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A Risk Scoring Model And Application To Measuring Internet Stock Performance

Author

Listed:
  • CHIEN-TA BRUCE HO

    (Institute of Electronic Commerce, National Chung Hsing University, Taiwan)

  • DESHENG DASH WU

    (Reykjavik University, Iceland;
    University of Toronto, Canada)

  • DAVID L. OLSON

    (Department of Management, University of Nebraska, USA)

Abstract

This paper proposes a risk scoring model to assess the performance of 27 US companies listed online by applying Data Envelopment Analysis (DEA) and comparing with the traditional financial measure Return on Equity (ROE). The DEA evaluation process involves two processes: (1) computation of operating efficiency and effectiveness to measure a company's operating performance, and (2) measurement of the return level per unit of risk to provide guidance for their investors. The risk scoring model is useful for both investors and company managers. For investors, it yields a new stock selecting strategy. For managers, it provides a risk-adjusted performance evaluation process. Empirical results show that for the Internet industry, the effectiveness of a company is more important than operating efficiency. Investors investing in efficient online companies yield higher returns.

Suggested Citation

  • Chien-Ta Bruce Ho & Desheng Dash Wu & David L. Olson, 2009. "A Risk Scoring Model And Application To Measuring Internet Stock Performance," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 133-149.
    • Handle: RePEc:wsi:ijitdm:v:08:y:2009:i:01:n:s0219622009003302
    DOI: 10.1142/S0219622009003302
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    Cited by:

    1. Yongrok Choi & Xiaoxia Ye & Lu Zhao & Amanda C. Luo, 2016. "Optimizing enterprise risk management: a literature review and critical analysis of the work of Wu and Olson," Annals of Operations Research, Springer, vol. 237(1), pages 281-300, February.
    2. Yongrok Choi & Xiaoxia Ye & Lu Zhao & Amanda Luo, 2016. "Optimizing enterprise risk management: a literature review and critical analysis of the work of Wu and Olson," Annals of Operations Research, Springer, vol. 237(1), pages 281-300, February.
    3. Chenquan Gan & Jiabin Lin & Da-Wen Huang & Qingyi Zhu & Liang Tian, 2023. "Advanced Persistent Threats and Their Defense Methods in Industrial Internet of Things: A Survey," Mathematics, MDPI, vol. 11(14), pages 1-23, July.

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