財務困境的決定因素：以台灣公司為例__國立東華大學博碩士論文全文影像系統

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作者:	胡麗玉
作者(英文):	Oh Maressa Wintora
論文名稱:	財務困境的決定因素：以台灣公司為例
論文名稱(英文):	The Determinant of Financial Distress: Evidence from Taiwan Companies
指導教授:	王詩韻
指導教授(英文):	Shin-Yun Wang
口試委員:	羅德謙翁培師
口試委員(英文):	Te-Chien Lo Pei-Shih Weng
學位類別:	碩士
校院名稱:	國立東華大學
系所名稱:	財務金融學系
學號:	610736027
出版年(民國):	109
畢業學年度:	109
語文別:	英文
論文頁數:	76
關鍵詞(英文):	Financial Distress、Financial Ratios、Risk、Bankruptcy、Taiwan
相關次數:	推薦:0 點閱:33 評分: 下載:20 收藏:0

The unhealthy financial situation can be a massive and can cause long term distress which can result in limitations of capital flows, investments activities, and performance of companies. This study is using a sample of 110 companies in Taiwan and it is selected randomly from Taiwan Economic Journal (TEJ) during the period 2016-2018 with quarterly data. Logistic Regression issued to analyze the relationships between financial distress and firms’ characteristics and risk. The dependent variables consist of dummy variable, which has negative Earning per Share (EPS) and positive Earning per Share (EPS). EPS is the portion of company’s profit allocated to each outstanding share of common stock. Financially distressed companies (have negative EPS) are coded 1 and healthy companies (if not) are coded 0. The independent variables are profitability, liquidity or solvency, turnover, total assets, net sales, and ROE (Return on Equity). The results of the analysis show that profitability, turnover and total assets have correlation with the financial distress of Taiwanese companies. Profitability and turnover are found to be significant and have a positive relationship with financial distress, while total assets has a negative relationship with financial distress. However, there are no significant effect among Solvency/Liquidity, Net Sales, and ROE to the financial distress. The determinant of financial distress at industry level shows that total assets and ROE are found to be significant and have a negative relationship with financial distress on electronics industry. As the results of textile industry show that profitability is found to be significant and have a negative relationship with financial distress. Then, turnover is significant and have a positive relationship with financial distress. The results of other industries show that profitability and total assets are found to be significant and have a negative relationship with financial distress.

CHAPTER 1 INTRODUCTION 1
1.1 Research Background 1
1.2 Research Questions 5
1.3 Research Aims 6
1.4 The Significance of the Research 6
CHAPTER 2 LITERATURE REVIEW 9
2.1 Financial Distress 9
2.2 Financial Distress Prediction Models 15
2.3 Binary Logistic Regression 17
2.4 Financial Ratios 21
CHAPTER 3 RESEARCH METHODOLOGY 29
3.1 Research Type 29
3.2 Data Source 29
3.3 Research Framework 30
3.4 Definition of Operating Variables 30
3.5 Data Analysis Techniques 32
CHAPTER 4 RESULTS 35
4.1 Descriptive Statistics and Deviance Residual 35
4.2 Logistic Regression 37
4.3 Histogram 46
4.4 Scatterplot 50
CHAPTER 5 CONCLUSION 52
REFERENCES 54
APPENDIX 64

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2. Al-khatib, H. B. & Al-Horani, A. (2012). Predicting financial distress of public companies listed in Amman Stock Exchange. European Scientific Journal, 8(15), 1-17.
3. Altman, E. (1968). Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.
4. Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). ZETA Analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1, 29-54.
5. Altman, E.I. (1993). Corporate Financial Distress and Bankruptcy, (2nd ed.). John Wiley & Sons, New York.
6. Altman, Edward. I. (2013). Predicting financial distress of companies: revisiting the Z-Score and ZETA models. Handbook of Research Methods and Applications in Empirical Finance, 17, 428-456.
7. Amirulloh, M. & Isbanah, Y. (2017). Analisis Model Prediksi Financial Distress dan Determinan yang Mempengaruhinya (Studi pada Perusahaan Sektor Pertambangan di BEI Tahun 2014-2016). Seminar Nasional Call for Paper, 83-98.
8. Anandarajan, M., Lee, P. & Anandarajan, A. (2001) Bankruptcy prediction of ﬁnancially stressed ﬁrms: An examination of the predictive accuracy of artiﬁcial neural networks. International Journal of Intelligent Systems in Accounting, Finance and Management, 10 (2):69– 81.
9. Andrade, G., & Kaplan, S. N. (1998). How costly is financial (not economic) distress: Evidence from highly leveraged transactions that became distressed. Journal of Finance, 53(5), 1442-1493.
10. Anggraini, Dewi. (2016). Financial Distress Model Prediction for Indonesian Companies. International Journal of Management and Administrative Sciences, 3(4), 74-84.
11. Bae, Jae Kwon. (2012). Predicting Financial Distress of the South Korean Manufacturing Industries. Expert Systems with Applications: An International Journal. 39(10), 9159-9165.
12. Ball, R., & Foster, G. (1982). Corporate financial reporting: A methodological review of empirical research. Journal of Accounting Research, 20, 161-234.
13. Beaver, W. H. (1966). Financial Ratio as Predictor of Failure, Empirical Research in accounting: Selected Studies. Journal of Accounting Research, 5, 71-111.
14. Beaver, William. H. (1968). Alternative Accounting Measures as Predictors of Failure. The Accounting Review, 43(1), 113-122.
15. Binh, P. V. N., Trung, D. T., & Duc, V. H. (2018). A Prediction of Financial Distress of Listed Firms in Vietnam A Sector Analysis. Review of Pacific Basin Financial Markets and Policies.
16. Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637-654.
17. Boyd, C. R., Tolson, M. A., & Copes, W. S. (1987). Evaluating trauma care: The TRISS method. Trauma Score and the Injury Severity Score. The Journal of Trauma. 27 (4), 370–378.
18. Casey, C. J. & N. J. Bartczak (1985). Using operating cash flow data to predict financial distress: Some extensions. Journal of Accounting Research, 23, 384-401.
19. Coats, P. K., & Fant, L. F. (1993). Recognizing Financial Distress Patterns Using a Neural Network Tool. Financial Management, 22(3), 142.
20. Dichev, I. D. (1998). Is the risk of bankruptcy a systematic risk? The Journal of Finance, 53(3), 1131–1147.
21. Dietrich, J. R. (1984). Discussion of methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 83-86.
22. Dimitras, A.I., Zanakis, S.H., & Zopounidis, C. (1996). A survey of business failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research, 90(3), 487-513.
23. Dun & Bradstreet. (2016). Global Bankruptcy Report 2016. USA: The Dun & Bradstreet Corporation.
24. Elloumi, F., & Gueyié, J. P. (2001). CEO compensation, IOS and the role of corporate governance. Corporate Governance: The International Journal of Business in Society, 1(2), 23-33.
25. Elloumi, F., & Gueyie, J. P. (2001). Financial Distress and Corporate Governance: an empirical analysis. Corporate Governance International Journal of Business in Society, 1(1), 15-23.
26. Fahmi, I. (2011). Analisis Laporan Keuangan. Bandung: CV Alberta.
27. Fahmi, Irham. (2012). Analisis Kinerja Keuangan. Bandung: Alfabeta.
28. Fama, E., & French, K. (2004). New lists: fundamentals and survival rates. Journal of Financial Economics, 73(2), 229-269.
29. Fulmer, J. G. (1984). A Bankruptcy Classification Model for Small Firms. The Journal of Commercial Bank Lending, 66(11), 25-37.
30. Geng, R., Bose, I., & Chen, X. (2015). Prediction of financial distress: An empirical study of listed Chinese companies using data mining. European Journal of Operational Research, 241(1), 236-247.
31. Grice, S. & Dugan, M. (2001). The limitations of bankruptcy prediction models: some cautions for the researcher. Review Quantitative Finance Accounting, 17(2), 151–166.
32. Grover, J. & Lavin, A. (2001). Financial Ratios, Discriminant Analysis and The Prediction of Corporate Bankruptcy: a Service Industry Extension of Altman’s Z-Score Model of Bankruptcy Prediction. Working Paper. Southern Finance Association Annual Meeting.
33. Grusky, D. B., Western, B., & Wimer, C. (2011). The Great Recession. New York: Russel Sage Foundation.
34. Gruszczynski, M. (2004). Financial Distress of Companies in Poland. International Advances in Economic Research, 10(4), 249-256.
35. Haq, S., Arfan, M., & Siswar, D. (2013). Analisis rasio keuangan dalam memprediksi financial distress (studi pada perusahaan yang terdaftar di bursa efek indonesia). Jurnal Akuntansi Pascasarjana Universitas Syiah Kuala, 2(1), 37-46.
36. Heniwati, E., & Essen, E. (2020). Which Retail Firm Characteristics Impact On Financial Distress? Jurnal Akuntansi dan Keuangan, 22(1), 40-46.
37. Hery. (2016). Analisis Laporan Keuangan. Jakarta: PT. Grasindo.
38. Hidayat, M. A., & Meiranto, W. (2014). Prediksi financial distress perusahaan manufaktur di Indonesia. Journal of Accounting, 3(3), 1-11.
39. Hua, Z., Wang, Y., Xu, X., Zhang, B., & Liang, L. (2007). Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems with Applications, 33(2), 434–440.
40. Hui, H., & Zhao, J. (2008). Relationship between Corporate Governance and Financial Distress: An Empirical Study of Distressed Companies in China. International Journal of Management, 25(4), 654-664.
41. Johnsen, T., & Melicher, R. W. (1994). Predicting corporate bankruptcy and financial distress: Information value added by multinomial logit models. Journal of Economics and Business, 46(4), 269–286.
42. Kartika, R. & Hasanuddin. (2019). Analisis Pengaruh Likuiditas, Leverage, Aktivitas, dan Profitabilitas terhadap Financial Distress pada Perusahaan Terbuka Sektor Infrastruktur, Utilitas, dan Transportasi Periode 2011-2015. Jurnal Ilmu Manajemen, 15(1), 1-16.
43. Kasmir. (2017). Analisis Laporan Keuangan. Jakarta: Raja Grafindo Persada.
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