Showing papers on "Market capitalization published in 2022"

Achieving Financial Sustainability through Revenue Diversification: A Green Pathway for Financial Institutions in Asia

Abstract: Establishing balanced and sustainable development is critical for improving banks’ capability and performance. Financial development has enormous significance in an environment of increasingly contestable international markets, and can be achieved by enhancing banking efficiency and performance. The bank efficiency is estimated through data envelopment analysis (DEA). By applying the quantile regression technique, this research examines the impact of revenue diversification (RD) on the bank efficiency (BE) of seven Asian emerging economies over 2008–2019. In this regard, non-performing loans (NPLs), non-interest income, capitalization, and gross domestic product (GDP) are taken as control variables. The empirical findings indicate that RD, market capitalization, non-interest income, and GDP have a significant positive impact on BE, whereas NPLs have a significant negative relationship with BE. These results have significant strategic implications for managers, regulators, and policymakers, who share a common interest in boosting financial sustainability and performance.

Sustainable development goals (SDG) reporting: an analysis of disclosure

Abstract: Purpose The global agenda of sustainable development goals (SDGs) has posed a major challenge to corporate organizations by addressing sustainability issues within their business model and strategy. Based on this premise, this study provides empirical examination of SDG reporting of the top fifty (50) listed companies in Nigeria for the period of 2016–2018. Design/methodology/approach The study adopts survey method and content analysis technique to analyze corporate SDG reporting of the selected firms. The study examines the top-50 listed firms in Nigeria based on their market capitalization. Questionnaires were distributed to financial managers of the top-50 listed firms and staffs of the big four audit firms from the governance and sustainability department. The fifty (50) firms selected are as follows: 17 firms from the financial sector, 13 firms from the consumer goods sector, 5 firms from the healthcare sector, 6 firms from the oil and gas sector, 5 firms from the industrial goods sector and 4 firms from the information technology sector. The content analysis was utilized through the PwC framework, Global Reporting Initiative (GRI) framework and International Integrated Reporting Council (IIRC) framework to gage the extent of firms' compliance regarding corporate SDG reporting. Also, the business reporting indicators for each SDG developed by GRI was employed to determine the compliance level of the selected firms with respect to corporate SDG reporting. Findings The empirical evidence shows that corporate organizations in Nigeria have performed poorly in corporate SDG reporting. The result of the survey reveals that lack of regulatory framework and voluntary disclosure are the major factors that contributes to low level of SDG reporting by Nigerian firms. Also, the result of the content analysis shows poor reporting on SDG activities. The result of the research survey indicates that voluntary disclosure, lack of management commitment and lack of regulatory enforcement accounts for low SDG disclosure by the selected Nigerian firms. Practical implications This study's findings call for clear responsibility and a strong drive for SDG performance from corporate institutions in Nigeria. Whilst the overall responsibility rests on the government, the actualization of SDG cannot be achieved without support from corporate organizations. The empirical approach used in this study emphasizes the need for corporate organizations to embrace sustainable practices and to integrate SDG information into their reporting cycle. Originality/value This study contributes to growing literature in the area of corporate reporting and SDG research in Nigeria and other emerging economies.

Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

Abstract: The creation of trustworthy models of the equities market enables investors to make better-informed choices. A trading model may lessen the risks that are connected with investing and make it possible for traders to choose companies that offer the highest dividends. However, due to the high degree of correlation between stock prices, analysis of the stock market is made more difficult by batch processing approaches. The prediction of the stock market has entered a technologically advanced era with the advent of technological marvels such as global digitization. For this reason, artificial intelligence models have become very important due to the continuous increase in market capitalization. The novelty of the proposed study is the development of the robustness time series model based on deep leaning for forecasting future values of stock marketing. The primary purpose of this study was to develop an intelligent framework with the capability of predicting the direction in which stock market prices will move based on financial time series as inputs. Among the cutting-edge technologies, artificial intelligence has become the backbone of many different models that predict the direction of markets. In particular, deep learning strategies have been effective at forecasting market behavior. In this article, we propose a framework based on long short-term memory (LSTM) and a hybrid of a convolutional neural network (CNN-LSTM) with LSTM to predict the closing prices of Tesla, Inc. and Apple, Inc. These predictions were made using data collected over the past two years. The mean squared error (MSE), root mean squared error (RMSE), normalization root mean squared error (NRMSE), and Pearson’s correlation (R) measures were used in the computation of the findings of the deep learning stock prediction models. Between the two deep learning models, the CNN-LSTM model scored slightly better (Tesla: R-squared = 98.37%; Apple: R-squared = 99.48%). The CNN-LSTM model showed a superior performance compared with the single deep learning LSTM and existing systems in predicting stock market prices.

Shareholder Activism and Its Impact on Profitability, Return, and Valuation of the Firms in India

Abstract: The paper’s prime objective is to understand the impact of Shareholder activism on firm performance. This study is conducted in a unique setup where traditional activist investors such as pension funds and hedge funds are not present. However, the activism cases are increasing yearly in an emerging economy like India. We have created a comprehensive shareholder activism index (sha index) using multiple activisms and corporate governance factors. To measure firm performance, we have used valuation (Tobin’s Q and Market capitalization), profitability (operating profit margin and net profit margin), and return ratios (Return on capital and return on equity). Panel data analysis (PDA) is employed for the current study as it overcomes the shortcomings of the time series analysis and cross-sectional studies. The sample comprises 37 listed firms’ data for FY2017 to FY2020. Chosen firms have experienced activism instances at least once during the 2017–2020 period. As per our analysis, shareholder activism has a significant negative impact on valuation measured in market capitalization and profitability estimated by operating profit margin. Activism primarily impacts the other four parameters negatively, but it is insignificant. India is in the nascent stage of activism, partly explaining the insignificance of the effects of shareholder activism on firm performance. Also, activist investors are targeting companies. These attacks are not fructifying desired outcomes as promoters own over 50% stake in the listed companies. The latest data for FY2021 has not been considered for the study as covid-19 impacted the businesses during the financial year. Also, we cannot capture activism instances that are not reported in regulatory filings. Unlike past research in this area, we have used a comprehensive activism index as a proxy of activism and have employed PDA instead of event studies to assess the impact on firm performance. Also, this is the first such empirical study conducted in an emerging economy setup where neither large hedge nor pension funds are present.

Deconstruction of the Green Bubble during COVID-19 International Evidence

Abstract: Bubbles are usually chaotic but can be predictable, provided their formation matches the log periodic power law (LPPL) with unique stylized facts. We investigated Green Bubble behaviour in the stock prices of a selection of stocks during the COVID-19 pandemic, namely, those with the highest market capitalization from a basket of North American and European green energy or clean tech companies and the S&P Global Clean Energy Index. Moreover, the biggest Exchange Traded Fund (TAN) by market capitalization was also considered. The examined period is from 31 December 2019 to 11 October 2021, during which we detected 35 Green Bubbles. All of these followed the LPPL signature while calibrated through the 2013 reformulated LPPL model. In addition, the average drawdown emerged as four times that of the regular S&P-500 stock index (108% vs. 27%) under stressed conditions, such as the COVID-19 pandemic (stylized fact). Finally, the aftermaths of Green Bubbles, unlike regular bubbles, are not destructive, as these bubbles increase economic activity and infrastructure spending and are hence beneficial for holistic growth (described as Social Bubble Hypothesis). We document that there are benefits in adapting greener and more sustainable business models in energy production. Green and sustainable finance offers benefits and opportunities for stock exchanges, especially for energy stocks. As a result, many businesses are focusing on sustainability and adopting an eco-friendly business model, which helps the environment, helps sustainability and attracts investors.

Analysis of the cryptocurrency market using different prototype-based clustering techniques

Abstract: Abstract Since the emergence of Bitcoin, cryptocurrencies have grown significantly, not only in terms of capitalization but also in number. Consequently, the cryptocurrency market can be a conducive arena for investors, as it offers many opportunities. However, it is difficult to understand. This study aims to describe, summarize, and segment the main trends of the entire cryptocurrency market in 2018, using data analysis tools. Accordingly, we propose a new clustering-based methodology that provides complementary views of the financial behavior of cryptocurrencies, and one that looks for associations between the clustering results, and other factors that are not involved in clustering. Particularly, the methodology involves applying three different partitional clustering algorithms, where each of them use a different representation for cryptocurrencies, namely, yearly mean, and standard deviation of the returns, distribution of returns that have not been applied to financial markets previously, and the time series of returns. Because each representation provides a different outlook of the market, we also examine the integration of the three clustering results, to obtain a fine-grained analysis of the main trends of the market. In conclusion, we analyze the association of the clustering results with other descriptive features of cryptocurrencies, including the age, technological attributes, and financial ratios derived from them. This will help to enhance the profiling of the clusters with additional descriptive insights, and to find associations with other variables. Consequently, this study describes the whole market based on graphical information, and a scalable methodology that can be reproduced by investors who want to understand the main trends in the market quickly, and those that look for cryptocurrencies with different financial performance.In our analysis of the 2018 and 2019 for extended period, we found that the market can be typically segmented in few clusters (five or less), and even considering the intersections, the 6 more populations account for 75% of the market. Regarding the associations between the clusters and descriptive features, we find associations between some clusters with volume, market capitalization, and some financial ratios, which could be explored in future research.

Impact of ESG score on financial performance of Indian firms: static and dynamic panel regression analyses

Abstract: ABSTRACT The study attempts to empirically investigate the impact of ESG score on the financial variables that may affect the performance of firms in the Indian context; SEBI’s recent mandate on ESG reporting by the listed entities being the point of departure for the present discourse. A representative sample of 48 Indian firms having ESG scores under BSE-100 index is used in the analysis. The study period comprises the years 2011–2019. Static and dynamic panel regression analyses are conducted. The financial performance variables incorporated in this paper include ROA, ROE, firm size, market capitalization, PBDIT, Tobin’s Q and share price. It is demonstrated that ESG score influences these variables, however with time lags. The distinctive contribution of the current endeavour lies in establishing a long-term positive association between ESG disclosure and annual average share price for the listed firms in a developing economy like India. The results are implicative of the fact that ESG score is an emerging indicator for conceiving future financial performance and risk mitigation strategies, and therefore, of considerable importance from policy perspective.

Stock Price Crash Warning in the Chinese Security Market Using a Machine Learning-Based Method and Financial Indicators

Abstract: Stock price crashes have occurred frequently in the Chinese security market during the last three decades. They have not only caused substantial economic losses to market investors but also seriously threatened the stability and financial safety of the security market. To protect against the price crash risk of individual stocks, a prediction and explanation approach has been proposed by combining eXtreme Gradient Boosting (XGBoost), the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and SHapley Additive exPlanations (SHAP). We assume that financial indicators can be adopted for stock crash risk prediction, and they are utilized as prediction variables. In the proposed method, XGBoost is used to classify the stock crash and non-crash samples, while NSGA-II is employed to optimize the hyperparameters of XGBoost. To obtain the essential features for stock crash prediction, the importance of each financial indicator is calculated, and the outputs of the prediction model are explained by SHAP. Compared with the results of benchmarks using traditional machine learning methods, we found that the proposed method performed best in terms of both prediction accuracy and efficiency. Especially for the small market capitalization samples, the accuracy of classifying all samples reached 78.41%, and the accuracy of identifying the crash samples was up to 81.31%. In summary, the performance of the proposed method demonstrates that it could be employed as a valuable reference for market regulators engaged in the Chinese security market.

Analysis of the cryptocurrency market using different prototype-based clustering techniques

Abstract: Abstract Since the emergence of Bitcoin, cryptocurrencies have grown significantly, not only in terms of capitalization but also in number. Consequently, the cryptocurrency market can be a conducive arena for investors, as it offers many opportunities. However, it is difficult to understand. This study aims to describe, summarize, and segment the main trends of the entire cryptocurrency market in 2018, using data analysis tools. Accordingly, we propose a new clustering-based methodology that provides complementary views of the financial behavior of cryptocurrencies, and one that looks for associations between the clustering results, and other factors that are not involved in clustering. Particularly, the methodology involves applying three different partitional clustering algorithms, where each of them use a different representation for cryptocurrencies, namely, yearly mean, and standard deviation of the returns, distribution of returns that have not been applied to financial markets previously, and the time series of returns. Because each representation provides a different outlook of the market, we also examine the integration of the three clustering results, to obtain a fine-grained analysis of the main trends of the market. In conclusion, we analyze the association of the clustering results with other descriptive features of cryptocurrencies, including the age, technological attributes, and financial ratios derived from them. This will help to enhance the profiling of the clusters with additional descriptive insights, and to find associations with other variables. Consequently, this study describes the whole market based on graphical information, and a scalable methodology that can be reproduced by investors who want to understand the main trends in the market quickly, and those that look for cryptocurrencies with different financial performance.In our analysis of the 2018 and 2019 for extended period, we found that the market can be typically segmented in few clusters (five or less), and even considering the intersections, the 6 more populations account for 75% of the market. Regarding the associations between the clusters and descriptive features, we find associations between some clusters with volume, market capitalization, and some financial ratios, which could be explored in future research.

The ESG Reporting of EU Public Companies. Does the Company’s Capitalisation Matter?

Abstract: Large companies in the European Union are required to publish information related to environmental, social and governance (ESG) matters. The aim of our study is to determine the quality of ESG reporting in EU public companies (measured by the ESG-index) and its effect on their market capitalisation. Therefore, the results of our research will be both scientific and applicative, and they will be useful for investors when making investment decisions on the stock exchange. The research includes over 15,000 companies listed on 27 stock exchanges (in the “old” and “new” member states, EU-14 and EU-13, respectively), covering the period 2002 to 2019. The data were obtained from the Refinitiv database. We drew three conclusions after the research. Firstly, only 50% of the companies listed on the stock exchanges in the old EU member states and merely 5% of the companies from the new EU member states had reported ESG-indexes in any year of the research period. Secondly, we found a positive relationship between a company’s market capitalisation and the quality of its ESG reports. Thirdly, the market values of companies are positively but not strongly affected by the ESG-indexes.

A fuzzy ISM analysis of factors in growth of initial coin offerings (ICOs)

Abstract: Abstract In the previous few years, the value of cryptocurrencies and their market capitalization have grown at an incredibly rapid pace. There are currently over 1500 distinct trading currencies with a total market valuation of over 384 billion US dollars. It is the purpose of this study to examine the elements that have helped the ICO industry flourish over the past several years. Findings from previous studies and recommendations from industry professionals lead to the selection of 16 variables. ISM (Interpretive Structural Modeling) analysis was utilised in order to better comprehend the influence and interrelationships across seven different Fuzzy levels of identified obstacles. Based on the drive and reliance power of the Fuzzy matrix utilising Fuzzy MICMAC, the factors are further divided into four primary groups. High reliance and driving power can be found in all but one of the selected fifteen criteria, except for no or low regulation. The storm’s expansion in the field is directly linked to the relationship between all factors. This revolutionary fundraising approach is changing and accepted by common investors because of “no or low regulation,” but it cannot be managed directly by the ICOs industry’s connected participants.

Impact of COVID-19 effective reproductive rate on cryptocurrency

Abstract: Abstract The importance of cryptocurrency to the global economy is increasing steadily, which is evidenced by a total market capitalization of over $2.18T as of December 17, 2021, according to coinmarketcap.com (Coin, 2021). Cryptocurrencies are too confusing for laymen and require more investigation. In this study, we analyze the impact that the effective reproductive rate, an epidemiological indicator of the spread of COVID-19, has on both the price and trading volume of eight of the largest digital currencies—Bitcoin, Ethereum, Tether, Ripple, Litecoin, Bitcoin Cash, Cardano, and Binance. We hypothesize that as the rate of spread decreases, the trading price of the digital currency increases. Using Generalized Autoregressive Conditional Heteroskedasticity models, we find that the impact of the spread of COVID-19 on the price and trading volume of cryptocurrencies varies by currency and region. These findings offer novel insight into the cryptocurrency market and the impact that the viral spread of COVID-19 has on the value of the major cryptocurrencies.

Value drivers of development stage biopharma companies

Abstract: Abstract Objective Scholars previously estimated research and development (R&D) costs of the internal drug development process. However, little is known about the costs and value arising from externally acquired therapeutics. This study identifies and estimates the magnitude of factors associated with Biopharma acquisition value. Methods SDC Thomson Reuter and S&P Capital IQ were screened for majority acquisitions of US and EU Biopharma companies developing new molecular entities for prescription use (SIC code: 2834) from 2005 to 2020. Financial acquisition data were complemented with variables characterizing the target’s product portfolio extracted from clinicaltrials.gov, Drugs@FDA database, US SEC filings, and transaction announcements. A multivariate regression assesses the association of firm value with extracted variables. Results 311 acquisitions of companies developing prescription drugs were identified over the study period. Acquirers paid 37% ( p < 0.05) more for companies with biologics and gene therapeutics than small-molecule lead drugs. Multi-indication products were acquired for a 12% premium per additional indication ( p < 0.01). No significant valuation difference between companies developing orphan and non-orphan designated lead products was observed (18%, p = 0.223). Acquisition value positively correlated with the total number of further products, headquarter location in the US, underlying market conditions, and acquirer market capitalization ( p < 0.05). Conclusions Internal and external drug development consumes many financial and human resources, yet it is important for entrepreneurs, regulators, and payers to understand their precise magnitude and value drivers. This information permits the design of targeted pricing and industrial policies that incentivize the development of novel drugs in areas with high unmet needs.

Stock market development and agricultural growth of emerging economies in Africa

Abstract: PurposeThis research investigates the bond between stock market development and agricultural growth in African emerging economies from 1990 to 2020.Design/methodology/approachAgricultural value added to the gross domestic product measures agricultural growth and market capitalization and stock value traded measure stock market development.FindingsThe findings disclose that market capitalization negatively affects agricultural growth while stock value traded positively affects agricultural growth in the fully modified and dynamic ordinary least square techniques. The findings unveil bidirectional causality between labour and agricultural value added with unidirectional causality flow from agricultural value added to market capitalization and stock value traded.Research limitations/implicationsThe governments should promote agricultural growth initiatives which stimulate stock market development. Effective methods required to encourage credit flow to the agricultural enterprises through the stock markets' intermediation should be promoted using aggressive policies which eliminate credit flow bottlenecks. Policy makers and regulatory authorities should implement policies which attract investors to the agricultural sector and encourage companies' listing in the stock markets. The capital market funding should be expanded to boost economic growth through agricultural value added.Originality/valueLiterature reveals divergent results on the relationship between stock market development and agricultural growth. Earlier studies provide conflicting findings on the bond between stock market development and agricultural growth. Some findings indicate positive link between stock market development and agricultural growth, while others show a negative association. Studies' results reveal opposing directions of causality between stock market development and agricultural growth.

Environment, Social, and Governance Score and Value Added Impacts on Market Capitalization: A Sectoral-Based Approach

Abstract: The main goal of this study was to measure the impact of the environmental, social, and governance (ESG) sustainability score and value added to companies’ market capitalization. Therefore, financial and sustainable performance were measured in a sample of 5557 companies divided into 9 economic sectors of activity from 78 countries and 6 regions (Americas: 2144; Asia: 1770; Europe: 1232; Oceania: 311; Africa: 90; United Kingdom: 10). The analyzed sample consisted of publicly traded companies ranked by market capitalization (from small-cap to large-cap), for which the ESG score was measured in the analyzed period: the financial year was 2019, before the advent of the COVID-19 pandemic. Using two methods (multiple linear regression and complementary quantile regression), we found a direct link between the ESG score and value added variables and market capitalization, with distinct impacts at the economic sector level for ESG score and relatively constant impact for value added.

An Empirical Study of Volatility in Cryptocurrency Market

Abstract: Cryptocurrencies have gained a lot of attraction across the globe. Most observers of the cryptocurrency market will agree that crypto volatility is in a different league altogether. There has been a growing need to understand the nature of volatility in cryptocurrency. This paper analyzes the performance of four mostly traded, different cryptocurrencies in terms of their risk and return. The relationship between the return and returns volatility among different currencies has been examined considering the daily closing prices from 1 January 2017 to 30 June 2022, using the family of the GARCH model. The study has explored the spillover and asymmetric effect of volatility by using the DCC GARCH model and EGARCH model, respectively. The causal behavior among different cryptocurrencies has also been examined using Granger causality. There has been a strong spillover effect among different cryptocurrencies, Bitcoin and Ether, which are the top two cryptocurrencies with the highest market capitalization which have exhibited an asymmetric impact in their volatility as compared to the other two currencies, which are Litecoin and XRP.

OUP accepted manuscript

Abstract: Abstract We propose to institute a new tax on corporations’ stock shares for all publicly listed companies and large private companies headquartered in G20 countries. Each of these companies would have to pay 0.2% of the value of its stock in taxes each year. As the G20 stock market capitalization is around 100% of world GDP, the tax would raise approximately 0.2% of world GDP in revenue. Because stock ownership is highly concentrated among the rich, this tax would be progressive. The tax could be paid in kind by corporations (by issuing new stock) so that the tax does not raise liquidity issue nor affect business operations. In today’s globalized and fast-moving world, companies can become enormously valuable once they establish market power, even before they start making large profits (e.g., Amazon and Tesla). This tax would make them start paying taxes sooner than standard income taxes.

On the relationship between oil price, exchange rate and stock market performance in South Africa: Further evidence from time-varying and regime switching approaches

Abstract: Abstract The study examines the dynamics between oil price, exchange rate, and stock market performance in South Africa using DCC-GARCH, time-varying VAR, and multivariate Markov regime switching models. Monthly data on oil price, exchange rate, and market capitalization as a measure of stock performance from 2003(01) to 2019(7) were employed. The results of DCC-GARCH model show that dynamic conditional correlation among the variable was stable with few exceptionalities. The empirical findings from time-varying VAR show existence of feedbacks from stock market to oil price. Markov regime switching VAR model results show that exchange rate and market capitalization have significant effects on oil price in booming period. The study concludes that stock market performance provides an important policy help in stemming the erratic fluctuations in oil price. Appropriate knowledge of the linkage between oil price, exchange rate and stock market performance in South African economy is important because of her heavy reliance on oil importation. The upward change in oil price has so many overlapping effects on many sectors of the economy in form of increase in cost of goods and services (inflation) and lower standard of living among others. Hike in oil price is believed to also worsen the external value of Rands. This could send some dangerous signals to foreign investors in the stock market, which may undermine the performance of the stock market. Empirical knowledge of this dynamic could assist the policy makers to come up with mitigating policies to reduce the effects of oil price volatility on other economic fundamentals.

Nexus between corruption, market capitalization, exports, FDI, and country’s wealth: A pre-global financial crisis study

Abstract: The study investigates the impact of corruption, market capitalization, exports, and foreign direct investment on the wealth of 178 countries worldwide. Thus, the paper uses univariate and multivariate regressions to observe the nexus among exports, foreign direct investment, market capitalization, corruption, and wealth of nations. The findings indicate that corruption poses a significant hindrance to prosperity and development, as evaluated with respect to the Transparency International Corruption Perceptions Index. Additionally, the results showed that the world’s poorest nations are becoming less corrupt while the wealthiest ones are growing more corrupt. The paper also concludes that exports and market capitalization are critical for prosperity and development when combined with lower corruption levels. Furthermore, the analysis also suggests that inbound foreign direct investment favors the development of emerging countries. Surprisingly, market capitalization and exports had little impact on wealth of countries before the crisis period. Moreover, integrity also fosters economic growth. Overall, the study concludes that the causes of wealth are country-specific. AcknowledgmentsI thank the editor and the reviewers for the helpful comments and suggestions that significantly enhanced this work. The usual disclaimer applies.

Ownership and corporate governance across institutional contexts

Abstract: Research Question/Issue The purpose of this special issue (SI) is to encourage research examining the effect of different ownership types in and from different countries on corporate governance and objectives. Research Findings/Insights The articles included in this SI provide novel insights as to the influence of different types of owners, including states and sovereign wealth funds, families, and different types of institutional investors, as well as of other features of ownership structures, on various aspects of corporate governance, in a variety of institutional contexts. Theoretical/Academic Implications As companies face different institutional contexts when they operate internationally, and shareholders have become increasingly global, the resulting heterogeneity of shareholder types poses new challenges in our understanding of their behavior. This SI is a step in the direction of disentangling the complex implications of ownership for corporate governance across institutional contexts. Practitioner/Policy Implications Together with other owner types, the ever-increasing $100 trillion global asset management industry representing 40% of global market capitalization (PwC, 2020) fuels corporate governance and performance pressures on portfolio companies. The articles included in the SI provide useful insights as to the new ownership landscape and its consequences for operating firms in different countries.

Fitting Multi-Layer Feed Forward Neural Network and Autoregressive Integrated Moving Average for Dhaka Stock Exchange Price Predicting

Abstract: The stock market plays a vital role in the economic development of any country. Stock market performance can be measured by the market capitalization ratio as well as many other factors. The primary purpose of this study is to predict the movement of the stock market based on the total market capitalization of the Dhaka Stock Exchange (DSE) using autoregressive integrated moving average (ARIMA) models as well as artificial neural networks (ANN). The data set covers monthly time series data of total market capitalization from November 2001 to December 2018. This study also shows the best model for forecasting the movement of DSE market capitalization. The ARIMA (2,1,2) model is chosen from among the several ARIMA model combinations. From several artificial neural networks (ANN) models as a modern tool, a three-layer feed-forward topology using a backpropagation algorithm with five nodes in the hidden layer, one lag, and a learning rate equal to 0.01 is selected as the best model. Finally, these selected two models are compared based on the Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Theil’s U statistic. The results showed that the estimated error of ANN is less than the estimated error of the traditional method. Doi: 10.28991/ESJ-2022-06-05-09 Full Text: PDF

Correlates of Stock Market Development and Economic Growth: A Confirmatory Study from Ghana

Abstract: The study confirms the debate on whether stock market development correlates to economic growth. The dimensions used for the stock market development consisted of market liquidity, size, and capitalization. Economic growth was represented by the real gross domestic product (GDP) growth rate. Based on secondary data obtained from the Ghana Stock Exchange (GSE) and Ghana Statistical Service from 2014 to 2018, a correlational research design was adopted to analyze the data with SPSS 20v by using bivariate and regression. The study found that there is a high positive relationship between market liquidity and economic growth, a moderate negative relationship between market size and economic growth, and a moderate positive relationship between market capitalization and economic growth. Also, the stock market development of market liquidity, size, and capitalization predict 95.7 percent of economic growth. The study summarized that there is a high positive association between stock market development and economic growth as a confirmatory revelation, but all the relationship results were not statistically significant. The result points to the casualty of the relationship between stock market development and economic growth. The study recommends that more firms must be encouraged to be listed on GSE to enhance economic growth in Ghana.

Detection of structural regimes and analyzing the impact of crude oil market on Canadian stock market: Markov regime-switching approach

Abstract: Purpose This study aims to analyze the impact of the crude oil market on the Toronto Stock Exchange Index (TSX). Design/methodology/approach The focus is on detecting nonlinear relationship based on monthly data from 1970 to 2021 using Markov-switching vector auto regression (VAR) model. Findings The results indicate that TSX return contains two regimes: positive return (Regime 1), when growth rate of stock index is positive; and negative return (Regime 2), when growth rate of stock index is negative. Moreover, Regime 1 is more volatile than Regime 2. The findings also show the crude oil market has a negative effect on the stock market in Regime 1, while it has a positive effect on the stock market in Regime 2. In addition, the authors can see this effect in Regime 1 more significantly in comparison to Regime 2. Furthermore, two-period lag of oil price decreases stock return in Regime 1, while it increases stock return in Regime 2. Originality/value This study aims to address the effect of oil market fluctuation on TSX index using Markov-switching approach and capture the nonlinearities between them. To the best of the author’s knowledge, this is the first study to assess the effect of the oil market on TSX in different regimes using Markov-switching VAR model. Because Canada is the sixth-largest producer and exporter of oil in the world as well as the TSX as the Canada’s main stock exchange is the tenth-largest stock exchange in the world by market capitalization, this paper’s framework to analyze a nonlinear relationship between oil market and the stock market of Canada helps stock market players like policymakers, institutional investors and private investors to get a better understanding of the real world.

Environment, Social and Governance Reporting and Firm Performance: Evidence from GCC Countries

Abstract: The aim of this research is to investigate the impact of ESG reporting on firm performance, Environment, Social and Governance (ESG) are a triple-bottom-line approach that combines financial gains with adhering to social, governance and environmental norms. In addition, the study's objective is to determine the relationship between ESG disclosure and firm performance in Gulf Cooperation Council (GCC) listed companies. ESG scores and other samples for 91 firms from 6 GCC countries were collected for this purpose over a three-year period from 2019, 2020 and 2021. The sample comprised nine diverse industries. The dependent variables are Return on Assets (ROA) and market capitalization , experimental variables are environmental pillar score, social pillar score, governance pillar score and overall ESG score and the control variables: size, leverage. The study found that ESG scores and governance pillar scores have a positive impact on a firm’s market value but environmental and social pillar scores were not significant. In addition, there is a strong relationship between all ESG disclosures and ECG scores.