Abstract
BACKGROUND: 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. Graphical abstract The higher pipeline size(spip) and lower rate of return before (srr) the drug development setback (DDS) and the Market Value of Equity/Book Value of Total Liabilities ratio (sx4) are associated with a positive mean rate of return 120 days after the DDS.