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FUZZY ECONOMIC REVIEW, Volume 28

AN EXPLORING ANALYSIS OF THE CAUSES OF MSMEs FAILURE

Valeria Scherger. Universidad Nacional del Sur (UNS) - CONICET. E-mail: valeria.scherger@uns.edu.ar

Lisana B. Martinez. Universidad Nacional del Sur (UNS) - CONICET // Universidad Provincial del Sudoeste, Argentina. E-mail: lisanabelen.martinez@gmail.com

This paper investigates the factors contributing to the failure of MSMEs (Micro, Small, and Medium Enterprises) in Argentina. A framework based on a fuzzy model of business diagnosis is proposed to assess through experts´ opinions the impact of these factors and to identify the most significant causes of business failure.…
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AN ALGORITHMIC APPROACH FOR THE SELECTION OF BUSINESS PARTNERS BY USING BALANCED PICTURE FUZZY GRAPH

Fahad Ur Rehman. University of Management and Technology (UMT). E-mail: f2020265004@umt.edu.pk

Tabasam Rashid. University of Management and Technology (UMT). E-mail: tabasam.rashid@umt.edu.pk

Muhammad Tanveer Hussain. University of Management and Technology (UMT). E-mail: tanveerhussain@umt.edu.pk

The particular type of picture fuzzy graph (PFG) is picture fuzzy incidence graph (PFIG) which provides the detail about the impact of vertices on the edges. This article includes the basic characteristics and definitions of PFIG such as intense and feeble picture fuzzy incidence subgraphs, balanced picture fuzzy incidence graph…
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TOPIC MODELLING IN TWEETS ON RUSSIA’S INVASION OF UKRAINE

Maria Ioana Popa. Faculty of Economics and Business Administration, University of Craiova. E-mail: popa.maria.w3z@student.ucv.ro

Ioana-Andreea Gîfu. Faculty of Economics and Business Administration, University of Craiova. E-mail: gifu.ioana.w3b@student.ucv.ro

Vasile Georgescu. Doctoral School of Economics, University of Craiova. E-mail: v_geo@yahoo.com

The Ukraine–Russia crisis erupted into a war on February 24, 2022, due to Russian invasion of Ukrainian territory, and quickly became one of the hottest topics on Twitter (now rebranded as X), with millions of tweets generated every day. In this paper, we use Natural Language Processing methods, such as…
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PREDICTING RETURNS IN EMERGING MARKETS: A CNN-LSTM APPROACH TO SECTORAL NETWORK ANALYSIS

Nantaphong Boonpong. Faculty of Business Administration and Accountancy, Khon Kaen University. Thailand. E-mail: nantaphong.bo@kkumail.com

Pongsutti Phuensane. Faculty of Business Administration and Accountancy, Khon Kaen University. Thailand. E-mail: pongphu@kku.ac.th

This study investigates the interconnectedness of economic sectors in emerging markets and applies a hybrid machine learning approach, CNN-LSTM, to predict sector index returns in Indonesia, Malaysia, and Thailand. Our findings reveal the interdependence among asset classes within each market and demonstrate that CNN-LSTM can extract non-linear relationships better than…
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