Impact of reorganization announcements on distressed-stock returns

Economic Modelling

Volumn 24, Issue 5, 2007, Pages 749-767

  Chi, Li Chiu ^a   Tang, Tseng Chung ^a  

^a NATIONAL FORMOSA UNIVERSITY   (Taiwan)

Author keywords

Corporate reorganization; Distressed stock investing; Hybrid genetic algorithm and backpropagation network

Indexed keywords

EID: 34250625653     PISSN: 02649993     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.econmod.2007.02.007     Document Type: Article

References (49)

1. Altman E.I. Workouts and Turnarounds: The Handbook of Restructuring and Investing in Distressed Companies (1991), Irwin, Homewood, IL
2. Altman E.I. Market Dynamics and Investment Performance of Distressed and Defaulted Debt Securities. Working paper at New York University (1998)
3. Altman E.I. Bankruptcy and Distressed Restructurings: Analytical Issues and Investment Opportunities (1999), Beard Books, MD
4. Bhargava R., Radhakrishnan R., John L.K., and Evans B.L. Characterization of MMX-enhanced DSP and Multimedia Applications on a General Purpose Processor. Digest of Workshop on Performance Analysis and Its Impact on Design (PAID98), Barcelona, Spain (1998)
5. Brown S., and Warner J. Using daily stock returns: the case of event studies. Journal of Financial Economics 14 (1985) 3-31
6. Burrell P.R., and Folarin B.O. The impact of neural networks in finance. Neural Computing & Applications 6 (1997) 193-200
7. Callen J.L., Kwan C., Yip P., and Yuan Y. Neural network forecasting of quarterly accounting earnings. International Journal of Forecasting 12 (1996) 475-482
8. Chandra R., Rohrbach K., and Willinger L. A comparison of the power of parametric and nonparametric tests of location shift for event studies. Financial Review 30 (1995) 685-710
9. Chi L.C., and Tang T.C. Artificial neural networks in reorganization outcome and investment of distressed firms: the Taiwanese case. Expert Systems with Applications 29 (2005) 641-652
10. Cochran B., Rose L., and Fraser D. A market evaluation of FDIC assisted transactions. Journal of Banking and Finance 19 (1995) 261-279
11. Corrado C.J., and Zivney T.L. The specification and power of the sign test in event study hypothesis tests using daily stock returns. Journal of Financial and Quantitative Analysis 27 (1992) 465-478
12. Datta S., and Iskandar-Datta M.E. The information content of bankruptcy filing on security holders of the bankrupt firm: an empirical investigation. Journal of Banking and Finance 19 (1995) 903-919
13. Dissanaike G. Does the size effect explain the UK winner-loser effect?. Journal of Finance & Accounting 29 1-2 (2002) 139-154
14. Dorsey R.E., Johnson J.D., and Mayer W.J. A genetic algorithm for the training of feedforward neural networks. Advances in Artificial Intelligence in Economics. Finance & Management 1 (1994) 93-111
15. Dorsey R.E., and Mayer W.J. Genetic algorithms for estimation problems with multiple optima, non-differentiability, and other irregular features. Journal of Business and Economic Statistics 13 1 (1995) 53-66
16. Erwin G.R., and Miller J.M. The intra-industry effects of open market share repurchases: contagion or competitive?. Journal of Financial Research 21 4 (1998) 389-406
17. Fama E., and French K. The cross-section of expected stock returns. Journal of Finance 47 (1992) 427-465
18. Gilson S., John K., and Lang L. Troubled debt restructurings: an empirical study of private reorganizations of firms in default. Journal of Financial Economics 27 (1990) 315-353
19. Guadagni P.M., and Little D.C. A logit model of brand choice calibrated on scanner data. Marketing Science 2 3 (1983) 203-238
20. Harpel J. Fallen angels: Jim Harpel searches for stocks with big turnaround potential. Barron's 72 16/17 (1992) 21-29
21. Hotchkiss E.S., and Mooradian R. Vulture investors and the market for control of distressed firms. Journal of Finance 46 (1997) 861-878
22. Howe J.T. The New High-yield Debt Market: A Handbook for Portfolio Managers and Analysts (1990), Harper Business, New York
23. Indro D.C., Leach R.T., and Lee W. Sources of gains to shareholders from bankruptcy resolution. Journal of Banking & Finance 23 (1999) 21-47
24. Iqbal Z. The effects of bankruptcy filings on the competitors' earnings. International Review of Economics & Finance 11 (2002) 85-99
25. John K. Managing financial distress and valuing distressed securities: a survey and a research agenda. Financial Management 22 (1993) 60-78
26. Kim S.H., and Chun S.H. Graded forecasting using an array of bipolar predictions: application of probabilistic neural networks to a stock market index. International Journal of Forecasting 14 (1998) 323-337
27. Lakonishok J., Shleifer A., and Vishny R.W. Contrarian investment, extrapolation, and risk. Journal of Finance 49 (1994) 1541-1578
28. Lang L., and Stulz R. Contagion and competitive intraindustry effects of bankruptcy announcements: an empirical analysis. Journal of Financial Economics 32 (1992) 45-60
29. Leung M.T., Hazem D., and Chen A.S. Forecasting stock indices: a comparison of classification and level estimation models. International Journal of Forecasting 16 (2000) 173-190
30. Lo A.W., and MacKinlay A.C. Stock market prices do not follow random walks: evidence from a simple specification test. Review of Financial Studies 1 (1988) 41-66
31. Morse D., and Shaw W. Investing in bankrupt firms. Journal of Finance 43 (1988) 1193-1206
32. Moulton W.N., and Thomas H. Bankruptcy as a deliberate strategy: theoretical consideration and empirical evidence. Strategic Management Journal 14 (1993) 125-135
33. O'Hara M. Presidential address: liquidity and price discovery. Journal of Finance 58 (2003) 1335-1354
34. Ooghe, H, Balcaen, S. Are failure prediction models transferable from one country to another? An empirical study using Belgian financial statements. Vlerick Leuven Gent Management School. Vlerick Working Paper; 2002.
35. Pendharkar P.C. An empirical study of design and testing of hybrid evolutionary - neural approach for classification. Omega 29 (2001) 361-374
36. Platt H.D. Why Companies Fail (1985), D.C. Heath and Company, MA
37. Putnam III G. Investing in Bankruptcies and Turnarounds (1991), Harper Business, New York
38. Refenes A., and Bolland P. Modelling Quarterly Returns on the FTSE: A Comparative Study with Regression and Neural Networks (1996), Irwin, IL
39. Renshaw E. Modelling the stock market for forecasting purposes. Journal of Portfolio Management 1 (1993) 76-81
40. Rumelhart D.E., and McClelland J.L. Parallel Distributed Processing: Explorations in the Theory of Cognition (1986), MIT Press, Cambridge, MA
41. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning representations by back propagating errors. Nature 323 (1986) 533-536
42. Sadka R. The momentum and post-earnings-announcement drift anomalies: the role of liquidity risk. Journal of Financial Economics 80 2 (2006) 309-349
43. Scholes M., and Williams J. Estimating betas from nonsynchronous data. Journal of Financial Economics 5 (1977) 309-327
44. Tang T.C., and Chi L.C. Predicting multilateral trade credit risks: comparisons of Logit and Fuzzy Logic models using ROC Curve analysis. Expert Systems with Applications 28 (2005) 547-556
45. Tashjian E., Lease R., and McConnell J. Prepacks: an empirical analysis of prepackaged bankruptcies. Journal of Financial Economics 40 (1996) 135-162
46. Thawornwong S., and Enke D. The adaptive selection of financial and economic variables for use with artificial neural networks. Neurocomputing 56 (2004) 205-232
47. Turban E. Expert Systems and Applied Artificial Intelligence (1992), Macmillan, New York
48. Werbos P. The Roots of the Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting (1993), John Wiley and Sons Inc, New York
49. Wong B.K., Bodnovich T., and Selvi Y. A bibliography of neural network applications research: 1988-1994. Expert Systems 12 (1995) 253-261