Price strategies and economic uncertainty: Case-study for the Argentinian pharmaceutical sector using machine learning; Estrategias de precios e incertidumbre económica: Un caso de estudio aplicado al sector farmacéutico argentino usando aprendizaje automatizado - Emiliano Gutiérrez; Juan Marcelo Virdis; Leandro Meller; Diego Leandro Domínguez (Revista de Métodos Cuantitativos para la Economía y la Empresa, 2024)
Introduction: In August 2019 an unexpected presidential election result caused a change in expected exchange and inflation rates. The objective of this study is to analyze the relation between market share and the decision of increasing prices in the pharmaceutical industry in Argentina.Methods: Online weekly data on variations of some medicine's prices were obtained using web scraping, and then classification algorithms (Random Forests, Gradient Boosting Machine and logistic regression) were applied.Results: The results were mixed: market share was found to have high importance in tree-based methods. (Random Forests and Gradient Boosting Machine). However, in logistic regression, this variable wasn't significant. Conclusions: Exchange rate volatility after the election result caused several changes on price expectations, and pharmaceutical market structure influenced the resulting price reactions. Laboratories which owned a higher market share rose their prices first.
Keywords: C89; I10; 330; Pharmaceutical market; uncertainty; machine learning