Companies In Stock Market Research Articles

How the Financial Performance of Coal Mining & Metal, and Mineral Mining Companies in Indonesia's Stock Market From 2017 to 2022 Influenced Their Stock Prices

This research ascertained the effect of return on asset, debt to equity, company size, and current ratio on stock prices. The study applied descriptive quantitative. The method includes library research that utilizes data from secondary sources. The population in this study are all coal mining, metal, and mineral mining companies registered on the Indonesia Stock Exchange in the 2017-2022 period. Using the purposive sampling method, 26 companies are obtained with a total of 31 data observations. In analysing the data, the research used panel data analysis (fixed effect). Meanwhile data testing was done by using the E-views 10 program. The findings revealed that partial return on assets and company size had an effect on stock prices, whereas debt-to-equity and current ratios had no effect on stock prices. The study found that several factors which are return on asset, debt-to-equity, company size, and current ratio all simultaneously impact stock prices.

Public Environmental Concern and Corporate Environmental Governance

We examined the impact of public environmental concerns (PEC) on corporate environmental governance (CEG). Using Chinese listed companies in Shanghai and Shenzhen stock market as our research sample, we find that the higher the regional PEC, the better the level of CEG, and the positive effect of PEC on CEG is more significant in the sample of non-state owned enterprises. An examination of the influence mechanism finds that PEC increase government environmental attention and therefore more stringent government environmental regulations are developed to constrain corporate environmental decisions. This study will help us better understand the unique role of the public in corporate environmental strategy in the Internet era.

Enhancing operational efficiency in Vietnamese securities companies: Lessons from global practices and strategic recommendations

This study aims to improve the operational efficiency of Vietnamese securities companies by analyzing international practices and deriving pertinent lessons. Employing a research sample of secondary data, including audited financial statements and financial safety ratio reports from 70 Vietnamese stock market companies between 2013 and 2020, the study adopts analytical, synthetic, and comparative methodologies, underpinned by historical materialism. It meticulously investigates and evaluates practical aspects of business efficiency in securities companies, pinpointing specific challenges impeding operational efficiency in the Vietnamese context. The findings emphasize the need for substantial financial resources, advanced technical infrastructure, enhanced human resource quality and research activities, development of consulting services, and diversification of business operations and investor clientele. Additionally, it highlights the importance of flexible business strategies and effective utilization of company strengths. These insights offer a blueprint for Vietnamese securities firms to improve their performance and competitiveness in the global market.

The Impact of Stock Market Listing on the Financial Performance of Companies within the Rwanda Stock Exchange (RSE)

The study, titled Stock Market Listing and Company Performance in Rwanda aimed to explore the connection between a company's stock market listing and its performance. Examined within were firms listed in the Rwanda Stock Exchange, specifically Bralirwa, KCB, and BK. Utilizing a quantitative research design, the study relied solely on secondary data, primarily the financial statements of the three companies that operated in Rwanda between 2008 and 2015. The scope included BK, Bralirwa, and KCB. Data gathered from 2010 to 2012 was structured in tables, with financial ratios computed and subsequent analysis conducted using SPSS to ascertain the relationship between stock listing and financial performance. The results demonstrated a positive yet statistically insignificant relationship between stock listing and the financial performance of the listed firms. Notably, the correlation between financial leverage and financial performance lacked statistical significance (R = 0.303, P > 0.01) and showed a negative correlation with market ratio (0.582, P > 0.01). Recommendations included a call for improved liquidity management, especially in working capital for firms like BK, to address the impact on liquidity levels while maintaining financial stability. Stakeholders were encouraged to recognize the linkage between stock listing and financial performance and adopt suitable measures to assess and analyze the financial status of companies. Moreover, the study revealed that companies in Rwanda tend to rely more on short-term debt than long-term debt, potentially due to the underdeveloped bonds market in the country. Liquidity ratios exhibited a negative relationship with financial leverage, indicating that highly profitable and well-performing companies in Rwanda tend to have less debt and rely more on internal financing sources, aligning with the pecking order theory. Furthermore, the study emphasized considering the market value of capital structure in evaluating stock listing, given its stronger connection to financial performance compared to the book value. Keywords: Stock Market, Listing, Performance of Companies, Rwanda Stock Exchange, Liquidity.

Employee ownership in the US: some issues on ESOPs – overcoming the barriers to further development

PurposeThis viewpoint will present some statistical information about employee ownership in the US and interpret and analyze this information in order to address the barriers question using material from qualitative interviews that the authors have conducted over the last ten years with practitioners in the field. There have been few actual empirical studies that sort out the different barriers to employee ownership. The authors have chosen to focus on employee stock ownership plan (ESOP) in the US because this is the principal example from which people could learn from, and the high prevalence of ESOPs plays an important role in the US. This overview will present interpretations of these interviews with conceptual arguments that cannot always be supported with either overwhelming empirical studies or arguments that conclusively eliminate one or other explanation. This is an initial attempt to bring some comprehensive treatment and data to this incipient discussion. This is based on an interpretive analysis of qualitative interviews without quantification or social survey methods used for measurement. The advantage of this approach is that it lays out a completely different level of analysis of the barriers to employee ownership in the US that is “closer to the ground” and more based in the views of front-line practitioners who are actually implementing it.Design/methodology/approachAnalysis and interpretation of qualitative interviews.FindingsThe list of barriers that has been identified is not exhaustive. The preliminary conclusions are that (not necessarily in this order) limitations of investment banking models, poor supportive infrastructure, complexity and cost and regulatory issues, the lack of support by political parties and social movements, the sale of companies due to financial considerations and legal complexities and lack of clarity and resistance by Federal agencies are major barriers in the US. Various sectors of Wall Street has been amenable to employee ownership with the proper government and private sector support. What is needed now is a series of quantitative surveys and qualitative interviews of retiring business owners in closely held companies and of CEOs and CFOs in stock market companies in order to gauge the barriers that they believe are blocking their own action in the employee share ownership area. The Rutgers Institute for the Study of Employee Ownership and Profit Sharing is working on such a research agenda at this time. In addition, with the future size of the US employee ownership sector at stake, a more intensive one-year interview project would make sense in order to present these different explanations to key actors and practitioners and ask them to provide evidence to prove or disprove the relevance of the different barriers.Research limitations/implicationsEmpirical research which can resolve which barriers are more important than others is presented, when possible; however, studies that provide metrics to compare different barriers are not available and need to be carried out.Practical implicationsOther countries considering employee ownership policies can learn from the US experience. US policymakers and legislators can learn from an original, recent discussion of barriers.Social implicationsIf employee ownership sectors are to be developed, a careful discussion of barriers is most relevant.Originality/valueOriginal document by the authors based on original interviews.

Pengaruh Kebijakan Dividen, Kepemilikan Manajerial, Penghindaran Pajak, Profitabilitas, Dan Leverage Terhadap Nilai Perusahaan

The firm value measures the amount of a company's stock market price against its book value as an illustration of investors' perceptions of their confidence in the company for good performance prospects in the future. This study aims to analyze the effect of dividend policy, managerial ownership, tax avoidance, profitability, and leverage on firm value in non-financial companies on the Indonesia Stock Exchange for the period 2020 - 2022. The sampling technique used in this study was purposive sampling. This research sample consists of 165 companies that meet the criteria as a unit of analysis. The analysis method used is multiple linear regression analysis. The results provide empirical evidence that dividend policy, managerial ownership, profitability, and leverage affect firm value. Meanwhile, tax avoidance does not affect firm value.

Social Media and Company Stock Performance: A Thematic and Bibliometric Review

Theoretical background: The latent impact of the social media on company stock market performance or equity value has already been empirically studied. It is still in its infancy, although it has received increasing attention over the past few years. No efforts have yet been made to systematically review these studies in order to provide researchers and practitioners with an overview of the state-of-the-art links between social media and stock market performance of companies. This makes our work different from other recently published review papers on social media. Purpose of the article: The goal of this paper is to review, systematize, and integrate existing research on links between social media releases and stock performance, including research production timeline, global contributions, source analysis, affiliations, author locations, and citations of studies on social media (Twitter especially). A further objective is to comprehend the conceptual and intellectual structure of the relevant literature and to identify the knowledge base of social media use in investor relations and financial communication. Research methods: This study employs thematic and bibliometric analysis methodology on 135 peer reviewed papers obtained from two databases (Web of Science and Scopus) and provides an analysis of science mapping, including co-citation analysis, bibliometric coupling, word analysis, and trending topics regarding the relationship between social media releases and stock performance. Main findings: Our results provide three emerging clusters: (1) company stock performance, (2) investor or sentiment analysis, (3) user-generated content and several niche topic clusters: (4) corporate governance and disclosure (5) capabilities and earnings management and (6) economic and social effects. This study indicates that social media have significantly altered the corporate information landscape. Companies and information consumers must incorporate the new channels into their information dissemination or acquisition, and decision-making processes.

Multi-attribute group decision-making based on q-rung orthopair fuzzy Aczel–Alsina power aggregation operators

This article aims to develop the Aczel–Alsina operational laws based on power aggregation operators (PAOs) for the q-Rung orthopair fuzzy set (FS) (q-ROFS) framework. Changing the parameter can alter these sets according to the degree of fluctuation, providing a range of options. The decision-making sciences use “q-ROFS” to describe the family of the most distinct fuzzy information thoughts. In response to the degree of variation, the q-ROFSs can increasingly adjust the information region by fluctuating the restriction q≥1, making numerous options. But on the other hand, PAOs have the advantage of vanishing the influence of awkward data from the final results. To take advantages of POAs, based on Aczel- Alsina (AA) operational laws, q-Rung orthopair fuzzy (q-ROF) AA power-weighted averaging (q-ROFAAPWA) and q-ROF AA power-weighted geometric (q-ROFAAPWG) operators are stated. Also, it studied that the proposed AOs fulfilled the conditions of boundedness, monotonicity, and idempotency. Based on these newly constructed AOs, a technique for dealing the multi-attribute group decision-making (MAGDM) problems is suggested. Numerous research, correlations with another modern approach and a numerical example of selecting stock market companies have been used to show the suggested system’s applicability. The sensitivity analysis of the developed method is examined. A comparative study with other prevailing methods is also providing for superiority analysis. Finally, we demonstrated that in the results and discussion section, the proposed AOs in the q-ROPFS framework are more reliable in aggregating information than intuitionistic fuzzy (IF) set (IFS) and Pythagorean FS (PyFS) frameworks.

The loss of political connections and corporate financialization

This paper examines firms' financial asset allocation motives from the perspective of loss of political connections. Based on listed companies in China's stock market, we show that the loss of political connections leads enterprises to hold more financial assets based on preventive saving motives, leading to a stronger tendency of financialization, and this effect is significant only among non-state enterprises but not among state enterprises. Meanwhile, non-SOEs with low debt financing levels tend to hold more financial assets, while non-SOEs with high debt financing capacity keep their financial investment levels relatively the same.

The Effect Of Retained Earnings On Trading Volume Activity (TVA) With Return On Assets As A Moderation Variable

Trading Volume Activity (TVA) indicates the amount of stock trading volume in a company's stock market. Companies with good stock volume and financial performance can increase stock trading volume. The research aims to empirically examine the effect of retained earnings on Trading Volume Activity (TVA) with Return On Assets (ROA) as a moderating variable. The study uses data from the 2018-2020 financial statements of companies in the consumption and construction services sector listed on IDX and analyzed using moderation regression analysis. The results show that the variable retained earnings is simultaneously significant to Trading Volume Activity (TVA) with Return On Assets (ROA) as a moderating variable. Partially retained earnings can significantly influence Trading Volume Activity (TVA), while ROA can moderate the retained earnings variable on Trading Volume Activity (TVA). The research contributes that companies can increase the amount of retained earnings and increase ROA so that trading volume can be increased. The more companies trade, the value-added received will increase

The cost of foreign ownership: Voluntary sustainability reporting and financial performance in an emerging economy

PurposeMany emerging economy firms are under foreign owners' pressure to embrace the challenges of addressing corporate social responsibility (CSR) and consider adopting sustainability initiatives. However, it is not clear how foreign ownership plays a role to enable or inhibit these emerging economy firms from translating sustainability initiatives into improved financial performance. Utilizing neo-institutional theory, the authors argue that emerging economy firms that voluntarily report sustainability gain legitimacy in the eyes of shareholders and improve stock market performance. However, emerging economy firms may not have the resources to reconcile the internal stakeholders' various legitimacy requirements to promote sustainability practices, resulting in a negative association with accounting performance. Foreign ownership attenuates the relationship between sustainability reporting and firm performance due to the different legitimacy requirements in foreign markets.Design/methodology/approachTo test the study’s hypotheses, the authors collected and analyzed a large sample of publicly listed firms between 2010 and 2016 in Taiwan where the types of foreign ownership include foreign trust funds, foreign financial institutions and other foreign legal entities. Regression analyses were conducted to investigate whether the firms that report their sustainable practices have better financial performance, including stock market performance and accounting performance. Additionally, a three-step procedure was employed to address the endogeneity issue with a binary explanatory variable.FindingsThe positive stock market reaction to the emerging economy firms' voluntary sustainability reporting supports legitimacy gained among investors. By contrast, sustainability reporting has a negative association with accounting performance due to the difficulty of reconciling different legitimacy requirements among various stakeholders in emerging economies. Further, foreign ownership, particularly the trust fund, exhibits a negative moderating effect on the relationship between sustainability reporting in aligning corporate practices with sustainable development goals (SDGs) and the company's stock market performance.Originality/valueBy examining the less tested contingent role played by foreign ownership in the emerging economy firms' sustainability reporting, the authors provide insights into the influence exerted by different types of foreign ownership on firms' financial performances beyond previous studies that focus on family ownership, state ownership, or managerial ownership in emerging economies. The findings shed light on corporate sustainability strategy and foreign direct investment policies for an emerging economy.

Моделювання оптимального інвестиційного портфеля орієнтованого на мінімізацію ризику

Abstract. Introduction. Portfolio optimization modelling is a study aimed at determining the best way to allocate an investor's funds between securities of different companies. The study uses a number of methods and approaches, such as: multicriteria optimization methods - optimization of criteria for minimum and maximum, modeling of the optimal stock portfolio with minimal risk, to determine the best way to allocate funds that will help investors achieve maximum return while minimizing risk. Purpose. There are a lot of publicly traded companies in the modern American stock market. Having chosen the companies that may grow in the future, the problem of allocating your funds among them remains relevant. Building an optimal portfolio of securities using various economic and mathematical methods solves the problem of allocating financial resources, focusing on the desired future profit and the level of risk exposure. Results. The model for this study was built using one of the methods of multi-criteria optimization - criteria convolution, taking into account portfolio diversification and the specified constraints. The optimization is based on the "Modern Portfolio Theory" of the prominent scientist Harry Markowitz. Conclusions. As a result, we built an optimal and diversified portfolio of shares, in which each company on the list represents at least 1%. All constraints have been met and the main conditions have been fulfilled - the portfolio minimizes risk and maximizes profit. With a minimum risk of 5.39%, we expect a return of 1.75%. Such results can be obtained if we use a convolution of the criteria where the preference is given to minimizing risk - 0.7. The largest contributors to the portfolio were the following companies: T-Mobile Us Inc., McKesson Corporation, The Kroger Co., Microsoft Corporation, and Apple Inc. These companies account for a significant portion of the portfolio - 65%. Given the focus on portfolio diversification and the ratio of risk to potential return, we can say that the portfolio is efficient and can be used in practice.

From Ranking Search Results to Managing Investment Portfolios: Exploring Rank-Based Approaches for Portfolio Stock Selection

The task of investing in financial markets to make profits and grow one’s wealth is not a straightforward task. Typically, financial domain experts, such as investment advisers and financial analysts, conduct extensive research on a target financial market to decide which stock symbols are worthy of investment. The research process used by those experts generally involves collecting a large volume of data (e.g., financial reports, announcements, news, etc.), performing several analytics tasks, and making inferences to reach investment decisions. The rapid increase in the volume of data generated for stock market companies makes performing thorough analytics tasks impractical given the limited time available. Fortunately, recent advancements in computational intelligence methods have been adopted in various sectors, providing opportunities to exploit such methods to address investment tasks efficiently and effectively. This paper aims to explore rank-based approaches, mainly machine-learning based, to address the task of selecting stock symbols to construct long-term investment portfolios. Relying on these approaches, we propose a feature set that contains various statistics indicating the performance of stock market companies that can be used to train several ranking models. For evaluation purposes, we selected four years of Saudi Stock Exchange data and applied our proposed framework to them in a simulated investment setting. Our results show that rank-based approaches have the potential to be adopted to construct investment portfolios, generating substantial returns and outperforming the gains produced by the Saudi Stock Market index for the tested period.

The effects of dividend policy to stock prices of listed companies in Vietnam stock market

Dividend policy is one of the most important policies in corporate finance management. Understanding the impact of dividend policy on the distribution of profits, corporate value and thus on the stock price is important for business managers to make policies and for investors to make investment decisions. This study is conducted to evaluate the impact of dividend policy on share prices for companies listed on Vietnam’s stock market in the period from 2010 to 2018, based on the availability of continuous dividend payment data. Using the FGLS method with panel data of 100 companies listed on the HoSE and HNX, we find evidence of the impact of dividend policy on stock prices, supporting supports the bird in the hand and the signal detection theories. The findings of this study help to suggest a few recommendations for business managers and investors.

Discovering the influences of the patent innovations on the stock market

With the implementation of the innovation-driven development strategy, increasing technical innovations are patented by the individuals or the companies. As a form of intellectual properties, the patent has attracted attention from individuals and companies. Although there are some researches on the economic function of patent, few quantitative researches discuss on whether patents can work on the company stock market. To discover the relations between the company patents and the stock market, we explore a method to analyze the influence of patent activity on the company stock market. We collect the patent data and the stock data of listed companies, from which patent and market activities are extracted. By the recursive discrete wavelet transform, the patent and market activities are decomposed into multi-scale wavelets. These wavelets are fed into a patent and market activity based stock market trend prediction model, in which the influences of patent activity are analyzed. We compare our model with the state-of-the-art model on 4 measurements for 3 manufacturing datasets. The experimental results show that the patent activities have positive effect on market trend prediction in about 30% manufacturing listed companies and that the measurements of Shanghai/Shenzhen Stock Exchange often outperform that of USA in years 2016–2019 for the manufacturing listed companies.

Strategic Positioning Affect Corporate Financialization

This paper examines the impact of corporate strategy aggressiveness on the trend of financialization. Taking Chinese A-share listed companies of Shanghai and Shenzhen stock market from 2007 to 2018 as samples, the study finds that there is a positive correlation between corporate strategy aggressiveness and corporate financialization, that is, the more aggressive the company is, the higher its degree of financialization will be. Further research in this paper finds that there is a grouping moderating role of profitability and analyst concerns in the mechanism of the impact of corporate strategy on corporate financialization. Companies with weaker profitability and higher analyst attention level are more likely to have financialization tendency. The research of this paper expands the influencing factors of corporate financialization, and confirms that the degree of corporate strategy aggressiveness has a positive impact on corporate financialization, which has important practical significance for promoting the further development of strategic management practice of China's non-financial listed companies.

Companies’ Stock Market Performance in the Time of COVID-19: Alternative Energy vs. Main Stock Market Sectors

The paper aims to detect the differences in stock market performance between companies from the alternative energy sector and main stock market sectors in the first and second years of the COVID-19 pandemic. We used Global Industry Classification Standard to analyse eleven main stock market sectors and the alternative energy sector. Based on the one-factor variance analysis—ANOVA, we reveal the statistically significant differences between the analysed stock market sectors in both 2020 and 2021. The analysis implied that the performance of stock market companies during COVID-19 is sector-specific. Tukey’s Honestly Significant Difference (HSD) test for pairwise comparison indicates that the alternative energy sector shows the most differentiation. Its average rate of return in 2020 is the highest and is significantly different for all eleven stock market sectors, while the top constituents from the conventional energy and financial sectors suffered the most. In 2021, a reverse trend in the stock prices can be observed. Companies from the conventional energy and financial sectors achieved the highest positive average weekly rates of return among all of the analysed stock market sectors, while the alternative energy sector performed significantly worse than the other sectors did. Nevertheless, throughout the entire analyses period of 2020–2021, the companies from the alternative energy sector turned out to be the biggest stock market beneficiaries. This study might imply that the COVID-19 pandemic has not hampered but has instead accelerated growing concerns about the environment and climate change.

Comparing Two ARIMA Models for Daily Stock Price Data

Abstract: Analyzing the past data and planning for future is very important for every public and private organizational decisions. Now a days individuals also using forecasting methods to invest in Stock market. Investments in mutual funds and in registered companies in companies in stock market is the order of the day. In this paper, advanced forecasting methods are fitted to the time related stock price data to study its effectiveness in forecasting future events. Auto correlation and standard models have been analyzed before fitting this model to the above data. The forecasting can be done by using the ARIMA time series(using auto. arima) model. A particular reference have been made to Box and Jenkins approach for day to day stock price data values of Exxon Mobile Corporation from '1995-01-01 to 2020-03-01. With usual statistical software R. Here, ARIMA(1,1,1,) is fitted to this data, These results are compared with the model ARIMA(1,1,1,) by using accuracy measures. Keywords: ARIMA: Auto Regressive Integrated Moving Average ACF: Auto Correlation Function PACF: Partial Auto Correlation Function AIC: Akaikae Information Criterion RMSE: Root mean square error XOM: Exxon Mobil Corporation

The Globalization and Business Performance of Corporate Value Chain

Purpose - This paper empirically investigates the relationship between the corporate value chain and performance of non- financial businesses of South Korean stock market companies. It aims to explore the evidence that can be used to infer the relationship between value chains and corporate performance in the case of firms forming a value chain with other companies with the means of an equity investment or a special business relationship. Design/methodology - Non-financial corporations listed from 2011 to 2017 on the securities market of South Korea are analyzed. The data used for analysis are found for transactions with the related party by year for all the corporations of non-financial industries in the securities market. Multiple analysis attempts are conducted including the relationship between the value chain and productivity, corporate value, risk-adjusted corporate value, and mediation effects of productivity. The empirical model employs sixteen variables including the value chain index which identifies its impact on various aspects of business performances. Findings - The results of this study clearly supports the phenomenon that corporate productivity and value are enhanced when the corporation expands its value chain established with domestic related firms and overseas companies. Such a positive effect is statistically significant even after the possible risk factors that accompany the expansion of value chain were considered, and productivity plays the role as a medicating variable in the effect of the value chain on the corporation values. Originality/value - The findings of this study confirms that domestic companies’ expansion of their value chain centered on the related firms overseas that helped them in terms of the maximization of their productivity and corporate values. This study shows that Korean government’s policy on expanding the corporate GVC can enhance the productivity and value of firms. The expansion of value chain and its impact on business performance has not been explored thoroughly, although it is getting more and more important in the global trade operation.

A Study on Stock Price Prediction using LSTM Model

Predicting stock price of any stock is a challenging task because the Volatility of stock market the nature of stock price is dynamic, chaotic, noisy and sometimes totally unexpected. The other most difficult task is to analyze and decide financial time series data that improves investment returns and help in minimizing losses. Technical analysis is a method that help in analyzing a stock and predict its future price via evaluating securities. There are already many Indicators and other tools for technical analysis in stock market. Some famous indicators such as SMA (Simple Moving Average), EMA (Exponential Moving Average), WMA (Weight Moving Average), VWMA (Volume Weight Moving Average), DEMA (moving averages), MACD (Moving Average Convergence/Divergence), ADX (Average Di- reactional Movement Index), TDI (Trend Detection Index), Arun, VHF (trend indicators), stochastic, RSI (Relative Strength Index), SMI(Stochastic Momentum Index, volume indicators are also available for technical analysis. Here, we have used the LSTM Model to predict future price of some big companies of stock market in NSE.