A comparison of fuzzy strategies for corporate acquisition analysis

Analysing all prospective companies for acquisition in large market sectors is an onerous task. A strategy that results in a shortlist of companies that meet certain basic criteria is required. The short-listed companies can then be further investigated in more detail later if desired. Fuzzy logic systems (FLSs) imbued with the expertise of a focal organisation's financial experts can be of great assistance in this process. In this paper an investigation into the suitability of FLSs for acquisition analysis is presented. The nuances of training and tuning are discussed. In particular, the difficulty of obtaining suitable amounts of expert data is a recurring theme throughout the paper. A strategy for circumventing this issue is presented that relies on the design of a conventional fuzzy logic rule base with the assistance of a financial expert. With the rule base created, various scenarios such as the simulation of multiple experts and the creation of expert training data are investigated. In particular, two scenarios for the creation of simulated expert data are presented. In the first the responses from the different experts are averaged, and in the second scenario the responses from all the different experts are preserved in the training data. This paper builds on previous work with scalable membership functions, however. the use of fuzzy C-means clustering and backpropagation training, are new developments. Additionally, a type-2 FLS is developed and its potential advantages are discussed for this application. The type-2 system facilitates the inclusion of the opinions of multiple experts. Both the type-1 and type-2 FLSs were trained using the backpropagation algorithm with early stopping and verified with five-fold cross-validation. Multiple runs of the five-fold method were conducted with different random orderings of the data. For this particular application, the type-1 system performed comparably with the type-2 system despite the considerable amount of variation in the expert training data. The training results have proven the methods to be capable of efficient tuning of parameters, and of reliable ranking of prospective companies. (c) 2007 Elsevier B.V. All rights reserved.