Academic literature on the topic 'Yonakawatta (colombo, Sri Lanka) Economic conditions'

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. The purpose of this paper is to propose a new statistical approach for portfolio selection and 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 a nonlinear forecasting methodology based on grey mechanism for forecasting 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.

Purpose The purpose of this paper is to examine the evolutionary nature of herding phenomenon in the context of a frontier stock market, the Colombo Stock Exchange of Sri Lanka. Design/methodology/approach This study applies the cross-sectional absolute deviation methodology for daily frequencies of data of all the common stocks listed during the period from April 2000 to March 2018. The regression coefficients are estimated by using both the ordinary least square and the quantile regression procedures. Findings The findings reveal significant changes to the pattern of herding over different market periods, each with specific characteristics. Herding is strongly evident in up and down market days in the 2000-2009 period, during which the market was highly uncertain with the impact of the political instability of the country due to the Civil War on the stock trading. Even after this Civil War period, herd tendency is strongly manifested toward the up market direction as a result of the investors’ optimism about the country’s economy and political stability, which caused to a speculative bubble in the market. After that, it is turned into negative herding due to the panic selling occurred in view of the uncertainty of the inflated prices, which led to a market crash. Notably, herding appears to be consistently absent over the period after the crash, despite the presence of herd motives such as high market uncertainties triggered by political instability and economic crisis during that period. Research limitations/implications The findings suggest that herd behavior is an evolving phenomenon in financial markets. Consistent with the adaptive market hypothesis, the absence of herding evident after the market crash could be attributed to the investors’ learning of the irrationality of herding/negative herding for adapting to market conditions. As a result, herding and negative herding tendencies declined and disappeared at the aggregate market level. Originality/value This study contributes to the literature by providing novel evidence on the evolutionary nature of behavioral biases, particularly herding, as predicted by the adaptive market hypothesis. With the application of the quantile regression procedure, in addition to customary used ordinary least squares approach, it also provides robust evidence on this phenomenon.

Background: Tuberculosis (TB) is a major global health problem, commonly seen in underdeveloped countries. The probability of contracting the disease is significantly higher among the economically vulnerable and the socially disadvantaged. Risk factors associated with TB can also change over time. In the Sri Lankan context, no study has explored how these factors impact patients. Therefore, we aimed to explore social status, associated risk factors and lifestyle changes during the treatment period of TB patients attending a tertiary respiratory center in Colombo, Sri Lanka. Methods: The descriptive cross-sectional study was conducted in 2011. The study population consisted of diagnosed tuberculosis patients above the age of 15 years. Patient records were retrieved from the TB patient registry for the Colombo district. Systematic sampling was used to identify patients to be invited to the study. An interviewer-administered questionnaire was used for data collection. Data were collected on social status (example, level of education, employment, and income), associated risk factors (example, smoking and alcohol consumption, contact history, narcotic drug use) and lifestyle changes during treatment (example, employment status, social interactions). The analysis included a logistic regression model to explore the association between social status and risk factors. Results: The total number of patients included in the study was 425. Tuberculosis was found to be strongly prevalentamong participants from the lower socio-economic status. It was also common in participants with a low level of education, unemployed, if employed, those who are engaged in unskilled employment and have low levels of income. Risk factors associated with the patients were smoking, alcohol consumptions, narcotic drug use, imprisonment, closecontact history with active TB patients and chronic medical conditions. Changes in employment and the reduction ofsocial-interactions were the main lifestyle changes of the participants occurred during the treatment period. The analysis also showed positive correlation between low-level social status and sputum smear infectivity, and use of dangerous drugs. Even after adjusting for confounders, tuberculosis negatively affected social interactions and income levels of participants from the low social status. Conclusion: Low socio-economic status negatively affected the lifestyle and social interactions of patients during the treatment period. Though competent treatment programs exist in Sri Lanka, it is still important to identify and mitigate risk factors associated with tuberculosis patients. A comprehensive multi-disciplinary approach considering patient lifestyle, and the implications of the disease and treatment on social interactions may strengthen the current preventive strategies.