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Record W2029529093 · doi:10.5539/ijef.v4n2p260

The Effects of Board Size on Financial Performance of Banks: A Study of Listed Banks in Nigeria

2012· article· en· W2029529093 on OpenAlexvenueno aff
Olubukunola Ranti Uwuigbe, A. S. Fakile

Bibliographic record

VenueInternational Journal of Economics and Finance · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceAccountingBusinessStock exchangeOn boardAgency (philosophy)Value (mathematics)Financial sectorFinanceEngineering

Abstract

fetched live from OpenAlex

A critical review of the Nigerian banking system over the years shows that one of the problems confronting the sector has been that of poor corporate governance. In an attempt to investigate the linkage between corporate governance and financial performance of banks, this study contributed to the existing literature by assessing the effect of size of boards on the performance of banking sector in a developing economy like Nigeria. This study made use of a range of data drawn from the Nigerian Stock Exchange fact book (2008), which contains information on board size and the performance proxies. Regressing performance on board size, it was observed that banks with board size below 13 are more viable than those with board size above 13. The study further observed that banks with larger boards recorded profits lower than those with smaller boards. Therefore, this study concludes that there is a significant negative relationship between board size and bank financial performance with a t- value of -1.977 and a p- value of 0.053. This is because, increase in board size occurs with increase in agency problems (such as director free-riding) within the board and the board becomes less effective. However, the paper recommends a smaller board size for better financial performance and to reduce the problem of free-rider of banks in Nigeria.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.201
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations46
Published2012
Admission routes1
Has abstractyes

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