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Modeling innovation efficiency, its micro-level drivers, and its impact on stock returns

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  • Atta Mills, Ebenezer Fiifi Emire
  • Zeng, Kailin
  • Fangbiao, Liu
  • Fangyan, Li

Abstract

Motivated by the COVID-19 pandemic and ensuing challenges to human and economic welfare, this research seeks to evaluate innovation efficiency, its micro-level drivers, and its impact on stock returns. This study considers innovation-related activities during two growth phases experienced by 138 listed pharmaceutical manufacturing companies in China: research and development (R&D) and marketing. A dynamic two-phase network data envelopment analysis (DEA) model measured R&D efficiency, marketing efficiency, and dynamic integrated innovation efficiency. A projection difference analysis was presented to offer a feasible solution to address inefficient companies. Then, panel regression methods were adopted to examine micro-level drivers of innovation efficiency. Additionally, a portfolio formation test was used to investigate innovation efficiency as a characteristic impacting stock returns. The results indicate that the listed companies are generally innovation inefficient. Only two are DEA innovation efficient. Low marketing and R&D efficiencies are impeding innovation efficiency amelioration, with the most losses attributed to the marketing phase. Most companies lack good conditions for R&D and the commercialization of scientific and technological breakthroughs. Institutional investors and financial analysts' coverage have a positive and significant impact on innovation efficiency. Foreign institutional ownership and stock market overvaluation impact listed companies with high innovation efficiency positively. There is a negative and significant effect of size on innovation efficiency. However, growth in pharmaceutical manufacturing company size positively influences innovation efficiency at higher levels. The portfolio formation test results reveal that investors consider companies with high innovation efficiency as high-profile targets that provide high stock returns. The findings of this study offer stakeholders avenues to shape the initiation, process, and outcome of innovation.

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  • Atta Mills, Ebenezer Fiifi Emire & Zeng, Kailin & Fangbiao, Liu & Fangyan, Li, 2021. "Modeling innovation efficiency, its micro-level drivers, and its impact on stock returns," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    • Handle: RePEc:eee:chsofr:v:152:y:2021:i:c:s0960077921006573
    DOI: 10.1016/j.chaos.2021.111303
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    References listed on IDEAS

    as
    1. Brav, Alon & Jiang, Wei & Ma, Song & Tian, Xuan, 2018. "How does hedge fund activism reshape corporate innovation?," Journal of Financial Economics, Elsevier, vol. 130(2), pages 237-264.
    2. Lior Menzly & Oguzhan Ozbas, 2010. "Market Segmentation and Cross‐predictability of Returns," Journal of Finance, American Finance Association, vol. 65(4), pages 1555-1580, August.
    3. Guan, Jiancheng & Chen, Kaihua, 2012. "Modeling the relative efficiency of national innovation systems," Research Policy, Elsevier, vol. 41(1), pages 102-115.
    4. Li, Hongkuan & He, Haiyan & Shan, Jiefei & Cai, Jingjing, 2019. "Innovation efficiency of semiconductor industry in China: A new framework based on generalized three-stage DEA analysis," Socio-Economic Planning Sciences, Elsevier, vol. 66(C), pages 136-148.
    5. Fadzlan Sufian & Muzafar Shah Habibullah, 2010. "Developments in the efficiency of the Thailand banking sector: a DEA approach," International Journal of Development Issues, Emerald Group Publishing Limited, vol. 9(3), pages 226-245, September.
    6. Philippe Aghion & John Van Reenen & Luigi Zingales, 2013. "Innovation and Institutional Ownership," American Economic Review, American Economic Association, vol. 103(1), pages 277-304, February.
    7. Lo, Andrew W & MacKinlay, A Craig, 1990. "When Are Contrarian Profits Due to Stock Market Overreaction?," The Review of Financial Studies, Society for Financial Studies, vol. 3(2), pages 175-205.
    8. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    9. Zhongmin Liu & Jia Lyu, 2020. "Measuring the innovation efficiency of the Chinese pharmaceutical industry based on a dynamic network DEA model," Applied Economics Letters, Taylor & Francis Journals, vol. 27(1), pages 35-40, January.
    10. Sunder, Jayanthi & Sunder, Shyam V. & Zhang, Jingjing, 2017. "Pilot CEOs and corporate innovation," Journal of Financial Economics, Elsevier, vol. 123(1), pages 209-224.
    11. Wang, Chun-Hsien & Lu, Yung-Hsiang & Huang, Chin-Wei & Lee, Jun-Yen, 2013. "R&D, productivity, and market value: An empirical study from high-technology firms," Omega, Elsevier, vol. 41(1), pages 143-155.
    12. Kaoru Tone & Miki Tsutsui, 2014. "Slacks-Based Network DEA," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 231-259, Springer.
    13. Qi, Shaozhou & Peng, Huarong & Zhang, Xiaoling & Tan, Xiujie, 2019. "Is energy efficiency of Belt and Road Initiative countries catching up or falling behind? Evidence from a panel quantile regression approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    14. Schlag, Christian & Zeng, Kailin, 2019. "Horizontal industry relationships and return predictability," SAFE Working Paper Series 256, Leibniz Institute for Financial Research SAFE.
    15. Tarun Chordia & Bhaskaran Swaminathan, 2000. "Trading Volume and Cross‐Autocorrelations in Stock Returns," Journal of Finance, American Finance Association, vol. 55(2), pages 913-935, April.
    16. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    17. Maria Guadalupe & Olga Kuzmina & Catherine Thomas, 2012. "Innovation and Foreign Ownership," American Economic Review, American Economic Association, vol. 102(7), pages 3594-3627, December.
    18. McDonald, John, 2009. "Using least squares and tobit in second stage DEA efficiency analyses," European Journal of Operational Research, Elsevier, vol. 197(2), pages 792-798, September.
    19. Jaeho Shin & Changhee Kim & Hongsuk Yang, 2018. "The Effect of Sustainability as Innovation Objectives on Innovation Efficiency," Sustainability, MDPI, vol. 10(6), pages 1-13, June.
    20. Guan, Jian Cheng & Yam, Richard C.M. & Mok, Chiu Kam & Ma, Ning, 2006. "A study of the relationship between competitiveness and technological innovation capability based on DEA models," European Journal of Operational Research, Elsevier, vol. 170(3), pages 971-986, May.
    21. H.S. Pannu & U. Dinesh Kumar & Jamal A. Farooquie, 2011. "Efficiency and productivity analysis of Indian pharmaceutical industry using data envelopment analysis," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 10(1), pages 121-136.
    22. Brent W. Ambrose & Michael J. Highfield & Peter D. Linneman, 2005. "Real Estate and Economies of Scale: The Case of REITs," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 33(2), pages 323-350, June.
    23. Hashimoto, Akihiro & Haneda, Shoko, 2008. "Measuring the change in R&D efficiency of the Japanese pharmaceutical industry," Research Policy, Elsevier, vol. 37(10), pages 1829-1836, December.
    24. Schlag, Christian & Zeng, Kailin, 2019. "Horizontal industry relationships and return predictability," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 310-330.
    25. David Hirshleifer & Angie Low & Siew Hong Teoh, 2012. "Are Overconfident CEOs Better Innovators?," Journal of Finance, American Finance Association, vol. 67(4), pages 1457-1498, August.
    26. Luong, Hoang & Moshirian, Fariborz & Nguyen, Lily & Tian, Xuan & Zhang, Bohui, 2017. "How Do Foreign Institutional Investors Enhance Firm Innovation?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(4), pages 1449-1490, August.
    27. Wang, Qunwei & Hang, Ye & Sun, Licheng & Zhao, Zengyao, 2016. "Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 254-261.
    28. Yuqi Gu & Connie X. Mao & Xuan Tian, 2017. "Banks' Interventions and Firms' Innovation: Evidence from Debt Covenant Violations," Journal of Law and Economics, University of Chicago Press, vol. 60(4), pages 637-671.
    29. Egor V. Dudukalov & Natalia D. Rodionova & Yana E. Sivakova & Elena Vyugova & Irina V. Cheryomushkina & Elena G. Popkova, 2016. "Global Innovational Networks: Sense and Role in Development of Global Economy," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 10(4), December.
    30. García-Sánchez, Isabel-María & Aibar-Guzmán, Cristina & Aibar-Guzmán, Beatriz, 2020. "The effect of institutional ownership and ownership dispersion on eco-innovation," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    31. He, Jie (Jack) & Tian, Xuan, 2013. "The dark side of analyst coverage: The case of innovation," Journal of Financial Economics, Elsevier, vol. 109(3), pages 856-878.
    32. Kaihua Chen & Jiancheng Guan, 2012. "Measuring the Efficiency of China's Regional Innovation Systems: Application of Network Data Envelopment Analysis (DEA)," Regional Studies, Taylor & Francis Journals, vol. 46(3), pages 355-377, April.
    33. Zhong, Wei & Yuan, Wei & Li, Susan X. & Huang, Zhimin, 2011. "The performance evaluation of regional R&D investments in China: An application of DEA based on the first official China economic census data," Omega, Elsevier, vol. 39(4), pages 447-455, August.
    34. Pang-tien Lieu & Tsai-lien Yeh & Yung-ho Chiu, 2005. "Off-balance sheet activities and cost inefficiency in Taiwan's Banks," The Service Industries Journal, Taylor & Francis Journals, vol. 25(7), pages 925-944, October.
    35. Hong, Jin & Feng, Bing & Wu, Yanrui & Wang, Liangbing, 2016. "Do government grants promote innovation efficiency in China's high-tech industries?," Technovation, Elsevier, vol. 57, pages 4-13.
    36. Seema Sharma & V. J. Thomas, 2008. "Inter-country R&D efficiency analysis: An application of data envelopment analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(3), pages 483-501, September.
    37. Zhu, Huiming & Guo, Yawei & You, Wanhai & Xu, Yaqin, 2016. "The heterogeneity dependence between crude oil price changes and industry stock market returns in China: Evidence from a quantile regression approach," Energy Economics, Elsevier, vol. 55(C), pages 30-41.
    38. Badrinath, S G & Kale, Jayant R & Noe, Thomas H, 1995. "Of Shepherds, Sheep, and the Cross-autocorrelations in Equity Returns," Review of Financial Studies, Society for Financial Studies, vol. 8(2), pages 401-430.
    39. Aija Leiponen & Constance E. Helfat, 2010. "Innovation objectives, knowledge sources, and the benefits of breadth," Strategic Management Journal, Wiley Blackwell, vol. 31(2), pages 224-236, February.
    40. Hongbo Lai & Hao Shi & Yang Zhou, 2020. "Regional technology gap and innovation efficiency trap in Chinese pharmaceutical manufacturing industry," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-17, May.
    41. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    42. Brennan, Michael J & Jegadeesh, Narasimhan & Swaminathan, Bhaskaran, 1993. "Investment Analysis and the Adjustment of Stock Prices to Common Information," Review of Financial Studies, Society for Financial Studies, vol. 6(4), pages 799-824.
    43. Timo Kuosmanen & Mika Kortelainen, 2012. "Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints," Journal of Productivity Analysis, Springer, vol. 38(1), pages 11-28, August.
    44. Forsman, Helena, 2011. "Innovation capacity and innovation development in small enterprises. A comparison between the manufacturing and service sectors," Research Policy, Elsevier, vol. 40(5), pages 739-750, June.
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