OCI-CBR: A hybrid model for decision support in preference-aware investment scenarios. (January 2023)

• Record Type: Journal Article
• Authors: Pérez-Pons, María Eugenia; Parra-Dominguez, Javier; Hernández, Guillermo; Bichindaritz, Isabelle; Corchado, Juan M.
• Is Part Of: Expert systems with applications. Volume 211(2023)
• Publication Date: 2023-01
• Subjects: Case-base reasoning -- Capital investment -- Recommender systems -- Hybrid models -- Machine learning; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118568
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: This article proposes an adaptable hybrid model for recommending effective investments in different scenarios. Currently, a wide variety of methodologies are used for company valuation, especially those that take into account financial statements. However, for private held companies, there is no method that would be capable of predicting, with full certainty, the future success of an investment. The Optimal Capital Investment Case-Base Reasoning (OCI-CBR) consists of a case-based reasoning system that uses a classification algorithm to prune the case base according to a projected increase in certain company attributes. Once the cases have been pruned and the case is fed with the most profitable investment opportunities, the case-based reasoning system recommends optimal investments to potential investors. The complete model is conceived as an intelligent hybrid model that optimizes the case base by employing different algorithms for data retrieval and reuse. The system makes recommendations based on the investor's preferences and the investment decisions of other investors with similar profiles or interests. Highlights: Hybrid decision support system for capital investment. Machine learning pruning techniques for fault detection and diagnosis of companies. A CBR methodology is used to make recommendations for optimal investment.