Valuation of Commercial Insurance Companies with Focus on Relative Valuation
Bibliographic record
Abstract
Considering the insufficient current state of theory in the field of the insurance companies' valuation, the aim of this dissertation thesis was firstly to comprehensively analyze issues of commercial insurance companies' valuation and secondly to develop an appropriate body of knowledge that could contribute to the development of a practical methodology to be used in valuation practice. This dissertation thesis is divided into three consecutive parts. The first part assessed the accounting issues of commercial insurance companies with an impact on financial analysis and planning and on further procedure of insurance companies' valuation. In this section, the structure of financial statements of insurance companies was described and specific items of financial statements were discussed, especially concerning financial investments and technical reserves. The book keeping methods, reporting and testing while abiding to the Czech accounting regulations were analyzed. As well as, different accounting procedures required by IFRS or US GAAP were pointed out. Moreover, specific ratios convenient for a financial analysis of insurance companies were recorded. For an overall rating of the financial health, the value creation test was suggested in an equity form. Subsequently, the value drivers of insurance company were identified and recommendations for the complex processes of financial planning were given. Such a complex financial planning process should include a strategic analysis applicable for the purposes of a premium growth rate forecast. The second part includes the various methods of valuation, such as income approach, asset approach and relative valuation. Application of conventional valuation techniques was assessed and necessary modifications of classical methodology were suggested with respect to the specifics of the insurance market. Special attention has been paid to the fair value reporting of certain balance sheet items, as well as the creation of hidden reserves, respectively hidden debt. Modifications of valuation formulas were stated and/or suggested per each introduced method. The scope of this part also included methods of Embedded Value and Appraisal Value, which are used in practice for reporting purposes in the fields of life insurance, and their applicability in the field of business valuation was then assessed. The third part contains an extensive empirical study that focuses on one of the methods of the valuation multiples estimation, for the purposes of life insurance companies, namely so-called sector regressions. Analysis and testing were performed on historical data of traded life insurance companies from Europe, the USA and Canada for the period of 2000 to 2011. Only profitable companies were taken into account in each given year. Data were drawn from S&P Capital IQ database. Multiples MV/E, MV/BV, MV/Prem and D/MV, which were identified as utilizable (dependent variables) in the theoretical part of this thesis, were tested together with 13 financial indicators of life insurance companies (independent variables). Extreme observations of the tested multiples, as well as the indicators, were removed. The major outcome of this empirical study is firstly an identification of key financial indicators which have a major impact on valuation multiples of life insurance companies, and secondly, computation of time consistent regression formulas. Such formulas make for a relatively simple tool for the estimation of valuation multiples (and subsequently market value) of non-traded life insurance companies based on key financial indicators.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".