When to Sell or Hold a Stock:Empirical Evidence from an Emerging Market

Data from an emerging market were used to determine when to sell or hold a stock for a single model of a stock whose price is assumed to be a geometric Brownian motion in which the jump Markov process changes back and forth between positive and negative values

The Effect of Fixed and Random Models in the Interpretation of Biological Data

Problem statement: Data on variation of sugar content in maize Ogi, fermented maize flour, obtained from 4 maize hybrids subjected to 5 different days of fermentation were used to test the effects of fixed and random statistical models on the interpretation of biological results. Approach: The data were subjected to analysis of variance using both fixed and random models. Results: Highly significant difference (p = 0.1) was present among hybrids, days of fermentation and interaction of hybrids and days, where the fixed model was used. On the other hand, where the random model was assumed, the interaction component of variance was found not to be significantly different from zero contrary to the findings with the fixed model. Conclusion/Recommendations: The results indicate that the statistical model used may influence interpretation of biological results

Optimal advert placement slot – using the knapsack problem model

The Knapsack problem model is a general resource allocation model in which a single resource is assigned to a number of alternatives with the objective of maximizing the total return. In this work, we applied the knapsack problem model to the placement of advert slots in the media. The aim was to optimize the capital allocated for advert placements. The general practice is that funds are allocated by trial and error and at the discretions of persons. This approach most times do not yield maximum results, lesser audience are reached. But when the scientific Knapsack problem model was applied to industry data, a better result was achieved, wider audience and minimal cost was attained

Analytical Solution of the Extended Schwartz and Moon (2000, 2001) Growth Option Model

In the Schwartz and Moon growth option model and other extended versions of the model, the procedure of the valuation process is to use the discrete version of the continuous-time model and then use the Monte Carlo simulation for the valuation. We are of the opinion that if the equation for valuing the company is well posed and necessary adjustments made, our derived equation could be solved by a stochastic partial differential equation

A one-way dissection of high-order compact scheme for the solution of 2D Poisson equation

We present a one-way dissection formulation of high-order compact scheme for the solution of 2D Poisson equation. One-way dissection is a type of matrix reordering, divide and conquers procedure. Efficient and concise compact schemes of 4th and 6th orders are derived using the truncation errors of the Taylors’ series expansion of the governing equation. The system is split into sub-domains and each sub-domain is treated separately. Two test problems are solved to show the fourth order performance of the scheme. The direct method is used to achieve a quick solution to the problems

EFFECT OF GOEMETRY ON THE ANALYSIS OF THERMAL EXPLOSION OF A STRONGLY EXOTHERMIC CHEMICAL REACTONS

This study is devoted to investigate the effect of geometry on thermal explosion of a strong exothermic chemical reaction with variable pre-exponential factor under Bimolecular, Arrhenius and Sensitised reaction rate, neglecting the consumption of the material are examined. Analytical solutions are constructed for the governing nonlinear boundary-value problem using perturbation technique together with a special type Hermite-pade approximation and important properties of the temperature field including bifurcations and thermal criticality are discussed. It is shown that temperature field is highly influenced by the geometry

A Neural-CBR System for Real Property Valuation

In recent times, the application of artificial intelligence (AI) techniques for real property valuation has been on the increase. Some expert systems that leveraged on machine intelligence concepts include rule-based reasoning, case-based reasoning and artificial neural networks. These approaches have proved reliable thus far and in certain cases outperformed the use of statistical predictive models such as hedonic regression, logistic regression, and discriminant analysis. However, individual artificial intelligence approaches have their inherent limitations. These limitations hamper the quality of decision support they proffer when used alone for real property valuation. In this paper, we present a Neural-CBR system for real property valuation, which is based on a hybrid architecture that combines Artificial Neural Networks and Case- Based Reasoning techniques. An evaluation of the system was conducted and the experimental results revealed that the system has higher satisfactory level of performance when compared with individual Artificial Neural Network and Case- Based Reasoning systems

On the Application of the Open Jackson Queuing Network

In real life, waiting for service is a common phenomenon. As a system gets congested, service delay is inevitable; as the service delay increases, waiting time in the queue gets longer. In a typical hospital, the network is made up of various departments (nodes). In this study we considered the inflow and outflow of an hospital network; this is depicted in the schematic diagram. For an efficient hospital planning, a good patient flow means that patient queuing time is minimized, while poor patient flow means the patient suffer considerable queuing delays. This paper presents the results of a study carried out in a University Hospital Centre; the queuing model adopted used the Open Jackson Queuing Network to minimize the waiting times in the queues. The data collection was done for a period of two weeks, with a week interval in order to observe the system for any anomaly. For each node, the number of arrivals and departures together with the service times were recorded at an interval of five minutes. The study showed that for a good hospital planning, the more the personnel (servers) are made to focus on their assignments, the lesser the time the patients will spend on the queue and this leads to more efficient patient flo

Application of Kumaraswamy Inverse Exponential Distribution to Real Lifetime Data

In this research, the Kumaraswamy Inverse Exponential distribution being a generalization of the Inverse Exponential distribution was applied to six real lifetime datasets. The idea is to assess its flexibility and superiority over its sub-models. Some other properties of the Kumaraswamy Inverse Exponential distribution were investigated in minute details. It was demonstrated and confirmed that the Kumaraswamy Inverse Exponential distribution performed better than the competing probability models except for data sets with variances far above the means. The performance was judged based on the log-likelihood and Akaike Information Criteria (AIC) values posed by the distributions