Mostafania, Mohammad (2014) Do Fire Sales Really Exist? An Empirical Study on Distressed Targets' Premiums. Masters thesis, Concordia University.
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Abstract
Firms can be de-listed from public market due to different reasons. They could go bankrupt which would be a negative outcome for their shareholders, they could merge with or acquired by other firms, or they could go private, outcomes that are usually pleasant for their stakeholders. What if a firm becomes an acquisition target in the period right before going bankrupt? Is it still a positive event for shareholders or because of the firm’s distressed situation there will be no positive return for them? We estimate the likelihood of firm failure and examine the premium offered for distressed public firms in both contractions and normal economic periods. We use Survival Analysis and Artificial Neural Networks, both using multi-period inputs, to categorize firms into distressed and not-distressed groups. These models claim to be more successful compared to the single-period static models widely used in the extant literature. Results of analyzing 1378 targets in different market conditions shows that acquirers tend to overpay for distressed targets and even more in contraction periods. on the other hand, we observe a huge discount when we calculate the mean target premium in reference to targets' highest price in the 52-week period before the announcement . It seems that acquirers bid reference is not the current market valuation, but the target’s best position in the one year prior to announcement.
| Divisions: | Concordia University > John Molson School of Business > Finance |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Mostafania, Mohammad |
| Institution: | Concordia University |
| Degree Name: | M. Sc. |
| Program: | Administration (Finance option) |
| Date: | 5 December 2014 |
| Thesis Supervisor(s): | Betton, Sandra |
| Keywords: | Survival Analysis, Fire Sale, Artificial Neural Networks, Logistic Regression, Distressed Firms, Bankruptcy |
| ID Code: | 979597 |
| Deposited By: | MOHAMMAD MOSTAFANIA |
| Deposited On: | 13 Jul 2015 16:25 |
| Last Modified: | 22 Jul 2019 18:00 |
References:
Aharony, J., Jones, C. P., & Swary, I. (1980). An Analysis of Risk and Return Characteristics of Corporate Bankruptcy Using Capital Market Data. The Journal of Finance, 35(4), 1001–1016.Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.
Altman, E. I., Marco, G., & Varetto, F. (1994). Corporate distress diagno- sis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking & Finance, 18(3), 505–529.
Ang, J., & Mauck, N. (2011). Fire sale acquisitions: Myth vs. reality. Journal of Banking & Finance, 35(3), 532-543.
Atiya, A. F. (2001). Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on Neural Networks, 12(4), 929-935.
Beaver, W. H. (1966). Financial Ratios As Predictors of Failure. Journal of Accounting Research, 4, 71–111.
Beaver, W. H. (1968). Alternative Accounting Measures as Predictors of Failure. The Accounting Review, 43(1), 113–122.
Beaver, W. H. (1968). Market Prices, Financial Ratios, and the Prediction of Failure. Journal of Accounting Research, 6(2), 179–192.
Boritz, J. E.,& Kennedy, D. B. (1995). Effectiveness of Neural-Network Types for Prediction of Business Failure. Expert Systems with Applications, 9(4), 503-512. Bruin, J. 2006. newtest: command to compute new test. UCLA: StatisticalConsulting Group. http://www.ats.ucla.edu/stat/stata/ado/analysis/.
Coats, P. K., & Fant, L. F. (1993). Recognizing Financial Distress Patterns Using a Neural-Network Tool. Financial Management, 22(3), 142-155.
Collins, R. A. (1980). An Empirical Comparison of Bankruptcy Prediction Models. Financial Management, 9(2), 52–57.
Courtis, J. K. (1978). Modelling a financial ratios categoric framework. Journal of Business Finance & Accounting, 5(4), 371-386.
Coval, J., Stafford, E., & National Bureau of Economic Research. (2005). Asset fire sales (and purchases) in equity markets NBER working paper series working paper 11357 Retrieved from http://papers.nber.org/papers/W11357.
Deakin, E. B. (1972). A Discriminant Analysis of Predictors of Business Failure. Journal of Accounting Research, 10(1), 167–179.
Denis, D. J., Denis, D. K., & Sarin, A. (1997). Agency problems, equity owner- ship, and corporate diversification. Journal of Finance, 52(1), 135-160.
Diamond, Jr., H. 1976. Pattern recognition and the detection of corporate failure. Ph.D. dissertation, New York University.
Dimitras, A. I., Zanakis, S. H., & Zopounidis, C. (1996). A survey of busi- ness failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research, 90(3), 487-513.
Eckbo, B. E. (2009). Bidding strategies and takeover premiums: A review. Journal of Corporate Finance, 15(1), 149-178.
Farrar, D. E. (1982). Citation Classic - Multicollinearity in Regression-Analysis - the Problem Revisited. Current Contents/Social & Behavioral Sciences(7), 22-22.
Foster, G. (1986). Financial statement analysis (2nd ed.). Englewood Cliffs, N.J.: Prentice-Hall.
Garlappi, L., & Yan, H. (2011). Financial Distress and the Cross-section of Equity Returns. Journal of Finance, 66(3), 789-822.
Gilbert, L. R., Menon, K., & Schwartz, K. B. (1990). Predicting bankruptcy for firms in financial distress. Journal of Business Finance & Accounting, 17(1), 161–171.
Zhang, G., Y Hu, M., Eddy Patuwo, B., & C Indro, D. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European journal of operational research, 116(1), 16-32.
Kiefer, N. M. (1988). Economic Duration Data and Hazard Functions. Journal of Economic Literature, 26(2), 646-679.
Kleinbaum, D. G., & Klein, M. (2012). Survival analysis : a self-learning text (3rd ed.). New York: Springer.
Kothari, S.P. & Warner, Jerold B., The Econometrics of Event Studies (October 20, 2004). Available at SSRN: http://ssrn.com/abstract=608601
Koutsomanoli-Filippaki, A., & Mamatzakis, E. (2009). Performance and Merton- type default risk of listed banks in the EU: A panel VAR approach. Journal of Banking & Finance, 33(11), 2050-2061.
Lee, S., & Choi, W. S. (2013). A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis. Ex- pert Systems with Applications, 40(8), 2941-2946.
Leshno, M., & Spector, Y. (1996). Neural network prediction analysis: The bankruptcy case. Neurocomputing, 10(2), 125-147. .
Li, E. Y. (1994). Artificial neural networks and their business applications. Information & Management, 27(5), 303-313.
Messier, W. F., & Hansen, J. V. (1988). Inducing Rules for Expert System- Development - an Example Using Default and Bankruptcy Data. Management Sci- ence, 34(12), 1403-1415. doi: Doi 10.1287/Mnsc.34.12.1403.
Morris, R. (1997) Early Warning Indicators of Corporate Failure (Aldershot: Ashgate). Nobes, C. and Parker, R. (1999) Comparative International Accounting (Hemel Hempstead: Prentice-Hall).
Mossman, C. E., Bell, G. G., Swartz, L. M., & Turtle, H. (1998). An empirical comparison of bankruptcy models. Financial Review, 33(2), 35–54.
Odom, M. D., & Sharda, R. (1990). A Neural Network Model for Bankruptcy Prediction. Ijcnn International Joint Conference on Neural Networks, Vols 1-3, B163- B168.
Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109–131.
Pagano, M., Panetta, F., & Zingales, L. (1998). Why do companies go public? An empirical analysis. Journal of Finance, 53(1), 27-64.
Platt, H. D., & Platt, M. B. (1990). Development of a class of stable predic- tive variables: the case of bankruptcy prediction. Journal of Business Finance & Accounting, 17(1), 31–51.
Queen, M., & Roll, R. (1987). Firm Mortality: Using Market Indicators to Predict Survival. Financial Analysts Journal, 43(3), 9–26.
Rose, P. S., Andrews, W. T., & Giroux, G. A. (1982). Predicting business failure: A macroeconomic perspective. Journal of Accounting, Auditing and Finance, 6(1), 20-31.
Santomero, A. and J. Vinso (1977) Estimating the probability of failure for firms in the banking System, Journal of Banking and Finance, Sept. 1977.
StataCorp. 2013. Stata: Release 13. Statistical Software. College Station, TX: StataCorp LP.
Shleifer, A., & Vishny, R. W. (1992). Liquidation Values and Debt Capacity - a Market Equilibrium Approach. Journal of Finance, 47(4), 1343-1366.
Shumway, T. (2001). Forecasting Bankruptcy More Accurately: A Simple Haz- ard Model. The Journal of Business, 74(1), 101–124.
Tam, K. Y., & Kiang, M. Y. (1992). Managerial Applications of Neural Networks - the Case of Bank Failure Predictions. Management Science, 38(7), 926-947.
Theodossiou, P. T. (1993). Predicting Shifts in the Mean of a Multivariate Time Series Process: An Application in Predicting Business Failures. Journal of the American Statistical Association, 88(422), 441–449.
Wang, J., Meric, G., Liu, Z. G., & Meric, I. (2009). Stock market crashes, firm characteristics, and stock returns. Journal of Banking & Finance, 33(9), 1563-1574. White, R. W., & M. Turnball. The Probability of Bankruptcy: American Rail- roads. Working paper, Institute of Finance and Accounting, London University
Graduate School of Business, February 1975.
White, R. W., & M. Turnball. The Probability of Bankruptcy for American Industrial Firms. Working paper, July 1975.
Zmijewski, M. E. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, 22, 59–82.
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