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Measuring bank performance with a dynamic network Luenberger indicator

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Abstract

We construct and estimate a dynamic network Luenberger productivity indicator for Japanese banks during fiscal years 2006–2012. The network aspect to the model recognizes that banks produce deposits in the first stage of production using inputs such as labor, physical capital, and equity capital and then in the second stage use those deposits to generate a portfolio of loans and securities investments. Because of asymmetric information between borrower and lender and uncertainty about the future state of the economy the second stage of production also generates an undesirable by-product: some loans become nonperforming. The dynamic aspect to the model recognizes that nonperforming loans generated in one period will typically constrain production in a subsequent period. Moreover, bank managers have discretion over when to transform deposits into the portfolio of loans and investments so that when faced with a high risk lending environment managers can choose to save some deposits as excess reserves for use in a subsequent period when they anticipate a more favorable lending environment.

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Notes

  1. The static Malmquist index ignores dynamic variables such as carryovers in its construction.

  2. For the static Luenberger indicator, see Chambers (1998, 2002), Chambers et al. (1996), and Briec and Kerstens (2004).

  3. The static directional distance function takes the form \(\overrightarrow{D}(x,y,b;g_{x} ,g_{y},g_{b} )=\max \{\alpha :\;(x-\alpha g_{x},y+\alpha g_{y} ,b-\alpha g_{b} )\in T\}\).

  4. On July 1, 2013, Mizuho Bank and Mizuho Corporate Bank merged and began operating as Mizuho Bank. Hence, both banks still existed as different entities at the end of our sample period.

  5. Bank of Japan http://www.boj.or.jp/en/statistics/boj/other/reservereq/junbi.htm/.

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Acknowledgments

An earlier version of this paper, entitled “Modeling Bank Production with Dynamic-Network Data Envelopment Analysis”, was presented at the 12th International Conference of Data Envelopment Analysis (DEA 2014) held at University of Malaya (Kuala Lumpur, Malaysia) in March 15–17, 2014. We are grateful for the helpful and insightful suggestions of three anonymous reviewers. We are also grateful to the Grant-in-Aid for Scientific Research from Culture, Sports, Science and Technology, Grant No. 25282090 (B).

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Authors and Affiliations

  1. Faculty of Commerce, Fukuoka University, 8-19-1 Nanakuma, Jonan-Ku, Fukuoka, 814-0180, Japan

    Hirofumi Fukuyama

  2. Department of Economics and Finance, Southeast Missouri State University, Girardeau, MO, USA

    William L. Weber

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  1. Hirofumi Fukuyama
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Correspondence to Hirofumi Fukuyama.

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Fukuyama, H., Weber, W.L. Measuring bank performance with a dynamic network Luenberger indicator. Ann Oper Res 250, 85–104 (2017). https://doi.org/10.1007/s10479-015-1922-5

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