science, 2, 17-22.
Debreceny, R. S., & Gray, G. L. (2010), Data mining journal entries for fraud detection: An exploratory study. International Journal of Accounting Information Systems, 11, 157-181.
Dechow, P. M., Ge, W. L., Larson, C. R., & Sloan, R. G. (2011), Predicting Material Accounting Misstatements. Contemporary Accounting Research, 28, 17-82.
Dilla, W. N., & Raschke, R. L. (2015), Data visualization for fraud detection: Practice implications and a call for future research. International Journal of Accounting Information Systems, 16, 1-22.
Dong, W., Liao, S., & Zhang, Z. (2018), Leveraging Financial Social Media Data for Corporate Fraud Detection. Journal of Management Information Systems, 35, 461-487.
Frazier, K. B., Ingram, R. W., & Tennyson, B. M. (1984), A methodology for the analysis of narrative accounting disclosures. Journal of Accounting Research, 22(1), 318-331.
Geva, T., & Zahavi, J. (2013), Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news. Decision Support Systems, 57, 212-223.
Gray, G. L., & Debreceny, R. S. (2014), A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits. International Journal of Accounting Information Systems, 15, 357-380.
Hájek, P. (2018), Combining bag-of-words and sentiment features of annual reports to predict abnormal stock returns. Neural Computing and Applications, 29(7), 343-358.
Heidinger, D., & Gatzert, N. (2018), Awareness, determinants and value of reputation risk management: Empirical evidence from the banking and insurance industry. Journal of Banking and Finance, 91, 106-118.
Humpherys, S. L., Moffitt, K. C., Burns, M. B., Burgoon, J. K., & Felix, W. F. (2011), Identification of fraudulent financial statements using linguistic credibility analysis. Decision Support Systems, 50, 585-594.
Jiang, P., Liua, X., Zhang, J., & Yuan, X. (2016), A framework based on hidden Markov model with adaptive weighting for microcystin forecasting and early-warning. Decision Support Systems, 84, 89-103.
Kanapickienė, R., & Grundienė, Ž. (2015), The Model of Fraud Detection in Financial Statements by Means of Financial Ratios. Procedia - Social and Behavioral Sciences, 213, 321-327.
Khandani, A.E., Kim, A. J., & Lo, A. W. (2010), Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34, 2767-2787.
Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007), Datamining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4), 995-1003.
Kwak, W., Shi, Y., Cheh, J. J., Lee, H. (2004), Multiple criteria linear programming data mining approach: an application for bankruptcy prediction. Data Mining and Knowledge Management, 164-173.
Li, W. F., Chen, H.C., & Nunamaker, J. F. Jr. (2016), Identifying and Profiling Key Sellers in Cyber Carding Community: AZSecure Text Mining System. Journal of Management Information Systems, 33, 1059-1086.
Lin, F., Liang, D., & Chen, E. (2011), Financial ratio selection for business crisis prediction. Expert Systems with Applications, 38(12), 15094-15102.
Lopatta, K., Gloger, M. A., Jaeschke, R. (2017), Can Language Predict Bankruptcy? The Explanatory Power of Tone in 10K Filings. Accounting Perspectives, 16(4), 315-343.
Loughran, T., & McDonald, B. (2011), When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks. The Journal of Finance, 66(1), 35-65.
Mayew, W. J., Sethuraman, M., & Venkatachalam, M. (2015), MD&A disclosure and the firm`s ability to continue as a going concern. The Accounting Review, 90, 1621-1651.
Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y. J., & Sun, X. (2011), The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50, 559-569.
Nia, S. H. (2015), Financial ratio between fraudulent and non-fraudulent firms: evidence from Tehran Stock Exchange. Journal of Accounting and Taxation, 7(3), 38-44.
Ohlson, J. A. (1980), Financial ratio and the probabilistic prediction of bankruptcy. Journal of Accounting Research. 1980(1), 109-131.
Olson, D. L., Delen, D., & Meng, Y. Y. (2011), Comparative analysis of data mining methods for bankruptcy prediction. Decision Support Systems, 52, 464-473.
Persons, O. S. (1995), Using financial statement data to identify factor associated with fraudulent financial reporting. Journal of Applied Business Research, 11, 38-46.
Ravisankar, P., Ravi, V., Rao, G. R., & Bose, I. (2010), Detection of financial statement fraud and feature selection using data mining techniques. Decision Support Systems, 50, 491-500.
Rich, K. T., Roberts, B. L., & Zhang, J. X. (2016), Linguistic Tone of Municipal Management Discussion and Analysis Disclosures and Future Financial Reporting Delays. Journal of Emerging Technologies in Accounting, 13(2), 93-107.
Saha, P., Bose, I., & Mahanti, A. (2016), A knowledge based scheme for risk assessment in loan processing by banks. Decision Support Systems, 84, 78-88.
Siering, M., Koch, J. A., & Deokar A. V. (2016), Detecting Fraudulent Behavior on Crowdfunding Platforms: The Role of Linguistic and Content-Based Cues in Static and Dynamic Contexts. Journal of Management Information Systems, 33, 421-455.
Smith, M., & Taffler, R. J. (2000). The chairman’s statement: A content analysis of discretionary narrative disclosures. Accounting, Auditing, and Accountability, 13(5), 624-646.
Spathis, C. T. (2002), Detecting false financial statements using published data: Some evidence from Greece. Managerial Auditing Journal, 17(4), 179-191.
Tennyson, B. M., Ingram, R. W., & Dugan, M. T. (1990), Assessing the information content of narrative disclosures in explaining bankruptcy. Journal of Business Finance & Accounting, 17, 391-410.
Tinoco, M. H., & Wilson, N. (2013), Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International Review of Financial Analysis, 30, 394-419.
Wang, Y.B., & Xu, W. (2018), Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud, Decision Support Systems, 105, 87-95.
Yekini, L. S., Wisniewski, T. P., & Millo, Y. (2016), Market reaction to the positiveness of annual report narratives. The British Accounting Review, 48(4), 415-430.
Zainudin, E. F., & Hashim, H.A. (2016), Detecting fraudulent financial reporting using financial ratio. Contemporary Accounting Research, 28, 17-82.
Zhou, L., Burgoon, J. K., Twitchell, D. P., Nunamaker, J. F., & Twitchell, D. P. (2004), Automating linguistics-based cues for detecting deception in text-based asynchronous computer-mediated communication. Group Decision and Negotiation, 13, 81-106.
Zmijewsk, M. (1984), Method Logistical issue related to the estimation of financial distress prediction model. Journal of Accounting Research, 22, 59-82. |
