Author/Authors :
Abramavičius, Silvijus Laboratory of Preclinical Drug Investigation - Institute of Cardiology - Lithuanian University of Health Sciences, Kaunas, Lithuania , Stundžienė, Alina School of Economics and Business - Kaunas University of Technology, Kaunas, Lithuania , Korsakova, Laura Institute of Physiology and Pharmacology - Lithuanian University of Health Sciences, Kaunas, Lithuania , Venslauskas, Mantas Institute of Mechatronics -- Kaunas University of Technology, Kaunas, Lithuania , Stankevičius, Edgaras Laboratory of Preclinical Drug Investigation - Institute of Cardiology - Lithuanian University of Health Sciences, Kaunas, Lithuania
Abstract :
Investments in pharmaceutical companies remain challenging due to the inherent uncertainties of risk assessment. Objectives Our paper aims to assess the impact of the drug development setbacks (DDS) on the stock price of pharmaceutical companies while taking into account the company’s financial situation, pipeline size and trend of the stock price before the DDS. Methods The model-based clustering based on finite Gaussian mixture modeling was employed to identify the clusters of pharmaceutical companies with homogenous parameters. An artificial neural network was constructed to aid the prediction of
the positive mean rate of return 120 days after the DDS. Results Our results reveal that a higher pipeline size and a lower rate of return before the DDS, as well as a lower ratio of the market
value of the equity and the book value of the total liabilities, are associated with a positive mean rate of return 120 days after the DDS. Conclusion In general, the DDS have a negative impact on the company’s stock price, but this risk can be minimized by investors choosing the companies that satisfy certain criteria.
Keywords :
Pharmaceutical companies , Drug development setbacks , Stock price , Investment risk assessment
