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- Item11-day cycle of stock prices in Kenya around profit warnings(Strathmore University, 2020) Kagiri, Jonathan NjengaA profit warning is a statement issued by a company in order to inform the public that the profits for a specified period will be significantly different from the expected profit levels. The Capital Markets Authority, which is responsible for the regulation of the stock exchange, in a bid to reduce the levels of information asymnetry and conflicts of interest between managers and shareholders, made it a requirement for all companies listed on the Nairobi Stock Exchange to issue profit warnings if their profit will be 25% less than what was expected. This study aims to view the abnormal returns surrounding a profit warning on the returns within a 1 0-day scope of the release of a profit warning. The theories and hypotheses this study relies on are the agency theory, the efficient market hypothesis and the signalling theory. An event study methodology was used, with abnormal returns being derived as a regression analysis result of the stock versus the market returns. The result being that the abnormal return is significantly different on the trading day after the profit warning and two days after the profit warning.
- ItemEvaluation of generalized linear models for modelling claim frequencies in vehicle insurance(Strathmore University, 2020) Kinyanjui_, Wanjiru;This study aims at evaluating the generalised linear models used for modelling vehicle insurance claim frequency. Modelling claim frequency, in turn, helps in the pricing and estimation of premiums. In this paper, claim frequencies will be modelled with respect to other risk factors present in the vehicle insurance data. This study makes use of data present in the CASdatasets that can be downloaded as an R package. The specific data used is brvehins1e that contains 393,071 observations. I further went ahead to randomly select 10,000 observations for computational purposes. The four generalised linear models namely; Poisson, negative Binomial, zero inflated Poisson and zero inflated negative Binomial models were fitted to the data to evaluate how well they fit. Comparison of the models was done using the Akaike' s Information Criteria, Bayesian Schwartz Information _Criteria and as well as performing a Vuong Test. Significant variables ii1. the model were determined using the p-values. The negative binomial model was determined as the better model when compared to the Poisson model. The zero inflated negative Binomial model was also seen to provide a better fit compared to the zero inflated Poisson model.
- ItemComparison between parametric and non-parametric methods of estimating comprehensive motor claim severity distributions(Strathmore University, 2020) Kimani-, Ruth Wangari;Claim severity is the amount of loss associated with an insurance claim. Insurers compensate policyholders who have suffered a loss from the occurrence of an insured risk. Insurance companies have been estimating claim severity by using normal distribution meaning; they assume an average cost of motor claims to estimate the total claims amount. However, this method is not very efficient because not all motor claims follow a normal distribution. To deal with this, there has been an introduction to using other parametric distributions such as the gamma and log-normal distribution. Parametric distributions do not consider the outlier claims that do not follow any of the parametric distributions and this is what led to using non-parametric distributions. The data used in this research study consisted of an auto-insurance portfolio of a company operating in Sweden, which was compiled by the Swedish Committee on the Analysis of Risk Premium in Motor Insurance, (Hallin & Ingenbleek, 1983). The motor insurance data is cross sectional and it involves the third-party liability auto-insurance claims for the year 1977. The only variable I worked with were the claim amounts. The main aim of this research study was to employ both parametric and non-parametric models in estimating the claim size distribution. From the data analysis that was carried out, it can be concluded that the non-parametric method is the most suitable one for estimating motor claims severity distributions .
- ItemPricing an insurance product for the Kenya school of law tuition and bar exam fee(Strathmore University, 2020) Kamau, KelvinEducation insurance is one of the risk mitigation mechanisms adopted by parents and guardians to secure their dependents' future. This approach largely helps guardians against the risk of being unable to pay for the dependents' education due to several fmancial shortfalls. Traditionally, several insurance companies have come up with this kind of product targeting the parents and guardians. The product has worked effectively in the market for a good number of years. However, the need to save for professionalism and the performance of students in the professional field has led to questions around the insurance industry. This study has taken a different tum and developed a whole new product based on the behavior and outcome of major professional trainings and qualifications. The study developed a savings plan for the Kenya School of Law tuition fees for the advocates. It was realized that by paying a premium of KSH 4,542 for 4 years while still in campus, the students can be able to comfortably go through the KSL training without fmancial difficulties. In addition, the study also developed an insurance cover that caters for retakes and resist in case of a failure in some of the bar exams. Buy paying a single premium ofKSL 8,750, the students are in a position to redo a number of papers that they fail.