Supporting management decisions for M&A transactions based on the strategic allocation of intangible assets


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In the context of mergers and acquisitions (M&A), management decisions regarding asset allocation play a key role in determining the strategic value of intangible assets. This study investigates the allocation of such assets, particularly goodwill, in relation to enterprise value on balance sheets across global M&A transactions within the B2C sector from 2000 to 2021. Utilizing data from the Markables database, which includes 543 transactions, this study presents robust and quantile regression analyses to effectively address challenges arising from non-normally distributed data. The findings underscore a significant correlation between the strategic allocation of intangible assets, especially goodwill, and enterprise value, highlighting their essential role in reflecting future earning potential and growth prospects. Additionally, the study reveals specific factors, including transaction type (asset vs. share deals) and timing (transaction year), that influence these asset allocation decisions. These insights are critical for enhancing management decisions in valuation and strategic financial planning during M&A. By elucidating these dynamics, this paper significantly contributes to the literature on management accounting and corporate finance, offering a granular understanding of the valuation of intangible assets in business combinations.

This study emerged from a cooperative project between the University of Applied Sciences Kufstein (Austria) and the University of Economics in Bratislava (Slovakia) 2023-05-15-003 “Enhancing long-term business value towards environmentally and socially sustainable economy,” which was funded by the performance committee of the Austria-Slovakia Action initiative.

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    • Figure A1. Diagrams for the estimated parameters for the variables GOOD, ITANG and TANG
    • Table 1. Variables of the study
    • Table 2. Descriptive statistics and correlation analyses
    • Table 3. Robust regression analyses
    • Table 4. Robustness test of the results using quantile regression and a modified robust regression with non-linear effects
    • Conceptualization
      Giuseppe Sorrentino, Mario Situm, Yuliia Serpeninova
    • Data curation
      Giuseppe Sorrentino, Mario Situm
    • Formal Analysis
      Giuseppe Sorrentino, Mario Situm
    • Investigation
      Giuseppe Sorrentino, Mario Situm, Milos Tumpach, Zuzana Juhaszova
    • Methodology
      Giuseppe Sorrentino, Mario Situm
    • Resources
      Giuseppe Sorrentino, Mario Situm
    • Software
      Giuseppe Sorrentino, Mario Situm, Milos Tumpach
    • Writing – original draft
      Giuseppe Sorrentino, Mario Situm, Yuliia Serpeninova, Milos Tumpach, Zuzana Juhaszova
    • Project administration
      Mario Situm, Yuliia Serpeninova
    • Supervision
      Mario Situm, Yuliia Serpeninova, Milos Tumpach
    • Funding acquisition
      Yuliia Serpeninova
    • Writing – review & editing
      Yuliia Serpeninova, Milos Tumpach
    • Validation
      Milos Tumpach, Zuzana Juhaszova
    • Visualization
      Milos Tumpach, Zuzana Juhaszova