Stock Exchange Index Research Articles

Volatility forecasting for stock market incorporating media reports, investors' sentiment, and attention based on MTGNN model

AbstractIn this paper, the self‐monitoring learning model FinBERT is used to identify text emotions, and the sliding time window time‐lagged cross‐correlation (WTLCC) method is utilized to screen Baidu Index keywords for the Shanghai Stock Exchange Index and 18 A‐share listed companies. There are five different types of indicators constructed: news media sentiment, public attention, investor sentiment, investor sentiment disagreement, and media sentiment disagreement. To accurately describe the structure of sentimental contagion, this paper combines graph neural network to learn and output the sentimental contagion graph, and then constructs multivariable time series forecasting with graph neural networks (MTGNN) volatility forecasting model, which can extract the spatial–temporal dependence of variables in pairs. The results show that MTGNN model possesses the highest forecasting accuracy, which performs 30.30% lower on average across four evaluation indicators for Shanghai Stock Exchange Index than temporal pattern attention–long short‐term memory model, which ranks second. For all of the models considered in this paper, adding sentimental contagion mechanism can significantly improve the volatility forecasting accuracy. The error of MTGNN is reduced the most, with a 15.21% average reduction for the Shanghai Stock Exchange Index. The contagion relationship among media reports, investor sentiment, and attention can help provide new ideas for enhancing the precision of volatility forecasting from the public opinion environment in the financial market.

Comparison of Prediction Effectiveness in Deep Learning Perspective of China’s Data Finance

The study has developed a financial time series forecasting model, the Transformer-Encoder model, which utilizes the attention mechanism. This model has been applied to predict the closing price of the Shanghai Stock Exchange (SSE) index, a reliable indicator of financial trends. Furthermore, the study has conducted a comparative analysis, evaluating the performance of our model against other deep learning models, machine learning models, and traditional time series data forecasting models across short, medium, and long-term forecasting periods. Our study has yielded the following key findings: Firstly, the Transformer-Encoder model, leveraging the attention mechanism, demonstrates strong performance in predicting closing prices across short, medium, and long-term periods. This indicates the model’s viability in handling non-stationary financial data and its potential as a forecasting tool applicable to time series prediction problems within the economic sphere. Secondly, compared to alternative deep learning models, machine learning models, and traditional time series data forecasting models, our proposed model consistently outperforms them.

Vine Copula Approach to Understand the Financial Dependence of the Istanbul Stock Exchange Index

AbstractRecently, the complex dependence patterns among various stocks gained more importance. Measuring the dependency structure is critical for investors to manage their portfolio risks. Since the global financial crisis, researchers have been more interested in studying the dynamics of dependency within stock markets by using novel methodologies. This study aims to investigate a Regular-Vine copula approach to estimate the interdependence structure of the Istanbul Stock Exchange index (ISE100). For this purpose, we consider 32 stocks related to 6 sectors belonging to ISE100. To reflect the time-varying impacts of the 2008–2009 global financial crisis, the dependence analysis is conducted over pre-, during-, and post-global financial crisis periods. Portfolio analysis is considered via a rolling window approach to capture the changes in the dependence. We compare the Regular-Vine-based generalized autoregressive conditional heteroskedasticity (GARCH) against the conventional GARCH model with different innovations. Value at risk and expected shortfall risk measures are used to validate the models. Additionally, for the constructed portfolios, return performance is summarized using both Sharpe and Sortino ratios. To test the ability of the considered Regular-Vine approach on ISE100, another evaluation has been done during the COVID-19 pandemic crisis with various parameter settings. The main findings across different risky periods illustrate the suitability of using the Regular-vine GARCH approach to model the complex dependence among stocks in emerging market conditions.

The Impact of the Macroeconomic Factors on the Bucharest Stock Exchange During the Latest Crisis

The purpose of the paper is to observe and analyze how the dynamics of macroeconomic factors impact the evolution of the Bucharest Stock Exchange (BVB) during the latest crisis (Covid-19 pandemic and the Ukrainian war), through the lens of various market stock indices (BET, BetPlus, BET-FI and BETNG). After the monthly values of both the macroeconomic factors (inflation rate, unemployment rate, RONEURO exchange rates, industrial production index, average salary, money supply, interest rate and oil price) and Bucharest Stock Exchange indices were collected, during the period January 2020- June 2023, the descriptive statistics was used to describe all the observed data (mean value, standard deviation, minimum/ maximum value). Finally, the multiple regression was used to see exactly how the Romanian stock market indices are influenced by any changes of macroeconomic factors.

The Impact of Surplus, Promoter Holding, Leverage and Retained Earnings on Dividend Payment Decisions: Evidence from Small- and Mid-sized Firms in India

Free cash flow is a crucial component in deciding the dividend payout ratio; but literature has not sufficiently explored the effects of the same, specifically on dividend payment decisions. We therefore examine whether a firm’s deficit or cash surplus affects ‘cash dividend payout’ for Indian small- and mid-sized firms. Also, we have explored the idea of studying the effect of promoter holding on the dividend policy of mid-cap and small-cap firms listed in the stock exchange of India. Our study also determines the relationship between retained earnings and the dividend policy. The sample of the study includes 174 non-financial companies from the NIFTY MidSmallcap 400 Index from April 2011 till March 2019. Applying the Logit and Tobit models, we have arrived at the findings. Our findings demonstrate that surplus cash positively influences the decision of paying dividends in both mid-cap and small-cap firms. The findings also indicate that mid-cap firms use retained earnings for scaling up rather than distributing dividends and small-cap firms distribute dividends. We prove that Indian business houses with high promoter holdings are less in favour of distributing dividends. The results have the limitation of geography and period and may not be valid for other countries.

Crude Oil Prices and the Egyptian Economy Evidence from the Stock Market

This paper investigates two important things; the role of crude oil prices in explaining the Egyptian stock market return, and what factors derive from Egyptian crude oil prices. Using 6 log difference time series variables this paper finds that multiple regression is an inappropriate model to test the two goals mentioned above. On the other hand, using Vector Autoregression (VAR Model) is much more profitable in achieving the paper’s goals. Although the VAR model results are more reliable, crude oil price fails to explain the Egyptian stock market return because Egypt is not a big oil exporter. Furthermore, the VAR model shows that the Energy sector index, stock world index (S&P 500 the proxy), stock exchange index, exchange rate, and the global financial crisis are all factors that derive and determine the Egyptian crude oil price.

Asymptotic normality of Nadaraya–Waton kernel regression estimation for mixing high-frequency data

High-frequency data is widely used and studied in many fields, especially in the econometrics and statistics. In this paper, the asymptotic normality of Nadaraya–Waton (NW) kernel regression estimator under ρ-mixing high-frequency data is studied. We first derive some moment inequalities for ρ-mixing high-frequency data, and then use them to study the asymptotic normality of NW kernel regression estimator, and give Berry–Esseen upper bounds. The numerical simulations report that the kernel regression estimator of high-frequency data has good asymptotic normality. Our empirical analysis is to fit the correlation between the five minute interval price increment and the corresponding trading volume for the Shanghai Stock Exchange Index, Entrepreneurship Index and Real Estate Index. These kernel regression curves better reflect people's investment behaviour.

COVID-19’s Impact on the Travel & Leisure Indices: Volatility Spillover Analysis

The travel and leisure sector is extremely sensitive to fluctuations in economic circumstances, consumer demand, and market volatility. This sector has been greatly affected by the Covid-19 pandemic due to both travel restrictions and social distancing measures that have limited demand. This study aims to examine the impact of Covid-19 on the volatility spillovers of travel and leisure indices in nine significant markets. It examines the differences in spillover effects pre- and during-Covid-19, employing comparative analysis. The results show that the pandemic has led to an increase in volatility spillover between travel and leisure indices. Moreover, significant changes are evident in the spillover effects between periods, reflecting the dynamic nature of volatility transmission. The results show that changes in consumer spending and restrictions on international travel, in the travel and leisure industry have a bigger impact on volatility spillovers in this sector than on stock exchange indices. These outcomes hold significant implications for sector participants, underscoring the need for resilient strategies to mitigate the impact of global crises on market volatility spillovers.

A Method for Assessing Financial Market Price Behavior: An Analysis of the Shanghai Stock Exchange Index

A Method for Assessing Financial Market Price Behavior: An Analysis of the Shanghai Stock Exchange Index

Testing the Causality and Cointegration between the Performance of Euronext Stock Exchange Indexes

Testing the Causality and Cointegration between the Performance of Euronext Stock Exchange Indexes

An Inquiry into the Volatility of the Nepal Stock Exchange Index

An Inquiry into the Volatility of the Nepal Stock Exchange Index

ESG disclosures and corporate performance: A non-linear and disaggregated approach

The present study explores the association between Environmental Social Governance (ESG) disclosure and the operational, financial, and market-based performance of Indian corporations. The ESG score has been disaggregated into three sub-parts, i.e., EDS, GDS, and SDS (environmental, social, and governance disclosure scores, respectively). Using a sample of 305 companies listed in the NSE 500 (the largest stock exchange in India), linear and non-linear relationships among the variables have been investigated. The data has been compiled from 2014 to 2022 since the Indian government implemented the CSR mandate from the year 2014. The linear results of the dynamic panel data analysis technique have indicated that ESG disclosures significantly impact the market performance of business enterprises. Further, the non-linear results have indicated a U-shaped association between the composite ESG score and market performance. The disintegration of ESG score has revealed that while the social and governance components have a positive linear relationship with all performance indicators, the environmental sub-component has a U-shaped relationship with market performance. Hence, as the findings indicate that ESG-related activities impact firms positively after reaching a threshold, long-term planning and substantial resources are required to be dedicated in this direction.

Impact of COVID-19 on Fractal Capital Market Recursion

We present the optimization of the current prices function of the Mexican Stock Exchange index and we will focus our attention on aspects of maximizing stock market margins with limits in the bookkeeping operation. We ourselves will model the possible stochastic recursion scenarios in "n" as a fractal annihilation factor within this stock market. Finally we will obtain the expected delta of the price range and its Hamiltonian to minimize the operational risk of the capital market with the presence of COVID19 in Mexico.

Estimating the effect of exchange rate fluctuations on general index of stock prices in Khartoum stock exchange using GARCH model (2004-2019)

The study aimed to estimate the effect of exchange rate fluctuations on the general index of stock prices in the Khartoum Stock Exchange using the general conditional variance models. The study problem was formulated in the following main question: What is the effect of exchange rate fluctuations on the general index of stock prices in the Khartoum Stock Exchange? The study was built on the following main hypothesis: There is a statistically significant relationship between exchange rate fluctuations and the general index of stock prices in the Khartoum Stock Exchange. The study followed the quantitative standard approach using the general conditional variance models and the ordinary least squares. The study found a direct relationship between exchange rate fluctuations and the general index of stock prices in the Khartoum Stock Exchange. The study recommended the adoption of effective macroeconomic policies to reduce the exchange rate and support it with production to reflect positively on the general performance of the Khartoum Stock Exchange Index, which increases opportunities and potentials for local and foreign investment.

Stay or Leave? How the Policy of Business Cooperation with Russia Affected Value and Price of Listed Companies: Evidence from Dow Jones, DAX and WIG20

Theoretical background: The crisis situation in the aftermath of the military conflict triggered by Russia’s invasion of Ukraine has caused a significant reaction from financial markets. Not only the Russian RTSI index, but also the stock indexes of neighbouring countries (Poland, Germany), as well as the U.S. indices (NDX, US30, US500), declined in value in the following months of the war, which was a direct result of the declining capitalisation of their component companies. A team of Yale University researchers undertook a classification study on a group of more than 1,000 global companies, grouping them according to the strategy (business model) they adopted in the face of the response to Russian aggression against Ukraine. These groups were, in particular: (1) Grade F – defying Demands for Exit or Reduction of Activities: companies that are just continuing business-as-usual in Russia; (2) Grade D – Holding Off New Investments/Development: companies postponing future planned investment/development/marketing while continuing substantive business; (3) Grade C – Reducing Current Operations: companies that are scaling back some significant business operations but continuing some others; (4) Grade B – Keeping Options Open for Return: companies temporarily curtailing most or nearly all operations while keeping return options open; and (5) Grade A – Clean Break – Surgical Removal, Resection: companies totally halting Russian engagements or completely exiting Russia. This work provided the basis for an in-depth analysis of how financial markets (investors) reacted to companies that used extreme responses (completely breaking business ties with Russia as opposed to maintaining the existing business model and business ties with Russia). Depending on the adopted firm strategy, according to the Yale methodology, it can be observed that the impact of war on value was not uniform, and differences in value migration could be discerned. Purpose of the article: In the article, the authors undertook to evaluate the reaction of financial markets in the period 31 December 2021 – 30 September 2022 on the basis of three stock market indices: (1) the US (Dow Jones Industrial Average); (2) the German (DAX 40 – Deutscher Aktien Index); (3) the Polish (WIG 20 – Warsaw Stock Exchange Index) with a particular focus on the date: February 24, 2022, when Russia’s military aggression in the Ukrainian area began. The authors assessed how the volatility of the shares of selected companies in these indices evolved, and whether it was influenced by the social responsibility of the business conducted, as expressed by the severance of business relations with Russia, their periodic suspension or the lack of any response to the acts of war that began. Furthermore, a significant aim of the study was to assess the phenomenon of value migration in companies based on their business relationship strategies with Russia, classified according to the criteria upon which the Yale list was built. Research methods: Critical analysis of the literature on the subject, statistical analysis, technical analysis of stock index volatility, induction and synthesis. Main findings: In view of the object of research described in this way, in accordance with the idea accompanying the CSR policy, the following research hypothesis was adopted in the study: investment sentiment toward multinational corporations that, as a result of Russia’s aggression in Ukraine, ceased business contacts with Russia altogether, differs from investor sentiment toward those organisations that did not take such action and continued business/economic cooperation with Russia.

Investigating the effects of Information and Communication Technology (ICT) on capital market uncertainty by considering its impact on the textile industry: a case study for Iran

Information and Communication Technology (ICT) plays a vital role in sharing information and greater participation in exchanges and trading of investors' shares. Because the use of information and communication technology in financial markets reduces marketing costs, it has a significant role. Therefore, the primary purpose of this research article is to investigate the impact of ICT on capital market uncertainty in Iran. In this study, the GARCH model has been used to estimate the capital market uncertainty index. The results of ARDL model estimation using quarterly data in the period 2011: 1 to 2020: 4 in the short and long term showed the effect of the ratio of online transactions to the total volume of transactions as an ICT indicator on capital market uncertainty in the short term. Long-term are different. In the short run, increasing the ratio of online transactions to the total volume of transactions has increased capital market uncertainty. It has reduced capital market uncertainty in the long run. Other model results showed that inflation, economic growth, and exchange rate significantly affect the performance uncertainty of the total stock exchange index as an indicator of capital market uncertainty. And it takes about 7.5 seasons on average to completely offset the imbalance in capital market uncertainty.

Listing of Section 8 Companies in Indian Stock Exchange and Comparative Analysis

This research investigates the implications of listing Section 8 Companies on the stock exchange in the Indian context. Section 8 Companies, primarily driven by social objectives, play a crucial role in the country's philanthropic landscape. However, the decision to list on the stock exchange brings forth unique challenges and opportunities. The study aims to ascertain whether listing enhances the socio-economic impact of these entities and compares the performance of listed Section 8 Companies with their unlisted counterparts.

TESTING THE MARKET EFFICIENCY WHEN INTEREST RATES CHANGE: CASE IN INDONESIA

Until the end of September 2023, the interest rate policy in the Republic of Indonesia shifted from 3.50% to 5.75%. The objective of this study is to examine whether changes in interest rates have an impact on the market efficiency in Indonesia. The daily market returns of Indonesia Stock Exchange (IDX) indices from January 3rd, 2022, until October 9th, 2023 are employed. To examine the market efficiency in weak form, this study conducts the normality test by the Kolmogorov-Smirnov test, the unit root problem test by Augmented Dickey-Fuller test, and the variance ratio test. This study finds that most market indices experienced a decline in returns and were less volatile when interest rates shifted to higher levels. This study also finds that most market indices tend to remain efficient in weak form at both low and high interest rates. The finding implies that those indices tend not to be influenced by policy changes in interest rates. However, the findings also show that several market indices experience changes in conditions when interest rates shift. As the studies on the relationship between changes in interest rates and market efficiency are scarce, the novelty of this study is the use of event windows to get specific impact of changes in interest rates on market efficiency.

The Impact of Trading Volume, Exchange Rate on Istanbul Index Price: Empirical Results Using ARDL Model

This study aimed to investigate the relationship and the effect of the change in the trading volume and the exchange rate of the Turkish lira on the closing price of the Istanbul 100 Index. Therefore, weekly data were collected for the index, trade volume and exchange rate for the period 2016-2021. After that, we run diagnostic test to ensure that our data fit the necessarily robust characteristics. Thus, we used the unit root test and conclude that the dependent variable is stationary after first difference and other variables are stationary at level and after first difference. Afterwards, we checked that the residuals are fitting our main condition where it should be serially uncorrelated with homogenous slope. The ARDL has been used to explore the short and long run relationship and interaction between variables. First, we reported the bound test results and the F-state showed to be significant at 1%, which is mean that our model has a long-run interaction with the dependent variable. Secondly, we run the long-run ARDL test to check the impact of each independent variable on the dependent variable. The results showed that the exchange rate has statistically significant effect on the index price. The results showed that the exchange rate of the Turkish lira against the dollar negatively affected the price of the Istanbul Stock Exchange index. While, the trading volume was insignificant in the long-run. However, the results of short run cointegration is different. The current and lagged value of trading volume were significant. Finally, we tested the stability of our model. The results were different between CUSUM and CUSUMSQ. Where they CUSUM results came up to show the stability of coefficient in the model. Nevertheless, the CUSUMSQ showed that we have unexpected change in the structure

A Study on Technical Analaysis of Indian FMCG Sector

The Indian fast-moving consumer goods (FMCG) sector plays a crucial role in the country's economy, experiencing strong growth and catering to the needs of its vast population. This research aims to conduct a close examination of the Indian FMCG sector using technical analysis techniques in order to identify patterns, trends, and potential investment opportunities. Using a quantitative approach, this study employs various technical indicators and chart patterns to assess the price and volume movements of FMCG sector stocks listed on Indian stock exchanges. Historical price data and trading volumes are collected and analyzed to identify recurring patterns and trends that can assist investors in making well-informed decisions. The study covers a time period between 26/04/2021 to 02/06/2021, allowing for a comprehensive evaluation of the FMCG sector's performance and behaviour. Few technical analysis tools such as moving averages convergence and divergence (MACD), relative strength index (RSI), on balance volume (OBV), and trendlines are utilized. By applying these techniques, the study aims to identify potential signals for buying and selling, levels of support and resistance, and price targets within the Indian FMCG sector. Additionally, the research explores the impact of market trends, economic factors, and industry-specific events on the sector's performance. The findings of this study contribute to the existing knowledge in technical analysis and provide valuable insights for investors and traders interested in the Indian FMCG sector. The results enhance understanding of the sector's price movements, volatility, and potential investment opportunities, enabling market participants to develop effective trading strategies and risk management approaches. It is important to note that this study solely focuses on the technical analysis of the Indian FMCG sector and does not consider fundamental analysis or qualitative factors that may influence investment decisions. Therefore, readers are encouraged to combine the findings of this study with additional research and analysis before making investment choices.