Impact of Corporate Governance on Corporate Value for Companies Listed at the Nairobi Securities Exchange
Bibliographic record
Abstract
The study objective is to establish the impact of corporate governance on corporate value of firms listed at the Nairobi Securities Exchange. From time to time, capital market authorities have issued guidelines and regulations for good corporate governance in different areas in order to ensure solid and sound management of listed companies and to align the interests of all stakeholders thus ensuring the firm’s sustainability and optimization of the company’s value. Despite these policies, cases of failure and corporate underperformance caused by unsound corporate governance continue to increase in frequency and magnitude. The paper tested the hypothesis that there is no significant influence of corporate governance on corporate value. Corporate governance measurements variables were board independence, board size, board composition and board gender diversity while corporate value was measured by Tobin Q. Data was obtained from past audited financial statements of firms quoted at the NSE. The study used census survey for sixty-four listed companies. The analysis covered a five years period between 2013 and 2017. The study applied agency theory as the anchoring theory. Descriptive statistics and diagnostic tests were conducted on the data thereafter inferential statistics namely correlations analysis and regression analysis were used to test the hypothesis. When the data on the study variables was subjected to descriptive statistics, the results showed a significant relationship between the variables. The panel data approach was considered more appropriate because the sample data contained both cross-sectional and time-series data. The study revealed positive and significant relationship between corporate governance and corporate value.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".