Predicting and Optimizing Future Bank Performance

  IJMTT-book-cover
 
International Journal of Mathematics Trends and Technology (IJMTT)
 
© 2022 by IJMTT Journal
Volume-68 Issue-9
Year of Publication : 2022
Authors : Qiaopeng Ma, Zhongqiang Liu
 10.14445/22315373/IJMTT-V68I9P505

How to Cite?

Qiaopeng Ma, Zhongqiang Liu, "Predicting and Optimizing Future Bank Performance ," International Journal of Mathematics Trends and Technology, vol. 68, no. 9, pp. 21-33, 2022. Crossref, https://doi.org/10.14445/22315373/IJMTT-V68I9P505.

Abstract
This article selects 25 listed banks in China, and collects their annual financial data from 2016 to2021. According to existing research, we identify 5 influencing variables to explain weighted average return on equity (ROE) of banks. Noticing good fitting effect of nonlinear regression, we use nonlinear regression model to fit the annual financial data. Then taking fitted regression models as objective function, we solve nonlinear programming problems and obtain corresponding optimized results, which are compared with actual weighted average ROE of the 25 banks. By comparison, we observe that optimized values are significantly greater than corresponding actual values, which indicates that there is room for improvement in weighted average ROE. Based on regression models obtained, one-year predicted performance and three-year mean predicted performance are calculated to predict future bank performance. Combining optimized and predicted values, banks can optimize their performance in the future by adjusting their own internal influencing variables.

Keywords : Mean predicted performance, Nonlinear regression model, Nonlinear programming, Optimized performance, Weighted average ROE.

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