| Title: | Meta analysis of business valuation solutions –are AI based methods better? |
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| Authors: | ID Herman, Aljaž (Author)
ID Mumel, Damijan (Author)
ID Jagrič, Timotej (Author) |
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| Files: |  https://journal.doba.si/OJS/index.php/jimb/article/view/JIBM.2024.16.2.6/343
 Meta_analysis_of_business_valuation_solutions.pdf (577,12 KB)
MD5: 06E04B83DCBCF383FB9CEB893E8E3FF0
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.02 - Review Article |
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| Organization: | EPF - Faculty of Business and Economics
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| Abstract: | Purpose of the article–this article addresses the challenge of accurately assessing business value in today's dynamic environment, exploring the limitations of traditional valuation methods and the potential of modern, technology-driven approaches.Research methodology–the study uses qualitative research methods, including content analysis, deductive reasoning, and comparative analysis, to review various business valuation techniques.Findings –the research finds that traditional methods like Discounted Cash Flow and Relative Valuation are outdated, failing to capture all value factors. Modern approaches, such as simulation-based valuation, machine learning, and neural networks, combine traditional methods with advanced techniques. These methodologies utilize vast datasets and sophisticated algorithms, enhancing predictive accuracy and understanding of market dynamics. Neural networks excel in analysing complex patterns and adapting to market shifts. However, no single method can capture all nuances, necessitating diverse approaches and acknowledging the subjective nature of valuations |
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| Keywords: | business valuation, traditional and advanced valuation methods, machine learning, neural networks, artificial intelligence |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 28.11.2024 |
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| Publisher: | Doba Epis |
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| Year of publishing: | 2024 |
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| Number of pages: | str. 1-16 |
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| Numbering: | Vol. 16, no. 2 |
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| PID: | 20.500.12556/DKUM-92440  |
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| UDC: | 004.8 |
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| ISSN on article: | 1855-6175 |
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| COBISS.SI-ID: | 219901955 |
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| DOI: | 10.32015/JIBM.2024.16.2.6 |
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| Publication date in DKUM: | 01.07.2025 |
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| Views: | 216 |
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| Downloads: | 15 |
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| Metadata: |    |
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| Categories: | Misc.
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