Revisiting Insurance Capital Structure, Risk-Taking Behaviour and Performance between 1995 – 2002
Bibliographic record
Abstract
This paper examines the effect of capital structure and the moderation effect of risk-taking behaviour of insurance firms on performance of insurers in Nigeria from 1995 to 2002. This study became necessary as literatures in this area and regime are scarce. Secondary data from financial reports of each insurance firm were used. Descriptive statistics were used to describe the characteristics of the data while a two-stage estimation procedure of the fixed effect and random effect models were used to test the hypothetical framework of the study. Result shows that insurance capital structure (measured by equity ratio) had an insignificant negative effect on insurance performance while it had a significant positive effect on insurance performance if measured by technical provision ratios. On average, risk taking behaviour moderates the relationship between technical provision ratio and insurance performance. This study focused on capital structure and moderation effect of risk on performance of insurers in non risk-based capital era. Further study on risk-based capital era will provide more on performance of insurers before and after the implementation of risk-base capital requirement. These findings provide important insight to managers and regulators and investors by fostering more understanding of how to manipulate insurance capital and which source of fund should be used to embark on risky investment to attain superior performance. This investigation adds to literature on insurance capital structure, regulation and risk management and insurance performance in Nigeria.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".