Financial distress and cycle-sensitive corporate investments

References

• Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609. doi: 10.1111/j.1540-6261.1968.tb00843.x
   Web of Science ®Google Scholar
• Altman, E. I., & Narayanan, P. (1997). An international survey of business failure classification models. Financial Markets, Institutions and Instruments, 6(2), 1–57. doi: 10.1111/1468-0416.00010
   Google Scholar
• Aziz, A., & Dar, A. (2006). Predicting corporate bankruptcy: Where we stand? Corporate Governance, 6(1), 18–33. doi: 10.1108/14720700610649436
   Google Scholar
• Aziz, A., Emanuel, D. C., & Lawson, G. H. (1988). Bankruptcy prediction – an investigation of cash flow based models. Journal of Management Studies, 25(5), 419–437. doi: 10.1111/j.1467-6486.1988.tb00708.x
   Web of Science ®Google Scholar
• Aziz, A., & Lawson, G. H. (1989). Cash flow reporting and financial distress models: Testing of hypotheses. Financial Management, 18, 55–63. doi: 10.2307/3665698
   Web of Science ®Google Scholar
• Balcaen, S., & Ooghe, H. (2004, June). 35 years of studies on business failure: An overview of the classical statistical methodologies and their related problems (Working Paper Series, Faculteit Economie en Bedrijfskunde, Universiteit Gent. No. 04/248, pp. 1–56). Ghent.
   Google Scholar
• Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111. doi: 10.2307/2490171
   Web of Science ®Google Scholar
• Bellovary, J., Giacomino, D., & Akers, M. (2007). A review of bankruptcy prediction studies: 1930 to present. Journal of Financial Education, 33, 1–42.
   Google Scholar
• Bhattacharjee, A., Higson, C., Holly, S., & Kattuman, P. (2009). Macro economic instability and business exit: Determinants of failures and acquisitions of large UK firms. Economica, 76, 108–131. doi: 10.1111/j.1468-0335.2007.00662.x
   Web of Science ®Google Scholar
• Christidis, A. C.-Y., & Gregory, A. (2010). Some new models for financial distress prediction in the UK (University of Exeter Business School. No. 10/04, pp. 1–46). Exeter: Centre for Finance and Investment, University of Exeter Business School, Xfi.
   Google Scholar
• Gentry, J. A., Newbold, P., & Whitford, D. T. (1985a). Predicting bankruptcy: If cash flow's not the bottom line, what is? Financial Analysts Journal, 41(5), 47–56. doi: 10.2469/faj.v41.n5.47
   Google Scholar
• Gentry, J. A., Newbold, P., & Whitford, D. T. (1985b). Classifying bankrupt firms with funds flow components. Journal of Accounting Research, 23(1), 146–160. doi: 10.2307/2490911
   Web of Science ®Google Scholar
• Johnson, C. G. (1970). Ratio analysis and the prediction of firm failure. Journal of Finance, 25, 1166–1168. doi: 10.1111/j.1540-6261.1970.tb00879.x
   Web of Science ®Google Scholar
• Keasey, K., & Watson, R. (1991). Financial distress prediction models: A review of their usefulness. British Journal of Management, 2(2), 89–102. doi: 10.1111/j.1467-8551.1991.tb00019.x
   Google Scholar
• Laitinen, E. K. (2011). Modelling cash-flow solvency: Search for default patterns. EAA 2011 Rome.
   Google Scholar
• Männasoo, K. (2008). Patterns of firm survival in Estonia. Eastern European Economics, 46(4), 27–42. doi: 10.2753/EEE0012-8775460402
   Web of Science ®Google Scholar
• McKee, T. E. (2003). Rough sets bankruptcy prediction models versus auditor signaling rates. Journal of Forecasting, 22, 569–586. doi: 10.1002/for.875
   Web of Science ®Google Scholar
• McKee, T. E., & Lensberg, T. (2002). Genetic programming and rough sets: A hybrid approach to bankruptcy classification. European Journal of Operational Research, 138, 436–451. doi: 10.1016/S0377-2217(01)00130-8
   Web of Science ®Google Scholar
• Min, J. H., & Lee, Y.-C. (2005). Bankruptcy prediction using support vector machine (SVM) with optimal choice of kernel function parameters. Expert Systems with Applications, 28, 603–614. doi: 10.1016/j.eswa.2004.12.008
   Web of Science ®Google Scholar
• Park, C.-S., & Han, I. (2002). A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction. Expert Systems with Applications, 23(3), 255–264. doi: 10.1016/S0957-4174(02)00045-3
   Web of Science ®Google Scholar
• Piramuthu, S., Ragavan, H., & Shaw, M. J. (1998). Using feature construction to improve the performance of the neural networks. Management Science, 44(3), 416–430. doi: 10.1287/mnsc.44.3.416
   Web of Science ®Google Scholar
• Ravi Kumar, P., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques – a review. European Journal of Operational Research, 180, 1–28. doi: 10.1016/j.ejor.2006.08.043
   Web of Science ®Google Scholar
• Richardson, F., Kane, G., & Lobingier, P. (1998). The impact of recession on the prediction of corporate failure. Journal of Business Finance and Accounting, 25(1 & 2), 167–186. doi: 10.1111/1468-5957.00182
   Google Scholar
• Sharma, D. S. (2001). The role of cash flow information in predicting corporate failure: The state of the literature. Managerial Finance, 27(4), 3–28. doi: 10.1108/03074350110767114
   Google Scholar
• Statistics Estonia. (2013). Gross domestic product by expenditure approach. Tables: NAA1061 and NAA0061. Statistics Estonia Database. Retrieved from http://pub.stat.ee/px-web.2001/I_Databas/Economy/23National_accounts/01Gross_domestic_product_(GDP)/06Gross_domestic_product_by_expenditure_approach/06Gross_domestic_product_by_expenditure_approach.asp
   Google Scholar
• Taffler, R. (1983). The assessment of company solvency and performance using a statistical model. Accounting & Business Research, 13(52), 295–308. doi: 10.1080/00014788.1983.9729767
   Google Scholar
• Verikas, A., Kalsyte, Z., Bacauskiene, M., & Gelzinis, A. (2010). Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: A survey. Soft Computing, 14, 995–1010. doi: 10.1007/s00500-009-0490-5
   Web of Science ®Google Scholar
• White, M. (1980). Public policy toward bankruptcy: Me-first and other priority rules. Bell Journal of Economics, 11, 550–564. doi: 10.2307/3003379
   Google Scholar