Corporate valuation of agricultural enterprises for informed investment decision-making

Dokić, Dragan and Popović, Vera and Nedeljković, Miroslav and Ljubojević, Boris and Gantner, Vesna (2026) Corporate valuation of agricultural enterprises for informed investment decision-making. In: Book of Proceedings of the XV International Symposium on Agricultural Sciences AgroReS. University of Banja Luka, Faculty of Agriculture, Banja Luka, pp. 291-299. ISBN 978-99976-84-09-7

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Abstract

The study evaluated the financial performance and valuation indicators of selected agricultural corporations listed on the Croatian capital market during the period 2021–2025. The analysis includes Kutjevo p.l.c., Koka Varaždin p.l.c., Croatian Sugar Industry p.l.c., Žito Group p.l.c., and Podravka p.l.c. The research applied a quantitative approach based on secondary financial data obtained from annual reports, the Zagreb Stock Exchange, and the Financial Agency (FINA). The valuation framework is based on three standard financial models: discounted cash flow (DCF), Capital Asset Pricing Model (CAPM), and Economic Value Added (EVA). Comparative and descriptive analyses were used to examine differences in asset values, expected returns on equity, and value creation among companies. The results indicate significant differences in financial scale and performance indicators across the analyzed corporations. Larger companies, particularly Podravka p.l.c. and Žito Group p.l.c., recorded higher total asset values and EVA results, while smaller companies demonstrated lower but relatively stable financial performance. CAPM-based estimates of expected returns show moderate variation across firms, reflecting differences in systematic risk and market positioning. The findings suggest that valuation indicators provide useful comparative information for assessing financial performance in capital-intensive agricultural sectors. However, the results should be interpreted as descriptive evidence rather than causal relationships due to methodological limitations related to secondary data analysis and model assumptions.

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