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Predicting Japanese bank stock performance with a composite relative efficiency metric: A new investment tool

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  • Avkiran, Necmi K.
  • Morita, Hiroshi

Abstract

The paper's main objective is to predict bank stock performance one year ahead with a composite efficiency metric from relative contextual financial analysis. We bring together financial ratios, generalized data envelopment analysis and simulated annealing to rank Japanese banks on stock performance predicted from relative efficiency scores. An application of this ranking in a profitable investment strategy by designating long and short portfolios underscores the potential commercial value of the method. The method can also be used to monitor the effectiveness of ratios in forecasting stock performance and it is conducive to selecting predictive ratios when markets are changing rapidly.

Suggested Citation

  • Avkiran, Necmi K. & Morita, Hiroshi, 2010. "Predicting Japanese bank stock performance with a composite relative efficiency metric: A new investment tool," Pacific-Basin Finance Journal, Elsevier, vol. 18(3), pages 254-271, June.
    • Handle: RePEc:eee:pacfin:v:18:y:2010:i:3:p:254-271
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    Cited by:

    1. Avkiran, Necmi K., 2011. "Association of DEA super-efficiency estimates with financial ratios: Investigating the case for Chinese banks," Omega, Elsevier, vol. 39(3), pages 323-334, June.
    2. Chrysovalantis Gaganis & Iftekhar Hasan & Fotios Pasiouras, 2013. "Efficiency and stock returns: evidence from the insurance industry," Journal of Productivity Analysis, Springer, vol. 40(3), pages 429-442, December.
    3. Azad, A.S.M. Sohel & Yasushi, Suzuki & Fang, Victor & Ahsan, Amirul, 2014. "Impact of policy changes on the efficiency and returns-to-scale of Japanese financial institutions: An evaluation," Research in International Business and Finance, Elsevier, vol. 32(C), pages 159-171.
    4. Necmi Avkiran & Alan McCrystal, 2014. "Intertemporal analysis of organizational productivity in residential aged care networks: scenario analyses for setting policy targets," Health Care Management Science, Springer, vol. 17(2), pages 113-125, June.
    5. repec:zbw:bofrdp:2013_014 is not listed on IDEAS
    6. Héctor Darío Balseiro Barrios & Jorge Armando Luna Amador & Francisco Javier Maza Ávila, 2021. "Análisis de eficiencia financiera de las empresas cotizantes en el mercado accionario colombiano para el periodo 2012- 2017," Revista Finanzas y Politica Economica, Universidad Católica de Colombia, vol. 13(1), pages 19-41, March.
    7. Sana Ben Abdallah & Dhafer Saidane & Mihaly Petreczky, 2023. "Application of Robust Control for CSR Formalization and Stakeholders Interest," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 891-934, October.
    8. Carlini, Federico & Cucinelli, Doriana & Previtali, Daniele & Soana, Maria Gaia, 2020. "Don't talk too bad! stock market reactions to bank corporate governance news," Journal of Banking & Finance, Elsevier, vol. 121(C).
    9. Avkiran, Necmi K. & Goto, Mika, 2011. "A tool for scrutinizing bank bailouts based on multi-period peer benchmarking," Pacific-Basin Finance Journal, Elsevier, vol. 19(5), pages 447-469, November.
    10. Mohamed Mehdi Jelassi & Ezzeddine Delhoumi, 2021. "What explains the technical efficiency of banks in Tunisia? Evidence from a two-stage data envelopment analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-26, December.
    11. Necmi Avkiran & Lin Cai, 2014. "Identifying distress among banks prior to a major crisis using non-oriented super-SBM," Annals of Operations Research, Springer, vol. 217(1), pages 31-53, June.

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