Predicting Efficiency in <scp>A</scp>ngolan Banks: A Two‐Stage <scp>TOPSIS</scp> and Neural Networks Approach

Barros, Carlos Pestana; Macanda, Nkanga Pedro João

doi:10.1111/saje.12103

Cited by 12 publications

(12 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…His results also revealed an inverted U‐shaped relationship between cost and income diversification. Thus far, to the best of our knowledge, examination of such high efficiency among the African banks has not been examined in the context of M&As (Wanke et al ., ; Wanke et al ., ; Barros et al ., ).…”

Section: Literature Reviewmentioning

confidence: 98%

“…Wanke et al . ( analyzed the efficiency of Angolan banks with a DEA model, using the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). Wanke et al .…”

Section: Literature Reviewmentioning

confidence: 99%

“…Secondly, based on previous bank efficiency studies, this paper evaluates relative efficiency among virtual Mozambican banks and its major drivers. To the best of our knowledge, this is the first time Mozambican banks have been analyzed in terms of potential M&A, in contrast to previous studies on African banking (Wanke et al ., ; Wanke et al ., ; Barros et al ., ). Thirdly, the present analysis includes an assessment of the impact of different contextual variables related to ownership structure, accounting principles, and the previous occurrence of M&A in relation to the strategic fit of both bidder and target companies.…”

Section: Introductionmentioning

confidence: 98%

See 2 more Smart Citations

The Development of the Mozambican Banking Sector and Strategic Fit of Mergers and Acquisitions: A Two‐Stage DEA Approach

Barros¹,

Azad²,

Constantino³

2016

African Development Review

Self Cite

View full text Add to dashboard Cite

This paper examines the strategic fit of mergers and acquisitions (M&A) in Mozambican banks, using a two-stage data envelopment analysis (DEA) approach to compute the impact of contextual variables on efficiency scores and returns to scale of the resulting virtual merged banks. In an 'M&A DEA' model, different pairs of bidder and target companies are considered. In the first stage an M&A DEA model is used. Simplex regression is adopted in the second stage. The results reveal that ownership (public or foreign) impacts virtual efficiency levels. However, the findings also show that care must be exercised with M&A because the resultant banking organization could be oversized when foreign ownership is predominant, especially when public ownership is low. Given the relative sizes of the markets in terms of productive resources, M&A involving Mozambican banks can easily lead to decreasing returns to scale. Thus, a greater emphasis should be given to merging purely public banks with foreign ones and vice versa. Ã Research for this paper was undertaken with the support of the Calouste Gulbenkian Foundation.

show abstract

Section: Literature Reviewmentioning

confidence: 98%

“…Wanke et al . ( analyzed the efficiency of Angolan banks with a DEA model, using the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). Wanke et al .…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

The Development of the Mozambican Banking Sector and Strategic Fit of Mergers and Acquisitions: A Two‐Stage DEA Approach

Barros¹,

Azad²,

Constantino³

2016

African Development Review

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hence eight hidden nodes were thus used in the resulting ANN. The high number of hidden nodes justifies the use of ANN given that this allows greater levels of interactions between the input variables as the hidden levels which tends to provide improved performance relative to linear models [75].…”

Section: Artificial Neural Networkann Modelmentioning

confidence: 99%

Artificial Neural Network to Model Managerial Timing Decision: Non-linear Evidence of Deviation from Target Leverage

Hussain¹,

Kamarudin²,

Thaker³

et al. 2019

IJCIS

View full text Add to dashboard Cite

The current study highlights the utilization of a non-linear model to analyze an important decision-making process in the study of corporate finance where managers are deciding on the capital structure of a firm. This study compares the results from based on the unbalanced panel data multiple regression for firm fixed effects relative to the artificial neural networks, i.e., ANN, with known determinants of capital structure as control variables for a sample of UK firms respectively. Results of the study show that firms are timing away from target levels which challenges the current findings in the literature. The ANN model achieves a better fit based on the root of mean-squared error (RMSE) values which provides a more accurate forecast. Thus, the nature of balancing between cost of being off-target versus benefits gained from timing the equity market is non-linear and which is captured by ANN. Implications from the study allow market players to understand the process of achieving optimal capital structure to maximize firm value and thus benefit all stakeholders.

show abstract

“…There are also some arguments in respect to local banks as to whether the type of ownership-state or private-may have an impact, although private banks are often expected to be more efficient. Fewer exceptions are related to incipient markets or underdeveloped countries (Wanke, Azad, Barros, & Hadi-Vencheh, 2015;Wanke, Barros, Azad, & Constantino, 2016;Wanke, Barros, & Emrouznejad, 2016;Wanke, Barros, & Macanda, 2015).…”

Section: Introductionmentioning

confidence: 99%

A dynamic network DEA model for accounting and financial indicators: A case of efficiency in MENA banking

Wänke¹,

Azad²,

Emrouznejad

et al. 2019

International Review of Economics & Finance

Self Cite

View full text Add to dashboard Cite

Middle East and North Africa (MENA) countries present a banking industry that is well-known for regulatory and cultural heterogeneity, besides ownership, origin, and type diversity. This paper explores these issues by developing a Dynamic Network DEA model in order to handle the underlying relationships among major accounting and financial indicators.Firstly, a relational model encompassing major profit sheet, balance sheet, and financial health indicators is presented under a dynamic network structure. Subsequently, the dynamic effect of carry-over indicators is incorporated into it so that efficiency scores can be properly computed for these three substructures. The impact of contextual variables related to bank ownership, its type, and whether or not it has undergone a previous merger and acquisition process is tested by means of a stochastic non-linear model solved by differential evolution, which combines bootstrapped Simplex, Tobit, Beta, and Simar and Wilson truncated regression results. The results reveal that bank type, origin, and ownership impact efficiency levels differently in terms of profit sheet, balance sheet, and financial health indicators, although the impact of culture and regulatory barriers seem to prevail at the country level.Abstract: Middle East and North Africa (MENA) countries present a banking industry that is well-known for regulatory and cultural heterogeneity, besides ownership, origin, and type diversity. This paper explores these issues by developing a Dynamic Network DEA model in order to handle the underlying relationships among major accounting and financial indicators. Firstly, a relational model encompassing major profit sheet, balance sheet, and financial health indicators is presented under a dynamic network structure. Subsequently, the dynamic effect of carry-over indicators is incorporated into it so that efficiency scores can be properly computed for these three substructures. The impact of contextual variables related to bank ownership, its type, and whether or not it has undergone a previous merger and acquisition process is tested by means of a stochastic non-linear model solved by differential evolution, which combines bootstrapped Simplex, Tobit, Beta, and Simar and Wilson truncated regression results. The results reveal that bank type, origin, and ownership impact efficiency levels differently in terms of profit sheet, balance sheet, and financial health indicators, although the impact of culture and regulatory barriers seem to prevail at the country level.

show abstract

Predicting Efficiency in Angolan Banks: A Two‐Stage TOPSIS and Neural Networks Approach

Cited by 12 publications

References 88 publications

The Development of the Mozambican Banking Sector and Strategic Fit of Mergers and Acquisitions: A Two‐Stage DEA Approach

The Development of the Mozambican Banking Sector and Strategic Fit of Mergers and Acquisitions: A Two‐Stage DEA Approach

Artificial Neural Network to Model Managerial Timing Decision: Non-linear Evidence of Deviation from Target Leverage

A dynamic network DEA model for accounting and financial indicators: A case of efficiency in MENA banking

Contact Info

Product

Resources

About