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An Application of Support Vector Machines in the Prediction of Acquisition Targets: Evidence from the EU Banking Sector

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Handbook of Financial Engineering

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References

  1. Altunbas, Y., and Ibanez, D. M. Mergers and acquisitions and bank performance in Europe: The role of strategic similarities, European Central Bank, Working paper no. 398, October, 2004.

    Google Scholar 

  2. Altunbas, Y., Molyneux, P., and Thorton, J. Big-bank mergers in Europe: An analysis of the cost implications. Economica, 64:317–329, 1997.

    Article  Google Scholar 

  3. Asterbo, T., and Winter J. K. More than a dummy: The probability of failure, survival and acquisition of firms in financial distress, University of Waterloo, Working paper, 2001.

    Google Scholar 

  4. Barnes, P. The prediction of takeover targets in the U.K. by means of multiple discriminant analysis. Journal of Business Finance & Accounting, 17(1):73–84, 1990.

    Article  MathSciNet  Google Scholar 

  5. Barnes, P. Can takeover targets be identified by statistical techniques?: Some UK evidence. The Statistician, 47(4):573–591, 1998.

    Google Scholar 

  6. Barnes, P. Predicting UK takeover targets: Some methodological issues and an empirical study. Review of Quantitative Finance and Accounting, 12(3):283–301, 1999.

    Article  Google Scholar 

  7. Barnes, P. The identification of U.K. takeover targets using published historical cost accounting data. Some empirical evidence comparing logit with linear discriminant analysis and raw financial ratios with industry-relative ratios. International Review of Financial Analysis, 9(2):147–162, 2000.

    Article  Google Scholar 

  8. Bartley, J. W., and Boardman, C. M. The relevance of inflation adjusted accounting data to the prediction of corporate takeovers. Journal of Business Finance and Accounting, 17(1):53–72, 1990.

    Article  Google Scholar 

  9. Beitel, P., and Schiereck D. Value creation at the ongoing consolidation of the European banking market. Presented at the Xth International Conference on Banking and Finance, Tor Vergata University Rome, December 5–7, 2001.

    Google Scholar 

  10. Beitel, P., Schiereck, D., and Wahrenburg, M. Explaining M&A success in European banks. European Financial Management, 10:109–140, 2004.

    Article  Google Scholar 

  11. Burges, C. J. C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2:121–167, 1998.

    Article  Google Scholar 

  12. Campa, J. M., and Hernando, I. M&As performance in the European financial industry. Journal of Banking and Finance, 30(12):3367–3392, 2006.

    Article  Google Scholar 

  13. Cao, L. Support vector machines experts for time series forecasting. Neurocomputing, 51(April):321–339, 2003.

    Google Scholar 

  14. Cheh, J. J., Weinber, R. S., Yook, K. C. An application of an artificial neural network investment system to predict takeover targets. The Journal of Applied Business Research, 15 (4):33–45, 1999.

    Google Scholar 

  15. Cudd, M., and Duggal, R. Industry distributional characteristics of financial ratios: An acquisition theory application. The Financial Review, 41, 105–120, 2000.

    Article  Google Scholar 

  16. Cybo-Ottone, A., and Murgia, M. Mergers and shareholder wealth in European banking. Journal of Banking and Finance, 24(6):831–859, 2000.

    Article  Google Scholar 

  17. Diaz, B. D., Olalla, M. G., and Azorfa, S. S. Bank acquisitions and performance: evidence from a panel of European credit entities. Journal of Economics and Business, 56(5):377–404, 2004.

    Article  Google Scholar 

  18. Dietrich, J. M., and Sorensen, E. An application of logit analysis to prediction of merger targets. Journal of Business Research, 12, 393–402, 1984.

    Article  Google Scholar 

  19. Doumpos, M. A stacked generalization framework for credit risk assessment. Operational Research: An International Journal, 2(2):261–278, 2002.

    Google Scholar 

  20. Doumpos, M., Gaganis, C., and Pasiouras, F. Explaining qualifications in audit reports using a support vector machine methodology. Intelligent Systems in Accounting, Finance and Management, 13(4):197–215, 2005.

    Article  Google Scholar 

  21. Doumpos, M., Kosmidou, K., and Pasiouras, F. Prediction of acquisition targets in the UK: A multicriteria approach. Operational Research: An International Journal, 4(2):191–211, 2004.

    Google Scholar 

  22. Dunis, C.L., and Klein, T. Analysing mergers and acquisitions in European financial services: An application of real options. The European Journal of Finance, 11(4):339–355, 2005.

    Article  Google Scholar 

  23. Espahbodi, H., and Espahbodi, P. Binary choice models for corporate takeover. Journal of Banking and Finance, 27(4):549–574, 2003.

    Article  Google Scholar 

  24. European Central Bank. Mergers and acquisitions involving the EU banking industry: Facts and implications, December, 2000.

    Google Scholar 

  25. European Central Bank. Financial stability review, December, 2004.

    Google Scholar 

  26. European Central Bank. Financial stability review, June, 2005a.

    Google Scholar 

  27. European Central Bank. Financial stability review, December, 2005b.

    Google Scholar 

  28. European Central Bank. EU banking structures, October, 2006a.

    Google Scholar 

  29. European Central Bank. EU banking sector stability, November, 2006b.

    Google Scholar 

  30. European Central Bank. Financial stability review, June, 2006c.

    Google Scholar 

  31. European Central Bank. Financial stability review, December, 2006d.

    Google Scholar 

  32. Focarelli, D., Panetta, F., and Salleo, C. Why do banks merge?. Paper no. 361, Banca d'Italia Research Department, 1999.

    Google Scholar 

  33. Fung, G., and Mangasarian, O. L. Proximal support vector machine classifiers, In F. Provost, and R. Srikant, Editors, Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, New York, 2001, 77–86.

    Google Scholar 

  34. Gaganis, C., Pasiouras, F., and Doumpos, M. Probabilistic neural networks for the identification of qualified audit opinions. Expert Systems with Applications, 32:114–124, 2007.

    Article  Google Scholar 

  35. Gaganis, C., Pasiouras, F., and Tzanetoulakos, A. A comparison and integration of classification techniques for the prediction of small UK firms failure. The Journal of Financial Decision Making, 1(1):55–69, 2005.

    Google Scholar 

  36. Goddard, J., Molyneux, P., and Wilson, J. O. S. European Banking: Efficiency, Technology and Growth. John Wiley & Sons, New York, 2001.

    Google Scholar 

  37. Group of Ten. Report on consolidation in the financial sector, January 25, 2001; available at http://www.imf.org/external/np/g10/2001/01/Eng/

  38. Gupton, G. M., and Stein, R. M. LossCalc™: Moody's model for predicting loss given default (LGD). Moody's Investors Service Global Credit Research, Special comment, February, 2002.

    Google Scholar 

  39. Hannan, T., and Rhoades, S. Acquisition targets and motives: The case of the banking industry. The Review of Economics and Statistics, 69(1):67–74, 1987.

    Article  Google Scholar 

  40. Hardle, W., Moro, R. A., and Schafer, D. Rating companies with support vector machines. German Institute for Economic Research, Discussion paper 416, 2004.

    Google Scholar 

  41. Hardle, W., Moro, R. A., and Schafer, D. Predicting bankruptcy with support vector machines. School of Business and Economics, Humboldt-Universitaet zu Berlin, Discussion paper 2005-009, 2005.

    Google Scholar 

  42. Harper, I. R. Mergers in financial services: Why the rush?. The Australian Economic Review, 33(1):67–72, 2000.

    Article  Google Scholar 

  43. Hasbrouck, J. The characteristics of takeover targets: Q and other measures. Journal of Banking and Finance, 9(3):351–362, 1985.

    Article  Google Scholar 

  44. Hayden, E. Are credit scoring models sensitive with respect to default definitions? Evidence from the Austrian market. University of Vienna, Working paper, 2003.

    Google Scholar 

  45. Huang, W., Nakamori, Y., and Wang, S.-H. Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32(10):2513–2522, 2005.

    Article  MATH  Google Scholar 

  46. Huang, Z., Chen, H., Hsu, C.-J., Chen, W.-H., and Wu S. Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems, 37(4):543–558, 2004.

    Article  Google Scholar 

  47. Huizinga, H. P., Nelissen, J. H. M., and Vander Vennet, R. Efficiency effects of bank mergers and acquisitions in Europe. Tinbergen Institute, Discussion paper, 2001–088/3, 2001.

    Google Scholar 

  48. Kim, W. G., and Arbel, A. Predicting merger targets of hospitality firms (a Logit model). International Journal of Hospitality Management, 17(3):303–318, 1998.

    Article  Google Scholar 

  49. Kocagil, A. E., Reyngold, A., Stein, R. M., and Ibarra, E. Moody's RiskCalc™ model for privately-held U.S. banks. Moody's Investors Service, Global Credit Research, July, 2002.

    Google Scholar 

  50. Lee, Y.-C. Application of support vector machines to corporate credit rating prediction. Expert Systems with Applications, 33(1):67–74, 2007.

    Article  Google Scholar 

  51. Lepetit, L., Patry, S., and Rous, P. Diversification versus specialization: An event study of M&As in the European banking industry. Applied Financial Economics, 14(9):663–669, 2004.

    Article  Google Scholar 

  52. Manne, H. G. Mergers and the market for corporate control. Journal of Political Economy, 73(April): 110–120, 1965.

    Article  Google Scholar 

  53. McKee, T. E., and Lensberg, T. Genetic programming and rough sets: A hybrid approach to bankruptcy classification. European Journal of Operational Research, 138(2):436–451, 2002.

    Article  MATH  MathSciNet  Google Scholar 

  54. Min, J. H., Lee, Y.-C. Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4):603–614, 2005.

    Article  Google Scholar 

  55. Min, S.-H., Lee, J., and Han, I. Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications, 31(3):652–660, 2006.

    Article  Google Scholar 

  56. Moore, R. R. Banking's merger fervor: Survival of the fittest? Federal Reserved Bank of Dallas Financial Industry Studies, December, 9–15, 1996.

    Google Scholar 

  57. Nargundkar, S., and Priestley, J. Model development techniques and evaluation methods for prediction and classification of consumer risk in the credit industry. In P. Zhang, Editor, Neural Networks for Business Forecasting. IRM Press, Hershey, PA, 2003.

    Google Scholar 

  58. Pai, P.-F., and Lin, C.-S. A hybrid ARIMA and support vector machine model in stock price forecasting. Omega, 33(6):497–505, 2005.

    Article  Google Scholar 

  59. Palepu, K. G. Predicting takeover targets: A methodological and empirical analysis. Journal of Accounting and Economics, 8:3–35, 1986.

    Article  Google Scholar 

  60. Pasiouras, F., and Gaganis, C. Are the determinants of banks' acquisitions similar across EU countries? Empirical evidence from the principal banking sectors. Applied Economics Letters (forthcoming, 2007a).

    Google Scholar 

  61. Pasiouras, F., and Gaganis, C. Financial characteristics of banks involved in acquisitions: Evidence from Asia. Applied Financial Economics, 17(4):329–341, 2007b.

    Article  Google Scholar 

  62. Pasiouras, F., and Zopounidis, C. Consolidation in the Greek banking sector: Which banks are acquired? Managerial Finance (forthcoming, 2008).

    Google Scholar 

  63. Pasiouras, F., Tanna, S., and Zopounidis C. Application of Quantitative Techniques for the Prediction of Bank Acquisition Targets, World Scientific, Singapore, 2005.

    Google Scholar 

  64. Pasiouras, F., Tanna, S., and Zopounidis, C. The identification of acquisition targets in the EU banking industry: an application of multicriteria approaches. International Review of Financial Analysis (forthcoming, 2006).

    Google Scholar 

  65. Peterson, W., Birdsall, T., and Fox, W. The theory of signal detection. IRE Professional Group on Information Theory, PGIT-4, 171–212, 1954.

    Google Scholar 

  66. Platt, H. D., and Platt, M. B. Development of a class of stable predictive variables: The case of bankruptcy prediction. Journal of Business Finance and Accounting, 17:31–51, 1990.

    Article  Google Scholar 

  67. Platt, H. D., and Platt, M. B. A note on the use of industry-relative ratios in bankruptcy prediction. Journal of Banking and Finance, 15:1183–1194, 1991.

    Article  Google Scholar 

  68. Powell, R. G. Modelling takeover likelihood. Journal of Business Finance and Accounting, 24(7–8):1009–1030, 1997.

    Article  Google Scholar 

  69. Powell, R. G. Takeover prediction and portfolio performance: A note. Journal of Business Finance and Accounting, 28(7–8):993–1011, 2001.

    Article  Google Scholar 

  70. Rodriguez, A., and Rodriguez, P. N. Understanding and predicting sovereign debt rescheduling: A comparison of the areas under receiver operating characteristic curves. Journal of Forecasting (forthcoming, 2006).

    Google Scholar 

  71. Salcedo-Sanz, S., Fernández-Villacañas, J.-L., Segovia-Vargas, M. J., and Bousoño-Calzón, C. Genetic programming for the prediction of insolvency in non-life insurance companies. Computers & Operations Research, 32(4):749–765, 2005.

    Article  MATH  Google Scholar 

  72. Schölkopf, B., and Smola, A. Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. MIT Press, Cambridge, MA, 2002.

    Google Scholar 

  73. Scholtens, B., and Wit, R. Announcement effects of bank mergers in Europe and the US. Research in International Business and Finance, 18(2):217–228, 2004.

    Article  Google Scholar 

  74. Sealey, C., and Lindley, J. T. Inputs, outputs and a theory of production and cost at depositary financial institutions. Journal of Finance, 32, 1251–1266, 1977.

    Article  Google Scholar 

  75. Shin, K.-S., Lee, T. S., and Kim, H.-J. An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28(1):127–135, 2005.

    Article  Google Scholar 

  76. Simkowitz, M., and Monroe, R. J. A discriminant analysis function for conglomerate mergers. Southern Journal of Business, 38:1–16, 1971.

    Google Scholar 

  77. Slowinski, R., Zopounidis, C., and Dimitras, A. I. Prediction of company acquisition in Greece by means of the rough set approach. European Journal of Operational Research, 100(1):1–15, 1997.

    Article  MATH  Google Scholar 

  78. Sobehart, J., and Keenan, S. Measuring default accurately. Risk, 11(41):31–33, 2001.

    Google Scholar 

  79. Stein, R. M. Benchmarking default prediction models: Pitfalls and remedies in model validation. Moody's KMV, Technical report 020305, 2002.

    Google Scholar 

  80. Stevens, D. L. Financial characteristics of merged firms: A multivariate analysis. Journal of Financial and Quantitative Analysis, 8(March):149–158, 1973.

    Article  Google Scholar 

  81. Tanner, W., and Swets, J. A decision-making theory of visual detection. Psychological Review, 61:401–409, 1954.

    Article  Google Scholar 

  82. Tartari, E., Doumpos, M., Baourakis, G., and Zopounidis, C. A stacked generalization framework for the prediction of corporate acquisitions. Foundations of Computing and Decision Sciences, 28(1):41–61, 2003.

    Google Scholar 

  83. Tay, F. E. H., and Cao, L. J. Application of support vector machines in financial time series forecasting. Omega, 29(4):309–317, 2001.

    Article  Google Scholar 

  84. Tay, F. E. H., and Cao, L. J. Modified support vector machines in financial time series forecasting. Neurocomputing, 48(1–4):847–861, 2002.

    Article  MATH  Google Scholar 

  85. Tourani-Rad, A., and Van Beek, L. Market valuation of European bank mergers. European Management Journal, 17(5):532–540, 1999.

    Article  Google Scholar 

  86. Valkanov, E., and Kleimeier, S. The role of regulatory capital in international bank mergers and acquisitions. Research in International Business and Finance, 21(1):50–68, 2007.

    Article  Google Scholar 

  87. Vander Vennet, R. The effect of mergers and acquisitions on the efficiency and profitability of EC credit institutions. Journal of Banking and Finance, 20(9):1531–1558, 1996.

    Article  Google Scholar 

  88. Vapnick, V. N. Statistical Learning Theory. Wiley, New York, 1998.

    Google Scholar 

  89. Walkner, C., and Raes, J.-P. Integration and consolidation in EU banking: An unfinished business. European Commission, Directorate-General for Economic and Financial Affairs, Economic papers no. 226, April, 2005

    Google Scholar 

  90. Walter, R. M. The usefulness of current cost information for identifying takeover targets and earning above-average stock returns. Journal of Accounting, Auditing and Finance, 9:349–377, 1994.

    Google Scholar 

  91. Wheelock, D. C., and Wilson, P. W. Why do banks disappear? The determinants of U.S. bank failures and acquisitions. The Review of Economics and Statistics, 82(1):127–138, 2000.

    Article  Google Scholar 

  92. Wheelock, D. C., and Wilson, P. W. Consolidation in U.S. banking: Which banks engage in mergers? Review of Financial Economics, 13(1–2):7–39, 2004.

    Article  Google Scholar 

  93. Wu, C.-H., Tzeng, G-H., Goo, Y-J., and Fang, W.-C. A real-valued genetic algorithm to optimize the parameters of support vector machines for predicting bankruptcy. Expert Systems with Applications, 32(2):397–408, 2007.

    Article  Google Scholar 

  94. Zopounidis, C., and Doumpos, M. Multi-group discrimination using multi-criteria analysis: Illustrations from the field of finance. European Journal of Operational Research, 139(2):371–389, 2002.

    Article  MATH  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. School of Management, University of Bath, Claverton Down, Bath, BA2 7AY, UK

    Fotios Pasiouras

  2. Financial Engineering Laboratory, Department of Production Engineering and Management, Technical University of Crete, University Campus, Chania, 73100, Greece

    Chrysovalantis Gaganis & Constantin Zopounidis

  3. Department of Economics, Finance and Accounting, Faculty of Business, Environment and Society, Coventry University, Priory Street, Coventry, CV1 5FB, UK

    Sailesh Tanna

Authors
  1. Fotios Pasiouras
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  2. Chrysovalantis Gaganis
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  3. Sailesh Tanna
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  4. Constantin Zopounidis
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Correspondence to Fotios Pasiouras , Chrysovalantis Gaganis , Sailesh Tanna or Constantin Zopounidis .

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Editors and Affiliations

  1. Department of Production Engineering and Management, Financial Engineering Laboratory Technical University of Crete, University Campus, 731 00, Chania, Greece

    Constantin Zopounidis  & Michael Doumpos  & 

  2. Department of Industrial and Systems Engineering, University of Florida, 303 Weil Hall, 116595, Gainesville, FL, 32611-6595, USA

    Panos M. Pardalos

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Pasiouras, F., Gaganis, C., Tanna, S., Zopounidis, C. (2008). An Application of Support Vector Machines in the Prediction of Acquisition Targets: Evidence from the EU Banking Sector. In: Zopounidis, C., Doumpos, M., Pardalos, P.M. (eds) Handbook of Financial Engineering. Springer Optimization and Its Applications, vol 18. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-76682-9_14

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