An integrated framework for predicting the best financial performance of banks: evidence from Egypt

Mousa, Mohamed El-Sayed; Kamel, Mahmoud Abdelrahman

doi:10.1108/jm2-02-2021-0040

Cited by 4 publications

(2 citation statements)

References 53 publications

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“…Subsequently, Banker et al (1984) developed and expanded the concept of a ratio, and the scope of the classical CCR model application to be known as BCC that focuses on VRS, which assumes that a mix of inputs may not only produce the same amount of outputs but may produce higher or lower amounts within the output. (Mousa and Kamel, 2021; Jothimani et al , 2017).…”

Section: Review Of Literaturementioning

confidence: 99%

Organizational performance assessment based on psychological empowerment and employee engagement: PCA-DEA-SEM approach

Mousa

Kamel²

2022

JM2

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Purpose This study aims to examine performance assessment of organizational units through psychological empowerment (PE) and employee engagement (EE) approach and whether this relationship differs among efficient and inefficient organization units. Design/methodology/approach This study drew on merging the principal component analysis (PCA), data envelopment analysis (DEA) and partial least square-multigroup analysis (PLS-MGA) to benchmark the performance of organizational units affiliated with Zagazig University in Egypt using PE dimensions as inputs and EE as output. Besides investigating whether PE inputs have the same effect among efficient and inefficient units. Findings Performance assessment based on independent data showed that all the investigated organizational units are not at the same efficiency level. The results revealed that there are eight efficient units versus seven inefficient ones. Moreover, PLS-MGA results demonstrated that no significant differences concerning the impact of PE inputs on EE between efficient and inefficient units groups. Nevertheless, the effect of these inputs was slightly higher in the former. Originality/value Studies on EE performance in the service sector are scarce in the literature, this study is a novel contribution of exploring EE efficiency in Egypt as a developing economy. Specifically, using the PCA-DEA-structural equation modeling approach.

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Section: Review Of Literaturementioning

confidence: 99%

Organizational performance assessment based on psychological empowerment and employee engagement: PCA-DEA-SEM approach

Mousa

Kamel²

2022

JM2

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“…In Mousa and Kamel (2021), the authors seek to develop and test a framework for the integration of data envelopment analysis (DEA) and ANN, to predict the best financial performance concerning return on assets and return on equity for banks listed on the Egyptian Exchange.…”

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confidence: 99%

Guest editorial: Artificial intelligence and machine learning in business and management

Abdelaziz

Kunze²,

Torre³

et al. 2022

JM2

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Artificial intelligence (AI) is the enhancement of computers so that they can perform complex activities and tasks in ways that can be labeled as intelligent. Machine learning (ML) is a branch of AI where algorithms are used to learn from data to make decisions or predictions. Timely research subjects in AI and ML are: reinforcement learning, ethics in AI, quantum computing, convergence of AI and other emerging technologies, facial recognition, biased data, neural networks, socioeconomic models, deep learning and privacy protection (Alpaydin, 2014;Jiang, 2021;Shalev-Shwartz and Ben-David, 2014).AI and ML are drawing considerable attention these days. An already large and fastgrowing literature covers a wide range of real or soon-to-materialize applications, from dealing with autism and detecting breast cancer to selecting job candidates, setting legal sanctions and deciding upon conditional liberations. Managers, among many others, are now in need of a compass that would allow them to navigate lucidly through the hype (Bryson et al., 2021;La Torre et al., 2021; Jammeli et al., 2021 and the references therein).This special issue aims to start developing such a compass. It includes novel

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Measuring the efficiency of banks using high-performance ensemble technique

Thabet,

Darwish,

Ali

2024

Neural Comput & Applic

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The importance of technology and managerial risk management in banks has increased due to the financial crisis. Banks are the most affected since there are so many of them with poor financial standing. Due to this problem, an unstable and inefficient financial system causes economic stagnation in both the banking sector and overall economy. Data envelopment analysis (DEA) has been used to examine decision-making units (DMUs) performance to enhance efficiency. Currently, with the rapid growth of big data, adding more DMUs will likely require a large amount of memory and CPU time on the computer system, which will be the biggest challenge. As a result, machine learning (ML) approaches have been used to analyze financial institution performance, but many of them have variances in predictions or model stability, making measuring bank efficiency extremely difficult. For this, ensemble learning is commonly used to evaluate the performance of financial institutions in this context. This paper presents a robust super learner ensemble technique for assessing bank efficiency, with four machine learning models serving as base learners. These models are the support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), and AdaBoost classifier (ADA) which represent the base learners and their results utilized to train the meta-learner. The super learner (SL) approach is an extension of the stacking technique, which generates an ensemble based on cross-validation. One important benefit of this cross-validation theory-based technique is that it can overcome the overfitting issue that plagues most other ensemble approaches. When SL and base learners were compared for their forecasting abilities using different statistical standards, the results showed that the SL is superior to the base learners, where different variable combinations were used. The SL had accuracy (ACC) of 0.8636–0.9545 and F1-score (F1) of 0.9143–0.9714, while the basic learners had ACC of 0.5909–0.8182 and F1 of 0.6897–0.9143. So, SL is highly recommended for improving the accuracy of financial data forecasts, even with limited financial data.

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An integrated framework for predicting the best financial performance of banks: evidence from Egypt

Cited by 4 publications

References 53 publications

Organizational performance assessment based on psychological empowerment and employee engagement: PCA-DEA-SEM approach

Organizational performance assessment based on psychological empowerment and employee engagement: PCA-DEA-SEM approach

Guest editorial: Artificial intelligence and machine learning in business and management

Measuring the efficiency of banks using high-performance ensemble technique

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