Working capital management is one of the crucial factors in shaping the financial performance of firms while ensuring their existence and growth. The study aims to investigate the determining factors of working capital requirements in the Sri Lankan context. The study was conducted using secondary data, manually collected from a sample of 37 firms listed on the Colombo Stock Exchange over a six-year period from 2013/14-2018/19. The working capital requirement has been proxied through the working capital ratio as the dependent variable of the study whereas firm-specific and macroeconomic determinants were taken as the independent variables. Profitability, Cash conversion cycle, Leverage, Tobin’s Q, Firm size, and Altman Z-score have been used as firm-specific determinants and the GDP growth rate, Interest rate, and Inflation has been considered as the macroeconomic determinants of the study. Initially, summary statistics were obtained for the sample along with the diagnostic tests of normality and multicollinearity. The regression models were specified to test the hypotheses developed for the study where the analysis was performed using the Statistical Package for the Social Sciences (SPSS). The results were obtained by carrying out the Ordinary Least Square (OLS) regression method as an overall and specific analysis. Results of the overall analysis indicated that all firm-related variables except firm size have a significant relationship with the working capital requirements. Nevertheless, only the interest rate demonstrated a significant association with working capital amongst the macroeconomic factors analyzed in the study. The specific analysis provided slightly different results with both firm size and GDP growth reflecting a significant relationship with the working capital of firms. It was concluded that the profitability, cash conversion cycle, Altman z-score, leverage, and interest rate are the most influential determinants of working capital requirements amongst the factors considered under the analysis and also that the firm-specific variables are crucial over macro-economic variables in determining working capital requirements in the Sri Lankan context.

The stock markets of a country play a vital role in its economy. Stock market indices are vital fragments of information for investors. It is very important to develop models that reflect the pattern of the stock price movements for different sectors since it becomes very significant to investors and policymakers. Therefore, the aim of this research study was to develop models to forecast different sector indices in Colombo Stock Exchange and to compare sector-wise models. The investigation was performed using secondary data for a sample of ten listed sectors in the Colombo Stock Exchange (CSE) for the thirty-four years from 2nd January 1985 to 31st March 2019. Secondary data were collected by using the data library maintained by Colombo Stock Exchange. Financial time series data analysis techniques were used to analyze the collected data. It was applied the ARCH family models in this research study, which included the Autoregressive conditional heteroscedasticity model (ARCH), Generalized Autoregressive conditional heteroscedasticity model (GARCH), Threshold Autoregressive conditional heteroscedasticity model (TARCH), Exponential generalized autoregressive conditional heteroscedastic model (EGARCH), Integrated Generalized Autoregressive conditional heteroscedasticity model (IGARCH) and Power Autoregressive conditional heteroscedasticity model (PARCH) since the sector indices are financial time series data. Findings revealed that the appropriate model to forecast the sector indices of Oil Palms sector, Services sector and Stores & Supplies sector as PARCH (2,1) model, Beverage, Food & Tobacco sector as PARCH (1,1) model, Chemicals & Pharmaceuticals sector as PARCH (2,2) model, Banking Finance & Insurance sector and Investment Trusts sector as IGARCH (2,2) model, Footwear & Textiles sector as EGARCH (1,1) model, the Manufacturing sector as EGARCH (1,3) model and Hotels & Travels sector as TARCH (1,1) model. The findings of this research study are useful to policymakers and investors for their decision-making.