Market Risk and Financial Performance of Listed Non-bank Financial Institutions in Kenya
Bibliographic record
Abstract
Non-Bank Financial Institutions (NBFIs) have surfaced as a growing segment in the financial sector as they complement the mainstream banking institutions to provide financial services to customers. In the present day’s dynamic business environment, these financial institutions face market risks including interest rate risk, foreign exchange risks, commodity risks and equity. The objective of the study was to examine the effect of interest rate risk on the financial performance of NBFIs listed on the Nairobi Stocks Exchange (NSE) over the period 2012–2017. The study focused on balance sheets components and financial ratios of 9 listed non-bank financial institutions in Kenya. The study used unbalanced panel data from 9 listed non-bank financial institutions over the period 2008–2017.The study used the net profit margin to assess financial performance of the companies while the degree of financial leverage indicators of interest rate risk. The financial performance was regressed against the market risk factors using the random effects models based on the Hausman and the LM tests specifications. The results show that financial leverage has a significant positive impact on the performance of the NBFIs with a p value of 0.000. The study concluded that interest risk has momentous effects on financial performance of the listed NBFIs in Kenya. Therefore, the study recommended that the management of the NBFIs should employ effective risk management strategies to mitigate their effects of the market risks.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".