Stock Exchange Index Research Articles

Financial Profitability Analytics of Selected Fertilizer Companies in India

Governments of every country formulates their policies which enhance the growth of the agriculture sector and thereby push the availability of the resources for the growth of the industrial sectors and thereby generates the real income for the livelihood of their citizens. The current market value of the Indian chemical and fertilizer companies is approximately around Rs. 898.5 billion for the financial year 2021-2022 and its projected growth is probably 4.7% of CAGR during the period 2023 to 2028 and the market growth is being witnessed by increased demand for food manufacturing industries. The Indian government consistently acting proactively to tap the projected grow of the chemical and fertilizer companies which leads to achieve the objectives of Atmanirbhar Bharat and become most efficient player all over the world. The below chart clearly indicates that the Indian fertilizer market will reach US$ 1460.8 billion by the end of the year 2029 from US$ 937 billion which is quite remarkable. For the study five fertilizer and chemical companies which are listed at the recognized stock exchange in India have been selected randomly and profitability ratio in relation to selected elements of financial statement used for the period from March 2016 to March 2022. Single Factor Analysis of Variance (ANOVA) has been used for testing the hypothesis and the results derived. The results shows that there is positive relationship between investment in fixed assets and generation of profit and for achieving desired profit the management should be aggressive to increase the investments over the fixed assets rather than focusing on net worth, capital employed and total assets.

Assessing the Influence of Corporate Governance on Corporate Social Responsibility Perceptions Between Firms in Turkish Governance and Main Stock Exchange Indexes

Assessing the Influence of Corporate Governance on Corporate Social Responsibility Perceptions Between Firms in Turkish Governance and Main Stock Exchange Indexes

EMERGING TREND IN INDIAN STOCK MARKET: A RISE IN RETAIL PARTICIPATION

Indian economy is one of the fast-growing economies in the world and is expected to reach a 5 trillion-dollar mark by 2025 leading to become the third largest economy. The Indian stock market is considered as one of best performing markets globally. Stock market provides investors with various opportunities to invest in different classes. This mobilization of investment is very much crucial for the fund’s movement and for the ultimate growth and progress of the economy. The investors count increased to huge number with the introduction of online stock trading first by national stock exchange and thereafter by Bombay Stock Exchange. The present study is basically an attempt to briefly describe and discuss the emerging scenario in the Indian Stock Exchange with special focus on retail participation with the technological development which is shaping and transforming the structure of the Indian Stock Market

A Study on Position of SEBI in Indian Securities Market

The arrangement and processes of the SEBI have been developed over the year. In 1602 when Amsterdam stock exchange was admitted by the East India Company for dealings in its hold securities the establishment of the native share and stock Brokers Association which is now remand as Bombay Stock Exchange in 1875 in the existing India undoubtedly marked a beginning of the stock exchange in India despite being the first ever stock exchange in Asia continent. The most basic legislative efforts to control the securities market in India was made by the Bombay Securities Contracts control act 1925, which was enacted to regulate and control certain contras acts for purchase and sale of securities in the city of Bombay.

Novel optimization approach for realized volatility forecast of stock price index based on deep reinforcement learning model

Accurately predicting volatility has always been the focus of government decision-making departments, financial regulators and academia. Therefore, it is very crucial to precisely predict the realized volatility (RV) of the stock price index. In this paper, we take the RV sequences of Shanghai Stock Exchange Composite Index (SSEC), Standard & Poor 500 index (SPX) and Financial Times Stock Exchange Index (FTSE) as the research objects, and propose a predictive model based on optimized variational mode decomposition (VMD), deep learning models including deep belief network (DBN), long short-term memory network (LSTM) and gated recurrent unit (GRU), and reinforcement learning Q-learning algorithm. Firstly, the original RV sequence is decomposed by using the VMD ideal parameters optimized by grey wolf optimizer (GWO) to obtain the intrinsic mode functions (IMFs). Then, DBN, LSTM and GRU are used to predict same IMF simultaneously. Finally, the optimal weights of the above three models are determined by the Q-learning algorithm to construct an integrated model, and the final results are obtained after accumulating the predicted values of each IMF. The predictive performance of the model was evaluated by four loss functions: the mean average error (MAE), mean squared error (MSE), heterogeneous mean average error (HMAE), heterogeneous mean squared error (HMSE) and modified Diebold and Mariano test (MDM). The experimental results show that the constructed GVMD-Q-DBN-LSTM-GRU method has better performance that the comparison model in both emerging and developed markets.

Assessing the Impact of Syrian Refugee Influx on the Jordanian Stock Exchange Market

The past decade has witnessed significant turmoil and political conflicts in several Middle Eastern countries, such as Egypt, Syria, and Libya, called the Arab Spring. These revolutions did not only affect the countries mentioned previously; their neighboring countries were also directly affected. This study explores the impact of the Syrian refugee influx on the stock exchange market of one of its neighboring countries, namely Jordan. The Syrian civil war represents a recent catastrophic event that has resulted in over three million refugees migrating to various countries worldwide. The main objective of this paper is to examine the effect of the Syrian war on Jordan’s stock exchange market. The study utilizes the stock exchange indices as indicators of the performance of the exchange market, including Financials, Services, Industries, and General indices as dependent variables, and seven dummy variables are defined as representatives of the main events occurring in the Syrian civil war during the period 2011–2018 as independent variables. Multiple statistical analysis techniques, including correlation coefficients, error functions, and stepwise regression, are employed to analyze the selected variables. The findings reveal an inverse influence of the Syrian war on Jordan’s stock market. These findings can potentially enhance the development of prediction models for stock indices in Jordan and other countries by incorporating relevant variables.

Investigating the link between socially-responsible HRM and organizational sustainability performance – an HRD perspective

PurposeThe paper aims to investigate the impact of socially-responsible human resource (SR-HR) practices on organizational sustainability performance (OSP) in Indian business organizations that are mandated to publish business sustainability and responsibility reporting.Design/methodology/approachData were obtained from 620 working professionals employed in the organizations listed on National Stock Exchange in India. The proposed hypotheses were tested by deploying the statistical technique of multiple regression analysis using SPSS Version-21.FindingsThe results demonstrated that overall, SR-HR practices impact OSP. More precisely, all the dimensions of SR practices are positively associated with the organization’s financial performance, environmental performance and social performance (SP). There was a relatively higher significant impact of legal-oriented human resource management (HRM) on organizational economic and ecological performance. However, in the case of SP, a substantial effect of employee-oriented HRM was found.Practical implicationsStudy findings encourage HR practitioners to invest in SR-HR practices to build and strengthen employees’ abilities and contributing to sustainability goals.Originality/valueThis study is one of the few studies conducted in the Indian context that highlights the relevance of the convergence of HRM, human resource development and corporate social responsibility to realize sustainability goals.

Impacts of U.S. Stock Market Crash on South African Top Sector Indices, Volatility, and Market Linkages: Evidence of Copula-Based BEKK-GARCH Models

This paper examines the effects of the Standard and Poor’s 500 (SP500) stock index crash during the global financial crisis and the COVID-19 pandemic periods on the South African top sector indices (basic materials, consumer goods, consumer services, financials, healthcare, industrials, technology, and telecommunication). The results of a copula-based BEKK-GARCH approach technique demonstrate the existence of price and volatility spillover during times of stock crashes. We discover that during a stock crisis, strong shocks and higher volatility spillover effects from the United States (U.S.) SP500 index to the top sector indices of the South African Johannesburg Stock Exchange (JSE) markets are more significant. However, there is no integrated economy, as the results did not show any spillover effects from South Africa to U.S. markets. Furthermore, the Gumbel copulas have higher dependence parameters, implying that extreme co-movements occur in the upper tails, suggesting the possibility of a large transmission of shocks from the SP500 to the eight top sector indices of the JSE and showing an asymmetric dependence between these markets. This result is important for investors willing to invest in the South African sector of equity markets to develop hedging strategies to prevent risk spillover from developed markets.

Analysis of the Linkage Relationship between the Mainland Stock Market and the Taiwan Stock Market during the Post -epidemic Period Based on the VAR

This article use The Shanghai Composite Index, Shenzhen component Index and the Taiwan weighted stock index from January 4, 2021 to January 20 , 2023 as the sample. Establish an analysis of the connected relationship between mainland stock markets and Taiwan stock markets during the post-epidemic period. Conduct empirical analysis through stable test, Grandie Cause and effect testing, pulse response analysis, variance decomposition and other methods.The empirical results show that Taiwan's weighted stock price index is the reason for the Shanghai Composite Index and the Shenzhen Stock Exchange. The mainland stock market has a significant guiding role in the Taiwan stock market in the short term. Compared with the Shanghai Composite Index, the Shenzhen Stock Exchange Index has a closer relationship with the Taiwan's weighted stock price index.

Sources of uncertainty and their impact on stock prices evidence from emerging economies

Abstract This study investigates the short- and long-term effects of various sources of uncertainty on the share prices of key exchanges in emerging nations. The sample comprises monthly time series data from January 2017 to December 2021 for China, India, Russia, and Brazil. The study contains a version of Autoregressive-Distributive-Lag (ARDL) with error correction as well as other relevant approaches to time series. Economic policy, climate policy, pandemics, and Twitter-based uncertainty may cause a long-term decline in SSE (Shanghai Stock Exchange) composite index and BSE (Bombay Stock Exchange) Sensex index. In China, geopolitical, climatic, and pandemic uncertainty are short-term sources of uncertainty, and in India, economic policy, geopolitical, and pandemic uncertainty. Moreover, no sources of uncertainty have a long-term impact on Russia’s Moscow Exchange (MOEX) index. All sources except climate uncertainty are short-term MOEX index contributors. Pandemics and Twitter-based uncertainty are long-term sources, whereas economic policy and Twitter-based uncertainty are short-term sources for Brazilian Stock Exchange (BOVESPA) Index. This research adds to the literature by examining the relationship between distinct sources of uncertainty and an emerging market share prices index. It provides the behavior of leading share price indexes in the presence of uncertainty. The study’s conclusions only apply to emerging economies. Future research may take into account a panel dataset consisting of a large number of emerging nations to examine the same set of variables.

Optimal Portfolio Investment and Consumption Strategies in Indian Equity Market

A number of investment strategies map out to maximize portfolio growth are tested on a long run Indian equity market. The application of the optimal portfolio techniques produces magnificent rate of growth, In spite of the fact that the assumptions of normality. Optimal portfolios are constructed by rebalancing the portfolio weights of four indices and ten sectors of National stock exchange indices. The purpose of this paper is to examine the Optimality of the Portfolio in the NSE and examine hypothesis the application of efficient market. The sample of stocks is not large in spite of its comprehensiveness from the local stock market . This paper also analyzes optimal portfolio and consumption strategies with unobservable states and predictability of risky asset returns. We develop approximate analytical solutions to the unconstrained dynamic problem. The approximation is shown to be fast and accurate. The computation time of the approximate solution is shown to be practically independent of the number of assets when no predictors are present and only marginally affected by the number of predictors. While the portfolio policy strongly depends on the current state of the economy, the consumption-to-wealth ratio is violently state-independent. Hedging demands are negligible with regimes and no predictability, but are important with predictability. On the other hand, the consumption to wealth ratio is not very impacted by the predictor. We provide an out of sample statistical assessment of the returns provided by a multi-regime strategy with respect to a single-regime and to a 1/N strategy.

LONG-RUN AND SHORT-RUN CAUSALITY BETWEEN STOCK PRICE INDICES AND MACROECONOMIC VARIABLES: EVIDENCE OF PANEL VECM ANALYSIS FROM BOSNIA AND HERZEGOVINA, CROATIA, NORTH MACEDONIA AND SERBIA

The purpose of this paper is to identify the long-run and short-run relationship between the values of the Macedonian Stock Exchange Index composed of 10 most liquid listed stocks (MBI10), the Zagreb Stock Exchange Index (CROBEX) composed of the most liquid listed stocks, the Sarajevo Stock Exchange Index (SASX-10) composed of 10 most liquid listed stocks and the Belgrade Stock Exchange Index composed of 15 most liquid listed stocks (BELEX 15) and the selected macroeconomic variables. In order to identify the macroeconomic variables that affect the values of the selected stock indices, the analytical-synthetic method and the statistical method are applied. The statistical method uses econometric models for data analysis and interpretation and includes the application of the following econometric tools: Panel unit root test, Fisher -Johansen cointegration test, application of the panel vector error correction model (PVECM) and the Wald test statistics. The results of PVECM between the values of the selected stock indices and independent variables such as industrial production index 2015=100, average monthly gross wages, shows existence of conditionality or causal relationship on the long-run, when independent variable Harmonized Index of Consumer Prices (HICP) according to the COICOP classification 2015=100 is exluded from the model. By applying PVECM, it can be concluded that there is a long run causality running from independent variable to dependent variable, meaning that between the values of the selected stock indices and industrial production and average gross wages there is speed of adjustment towards long run equilibrium.

Forecasting the Daily Stock Prices of the Philippine Stock Exchange Index During the Opening and Closing of the Market

While there are many factors and other alternative methods to forecast the future performance of the Philippine Stock Market Index, the researchers have elected to utilize a quantitative approach based on technical market factors available which consists mainly of intra-day daily trading levels of the index.

A Behavioral Interpretation of Volatility Patterns in Brazilian Stock Market: Analysis of Pre and Post-COVID-19 Periods from 2019 to 2021

There has recently been an increasing number of new investors in the Brazilian stock markets, providing new asset allocation opportunities as well as challenges in terms of individual judgment and decision-making. The departure point of this paper is the conjecture that the Ibovespa (Brazilian stock exchange index) might reveal asymmetric volatility patterns during the January 2019 to February 2021 period that call for behavioral financial explanations. More specifically, this paper aims to subject this bold conjecture to an empirical test. It draws on a threshold autoregressive conditional heteroskedasticity (TARCH) model to investigate the existence of asymmetric reactions in the period under study. Our findings suggest that the traditional approach to finance fails to explain volatility patterns in the Brazilian stock market behavior, since they do not fit well with predictions based on the efficient market hypothesis. Inspired by Vasileiou’s (2021) account of what occurred with the New York Stock Exchange during COVID-19, we then embark on a brief survey of the vast literature on applied behavioral economics and finance to provide an interpretation of the heuristics and biases that may be non-negligible mental mechanisms underlying the Ibovespa’s detected asymmetric responses.

The influence of corporate governance factors on intellectual capital performance: Panel data evidence from the Indian banking sector

This study empirically examined the relationship between corporate governance factors, namely CEO duality, independent directors, board meeting frequency, board size, gender diversity, audit committee size and audit committee meetings, and intellectual capital performance. The above premise is studied using data of 26 commercial banks listed on the Indian Stock Exchange (NSE) from 2010 to 2020. The study used purposive sampling as the methodology and multiple regression models with VAIC and ROA as attributes. VAIC measures the efficiency of intellectual capital. ROA is used to determine financial performance. The results of the study reveal that the use of observational data, independent directors, frequency of board meetings and audit committee size has a positive and significant effect on intellectual performance at a 10% significance level. According to the study’s findings, audit committee meetings have a positive impact on intellectual capital performance at a 1% significance level, while board size has a negative impact at a 5% significance level. Among the study results, CEO duality, board meeting frequency and board size have a positive and significant effect on financial performance with 1% significance. Board gender diversity has a negative impact on financial performance. The study’s findings indicate that there is no single best way to design corporate governance that applies to all corporate situations, and that good corporate governance factors have a significant impact on improved intellectual capital performance.

GARCHNet: Value-at-Risk Forecasting with GARCH Models Based on Neural Networks

This paper proposes a new GARCH specification that adapts the architecture of a long-term short memory neural network (LSTM). It is shown that classical GARCH models generally give good results in financial modeling, where high volatility can be observed. In particular, their high value is often praised in Value-at-Risk. However, the lack of nonlinear structure in most approaches means that conditional variance is not adequately represented in the model. On the contrary, the recent rapid development of deep learning methods is able to describe any nonlinear relationship in a clear way. We propose GARCHNet, a nonlinear approach to conditional variance that combines LSTM neural networks with maximum likelihood estimators in GARCH. The variance distributions considered in the paper are normal, t and skewed t, but the approach allows extension to other distributions. To evaluate our model, we conducted an empirical study on the logarithmic returns of the WIG 20 (Warsaw Stock Exchange Index), S&P 500 (Standard & Poor’s 500) and FTSE 100 (Financial Times Stock Exchange) indices over four different time periods from 2005 to 2021 with different levels of observed volatility. Our results confirm the validity of the solution, but we provide some directions for its further development.

Stock Markets and Major Sport Events: Evidence from Cricket World Cup 2019

The effect of major sport events on stock markets has been studied by many researchers, most of them focused on soccer without considering if it’s the most popular sport in the countries under study and without considering if the matches are important or not. The enquiry that the researcher tried to answer in this study was whether the stock market is affected by the results of the major event of the sport that is the most popular in the country of that market. To answer this question, the researcher studied the effect of the world cup of cricket, the most popular sport in India, on the main Indian stock exchanges: Bombay stock exchange and national stock exchange. A quantitative approach was followed to conduct this study using data for the period from May 24,2018 to July 15, 2019. The collected data was analyzed using ordinary least squares method and one sample t-test. Study conclusions indicated that loss results in the world cup have a significant positive effect on the stock prices in the two stock exchanges while there was no significant effect for winning results.

Supervised Machine Learning Method for Ontology-based Financial Decisions in the Stock Market

For changing semantics, ontological and information presentation, as well as computational linguistics for Asian social networks, are one of the most essential platforms for offering enhanced and real-time data mapping, as well as huge data access across diverse big data sources on the web architecture, information extraction mining, statistical modeling and data modeling, database control, and so on. The concept of opinion or sentiment analysis is often used to predict or classify the textual data, sentiment, affect, subjectivity, and other emotional states in online text. Recognizing the message's positive and negative thoughts or opinions by examining the author's goals will aid in a better understanding of the text's content in terms of the stock market. An intelligent ontology and knowledge Asian social network solution can improve the effectiveness of a company's decision making support procedures by deriving important information about users from a wide variety of web sources. However, ontology is concerned primarily with problem-solving knowledge discovery. The utilization of Internet-based modernizations welcomed a significant effect on the Indian stock exchange. News related to the stock market in the most recent decade plays a vital role for the brokers or users. This article focuses on predicting stock market news sentiments based on their polarity and textual information using the concept of ontological knowledge-based Convolution Neural Network (CNN) as a machine learning approach. Optimal features are essential for the sentiment classification model to predict the stock's textual reviews' exact sentiment. Therefore, the swarm-based Artificial Bee Colony (ABC) algorithm is utilized with the Lexicon feature extraction approach using a novel fitness function. The main motivation for combining ABC and CNN is to accelerate model training, which is why the suggested approach is effective in predicting emotions from stock news.

The Relationship between the Gold Price, Crude Oil Price, Exchange Rate and Chinese Stock Market Indexes

One of the trickiest problems for investors is how the financial and commodity markets interact with each other. The volatility in one market might affect the price of the other market. This essay aims to clarify the relationship between gold, crude oil, exchange rates, and Chinese stock market indices. In order to do this, the Shanghai Stock Exchange Index and the China Industrial Index, two indices that reflect the Chinese financial market, were subjected to the DCC-GARCH model (Generalized Autoregressive Conditional Heteroskedasticity Model). By capturing the dynamic correlations of the time series' variance, covariance, and correlation coefficient, this model aids in illuminating the long-run dynamic correlation between returns. The findings show that gold and crude oil are positively correlated with both the Shanghai Stock Exchange Index and the China Industrial Index, while the correlations between the USD CNY exchange rate and the two Chinese stock indices are negatively correlated, using monthly data from 2000-01-03 to 2022-08-30. This paper shows that stock prices in China are weakly linked to international commodity markets and exchange rate with the US. Additionally, throughout the observation period, financial crisis (2008) and the COVID-19 pandemic breakout (2020) enhanced comovement between the Chinese stock market, commodities, and exchange rate in general.

The effect of gold, dollar and Composite Stock Price Index on cryptocurrency

This paper aims to analyze cryptocurrency volatility by examining the effect of Gold, Dollar Index, and Composite Stock Price Index (IHSG) as independent variables and on Bitcoin and Ethereum as dependent variables. The cryptocurrency objects in this study are Bitcoin and Ethereum, which have the largest market capitalization. The data in this study used the period January 1, 2018, to December 31, 2021. This study used GARCH analysis. This study's results indicate that Bitcoin's volatility is influenced by the price of Bitcoin itself, gold, and the stock exchange index, and Ethereum and the stock exchange index influence Ethereum. This shows that the cryptocurrency market is inefficient as the prices are also affected by past prices.