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A plethora of information on indices of popular bourses across the globe has heisted investors off their mental peace to give them nightmares. Operational nuances and informed interpretations of the movement in the stock market indices of bourses operating at the international level, regional level, and national level and of those of sector specific have always been a daunting task to myriad of investors actively functioning in the market arena. This paper seeks to demystify the modus operandi of developing indices of bourses using pragmatic episodes and thereby aid the investors in comprehending the subtleties associated with it and the niceties involved in its dexterous maneuvering and making robust decisions for triumphant innings in the bourses. This paper provides model of insights in computation of stock market indices with techniques such as market capitalization weighted index, free-float weighted index, price weighted index, equal weighted index, impact cost, and index management. [ABSTRACT FROM AUTHOR]

India is on the path of Digital India, FII and DII's in the country boost the country's economy. Both FII and DII have dominant role in capital formation which assists in assets formation along with investments in the stock market form of trading of various securities. FII funds a capital sources in India from foreign countries, affect the financial system and boost the economical growth. During pandemic, covid -19 the Indian economy has faced severe changes which affected the DII and FDI funds too. The research paper points on the changes in the FII and DII funds due to pandemic with reference to increase in covid cases. Design/Methodology/Approach-The paper employs quantitative approach have exploratory research design. The secondary has been collected from authorized government sites i.e. www.moneycontrol.com & www.statista.com for infusion of FII and DII funds in India and data sheet of COVID-19 cases in India(January 2020 to December 2020). Furthermore, for analyze of data SPSS version 20 has been used. Correlation & regression and ANOVA; Statistical techniques have been applied to reach the findings. The findings exhibits that there has been a significant changes in FII and DII during the COVID first wave period as compared to the pre COVID. Being FII and DII are very important for any countries economy, therefore studying the same and especially for the COVID period is highly essential from the Future perspective. This paper is original as till date no study has been conducted in detail to study the pattern and impact of FII and DII during the COVID times. [ABSTRACT FROM AUTHOR]

Initial public offerings (IPOs) have a special place in finance, probably because they are the first opportunity to value a number of financial assets of company by variousparticipants of capital market. In India, initial public offerings (IPOs) have attracted many investors. Because companies coming out with IPOs are new and have no historical data to analyze. The success of IPO is critical for stockholders, companies, and the economy. Objectives: 1. To Study different IPOs during 2020-22 2. To analyze performance of Indian IPOs in short Run time frame. Research Approach The work in this research paper is carried out on the basis of empirical evidence. The study encompasses 33 IPOs which got listed on NSE of India for the period between 2020-2022. Thework is based on data collected from sources which are secondary in nature. The data required for research work is taken from website of National stock Exchange of India in the form of price quotes of shares and Nifty values. Themarket adjusted short term performance model, wealth relative index model along with other statistical measures is used for the study. Scope of work- The research work is restricted to companies which came out with public issues during the above specified period and got listed on NSE of India. Period -Year 2020-2022 Limitations of Study-The study covers external factors which affects the IPO performance. Originality-This research work is original and examines stock's listing profit or loss and its performance after the listing on recognized stock exchange. [ABSTRACT FROM AUTHOR]

Blockchain technology is one of the fast growing technologies used for the financial sector now days. Financial information is very sensitive and always organizations try to keep them secret and secure otherwise it can create a big loss and hazard. Stock market is most important financial sector that works on two concepts; one is to increase the profit and second is to reduce the risk. In this research paper I try to discuss how blockchain technology is gaining importance in stock market. Mainly blockchain will help us to make optimal stock exchanges through automation and decentralization. Across the globe in each and every sector blockchain technology make inroads, so stock market also. Some of the country is still preparing them to use the blockchain technology. This Technology offers a big amount of security especially in financial sector dealing in money matters with customers. [ABSTRACT FROM AUTHOR]

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This study examines the impacts of domestic and counter-terrorist attacks on India's securities markets after collecting time series data of SENSEX and NVIX during the period 1980-2015 for the empirical work. We employ the conditional volatility framework to estimate stock market volatility around the terrorist events, presenting empirical results that corroborate earlier studies and report a significant impact of terrorist activities on investors' sentiment. Stock market volatility remains volatile for pre- and post-terrorist events. This paper is the first empirical research investigating the impact of terrorist attacks on stock market performance in an emerging economy like India, such as stock returns and investors' fear-gauge-index (NVIX). The implications of the study underpin a forward-looking direction on stock market volatility and investors' behavior towards risky capital investment decisions. [ABSTRACT FROM AUTHOR]

This paper proposes a hybridized framework of Support Vector Machine (SVM) with K-Nearest Neighbor approach for Indian stock market indices prediction. The objective of this paper is to get in-depth knowledge in the stock market in Indian Scenario with the two indices such as, Bombay Stock Exchange (BSE Sensex) and CNX Nifty using technical analysis methods and tools such as predicting closing price, volatility and momentum of the stock market for the available data. This hybrid model uses SVM with different kernel functions to predict profit or loss, and the output of SVM helps to compute best nearest neighbor from the training set to predict future of stock value in the horizon of 1 day, 1 week and 1 month. The proposed SVM and KNN based prediction model is experienced with the above mentioned distinguished stock market indices and the performance of proposed model has been computed using Mean Squared Error and also been compared with recent developed models such as FLIT2NS and CEFLANN respectively. The limitation of both of those existing models undergoes complex weight updating procedures, whereas, proposed SVM-KNN hybridized model scales relatively well to high dimensional data and the trade-off between classifier complexity and error can be controlled explicitly and have better prediction capability. [ABSTRACT FROM AUTHOR]

The paper presents a framework to forecast futures prices of stocks listed on the National Stock Exchange (NSE) in India during normal (unaffected by the COVID-19 pandemic) and new normal times (affected by COVID-19 and a macroeconomic slowdown). The model leverages a structural model that determines the relevance of the explanatory features used in the study; namely, spot prices, market sentiment, sectoral outlook, historic and implied volatility, crude price volatility, and exchange rate volatility. The proposed Ensemble Feature Selection (EFS) methodology comprising Boruta and Regularized Random Forest (RRF) algorithms is used to screen the explanatory features. Two advanced Artificial Intelligence techniques—Regularized Greedy Forest (RGF) and Deep Neural Network (DNN)—are used in conjunction with Kernel Principal Component Analysis (KPCA) and Autoencoder (AE) for forecasting. To understand the extent and nature of the influence of the explanatory variables, the Explainable Artificial Intelligence (AI) approach has been used. Statistical checks confirm that our hybrid framework is effective. The results indicate that the relative importance of the explanatory variables in forecasting futures prices differs depending on the company concerned and the period under consideration. • Present a framework to forecast futures prices of Indian stocks during normal and new normal times. • Boruta and Regularized Random Forest algorithm have been adopted to screen the explanatory features. • RGF and DNN have been used in conjunction with KPCA and Autoencoder for forecasting. • XAI approach has been used to understand the extent and nature of influence of the explanatory variables. • The relative importance of the explanatory variables depends on the company concerned, and on the period under consideration. [ABSTRACT FROM AUTHOR]

Interlocking directorates play a crucial role in the corporate governance system. In this paper we analyse the structural characteristics of the network of the interlocking directorate of National Stock Exchange (NSE) listed Indian companies using the tools of social network analysis to examine the effects of the underlying network on the performance of companies and directors. A component analysis of the network shows that 78.5% of the companies fall under one giant component with the largest island containing 6 companies. The giant component was further analysed for clusters and centrality measures. The results show that the highly boarded directors who constitute just 2.25% of the director population are associated with 42% of the total listed companies which account for 65.5% of the total market capitalisation. The top central actors in both director as well as company networks have been identified. The calculated values of mean path length and global clustering coefficient provide evidence for the existence of small world structure in the Indian corporate field. [ABSTRACT FROM AUTHOR]

Every country whether developed or developing is suffering from the stock market anomaly better known as Weekend effect. "Weekend effect" or "Day of week" has been a hot research topic among academicians for decades. The most popular anomaly is the Monday effect, meaning that Monday average rate of return is significant lower than the other day's Average return. The presence of the Monday effect in the stock market violates the basic premises of the Efficient Market Hypothesis. Besides that, it has the major implication in the designing the investment strategy. This paper attempted to find out empirically the week effect anomaly in the Indian equity market, using daily observations from the year August 2002 to December 2011. Two popular Indices of Indian Stock Market are taken in to consideration i.e. S&P CNX Index and Sensex. Both the indices have shown the same result that there is Wednesday effect on the Indian Stock market. Besides, there was no significant difference in the mean returns among the different days of the week. The seasonality results showed that there was no significant day of week effect in the Indian Stock market during the study period. [ABSTRACT FROM AUTHOR]

Forward Rate Unbiased Hypothesis (FRUH) has been the subject of intensive scrutiny by researchers. Majority of the work has focused on the forward and the spot rate of the currency of a single country. Cross country FRUH comparison has been rare. This paper is an attempt to fill the lacuna by comparing the FRUH in the Indian and the Tunisian Forex Market. The dataset used in this study consists of 238 weekly observations of the Tunisian and Indian spot and forward exchange rates for the four-year period starting 01 April 2004 to 16 October 2008. Analysis shows that the FRUH does not hold in both the markets. However, FRUH seems to be more severe in the Indian markets than in the Tunisian markets. Furthermore, the slope coefficient's in the Indian case were negative as opposed to the Tunisian case suggesting that India is a more developed economy as compared to Tunisia. Based on our evidences, we highlight some reasons as to why the FRUH fails and suggest areas for further research. [ABSTRACT FROM AUTHOR]

This paper examines the risk-return relationship in Indian stock market using symmetric and asymmetric GARCH-in-mean (GARCH-M) models. First the standard GARCH-M model is used, next since variance is proxy for risk, we ascertain if there is any significant relation between asymmetric variance and return using TGARCH-M, EGARCH-M and Power GARCH models. The study uses daily data on popular index S&P CNX Nifty of National Stock Exchange, India, during a period of two decades from July, 1990 to November, 2010. Further the period of Asian crisis is covered during the pre-derivative period of a decade and sub-prime crisis is covered during the post-derivative period of a decade to see the behaviour of volatility and risk-return relationship. The results show that both the TGARCH-M and PGARCH-M models are good for Indian market conditions. The asymmetric models find strong evidence of time-varying, highly persistent and predictable volatility in Indian market. It establishes that return is positively related to risk in Indian market during all periods. The risk-return relationship is positive and significant only at higher lags that too in the presence of dummyfut (variable which takes value 1 after derivatives and 0 before it). Further during sub-period analysis we find that risk-return parameter is higher in magnitude in pre-derivative period compared to post-derivative period. In both periods both the crisis had negative effect on return and positive effect on volatility. The rise in volatility during Sub-prime crisis is sharper compared to during Asian crisis. The findings are useful for financial decision making. [ABSTRACT FROM AUTHOR]

This paper makes an effort to investigate the presence of feedback trading behavior, if any with reference to foreign institutional investors (FIIs) and mutual fund investments in relation with the benchmark market index CNX SandP Nifty. The study used the daily data for the period started from April 1st, 2001 to December 31st, 2009 and applied various econometric models to identify the investment behavior of various institutional investors. The study concluded that the FII's are engaged in the positive feedback trading activities and inducing volatility in Indian stock market, whereas the local mutual funds are found to be involved in negative feedback trading in Indian stock market, which provide support to the market when it becomes volatile. [ABSTRACT FROM AUTHOR]

In this research paper, attempt has been made to explore the relation especially the causal relation between stock market index i.e. BSE Sensex and three key macro economic variables of Indian economy by using correlation, unit root stationarity tests and Granger causality test. Monthly data has been used from April,1995 to March, 2009 for all the variables, like, BSE Sensex, wholesale price index (WPI), index of industrial production(IIP) and exchange rate(Rs/$). Results showed that the stock market index, the industrial production index, exchange rate, and wholesale price index contained a unit root and were integrated of order one. Granger causality test was then employed. The Granger causality test indicated that IIP is the only variable having bilateral causal relationship with BSE Sensex. WPI is having strong correlation with Sensex but it is having unilateral causality with BSE Sensex. Therefore, it is concluded that, Indian stock market is approaching towards informational efficiency at least with respect to two macroeconomic variables, viz. exchange rate and inflation (WPI). [ABSTRACT FROM AUTHOR]

This paper makes an attempt to assess the impact of the global financial crisis on both the Indian conomy and the Indian labour force. Recession set off with the collapse of the US investment bank, Lehman Brothers leading to heavy losses in Asian stock markets. The turmoil in the US credit markets has affected other countries too, including India. There have been major job losses in the banking, financial, insurance segment and the IT-BPO sector. On the whole about 1.5 million job losses have been recorded for the year 2009. The global financial crisis has caused obvious issues in the private sector as to discharge, retrenchment, redundancy, closure, the rights of workers against insolvent companies and programmes dealing with unemployment. The labour laws in India, enacted to protect the interests of the labour, have been ineffective as illustrated by their implementation in the BPOs and SEZs in India. In the context of the global financial crisis, a comprehensive view on labour market reforms is required, one that addresses the need of both employers and workers, and includes the unorganised sector as well. [ABSTRACT FROM AUTHOR]

Abstract: This paper applies two alternative methods of estimation, viz., fully modified OLS (FMOLS) and generalized method of moments (GMM), to analyse the determinants of the capital structure of Indian firms using a panel of 1169 non-financial firms listed in either the Bombay Stock Exchange or the National Stock Exchange over the period 1995–2008. The results thus obtained are robust across the estimation methods. Among the three alternative theories of capital structure, the pecking order theory and the static trade-off theory both seem to explain Indian firms’ decisions. However, there is little evidence to support the agency cost theory. [Copyright &y& Elsevier]