International Research Symposium on Pure and Applied Sciences (IRSPAS)
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Item Studying the behaviour of export quantities of Tuna fish in Sri Lanka(4th International Research Symposium on Pure and Applied Sciences, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Sachithra, S. A. L.; Liyanage, U. P.; Wijeyaratne, W. M. D. N.Being an island in the Indian Ocean, Sri Lanka claims a large sea area and abundant fish resource with high facilitate suitable for large scale fishery industry. According to the Central Bank of Sri Lanka, the contribution of fisheries to the Gross Domestic Production (GDP) of the country ranges between 1.3% and 1.6%. Consequently, fishery industry already plays a vital role in economics and social development of Sri Lanka. Due to weather conditions, seasonal effects, changes of government tax policies and trade agreements, e.g. GSP+ and etc., there is a high fluctuation in export quantity of fishery products in Sri Lanka. Thereby, it is essential to study the variation patterns and forecast harvest and income generated by fishery products towards monitory strategy planning. Among the various types of fish, tuna is one of the species that is important in financial earnings. Out of all fisheries exports, Sri Lanka earns the highest income worth 50.8% by exporting tuna fish in 2016, according to the statistics from Ministry of Fisheries and Aquatic Development of Sri Lanka (SLMFAD). This study was conducted to analyze the export quantities of tuna fish and forecast the future export quantities. Monthly export quantities from January, 2010 to June, 2018 were collected from SLMFAD. In preliminary analysis, United States, Japan, and Canada are identified as the top countries in which Sri Lanka exports the highest quantity of tuna fish. To study the changes in export patterns and their associated relations, Statistical Change-Point Analysis was conducted. The results revealed a high correlation between the changes of export patterns with events such as country’s peace restoration, economic stability, infrastructure facilities, introduction of different capacity changes and termination of development projects. Towards forecasting the export patterns time series data analysis techniques were used. Unit root tests; Augmented-Dickey-Fuller Test (ADF) and Kwiatkowski-Phillips-Schmidt-Shin test (KPSS) were used to test the stationarity of the time series data. Based on Akaike information criterion (AIC) value, SARIMA (1,1,2)(1,0,0)12 model was identified as the best. Ljung-Box test, Jarque-Bera test and Heteroscedasticsity test were used to check the behavior of the residuals of this fitted models. Accuracy of the models were compared by root mean squared error (RMSE), and mean squared error (MSE). With 0.8485 of RMSE and 0.6038 of MSE, SARIMA (1,1,2)(1,0,0)12 model can be considered as the most suitable model to forecast the export tuna quantity from Sri Lanka.Item Stable forecasting of tax revenues of selected countries assisted by Clustering Approach(4th International Research Symposium on Pure and Applied Sciences, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Karunarathne, A. W. S. P.; Liyanage, U. P.; Hewaarachchi, A. P.Tax is one of the main income of a government that utilizes in public welfare and future investment. Taxation has goals: reducing the inequalities through a policy of redistribution of income, administrating the levels of inflation as well as deflation, protecting the local industries from foreign competitions through levies, and discouraging the undesirable activities such as consumption of tobacco. Additionally, taxation provides a major portion of Gross Domestic Product (GDP), depending on the country’s fiscal policy. Tax forecasting is essential towards strategizing government plans and future activities. However, tax revenue highly fluctuates due to many factors which include natural disasters, instability of political environment and government monitory policies. This study aims to find the set of best statistical forecasting models, by comparing the behavioral similarities of different tax revenues identified by clustering approach. Here, tax revenue data from 1972 to 2017 of 24 countries belonging to developing status: developed, developing and under-developed have been analyzed. Comparable and homogenize measure is obtained considering the tax revenue as a percentage of GDP. The countries with similar tax revenue are identified by using K-Means clustering. Consequently, the selected countries were clustered into five classes depending on their tax revenue as a percentage of GDP. The analysis shows that the tax revenue has similar behavior based on the similarities of countries’ developing status. Tax revenues data in each cluster were analyzed to identify the best fitted time series models. It has been found that models of the types Autoregressive Moving Average (ARMA) and Autoregressive (AR) are best fitted models for the representing tax revenue of the corresponding clusters. As an example, ARMA (2,2) model was fitted to one cluster and AR (1) model was fitted for another cluster of countries. According to the type of the model and their range of parameter values, it is found that similar models can be used to represent the tax revenue data within the underlying cluster. That is, there exist cluster specific models in the sense of model type and their parameter ranges. This finding can be utilized towards forecasting tax revenue in the case of the revenue data are highly affected with a qualitative factor, for example, political instability. In summary, through the clustering approach, stable forecasting of revenue data of a given country can be performed.Item A clustering based quantitative approach on selecting companies in an investment portfolio in Colombo Stock Exchange(4th International Research Symposium on Pure and Applied Sciences, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Kumara, M. S. M. S.; Liyanage, U. P.Portfolio management is a powerful concept in financial sector, heavily studied by both investors and researchers. Conventionally, investment portfolios on stocks available in stock markets, constitute by set of stocks belonging to numerous companies and their associated allocations. Additionally, the standard portfolio procedure results the optimum allocation of shares of selected set of companies, with the minimum risk. Nevertheless, the selection of companies in a portfolio is utterly depends on the experiences as well as the gut-feelings of investor or the broker. Thereby, this selection criterion is essentially conditional on qualitative measures that have no numerical justifications. This research aims to introduce a quantitative approach towards selecting companies into a portfolio based on their historical data so that the portfolio optimization procedure can overcome the qualitative bias. The analysis has been conducted using the stocks belonging to companies registered at the Colombo Stock Exchange (CSE), Sri Lanka. The data consisting of daily share prices of 291 companies registered at CSE for the period 2012-2016. The company risk is measured by the volatility of its stock prices over the time. In standard portfolios, there is a mix of companies with various risks. Technically, here a novel mechanism to determine composition of companies in such portfolio based on risk levels has been introduced. Different risk levels are determined by using K-Mean clustering technique applied on the volatility of companies. Since the history of stock prices essentially determine the risk levels, the volatility has been captured so that it would reflect the historical behavior of the company’s stock prices. Consequently, volatility has taken as a vector that has elements consisting of corresponding variance measured by quarterly basis. Number of quarters resulting the dimension of volatility-vector, is selected as four in this study. The clustering procedure determining the risk levels is based on the volatility-vectors computed on each company, used to obtain five classes of companies with different risk levels. Sorting the classes by mean risk from low to high, allows to select the composition of companies in the considered portfolio. In this research, to establish the portfolio, proportion of companies (0:3:4:3:0) belonging to classes from low to high risks, are selected. This selection allows to balance the risk among companies within the portfolio. The study shows that portfolios have higher return can be constructed by such selections from the clusters appropriately. Further investigation of selection criterion based on such proportions have been analyzedItem Algorithm to identify the original web links and suggest optimized mirror links for download content within a web page.(International Research Symposium on Pure and Applied Sciences, 2017 Faculty of Science, University of Kelaniya, Sri Lanka., 2017) Karunarathna, G. L. S. I.; Liyanage, U. P.Invention of the Internet has become a revolutionary change to the world. People use different technologies to connect to the Internet. Surfing Internet has become a stressful activity due to the existence of various spams and redirections. Consequently, internet surfers suffer from wasting time and money on in relevant web contents every day. Further, looping redirections caused to distract many internet surfers all over the world. Though the adware blockers come to the stage for preventing unwanted ads, it does not come with handy solution for assisting web surfers to direct the desired web content or resource. At the same time, there can be lots of mirror links, which are available for refer the same web content or resource. If the web surfer is provided desired content targeted and optimized mirror link/s that has minimum traffic and higher bandwidth with minimum estimate time to download the file, it will be much more useful. The purpose of the research is to achieve the solution for suggest original link to download and provide optimized download link. A chrome extension, which is run in chrome browser, is built with all the proposing components and algorithms in order to proof of the concept. Through this highlight original resource link in the web page and pointing fake/redirect links in the web page. Define an algorithm to suggest optimized mirror link to download among the original mirror links. The tool supposed to cache all the metadata of the referred links and validate links time to time with update latest state of the links. The ultimate objective is to derive an algorithm to avoid fake web redirection links and download resources in cost effective manner. Additionally, the software solution implementing this algorithm protect the computer system by avoiding the links that contain harmful malwares and virus. This proposed software solution will develop as platform independent chrome extension and deploy to ensure the optimum and safe internet surfing.