Large-cap Companies Research Articles

Exploring the asymmetric relationship between macroeconomic factors and corporate profitability in the MSCI Colombia index

PurposeThis study aims to explore the asymmetric effects of macroeconomic factors on the profitability of large-cap companies in an emerging country like Colombia, using the Morgan Stanley Capital International (MSCI) Colombia index as the basis.Design/methodology/approachWe employ a combination of singular spectrum analysis (SSA) and principal component analysis (PCA) to identify and estimate four key macroeconomic factors that account for approximately 47.8% of Colombia's macroeconomy. These factors encompass indicators related to inflation and cost of living, foreign trade and exchange rate, employment and labor force and trade and production in Colombia. We utilize the distributed lag nonlinear model (DLNM) to analyze the asymmetric relationships between these factors and corporate profitability, considering different scenarios and lags.FindingsOur analysis reveals that there are indeed asymmetric relationships between the identified macroeconomic factors and corporate profitability. These relationships exhibit variability over time and lags, indicating the nuanced nature of their impact on corporate performance.Originality/valueThis study contributes to the existing literature by applying a novel methodology that combines SSA and PCA to identify macroeconomic factors within the Colombian context. Additionally, our focus on asymmetric relationships and their dynamic nature in relation to corporate profitability, using DLNM, adds original insights to the research on this subject.

Portfolio Size on Profit Optimization: The Case of Large-Cap Companies in the Philippine Stock Market

Portfolio Size on Profit Optimization: The Case of Large-Cap Companies in the Philippine Stock Market

Market Capitalization and Its Impact on Equity Valuation a Mixed Method Study of The Indian Capital Market

An investor must perform research on a stock before investing in it. It is critical for the intelligent investor. As a result, stock prices have long piqued the interest of investors. Researchers have worked hard to identify the elements that influence stock prices and so returns. In the current study, 300 companies listed on India's National Stock Exchange (NSE) were divided into three categories based on market capitalization. The study relied on both primary and secondary data to reach its findings. Logistic regression is used to examine secondary data in the form of historical financial information. The outcomes of secondary data analysis are then confirmed using primary data gathered through interviews with domain experts. The study also revealed the importance of the new variables PEG ratio and economic value added (EVA) in Indian scenario to predict the market price of large cap companies. The study's findings can help market participants such as fund managers, stocks analysts, portfolio managers, retail investors, and high-net-worth individuals. The findings of the study will also contribute to the current body of knowledge on equity valuation.

Indian Stock Markets’ Reaction to the Nationwide Lockdown due to COVID-19: Evidence from Event Study Analysis

Indian Stock Markets’ Reaction to the Nationwide Lockdown due to COVID-19: Evidence from Event Study Analysis

Investigating the impact of company announcements on stock prices: an application of machine learning on Australian lithium market

This paper investigates the effects of various types of announcements made by lithium producers on stock prices. We collected data from 40 lithium-producing companies listed on the world's largest stock exchanges, spanning from May 2020 to September 2022. To analyze the impact of announcements such as quoted and unquoted securities, market announcements, company reports, public meetings and presentations, financial announcements, and technical announcements on stock prices, we employed an extreme gradient boosting (XGBoost) model. Our results indicate that stock exchange market announcements and announcements about public meetings and presentations significantly influenced the stock prices of all eight large-cap companies studied. Announcements about public meetings and presentations were crucial predictors of stock prices for 73% of all companies analyzed. Additionally, positive financial announcements were key predictors for 70% of the companies. These findings suggest that investors should consider these predictors when making investment decisions in the lithium-related stock market. This study contributes to the existing literature by providing empirical evidence on the impact of different types of announcements made by lithium producers on stock prices. Furthermore, the XGBoost model used in this study can be applied to other industries and markets to analyze the impact of various types of announcements on stock prices.

Impact Of dividend decision on the market capitalization of the firm: Evidence from Indian large cap companies

The percentage of the profit distributed to the shareholders is known as the dividend. The decision at hand is how much of the company's profit should be divided to the shareholders after taxes have been paid. It also contains the portion of the profit that needs to be invested back into the company. The retained earnings boost the company's potential for future earnings when the present income is reinvested. The amount of retained earnings has an impact on the company's choice of financing as well. The decision to declare a dividend should be made with the goal of maximizing shareholder value in mind. The present study attempted to evaluate the impact of dividend decision on the market capitalization using regression model. The regression analysis model revealed the significant impact of the independent variable on the market price of the share as well as the dividend policy determinants on the subsequent market price variable.

The ESG Discourse Is Neither Timeless Nor Stable: How Danish Companies ‘Tactically’ Embrace ESG Concepts

During the last decade, ESG has become a globally widespread doctrine of good investment principles. ESG defines investing for broader, extra-financial goals by the use of “environmental, social and governance” (ESG) factors. While commentators generally agree that ESG has become a crucial arena for defining responsible investment, research is so far scarce on the conceptual development of the ESG discourse as well as how companies articulate it in their public communication. By analysing ESG concepts, this article combines methods derived from corpus linguistics with dispositional analytics, inspired by Michel Foucault. The data material consists of 281 annual reports, which contain the self-representation of 24 Danish large-cap companies, including how they communicated their ESG policies from 2010 to 2021. The analysis displays the proliferation of specific ESG keywords as well as changes over time in their frequency, proportional to each other. We supplement the quantitative analyses with dispositional analytics, considering how the dispositives of law, discipline, and security condition Danish companies’ adoption of ESG. We also discuss how companies use ESG concepts ‘tactically’ to navigate a context, in which the dispositives ‘over-determine’ urgent environmental, social, and governance issues.

Market response and future performance of inefficient investment: Over-investment or under-investment

There have been many studies on the market response to investment spending, but only a few have examined the market response to the issue of over-investment or under-investment. This study examines the effect of the issue on market response and future financial performance. The sample includes large-cap companies listed on the Indonesia Stock Exchange (IDX) for 2016–2021. Samples must have at least 120 active trading days for each year. Two hundred and thirty-two observations meet the qualifications. This study adopts the investment inefficiency model developed by previous studies to measure over-investment or under-investment. Residual inefficient investment models are used as over-investment or under-investment scores, in addition to the dummy of the residual category. Market response is measured by cumulative abnormal returns (CAR), market capitalization (MCAP), and market-to-book value (MTB).Meanwhile, a firm’s performance uses return on assets (ROA) and return on equity (ROE). The results show that the coefficient of the inefficient investment variable, using both the residual value and the dummy variable, shows a negative direction, which means the market responds negatively to over-investment or under-investment. However, the value of t is significant at the <0.01 level on the market response variable as measured by MTB, but not significant for the other two proxies. Thus, hypothesis 1 is supported, although not for all market response proxies. The value of the inefficient investment coefficient also shows a negative direction when testing hypothesis 2 and is significant at the <0.1 level. These results are consistent with future performance variables measured by ROA and ROE. AcknowledgmentThe study was supported by PDUPT (Higher Education Primary Research Grant) from the Ministry of Education, Culture, Research and Technology, Government of Indonesia.

Impact of Covid-19 on U.S. Fun Industry based on Fama and French Five-Factor Model

As the Covid-19 pandemic sweeping the globe in 2020, it had a profound impact on the economic circumstances of the world. This paper applied Fama-French five-factor model to evaluate the influence of the Covid-19 on the fun industry in America. The fun industry’s data and daily return data from Kenneth R.French’s database were adopted to make multiple linear regression. The difference of coefficients of the five factors illustrates the significant changes in the entertainment industry due to the pandemic. The sensitivity to the market risk increased as the coefficients of MKT added from 0.73 to 1.02. The income effect of small companies has gradually been higher than that of large-cap companies as coefficients of SMB reached 0.99 from 0.54 during the pandemic. Stocks of fun company with a high book-to-market ratio performed better in general during the covid-19 as the HML factor became significant with a positive value of 0.66. The firm’s operating profitability has become a crucial factor in investors’ decisions for the entertainment industry under the uncertainty provided by the Covid-19 pandemic as the RMW factor became significant with a positive value of 0.65. Investors are more willing to put their money into the aggressive companies as CMA factor became substantial with a negative value of -2.44.

A Study on Performance Evaluation of Top 5 Leading Mutual Fund Schemes in India

Mutual funds are created as a part of an investment which invest in new financial instruments like stocks and bonds. Investing in them allows an investor to gain, bonds or fixed income securities, commodities, etc. Mutual funds function by pooling the cash together from many investors and that cash gets used to buy the stocks bonds and other securities. among many schemes offered by mutual funds companies blue chip schemes are a type of money equity fund, that primarily finance and equity related securities of large cap companies and even large-cap funds are also coined as the blue chip funds. The blue-chip Fund invests in a company that have a reputation for paying a regular dividend. The disturbing profit consistently over a long period and which has a risk return by investing the blue- chip schemes, which are essentially equity funds are known to be the best mutual fund schemes and safest to invest. The research type is descriptive and data source is collected from secondary data by using the sample top 5 blue chip funds in India for five years. The technical measures like have been Sharpe’s, Treynor’s and Jensen’s ratio.

Industrial digitalization in the industry 4.0 era: Classification, reuse and authoring of digital models on Digital Twin platforms

Digital Twins (DTs) are real-time digital models that allow for self-diagnosis, self-optimization and self-configuration without the need for human input or intervention. While DTs are a central aspect of the ongoing fourth industrial revolution (I4.0), this leap forward may be reserved for the established, large-cap companies since the adoption of digital technologies among Small and Medium-size Enterprises (SMEs) is still modest. The aim of the H2020 European Project ”DIGITbrain” is to support a modular construction of DTs by reusing their fundamental building blocks, i.e., the Models that describe the behavior of the DT, their associated Algorithms and the Data required for the evaluation. By offering these building blocks as a service via a DT Platform (a Digital Twin Environment), the technical barriers among SMEs to adopt these technologies are lowered. This paper describes how digital models can be classified, reused and authored on such DT Platforms. Through experimental analyses of three industrial cases, the paper exemplifies how DTs are employed in relation to product assembly of agricultural robots, polymer injection molding, as well as laser-cutting and sheet-metal forming of aluminum.

"Determinants of Capital Structure of Selected Large Cap Companies Listed in India"

"Determinants of Capital Structure of Selected Large Cap Companies Listed in India"

Factors Affecting Movement of Indian Stock Market: A Study with Special Reference to CNX Nifty

Stock markets are often referred to as barometer of an economy. Stock market indices not only tell us about the condition of stock markets but in a way they also reflect the health of an economy. Stock prices do not move in vacuum rather they are affected by a large number of factors. The objective of the study is to identify the factors affecting the movement of Indian stock market and the impact of these factors on the same. For the purpose of this study, CNX Nifty has been used to represent the movement of Indian stock markets. CNX Nifty is a well-diversified index consisting of 50 large cap companies representing most of the important sectors of Indian economy. The study deals with the time series data and consists of eleven variables i.e. Oil Prices, S&P 500, Nikkei, FTSE, NASDAQ, Wholesale Price Index as a proxy for inflation. Index of Industrial Production, Foreign Exchange Rate, Current Account Deficit, FII Investment and CNX Nifty. The study has been conducted over a period of 32 quarters from financial year 2005-06 to 2012-13. Researchers have used Principal Component Analysis and Regression Analysis for conducting the study and drawing the conclusions. All these variables have been clubbed into two factors namely Macroeconomic Factors and International Factors. The study revealed that both these factors i.e. macroeconomic factors as well as international factors have significant impact on movement of CNX Nifty.

A Comparative Evaluation of Performance of Nifty IT Companies in Relation with Nifty IT Index

The Information Technology sector is central to the nation’s security, economy and public health. It is one of the fastest growing sectors in Indian Stock Market. This paper evaluated the performance of companies listed in Nifty IT index with an objective to find out the significance level of each company with Nifty IT index with the help of paired sample T-test. The study covered five years starting from 1s January 2017 to 31 December 2021. Mean returns and standard deviation is calculated to analyze and compare the risk return characteristics of the companies listed in Nifty IT index. Correlation is also calculated to know the relationship of each company with Nifty IT index. The result of the study revealed that L&T Infotech, Mindtree, Mphasis, Coforge and TCS are statistically significant. HCLTech, Infosys, Tech Mahindra, Wipro and L&T Services are not statistically significant. This paper has suggested the investors to invest in those companies which are in consistent with the broader indices. Large cap companies are safe to invest than compared to mid and small cap companies.

S&P 500 Stock Price Prediction Using Technical, Fundamental and Text Data

We summarized both common and novel predictive models used for stock price prediction and combined them with technical indices, fundamental characteristics and text-based sentiment data to predict S&P stock prices. A 66.18% accuracy in S&P 500 index directional prediction and 62.09% accuracy in individual stock directional prediction was achieved by combining different machine learning models such as Random Forest and LSTM together into state-of-the-art ensemble models. The data we use contains weekly historical prices, finance reports, and text information from news items associated with 518 different common stocks issued by current and former S&P 500 large-cap companies, from January 1, 2000 to December 31, 2019. Our study's innovation includes utilizing deep language models to categorize and infer financial news item sentiment; fusing different models containing different combinations of variables and stocks to jointly make predictions; and overcoming the insufficient data problem for machine learning models in time series by using data across different stocks.

Sector-by-sector analysis of dependence dynamics between global large-cap companies and infectious diseases: A time-varying copula approach in EBOV and COVID-19 episodes.

Infectious diseases and widespread outbreaks influence different sectors of the economy, including the stock market. In this article, we investigate the effect of EBOV and COVID-19 outbreaks on stock market indices. We employ time-varying and constant bivariate copula methods to measure the dependence structure between the infectious disease equity market volatility index (IEMV) and the stock market indices of several sectors. The results show that the financial and communication services sectors have the highest and the lowest negative dependency on IEMV during the Ebola virus (EBOV) pandemic, respectively. However, the health care and energy sectors have the highest and lowest negative dependency on IEMV during the COVID-19 outbreak, respectively. Therefore, the results confirm the heterogeneous time-varying dependency between infectious diseases and the stock market indices. The finding of our study contributes to the ongoing literature on the impact of disease outbreaks, especially the novel coronavirus outbreak on global large-cap companies in the stock market.

Impact of ESG Factors on Emerging Market Stock Returns

Empirical evidence indicates that environmental, social, and governance (ESG) practices are associated with a company’s financial performance, but little has been mentioned about investors’ attention toward stock performance. In this article, the authors conduct a test of the relationship between ESG performance disclosures and a company’s financial or capital market performance. They choose a stratified sample of large-cap companies across eight sectors drawn from the S&P 100 from 2016–2021. The changes in ESG ratings—namely, for governance and the environment—exhibit a small but significant impact on the stock’s performance. Their results show that a few ESG disclosures are positively related and dominate over the usual company-level determinants of return on assets. They find corporate governance practices to be more significant than climate change disclosures. These outcomes are robustly demonstrated on use cases of top stocks with investor safety recommendations. The results could have useful implications for investors, fund managers, and regulators.

Financial Implications of Capital Controls Enforcement on Large-Cap Companies Listed on the Athens Stock Exchange

The aim of this study is to examine the effects of the capital controls on the process and financial performance of large-cap companies operating in Greece listed on the Athens Stock Exchange. More specifically, the markets’ behavior is studied, as well as the reactions of the investors after the announcement of the implementation of the measure. The mixed method (quantitative research with questionnaires and qualitative research with interviews with financial managers and senior executives) was used. The main findings of the research are that the measure of capital controls: was deemed necessary and effective based on its aims, which were (a) the protection of the mass withdrawal of deposits and (b) the stabilization and security of the financial system had a significant effect on increasing the use of electronic banking. But also had significant negative effects: on the investment decisions of companies, on their competitiveness and reputation abroad, on the ability to raise new capital, on their liquidity and therefore on their ability to cope with key functions, as well as on their imports and exports. Keywords: Capital controls, listed large-cap companies, crisis, Athens Stock Exchange, Greece.

INDIAN IT FIRMS CREATING MANAGEMENT CODES FOR FOREIGN EXCHANGE RISK

Purpose: India has been a preferred I.T. service sourcing nation globally and has been registering high growth. India has a significant pie of the global sourcing market, accounting for nearly 55 % share. It covers significant global through its more than one thousand centres spread across continents. With a year-on-year growth of 6.1%, India’s I.T. and ITES industry will increase to the U.S. $ 350 million by 2025. The extensive expanse of geographical coverage also translates into foreign exchange risk; hence foreign exchange risk management becomes an important strategy. The current study attempts to assess the impact of foreign exchange risk management on the Indian sector over 2007-2017; the period includes the 2008 financial crisis taken up in the current study.
 Design/Methodology/ Approach: We analyzed the Indian I.T. companies listed on the BSE Ltd on their exposure, approach, and management towards foreign exchange risk. We investigated their annual reports from 2007 -2017 to understand their exposure and the adopted external foreign exchange risk management techniques. We further assessed the impact of these foreign exchange risk management techniques on the firm’s value.
 Findings: The impact of foreign exchange risk management was significant on small-cap I.T. companies for the study period. Though for the during the 2008 crisis term, it was found to be insignificant.
 Practical/Implications: Foreign exchange risk management is crucial for Indian I.T. companies indulging in cross-border trade. The current study incorporates external methods of managing foreign exchange risk management; hence even if the impact were found to be insignificant for Mid Cap and some Large-cap companies, they would be practicing internal hedging methods, which puts a strong case tapping trillion-dollar business through a fully functional competitive International Financial Centre.
 Originality/Value: Our paper contributes to the literature on Foreign exchange risk management by Indian I.T. companies, which contributes handsomely to India’s GDP and Foreign exchange reserve.
 JEL Classification Codes: F31, G32.

International variations in ESG disclosure – Do cross-listed companies care more?

We study the quantity of ESG disclosure of 1,963 large-cap companies headquartered in 49 countries. Using the Bloomberg ESG disclosure score as the measure of disclosure quantity, we find that firm characteristics explain most of the variation in firms' ESG disclosure, whereas variations in country factors such as corruption and political rights explain less. We empirically examine and extend the theoretical framework of the liability of foreignness in capital markets. Our results support the notion that cross-listed firms disclose more ESG data than those only listed in their home market to mitigate the liability of foreignness in external capital markets. We also find that an increased percentage of foreign ownership does not augment ESG disclosure. Companies which opt to increase foreign equity ownership at home do not encounter the challenges of foreignness. Our findings suggest that cross-listed status is likely to reduce the importance of country factors for variations in ESG disclosure quantity.