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Purpose: This study investigates internal/own shock in the domestic market and three external volatility spillovers from India, the UK, and the USA to the Sri Lanka stock market.

Need for the Study: The external market’s internal/own shocks and volatility spillovers influence portfolio choices in domestic stock market returns. Hence, it is required to investigate the internal shock in the domestic market and the external volatility spillovers from other countries.

Methodology: This study employs a quantitative method using ARMA(1,1)-GARCH(1,1) model. All Share Price Index (ASPI) is the proxy for the Colombo Stock Exchange (CSE) stock return. It uses daily time-series data from 1^st April 2010 to 21^st June 2023.

Findings: The findings revealed that internal/own and external shocks substantially impact the stock price volatility in CSE. Significant volatility clusters and persistence with extended memory in ASPI confirm internal/own shock in the market. Furthermore, CSE receives significant volatility shock from the USA, confirming external shock. This study’s findings highlight the importance of considering internal and external shocks in portfolio decision-making.

Practical Implications: Understanding the influence of internal shocks helps investors manage their portfolios and adapt to market volatility. Recognising significant volatility spillovers from external markets, especially the USA, informs diversification strategies. From a policy standpoint, the study emphasises the need for robust regulations and risk management measures to address shocks in domestic and global markets. This study adds value to the literature by assessing the sources of volatility shocks in the CSE, employing the ARMA-GARCH, a sophisticated econometrics model, to capture stock returns volatility, enhancing understanding of the CSE’s volatility dynamics.

Internationalization of capital markets gives opportunities to investors to invest their money in the country of their choice, not just in their own country. The relationships between international stock markets have become increasingly important in recent times. The purpose of this paper is to study the inter‐linkages between stock markets of India, Pakistan and Sri Lanka.

This paper studies the inter‐linkages between stock markets of India, Pakistan and Sri Lanka. Daily closing levels of the benchmark indices in the three countries are taken for a period of January 2003‐June 2010. While line charts, correlogram and unit‐root test are applied to check the stationary nature of the series; Granger's causality model, vector auto regression (VAR) model and variance decomposition analysis are performed to find out the linkages between the markets under study.

The paper concludes that while the National Stock Exchange (India) Granger causes Karachi Stock Exchange (Pakistan) and Colombo Stock Exchange (Sri Lanka), the vice versa is not true. These results of Granger's causality model are also confirmed by the VAR models.

Studies have been conducted in large numbers to test the linkages and integration between stock exchanges of the developed nations, namely the USA, Canada, Europe and Japan. Even the studies that have focused on the developing and under‐developed nations have studied the linkages of those with the developed nations. Little research has been conducted about the inter‐linkages between the nations from Asia. Even fewer studies have focused on stock exchanges in the South‐Asian region. This research paper focuses on the return from the benchmark stock exchanges from these three countries and also on the linkages between India, Pakistan and Sri Lanka.

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.

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.

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.

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.

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.

Purpose: This chapter examines the effect of COVID-19 on the stock market volatility (SMV) in the Colombo Stock Exchange (CSE), Sri Lanka.

Need for the Study: The study is necessary to understand investor behaviour, market efficiency, and risk management strategies during a global crisis.

Methodology: Utilising daily All Share Price Index (ASPI) data from 2 January 2018 to 31 August 2021, the data are divided into subsamples corresponding to the pre-pandemic period, the pandemic period, and distinct waves of the pandemic. The impact of the pandemic is investigated using the Mann–Whitney U test, the Kruskal–Wallis test, and the Exponential Generalised Autoregressive Conditional Heteroscedasticity (EGARCH) model.

Findings: The pandemic considerably affected CSE – the Mann–Whitney U test produced different market returns during the pre-COVID and COVID eras. The Kruskal–Wallis test improved performance during COVID-19 but did not continue to do so across COVID-19 waves. The EGARCH model detected increased volatility and risk during the first wave, but the second and third waves outperformed the first. COVID-19 had a minimal overall effect on CSE market results. GARCH and Autoregressive Conditional Heteroskedasticity (ARCH) models identified long-term variance memory and volatility clustering. The News Impact Curve (NIC) showed that negative news had a more significant impact on market return volatility than positive news, even if the asymmetric term was not statistically significant.

Practical Implications: This study offers significant insight into how Sri Lanka’s SMV is affected by COVID-19. The findings help create efficient mitigation strategies to mitigate the negative consequences of future events.

This paper aims to examine the patterns of intellectual capital reporting (ICR) of large listed firms in a developing nation, Sri Lanka. The aim of this study is to highlight the differences in ICR practice between developing and developed nations.

The paper begins by examining each of the top 30 firms by market capitalization listed on the Colombo stock exchange in 1998/1999 and 1999/2000. Using the content analysis method, it reviews the annual reports of these firms to determine the types of intellectual capital (IC) items reported in Sri Lanka. It then compares these findings with a similar study undertaken in Australia during the same period.

The findings in this paper highlight the need for a uniform ICR definition and a reporting framework that provides comparative and consistent reporting under the auspices of a regulatory body. ICR differences were identified between Sri Lankan and Australian firms, and it is argued that these differences can be attributed to economic, social and political factors.

This paper highlights important policy issues for Australia, Sri Lanka and other nations. These issues are even more pertinent in the light of the gradual international adoption of the International Financial Reporting Standards (IFRSs), formulated by the International Accounting Standards Board (IASB).

Most papers on intellectual capital reporting have focused on firms in developed countries. This paper offers insights into comparative reporting practices between a developed and a developing country.

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.

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.

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.

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.

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.

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.

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 mainly attempted to understand the trends and suitable forecasting model in order to predict the future 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.

The results disclosed that, grey prediction models generate smaller forecasting errors than traditional time series approach for limited data forecastings.

Finally, the authors strongly believed that, it could be better to use the improved grey hybrid methodology algorithms in real world model approaches.

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.

Drawing on agency theory and legitimacy theory perspectives, the purpose of this paper is to investigate the influence of board characteristics on sustainability reporting of listed companies in the Colombo Stock Exchange (CSE), Sri Lanka.

A sample of 148 listed companies was drawn from the CSE using stratified random sampling method and data were collected from the 2012 annual reports. The proposed hypotheses were tested using a hierarchical binary logistic regression.

This study documents that board size and dual leadership are positively associated with sustainability reporting and boards with female directors are negatively associated with sustainability reporting. This study also found that sustainability reporting is likely to be influenced by firm size and firm growth. Additionally, the study also reveals that younger firms are likely to adopt sustainability reporting.

This is the first study to examine the influence of board characteristics on sustainability reporting in Sri Lanka, considered as a developing economy with an emerging equity market.

This study examines return and volatility spillovers from the US and Japanese stock markets to three South Asian capital markets – (i) the Bombay Stock Exchange, (ii) the Karachi Stock Exchange and (iii) the Colombo Stock Exchange. We construct a univariate EGARCH spillover model that allows the unexpected return of any particular South Asian market to be driven by a local shock, a regional shock from Japan and a global shock from the USA. The study discovers return spillovers in all three markets, and volatility spillovers from the US to the Indian and Sri Lankan markets, and from the Japanese to the Pakistani market. Regional factors seem to exert an influence on these three markets before the Asian financial crisis but the global factor becomes more important in the post-crisis period.

The purpose of this paper is to examine the association between designated corporate governance attributes and the degree of earnings management in selected quoted companies in Sri Lanka.

In total, 70 listed companies in Colombo Stock Exchange (CSE) were selected based on the highest market capitalisation for the period covering from 2015 to 2017 and representing beverage, food and tobacco, diversified, hotel and travel, manufacturing, oil palms and health care sectors, which accounted for 59.9 per cent of the total market capitalisation of CSE.

This study found a positive relationship between CEO-Chair duality and earnings management.

The insights may also provide investors, economic analysts and regulators with early caution indicators of potential problems in a corporation regarding corporate governance failures and aid stakeholders in assessing the effectiveness and efficiency of the board and corporate governance structure and earnings management methods.

This study extends the extant research on board characteristics and real earnings management by adopting prominent research design and modernised data. This study offers evidence on how selected audit and board committee’s characteristics influence real earnings management practices.