Terms Of Market Capitalization Research Articles

Measuring the Performance of KSE100: The Impact of Demutualization

The purpose of this study is to assess how the demutualization process has affected the performance of the KSE100 Benchmark Index. The study utilized Descriptive Statistics and a Paired sample t-test, as well as daily data from KSE-100 spanning 2009 to 2019. This data was analysed in terms of market capitalization, traded value, daily stock returns, and trading volume. Our results show that, overall, market capitalization increased significantly during the period, except in the second phase. Daily stock returns for the KSE100 Index did not improve over the entire period, though the decrease was not statistically significant. Trading volume did not improve and decreased significantly in both the first and second phases, but increased after the listing of the Pakistan Stock Exchange on its own exchange. The overall conclusion is that the performance of the Index improved, which is a crucial finding for foreign investors looking to invest in the Pakistani stock market. The study is also significant for institutional investors, local investors, potential investors, and portfolio managers who are interested in the growth and development of the stock market. This study is a valuable addition to existing literature, as a significant portion of the existing literature on the demutualization of the Pakistan Stock Exchange is qualitative.

Investing In Renewable Energy Stocks: Performance of Yield Cos in US

Purpose – Renewable Energy company stocks is gaining taction for yielding lucrative returns in covid and post covid world. Moreover, there has been a lot of talk about ESG investing and its potential benefits to the future economy. Most of the top companies in terms of market capitalization often use yield cos to shift the risks and gain good returns.The purpose of the paper is to focus on how the renewable energy companies having yield cos operate and what makes their structure unique for investment. Design/methodology/approach – The stock data for the top 5 renewable companies with yield cos are taken from Yahoo Finance, Cap IQ for the years 2010 to 2023. Stock price analysis is carried out to determine how the stocks performed compared to S&P500 index. Findings – We could see that the yield co structure for the renewable companies remains an attractive and viable mechanism for renewable energy projects and the companies. The renewable companies most of the time bet the market index suggesting that the stocks have significant potential in the future. Originality/value – The paper primarily focuses on the renewable energy companies which follow the yield co structure. First, the historic trend from 2010 has been analyzed to determine how the companies evolved over time. Second, correlation analysis has been performed with the index to determine how it compared against the market. Third, detailed analysis has been performed on the impact of yield cos and its potential benefits.

Evaluation based on Relative Utility and Nonlinear Standardization (ERUNS) Method for Comparing Firm Performance in Energy Sector

The present paper introduces a new multi-criteria decision making (MCDM) model such as Evaluation based on Relative Utility and Nonlinear Standardization (ERUNS). The proposed method provides a number of unique features such as use of non-linear standardization combined with a new definition for calculating utility values of the alternatives and design flexibility. The new method is applied to compare the firm performances using Economic, Environment, Social and Governance (EESG) framework. Over the last few decades alongside economic gain, the firms have been equally emphasizing on environmental protection, social values and good governance. This paper aims to bring in the economic aspect in terms of market capitalization and enterprise value while uses rating scores for environment, social and governance to put forth an extended performance measurement framework. A set of eight leading firms belonging to the energy sector listed to NSE, India has been considered for the period FY 2022-23 as a case study. The criteria weights are calculated using the Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) method. It is observed that ERUNS provides a stable and reliable ranking result free from the rank reversal issue. This paper shall be of interest to both analysts and practitioners in terms of its robust method and practical performance assessment framework.

Commercial real estate prices in Europe after COVID-19

PurposeThe article aims to analyze the behavior of commercial real estate prices in Europe, with a focus on the post-coronavirus disease 2019 (COVID-19) pandemic period. The authors use national and city-level data for the various commercial real estate sectors in ten countries, as well as listed real estate data, to assess any differences across property type and space.Design/methodology/approachThe authors analyze the behavior of commercial real estate prices after the COVID-19 pandemic, emphasizing differences across property types. For that purpose, the authors use national and city-level direct real estate data for the ten largest countries in terms of market capitalization, as well as listed real estate data. The article then turns to discussing the likely trajectory of commercial real estate prices in the future.FindingsThe recent rise in interest rates and geopolitical instability have affected prices differently across sectors. Industrial properties benefited from the pandemic, although prices declined significantly in 2022. Residential properties continued their upward price trend and have been the best-performing property type during the last two decades. Retail real estate continued its downward price trajectory. Thus far, office markets do not appear to be significantly affected by structural changes in the sector. The data for listed real estate markets in Europe suggest that markets bottomed out in early 2023.Originality/valueThis paper provides for a better understanding of the behavior of commercial real estate prices in Europe since the COVID-19 pandemic. The authors assess whether the effects found during the COVID-19 crisis were temporary or long-lasting. Also, many economic and political uncertainties have emerged since the beginning of the Ukraine war in February 2022, and it is important to analyze the effects of such uncertainties on commercial real estate prices.

Monetary policy document analysis for prediction of monetary policy board decision

In terms of market capitalization, the bond market is larger than the stock market, and the bond market is affected by macroeconomic indicators. Despite this, there has been relatively little research, making it a good candidate for the use of data mining techniques. In this paper, a novel approach designed to predict the vote results of the Korean Monetary Policy Committee regarding the base interest rate was proposed. To predict sentence sentiment, prior monetary policy decision text was used as input for classification models. The sentence sentiment prediction model showed 83.7% performance when using a support vector machine. In addition, it was observed that the bigrams extracted from documents provided important descriptions of the Korean economy at the time. Finally, the document sentiment of monetary policy decision was calculated using aggregating sentence sentiment, and the vote results were predicted using this sentiment. As a result, when using the support vector machine to predict the Monetary Policy Committee vote results, the performance improved by 29.5% over the baseline model. Statistical tests confirmed whether there is a difference in document sentiments between unanimous and non-unanimous, and the null hypothesis was rejected at a significance level of 5%.

Herding behavior in the cryptocurrency market: the case of the Russia–Ukraine conflict

This study investigates the herding behavior in the cryptocurrency market during the period of the Russia and Ukraine conflict using intraday cryptocurrency price data of the five largest cryptocurrencies in terms of market capitalization. The empirical results indicate an anti-herding behavior during the whole period of the conflict, especially after the conflict officially happens. The research contributes to the growing literature on herding behavior in the cryptocurrency market by using intraday data and examining the Russia–Ukraine conflict period.

Customer satisfaction as a mediator between service quality and customer loyalty: a case study of Bank Central Asia

Purpose – This study aims to examine the effect of service quality on customer satisfaction and the impact of customer satisfaction on customer loyalty among banking service users in Indonesia. Design/methodology/approach – The population of this study consists of customers aged 18 years and above who use the mobile banking service provided by Bank Central Asia, one of the largest banking companies in Indonesia in terms of market capitalization. Purposive sampling was used to obtain a sample of 194 respondents who met the research criteria. Findings – The results of this study indicate that the reliability, customer service and support, and responsiveness variables have a significant positive influence on customer satisfaction, while the privacy and security variable was found to have no significant impact on customer satisfaction. Additionally, customer satisfaction was found to have a significant positive effect on customer loyalty. Research limitations/implications – The data for this study primarily came from East Java, Indonesia, highlighting the need for future research using longitudinal data to investigate the causal relationships between variables. Practical implications – The findings of this study can provide valuable insights into the influence of electronic service quality on customer loyalty in the Indonesian banking industry. It offers banks a deeper understanding of consumer behavior and the crucial satisfaction factors that drive long-term decision-making in banking services. Originality/value – This paper offers novelty by addressing the digitalization trend and competitive challenges faced by banking service providers in achieving customer satisfaction within the digital economic ecosystem. It specifically focuses on customers of Bank Central Asia, a leading banking company in Indonesia. The value of this paper lies in its insights for banking professionals, researchers, and policymakers in navigating the digital landscape and enhancing customer-centric digital banking services.

Making supply chains great again: examining structural changes to US manufacturing supply chains

PurposePresident Trump's tenure was accompanied by a series of protectionist measures that intended to reinvigorate US-based production and make manufacturing supply chains more “local”. Amidst these increasing institutional pressures to localise, and the business uncertainty that ensued, this study investigates the extent to which manufacturers reconfigured their supply bases.Design/methodology/approachBloomberg's Supply Chain Function (SPLC) is used to manually extract data about the direct suppliers of 30 of the largest American manufacturers in terms of market capitalisation. Overall, the raw data comprise 20,100 quantified buyer–supplier relationships that span seven years (2014–2020). The supply base dimensions of spatial complexity, spend concentration and buyer dependence are operationalised by applying appropriate aggregation functions on the raw data. The final dataset is a firm-year panel that is analysed using a random effect (RE) modelling approach and the conditional means of the three dimensions are plotted over time.FindingsOver the studied timeframe, American manufacturers progressively reduced the spatial complexity of their supply bases and concentrated their purchase spend to fewer suppliers. Contrary to the aims of governmental policies, American manufacturers increased their dependence on foreign suppliers and reduced their dependence on local ones.Originality/valueThe research provides insights into the dynamics of manufacturing supply chains as they adapt to shifting institutional demands.

Peluang Investasi Saham di Era Pandemi Covid-19

Covid-19 was announced to enter Indonesia for the first time in early 2020. This immediately had an impact for the Indonesian capital market which fall in various sectors. This can be seen from the Composite Stock Price Index (IHSG) which reached its lowest point at 3,912. However, entering 2021 as the second year of the pandemic era, the IHSG actually increased 10.08% compared to the previous year. This study aims to find out which industrial sectors and stocks have opportunities in the Covid-19 pandemic era throughout 2020 and 2021 in terms of market capitalization. The method used in this research is a literature study. The results of the analysis show that the industrial sector in the capital market that has investment opportunities in the Covid-19 pandemic era, seen from its market capitalization, are companies in the financial industry sector (BBCA, BBRI, BMRI, and BBNI), the consumer goods industry sector (UNVR, HMSP, and ICBP), as well as the telecommunications industry sector (TLKM).

The Study of Utilization of Machine Learning in the Automobile Sector

Numerous businesses and institutions are involved in the study, design, manufacture, and sale of automobiles; they are all part of the Automotive Sector. In terms of market capitalization, the automobile industry is among the top five most profitable in the world. Businesses in the car business and those closely related to it are the ones who really get to put wheels on the road. Most of the parts are included in this, including the engine and body, but not the wheels, batteries, or gas. The automotive industry has worldwide influence and significance notwithstanding the problem of excess capacity and poor profitability. The automotive industry is vital to global trade and production. More than half of the world's oil consumption is attributable to the production of about 60 million automobiles and light trucks annually. Self-driving capabilities, in-car Wi-Fi and navigation systems, parking assistance, programmable computers, energy- saving features, night-vision aid, all-around cameras, automatic emergency braking, sensors to detect pedestrians, and driver concentration aids, etc. are just a few of the technologies we use often in our cars. Several features in cars that we use often include autonomous driving capabilities, in-car internet, parking assistance, onboard computers, and parking sensors. Our ride is safe and comfortable because of these features. Automobiles rely on the driver's active participation to function properly. Although, "how can we achieve this, and what is the logic behind the automobile industry?" is the fundamental issue that has to be posed. Computer jargon or programming language may be a good way to describe the answer to this issue. Incorporating new languages into vehicles is now possible because of the abundance of available programming languages. The Automotive Group relies heavily on machine learning and AI to accomplish its goals. Keywords— Artificial Intelligence, Automotive Industry, Deep Learning, Convolutional Neural Network, Technology, Machine Learning

IMPACT OF FIRM PERFORMANCE ON STOCK RETURNS: EVIDENCE FROM LARGE-CAP, MID-CAP AND SMALL-CAP STOCKS

In this article, we analyse the impact of firm specific factors, namely, total income, net profit, and earnings per share (EPS) on stock returns of companies from the large cap, mid-cap and small- cap (LMS) categories using the panel quantile regression. We also use the ordinary least square method to compare our panel regression results. We select companies from NSE Large Cap100, NSE Mid Cap 100 and NSE Small Cap 100 Indices and each index is composed of 100 companies. These indices reflect the overall state of the Indian stock market. Our empirical analysis based on quarterly data from June 2010 to March 2022 shows some important findings. First, net sales significantly and negatively affect stock returns across the large, mid, and small cap stocks. Second, net profit and EPS have a substantial and positive impact on companies from all the categories. However, the coefficients are not significant across all quantiles. In short, the impact of firm specific factors on stock returns is not homogeneous across the LMS stocks implying that these factors do not influence stocks in a uniform way. The plausible reason may be that large, mid, and small cap companies share differences in terms of market capitalisation, growth potential and volatility.

Analysis of Chinese stock market based on Fama-French five-factor model

After almost 25 years of expansion since its founding in late 1990, our stock market has expanded to rank second in the world in terms of market capitalisation. However, the stock market's speculative nature, information asymmetry, and extreme volatility can frequently raise serious concerns. The Fama-French five-factor (FF) model is used in this study to determine the variables affecting stock returns. and compares it to the three-factor model to see which model has a stronger ability to explain stock returns in the A-share market in the People’s Republic of China from the standpoint of asset pricing. The monthly data of A-share listed businesses for a total of 264 months between January 2000 and December 2021 are chosen as the sample in this article. Five factors are built using these data. Additionally, descriptive statistics and correlation analysis are carried out for the Chinese A-share market's five-factor model. The returns of the first four variables for regression are used to explain the returns of the fifth component in a five-factor model redundancy test.Finally, the three-factor intercept model and the five-factor intercept model are then obtained by doing a linear regression on the five-factor model. The results of the empirical test allow for the following inferences: All five factors are significant and have more explanatory power than the three factors since the CMA factor is redundant in the five-factor intercept model and is replaced by CMAO.Significant variables in the three-factor intercept model include MKT ,SMB and HML.

Research on the Dynamic Changes in Tesla Stock Price with this turn’s Interest Rate Policy

Since the Federal Reserve's action of a series of interest rate increases in response to high inflation, the U.S. equity market as a whole and individual stocks have been under pressure from soft landing and recession concerns, which is reflected in stock prices. In this article, the impact of the Federal Reserve rate increase on Tesla stock returns will be evaluated. The applied VAR model will contribute to the analysis of the dynamic returns of stock returns. The results of the research will present the actual extent of the impact of the rate increase policy on the stock in quantitative terms, as Tesla is one of the top 10 multinational companies in terms of market capitalization has increased the reliability of the data model. The results imply that the Federal Reserve's decision to raise interest rates has an extremely negative impact on stock yields and that this impact diminishes over time. For policymakers, this monetary policy can be effective in the short run effectively suppressing asset prices rather than absorbing more liquidity for their capital markets. For investors, the period of interest rate hikes is not the appropriate time to invest since company profits will be affected by the strength of the local currency which will not meet the expected standards.

The instability of stablecoins

This paper examines the stability of the five largest stablecoins in terms of market capitalization through a fractional time series analysis. By using hourly data of Tether, USDC, Binance USD, DAI and PAX Dollar, we find strong evidence of instability of stablecoins, although these deviations from the $1 mark are gradually corrected at different speeds for all stablecoins except for DAI. For the latter, the deviations do not converge even in the long-run due to non-stationarity of the differentiated series between its price and the $1 mark. BUSD is found as the most stable stablecoin with the fastest correction speed. Further rolling-window analysis shows that stability of different stablecoins exhibits strong co-movement and time-variation.

Innovation Management Case Study

Apple Inc. (Apple), is one of the world's most valuable companies in terms of market capitalization. Apple led the global technology market by creating innovative products such as the Mac, iPod, iPhone, and iPad, all of which redefined their respective markets. This case examines Apple's approach to innovation, and the founder's role to foster a culture of innovation and inspire employees to create game-changing products by thinking outside the box. Apple has always surprised the world with its innovation, beginning with the 1976 invention of the Apple I computer circuit board and progressing to become one of the world's most successful personal computer and electronic devices manufacturer and, more recently, smart devices. Adopting a systematic review method the focus here is to discuss the innovation management benefits of Apple's distinct and ever-changing organizational model and provide a SWOT analysis. Recommendations are then given based on the SWOT analysis so that other companies can adapt to this competing rapidly changing environment.

Stock Prediction Using Machine Learning Algorithms

In the recent times, the stock markets have emerged as one of the top investment destinations for individual and retail investors due to the lure of huge profits that are possible with stock investments compared to more traditional and conservative forms of investments such as bank deposits, real estate and gold. The stock markets unlike other forms of investment are highly dynamic due to the various variables involved in stock price determination and are complex to understand for a common investor. Individual and small-time investors have to generate a portfolio of common stocks to reduce the overall risk and generate reasonable returns on their investment. This phenomenon has given way too many individual and retail investors incurring huge losses because their decisions are based on speculation and not on sound technical grounds. While there are financial advisory firms and online tools where individual investors can get professional stock investment advice, the reliability of such investment advice in the recent past has been inconsistent and not meeting the rigor of quantitative and rational stock selection process. Many of such stock analysts and the tools mostly rely on short term technical indicators and are biased by the speculation in the market leading to huge variances in their predictions and leading to huge losses for individual investors. While the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques is widely adopted in the financial domain, integration of AI/ML techniques with fundamental variables and long-term value investing is a lacking in this domain. Some of the stock portfolio tools available in the market use AI/ML techniques but are mostly built using technical indicators which makes them only suitable for general trend predictions, intraday trading and not suitable for long term value investing due to wide variances and reliability issues. The availability of a Financial Decision Support System which can help stock investors with reliable and accurate information for selecting stocks and creating an automated portfolio with detailed quantitative analysis is lacking. A Financial Decision Support System (DSS) that can establish a relationship between the fundamental financial variables and the stock prices that can VII automatically create a portfolio of premium stocks shall be of great utility to the individual investment community. As part of this thesis, the researcher has designed and developed a Financial Decision Support System (DSS) for selecting stocks and automatically creating portfolios with minimal inputs from the individual investors. The Financial DSS is based on a System Architecture combining the advantages of Artificial Intelligence (AI), Machine learning (ML) and Mathematical models. The design and development of the Financial DSS is based on the philosophy to combine various independent models and not rely on a single stock price model to increase the accuracy and reliability of the stock selections and increase the overall Return on Investment (ROI) of the stock portfolio. The Machine learning models are used to establish the relationship between fundamental financial variables and the price of the stock, a mathematical model is developed to calculate the intrinsic value of the stock taking in to account the full lifecycle of the stock which involves various phases and a comprehensive model to analyze the financial health of the stocks. The AI/ML stock models are independently trained using historical financial data and integrated with the overall Financial DSS. Finally, the Financial DSS tool with a graphical user interface is built integrating all the three models which shall be able to run on a general-purpose desktop or laptop. To reliably validate the Financial DSS, it has been subjected to wide variety of stocks in terms of market capitalization and industry segments. The Financial DSS is validated for its short term and long-term Return on Investment (ROI) using both historical and current real-time financial data. The researcher has reported that the accuracy of the AI/ML stock price models is greater than 90% and the overall ROI of the stock portfolios created by the Financial DSS is 61% for long term investments and 11.74% for short term investments. This system has the potential to help millions of individual investors who can make their financial decisions on stocks using this system for a fraction of cost paid to corporate financial consultants and value eventually may contribute to a more efficient financial system.

지속가능경영과 다양성: 제도주의이론 관점에서 본 이사회 내 여성 임원 비율

This paper examines the effects of corporate behaviors related to sustainable management which are presumably intended to attain institutional legitimacy on the diversity of board of directors in terms of the proportion of female executives in board composition. While previous studies focused mainly on roles of female board members in the advancement of corporate social responsibility or accounting transparency, we pay attention to the institutional factors that may affect female executives’ participation in the board of directors. The results of our empirical analysis of leading Korean corporations in terms of market capitalization from 2014 to 2018 show that corporate characteristics and behaviors related to sustainability and institutional dynamics, such as publishing sustainability report, sensitivity of environmental issues, and visibility within institutional fields, have positive effects on the participation of female executives in the board of directors, as predicted in our hypotheses.

The value of integrated reporting in South Africa

Background: South Africa is currently the only country in the world where its largest stock exchange has adopted integrated reporting on an ‘apply and explain’ basis, through the implementation of the King Code for Corporate Governance (King IV). However, there exists significant uncertainty regarding the value of adopting integrated reporting.Aim: The objective of this study is to establish whether organisations, perceived to produce higher-quality integrated reports, achieve better financial performance or if the value of integrated reporting lies in improving organisational legitimacy and managing stakeholders’ impressions.Setting: The sample consists of the Ernst Young (EY) ranked companies listed on the Johannesburg Stock Exchange (JSE) from 2011 to 2020.Method: The study examines whether the quality of integrated reporting is associated with various financial performance measures, namely liquidity, solvency, profitability, and market performance, using multinomial logistic regression.Results: The multinomial logistic regression model is weak and indicates no direct relationship between integrated reporting quality and financial performance. An investigation into specific variables in the model indicates that top-performing companies, in terms of integrated reporting quality, tend to have significantly lower price-to-book value ratios and higher return on equity values. Companies with the best quality integrated reports also appear to be larger in terms of market capitalisation than those companies who prepare integrated reports of lesser quality.Conclusion: The results of the study do not record a significant relationship between integrated reporting quality and financial performance. The results indicate that larger companies listed on the JSE produce better quality integrated reports. This may be an indication that companies produce integrated reports, not for their financial value-adding benefits but to maintain organisational legitimacy and to manage the impressions of stakeholders.

The looming crisis in the Chinese stock market? Left-tail exposure analysis of Chinese stocks to Evergrande

We estimate and explain the time-varying left-tail dependence between the returns of the China Evergrande Group, a prominent real estate developer in financial distress, and 275 liquid shares traded on major Chinese stock markets. Our sample starts on 15th October 2015, and ends on 29th April 2022. The analysis reveals that the most exposed were shares of larger corporations (in terms of market capitalization) and those in the real estate and utilities sectors. On the other hand, A-shares and shares in the information technology, health care, or even consumer sectors tend to be less exposed.

Evidence on the Existence of Inter-industry Differences in Capital Structure of Leading Indian Companies

The objective of this paper is to provide additional evidence on the inter industry differences in the Capital Structure of leading Indian companies in terms of market capitalization. Comparisons in the capital structure of 15 industrial groups namely Pharmaceutical, Non-Metallic Mineral Products, Power, Electric Equipment, Automotive, Chemical, Construction, Information Technology, Food-Beverages-Tobacco-Alcohol, Consumer Goods, Textile, Service, Metal, Retail/Wholesale and Others identified from 333 companies are made using three gearing ratios i.e. Total Debt to Net Worth ratio (TDNW), Long Term Debt to Net Worth ratio (LTDNW), Short Term Debt to Net Worth ratio (STDNW). One way ANOVA and Post hoc tests are employed for statistical analysis. Construction and Textile industries have a higher level of debt in their capital structure. However, Information Technology and Service industries are identified as least levered ones. Overall, the finding indicates IT and Service industry to be significantly different from other industry groups in case of all the debt ratios.