Assessing Bank Performance Using Dynamic SBM Model

Abstract

Global economic growth has led banks to expand their operations all over the world. The purpose of this research was to understand the efficiency of 18 large bank from all over the world during the period from 2013 to 2017. The performance was estimated by a dynamic slacks-based measure (SBM) model in data envelopment analysis (DEA). This model could be solved using inputs, outputs, and links. The banks variables were considered as follows: Assets, capitalization, and liabilities as inputs; revenue as output; and net interest income as a good link. The final empirical results exhibit the efficiency for each term, and the overall score. The data analysis recommends a feasible solution to refine inefficient terms based on the projections (slacks). This study visually observed the proficiency of the banking industry to equip enterprises with the best choice for their finances.

Dynamic operating efficiency and its determining factors of listed real‐estate companies in China: A hierarchical slack‐based DEA‐OLS approach

“…The listed real‐estate company will be excellent that is, DEA efficient if an overall efficiency score is equal to 1. The efficiency scores are categorized into five levels based on the nature of the scores as depicted by Wang et al (2019). The real‐estate company will be good, median, low, and very low if its overall efficiency scores are 0.7 to 0.99, 0.5 to 0.69, 0.3 to 0.49, and 0 to 0.29 respectively.…”

“…The dynamic SBM model is non‐radial, considers carry‐overs and deals with inputs and outputs on individual basis, in contrast to the radial techniques that consider proportional changes in inputs and outputs. Dynamic SBM based DEA model has been implemented in previous research work but in different areas: evaluating inter‐temporal efficiency based on fossil‐fuel carbon emissions in China and OECD countries, Guo, Lu, Lee, and Chiu (2017) applied the dynamic DEA model; investigation of operational efficiency of city‐owned commercial, state‐owned commercial and joint‐stock commercial banks in China using data from 2008 to 2012 to obtain results of inefficiences in productivity and profitability stages (Zha, Liang, Wu, & Bian, 2016); an input‐oriented dynamic SBM model is pursued for the operation of Chinese photovoltaic companies and compared business groups and specialized operation (Li, Liu, & Zha, 2016); assessing the efficiency of 18 global banks using data from 2013 to 2017 was performed with a dynamic SBM model and a feasible solution to refine inefficient terms was proposed (Wang et al, 2019); Li, Chiu, and Lu (2018) employs dynamic SBM model to evaluate energy efficiency and air quality index efficiency of 31 cities in China using data from the time period 2013 to 2016.…”

“…This is because of the cryptic nature of the emerging Chinese stock market, which contains about 127 real‐estate securities to most investors. In the light of this, evaluating efficiency will be valuable in gaining insight into how a firm operates (Wang, Luu, Nguyen, & Day, 2019). It is logical to ascertain that a firm with higher efficiency will have higher capital increment in the listed financial market.…”

Efficiency of China’s Listed Securities Companies: Estimation through a DEA-Based Method

“…Based on the operational process established in Figure 1, this section introduces the SBM-DEA to develop a modified efficiency evaluation model considering the impact of the risk factor. As a non-radial method, the SBM approach is better than the traditional DEA model in identifying output and input slacks, thus measuring securities firms' operational efficiency in a more accurate manner [28,29]. For the risk variable that is regarded as an undesirable output in DEA, there are two kinds of settings, strong disposability and weak disposability [17].…”

Risk Measurement of Stock Markets in BRICS, G7, and G20: Vine Copulas versus Factor Copulas

“…Therefore, study risk measurement would be able to prevent or give a signal to avoid huge potential loss. Diverse markets lead to different risks (see Luo et al (2015) [3], Shahzad et al (2018) [4], Airouss et al (2018) [5], and others), and according to Zhang & Zheng (2018) [6] and Wang et al [7], there is a huge difference in stock markets and banks between emerging markets and advanced countries, especially in terms of seasonality. Also, different countries have different levels of influence on the global economic performance (see [8] and Amavilah (2017) [9]).…”