Meta analysis of business valuation solutions – are AI based methods better?

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 Findings – the research finds that traditional methods like Discounted Cash Flow and Relative Valuation are outdated, failing to capture all value factors.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. Practical implications – the findings suggest that incorporating advanced methodologies can improve business valuations, leading to better decision-making and a deeper understanding of market dynamics and intrinsic value. Originality/Value – this study uniquely examines the transition from traditional to modern valuation methods, highlighting the superior performance of neural networks and the need for a multifaceted approach to business valuation.

Keywords

• traditional and advanced valuation methods
• machine learning
• neural networks
• artificial intelligence
• business valuation