Master of Science in Financial Mathematics

Permanent URI for this collectionhttp://192.248.9.226/handle/123/67

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item: Thesis-Abstract
Assessing the predictability of all share price index of Colombo stock exchange using different models : a case study during the COVID - 19 pandemic
(2023) Jayakody, G; Jayasinghe, JABU
The aim of this investigation was to assess the predictability of three models: Autoregressive Integrated Moving Average (ARIMA), Seasonal Auto-regressive Integrated Moving Average (SARIMA), and Dynamic Harmonic Regression (DHR) model, both prior to and following the Covid-19 outbreak. Every model was crafted with great care and then compared to determine the optimal method for predicting future outcomes. The findings suggested that, during the Covid-19 period, the DHR model outperformed the other models as it had the lowest Corrected Akaike’s Information Criterion (AIC) value. According to the Portmanteau test, the residuals were random and not correlated, indicating that all the models were adequate for making predictions. Although the rapid decline of CSE was captured by both the ARIMA and DHR models, the DHR model yielded more significant results. In contrast, prior to the pandemic, the ARIMA model performed well and effectively captured the underlying trend compared to other models. However, forecast errors indicated that DHR model was more appropriate for predicting daily share indices with long intricate seasonal variations compared to the SARIMA model. As a consequence, stakeholders were able to make accurate investment decisions even in the midst of the outbreak. Finally, the Engle’s ARCH test was conducted to analyze the occurrence of volatility clusters during the pandemic, and it was identified that there were notable fluctuations in volatility throughout the pandemic period.
item: Thesis-Abstract
An Analysis on deterministic chaos of exchange rates during COVID -19 outbreak
(2023) Thathsarani, AA; Ganegoda, NC
The COVID-19 influence has had a significant impact in a number of areas, including the economies of many nations. Exchange rates also show noticeable fluctuations during the pandemic period, hence analysis of the behaviour of these fluctuations is useful to make better decisions. This study aims to analyze the deterministic chaos of exchange rates during the COVID-19 period in 2020, using Poincare's definition and forecasting with ARIMA and ETS models, using R software. From 1'1 January 2016 to 3181 December 2020, daily rates between the US Dollar, Euro, British Pound, and Japanese Yen and the Sri Lankan Rupee were collected from reports of the Central Bank of Sri Lanka. Data from January 2016 to February 2020 are considered for model fitting and the forecast period is considered from March 2020 to December 2020, considering the COVID-19 first wave and the beginning of the second wave in Sri Lanka. According to Poincare's definition, the properties of deterministic chaos are depicted by all four rates, hence showing deterministic chaos during the considered COVID-19 period. Forecasts using ARlMA and ETS models also determine that the USD, GBP, and Japanese Yen show clear chaotic behaviour, while Euro rates show slight chaotic behaviour at the beginning of the COVID-19 outbreak in Sri Lanka.
item: Thesis-Abstract
A Clustering based approach to study the impact of COVID - 19 pandemic on Colombo stock exchange
(2023) Saumyamala, MGA; Dharmarathna, HAS
Investor’s main objective is to maximize the portfolio returns while minimizing the risk. Unsystematic risk inherent to the industry can be minimized by diversification and systematic risk is hard to avoid. Main reason for the systematic risk is economic recession and covid pandemic is the most recent recession affected Sri Lankan economy. The primary objective of this research is to study the impact of covid-19 pandemic on risk and return of all listed companies in Colombo stock exchange (CSE) using clustering-based approach. For this study stock market data for 3-year period from 1st January 2019 to 31st December 2021 were used. The first dataset of All share price index (ASPI) which represent the behavior of all the listed companies was used to identify the different periods. Using the background study and performing structural breakpoint tests the entire period was divided in to 5 meaningful periods. The second data set with daily open and close price of 289 companies were used for the clustering. Expected return and variance of each company in each period were used as the inputs for the clustering. K means clustering was used to achieve the main objective of clustering. When clustering the basic idea was to achieve meaningful clustering based on risk return tradeoff to facilitate better investment decision. Then the composition of clusters from period to period were analyzed with relevant to industry classification. The cluster analysis shows that first and second period in year 2019 before the covid pandemic can be divided in to 4 clusters. During covid first wave market can be clearly separated in to 4 clusters, during covid second wave 6 clusters and during the third wave 3 clusters. Some companies have performed unusually with high returns and high variance during covid first wave and third wave. Further by monitoring the shift of clusters from period to period set of companies were suggested as the high performing companies despite the covid pandemic for the Risk seeking investors. Another set of companies which obtain high returns during the pandemic by tolerating moderate level of risk were suggested for the Risk neutral investors. The remaining set of companies are suitable for the Risk Averse investors. Finally, the study concludes that there is an impact of the covid -19 pandemic for the number of listed entities in the stock market, composition of clusters and risk return level of individual companies at different stages of a Recession.
item: Thesis-Abstract
Sensitivity analysis of risk free interest rate to optimize portfolio gain with reference to Colombo stock exchange
(2023) Amarasinghe, APGTS; Ranasinghe, L; Denzil, M
Historically, investors made their investments based on the gain that they can earn out of it. Although there had been high risk involved in high return gaining investment activities, it was the habit of people to expect more gain from an investment. In modern world, investment strategies and investment evaluating methods are rapidly used to create optimal portfolios. These methods were mainly used to optimize investments on financial assets such as stocks, bonds, deposits, treasury bills etc. The application of Markowitz model is of rare use in Sri Lankan context and on Colombo Stock Exchange. Main objective of this study is to explore a range of optimal portfolios an investor can approach with changes in risk free rate and identify a safe range of risk free rate for optimal portfolio investment. In order to conduct this study monthly closing stock prices of 18 companies listed under ASPI were used as the data sample. Mainly secondary data was used in this study and the data were collected from Bloomberg official web site. Data was analyzed using simple mathematical equations, statistical methods with MS excel and MATLAB software. Findings of the study reveal that there is safe risks free rate range 6.72% - 8.64% where investor can diversify investment between Ceylon Cold Stores PLC and Teejay Lanka PLC, 83% and 17% respectively.
item: Thesis-Abstract
Modelineg Sri Lankan GDP using macroeconomic indicators
(2023) Karunarathne, AWSP; Piyatilake, ITS
Economics mainly divided into two parts, namely microeconomics and macroeconomics. Microeconomics study the individuals and business decisions while macroeconomics look at the decisions of county and government. That is, macroeconomics helps to understand the economy as a whole. Macroeconomic indicators are the key reflectors of the economic status of a country. Therefore, macroeconomic indicators have a notable role in sustaining the economic sustainable growth of a country. This study aimed at analyzing the relationships between macroeconomic indicators and the economic growth of Sri Lanka. Nineteen macroeconomic indicators were extracted from the Central Bank of Sri Lanka reports. The data were collected for the period of 1976-2018 from the World Bank website. This research mainly uses principal component analysis (PCA) in determining the existing patterns/similarities between the selected macroeconomic indicators. PCA method is specially applied because the selected macroeconomic variables were highly correlated. Forward regression analysis has been carried out to fit models with the use of identified principal components to determine the most prominent macroeconomic indicators which impact on Gross Domestic Product (GDP) and to identify the most reliable indicators which has the highest predictive power on GDP. The extracted two principal components (PCs) highly resemble the government activities and the human capital involved with the economy respectively. GDP can be predicted using the above said two PCs with a R-squared value of 99.74% which shows a high reliable predictive power. As this study focuses on large number of macroeconomic indicators it is very much essential in identifying the most prominent indicators among them. Therefore, as a novel concept Grey Relational Analysis (GRA) was constructed in ranking the selected macroeconomic indicators. Inflation, official exchange rate, and exports of goods and services have taken the first three rankings respectively, indicating that the government and responsible parties should pay more attention to avoid future economic recessions and to develop a sustainable economy.
item: Thesis-Abstract
Identifying factors influencing credit card in a Colombo suburb area logistic regression approach
(2022) Dasara HMT; Edirisinghe P.M
Credit cards are electronic products that are now becoming more important and wellknown around the world. The usage of credit cards is becoming highly connected to peoples’ day-to-day lives and credit cards have a greater impact on the rapidly changing lifestyles of busy people. However, penetration and usage of credit cards in Sri Lanka are still low when compared with other countries. Hence, identifying factors influencing the usage of credit cards would be of great importance for the banking industry, because through them, they can enact policies and take necessary measures to increase the number of Sri Lankans who use credit card. Though researchers have explored this study area in the recent past, In Sri Lanka, there is an empirical and knowledge gap. The purpose of this study is to figure out what factors influence or affect people's use of credit cards in Boralesgamuwa town area, which is a city in Colombo district, the western province of Sri Lanka. This is a cross-sectional study and was done in a non-contrived setting. The data was gathered using a questionnaire from 102 people in the Boralesgamuwa town area. Age, Gender, Education level, perceived cost, and Relatively advantages variables are highly affected by the use of credit cards in the Borelesgamuwa area. Thus, it was observed that customers' intention to use credit cards is highly dependent on those variables, and among those Perceived costs are discouraging the use of credit cards, relatively advantage encourages people to use credit cards. This result indicates the odds of using a credit card is 9.6 times higher for male than female. The odds of using a credit card is 10.2 times higher for those who have a diploma compared to up to A/L educated customers, The Odds of using a credit card is 18.4 times higher for who had a degree & above compared to up to A/L educated customers while other variables remain constant and change in one year in age changes the odds of use of a credit card increases by 1.5 units while other variable remain constant. The category of young age received a lot of responses. A young age group can be described using the odds value. Hence, the industries and policymakers should consider these when enacting policies, in order to improve the usage of credit cards among the people of the country.
item: Thesis-Abstract
Analyzing & predicting the impact of news on the ASI return of Colombo stock exchange through black-litterman model
(2022) Perera PRSG; Ranasinghe L
Considering the historical data, we can conclude that news on attacks, international political problems, natural disasters, and protests have a negative impact on the stock market return while changes in Government Acts and Election period have a positive impact on stock market return. Further, Monetary & Fiscal policy changes and news on major investments have a neutral impact on the return. The significance of the news has been tested using the t-distribution method. It concludes that Monetary & Fiscal policy changes and news on major investments don't have a significant impact on index returns while other selected events have a significant impact on the index returns. By applying the Black-Litterman model together with hypothetical views, one can develop a P matrix to derive/ predict the return of the index movement. Therefore, an investor/analyst can consider the significance level of the news items (t-distribution results) and the type of impact (negative/positive) when assigning numbers and views for the P matrix.
item: Thesis-Abstract
A Study of corporate financial distress prediction of Sri Lanka : an application of logistic regression analysis and multiple discriminant analysis
(2022) Perera BHD; Jayasooriya SD
A financial distressed situation means a company cannot settle its obligations, liabilities from the operating cash flows or value of total assets is lower than the aggregate value of the liabilities and equity. The probability of bankruptcy should be evaluated to reduce its‟ harmful effects. In such a situation, the firms should have to incur bankruptcy costs. It can be minimized through the evaluation of the possibility of financial distress. Up to now various types of models are generated to forecast bankruptcy. In this study, three models are evaluated to compare their distress predict ion abilit y within the Sri Lankan Context. They are Alt man‟s (1968) and Springate Model (1978) and Grover Model (2001). Therefore, the objective of this research is to identify the applicability of these models in forecasting the financial distress of listed companies in Sri Lanka. Those models are analyzed within the listed companies of the Colombo Stock Exchange. The relevant financial data is collected from the audited financial statements during the period of 2013/142017/18. Descriptive Statistics and Regression Analysis are used to analyze collected data with Multivariate Discriminant Analysis (MDA) as the main method of analysis. The objective of this method is to identify groups of samples from a group of predictors by finding the relationship of the variables which maximize the deviance among the populations being studied. The study findings reveal that Alt man‟s model has a higher accuracy rate in predicting financial distress in a non-distressed sample rather than a distressed sample and can predict financial distress before one year to bankruptcy. Yet the Springate model has an excellent predicting ability both in distressed and nondistressed samples. And also, it can reveal a symptom of financial distress before three years to the bankruptcy. Therefore, it can be concluded that the Springate model is performed well than Alt man‟s model wit hin the Sri Lankan context.
item: Thesis-Abstract
Time series model of water level fluctuation in Mahakanadarawa tank
(2022) Saranga AGS; Neluwala P
This research concludes an attempt to forecast water level changes using the best-fitted model of Mahakanadarawa tank by using Box and Jenkins methodology of univariate Auto-Regressive Integrated Moving Average (ARIMA) model. Data from 2010 to 2019 was analyzed and predicted values for the next 12 months were calculated. SARIMA (1, 1, 1) (1, 1, 1) 12 was identified as the tentative model, and Finally, the best-fitting models (0, 1, 1) (0, 1, 1)12) were discovered of water level fluctuations of Mahakanadarawa tank. Forecasted values were used to decide on the supply of water. Two major purposes were considered. Drinking water requirements and water for cultivation were focused.
item: Thesis-Abstract
Estimating and forecasting the yield curve : Sri Lankan government securities market
(2022) Yapa LTS; Welagedara V
In this study, I evaluate two versions of the Nelson and Siegel (1987) model, namely the Nelson-Siegel model using the methodology presented in Diebold and Li (2006) and Nelson-Siegel-Svensson model (1994), with the purpose of fitting the current yield curve and forecasting the yield curve for the Sri Lankan government securities market. The study finds that using the Svensson model which has an additional curvature factor compared to the Nelson -Siegel (Diebold and Li model) leads to a better in-sample fit of the term structure, and thus a better fit of the yield curve is observed. The superior in-sample fit of the Svensson model is clearly visible in the graphical outputs obtained and is further supported by the higher 𝑅 2 and lower RMSE associated with the Svensson model. The results obtained are robust for recent events such as the COVID -19 pandemic that affected the country. Forecasting performance of the two models, indicated opposite results compared to results obtained in the estimation of yield curves. Yield curves from Nelson-Siegel (Diebold and Li) model are predicted better compared to the Svensson model under both the short forecast horizon of one month and longer forecast horizon of six months. This is clearly exhibited in the lower RMSE associated with the Nelson -Siegel (Diebold and Li) model under the rolling window forecasting design that was applied using an AR(1) forecasting model.
item: Thesis-Abstract
Applicability of geometric brownian motion and geometric fractional brownian motion to forecast share prices of telecommunication services sector in Sri Lanka
(2022) Athukorala AKKK; Dissanayake R
The Brownian motion is a Mathematical concept which European botanist Robert Brown introduced in 1827 to study the behaviour of molecules. The Brownian motion concept was transformed into many versions, and Geometric Brownian Motion (GBM) and Geometric Fractional Brownian Motion (GFBM) is the latest transformation of this concept. The GBM and GFBM are mathematical models used to forecast prices of stocks, commodities, etc. In this study, the GBM and GFBM were tested to estimate the share prices of telecommunication industry companies in Sri Lanka. The two sample companies were selected by representing 18% of the population of the telecommunication industry group. The five-year share prices were collected from sample companies: Sri Lanka Telecom PLC and Dialog Axiata PLC. The two models were implemented by estimating parameters such as the drift, the volatility, probability measurement and the time interval. In addition, the Hurst component was generated by a MATLAB program for GFBM. This study is concluded that GBM is the most accurate model for forecasting share prices of the telecommunication industry group with minimum mean absolute percentage error (MAPE).
item: Thesis-Abstract
Modelling exchange rate of USD to Sri Lankan rupees with oil prices, gold prices, silver prices and return of all share price index of Sri Lanka
(2022) Shanika MH; Edirisinghe PM; Mathugama SC
This report contains the analysis of secondary values of US dollar foreign exchange rate (LKR per USD), Gold price (LKR per Troy ounce), Oil price (LKR per barrel), Silver price (LKR per Metric Ton), and Stock return (All Share Price Index) in Sri Lanka. The purpose of this study is to find the relationship among these variables and forecast the US dollar foreign exchange rate in Sri Lanka. This study has used the EViews8 data analysis package to develop time series models to identify the significance of the relationship between exchange rate and other factors using monthly data from October 2000 to December 2019. Log transformed first differenced series were used in Autoregressive Conditional Heteroskedasticity/ Generalized Autoregressive Conditional Heteroskedasticity modeling. The best model fits for the exchange rate was an exponential GARCH model with EGARCH (2,2). All variables were significant at the 5% level of significance and free from the serial correlation/ heteroskedasticity. The model is sufficient but residuals are not normal. Finally, USD forecasting was done for January to December 2019 using the best fitted model. The mean average percentage error value (5.27%) is in between the highly accurate range (0%-10%).
item: Thesis-Abstract
Pandemic outbreak, investor sentiment and stock market reaction :evidence from the frontier market, Sri Lanka
(2022) Rathnasekara RD; Nanayakkara N
This study examines the impact of COVID-19 pandemic, the resulting investor sentiment in determining stock returns of different sector portfolios, namely, healthcare, telecommunication, banking, insurance, and hotel companies in the Colombo Stock Exchange (CSE), Sri Lanka in the year 2020. The empirical work is drawn on two widely used event study and regression-based econometric analysis. Firstly, the event study methodology focuses on the impact on sector portfolio returns after the World Health Organisation (WHO) declared COVID-19 as a global pandemic on 11 March 2020. Statistically significant positive cumulative average abnormal returns (CARs) are observed surrounding the event day. The most striking phenomenon is positive and persisting CARs perceived after a long Island-wide lockdown curfew, which imposed with effect from 16 March 2020, is lifted on 11 th May 2020. CSE investors are likely to be more sensitive to local events than to global news, and persisting CARs indicate market inefficiency. A second-stage regression-based methodology is adopted to evaluate the impact of pandemic related news and to identify the influence of investor sentiment on sector portfolio returns and its persisting effects. Results reveal an initial negative sentiment effect on portfolio stock returns, followed by a positive sentiment thereafter. Initial negative effect is relatively robust on banks and hotel sector stock returns. A positive sentiment might emanate from overreaction to the subsequent rebound with the removal of lockdown curfew and the Government’s COVID-relief moratorium packages offered to businesses. Results indicate that CSE investors are likely to react with investment decisions based on psychological bias or sentiment, signifying irrational investor behaviour in CSE. This study provides current findings of investor sentiment, provoked by COVID-19 pandemic, on different sector portfolio returns in the frontier market, CSE, Sri Lanka. th th
item: Thesis-Abstract
Forecasting retail prices of the most commonly used rice in Sri Lanka
(2021) Fernando WSD; Cooray TMJA
This thesis focuses on Modeling and forecasting Maximum Retail Price (MRP) of Samba, Nadu, Kekulu White and Kekulu Red rice in Sri Lanka using Univariate and Multivariate Time Series approaches. Sri Lanka is a developing country with population of 21.4 million as estimated in 2020. Rice is the most commonly used food in Sri Lanka. Thus, the finding a model for the forecasting prices is most economical advantage for Sri Lankan government. The fluctuations of the prices of rice making a great risk of investing, buffer stock maintaining, international trade and other associated actions. Thus, it is vital to forecast future prices for decision making purposes. Our objective is to forecast the average weekly prices of selected four products. In this study, we consider weekly average retail prices of Samba, Nadu, Kekulu White and Kekulu Red from September 2017 to March 2019. Thus, each series consists of 93 data points. The missing values are estimated using expectation maximization algorithm. Data is collecting from Central Bank of Sri Lanka. First 83 data points are used to build the model and remaining 10 data points are used to validate the forecasting model. To select the best model, selection criteria based on the Akaike information criterion (AIC). We observe that the best model for the Samba prices is exponential smoothing. Nadu price is ARIMA (2,1,0) and best model for Kekulu White and Kekulu Red are ARIMA (1,1,0) and ARIMA (1,1,0) respectively. Then, the testing data set is used to validate the prediction. Since there is a strong correlation between prices, we consider vector auto regression (VAR) model to improve the forecasts. Among several plausible models VAR of order 2 results in the best model. Nadu prices is independent of other three prices. VAR models provides better forecasts for prices of Nadu, Kekulu White, Kekulu Red.
item: Thesis-Abstract
Evaluation of the potential impact of exports & imports commodities into the Sri Lankan economy
(2021) Rathnayaka RMIC; Dissanayake AR
The research attempts to evaluation impact of the Exports and Imports commodities in the country economic. Mainly, following objectives are elaborated, behavior of the exports and imports, to determine which type of commodity is mainly impact to the country economic, to understand theoretically link between the total exports and imports commodities groups and to revisit the total exports and imports groups by investigating hidden factors which are impacting to county economic. The data are collected mainly from 2007 to 2017 monthly basis from Central Bank of Sri Lanka and Census & Statistic Department Sri Lanka. Although total Exports and Imports commodities have been studied with many statistical and economical methods. The researcher has tried to study the similarities of commodities groups and differences. Various statistical techniques have been used such as Basic Statistical, Principle Components (PCA), Factor Analysis (FA) and VARI-MAX Factor Rotation. With the strong based on the preliminary analysis, there are strong correlations among the commodities. This paper is adopted the Principle Component and Factor Analysis to assess eights commodities groups by finding out the level of redundancy among them from the correlations matrix and grouping indicators with higher similarities into the same factors in Exports and Imports separately. According to the similarities, Agriculture Exports, Mineral Exports and Consumer Imports and Investment Goods Imports are categories accurately. But there is no proper grouping for Industrial Exports & Intermediate Imports. Based on the rearrangement of commodities, seven components are identified for Total Exports and eight components are identified for Total Imports according to Factor analysis and improving the results using VARI-MAX Rotations. Also, there are no rapid improvement for all commodities except Garments, Tea and Petroleum sectors. These groups are highly impact to the Gross Domestic Product (GDP) and it need reliable improvement to eliminate the negative impact for country economic. Furthermore, Balance of the Trade is a negative value. When determining which type of commodity is mainly impact to the country economic, Garment & Textile, Tea & Petroleum Products are critical in Export sector. But, there are lack of attention for agriculture sector.
item: Thesis-Abstract
Profitability analysis of selected multinational companies listed on London stock exchange by principal component method
(2021) Ponnudurai T; Dissanayake A.R
Investment decisions include higher level of risk due to the uncertainty nature in the business environment. Thus, investors take investment decisions more wisely by considering several internal and external factors. As a result, this study is mainly focusing on the profitable industries in a developed country like United Kingdom and it has considered analysing for the top 10 companies listed in the share market. Further, it investigates how changes in the financial performance of those selected companies influencing the profit of first 10 best. Thus, Multinational National Companies (MNC) in the UK during the period of 2005 to 2019 was selected as the sample of this study. Accordingly, eight performance indicators were used to carry out this study as current ratio, Quick ratio, Debt to equity ratio, Return on Asset ratio, earning per share ratio, Return on Investment ratio, Return on Equity ratio and number of employees. Moreover, secondary data was collected through annual financial statements of the organizations. In this research Principal Component Analysis (PCA) tools have been used to analysis and discuss the profitability of performance indicators of the selected MNC. As per the analysed data, principal component analysis was run to come up with variables from each component. However, all the variables involved in this analysis are not recorded within the expected range as some showed higher level of deviations. Accordingly, this study demonstrates the position of the MNCs in the UK to different stakeholders who are interested in the financial performance of each company by giving them a quick analyse to show the performance of each company. It also assists those who do financial reporting on picking the ratios which matter in reflecting the performance of their companies. The use of PCA gives unbiased ratios that are most significant to assess the performance financially. As a result, this study will be beneficial for the future investors in financial market to select the best MNC to make them invest decisions based on the financial performances of the organizations. Moreover, this study suggests future research to use other alternative multivariate methods to reduce the complexity of the obtained model.
item: Thesis-Abstract
Impact of age structure transition to the current account balance
(2021) Perera WGGP; Sivathas K; Dissanayake R
This research data included 46 panels of countries over a period of 39 years starting from 1980 to 2018. These countries were selected randomly from the World Bank classification based on income. Dataset was analyzed using different panel data models. Hypotheses were developed to find the relationship between the dependent variable, CAB and three independent variables; 0-24 years aged population as a percentage of total population, 65+ years aged population as a percentage of total population, growth rate of per capita GDP in current prices. Several models including pooled ordinary least squares (OLS) model, fixed effects model and random effects model were tested for the dataset. Chow test and Hausman test were used to select the most appropriate model. Fixed effects model (FEM) was selected as the best model to analyze the impact of age structure variables to CAB. According to the selected model, both age structure variables have negative impact on CAB. On average, CAB is declined by increases in shares of both young and elderly populations. More young and elderly population means higher dependent population. When expenditure for dependent population is getting higher, savings become less. When savings are decreased, CAB is declined, according to the savings-investments approach. Further, selected countries were divided into two groups according to the current account surplus and deficit. For those countries age adjusted CAB was calculated using the estimated coefficients of FEM calculated in order to check the robustness of the selected model. Following Chitgupi (2014)’s study, averages of the two demographic variables and Current Account Balance for a subset of years (2014-2018) from the period used in the estimation of model were used to calculate age adjusted CAB of the selected countries. An adjustment factor which determines the nature and impact of age structure on CAB was obtained by getting the difference between Age Adjusted CAB and the actual CAB. Sri Lanka specific analysis was conducted to check the behavior of Sri Lanka’s CAB during the period 1980-2018 with the age adjustment. Based on these results of country specific analysis, it is found that Sri Lanka is experiencing lower dependent populations during the period 19802017. Even though, Sri Lanka’s dependent population was getting lower during 1980-2018 period, the current account balance is decreasing year by year and the deficit in current account is also getting larger. It implies that having a larger proportion of working age population will not always make a positive impact to the current account.
item: Thesis-Full-text
Analysis of internal factors affecting share prices:
(2020) Umathevan B; Karunarathna KANK
The stock market plays an important role in economic progress of any nation and share price is a key aspect in stock market. Share price is the value of single share of a company's multiple sellable stocks. It represents not only present value of a company, but also the growth. However, the stock market is dependent on several factors and hence, it fluctuates and predicting becomes much more complicated. This study aimed to determine internal factors that influence share price of 24 diversified financial companies listed in Colombo Stock Exchange in the period from 2014 to 2019. Impacts of six variables namely return on assets, return on equity, book value per share, earnings per share, dividend per share and dividend yield on market price of shares in the respective sector were studied. Yeo and Johnson power transformation was used to transform the data and then used for model fitting. The panel data models: ordinary least square with common effect model; fixed effects model; and random effects model were tested. Among these models, the best model was fixed effects model. The results indicated that return on assets, return on equity, book value per share, earnings per share and dividend per share have positive relationship with share price and dividend yield has negative relationship with share price. Further, all these factors have significant impact on market price of share and dividend yield has higher influence whereas book value per share has lower influence on share price. Results of this study implies that investors can take most advantageous investment decisions and be guaranteed favorable returns if they take into consideration of these significant determinants In the future, it can be aimed to complement a study in various sectors with in internal and external variables of and a large timeframe. This would provide better insights on the determinants of share price
item: Thesis-Full-text
Age structural transitions and inflation dynamics in selected south Asian countries
(2020) Ariyarathna PAHR; Sivathas K; Dissanayake R
The aim of this study is to find out whether there is significant effect from age structural transitions on inflation dynamics in some selected South Asian countries such as Sri Lanka, India and Bangladesh. It has been shown that the age structural transitions can disrupt macroeconomic equilibriums of countries, if unattended. Sri Lanka is facing a decreasing youth dependency ratio growth and increasing elderly dependency ratio growth phase as a result of age structural transitions. This poses serious concerns in terms of obvious factors such as health budget and social security payments to elders in future. In this thesis, I endeavor to study whether age structural transition has an implication on an important macroeconomic indicator which is inflation. A structural VAR model has been constructed to answer this issue. Elderly dependency ratio growth, youth dependency ratio growth, real interest rate and output gap growth are the selected variables from 2003 to 2018 for these models. Cholesky decomposition and structural decomposition used to check the robustness of the models. The empirical results showed that the growth of youth dependency ratio is inflationary for Sri Lanka. But for India and Bangladesh growth of youth dependency ratio does not have any significant effect on inflation. Growth of elderly dependency ratio does not have any significant effect on inflation for Sri Lanka, India and Bangladesh. But the magnitude of the impact from elderly and youth dependency ratio growth on inflation is around 5% over the period of 10 months as the variance decomposition reveals for Sri Lanka and for India and Bangladesh it is around 2%.
item: Thesis-Full-text
Effect of macroeconomic variables to determine the share market performance in Sri Lanka
(2020) Ediriweera EAIN; Dissanayake AR
The stock exchange is considered an economic barometer that emphasises the economic condition of any country. The rise or fall of share prices indicates an economic boom or recession cycle. The better performance of the share market attracts the attention of investors and the exchange of shares continues the process of reinvestment and disinvestment which leads to economic growth via capital formation. The diminishing performance of the share market adversely impacts the creation of investments. It directly impacts economic growth as a component of aggregate demand and as a deterministic factor of the productive capacity of the economy. Thus, the study focused on identifying the effect of macroeconomic variables on the performance of the share market which would attract the attention of economic policy-makers in terms of enhancing the investments within Sri Lanka. The study focused on few key macroeconomic variables as in inflation rate, money supply, treasury bill rate, crude oil prices, gold prices, foreign exchange rate, and analysed the performance of All Share Price Index (ASPI) with those variables. The quantitative research approach was applied for the monthly data collected for 213 months from January 2002 – September 2019. Two factors were derived using Principle Component Factoring as Economic Growth Factor and Time Value of Money Factor. Based on both econometric and time series analysis, the study developed the Vector Autoregressive (VAR) model and the GARCH (1,1) model to analyse the performance of ASPI. The results of VAR model revealed that ASPI data for past months, and time value of money factor which includes inflation and treasury bill rate is more deterministic when analysing the performance of Sri Lankan stock exchange. GARCH (1,1) model also confirmed the same result in its conditional mean equation. However, the economic growth factor shows insignificant result in both the models in relation to the performance of Colombo Stock Exchange.