BSE Sensex Research Articles

Impact of Industrial Growth on Indian Stock Market: With Reference To BSE SENSEX and NSE CNX NIFTY

Impact of Industrial Growth on Indian Stock Market: With Reference To BSE SENSEX and NSE CNX NIFTY

The USA–China trade policy uncertainty and inference for the major global south indexes

PurposeThe recent dynamics of trade policy, especially that is associated with the United States of America (USA) and China, has not only triggered policy adjustments in two economies, it has also implied an uncertainty spillover to other economies across the globe. Consequently, the current study attempts to examine the effect of uncertainties in the USA–China trade policies on stock market indexes. In addition, the cointegration evidence between the USA–China trade policy uncertainty index and of the leading Global South fragile quintet (Brazil, Indonesia, South Africa, India and Turkey) stock market indices is investigated.Design/methodology/approachMainly, the FMOLS and DOLS Granger causality analysis with cointegration coefficient estimators were employed for the dataset over the monthly data period of March 2003 and July 2019.FindingsAccordingly, the study found a long-term relationship between the USA–China Trade Policy Uncertainty index and the stock exchange indexes. In addition, a causal relationship was established from the change in the USA–China Trade Policy Uncertainty index to the change in the stock market indexes of almost all of the examined countries (Brazil, Indonesia, South Africa, India and Turkey). In addition, the nonlinear Autoregressive Distributed Lag approach further offers evidence of asymmetric relationship among the examined indicators.Originality/valueMoreover, this study contributed to the existing literature because it employed the indexes of BIST100, BOVESPA, BSE Sensex 30, IDX Composite and South Africa 40 in a novel approach. Thus, the study posited a useful policy guideline for associated economic uncertainties arising from the trade dispute, such as the case of the world’s two largest trading giants or partners (i.e. the USA and China).

ANALISIS HARGA SAHAM SYARIAH INDONESIA JAKARTA ISLAMIC INDEX MASA SEBELUM DAN SAAT COVID 19

ABSTRACTThis study aims to test and analyze the Indonesian Jakarta Islamic Index's Islamic stock price which is influenced by 4 macroeconomic fundamentals (inflation, exchange rate $ / IDR, interest rates, stock trading volume) and shares of Indonesia's 5 largest trading partners (Shanghai Composite Index China shares, stocks Japanese Nikkei 225 Index, Singapore Straits Time Index shares, United States Dow Jones Industrial Average shares, India S&P BSE Sensex shares) before and during covid-19. The population in this study were Indonesian Islamic stocks listed on the IDX with the period March 2019 to September 2020. The sampling technique used a purposive sampling method and obtained one sample of the Jakarta Islamic Index (JII) stock index. The analytical method used is multiple linear regression analysis with SPSS application tools. Based on testing using the Simultaneous Model Test (Test F), it is known that the independent variables together (simultaneously) have a significant effect on the Sharia Indonesia Jakarta Islamic Index stock price before and during the Covid-19 period. While the results of the Hypothesis Test (t test) show that, the exchange rate of $ / Rp., China's Shanghai Composite Index (SCI) and the United States Dow Jones Industrial Average (DJIA) Stock Index have a significant effect on Islamic stocks in Indonesia Jakarta Islamic Index during covid-19. .

Attention to the Fads and Fashions in the Indian Stock Markets During COVID-19

Do people show fads and fashions in their attention searches? With the Google online search data during COVID-19, particularly from January to May 2020 for the socio-economic keywords, this study examines if online searches show short-run and long-run attention dynamics leading to fads and fashions in attention to the NSE Nifty and BSE Sensex indices. This study employs the methodology of cointegrating relationship with autoregressive distributed lag (ARDL) model and explains investors’ attention search dynamics at the ‘NSE Nifty Index’ and ‘BSE Sensex Index’ caused by socio-economic attention searches. It also examines if the dynamics of attention coordination are parsimonious in nature and it explores the same with the generalized autoregressive conditional heteroskedastic (GARCH-X) model. With the ARDL models, this study finds robust and unbiased cointegrating impacts of socio-economic attention searches on the attention search for the NSE Nifty index but these are not the best linear unbiased and efficient (BLUE) ones, while the same on the BSE Sensex Index are BLUE. For the NSE Nifty index, the attention dynamics at the GARCH-X specification are BLUE while for the BSE Sensex index, the GARCH-X specification also has some additional information in terms of the ARCH effect only.

The upshot of COVID-19 on sensex & sectoral Indices in Indian stock market – A comparative analysis during pre COVID & COVID period

This paper focused on studying the impact of COVID-19 on stock market Index (BSE SENSEX), more specifically on sectoral Indices in Indian stock market. At the time of this research, there were millions of Covid cases taken place all over the world. The main aim of this system is to know the impact of Covid cases on sectoral performance specifically assist the investor for picking the stocks from each sector during the covid period. There are several other economic factors influence on the investment decisions and performance of the stock market indices in India during the covid period. In this research, contributing factors such as FII, Crude oil price, influence on stock market indices have been studied during the covid period. So this study found that Crude oil price is an important factor determining some sectoral Indices in Indian stock market during the covid period. This study also focused on performance of sensex and sectoral Indices during pre covid and covid period. © 2021 Karadeniz Technical University. All rights reserved.

The Correlation analysis of COVID-19 result and Stock Market : Study of BSE-Sensex

It has been observed that from the month of Nov, 2020 BSE Sensex is back in its mood and is going has made an all time high. Now the market is in a jubilant mood. During the COVID-19 period the market has behaved like a roller costa and every good and bad news about the Pandemic has equivalently affected the Sensex too, if not in absolute terms at least in terms of its direction. The initial days of the Covid-19 pandemic has created a huge losses to the investors in the financial market. Almost all the economic activity was stopped which has resulted in a huge loss to the business. After the removal of the lockdown in phased wise manner the market particularly the stock market has a frequent swing and in last couple of month it has been able to reach all time high mark of 50 K. The present article aims to analyse the journey of the BSE Sensex during the COVID-19 period from January second week of 2020 till date

Herding in the crypto market: a diagnosis of heavy distribution tails

PurposeWith the unprecedented growth of digitalization across the globe, a new asset class, that is cryptocurrency, has emerged to attract investors of all stripe. The novelty of this newly emerged asset class has led researchers to gauge anomalous trade patterns and behavioural fallacies in the crypto market. Therefore, the present study aims to examine the herd behaviour in a newly evolved cryptocurrency market during normal, skewed, Bitcoin bubble and COVID-19 phases. It, then, investigates the significance of Bitcoin in driving herding bias in the market. Finally, the study gauges herding contagion between the crypto market and stock markets.Design/methodology/approachThe study employs daily closing prices of cryptocurrencies and relevant stocks of S&P 500 (USA), S&P BSE Sensex (Index) and MERVAL (Argentina) indices for a period spanning from June 2015 to May 2020. Quantile regression specifications of Chang et al.’s (2000) absolute deviation method have been used to locate herding bias. Dummy regression models have also been deployed to examine herd activity during skewed, crises and COVID-19 phases.FindingsThe descriptive statistics reveal that the relevant distributions are leptokurtic, justifying the selection of quantile regression to diagnose tails for herding bias. The empirical results provide robust evidence of crypto herd activity during normal, bullish and high volatility periods. Next, the authors find that the assumptions of traditional financial doctrines hold during the Bitcoin bubble. Further, the study reveals that the recent outbreak of COVID-19 subjects the crypto market to herding activity at quantile (t) = 0.60. Finally, no contagion is observed between cryptocurrency and stock market herding.Practical implicationsDrawing on the empirical findings, it is believed that in this age of digitalization and technological escalation, this new asset class can offer diversification benefits to the investors. Also, the crypto market seems quite immune to behavioural idiosyncrasies during turbulence. This may relieve regulators of the possible instability this market may pose to the entire financial system.Originality/valueThe present study appears to be the first attempt to diagnose leptokurtic tails of relevant distribution for crypto herding in the wake of two remarkable events: the crypto asset bubble (2016–2017) and the outbreak of coronavirus (early 2020).

Role of debt-to-equity ratio in project investment valuation, assessing risk and return in capital markets

In this paper, a case study was performed with an aim to analyze the asset returns for two different companies and the risk and returns from capital projects using standard capital asset pricing method. To demonstrate how the present values of future cash flows are influenced by discount rates when the debt-to-equity capital structure ratio is varied between 0 and 2.5 debt-to-equity. The breakeven sensitivity was also conducted in relation to different gross margin ratios of company. It was found that high value of debt-to-equity ratio yielded a flatter net present value with increase in gross margins. Capital appraisal techniques were applied to illustrate the project returns and annual cash flows and its relationship with change in cost of capital. Analysis showed that when average cost of capital is increased beyond threshold value, the net present value from the firm’s project investments reduced significantly. A covariance analysis was performed to determine individual returns from two stocks traded in BSE Sensex and S&P 500 indices using the beta values. Comparing the individual and total returns of two stocks revealed that returns not only increased with increasing beta values–but also varied with earnings potential, growth rate of firm, dividend payout ratios and trading stock price. The standard deviation on portfolio of two stocks has been computed for varying asset weight ratios. It has been found that positive correlation between two stocks increased equity risk when weight ratios are not balanced in portfolio, while a negative correlation reduced equity risk.

A remark on mean‐semivariance behaviour: Downside risk and capital asset pricing

AbstractIn the conventional capital asset pricing model (CAPM), standard deviation as a measure of risk penalizes both upward and downward movements. However, while downward movements in investments are risky, upward movements are favourable. Therefore, standard semideviation that treats only those negative differences in returns over the benchmark as risky was proposed as a measure of downside risk. Attempts have been made to obtain the formulation of beta in this context, called downside beta. In this work, CAPM in the mean‐semivariance framework (D‐CAPM), with semivariance measured in terms of negative deviation around expected return, is derived and established that the model proposed earlier with a version of downside beta with respect to this definition of semivaraince is invalid. The appropriate version of downside beta is obtained, and under the rectified formulation, the validity of the true D‐CAPM is empirically tested during July 2013–July 2018. The solution to the tangent point is also obtained. The proportion of investment in the stocks in the tangent portfolio and the downside beta for the stocks in the tangent portfolio are determined. We find that there is no evidence to disprove that D‐CAPM holds true considering either Nifty 50 or S&P BSE Sensex as market portfolio.

Santa Claus rally and the Indian stock market: A comprehensive analysis

The study examines the leading Indian stock market indices all of which reflect the “Santa Claus rally” effect where significantly higher daily returns accrue to investors in the last five trading days of December and the first two trading days of the following January. The study also reveals that the BSE SENSEX started exhibiting this effect only during the post-1991 period. Additionally, the results indicate that there is a size effect associated with the Santa Claus rally effect wherein small-cap companies outperform mid-cap and large-cap companies. The evidence also suggests that the effect is prominent in eight industry-specific sub-sectors.

A Variance-Ratio Test of Random Walk in the Stock Markets of Selected South Asian Countries

Over the past many years, researchers in finance, economics, and other related fields conduct intensive studies to examine whether stock prices follow a random walk. They, used various statistical techniques, often document mixed findings. In this paper, the random walk hypothesis is assessed using the relatively fresh data, NEPSE Index, BSE Sensex, DSE Index and KSE 100 Index of the Nepalese Stock market, Indian Stock Market, Bangladeshi stock market and Pakistani Stock Market from February 2012 to November 2017.The study is based on the Lo and Mackinlay variance ratio test. The empirical result explored that the Nepalese stock market follow the random walk model but Indian, Bangladeshi and Pakistani stock market do not follow the random walk.

Carbon Reduction and Sustainable Investment: A Way to Sustainable Development

The present empirically examines the impact of five variables, namely Economic level, Population-level, Urbanization level, Industry proportion, Fossil fuel energy consumption, and Methane emission on Carbon intensity in India. It also evaluates the volatility and impact of news on asymmetric volatility of sustainable indices in the Indian stock market, S&P BSE CARBONEX, S&P BSE GREENEX, and S&P BSE 100 ESG against S&P BSE SENSEX as poxy for the market index. The data for the study was collected from the World Bank Database and the official website of the Bombay Stock Exchange. The study used OLS regression to evaluate the impact of five variables on Carbon intensity and econometrics tools like GARCH and EGARCH to measure the volatility and impact of the news. The study found that Economic level, Fossil fuel energy consumption, Population-level, urbanization level, and Methane emission have a significant positive impact on carbon intensity. There is a negative relationship between economic level and carbon intensity. The volatility of SENSEX is higher than that of sustainability indices. The study found that there is an asymmetric impact of positive and negative news on stock volatility, as parameter γ is negative and significant for all indices.

Forecasting of bse sensex using auto regressive integrated moving average (arima) method

Forecasting involves the preparation of a statement concerning unknown events, which might occur in future. The main purpose of making forecast is to gain knowledge about uncertain events that are important to our present decisions. In this study, we discussed about Auto Regressive Integrated Moving Average (ARIMA) method. By using this method, we forecast the BSE SENSEX Closing point value and then compare the method with the help of RMSE measure.

Impact of Mergers and Acquisitions on Unemployment: A Case of Indian Economy

The mergers and acquisitions approval is focused on the product market mainly, but recently input market has also been considered in different jurisdictions. The study’s focal point was evaluating the impact of mergers and acquisitions on the unemployment rate in India. The secondary data was collected from CMIE. Data was collected on employment and mergers in manufacturing, service, mining, and real estate and construction industries from March 2016 to September 2020 quarterly for correlation analysis. Regression analysis was also conducted where the unemployment rate in India was dependent variable and number of acquisitions, value of acquisition, number of mergers, CPI, IIP, BSE Sensex trading volume, trade balance, central government expenditure were independent variables, where data was collected from January 2016 to November 2020 on monthly basis. The result indicated a negative and weak correlation between the mining and service industries mergers with the number of employees in the industries and positive yet weak correlation between manufacturing industry and real estate and construction industry mergers with the employment in the industry, but these results were insignificant. Regression analysis result deduced that number of acquisition, inflation, and trade balance had a positive yet insignificant impact on the unemployment rate in India, and acquisition value, number of mergers, IIP, BSE Sensex Trading volume, trade balance, and central government expenditure had a negative impact on the unemployment rate in India but insignificant, except IIP. Results indicate that volume of transactions and number of mergers and acquisitions had a distinctive impact on the unemployment level and industry specific research is required for the identification of oligopoly and oligopsony.

Interdependency Between Indian and US Market Indices: A Granger Causality Approach

The Granger causality model is used in the current study to analyze the short-run cause–effect relationship between two stock market indices between 2001 and 2021 using time series data of the daily closing prices of the BSE Sensex and S&P 500 indices listed in the Indian and US stock markets, respectively. The Granger causality model and the augmented Dickey–Fuller test for data stationarity were used in the study to examine the short-term causal link between two market indices during the time period. The outcomes demonstrated the connection between the Indian and US stock markets. The findings imply that both markets have a dynamic, bidirectional relationship. This study provides the investor’s essential inputs for investment decision-making and portfolio diversification. In the current era of globalization, the study is crucial because investors and fund managers now place a high priority on stock market integration. Through fund diversification across equity markets, this study subsequently makes it easier to reduce portfolio risk by providing useful insights on diversification strategies across the stock markets.

Forecasting of bse sensex using simple exponential smoothing (SES) method

Forecasting is the prediction of variable based on known past values of that variable or other related variables. The art of forecasting is learned by experience rather than by academic study. Forecasts must results in present action to improve future. In this study, we discussed about Simple Exponential Smoothing (SES) method. By using this method, we forecast the BSE SENSEX closing point value and then compare the method with the help of RMSE measure.

An assessment of the day-of the-week-effect in BRICS countries

The present study investigates the stock market anomalies in the stock prices of premier stock exchanges in the BRICS countries. To reach out the objectives of the study, the researchers have adopted different econometric tools such as Descriptive Statistics, Augmented Dickey-Fuller test, Phillips-Perron test, Kwiatkowski Phillips Schmidt and Shin test, Generalised Autoregressive Conditional Heteroskedasticity (GARCH (1, 1)) and Simple Regression Method. The results evident the Thursday effect on the SSEC index of China while the other days' effect is quite insignificant. Further, Bovespa-Brazil, RTSI-Russia, BSE Sensex-India, and JSE-South Africa did not exhibit any mid-day effect during the study period. As per the analysis of GARCH (1, 1), the Wednesday effect could be found for Bovespa but the Thursday effect does not exist. The effect of Thursday for RTSI, SSEC and JSE is significant while the effect of Monday is significant for BSE Sensex and JSE.

Market Efficiency of Indian Capital Market: An Event Study Around the Announcement of Results of Lok Sabha Election 2019

Market efficiency categorizes a stock market into three sections based on the reaction of share prices to private and public information. This paper mainly deals with reactions of stock market dynamics to information in political events considering the impact of result announcement of the Lok Sabha Elections 2019 on the Indian Stock market as reflected in the behaviour of share prices. Taking BSE 100 as the proxy market, daily closing stock prices of the 30 companies listed in BSE SENSEX was used. An estimation window of 120 trading days was taken prior to the event window. The standard Market model was applied to calculate the AAR and CAAR during the event window of 21 days. Further the Augmented Dickey Fuller (ADF) Test for unit root is applied to measure the stationary of the variables and the presence of ARCH/GARCH effect is tested to understand the volatility during the study period. The Runs Test was used to test the randomness of AAR and the paired sample t test was applied to check the impact of the event on the volume of trading. Consistent negative returns were observed following the event. But the absence of volatility and the insignificant results indicated that market efficiency Indian Stock Market is in a semi strong form.

Nexus between intellectual capital and business performance: evidence from India

PurposeThe prime intention of this study is to examine the influence of intellectual capital (IC) on the financial performance of Indian companies listed on Standard and Poor Bombay Stock Exchange Sensitive Index (BSE SENSEX).Design/methodology/approachThe study employs the data of 30 most significant and most prominent companies of India listed on BSE SENSEX for 10 years from 2009–2010 to 2018–2019. Value Added Intellectual Coefficient (VAICTM) methodology developed by Pulic (2000) was employed for measuring the efficiency of the IC.FindingsThe efficiency of IC is substantially and positively associated with the financial performance of the Indian companies as measured by return on assets (ROA), market-to-book (MB) ratio and return on equity (ROE). Amongst the three dimensions of VAIC, capital employed efficiency (CEE) was the most vital element in contributing to the firm financial performance, followed by human capital efficiency (HCE). Structural capital efficiency (SCE) only helps in enhancing the ROA of Indian firms.Research limitations/implicationsThe study results are only restricted to the 30 companies of India listed on S&P BSE SENSEX Index. Thus generalization of the result needs especial caution.Originality/valueThe study fills the void in the current literature of IC and business performance and extends the understanding of their relationship by providing empirical evidence.

A study of the interrelation between inflation and BSE consistency

This study examines the relationship between WPI measured inflation and BSE market consistency proxied by market capitalization, BSE Sensex 30 and trading turnover-both value and volume, during the period April, 2000 to March, 2014. The investigation has been made by applying the vector error correction model and Johansen co-integration test. The study analysed the correlation between the variables by using Karl Pearson Coefficient of correlation matrix, further confirmed through the test of significance. All the findings of the study were justified through the correlated variables. In addition the Johansen co-integration test results show that there is a long run, but there is a negative relationship between WPI measured inflation and selected proxies of BSE consistency during the evaluated period. However, no short run interrelation between the variables also observed during the study period. Keywords: Capital markets, Financial intermediation, Correlation, Co-integration, Vector error correction, Inflation, Market capitalization, BSE sensex 30.