Assessing Canadian Schedule I banks using DEA window analysis and the Malmquist Index

Yang, Zijiang; Cao, Yurong

doi:10.1109/icsssm.2010.5530262

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…rizes the route research has taken over the years. The first column states the authors and year of publication, the second gives the title of the paper and third column summarizes the input-output classification used.The following is a descriptive account of similar research based on accounting conventions:Yang and Cao 2010[22], examine the performance of seven of the largest Canadian Schedule 1 banks. They have used DEA analysis in order to calculate the efficiency scores for the Schedule 1 banks between 1998 and 2007.…”

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confidence: 99%

A Nonparametric and Aggregation Theoretic Approach for Measuring Productivity of US Banks during 2006-2016

Hasan¹

2022

AJOR

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Existing literature related to efficiency measurement and productivity analysis of banks is swarmed with the input-output classification of banks based on using accounting conventions. This usage varies from paper to paper. No two research papers are in consensus as to which classification should be used. This present work, however, uses the input-output classification of banks based on Barnett's generalized model of production for financial intermediaries originally proposed in Barnett (1987) [1]. This model is based on economic theory definitions of inputs and outputs of a bank. Using this classification, the paper applies Data Envelopment Analysis to US banks during 2006-2016. This new methodology seeks to resolve and fix the issue of lack of consensus regarding which inputs and outputs to use for productivity analysis of banks. Furthermore, a standardized way of measuring productivity across banks is developed which can be used all over the world. This is accomplished by using the Malmquist Index of Productivity which is a tool used under Data Envelopment Analysis. The paper further establishes the connection of this tool with Barnett's generalized model of production for financial intermediaries. Results indicate very high efficiency levels for US banks even post financial crisis. The reason for this performance is the cleansing of the financial system as unhealthy banks either left the scene or were merged. Better risk management, cost management and efficiency of structure of funding are some other reasons for high efficiency.

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confidence: 99%

A Nonparametric and Aggregation Theoretic Approach for Measuring Productivity of US Banks during 2006-2016

Hasan¹

2022

AJOR

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Comparison of US and Australia Banking Systems Using Data Envelopment Analysis

Mahabadi

Yang

2015

Liss 2014

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Window data envelopment analysis approach: A review and bibliometric analysis

et al. 2021

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Window Data Envelopment Analysis (WDEA) is a popular, effective, and applicable methods for dynamic performance assessment of peer decision making units (DMUs). WDEA is a non-parametric panel method that operates based on the principle of moving averages and establishes efficiency measures by treating each DMU in different periods as a separate DMU. By applying the WDEA approach, a decisionmaker (DM) can measure the efficiency of different DMUs in different periods through a sequence of overlapping windows. Also, WDEA can increase the discrimination power by increasing the number of DMUs when a limited number of DMUs is available. Given the advantages of the WDEA approach and its applications in realworld problems, this paper surveys and analyses 387 WDEA papers published from 1985 to 2020. The paper also recommends some suggestions, guidelines, and opportunities for future research. Notably, the findings show the applicability and efficacy of WDEA in the literature.

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Assessing Canadian Schedule I banks using DEA window analysis and the Malmquist Index

Cited by 4 publications

References 11 publications

A Nonparametric and Aggregation Theoretic Approach for Measuring Productivity of US Banks during 2006-2016

A Nonparametric and Aggregation Theoretic Approach for Measuring Productivity of US Banks during 2006-2016

Comparison of US and Australia Banking Systems Using Data Envelopment Analysis

Window data envelopment analysis approach: A review and bibliometric analysis

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