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Efficiency of China’s Listed Securities Companies: Estimation through a DEA-Based Method

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  • Tao Xu

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Jianxin You

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Yilei Shao

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

Abstract

Accurate assessment of the efficiency of securities companies is of great significance to improve the competitiveness of companies, due to their increasingly important role in supporting economic development. As the main contribution, this paper proposes a novel efficiency estimation framework for securities companies based on data envelopment analysis (DEA), which takes into account operational risks and technical heterogeneity. First, the risk variable is incorporated in the evaluation system as an undesirable output through the setting of weak disposability. Subsequently, the meta-frontier model is introduced to consider the impact of the technical heterogeneity of different companies to improve the accuracy of the assessment. Furthermore, this article also provides the meta-frontier Malmquist model, which can be utilized to analyze in detail technological progress. Finally, the securities companies listed in the Chinese stock market were selected as samples for empirical analysis. The efficiency evaluation model for securities companies proposed in this paper will provide a reference for related evaluation issues.

Suggested Citation

  • Tao Xu & Jianxin You & Yilei Shao, 2020. "Efficiency of China’s Listed Securities Companies: Estimation through a DEA-Based Method," Mathematics, MDPI, vol. 8(4), pages 1-16, April.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:4:p:589-:d:345754
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    References listed on IDEAS

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    1. William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), 2011. "Handbook on Data Envelopment Analysis," International Series in Operations Research and Management Science, Springer, number 978-1-4419-6151-8, September.
    2. Zhang, Wei David & Zhang, Shuo & Luo, Xueming, 2006. "Technological progress, inefficiency, and productivity growth in the US securities industry, 1980-2000," Journal of Business Research, Elsevier, vol. 59(5), pages 589-594, May.
    3. Meng-Chun Kao & Chien-Ting Lin & Lei Xu, 2012. "Do Financial Reforms Improve the Performance of Financial Holding Companies? The Case of T aiwan," International Review of Finance, International Review of Finance Ltd., vol. 12(4), pages 491-509, December.
    4. Serifsoy, Baris, 2007. "Stock exchange business models and their operative performance," Journal of Banking & Finance, Elsevier, vol. 31(10), pages 2978-3012, October.
    5. 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.
    6. Halkos, George & Petrou, Kleoniki Natalia, 2019. "Treating undesirable outputs in DEA: A critical review," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 97-104.
    7. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    8. Yang, Hongliang & Pollitt, Michael, 2010. "The necessity of distinguishing weak and strong disposability among undesirable outputs in DEA: Environmental performance of Chinese coal-fired power plants," Energy Policy, Elsevier, vol. 38(8), pages 4440-4444, August.
    9. Chiu, Ching-Ren & Liou, Je-Liang & Wu, Pei-Ing & Fang, Chen-Ling, 2012. "Decomposition of the environmental inefficiency of the meta-frontier with undesirable output," Energy Economics, Elsevier, vol. 34(5), pages 1392-1399.
    10. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
    11. Fare, R. & Grosskopf, S. & Pasurka, C., 1986. "Effects on relative efficiency in electric power generation due to environmental controls," Resources and Energy, Elsevier, vol. 8(2), pages 167-184, June.
    12. Wendong Lv & Xiaoxin Hong & Kuangnan Fang, 2015. "Chinese regional energy efficiency change and its determinants analysis: Malmquist index and Tobit model," Annals of Operations Research, Springer, vol. 228(1), pages 9-22, May.
    13. Anyu Yu & Yilei Shao & Jianxin You & Maoguo Wu & Tao Xu, 2019. "Estimations of operational efficiencies and potential income gains considering the credit risk for China’s banks," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(12), pages 2153-2168, December.
    14. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    15. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    16. Tao Ding & Ya Chen & Huaqing Wu & Yuqi Wei, 2018. "Centralized fixed cost and resource allocation considering technology heterogeneity: a DEA approach," Annals of Operations Research, Springer, vol. 268(1), pages 497-511, September.
    17. Pérez, Karen & González-Araya, Marcela C. & Iriarte, Alfredo, 2017. "Energy and GHG emission efficiency in the Chilean manufacturing industry: Sectoral and regional analysis by DEA and Malmquist indexes," Energy Economics, Elsevier, vol. 66(C), pages 290-302.
    18. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    19. Daniel Tyteca, 1997. "Linear Programming Models for the Measurement of Environmental Performance of Firms—Concepts and Empirical Results," Journal of Productivity Analysis, Springer, vol. 8(2), pages 183-197, May.
    20. Scheel, Holger, 2001. "Undesirable outputs in efficiency valuations," European Journal of Operational Research, Elsevier, vol. 132(2), pages 400-410, July.
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