Pricing strategies and economic uncertainty
case-study for the Argentinian pharmaceutical sector using machine learning
DOI:
https://doi.org/10.46661/rev.metodoscuant.econ.empresa.6407Keywords:
pharmaceutical market, uncertainty, machine learningAbstract
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.
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