A comparison of fuzzy strategies for corporate acquisition analysis

Fuzzy Sets and Systems

Volumn 158, Issue 18, 2007, Pages 2039-2056

  Glackin, Cornelius ^a   Maguire, Liam ^a   McIvor, Ronan ^a   Humphreys, Paul ^a   Herman, Pawel ^a  

^a UNIVERSITY OF ULSTER   (United Kingdom)

Author keywords

Clustering methods; Fuzzy systems; Interval valued fuzzy sets; Type 2 fuzzy sets

Indexed keywords

CLUSTERING METHODS; EXPERT TRAINING; INTERVAL VALUED FUZZY SETS;

BACKPROPAGATION; DATA ACQUISITION; FUZZY LOGIC; MERGERS AND ACQUISITIONS; PARAMETER ESTIMATION; STRATEGIC PLANNING;

FUZZY SETS;

EID: 34447335085     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fss.2007.03.020     Document Type: Article

References (46)

1. E. Altman, Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, J. Finance, September (1968) 189-209.
2. E. Altman, Z-score: the old reliable method for predicting bankruptcy still works, New York University, Leonard N. Stern School of Business, Stern in the News, 〈http://w4.stern.nyu.edu/news/news/2003/october/1001mc.htm〉, 1st October 2003.
3. Amadeus formulas, Wharton University of Pennsylvannia, Wharton Research Data Services, 〈http://wrds.wharton.upenn.edu/demo/bvd/description/Amadeus_Formula s.shtml〉, 2006.
4. Astudillo L., Castillo O., and Aguilar L.T. Intelligent control for a perturbed autonomous wheeled mobile robot: a type-2 fuzzy logic approach. J. Nonlinear Stud. 14 1 (2007) 37-48
5. A. Auerbach, How to analyze your business using financial ratios, FastTrac-Edward Lowe Business Builders Library, Ewing Marion Kaufman Foundation, 〈http://www.embarq.sbresources.com/SBR_template.cfm?DocNumber=PL12_ 1500.htm〉, 2006.
6. Baguley P., Page T., Koliza V., and Maropoulos P. Time to market prediction using type-2 fuzzy sets. J. Manuf. Technol. Management 17 4 (2006) 513-520
7. Bellman R.E., and Zadeh L.A. Decision-making in a fuzzy environment. Management Sci. 17 4 (1970) 141-164
8. Bevilacqua M., and Petroni A. From traditional purchasing to supplier management: a fuzzy logic-based approach to supplier selection. Internat. J. Logistics. Res. Appl. 5 3 (2002)
9. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms (1981), Plenum Press, New York
10. Compustat, Standard & Poor's, a division of The McGraw-Hill Companies incorporated, 〈http://www2.standardandpoors.com〉, 2006.
11. E.A. Cox, Fuzzy fundamentals, IEEE Spectrum, October (1992) 58-61.
12. E.A. Cox, Fuzzy logic for business and industry, Charles River Media, ISBN: 1-886801-01-0, 1994.
13. E.W. Davis, Global outsourcing: have U.S. managers thrown the baby out with the bath water? Business Horizons, July-August (1992) 58-65.
14. Dunn J.C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybernet. 3 (1973) 32-57
15. FAME Database, Sungard Data Management Solutions, 〈http://www.data.sungard.com/data/index.htm〉, 2006.
16. J. Figuero, J. Posada, M. Melgarejo, S. Rojas, A type-2 fuzzy controller for tracking mobile objects in the context of robotic soccer games, in: Proceedings IEEE International Conference on Fuzzy Systems, Reno, NV, May 2005, pp. 359-364.
17. J.K. Gentry, Reading business financial statements: use data to determine a company's financial health, FACSNET, 〈http://www.facsnet.org/tools/biz_econ/covering_biz/part2.php3 〉, 2006.
18. Gorzalczany M.B. A method of inference in approximate reasoning based on interval-valued fuzzy sets. Fuzzy Sets and Systems 21 (1987) 1-17
19. Gosset W.S. The probable error of a mean. Biometrika 6 1 (1908) 1-25. http://www.york.ac.uk/depts/maths/histstat/student.pdf 〈http://www.york.ac.uk/depts/maths/histstat/student.pdf〉
20. A. Greta, Introduction to fundamental analysis, 〈http://www.thestreet.com/basics/fundamental/145556.html〉, 2006.
21. Harrison B. Lean and Mean: the Changing Landscape of Corporate Power in the Age of Flexibility (1994), Basic Books, New York
22. P. Herman, G. Prasad, T.M. McGinnity, Investigation of the type-2 fuzzy logic approach to classification in an EEG-based brain-computer interface, in: Proc. Annual Internat. Conf. IEEE Engineering in Medicine and Biology Society, Conference vol. 27, 2005, pp. 5354-5357.
23. J.-S.R. Jang, C.-T. Sun, E. Mizutani, Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence, MATLAB Curriculum Series, Prentice-Hall, ISBN 0-13-261066-3, 1997.
24. John R.I., and Innocent P.R. Neuro-fuzzy clustering of radiographic tibia image data using type 2 fuzzy sets. Inform. Sci. 125 (2000) 65-82
25. G. Johnson, K. Scholes, The nature of strategy and strategic decisions, sixth ed., in: Exploring Corporate Strategy, Financial Times: Prentice Hall, 2002.
26. N.N. Karnik, J.M. Mendel, Type-2 fuzzy logic systems: type reduction, in: IEEE Systems, Man and Cybernetics, International Conference, vol. 2, 1998, pp. 2046-2051.
27. N.N. Karnik, J.M. Mendel, An introduction to type-2 fuzzy logic systems, University of Southern California, 〈http://sipi.usc.edu/∼mendel/software〉, 2005.
28. Kreinovich V., et al. Fuzzy rule based modelling as a universal approximation tool. In: Nguyen H.T., and Sugeno M. (Eds). Fuzzy Systems: Modeling and Control (1998), Kluwer, Boston, MA 135-195
29. Liang Q., and Mendel J.M. Modeling MPEG VBR video traffic using type-2 fuzzy logic systems, Granular Computing: An Emerging Paradigm (2000), Springer, Berlin
30. Q. Liang, L. Wang, Sensed signal strength forecasting for wireless sensors using interval type-2 fuzzy logic system, in: Proc. IEEE Internat. Conf. on Fuzzy Systems, Reno, NV, 25-30, May 2005.
31. P.Z. Lin, C.F. Hsu, T.T. Lee, Type-2 fuzzy logic controller design for Buck DC-DC converters, in: Proc. IEEE Internat. Conf. Fuzzy Systems, Reno, NV, 365-370, May 2005.
32. C. Lynch, H. Hagras, V. Callaghan, Using uncertainty bounds in the design of an embedded real-time type-2 neuro-fuzzy speed controller for marine diesel engines, in: Proc. IEEE Internat. Conf. Fuzzy Systems, Vancouver, Canada, July 2006, pp. 7217-7224.
33. MATLAB, The MathWorks Incorporated, 〈http://www.mathworks.com〉, 2006.
34. M. Matteucci, A tutorial on clustering algorithms, Dipartimento di Elettronica e Informazione, Politecnico di Milano, 〈http://www.elet.polimi.it/upload/matteucc/Clustering/tutorial_html /index.html〉, 2006.
35. T. McCloskey, R. McIvor, L.P. Maguire, P. Humphreys, T. O'Donnell, A user centred corporate acquisition system: a dynamic fuzzy membership function approach, Decision Support Systems, 2005, accepted for publication.
36. McIvor R., McCloskey A., Maguire L.P., and Humphreys P.K. Using a fuzzy approach to support financial analysis in the corporate acquisition process. J. Expert Systems Appl. 27 4 (2004) 533-547
37. J. Mendel, Uncertain rule-based fuzzy logic systems: introduction and new directions Prentice-Hall, N.J., ISBN 0-13-040969-3, 2001.
38. J. Mendel, K. Wu, Uncertainty versus choice in rule-based fuzzy logic systems, Proc. IEEE Internat. Conf. Fuzzy Systems, Honolulu, HI, 2002, pp. 1336-1342.
39. Mizumoto M., and Tanaka K. Some properties of fuzzy sets of type-2. Inform. Control 31 (1976) 312-340
40. Mizumoto M., and Tanaka K. Fuzzy sets of type-2 under algebraic product and algebraic sum. Fuzzy Sets and Systems 5 (1981) 277-280
41. U. Pareek, I. N. Kar, Estimating compressor discharge pressure of gas turbine power plant using type-2 fuzzy logic systems, Proc. IEEE Internat. Conf. Fuzzy Systems, Vancouver, Canada, July 2006, pp. 3113-3118.
42. Stone M. Cross-validatory choice and assessment of statistical predictions. J. Roy. Statist. Soc. B 36 1 (1974) 111-147
43. Value Line investment survey, Value Line incorporated, 〈http://www.valueline.com〉, 2006.
44. P.J. Werbos, The roots of backpropagation: from ordered derivatives to neural networks and political forecasting, Adaptive and Learning Systems for Signal Processing, Communications, and Control, Wiley, New York, ISBN: 0471598976, 1994.
45. Zadeh L.A. The concept of a linguistic variable and its application to approximate reasoning-I. Inform. Sci. 8 (1975) 199-249
46. Zeng J., and Liu Z.-Q. Type-2 fuzzy hidden Markov models and their application to speech recognition. IEEE Trans. Fuzzy Systems 14 3 (2006) 454-467