http://ir.lib.ruh.ac.lk/handle/iruor/7485 2026-05-12T14:34:20Z http://ir.lib.ruh.ac.lk/handle/iruor/10716 Hybrid grey exponentials moothing approach for predicting transmission dynamics of the COVID-19 outbreak in Sri Lanka Seneviratna, D.M.K.N.; Rathnayaka, R.M. Kapila Tharanga Purpose – The Coronavirus (COVID-19) is one of the major pandemic diseases caused by a newly discovered virus that has been directly affecting the human respiratory system. Because of the gradually increasing magnitudeoftheCOVID-19pandemicacrosstheworld,ithasbeensparkingemergenciesandcriticalissuesin the healthcare systems around the world. However, predicting the exact amount of daily reported new COVID cases is the most serious issue faced by governments around the world today. So, the purpose of this current study is to propose a novel hybrid grey exponential smoothing model (HGESM) to predicting transmission dynamics of the COVID-19 outbreak properly. Design/methodology/approach – As a result of the complications relates to the traditional time series approaches, the proposed HGESM model is well defined to handle exponential data patterns in multidisciplinary systems. The proposed methodology consists of two parts as double exponential smoothing and grey exponential smoothing modeling approach respectively. The empirical analysis of this study was carried out on the basis of the 3rd outbreak of Covid-19 cases in Sri Lanka, from 1st March 2021 to 15th June 2021. Out of the total 90 daily observations, the first 85% of daily confirmed cases were used during the training, and the remaining 15% of the sample. Findings – The new proposed HGESM is highly accurate (less than 10%) with the lowest root mean square error values in one head forecasting. Moreover, mean absolute deviation accuracy testing results confirmed that the new proposed model has given more significant results than other time-series predictions with the limited samples. Originality/value – The findings suggested that the new proposed HGESM is more suitable and effective for forecasting time series with the exponential trend in a short-term manner. 2021-01-01T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/10711 Short-term and long-term dynamics between macroeconomic indicators and market fluctuation: a study of Colombo stock exchange Rathnayaka, R.M. Kapila Tharanga; Seneviratna, D.M.K.N.; Jianguo, Wei As a developing market, the high volatile fluctuations with instability patterns are the common phenomenon in the Colombo Stock Exchange of Sri Lanka. The miscellaneous type of micro and macro-economic conditions directly effect on the market fluctuations. By using Vector Autoregressive Regression and Vector Error Correlation Model to capture the linear inter-dependencies, this study examines the equilibrium relationships between the stock market indices and macro-economic factors in Sri Lankan during the period from January, 2009 to December 2015. The study revealed that macroeconomic variables have direct effect on high volatility in stock market fluctuations in the Colombo Stock Exchange. Furthermore, the results show that Colombo stock exchange is sensitive to the macroeconomic variables such as market capitalization, real gross domestic product and broad money supply. 2016-01-01T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/10695 Grey system based novel approach for stock market forecasting Rathnayaka, R.M. Kapila Tharanga; Seneviratna, D.M.K.N.; Jianguo, Wei Purpose – Making decisions in finance have been regarded as one of the biggest challenges in the modern economy today; especially, analysing and forecasting unstable data patterns with limited sample observations under the numerous economic policies and reforms. The purpose of this paper is to propose suitable forecasting approach based on grey methods in short-term predictions. Design/methodology/approach – High volatile fluctuations with instability patterns are the common phenomenon in the Colombo Stock Exchange (CSE), Sri Lanka. As a subset of the literature, very few studies have been focused to find the short-term forecastings in CSE. So, the current study mainlyattemptedtounderstandthetrendsandsuitableforecastingmodelinordertopredictthefuture behaviours in CSE during the period from October 2014 to March 2015. As a result of non-stationary behavioural patterns over the period of time, the grey operational models namely GM(1,1), GM(2,1), grey Verhulst and non-linear grey Bernoulli model were used as a comparison purpose. Findings – Theresultsdisclosedthat,greypredictionmodelsgeneratesmallerforecastingerrorsthan traditional time series approach for limited data forecastings. Practical implications – Finally, the authors strongly believed that, it could be better to use the improved grey hybrid methodology algorithms in real world model approaches. Originality/value – However, for the large sample of data forecasting under the normality assumptions, the traditional time series methodologies are more suitable than grey methodologies; especially GM(1,1) give some dramatically unsuccessful results than auto regressive intergrated moving average in model pre-post stage. 2015-01-01T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/10575 Grey system based novel forecasting and portfolio mechanism on CSE Rathnayaka, R.M. Kapila Tharanga; Seneviratna, D.M.K.N.; Jianguo, Wei Purpose – Because of the high volatility with unstable data patterns in the real world, the ability of forecasting price indices is notoriously embarrassing and represents a major challenge with traditional time series mechanisms; especially, most of the traditional approaches are weak to forecast future predictions in the high volatile and unbalanced frameworks under the global and local financial depressions.Thepurposeofthispaperistoproposeanewstatisticalapproachforportfolioselectionand stock market forecasting to assist investors as well as stock brokers to predict the future behaviors. Design/methodology/approach – This study mainly takes an attempt to understand the trends, behavioral patterns and predict the future estimations under the new proposed frame for the Colombo Stock Exchange (CSE), Sri Lanka. The methodology of this study is carried out under the two main phases. In the first phase, constructed a new portfolio mechanism based on k-means clustering. In the second stage,proposed anonlinear forecasting methodology basedongrey mechanism forforecasting stock market indices under the high-volatile fluctuations. The autoregressive integrated moving average (ARIMA) predictions are used as comparison mode. Findings – Initially, the k-mean clustering was applied to pick out the profitable sectors running under the CSE and results indicated that BFI is more significant than other 20 sectors. Second, the MAE, MAPE and MAD model comparison results clearly suggested that, the newly proposed nonlinear grey Bernoulli model (NGBM) is more appropriate than traditional ARIMA methods to forecast stock price indices under the non-stationary market conditions. Practical implications – Because of the flexible nonlinear modeling capability, proposed novel concepts are more suitable for applying in various areas in the field of financial, economic, military, geological and agricultural systems for pattern recognition, classification, time series forecasting, etc. Originality/value – For the large sample of data forecasting under the normality assumptions, the traditional time series methodologies are more suitable than grey methodologies. However, the NGBM is better both in model building and ex post testing stagers under the s-distributed data patterns with limited data forecastings. 2016-01-01T00:00:00Z