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3 Foundations of Computing and Decision Sciences

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A stacked generalization framework for the prediction of corporate acquisitions

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Tartari E. , Doumpos M. , Baourakis G. , Zopounidis C.

Foundations of Computing and Decision Sciences

2003

tom Vol. 28, No. 1

41-61

Over the past decade the number of corporate acquisitions has increased rapidly worldwide. This has been mainly due to strategic reasons, since acquisitions play a prominent role in corporate growth. The prediction of acquisitions is of major interest to stockholders, investors, creditors and generally to anyone who has established a relationship with the acquired and non-acquired firm. Most of the previous studies on the prediction of corporate acquisitions have focused on the selection of an appropriate methodology to develop a predictive model and the comparison with other techniques to investigate the relative efficiency of the methods. On the contrary, this study proposes the combination of different methods in a stacked generalization context. Stacked generalization is a general framework for combining different classification models into an aggregate estimation which is expected to perform better than the individual models. This approach is employed to combine models for predicting corporate acquisitions which are developed through different methods into a combined model. Four methods are considered, namely linear discriminant analysis, probabilistic neural networks, rough set theory and the UTADIS multicriteria decision aid method. An application of the proposed stacked generalization approach is presented involving a sample of 96 UK firms.

Towards an intelligent decision support system for portfolio management

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Samaras G. D. , Matsatsinis N. F. , Zopounidis C.

Foundations of Computing and Decision Sciences

2005

tom Vol. 30, No. 2

141-162

The paper presents an Intelligent DSS to cover the needs in stock portfolio management. The system introduced develops the three tools of portfolio management: Fundamental Analysis, Technical Analysis and Market Psychology. In addition, it is also led by the investor's profile in order to make a personalized investment decision. The system integrates multi-criteria analysis methods and AI technologies, in order to obtain an efficient system, better suited to the rapidly changing conjunctures of financial markets. It introduces to the potential investor a complete fully justified investment suggestion for portfolio management of the Athens Stock Exchange.

Detecting falsified financial statements using multicriteria analysis: the case of Greece

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Spathis C. , Doumpos M. , Zopounidis C.

Foundations of Computing and Decision Sciences

2000

tom Vol. 25, No. 3

193-208

This paper develops a model for detecting factors associated with falsified financial statements (FFS). A sample of 76 firms described over ten financial ratios is used for detecting factors associated with FFS. The identification of such factors is performed using a multicriteria decision aid classification method (UTADIS-UTilites Additives DIScriminantes). The developed model is accurate in classifying the total sample correctly. The results therefore demonstrate that the model is effective in detecting FFS and could be of assistance to auditors, to taxation, to Stock Exchange officials, to state authorities and regulators and to the banking system.