La prise en compte du risque en comptabilité, le cas des industries extractives

Auteurs

DOI :

https://doi.org/10.53102/2024.38.02.1147

Mots-clés :

comptabilité, industries extractives, industrie pétrolière, évaluation financière, réserves pétrolières, modèle d’Ohlson

Résumé

Alors qu’ils cherchaient à définir une norme spécifique aux activités extractives, de nombreux normalisateurs comptables ont tenté de répondre à la question suivante : quelle estimation des perspectives de croissance future est utile aux investisseurs en complément des informations issues du bilan et du compte de résultat ? Notre étude emploie le modèle d’Ohlson (1995) sur un échantillon de 52 entreprises cotées pendant une période de 11 ans (1996-2006) et examine la pertinence informationnelle de quatre vecteurs orientés vers le futur : la valeur optionnelle des réserves de pétrole, les flux de trésorerie disponibles, les capex et la volatilité du prix du pétrole brut. Nos résultats suggèrent qu’une valeur heuristique des réserves considérées comme une option réelle sur un volume de pétrole découvert est la plus informative et la moins redondante des valeurs prospectives testées. Ils remettent en cause l’utilité de la communication de résultats futurs sous forme d’estimations ponctuelles.

Biographies des auteurs

Véronique Blum, Université Grenoble Alpes

Véronique Blum est Maître de conférences HDR à l’Université Grenoble Alpes. Sa recherche porte principalement sur l’évaluation financière et non financière des actifs incertains, immatériels et stratégiques.

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Charlotte Krychowski, Institut Mines-Télécom Business School, France

Charlotte Krychowski est Maître de conférences à Institut Mines-Télécom Business School. Ses principaux thèmes de recherche actuels portent sur les business models dans les industries numériques, la valorisation des actifs immatériels et les options réelles

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Publiée

10-11-2023

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25-06-2022

Comment citer

Blum, V., & Krychowski, C. (2023). La prise en compte du risque en comptabilité, le cas des industries extractives. Revue Française De Gestion Industrielle, 38(2), 45–66. https://doi.org/10.53102/2024.38.02.1147

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Statistiques

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