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Predicting Corporate Acquisitions: An Application of Uncertain Reasoning Using Rule Induction

Author

Listed:
  • Srinivasan Ragothaman

    (University of South Dakota)

  • Bijayananda Naik

    (University of South Dakota)

  • Kumoli Ramakrishnan

    (University of South Dakota)

Abstract

Artificial Intelligence (AI)-based rule induction techniques such as IXL and ID3 are powerful tools that can be used to classify firms as acquisition candidates or not, based on financial and other data. The purpose of this paper is to develop an expert system that employs uncertainty representation and predicts acquisition targets. We outline in this paper, the features of IXL, a machine learning technique that we use to induce rules. We also discuss how uncertainty is handled by IXL and describe the use of confidence factors. Rules generated by IXL are incorporated into a prototype expert system, ACQTARGET, which evaluates corporate acquisitions. The use of confidence factors in ACQTARGET allows investors to specifically incorporate uncertainties into the decision making process. A set of training examples comprising 65 acquired and 65 non-acquired real world firms is used to generate the rules and a separate holdout sample containing 32 acquired and 32 non-acquired real world firms is used to validate the expert system results. The performance of the expert system is also compared with a conventional discriminant analysis model and a logit model using the same data. The results show that the expert system, ACQTARGET, performs as well as the statistical models and is a useful evaluation tool to classify firms into acquisition and non-acquisition target categories. This rule induction technique can be a valuable decision aid to help financial analysts and investors in their buy/sell decisions.

Suggested Citation

  • Srinivasan Ragothaman & Bijayananda Naik & Kumoli Ramakrishnan, 2003. "Predicting Corporate Acquisitions: An Application of Uncertain Reasoning Using Rule Induction," Information Systems Frontiers, Springer, vol. 5(4), pages 401-412, December.
    • Handle: RePEc:spr:infosf:v:5:y:2003:i:4:d:10.1023_b:isfi.0000005653.53641.b3
    DOI: 10.1023/B:ISFI.0000005653.53641.b3
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    References listed on IDEAS

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    Cited by:

    1. Hyunseok Park & Janghyeok Yoon & Kwangsoo Kim, 2013. "Identification and evaluation of corporations for merger and acquisition strategies using patent information and text mining," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 883-909, December.
    2. Dafei Yin & Jing Li & Gaosheng Wu, 2021. "Solving the Data Sparsity Problem in Predicting the Success of the Startups with Machine Learning Methods," Papers 2112.07985, arXiv.org.
    3. Ioannis Anagnostopoulos & Anas Rizeq, 2021. "Conventional and neural network target‐matching methods dynamics: The information technology mergers and acquisitions market in the USA," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(2), pages 97-118, April.
    4. David Dilts & James Moore, 2009. "Do Arbitrators Use Just Cause Standards in Deciding Discharge and Discipline Cases? A Test," Journal of Labor Research, Springer, vol. 30(3), pages 245-261, September.
    5. Yan He & Yung-ho Chiu & Bin Zhang, 2020. "Prevaluating Technical Efficiency Gains From Potential Mergers and Acquisitions in China’s Coal Industry," SAGE Open, , vol. 10(3), pages 21582440209, July.

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