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Stock screening with use of multiple criteria decision making and optimization

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  • Sevastjanov, Pavel
  • Dymova, Ludmila

Abstract

A new method for the stock ranking based on the multiple criterion decision making and optimization is proposed. Two general criteria are used in the analysis. The first of them is based on the financial indices and may be treated as the criterion of firm's "health" or its financial performance. The second one is the two-criteria performance of firm based on the stock prices. It represents the firm's market success. The method rests on the selection of the stocks with a great correlation of the firm's financial performance and its market success. The local criteria are built in the form of the membership function of corresponding fuzzy subsets. Two different strategies for stock ranking and three most popular methods for local criteria aggregation are compared. As the example the values of financial rations and prices from database comprising the data of 162 firms from subsector of the biotechnology of US economy were used. It is shown that the proposed method makes it possible to select a small group of "good" stocks characterized by a great coincidence of firm's financial performance and its market success. The method rejects from the consideration all the "unsafe" firms, i.e., such ones that their market success is based rather on the public relations, rumors and other rather unreliable information. The method is addressed to those who prefer to select for a portfolio only the firms which demonstrate the closeness of their overall financial performance in the past year and success in the Stock Exchange in the following year.

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  • Sevastjanov, Pavel & Dymova, Ludmila, 2009. "Stock screening with use of multiple criteria decision making and optimization," Omega, Elsevier, vol. 37(3), pages 659-671, June.
    • Handle: RePEc:eee:jomega:v:37:y:2009:i:3:p:659-671
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    1. Constantin Zopounidis & Michael Doumpos, 2013. "Multicriteria decision systems for financial problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(2), pages 241-261, July.
    2. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    3. Li, Sheng-Tun & Chou, Wei-Chien, 2014. "Power planning in ICT infrastructure: A multi-criteria operational performance evaluation approach," Omega, Elsevier, vol. 49(C), pages 134-148.
    4. Edvardas Liachovičius & Viktor Skrickij & Askoldas Podviezko, 2020. "MCDM Evaluation of Asset-Based Road Freight Transport Companies Using Key Drivers That Influence the Enterprise Value," Sustainability, MDPI, vol. 12(18), pages 1-16, September.
    5. Pätäri, Eero & Karell, Ville & Luukka, Pasi & Yeomans, Julian S, 2018. "Comparison of the multicriteria decision-making methods for equity portfolio selection: The U.S. evidence," European Journal of Operational Research, Elsevier, vol. 265(2), pages 655-672.

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