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This study aims to explore the quality of corporate governance disclosure under an “apply and explain” regime in the context of an emerging economy (Mauritius), following a transition from the traditional “comply or explain” approach within the national code of corporate governance.

The research relies on a content analysis of corporate governance disclosure in 86 annual reports of companies listed on the Stock Exchange of Mauritius for the financial periods 2018–2019 and 2019–2020, one-way analysis of variance tests and draws on the typology of corporate governance explanations developed by Shrives and Brennan (2015), focusing on specificity, location and comprehensiveness dimensions. This paper draws on legitimacy theory and the concepts of substantive and symbolic disclosures to guide the interpretation of the findings.

From a specificity point of view, the disclosure index revealed significant variations, with the highest score being four times the lowest score. With regards to location and comprehensiveness, only around half of companies are making optimum use of a corporate governance report and providing explanations by principles. This paper also illustrated how some firms provided symbolic disclosures. Overall, there are disparities in the application of the code by companies, reflected in a blend of substantive and symbolic disclosures to maintain their legitimacy.

This study examines “apply and explain” disclosures in a emerging economy in contrast to the “comply or explain” approach studied so far in the literature. Merely professing a “well intended” shift to the “apply and explain” approach does not necessarily lead to improvements in the quality of corporate governance disclosures. Companies, governance professionals and regulatory bodies could formulate disclosure guidance to better underpin the implications of the “apply and explain” approach.

This study aims to assess the evolution of the value relevance of book value, earnings and its components in Mauritius, an African developing country, focusing on value relevance changes after International Financial Reporting Standards (IFRS) adoption and subsequent local reforms.

The study relies on a data set of 567 firm-year observations (2001–2018) and the Ohlson valuation model to investigate value relevance after IFRS adoption, the implementation of institutional reforms and enforcement reforms.

Firstly, the authors find support for a rise in the combined value relevance of earnings and book value, albeit that book value significantly contributes to changes over time. The findings highlight the combined importance of IFRS adoption with institutional and enforcement reforms to improve value relevance. Secondly, the authors do not find evidence of a shift in value relevance between earnings and book value. Third, the cash flow model reveals a higher level of significance relative to the earnings model.

The authors extend the value relevance literature in the context of African developing countries. The present findings underpin the need for a reinforcing of relevant institutional and enforcement frameworks to ensure the benefits of IFRS adoption materialise. The findings also offer a contribution of how developing countries’ experience IFRS post-adoption while adding to the dearth of studies analysing IFRS enforcement practices.

The purpose of this study is to investigate the impact of idiosyncratic and macroeconomic risks on capital structure on SADC countries.

Employing data from the African Financials database, the analysis is conducted over a ten year period spanning from 2009 to 2018 for 309 companies. Unit Root Fisher Chi-Square test and Granger Causality test were employed to test for unidirectional and bidirectional relationships cross-sectionally. To resolve endogeneity issues, System GMM was used as main topology for panel regression analysis.

The study confirmed that companies become risk averse when there is an increase in idiosyncratic and macroeconomic risk and therefore take less leverage. According to the perking order theory, a higher variability in earnings shows that the bankruptcy probability amplifies. Hence, institutions with high income employ more internal finance during periods of high idiosyncratic and macroeconomic uncertainty thereby lowering leverage. A positive significant and statistically relationship is also confirmed between idiosyncratic risk and leverage in Botswana, South Africa and Tanzania. Companies with higher leverage make riskier investment in line with the trade-off theory. In short, executives from the SADC region consider more importance to fluctuations in risk while accelerating or diminishing leverage in their capital structure.

The study is among one of the pioneering work underpinning the idiosyncratic risk and macroeconomic risk on capital structure and relying on a large number of companies across the SADC region. In this respect, it adds contribution to the existing literature on risks and capital structure to the socio-economic goals of the SADC region.

This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian forex market has been utilized as a case study, and daily data for nominal spot rate (during a time period of five years spanning from 2014 to 2018) for EUR/MUR, GBP/MUR, CAD/MUR and AUD/MUR have been applied for the predictions.

Autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models are used as a basis for time series modelling for the analysis, along with the non-linear autoregressive network with exogenous inputs (NARX) neural network backpropagation algorithm utilizing different training functions, namely, Levenberg–Marquardt (LM), Bayesian regularization and scaled conjugate gradient (SCG) algorithms. The study also features a hybrid kernel principal component analysis (KPCA) using the support vector regression (SVR) algorithm as an additional statistical tool to conduct financial market forecasting modelling. Mean squared error (MSE) and root mean square error (RMSE) are employed as indicators for the performance of the models.

The results demonstrated that the GARCH model performed better in terms of volatility clustering and prediction compared to the ARIMA model. On the other hand, the NARX model indicated that LM and Bayesian regularization training algorithms are the most appropriate method of forecasting the different currency exchange rates as the MSE and RMSE seemed to be the lowest error compared to the other training functions. Meanwhile, the results reported that NARX and KPCA–SVR topologies outperformed the linear time series models due to the theory based on the structural risk minimization principle. Finally, the comparison between the NARX model and KPCA–SVR illustrated that the NARX model outperformed the statistical prediction model. Overall, the study deduced that the NARX topology achieves better prediction performance results compared to time series and statistical parameters.

The foreign exchange market is considered to be instable owing to uncertainties in the economic environment of any country and thus, accurate forecasting of foreign exchange rates is crucial for any foreign exchange activity. The study has an important economic implication as it will help researchers, investors, traders, speculators and financial analysts, users of financial news in banking and financial institutions, money changers, non-banking financial companies and stock exchange institutions in Mauritius to take investment decisions in terms of international portfolios. Moreover, currency rates instability might raise transaction costs and diminish the returns in terms of international trade. Exchange rate volatility raises the need to implement a highly organized risk management measures so as to disclose future trend and movement of the foreign currencies which could act as an essential guidance for foreign exchange participants. By this way, they will be more alert before conducting any forex transactions including hedging, asset pricing or any speculation activity, take corrective actions, thus preventing them from making any potential losses in the future and gain more profit.

This is one of the first studies applying artificial intelligence (AI) while making use of time series modelling, the NARX neural network backpropagation algorithm and hybrid KPCA–SVR to predict forex using multiple currencies in the foreign exchange market in Mauritius.

The purpose of this paper is to assess the presence of the momentum effect on the Stock Exchange of Mauritius (SEM) and its implications for investors.

Data for stock trading activities of all listed companies on the SEM from 2001 through 2009 were subjected to the Jegadeesh and Titman methodology for the selection of stocks. The stock selection is based on their returns over the past three to 12 months, with holding periods that range from three to 12 months. In addition, the Capital Asset Pricing Model is used to estimate the risk adjusted returns for momentum portfolios and the impact of the strategies are evaluated for the “bullish” ‘(high growth) and “bearish” (moderate growth) periods.

The results show the existence/presence of the momentum effects on the SEM. However, the outcomes of the momentum strategies and particularly those of the winner and loser strategies are not consistently above (outperform) the effects of the Efficient Market Hypothesis. Accounting for heteroskedasticity did not alter the superiority of the Efficient Market Hypothesis.

The implication for investors with interest in trading on the SEM, or in Africa in general, is to focus on trading strategies that are in line with the Efficient Market Hypothesis.

The paper is a first formal attempt to fill the research gap on the momentum effect on the Mauritian equity market. The paper also contributes to the existing literature on momentum strategies in emerging markets.