Price volatilities make stock investments risky, leaving investors in critical position when uncertain decision is made. To improve investor evaluation confidence on exchange markets, while not using time series methodology, we specify equity price change as a stochastic process assumed to possess Markov dependency with respective state transition probabilities matrices following the identified state pace (i.e. decrease, stable or increase). We established that identified states communicate, and that the chains are aperiodic and ergodic thus possessing limiting distributions. We developed a methodology for determining expected mean return time for stock price increases and also establish criteria for improving investment decision based on highest transition probabilities, lowest mean return time and highest limiting distributions. We further developed an R algorithm for running the methodology introduced. The established methodology is applied to selected equities from Ghana Stock Exchange weekly trading data.Electronic supplementary materialThe online version of this article (doi:10.1186/2193-1801-3-657) contains supplementary material, which is available to authorized users.
Purpose
– The purpose of this study is to assess the level and variability of Ghanaian property and liability insurer’s reserve estimates to examine its sources and ascertain if reserve errors are random or not (i.e. manipulated or not).
Design/methodology/approach
– It uses information on insurer claim reserve provisions, claims outstanding, claims incurred and claims paid for the period of 2000-2010. Categorizing the sources of variation as endogenous and exogenous, the authors use the panel correlated standard error regression model to determine sources and magnitude of industry reserve error.
Findings
– The study finds that size, age, lag of loss reserve error, inflation rate and real gross domestic product are significant in determining the degree of reserve error variation. Type of ownership (domestic or foreign) is, however, not a significant source of variation. Further, the authors found that industry reserve errors are random (not manipulated) across firms, suggesting that sampled insurers act independently on reserve error decision making and are not influenced by industry trends and competition.
Research limitations/implications
– The main research study limitation is the difficulty involved in obtaining annual statements from insurance companies in Ghana. Reluctance of companies to make statements available impeded on the smooth flow of the study during data collection.
Practical implications
– Policy-wise, this suggest that regulatory bodies can uniquely set reserve error levels for existing firms with little influence on competition. Further, the Ghanaian insurance regulator does not to focus on the type of ownership (foreign or local) when setting regulatory standards. However, size of the company and age (length of operation) should be considered.
Originality/value
– This paper is the first empirical study to examine the loss reserve error and loss reserve variability of Ghanaian property and liability insurance companies.
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