An Empirical Study on Determinants of Business Performance of Korean Non-life Insurance Companies (Focused on ROA)
Bibliographic record
Abstract
This study examines the total asset profitability, which is an indicator of business performance, using panel data for 10 years from 2005 to 2015 for 10 domestic insurance companies. We analyze the factors affecting the ROA, compare the differences between before and after the enactment of the Capital Market Act, and assess the level of total assets of domestic insurance companies. Total Asset Margins As a result of analyzing the eight independent variables in order to identify the factors that affect the dependent variable, the factors affecting the total asset margins are (4) investment operating profit, insurance operating profit, business expense, appear. Among them, investment profits were the most influential factors. On the other hand, the factors affecting (-) the total asset profitability were analyzed as total capital, premium, leverage, and loss ratio. In particular, the total amount of capital has the largest negative impact on total assets. As a result of analyzing whether or not the total assets profit rate before and after enforcement of the Capital Market Act is the same, ROA, leverage, and period of operation were found to be the same before and after the Capital Market Act. On the other hand, insurance premiums, insurance operating profits, investment operating profits, business expenses, loss ratios, and total capital were analyzed before and after the implementation of the Capital Market Act. According to the results of the analysis of the total assets profit rate and the amount of the premium insurance, the second group has a 0.4% lower ROA than the first group but the third group is 41.8% lower than the first group. In other words, it can be seen that the ratio of total assets is lower than that of large companies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".