Skip to main content
SpringerLink
Log in

Data envelopment analysis in financial services: a citations network analysis of banks, insurance companies and money market funds

  • Original-Survey or Exposition
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Development and application of the data envelopment analysis (DEA) method, have been the subject of numerous reviews. In this paper, we consider the papers that apply DEA methods specifically to financial services, or which use financial services data to experiment with a newly introduced DEA model. We examine 620 papers published in journals indexed in the Web of Science database, from 1985 to April 2016. We analyse the sample applying citations network analysis. This paper investigates the DEA method and its applications in financial services. We analyse the diffusion of DEA in three sub-samples: (1) banking groups, (2) money market funds, and (3) insurance groups by identifying the main paths, that is, the main flows of the ideas underlying each area of research. This allows us to highlight the main approaches, models and efficiency types used in each research areas. No unique methodological preference emerges within these areas. Innovations in the DEA methodologies (network models, slacks based models, directional distance models and Nash bargaining game) clearly dominate recent research. For each subsample, we describe the geographical distribution of these studies, and provide some basic statistics related to the most active journals and scholars.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. Both software are freely available: http://interest.science.thomsonreuters.com/forms/HistCite/, http://mrvar.fdv.uni-lj.si/pajek/.

  2. Property-liability insurance pricing was proposed in 1926 to integrate underwriting and investment performance. A description of the development of property-liability insurance pricing models can be found in Cooper (1974) and D’Arcy and Doherty (1988).

References

  • Athanassopoulos, A. D. (1998). Non-parametric frontier models for assessing the market and cost efficiency of large scale bank branch networks. Journal of Money Credit and Banking, 30(2), 172–192. http://papers.ssrn.com/abstract=50740. Accessed December 11, 2015.

  • Avkiran, N. (2009). Opening the black box of efficiency analysis: An illustration with UAE banks. Omega, 37(4), 930–941. doi:10.1016/j.omega.2008.08.001.

    Article  Google Scholar 

  • Avkiran, N. K. (1999). The evidence on efficiency gains: The role of mergers and the benefits to the public. Journal of Banking & Finance, 23(7), 991–1013. doi:10.1016/S0378-4266(98)00129-0.

    Article  Google Scholar 

  • Avkiran, N. K. (2011). Association of DEA super-efficiency estimates with financial ratios: Investigating the case for Chinese banks. Omega, 39(3), 323–334. doi:10.1016/j.omega.2010.08.001.

    Article  Google Scholar 

  • Avkiran, N. K. (2015). An illustration of dynamic network DEA in commercial banking including robustness tests. Omega, 55, 141–150. doi:10.1016/j.omega.2014.07.002.

    Article  Google Scholar 

  • Avkiran, N. K., & Alpert, K. (2015). The influence of co-authorship on article impact in OR/MS/OM and the exchange of knowledge with Finance in the twenty-first century. Annals of Operations Research, 235(1), 51–73. doi:10.1007/s10479-015-1880-y.

    Article  Google Scholar 

  • Avkiran, N. K., & Morita, H. (2010). Benchmarking firm performance from a multiple-stakeholder perspective with an application to Chinese banking. Omega, 38(6), 501–508. doi:10.1016/j.omega.2009.12.007.

    Article  Google Scholar 

  • Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092. doi:10.1287/mnsc.30.9.1078.

    Article  Google Scholar 

  • Barberá-Tomás, D., Jiménez-Sáez, F., & Castelló-Molina, I. (2011). Mapping the importance of the real world: The validity of connectivity analysis of patent citations networks. Research Policy, 40(3), 473–486.

    Article  Google Scholar 

  • Barros, C. P., Dumbo, S., & Wanke, P. (2014). Efficiency determinants and capacity issues in Angolan insurance companies. South African Journal of Economics, 82(3), 455–467. doi:10.1111/saje.12056.

    Article  Google Scholar 

  • Barros, C. P., Managi, S., & Matousek, R. (2012). The technical efficiency of the Japanese banks: Non-radial directional performance measurement with undesirable output. Omega, 40(1), 1–8. doi:10.1016/j.omega.2011.02.005.

    Article  Google Scholar 

  • Barros, C. P., & Wanke, P. (2014). Insurance companies in Mozambique: A two-stage DEA and neural networks on efficiency and capacity slacks. Applied Economics. http://www.tandfonline.com/doi/abs/10.1080/00036846.2014.934436. Accessed June 5, 2015.

  • Basso, A., & Funari, S. (2001). A data envelopment analysis approach to measure the mutual fund performance. European Journal of Operational Research, 135(3), 477–492. doi:10.1016/S0377-2217(00)00311-8.

    Article  Google Scholar 

  • Basso, A., & Funari, S. (2003). Measuring the performance of ethical mutual funds: a DEA approach. Journal of the Operational Research Society, 54(5), 521–531. doi:10.1057/palgrave.jors.2601541.

    Article  Google Scholar 

  • Basso, A., & Funari, S. (2014a). Constant and variable returns to scale DEA models for socially responsible investment funds. European Journal of Operational Research, 235(3), 775–783. doi:10.1016/j.ejor.2013.11.024.

    Article  Google Scholar 

  • Basso, A., & Funari, S. (2014b). DEA models with a constant input for SRI mutual funds with an application to European and Swedish funds. International Transactions in Operational Research, 21(6), 979–1000. http://doi.wiley.com/10.1111/itor.12092.

  • Batagelj, V. (2003). Efficient algorithms for citation network analysis. Institute of Mathematics, pp. 1–29. http://arxiv.org/abs/cs/0309023. Accessed March 11, 2013.

  • Bekkers, R., & Martinelli, A. (2012). Knowledge positions in high-tech markets: Trajectories, standards, strategies and true innovators. Technological Forecasting and Social Change, 79(7), 1192–1216.

    Article  Google Scholar 

  • Berg, S. A., Førsund, F. R., Hjalmarsson, L., & Suominen, M. (1993). Banking efficiency in the Nordic countries. Journal of Banking & Finance, 17(2–3), 371–388. doi:10.1016/0378-4266(93)90038-F.

    Article  Google Scholar 

  • Berg, S. A., Førsund, F. R., & Jansen, E. S. (1992). Malmquist indices of productivity growth during the Deregulation of Norwegian Banking, 1980–89. Scandinavian Journal of Economics, 94, S211–S228. http://econpapers.repec.org/RePEc:bla:scandj:v:94:y:1992:i:0:p:s211-28. Accessed June 3, 2015.

  • Berger, A. N., Hunter, W. C., & Timme, S. G. (1993). The efficiency of financial institutions: A review and preview of research past, present and future. Journal of Banking & Finance, 17(2–3), 221–249. doi:10.1016/0378-4266(93)90030-H.

    Article  Google Scholar 

  • Berger, A. N., & Humphrey, D. B. (1997). Efficiency of financial institutions: International survey and directions for future research. European Journal of Operational Research, 98(2), 175–212. doi:10.1016/S0377-2217(96)00342-6.

    Article  Google Scholar 

  • Bhupatiraju, S., Nomaler, Ö., Triulzi, G., & Verspagen, B. (2012). Knowledge flows—Analyzing the core literature of innovation, entrepreneurship and science and technology studies. Research Policy, 41(7), 1205–1218.

    Article  Google Scholar 

  • Branda, M. (2015). Diversification-consistent data envelopment analysis based on directional-distance measures. Omega, 52, 65–76.

    Article  Google Scholar 

  • Branda, M. (2016). Mean-value at risk portfolio efficiency: Approaches based on data envelopment analysis models with negative data and their empirical behaviour. 4OR, 14(1), 77–99. doi:10.1007/s10288-015-0296-5.

    Article  Google Scholar 

  • Brandouy, O., Kerstens, K., & Van de Woestyne, I. (2015). Frontier-based vs. traditional mutual fund ratings: A first backtesting analysis. European Journal of Operational Research, 242(1), 332–342. doi:10.1016/j.ejor.2014.11.010.

    Article  Google Scholar 

  • Breschi, S., & Lissoni, F. (2003). Knowledge-relatedness in firm technological diversification. Research Policy, 32(January 2001), 69–87.

    Article  Google Scholar 

  • Camanho, A. S., Dyson, R. G., The, I., & Sim, T. (1999). Efficiency, size, benchmarks and targets for bank branches: An application of data envelopment analysis. Journal of the Operational Research Society, 50(9), 903–915. doi:10.1057/palgrave.jors.2600792.

    Article  Google Scholar 

  • Canhoto, A., & Dermine, J. (2003). A note on banking efficiency in Portugal, New vs. Old banks. Journal of Banking and Finance, 27(11), 2087–2098.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. doi:10.1016/0377-2217(78)90138-8.

    Article  Google Scholar 

  • Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA)—Thirty years on. European Journal of Operational Research, 192(1), 1–17. doi:10.1016/j.ejor.2008.01.032.

    Article  Google Scholar 

  • Cooper, R. W. (1974). Investment return and property-liability insurance ratemaking. Philadelphia: SS Huebner Foundation, University of Pennsylvania.

    Google Scholar 

  • Cooper, W. W., Seiford, L. M., Tone, K., & Zhu, J. (2007). Some models and measures for evaluating performances with DEA: Past accomplishments and future prospects. Journal of Productivity Analysis, 28(3), 151–163. doi:10.1007/s11123-007-0056-4.

    Article  Google Scholar 

  • Cummins, J. D., & Rubio-Misas, M. (2006). Deregulation, consolidation, and efficiency: Evidence from the Spanish insurance industry. Journal of Money Credit and Banking, 38(2), 323–355. doi:10.1353/mcb.2006.0029.

    Article  Google Scholar 

  • Cummins, J. D., Rubio-Misas, M., & Zi, H. (2004). The effect of organizational structure on efficiency: Evidence from the Spanish insurance industry. Journal of Banking & Finance, 28(12), 3113–3150. doi:10.1016/j.jbankfin.2004.05.004.

    Article  Google Scholar 

  • Cummins, J. D., & Xie, X. (2008). Mergers and acquisitions in the US property-liability insurance industry: Productivity and efficiency effects. Journal of Banking & Finance, 32(1), 30–55. doi:10.1016/j.jbankfin.2007.09.003.

    Article  Google Scholar 

  • Cummins, J. D., Weiss, M. A., Xie, X., & Zi, H. (2010). Economies of scope in financial services: A DEA efficiency analysis of the US insurance industry. Journal of Banking & Finance, 34(7), 1525–1539.

    Article  Google Scholar 

  • D’Arcy, S. P., & Doherty, N. A. (1988). The Financial Theory of Pricing Property-liability Insurance Contracts, Volumes 11–15. S.S. Huebner Foundation for Insurance Education.

  • de Nooy, W., Mrvar, A., & Batagelj, V. (2005). Exploratory social network analysis with Pajek. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Ding, Y., Liu, X., Guo, C., & Cronin, B. (2013). The distribution of references across texts: Some implications for citation analysis. Journal of Informetrics, 7(3), 583–592. doi:10.1016/j.joi.2013.03.003. Accessed April 21, 2013.

  • Eling, M., & Luhnen, M. (2010). Efficiency in the international insurance industry: A cross-country comparison. Journal of Banking & Finance, 34(7), 1497–1509. doi:10.1016/j.jbankfin.2009.08.026.

    Article  Google Scholar 

  • Elyasiani, E., & Mehdian, S. M. (1990). A nonparametric approach to measurement of efficiency and technological change: The case of large U.S. commercial banks. Journal of Financial Services Research, 4(2), 157–168. doi:10.1007/BF00352569.

    Article  Google Scholar 

  • Emrouznejad, A., & Marra, M. (2014). Ordered weighted averaging operators 1988–2014: A citation-based literature survey. International Journal of Intelligent Systems, 29(11), 994–1014.

    Article  Google Scholar 

  • Emrouznejad, A., Parker, B. R., & Tavares, G. (2008). Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Socio-Economic Planning Sciences, 42(3), 151–157. doi:10.1016/j.seps.2007.07.002.

    Article  Google Scholar 

  • Epicoco, M. (2013). Knowledge patterns and sources of leadership: Mapping the semiconductor miniaturization trajectory. Research Policy, 42(1), 180–195.

    Article  Google Scholar 

  • Fan, D., Lo, C. K. Y., Ching, V., & Kan, C. W. (2014). Occupational health and safety issues in operations management: A systematic and citation network analysis review. International Journal of Production Economics, 158, 334–344. doi:10.1016/j.ijpe.2014.07.025.

    Article  Google Scholar 

  • Farrell, M. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A, 120(3), 253–290.

    Article  Google Scholar 

  • Favero, C. A., & Papi, L. (1995). Technical efficiency and scale efficiency in the Italian banking sector: A non-parametric approach. Applied Economics, 27(4), 385–395. doi:10.1080/00036849500000123.

    Article  Google Scholar 

  • Fethi, M. D., & Pasiouras, F. (2010). Assessing bank performance with operational artificial intelligence techniques: A Survey. European Journal of Operational Research, 204(2), 189–198.

    Article  Google Scholar 

  • Fontana, R., Nuvolari, A., & Verspagen, B. (2009). Mapping technological trajectories as patent citation networks: An application to data communication standards. Economics of Innovation and New Technologies, 18(4), 311–336.

    Article  Google Scholar 

  • Garfield, E. (2009). From the science of science to Scientometrics visualizing the history of science with HistCite software. Journal of Informetrics, 1, 21–26.

    Google Scholar 

  • Garfield, E., Sher, I., & Torpie, R. (1964). The use of citation data in writing the history of science. (Institute for Scientific Information, Ed.) Institute for Scientific Information. Philadelphia: Institute for Scientific Information.

  • Gattoufi, S., Oral, M., & Reisman, A. (2004). Data envelopment analysis literature: A bibliography update (1951–2001). Journal of Socio-Economic Planning Science, 38(2–3), 159–229.

    Google Scholar 

  • Giokas, D. (1991). Bank branch operating efficiency: A comparative application of DEA and the loglinear model. Omega, 19(6), 549–557. doi:10.1016/0305-0483(91)90005-E.

    Article  Google Scholar 

  • Giokas, D. I. (2008). Assessing the efficiency in operations of a large Greek bank branch network adopting different economic behaviors. Economic Modelling, 25(3), 559–574. doi:10.1016/j.econmod.2007.10.007.

    Article  Google Scholar 

  • Glawischnig, M., & Sommersguter-Reichmann, M. (2010). Assessing the performance of alternative investments using non-parametric efficiency measurement approaches: Is it convincing? Journal of Banking & Finance, 34(2), 295–303. doi:10.1016/j.jbankfin.2009.07.017.

    Article  Google Scholar 

  • Golany, B., & Storbeck, J. (1999). A data envelopment analysis of the operational efficiency of bank branches. Interfaces, 29(3), 14–26.

    Article  Google Scholar 

  • Gonzalez, M., & Trujillo, L. (2009). Efficiency measurement in the port industry: a survey of the empirical evidence. Journal of Transport Economic and Policy, 43(2), 157–192. http://ideas.repec.org/p/cty/dpaper/07-08.html. Accessed June 3, 2015.

  • Grosskopf, S. (1996). Statistical inference and nonparametric efficiency: A selective survey. Journal of Productivity Analysis, 7(2–3), 161–176. doi:10.1007/BF00157039.

    Article  Google Scholar 

  • Hauner, D. (2008). Credit to government and banking sector performance. Journal of Banking & Finance, 32(8), 1499–1507. doi:10.1016/j.jbankfin.2007.07.012.

    Article  Google Scholar 

  • Herrera-Restrepo, O., Triantis, K., Seaver, W. L., Paradi, J. C., & Zhu, H. (2016). Bank branch operational performance: A robust multivariate and clustering approach. Expert Systems with Applications, 50, 107–119. doi:10.1016/j.eswa.2015.12.025.

    Article  Google Scholar 

  • Hollingsworth, B. (2003). Non-parametric and parametric applications measuring efficiency in health care. Health Care Management Science, 6(4), 203–218. doi:10.1023/A:1026255523228.

    Article  Google Scholar 

  • Hollingsworth, B., Dawson, P. J., & Maniadakis, N. (1999). Efficiency measurement of health care: A review of non-parametric methods and applications. Health Care Management Science, 2(3), 161–172. doi:10.1023/A:1019087828488.

    Article  Google Scholar 

  • Hoskin, J. R. M. (1990). L-moments: Analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society. Series B (Methodological), 52(1), 105–124.

    Google Scholar 

  • Hummon, N. P., & Doreian, P. (1989). Connectivity in a citation network: The development of the DNA theory. Social Networks, 11, 39–63. doi:10.1016/0378-8733(89)90017-8.

    Article  Google Scholar 

  • Jensen, M. C. (1968). The performance of mutual funds in the period 1945–1964. The Journal of Finance, 23(2), 389–416. doi:10.1111/j.1540-6261.1968.tb00815.x.

    Article  Google Scholar 

  • Joro, T., & Na, P. (2006). Portfolio performance evaluation in a mean-variance-skewness framework. European Journal of Operational Research, 175(1), 446–461. doi:10.1016/j.ejor.2005.05.006.

    Article  Google Scholar 

  • Juo, J.-C., Fu, T.-T., & Yu, M.-M. (2012). Non-oriented slack-based decompositions of profit change with an application to Taiwanese banking. Omega, 40(5), 550–561. doi:10.1016/j.omega.2011.11.002.

    Article  Google Scholar 

  • Kaffash, S. (2014). Oil price and bank performance in the Middle Eastern oil exporting countries. PhD thesis, Brunel University.

  • Kao, C., & Liu, S.-T. (2014). Multi-period efficiency measurement in data envelopment analysis: The case of Taiwanese commercial banks. Omega, 47, 90–98. doi:10.1016/j.omega.2013.09.001.

    Article  Google Scholar 

  • Kejžar, N., Černe, S. K., & Batagelj, V. (2010). Network analysis of works on clustering and classification from Web of Science. In H. Locarek-Junge & C. Weihs (Eds.), Classification as a tool for research (pp. 525–536). Berlin, Heidelberg: Springer. http://www.springerlink.com/index/10.1007/978-3-642-10745-0. Accessed November 15, 2013.

  • Kerstens, K., Mounir, A., & Van de Woestyne, I. (2011). Non-parametric frontier estimates of mutual fund performance using C- and L-moments: Some specification tests. Journal of Banking & Finance, 35(5), 1190–1201. doi:10.1016/j.jbankfin.2010.09.030.

    Article  Google Scholar 

  • Kirkwood, J., & Nahm, D. (2006). Australian banking efficiency and its relation to stock returns. Economic Record, 82(258), 253–267. doi:10.1111/j.1475-4932.2006.00338.x.

    Article  Google Scholar 

  • Lamb, J. D., & Tee, K.-H. (2012a). Resampling DEA estimates of investment fund performance. European Journal of Operational Research, 223(3), 834–841.

    Article  Google Scholar 

  • Lamb, J. D., & Tee, K.-H. (2012b). Data envelopment analysis models of investment funds. European Journal of Operational Research, 216(3), 687–696.

    Article  Google Scholar 

  • Lampe, H. W., & Hilgers, D. (2014). Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA. European Journal of Operational Research, 240(1), 1–21. doi:10.1016/j.ejor.2014.04.041.

    Article  Google Scholar 

  • Lampe, H. W., & Hilgers, D. (2015). Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA. European Journal of Operational Research, 240(1), 1–21. doi:10.1016/j.ejor.2014.04.041.

    Article  Google Scholar 

  • Liu, J., Lu, L. Y. Y., Lu, W. M., & Lin, B. J. Y. (2013a). A survey of DEA applications. Omega, 41(5), 893–902.

    Google Scholar 

  • Liu, J. S., Lu, L. Y. Y., & Lu, W.-M. (2016). Research fronts in data envelopment analysis. Omega, 58, 33–45.

    Article  Google Scholar 

  • Liu, J. S., Lu, L. Y. Y., Lu, W.-M., & Lin, B. J. Y. (2013b). Data envelopment analysis 1978–2010: A citation-based literature survey. Omega, 41(1), 3–15. doi:10.1016/j.omega.2010.12.006.

  • Liu, W., Zhou, Z., Liu, D., & Xiao, H. (2015). Estimation of portfolio efficiency via DEA. Omega, 52, 107–118. doi:10.1016/j.omega.2014.11.006.

    Article  Google Scholar 

  • Lozano, S., & Gutiérrez, E. (2008). Data envelopment analysis of mutual funds based on second-order stochastic dominance. European Journal of Operational Research, 189(1), 230–244.

    Article  Google Scholar 

  • Lucio-Arias, D., & Leydesdorf, L. (2008). Main-path analysis and path-dependent transitions in HistCite\(^{{\rm TM}}\) -based historiograms. Journal of the American Society for Information Science and Technology, 59(12), 1948–1962.

    Article  Google Scholar 

  • Martinelli, A. (2012). An emerging paradigm or just another trajectory? Understanding the nature of technological changes using engineering heuristics in the telecommunications switching industry. Research Policy, 41(2), 414–429. doi:10.1016/j.respol.2011.10.012.

    Article  Google Scholar 

  • Mina, A., Ramlogan, R., Tampubolon, G., & Metcalfe, J. S. (2007). Mapping evolutionary trajectories: Applications to the growth and transformation of medical knowledge. Research Policy, 36(5), 789–806. doi:10.1016/j.respol.2006.12.007.

    Article  Google Scholar 

  • Murthi, B. P. S., Choi, Y. K., & Desai, P. (1997). Efficiency of mutual funds and portfolio performance measurement: A non-parametric approach. European Journal of Operational Research, 98(2), 408–418. doi:10.1016/S0377-2217(96)00356-6.

    Article  Google Scholar 

  • Nerur, S. P., Rasheed, A. A., & Natarajan, V. (2008). The intellectual structure of the strategic management field: An author co-citation analysis. Strategic Management Journal, 29(3), 319–336. doi:10.1002/smj.659.

    Article  Google Scholar 

  • Oum, T. H., Waters, W., & Yu, C. (1999). A survey of productivity and efficiency measurement in rail transport. Journal of Transport Economic and Policy, 33(1), 9–42.

    Google Scholar 

  • Paradi, J. C., Rouatt, S., & Zhu, H. (2011). Two-stage evaluation of bank branch efficiency using data envelopment analysis. Omega, 39(1), 99–109. doi:10.1016/j.omega.2010.04.002.

    Article  Google Scholar 

  • Paradi, J. C., & Schaffnit, C. (2004). Commercial branch performance evaluation and results communication in a Canadian bank–a DEA application. European Journal of Operational Research, 156(3), 719–735. doi:10.1016/S0377-2217(03)00108-5.

    Article  Google Scholar 

  • Paradi, J. C., & Zhu, H. (2013). A survey on bank branch efficiency and performance research with data envelopment analysis. Omega, 41(1), 61–79. doi:10.1016/j.omega.2011.08.010.

    Article  Google Scholar 

  • Portela, M. C. A. S., & Thanassoulis, E. (2005). Profitability of a sample of Portuguese bank branches and its decomposition into technical and allocative components. European Journal of Operational Research, 162(3), 850–866. doi:10.1016/j.ejor.2003.10.018.

    Article  Google Scholar 

  • Portela, M. C. A. S., & Thanassoulis, E. (2007). Comparative efficiency analysis of Portuguese bank branches. European Journal of Operational Research, 177(2), 1275–1288. doi:10.1016/j.ejor.2006.01.007.

    Article  Google Scholar 

  • Rangan, N., Grabowski, R., Aly, H. Y., & Pasurka, C. (1988). The technical efficiency of US banks. Economics Letters, 28(2), 169–175. doi:10.1016/0165-1765(88)90109-7.

    Article  Google Scholar 

  • Rotolo, D., Rafols, I., Hopkins, M., & Leydesdorff, L. (2013). Scientometric intelligence on emerging technologies: Scientometrics overlay mapping. Journal of the Association for Information Science and Technology. Digital Libraries.

  • Sathye, M. (2001). X-efficiency in Australian banking: An empirical investigation. Journal of Banking & Finance, 25(3), 613–630. doi:10.1016/S0378-4266(00)00156-4.

    Article  Google Scholar 

  • Seiford, L. M., & Thrall, R. M. (1990). Recent developments in DEA. Journal of Econometrics, 46(1–2), 7–38. doi:10.1016/0304-4076(90)90045-U.

    Article  Google Scholar 

  • Seiford, L. (1996). A bibliography for data envelopment analysis (1978–1996). Annals of Operations Research, 73, 393–438.

    Article  Google Scholar 

  • Sharpe, W. F. (1966). Mutual fund performance. The Journal of Business, 39(1), 119–138. http://www.jstor.org/stable/2351741.

  • Sherman, H. D., & Gold, F. (1985). Bank branch operating efficiency: Evaluation with data envelopment analysis. Journal of Banking & Finance, 9(2), 297–315. doi:10.1016/0378-4266(85)90025-1.

    Article  Google Scholar 

  • Siriopoulos, C., & Tziogkidis, P. (2010). How do Greek banking institutions react after significant events? A DEA approach. Omega, 38(5), 294–308.

    Article  Google Scholar 

  • Soteriou, A., & Zenios, S. A. (1999). Operations, quality, and profitability in the provision of banking services. Management Science, 45(9), 1221–1238. doi:10.1287/mnsc.45.9.1221.

    Article  Google Scholar 

  • Sturm, J.-E., & Williams, B. (2004). Foreign bank entry, deregulation and bank efficiency: Lessons from the Australian experience. Journal of Banking & Finance, 28(7), 1775–1799. doi:10.1016/j.jbankfin.2003.06.005.

    Article  Google Scholar 

  • Thanassoulis, E. (1999). Data envelopment analysis and its use in banking. Interfaces, 29(3), 1–13. http://pubsonline.informs.org/doi/abs/10.1287/inte.29.3.1?journalCode=inte. Accessed June 3, 2015.

  • Vassiloglou, M., & Giokas, D. (1990). A study of the relative efficiency of bank branches: An application of Data Envelopment Analysis. Journal of the Operational Research Society, 41(7), 591–597. doi:10.1057/jors.1990.83.

    Article  Google Scholar 

  • Wanke, P., & Barros, C. P. (2016). Efficiency drivers in Brazilian insurance: A two-stage DEA meta frontier-data mining approach. Economic Modelling, 53, 8–22. doi:10.1016/j.econmod.2015.11.005.

    Article  Google Scholar 

  • Whitley, E. A., & Galliers, R. D. (2007). An alternative perspective on citation classics: Evidence from the first 10 years of the European conference on information systems. Information & Management, 44(5), 441–455.

    Article  Google Scholar 

  • Worthington, A. C. (2004). Frontier efficiency measurement in health care: A review of empirical techniques and selected applications. Medical Care Research and Review, 61(2), 135–170. doi:10.1177/1077558704263796.

    Article  Google Scholar 

  • Xie, X. Y., Lu, W. L., Reising, J., & Stohs, M. H. (2011). Demutualisation, control and efficiency in the U.S. life insurance industry. Geneva papers on Risk and Insurance-Issues and Practice.

  • Yang, X., & Morita, H. (2013). Efficiency improvement from multiple perspectives: An application to Japanese banking industry. Omega, 41(3), 501–509. doi:10.1016/j.omega.2012.06.007.

    Article  Google Scholar 

  • Zhou, P., Ang, B. W., & Poh, K. L. (2008). A survey of data envelopment analysis in energy and environmental studies. European Journal of Operational Research, 189(1), 1–18. doi:10.1016/j.ejor.2007.04.042.

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the three anonymous referees for their useful insights which have helped improve the argument in this paper.

Author information

Authors and Affiliations

  1. Department of Accounting and Finance, University of Massachusetts Boston, 100 Morrissey Blvd, Boston, MA, 02125, USA

    Sepideh Kaffash

  2. Department of Management Science and Information Systems, University of Massachusetts Boston, 100 Morrissey Blvd, Boston, MA, 02125, USA

    Sepideh Kaffash

  3. Department Management Science and Entrepreneurship, Essex Business School, Essex University, Southend on Sea, Essex, UK

    Marianna Marra

Authors
  1. Sepideh Kaffash
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marianna Marra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marianna Marra.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaffash, S., Marra, M. Data envelopment analysis in financial services: a citations network analysis of banks, insurance companies and money market funds. Ann Oper Res 253, 307–344 (2017). https://doi.org/10.1007/s10479-016-2294-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-016-2294-1

Keywords

Search

Navigation