Stock price reaction to the drug development setbacks in the pharmaceutical industry

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.

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.

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References

  1. 1.

    Thakor RT, Anaya N, Zhang Y, Vilanilam C, Siah KW, Wong CH, et al. Just how good an investment is the biopharmaceutical sector? Nat Biotechnol. 2017;35:1149–57.

    CAS  Article  Google Scholar 

  2. 2.

    Popa C, Holvoet K, Van Montfort T, Groeneveld F, Simoens S. Risk-return analysis of the biopharmaceutical industry as compared to other industries. Front Pharmacol. 2018;9:1108.

    Article  Google Scholar 

  3. 3.

    Jekunen A. Decision-making in product portfolios of pharmaceutical research and development--managing streams of innovation in highly regulated markets. Drug Des Devel Ther. 2014;8:2009–16.

    Article  Google Scholar 

  4. 4.

    Rothenstein JM, Tomlinson G, Tannock IF, Detsky AS. Company stock prices before and after public announcements related to oncology drugs. JNCI J Natl Cancer Inst. 2011;103:1507–12.

    Article  Google Scholar 

  5. 5.

    Hwang TJ. Stock market returns and clinical trial results of investigational compounds: an event study analysis of large biopharmaceutical companies. PLoS One. 2013;8:e71966.

    CAS  Article  Google Scholar 

  6. 6.

    Agarwal V, Taffler R. Does financial distress risk drive the momentum anomaly? Financ Manag. 2008;37:461–84.

    Article  Google Scholar 

  7. 7.

    Eisdorfer A, Goyal A, Zhdanov A. Distress anomaly and shareholder risk: international evidence. Financ Manag. 2018;47:553–81.

    Article  Google Scholar 

  8. 8.

    Sedighi M, Jahangirnia H, Gharakhani M, Farahani Fard S. A novel hybrid model for stock Price forecasting based on metaheuristics and support vector machine. Data. 2019;4:75.

    Article  Google Scholar 

  9. 9.

    Evaluate Ltd. EvaluatePharma ® 2017 ®. In: EvaluatePharma ® 2017 [Online document] https://www.evaluate.com/products-services/pharma/evaluatepharma, .

  10. 10.

    Evaluate Ltd. EvaluatePharma ® 2018 ®. In: EvaluatePharma ® 2018 [Online document] https://www.evaluate.com/products-services/pharma/evaluatepharma, .

  11. 11.

    Jacquier E, Kane A, Marcus AJ. Geometric or arithmetic mean: a reconsideration. Financ Anal J. 2003;59:46–53.

    Article  Google Scholar 

  12. 12.

    Bessembinder H. Do stocks outperform Treasury bills? J Financ Econ. 2018;129:440–57.

    Article  Google Scholar 

  13. 13.

    Alaka HA, Oyedele LO, Owolabi HA, Kumar V, Ajayi SO, Akinade OO, et al. Systematic review of bankruptcy prediction models: towards a framework for tool selection. Expert Syst Appl. 2018;94:164–84.

    Article  Google Scholar 

  14. 14.

    Malsiner-Walli G, Frühwirth-Schnatter S, Grün B. Model-based clustering based on sparse finite Gaussian mixtures. Stat Comput. 2016;26:303–24.

    Article  Google Scholar 

  15. 15.

    Melnykov V, Maitra R. Finite mixture models and model-based clustering. Stat Surv. 2010;4:80–116.

    Article  Google Scholar 

  16. 16.

    Scrucca L, Fop M, Murphy TB, Raftery AE. Mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R J. 2016;8:289–317.

    Article  Google Scholar 

  17. 17.

    Raudys Š, Jain AK. Small sample size problems in designing artificial neural networks. Mach Intell Pattern Recognit. 1991;11:33–50.

    Google Scholar 

  18. 18.

    Günther F, Fritsch S neuralnet: Training of Neural Networks.

  19. 19.

    Beck MW. NeuralNetTools : visualization and analysis tools for neural networks. J Stat Softw. 2018;85:1–20.

    Article  Google Scholar 

  20. 20.

    Olden JD, Joy MK, Death RG. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol Model. 2004;178:389–97.

    Article  Google Scholar 

  21. 21.

    Di Zio M, Guarnera U, Rocci R. A mixture of mixture models for a classification problem: the unity measure error. Comput Stat Data Anal. 2007;51:2573–85.

    Article  Google Scholar 

  22. 22.

    Yiu KK, Mak MW, Li CK. Gaussian mixture models and probabilistic decision-based neural networks for pattern classification: a comparative study. Neural Comput & Applic. 1999;8:235–45.

    Article  Google Scholar 

  23. 23.

    Strand V, Girolomoni G, Schiestl M, Ernst Mayer R, Friccius-Quecke H, McCamish M. The totality-of-the-evidence approach to the development and assessment of GP2015, a proposed etanercept biosimilar. Curr Med Res Opin. 2017;33:993–1003.

    CAS  Article  Google Scholar 

  24. 24.

    Penman SH, Richardson SA, Tuna İ. The book-to-Price effect in stock returns: accounting for leverage. J Account Res. 2007;45:427–67.

    Article  Google Scholar 

  25. 25.

    Myers SC. Determinants of corporate borrowing. J Financ Econ. 1977;5:147–75.

    Article  Google Scholar 

  26. 26.

    Altman EI, Altman, I. E (2018) Applications of distress prediction models: what have we learned after 50 years from the Z-score models? Int J Financ Stud 6:70.

  27. 27.

    Wu Y, Gaunt C, Gray S. A comparison of alternative bankruptcy prediction models. J Contemp Account Econ. 2010;6:34–45.

    Article  Google Scholar 

  28. 28.

    Cai J, Zhang Z. Leverage change, debt overhang, and stock prices. J Corp Finan. 2011;17:391–402.

    Article  Google Scholar 

  29. 29.

    Lin F-L, Chang T. Does debt affect firm value in Taiwan? A panel threshold regression analysis. Appl Econ. 2011;43:117–28.

    CAS  Article  Google Scholar 

  30. 30.

    Frunza M-C, Frunza M-C. Efficient market hypothesis testing. Solving Mod Crime Financ Mark. 2016:303–10.

  31. 31.

    McKay DR, Peters DA. What’s the difference between a hedge fund and a mutual fund? Plast Surg (Oakville, Ont) 22:270–1. 2014.

  32. 32.

    Malkiel BG. The efficient market hypothesis and its critics. J Econ Perspect. 2003;17:59–82.

    Article  Google Scholar 

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Availability of data and material

available upon request.

Code availability

R software was used. Script is available upon request.

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Affiliations

Authors

Contributions

conceptualization, S.A., L.M., M.V. and E.S.; methodology, S.A., M.V. and A.S.; software, S.A., M.V.; validation, S.A. and A.S.; formal analysis, L.M. and E.S.; investigation, S.S. and A.S.; resources, M.V., S.A.; preparation of paper, M.V., E.S.; paper review and editing, A.S.; visualization, A.S.; supervision, S.A.

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Correspondence to Silvijus Abramavičius.

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Silvijus Abramavičius and Alina Stundžienė Shared first authorship

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Abramavičius, S., Stundžienė, A., Korsakova, L. et al. Stock price reaction to the drug development setbacks in the pharmaceutical industry. DARU J Pharm Sci (2021). https://doi.org/10.1007/s40199-020-00349-6

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Keywords

  • Pharmaceutical companies
  • Investment risk assessment
  • Stock price
  • Drug development setbacks