Stock Exchange Index Research Articles

Contagion Effect Between Bombay Stock Exchange (BSE), National Stock Exchange (NSE) and Nasdaq

This comprehensive study investigates the contagion effect and relationships among three major stock exchanges—specifically, the two largest stock exchanges in India, BSE (Bombay Stock Exchange) and NSE (National Stock Exchange), and the globally renowned NASDAQ—spanning an extensive eight-year period on a daily basis. Employing a robust analytical framework in E-views, the study conducts a suite of tests, including Unit Root tests, Granger causality analysis, Johansen's Cointegration test, Vector Error Correction Models (VECM), and Dynamic Ordinary Least Squares (DOLS). The results of our investigation reveal a significant cointegration among BSE, NSE, and NASDAQ, indicating a long-term relationship between these stock exchanges. Notably, the study establishes that BSE Granger causes NSE, given their shared national context. Moreover, in an international context, NASDAQ emerges as a Granger cause for both BSE and NSE, while NSE Granger causes NASDAQ. This nuanced understanding of Granger causality relationships has profound implications for investors seeking to optimize their portfolios. Importantly, our findings suggest a viable strategy for portfolio diversification, emphasizing the potential benefits of allocating investments across stock exchanges with limited correlation. Specifically, investors can strategically diversify their portfolios by considering exchanges that do not exhibit Granger causality relationships with each other. This insight provides a practical guide for investors looking to capitalize on opportunities in markets with distinct and uncorrelated movements, thereby enhancing the effectiveness of international portfolio diversification. In essence, this study underscores the feasibility of diversification between exchanges with no observed Granger causality relationships, shedding light on the nuanced dynamics of inter-market dependencies. Investors can leverage these findings to make informed decisions, mitigating risks, and capitalizing on diverse opportunities across global financial markets.

Coexistence of sustainability and growth in different economies

Research backgroundThe Sustainable Development Goals (SDGs) and their practical application to national economies have raised different lines of investigation among which this research focuses on interactions between macroeconomic indicators, namely Gross Domestic Product per Capita (GDPpC), Stock Exchange Indexes and CO2 emissions as indexes representative of SDGs achievement. Purpose of the articleThis research aims to analyze the current compatibility of the different SDG, more specifically those related to economic growth and climate sustainability. MethodsTwo methods have been followed: a descriptive methodology based on the Pearson Correlation Coefficient to assess potential behavioral parallelisms between the indexes studied. Inference, to define functional relations and potential causality traits between the macroeconomic indicators. Findings and value addedThe main added value of this research is to question the true simultaneous applicability of the different SDGs.

Türkiye in 2002-2023 Seçili Makroekonomik Değişkenlerin BİST Bankacılık Endeksine Etkisi: 2002-2023 Türkiye Örneği

The increasing number of financial instruments in the financial markets and the increase in product diversity in the real sector push the relevant markets to new searches in terms of both financing and competitiveness. In this context, in this study, the transformation of the recent global financial mobility and increasing financial competition in the macroeocomic variables in the Turkish economy and the possible effects of this transformation in selected macroeconomic variables on the banking sector, which is the most important pillar of the financial sector, were analyzed by determining the effects of the nominal effective exchange rate index (EERI), industrial production index (IPI) and consumer price index (CPI) on the banking stock exchange index between the periods 2002M01-2023M03 and the direction and degree of the relationship between the relevant variables were tried to be determined. In this context, since the Phillips-Perron unit root test results of the variables included in the analysis are stable at different stationary levels, the ARDL Boundary Test method was performed and the results obtained showed that; It was concluded that there is a positive and long-term relationship between the nominal EERI and the CPI and the banking stock exchange index, therefore the relevant variables are cointegrated, but the IPI and the banking stock exchange index are not long-term cointegrated.

Cointegration and causality relationship of Indian stock market with selected world markets

Background: The purpose of this study is to explore the trends and causes of established and emerging nations’ stock market integration with India. The National Stock Exchange (NSE) indices act as a counterweight to international market indices. This study investigates the sustained interest of foreign investors in the Indian stock market in the wake of capital market reforms, as well as whether it moves in tandem with other markets in Asia and the United States. Methods: Our study examined the possibility of cross-country cointegration between the largest economies and indices around the world using multiple financial econometric models, such as Augmented Dickey-Fuller, Unit Root, Correlation, and Johansen Cointegration. Results: The findings of this study significantly support the notion that Indian and international financial markets are highly integrated. Vector error correction model indicates that the Indian market (NSE) is highly cointegrated with the US market (National Association of Securities Dealers Automated Quotations) and increased volatility signifies global contagion. Conclusion: A cursory examination of the data reveals distinct investment and portfolio diversification options for global investors. This could assist regulators in formulating more effective rules regarding price discovery processes.

COVID-19 Pandemic and Indices Volatility: Evidence from GARCH Models

This study examines the impact of volatility on the returns of nine National Stock Exchange (NSE) indices before, during, and after the COVID-19 pandemic. The study employed generalized autoregressive conditional heteroskedasticity (GARCH) modelling to analyse investor risk and the impact of volatility on returns. The study makes several contributions to the existing literature. First, it uses advanced volatility forecasting models, such as ARCH and GARCH, to improve volatility estimates and anticipate future volatility. Second, it enhances the analysis of index return volatility. The study found that the COVID-19 period outperformed the pre-COVID-19 and overall periods. Since the Nifty Realty Index is the most volatile, Nifty Bank, Metal, and Information Technology (IT) investors reaped greater returns during COVID-19 than before. The study provides a comprehensive review of the volatility and risk of nine NSE indices. Volatility forecasting techniques can help investors to understand index volatility and mitigate risk while navigating these dynamic indices.

Prediksi Indeks Bursa Efek Indonesia 2023 Pendekatan ARIMA, Machine Learning dengan R Programming

The Indonesian Stock Exchange Index (BEI) is the main indicator of the Indonesian stock market. The problem in this research is a specific issue to solve the practical problem of predicting the future movement of the BEI index which has strategic value for investors, traders and companies in Indonesia. The aim of this research is to be able to predict the BEI index until the end of 2023, because the stock exchange plays a big role for the Indonesian economy as an economic and financial function. Traditional approaches such as ARIMA (Autoregressive Integrated Moving Average) have been used to predict the movement of the BEI index. However, in recent years, machine learning and data mining techniques have become popular as more sophisticated alternative approaches. This research uses a combined approach between ARIMA and machine learning with R Programming. Daily IDX index data from January 2012 to December 2022 will be taken from Yahoo Finance. This data will then be cleaned and processed using R Programming. The ARIMA approach will be used as a baseline to compare machine learning performance. The results focus on the estimated closing stock prices for the next 365 days or the average until the end of 2023. The Time Series value of the possible minimum/maximum predicted value for IDX shares in 2023 is a minimum predicted value of 6786,212 - 6849,559, a maximum predicted value of 6850,093 - 7086,012. Trends represent a good approach in predicting the future direction of closing prices.

Blockchain-Based Secure Stock Market Price Prediction Using Next Generation Optimized LSTM Model

Stock forecasting has long drawn people’s attention because the stock market is a crucial source of financing for publicly traded corporations and a sizable investment market. To fully use the evidence from dissimilar typical prices and recover the stock forecasting effect, a Blockchain-based secure stock value forecasting model TL-EMD-LSTM-MA (TELM) is projected. Other methods are selected for prediction according to the oscillation frequencies of the details, and the high-frequency components use the depth of the transfer learning method to train the stacked LSTM. Deep transferable learning-trained stacked LSTMs incorporate data from several equities and have a deeper understanding of the marketplace or commerce, which can significantly lower forecasting mistakes. Furthermore, it is possible to more accurately estimate the low-frequency components and the overall trend of the stock by employing the average movement approach. 500 stocks in the stock market are shown, as well as indices such as the Stock Exchange Index and Stock Exchange Component Index, the outcomes demonstrate that compared with other models, TELM has the least prediction error and the highest goodness of fit. Finally, simulate the stock trading process based on the stock closing price predicted by TELM, and the results show that TELM investment has low risk and high returns.

An integrated approach of ensemble learning methods for stock index prediction using investor sentiments

It has been evidenced by numerous studies that irrational investor sentiment is one of the critical factors leading to dramatic volatility in financial market prices. Therefore, how to effectively predict market prices by information on investor sentiment is a popular but complex topic for researchers, market investors, and financial regulators. In this research, we aim to investigate the effectiveness of stock index prediction using multiple investor sentiment features, and we propose an advanced price trend prediction and trading simulation approach for the Shanghai Stock Exchange index and the Shenzhen Component index by integrating the Boosting, Bagging, and NSGA-II methods. Additionally, the SHAP method is employed as a model interpretation approach to analyze the importance of the sentiment variables and quantify their contributions to the predictions from both local and global perspectives. According to the experimental results, it can be found that the proposed method outperforms the benchmark methods in terms of the hit ratio, accumulated return, and maximum drawdown. It indicates that the proposed method is capable of achieving high accuracy, low risk, and stable profit in price trend prediction and trading simulation of the Chinese stock indexes. Moreover, the SHAP approach incorporated in the proposed method improved the interpretability of the proposed model, which can provide a beneficial reference for market participants to clarify the important sentiment factors and make relative decisions.

Assessing market efficiency in Palestine Securities Exchange (PSE) market at weak form: Analysis from 2010–2022

The efficient market hypothesis (EMH) asserts that financial markets are efficient, meaning that current market values of stocks incorporate all available information. This study examines the weak-form efficiency of Palestine Stock Exchange stocks using the indices returns from 2010 to 2022. The study used parametric tests (Augmented Dickey-Fuller (ADF) unit root, serial autocorrelation) and nonparametric tests (Phillips-Perron (PP) unit root, run test, variance ratio test). The findings of these tests provide insights into the behavior of the Palestine Stock Exchange market. The run test outcomes reveal a statistically significant pattern in the data for general, insurance, and service indices, with a p-value below the significance level. Furthermore, the unit root tests indicate statistical significance for all indices with 0.00 p-values and t-statistic below the critical values of –2.86 for level and intercept, and –3.14 for level, trend, and intercept, signifying that the indices returns are stationary. In addition, serial autocorrelation test show that the general and Al-Quds indices show statistically significant links between consecutive observations at all four lags. However, the insurance, investment, and services indices show statistically significant results on three lags. The variance ratio test results challenge the random-walk hypothesis for all indices except industry and insurance. With low probability values, a discernible, long-term, predictable pattern is evident in the Palestine Stock Exchange indices. The analysis reveals that Palestine Stock Exchange index returns exhibit nonrandom behavior, suggesting predictability and patterns in daily returns, indicating the possibility of exploiting market inefficiencies in investment strategies.

Alternative Confidence Interval for Variability Parameters in the Normal Distribution with Applications to Stock Exchange Index Data Set

This work focusses on estimation the standard deviation (SD) and coefficient of variation (CV) in the normal distribution. These two measures are useful in applications and widely used to report the spread or variability of continuous data. We develop the confidence intervals for these parameters using the two pivotal quantity methods with the unbiased estimator of SD. The first confidence interval uses the pivot function based on a chi-square distribution and the second one is based on a generalized pivotal quantity. The performance of our approaches is conducted via simulations. We show that the confidence intervals for SD and CV based on the new pivots have coverage probabilities greater than existing confidence intervals. Furthermore, they have acceptable short expected lengths. We also provide two real-data sets on the SET50 index of Thailand to demonstrate the proposed methods.

The Impact of Micro Factors on the Performance of the Iraq Stock Exchange Index for the Period 2005-2021

Purpose: The purpose of this study is to determine the impact of some micro factors (liquidity and profitability factors) of the Iraqi banking sector in determining the return of the stock market represented by the return of the Iraqi financial market index using time series analysis according to the ARDL methodology for the period 2005-2021. Design/methodology/approach: The study adopted an econometrics analysis of time series using the ARDL (auto-regressive distributed lag) methodology for the period 2005-2021 to investigate the impact of some micro factors (liquidity and profitability ratios) in Iraqi banking sector listed in Iraq stock exchange market on market return (Iraq Stock Exchange Index as a benchmark). Findings: The results of this study reveal significant effects of the financial factors for micro factor on the Iraqi Stock Exchange index. It is interesting that the variables of banking liquidity showed a stronger logical relationship with the dependent variable in line with financial theory than the variables of banking profitability. The relationship between the micro factors model and the financial market index required a long-term adjustment equivalent to (0.2124) of the time needed to adjust to the balanced state according to the error correction equation. Research, Practical & Social implications: These results confirm the importance of taking into account micro-financial factors when making financing and investment decisions, as well as when evaluating the economic situation in the Iraqi banking sector. Understanding the impact of these factors and their relationships contributes to decision-making and enhances general forecasting of future economic volatility and risk management. Originality/value: This study provides valuable insights into the dynamics of the Iraqi financial market and highlights the importance of incorporating micro-financial factors into financial analyzes and investment strategies. The study recommends that future research should explore the complex links between macroeconomic factors and the financial market to gain a comprehensive understanding of the economic environment.

Forecasting TAIEX and FITX with Affirmative and Doubtful Investor Sentiments

Abstract The existing literature have used media messages as a proxy variable for investor sentiment, but they mainly classify sentiment into positive or negative categories based on the words used in news articles, without much attention to the degree of affirmative or doubtful conveyed by the words used. Thus, in addition to classifying news content into positive or negative sentiment, this study also measures the degree of affirmative or doubtful expressed in the news articles in order to achieve more accurate predictive results. The study converts qualitative text to two quantitative scores (sentiment ratio and affirmative ratio) and investigates the predictive ability of these two variables on stock index returns and volatility in Taiwan’s case. The empirical findings indicate that only affirmative ratio exhibits a significant and negative impact on the one-day ahead returns of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the Taiwan Stock Exchange Index Futures (FITX). Additionally, the volatility of returns in both future and spot markets is significantly influenced by both sentiment ratio and affirmative ratio. JEL Classifications: C22; G10; G40. Keywords: Investor sentiment; Text-mining; TAIEX; FITX; Affirmative ratio.

Cointegration and causality relationship of Indian stock market with selected world markets

Background: The purpose of this study is to explore the trends and causes of established and emerging nations' stock market integration with India. The National Stock Exchange (NSE) indices act as a counterweight to international market indices. This study investigates the sustained interest of foreign investors in the Indian stock market in the wake of capital market reforms, as well as whether it moves in tandem with other markets in Asia and the United States. Methods: Our study examined the possibility of cross-country cointegration between the largest economies and indices around the world using multiple financial econometric models, such as Augmented Dickey-Fuller, Unit Root, Correlation, and Johansen Cointegration. Results: The findings of this study significantly support the notion that Indian and international financial markets are highly integrated. Vector error correction model indicates that the Indian market (NSE) is highly cointegrated with the US market (National Association of Securities Dealers Automated Quotations) and increased volatility signifies global contagion. Conclusion: A cursory examination of the data reveals distinct investment and portfolio diversification options for global investors. This could assist regulators in formulating more effective rules regarding price discovery processes.

Balance assessment in selected stages of Parkinson’s disease using trend change analysis

BackgroundBalance disorders in patients diagnosed with Parkinson’s disease (PD) are associated with a change in balance-keeping strategy and reflex disorders which regulate the maintenance of vertical body posture. Center of foot pressure (COP) displacement signals were analyzed during quiet standing experiments to define such changes. The research aimed to apply stock exchange indices based on the trend change analyses to the assessment of a level of the Parkinson disease progression on the grounds of the analysis of the COP signals.Methods30 patients in two stages of PD, 40 elderly participants, and 20 individuals at a young age were studied. Each person was subjected to 3 measurements with open and closed eyes. A technical analysis of the COP displacement signal was performed, and the following quantities were determined: indices related to the number of trend changes (TCI), indices defining a mean time (TCI_dT), and mean displacement (TCI_dS) and mean velocity (TCI_dV) between such changes.ResultsThe results indicate a higher TCI value for PD than for aged-matched control group (p < 0.05). In the case of PD patients, there was also an increase in the TCI_dS value by 2–5 mm, which mainly contributed to the increase in TCI_dV. Statistically significant differences for the TCI_dT values occurred between all groups in which differences in the average COP velocity were noted.ConclusionsThe TCI and TCI_dV results obtained for the healthy participants enabled the development of indices supporting PD diagnostics. The causes of the TCI_dV changes in patients were determined, i.e., whether they resulted from an increase in the TCI_dT or TCI_dS between the moments of trend changes indicated by the developed algorithm. The developed methodology provides new information on the impact of PD on the strategy of maintaining balance, which was impossible to obtain using currently used analyses.Trial registration The conducted research is an observational study and does not include a health care intervention. Participants gave their consent to participate in the research and the procedure was approved by the Institutional Bioethics Committee.

Fama and French three and six-factor models: Evidence from Indian stock exchange

This study attempts to compare the performance of Fama–French three-factor model (FFTFM) and Fama and French six-factor model (FFSFM) in predicting the variations in expected returns of Nifty-100 listed stocks. Only 5/6 of the total listed companies are chosen, while the remaining 1/6 are ignored because they are not listed for the whole study period. The stocks are divided into two size groups and three groups based on B/M, OP, Inv and MOM using independent sorts to create 24 portfolios. The monthly average returns of the MC-MOM portfolios increase as momentum increases, in contrast to MC-B/M and MC-Inv portfolios. Almost all the portfolios with high returns are paired with significant risk, apart from the BM portfolio in size and profitability group. The findings prove that the FFSFM outperforms FFTFM on all the GRS test parameters. However, there is no significant improvement in explanatory power over the FFTFM.

Performance Evaluation of Technical Analysis in the Nepalese Stock Market: Implications for Investment Strategies

Purpose: This study aims to examine the performance of five widely used technical indicators on Nepal Stock Exchange (NEPSE) indexes. Specifically, the study evaluates indicators such as the simple moving average, moving average convergence and divergence, relative strength index, stochastic oscillator, and Bollinger Band. Design/methodology/approach: The study utilizes a descriptive and quantitative research design. Daily closing index data of NEPSE and its six sub-indices are analyzed for a substantial period, ranging from 1st September 2012 to 31st August 2022. The performance of the technical indicators is examined through modeling, backtesting, and statistical analysis. The technical trading rules of each indicator are incorporated into the modeling process, and the results are analyzed using different performance metrics. Furthermore, the predictability of the indicators is tested through standard statistical analysis and bootstrap techniques utilizing a random walk model. Findings: The results of the study reveal that the technical strategies, as represented by the analyzed indicators, generally support the effectiveness of technical analysis in the NEPSE. However, the relative strength index stands out as an exception, generating negative returns. The second stage of the analysis indicates that the simple moving average, relative strength, and Bollinger band fail to generate significant returns in certain indices. The findings from the bootstrap techniques further contradict the forecasting ability of technical strategies in the NEPSE, raising questions about their real performance in the Nepalese stock market. Practical Implications: This research provides valuable insights for investors, traders, and market participants in understanding the nuances and limitations of technical analysis in the context of the NEPSE. The findings suggest that technical indicators, notably the relative strength index, should be interpreted cautiously and that investing decisions should take into account additional factors. The study contributes to the ongoing discussion on the effectiveness of technical analysis in the Nepalese stock market, assisting practitioners to make informed decisions and better understand market dynamics.

Does Stock Market React to Terrorist Attack? An Evidence from 9/11 Attack Using Event Study

The global economy and financial markets around the world are said to be adversely affected by the terrorist attack of 11 September 2001. The present study empirically evaluates the impacts of terrorist attacks on the top 20 stock market indices of the world. The Event study approach is used to conclude that there is a significant impact of the terrorist attack on stock indices of the selected countries, as the following stock market indices showed an adverse abnormal return on the day of the event namely, New York Stock Exchange, NASDAQ, Shenzhen Stock Exchange, National Stock Exchange of India, Frankfurt Stock Exchange, Bombay Stock Exchange, Euronext Paris Exchange, Johannesburg stock exchange and Euronext Brussels. While the rest of the selected stock markets showed a positive abnormal return on the event date. In the following days of the event, the Stock exchange in China showed a negative abnormal CAR for 80 days, while stock exchanges in India showed a negative abnormal CAR in the 20 days following the event. Most of the stock exchanges were not much affected by the event, even if there was an effect it didn’t last long, as the abnormal return was positive after a while.

Role of perceived COVID-19 disruption, personality traits and risk perception in determining the investment behavior of retail investors: a hybrid regression-neural network approach

PurposeThis study aims to assess the impact of personality traits, risk perception and perceived coronavirus disease 2019 (COVID-19) disruption on the investment behavior of individual investors in the Indian stock market.Design/methodology/approachThis study adopts a survey approach. The sample comprises 315 active retail investors investing in the Indian stock exchange. Two-stage analysis technique regression and Artificial Neural Network (ANN) were used for data analysis. Study hypotheses were tested through regression and ANN was adopted to validate the regression results.FindingsTwo regression models were modeled to test the research hypotheses. Findings showed that risk perception and COVID-19 disruption have a significant positive and neuroticism has a significant negative impact on short-term investment decisions, while the role of conscientiousness in determining short-term investment decisions was not found significant. Results also showed a positive impact of neuroticism and conscientiousness and a negative impact of risk perception on long-term investment decisions. The role of COVID-19 disruption was found negative but insignificant in predicting long-term investment decisions.Practical implicationsThis study has practical implications for many parties like retail investors, financial advisors and policymakers. This study will assist the investors to realize that they do not always take rational financial decisions. This study will suggest the financial advisors to use the knowledge of behavioral finance in making the advisors' advisory and wealth management decisions. This study will also assist the policymakers to outline behaviorally well-informed policy decisions to protect the interests of investors.Originality/valueIndia is one of the fast-growing economies in the world. India has a vast population of active investors and determining investors' investment behavior adds novelty to this study as developed economies have remained the main focus of previous studies. The other novel feature of this study is that this study tries to assess the impact of COVID-19 disruption along with personality traits and risk perception on investment behavior. The other valuable factor of this study is the use of ANN to predict the relative importance of the exogenous variables.

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