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Purpose: Equity research in experimental psychology reveals investors' overreactions to bad news events. This study of asymmetric price structures in equity markets investigates whether such behavior predicts stock returns in an emerging market of India. Design/methodology/approach: The research decomposes Bombay Stock Exchange (BSE) Sensex returns into Extremely Positive Returns (EPR) and Extremely Negative Returns (ENR) based on extreme values at first and then tests their lead–lag relations. Findings: The empirical finding is consistent with the existing evidence of asymmetric news effects on stock returns in India. In precise, ENR robustly predicts one-month-ahead EPR for the sample period from January 1991 to March 2020. This predictive power persists even in the presence of popular valuation ratios and business cycle variables. Practical implications: The paper explains the rationale of extreme value modeling in price forecasting. Investors can find additional utility gains from market cycle information while predicting extreme returns in Indian stock market. Originality/value: The paper is unique to understand business cycle effects in extreme return reversals in emerging markets. [ABSTRACT FROM AUTHOR]

The study aims to rank the performance of Indian private sector banks on the basis of their financial capability. The study is focussed on those banks which are listed in the Bombay Stock Exchange (BSE), for the period from 2014–15 to 2018–19. The financial performance of selected Indian private banks is ranked using a hybrid Multi‐Criteria Decision Making (MCDM) technique, that is, AHP‐TOPSIS. The Analytical Hierarchy Process (AHP) is used to determine the weights of 10 financial performance pointers, and further Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to provide rankings and benchmarking of the private sector banks. Thereafter, overall ranking is given to the selected banks using interval‐valued TOPSIS (IV‐TOPSIS) for a combined period of 5 years. Sensitivity analysis has been performed to check the robustness of the method and its impact on the final ranking of the banks. Results showed that HDFC bank is the top performer and is ranked first for all the years consecutively during the period 2014–2019 and has set a financial indicator benchmark for other competitor banks. South Indian Bank showed the worst results in each year and stood last in the overall ranking. The IndusInd bank is followed after HDFC bank by improving its performance from 2014–19. With a slight margin, Kotak Mahindra Bank ranked after IndusInd. Through the AHP‐TOPSIS technique, each year the private sector banks ranking differs among themselves depending upon the financial performance pointers. Based on interval‐valued TOPSIS (IV‐TOPSIS), it was found that the overall HDFC was the top‐performing bank and South Indian Bank was the worst‐performing bank among all other banks. The results obtained from sensitivity analysis were found to be consistent with IV‐TOPSIS. The paper used the AHP technique, which is subjective in nature, and the results depend on the opinion of the experts. This study will assist the investors who want to channelize their funds in private banks having shares enlisted in BSE. It will accommodate banks to compare and judge their financial performance along with improving their performance and planning their future strategy. AHP‐TOPSIS utilized by past researchers is unable to bring out the cumulative performance for banks, but in this paper, IV‐TOPSIS is utilized, which can be effectively used to evaluate the cumulative performance of banks. The study tries to fill the gap in the literature by providing the overall ranking to private sector banks in India on the basis of financial indicators. [ABSTRACT FROM AUTHOR]

The paper aims to empirically investigate the influence of corporate social responsibility (CSR) on firm performance in Indian listed firms. We use regression analysis to determine the relationship between CSR and firm performance. Both accounting and market measures of firm performance have been deployed for the purpose of the study. We have found mixed evidence. While CSR has a positive impact on the accounting measure, it has a weak relationship with market-based returns. Our most significant finding in the study is that markets do not perceive the standard mandated CSR spends worthy of a reward. Instead, such spends are considered hygiene whereas expenditure made by firms in excess of the mandated CSR spends is rewarded by markets for their CSR conscious behaviour. We therefore advise the Indian companies to spend beyond the mandated CSR outlays if they are to improve on their market value. [ABSTRACT FROM AUTHOR]

This study explores the relationship of investment done by domestic institutional investors (DII) and stock market returns. The paper covers a bigger definition of DII's, bifurcating them into four categories, (a) mutual fund, (b) venture capital fund, (c) financial institutions and (d) insurance companies who have invested in national stock exchange of India. The study uses CNX Nifty 50 to represent the stock market of India. ARDL bound testing cointegration model is applied to find the relationship between the Dependent variable (nifty 50) and Independent variables (mutual fund, venture capital fund, Indian financial institution and insurance companies) This analysis will help regulatory authorities to improvise on policy making on investment in stock market for insurance companies, financial institution, venture capital fund and mutual fund. The surge in domestic institutional equity inflows, will help to insulate the Indian equity market from the high velocity traders of overseas. This is the first study to undertake domestic institutional investors at a disintegrated level. [ABSTRACT FROM AUTHOR]

The purpose of the study is to investigate the nature and extent of sustainability reporting practices of top 100 National Stock Exchange (NSE) listed companies in India. Further, this paper also analyses the difference in sustainability reporting practices of companies based on the Global Reporting Initiatives (GRI) reporting, ownership structure and industry type. Data were collected from the sustainability report (SR), business responsibility report, CSR report and annual report of the companies for the year ended 2017–2018 and 2018–2019. Content analysis of the reports, Independent sample t test and Cohen's d research techniques were used for data analysis. Notwithstanding the uniform disclosure norm, the results suggest an inconsistency in sustainability reporting practices of the companies. It was found that GRI reporting companies have significantly higher sustainability disclosure than non‐GRI reporting companies in India. Contrary to prior studies, the result indicates the absence of a statistically significant difference in sustainability reporting practices of government‐owned corporations and private companies. The results provide several practical implications for regulators, government, policymakers and companies. Findings suggest companies should move beyond compliance of regulatory norms and adopt sustainability code of conduct (i.e., GRI, UNGC principles, SDGs Mapping) to improve sustainability reporting practices. Policymakers and regulators should broaden the scope of existing disclosure norms (National Voluntary Guidelines) to further improve sustainability reporting in India. The present study is one of the first of its kind to investigate the nature and extent of sustainability reporting practices of NSE listed companies' post‐disclosure reforms in India. [ABSTRACT FROM AUTHOR]

This paper empirically investigates the impact of COVID‐19 on the volatility of stock prices in India with the help of a generalized autoregressive conditional heteroscedasticity model. Daily closing prices of stock indices, Nifty and Sensex from September 3, 2019 to July 10, 2020 has been used for the analysis. Further, the study has been attempted to make a comparison of stock price return in pre‐COVID‐19 and during COVID‐19 situation. Findings reveal that the stock market in India has experienced volatility during the pandemic period. While comparing the result during COVID period with that of the pre‐COVID, we found that the return on the indices is higher in the pre‐COVID‐19 period than during COVID‐19. [ABSTRACT FROM AUTHOR]

In this paper, we explore mutual information based stock networks to build regular vine copula structure on high frequency log returns of stocks and use it for the estimation of Value at Risk (VaR) of a portfolio of stocks. Our model is a data driven model that learns from a high frequency time series data of log returns of top 50 stocks listed on the National Stock Exchange (NSE) in India for the year 2014. The Ljung-Box test revealed the presence of Autocorrelation as well as Heteroscedasticity in the underlying time series data. Analysing the goodness of fit of a number of variants of the GARCH model on each working day of the year 2014, that is, 229 days in all, it was observed that ARMA(1,1)-EGARCH(1,1) demonstrated the best fit. The joint probability distribution of the portfolio is computed by constructed an R-Vine copula structure on the data with the mutual information guided minimum spanning tree as the key building block. The joint PDF is then fed into the Monte-Carlo simulation procedure to compute the VaR. If we replace the mutual information by the Kendall's Tau in the construction of the R-Vine copula structure, the resulting VaR estimations were found to be inferior suggesting the presence of non-linear relationships among stock returns. [ABSTRACT FROM AUTHOR]

In this paper, we study the effect of overall stock market sentiment in India on sectoral indices and on individual stock prices in terms of co-movement, dependence and volatility transmission along with the magnitude and persistence of the effects. The study uses wavelet decomposition framework for breaking down different financial time series into time-varying components. Quantile Regression, Wavelet Multiple Correlation and Cross-Correlation analysis, and Diebold-Yilmaz spillover analysis are then applied to investigate the nature of dependence, association, and spillover dynamics. For further focus, we have considered different time periods separately to identify the effect of market phases. Interesting results are obtained with respect to persistence of shocks, both across and within time periods. These have implications with respect to understanding market behavior and also perception of sectors and stocks. [ABSTRACT FROM AUTHOR]

In this paper, we explore the problem of establishing a network among the stocks of a market at high frequency level and give an application to program trading. Our work uses high frequency data from the National Stock Exchange, India, for the year 2014. To begin, we analyse the spectrum of the correlation matrix to establish the presence of linear relations amongst the stock returns. A comparison of correlations with pairwise mutual information shows the further existence of non-linear relations which are not captured by correlation. We also see that the non-linear relations are more pronounced at the high frequency level in comparison to the daily returns used in earlier work. We provide two applications of this approach. First, we construct minimal spanning trees for the stock network based on mutual information and study their topology. The year 2014 saw the conduct of general elections in India and the data allows us to explore their impact on aspects of the network, such as the scale-free property and sectorial clusters. Second, having established the presence of non-linear relations, we would like to be able to exploit them. Previous authors have suggested that peripheral stocks in the network would make good proxies for the Markowitz portfolio but with a much smaller number of stocks. We show that peripheral stocks selected using mutual information perform significantly better than ones selected using correlation. [ABSTRACT FROM AUTHOR]

The interaction between macroeconomic variables and share returns has been a debatable topic since decades. Much of the earlier studies have talked about significant relationships between stock market and macroeconomic indicators. In recent years Islamic finance has shown persistent growth worldwide. India has approximately 177 million Muslims, accounting for 12% of India's total population. Islamic finance differs from conventional investing in different ways like prohibition of gambling, pork, alcohol, entertainment, tobacco, profiting from interest rates etc. This paper aims to explore the long run and short run relationships of macroeconomic indicators and stock returns in the Indian context. The study is unique as it employs Shariah compliant indices to check on the stock market in India, a non - Islamic country. Data spans the period from January 2006 to July 2015. Monthly data is used and the selected macroeconomic variables are inflation rate (wholesale price index), interest rate (365 day government of India T-bill rate), money supply (M3) and exchange rate (US Dollar/Indian Rupee). 'Dow Jones Islamic Market India Total Return Index' is considered to track for the Shariah compliant stock index in Indian equity market. The study employs Johansen's co-integration and VECM to check for long run and short run equilibrium relationships. We check for long run equilibrium relationships through co-integration analysis. Johansen's test is a preferable multivariate cointegration test as it accounts for more than one cointegrating relationships. Granger causality is applied to check for the direction of causality. Variance decomposition in VAR system dynamics is also analyzed. Study reports the presence of a long run equilibrium relation between macroeconomic indicators and Dow Jones Islamic India market index. We are getting a positive and statistically significant relationship between WPI and stock returns as well as money supply and stock returns. Interest rate is showing negative and statistically significant relation with stock returns. Further uni-directional causality is observed from money supply and exchange rate to Dow Jones Islamic India market index. We can say that exchange rate as well as money supply granger causes Dow Jones Islamic India Market Index. Variance decomposition of exchange rate shows that 21.66% of variance in exchange rate is explained by Dow Jones index. Variance decomposition of Dow Jones index shows that money supply M3 explains 16.01% of variation in Dow Jones index. India's Islamic capital markets are showing signs of market inefficiency because of long run equilibrium relationships. This paper caters to the needs of Islamic investors and also to the policy makers as it provides a broad understanding of dynamic relationships between Shariah compliant stock returns and macroeconomic factors in India. [ABSTRACT FROM AUTHOR]

• A systematic approach for optimal portfolio construction based on sector analysis is presented. • The concepts of probability theory, econometrics, and deep learning are integrated. • Evidence theory and Granger causal network are unified to select strong and diverse economic sectors. • Deep recurrent neural network is used for optimal ratio allocation to portfolio securities. • The proposed model is designed for investment in National Stock exchange of India. With the emerging areas of economy, the diverse sector-based investment portfolios are considered more significant. This paper presents an integrated approach of portfolio construction based on sector analysis for investment in National Stock Exchange of India. The model is developed in four stages: First, multiple evidences are collected using historical data to analyze the performance of different sectors, and ranked using Dempster-Shafer evidence theory. The top-ranked sectors are considered as the strong sectors. Second, Granger causality test is employed for determining interdependencies between the strong sectors using the past closing price time series. Then, the strong sectors with no causal relationships are selected to diversify the investment. Third, leading stocks of the selected sectors are picked on the basis on their performance in previous years. Thus, the portfolio of best stocks from strong and diverse sectors is constructed. Fourth, for optimizing of the constructed portfolio or to obtain optimal ratio allocations of stocks in the portfolio, an optimization function is constructed and simulated using a deep recurrent neural network. The results obtained by the proposed portfolio are found very significant which confirms its effectiveness. The applicability of the proposed model is verified by empirical study over the existing models. [ABSTRACT FROM AUTHOR]

This paper investigates the properties of realized volatility and correlation series in the Indian stock market by employing daily data converting to monthly frequency of five different stock indices from January 2, 2006 to November 30, 2014. Using non-parametric estimation technique the properties examined include normality, long-memory, asymmetries, jumps, and heterogeneity. The realized volatility is a useful technique which provides a relatively accurate measure of volatility based on the actual variance which is beneficial for asset management in particular for non-speculative funds. The results show that realized volatility and correlation series are not normally distributed, with some evidence of persistence. Asymmetries are also evident in both volatilities and correlations. Both jumps and heterogeneity properties are significant; whereas, the former is more significant than the latter. The findings show that properties of volatilities and correlations in Indian stock market have similarities as that show in the stock markets in developed countries such as the stock market in the United States which is more prevalent for speculative business traders. [ABSTRACT FROM AUTHOR]

This paper examines the profitability of the Indian stock market using an extensive new data set that includes 1,515 stocks and covers a time-period spanning 1992 to 2014. Using both the popular Jegadeesh-Titman and the 52-week momentum trading strategies, we discover that portfolios of all stocks and various portfolios of industry stocks are profitable. These profits, we find, disappear once we account for a range of market and macroeconomic factors, suggesting that market and industry profits are compensation for risks. Our results survive a battery of robustness tests. [ABSTRACT FROM AUTHOR]

Firstly this research paper aims at discussing how the economic scenario post 1990s forced the Indian economy to open up with the three triggers of Liberalisation, Privatisation and Globalisation. This further discusses the opening of economy for the foreign investors under the given scenario. It narrates the positives and negatives of FIIs in India in terms of their impact on the Indian economy. Secondly it also studies the movement of Indian stock market i.e. BSE Index vs. FIIs investment. Thirdly and more importantly this paper has also made a herculean efforts for studying the impact of FIIs investment on the volatility of Indian stock market on the basis of daily as well as monthly data (from 2000 to 2012). For daily data 3000 observations have been used and for monthly data 156 observations have been used. GARCH and ARCH techniques have been applied for the same. The time period from 2000 to 2012 has been divided into two phases i.e. pre crisis period from 2000 to 2008 and post crisis period from 2009 to 2012. It has been found in the study that FIIs flows have had an impact on the volatility of the Indian stock market in the pre crisis as well as post crisis period. [ABSTRACT FROM AUTHOR]

Abstract: In the last decade, the efficient market hypothesis and its validity for emerging markets grew as a fertile topic of debate in Finance. However, the dilemma of market efficiency still remains intractable. It is more likely that any literature review in respect of market efficiency would produce contradictory results: for a single paper producing empirical evidence supporting the market efficiency, we can perhaps find a contradictory paper which empirically establishes market inefficiency. Paradoxically, popular models in finance developed in 1970s or 1980s were based on the assumption that the market under consideration was efficient. The conventional bond or stock or option pricing models are common examples of this type. In an alternative approach, we have worked out a model which incorporates market sentiments in the domain of the standard rational model of asset pricing. Our model would be well applicable for a ‘less than’ efficient market and, therefore may be the useful input in investors’ policies. [Copyright &y& Elsevier]

In this paper we attempt to automate the process of fitting the uncertain parameters of a multi-objective portfolio selection problem by generating L-R fuzzy numbers that belong to power reference function family. Such an approach is advantageous when the fuzzy parameters of the portfolio are best represented as general functional forms. We also propose four new portfolio selection models in a multi-criteria credibilistic framework. The key financial criteria considered are return, illiquidity (antagonistic to liquidity) and risk. In the absence of joint possibility distribution of these parameters, the return and illiquidity of the entire portfolio are considered as historical data set instead of return and illiquidity of the individual assets. In the process of fitting the most appropriate L-R fuzzy number, the vagueness within the information and deviation of the L-R fuzzy number from historical data is measured using entropy and cross-entropy respectively. These principles are embedded into the modelling process of proposed portfolio selection problems. One of the key contribution of this study is that we propose and design a sub-algorithm namely “Entropy-Cross Entropy (ECE) Algorithm” that is appended within an “MIBEX-SM” genetic algorithm and is used to solve the proposed portfolio optimization problems. This proposed solution methodology results in an automated system that is intelligent enough to extract information required for fitting of L-R fuzzy number from the historical data and does not need any human intervention in terms of stating the parameters of the problem. We also conduct an empirical study to demonstrate the impact of the solution approach and applicability of the proposed models in practical applications of portfolio selection. For this purpose we collected historical data from National Stock Exchange (NSE), Mumbai, India. The data for a period of 2008–2013 is first used to train the models. Then the data sets of one-year period of 2013 − 2014 and two-year period of 2013 − 2015 are used to test the performance of the models. We introduce a decision support system strategy of comparing the performance of models by proposing a modified version of Sharpe ratio in fuzzy context, named as “Credibilistic Sharpe Ratio (CrSR)”. [ABSTRACT FROM AUTHOR]

This paper examines the connection between oil price changes and size of firms listed in Indian stock market. A three-factor Fama-French model along with oil price variables is estimated using data from a panel of 1535 firms. Results show that there is a positive relationship between growth in oil price and stock return. Similarly, the size of the firm is also positively related to stock return. However, oil price uncertainty shows a negative relationship with stock return. Overall, these results imply that targeted measures should be put in place by the policy makers to safeguard the interests of the small and medium size firms in India. [ABSTRACT FROM AUTHOR]

The permission to Foreign Institutional Investors (FIIs) in 1992, to invest in Indian stock market was the first major step of government towards the implication of the globalization policies regarding Indian financial system. Since then, FIIs have been contributing significantly and emerged as one of the main sources of equity capital flows to Indian stock markets. Present paper provides a glimpse of the latest investment trends of FIIs during year 2008-09 and 2009-10 (so far). The paper also provides an overview of FIIs related regulatory environment and recent policy changes to attract outgoing FIIs capital from Indian stock markets after global financial crisis. [ABSTRACT FROM AUTHOR]

Abstract: A growing body of research points to the fact that capital markets react to environmental news and thus create incentives for pollution control in both developed and emerging market economies. In this paper, we conduct an event study to examine the impact of environmental rating of large pulp and paper, auto, and chlor alkali firms on their stock prices. We find that the market generally penalizes environmentally unfriendly behaviour in that announcement of weak environmental performance by firms leads to negative abnormal returns of up to 30%. A positive correlation is found between abnormal returns to a firm''s stock and the level of its environmental performance. These findings should be viewed as further evidence of the important role that capital markets could play in environmental management, particularly in developing countries where environmental monitoring and enforcement are weak. [Copyright &y& Elsevier]

The growing emphasis on exports, education, science and technology over a period of time have made the BRIC countries one of the most promising emerging market basket to invest in. The paper attempts to analyze the impact of the Foreign Institutional Investor's Equity Investment Behavior in India on the Stock Market Volatility of the BRIC countries during the Subprime Crisis by employing ARMA (1,1) GARCH (1,1) model. A dummy variable has been introduced to account for the purchase and sale behavior of the FIIs. The negative investment flow increased the volatility in the stock markets of Brazil, India and China except Russia during the crisis period. When the FIIs push the sell button in the Indian economy, the volatility in the stock markets of Brazil, India and China also increases. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between stock market returns and volatility in the Indian stock markets using AR(1)-EGARCH(p, q)-in-Mean model. The study considers daily closing prices of two major indexes of Indian stock exchanges, viz., S&P CNX NIFTY and the BSE-SENSEX of National Stock Exchange (NSE) and Bombay Stock Exchange (BSE), respectively for the period from July 1, 1997 to December 31, 2013. The empirical results show positive but insignificant relationship between stock returns and conditional variance in the case of NSE Nifty and BSE SENSEX stock markets. Besides, the analysis reveals that volatility is persistent and there exists leverage effect supporting the work of Nelson (1991) in the Indian stock markets. The present study suggests that the capital market regulators, investors and market participants should employ the asymmetric GARCH-type model that sufficiently captures the stylized characteristics of the return, such as time varying volatility, high persistence and asymmetric volatility responses, in determining the hedging strategy and portfolio management and estimating and forecasting volatility for risk management decision making at Indian Stock Exchange. [ABSTRACT FROM AUTHOR]

This paper presents a computationally efficient functional link artificial neural network (CEFLANN) based adaptive model for financial time series prediction of leading Indian stock market indices. Financial time-series data are usually non-stationary and volatile in nature. The proposed adaptive CEFLANN based model employs the least mean square (LMS) algorithm with a new cost function to train the weights of the networks. The mean absolute percentage error (MAPE) with respect to actual stock prices is selected as the performance index to estimate the quality of prediction. The CEFLANN model inputs are chosen from the past stock prices of different market sectors along with technical indicators to determine best stock trend prediction one day ahead in time. Further to improve the performance of the CEFLANN model, weights are optimized using an adaptive differential evolution (DE) algorithm and its overall prediction performance is compared with the improved LMS algorithm showing the effectiveness of the DE in producing more accurate forecast. We have selected different combinations of important technical indicators to have a strong control on changes in stock indices. [ABSTRACT FROM AUTHOR]

This paper seeks to analyze the dynamical structure of the Indian stock market by considering two major Indian stock market indices, namely, BSE Sensex and CNX Nifty. The recurrence quantification analysis (RQA) is applied on the daily closing data of the two series during the period from January 2,2002, to October 10,2013. A Rolling Window of 100 and step size 21 are applied in order to see how both the series behave over time. The analysis based on three RQA measures, namely, % determinism (DET), laminarity (LAM), and trapping time (TT), provides conclusive evidence that the Indian equity market is chaotic in nature. Evidences for phase transition in the Indian equity market around the time of financial crisis are also found. [ABSTRACT FROM AUTHOR]

The present paper examines the relation between commodity market and capital market using 22 years monthly data for the period April, 1991 to March, 2013. Gold price represents commodity market and BSE Sensex is taken as proxy for capital market in India. The unit root test clarified that gold price and stock price were found to be integrated of order one using Augmented Dicky-Fuller test for unit root. The Granger causality test confirmed the presence of unidirectional causality which runs from gold price to stock price. It is established from the Johansen cointegration test that gold price and stock price are cointegrated, indicating an existence of long run equilibrium relationship between the two. VECM and wald test finally confirmed that there is a bi-directional relationship in the long-run between the two variables. We find a negative but low correlation between gold and sensex. Based on these results, we incline to suggest the favourable property of gold as an investment asset for the Indian emerging market. At least, gold provides a diversification and safe haven benefit to investors in the Indian market. The domestic Indian gold market tends to have resistance to heightened risk in the stock market as it preserves its low negative relation with stock market variations regardless of the market conditions. [ABSTRACT FROM AUTHOR]

Historically, IPOs have been observed to be underpriced. There are cycles in both the volume and the average underpricing of IPOs which may be attributable to the prevailing economic conditions. A large number of IPOs are made when there is a stock market boom. In contrast there is generally a decline in the IPO activity when the stock market conditions become adverse. The stock market crash may cause a short term behavioural change amongst investors or a structural change in the market which may affect IPO underpricing. The present paper investigates the impact of Indian stock market crash in the year 2008 on the underpricing of book-built IPOs. A significant difference is found in the level of IPO underpricing in the post stock market crash period as compared to pre stock market crash period. [ABSTRACT FROM AUTHOR]

In this paper, we study the impact of accounting fraud revelation on trading behaviour of investors. Using daily investor account holdings data from the National Stock Depository Limited (NSDL), the largest depository in India, we find that investors directly exposed to accounting fraud cash out almost 10.7 percentage points of their overall portfolio relative to investors not directly exposed. The cashing out is largely restricted to the bad stock. Over the period of a month, however, there is no difference in the trading behaviour of the treated and control investors. • This paper studies the effect of an accounting scandal on retail investment behaviour. • Those with direct exposure (treated) to fraud cash out more relative to those without direct exposure (control). • The cashing out is largely restricted to the \bad stock". • Over the period of a month, there is no di_erence in the trading behaviour of treated and control investors. • Results are contrary to those found in developed markets. [ABSTRACT FROM AUTHOR]

Green investing has recently received considerable attention in India in light of the local government plans to significantly reduce the level of CO 2 emissions through conducting and funding various types of eco-friendly infrastructures and green projects on a national scale. Given that investment in environmentally friendly projects is still relatively new in India, the stock prices of Indian green companies could be highly volatile and very prone to risk transmission from other assets, which requires a precise estimation of the time-varying volatility of these green stocks to understand their underlying risk. In this paper, we examine if the stock volatility of Indian green companies can be predicted based on the information contents of commodity market implied volatility indexes (VIX) by employing a GARCH-based quantile regression model on daily data. The results show that risk significantly transmits from crude oil, gold, and silver markets to various Indian green stock indexes. The impact of the transmission from commodity market VIXs is stronger during the bearish stock market periods compared to bullish stock market periods, suggesting that green stock indexes are more likely to receive volatility from energy and precious metal markets during the periods of high uncertainty. Our findings are useful to socially responsible investors who focus not only on the environmental performance of a firm but also consider its financial performance. [ABSTRACT FROM AUTHOR]

This paper analyses the profitability of moving average based trading rules in the Indian stock market using four stock index series. The study finds that most technical trading rules are able to capture the direction of market movements reasonably well and give significant positive returns both in long and short positions. But these returns cannot be exploited fully due to real world transaction costs. Although the transaction cost has come down over the years, various components of transaction costs due to the bid-ask spread, brokerage, etc. will never be zero. The trading rules based on short term moving averages may be able to detect trends in financial series very quickly, but those rules also generate a large number of trades causing higher transaction costs. Thus technical traders have to pay more attention to minimizing transaction costs while choosing a trading rule. Nevertheless, profit opportunities from technical analysis continue to remain an interesting and debatable issue in the Indian stock market. [ABSTRACT FROM AUTHOR]

The objective of this paper is to examine the association between the Corporate Leverage (cl) and the Dividend Policy (dp) of firms across industries in India in respect of Size of Corporate Firms. The investigation is conducted on a panel sample of 73 firms across industries [Cement, Chemical and Fertilizer, it, Oil and Gas, Pharmaceutical, Shipping, and Textiles], which listed their shares in National Stock Exchange (nse) in India for the period 1996-2007. The impacts of Capital Structure (cs) variables (leverage) on dp measures - dividend payout (Net dividend paid/net income) in the presence of some basic fundamental variables are considered to be the determinants of dp, using the Multiple Regression Technique (ols method). The results of the cross-sectional ols Model for the selected sample firms under various sectors show that there is a significant effect of selected independent variables, dpoα+β1dpot-1 +β2pat+β3tde+β4cf+β5size+β6inv+ β1ltd + β8std + e. Therefore, this study proves that the dp of Small Size, Medium Size, Large Size, and Overall Corporate Firms across industries in India is dependent on the level of debt in cs. [ABSTRACT FROM AUTHOR]

This paper studies the impact of market opening to FIIs, on Indian stock market behavior. India announced its policy regarding the opening of stock market to FIIs for investment in equity and related instruments on 14th September 1992. Using stock market data related to Bombay Stock Exchange, for both before and after the FIIs policy announcement day. An empirical examination has been conducted to assess the impact of the market opening on the returns and volatility of stock return. We found that while there is no significant changes in the Indian stock market average returns, volatility is significantly reduced after India unlocked its stock market to foreign investors. [ABSTRACT FROM AUTHOR]

The present paper is an attempt to examine the volatility in the individual stocks listed at NSE using daily closing prices of 29 selected companies. The companies have been selected from the list of S&P CNX Nifty covering the period from 1996-97 to 2006-07. The data have been analyzed by working out standard deviation of daily returns. The study reveals that ACC, HDFC, ITC, MTNL, SBIN and SIEMENS have been comparatively less volatile than other securities. On the other hand, the securities viz. BAJAJAUTO, DRREDDY, GLAXO, GRASIM, HDFCBANK, INFOSYSTCH, M&M, ONGC, TATAPOWER and VSNL were highly volatile during the period of study. Further, the study finds that the period after 2000-01 has registered comparatively less volatility than preceding period. [ABSTRACT FROM AUTHOR]

The economic crises of 1991 forced India to reassess its protectionist, closed economy system. Globalisation was the imminent choice. The opening up and integration of the Indian economy with the rest of the world brought a dramatic change in its business environment and governance standards. The Indian stock markets matured and we have witnessed a tremendous interest among international investors in entering this emerging market. This paper traces the history of corporate governance in India and discusses key unresolved issues against the backdrop of sweeping reforms in the stock market structure, systems and regulation.International Journal of Disclosure and Governance (2008) 5, 205–235. doi:10.1057/jdg.2008.9 [ABSTRACT FROM AUTHOR]

• A multi-period multi-objective portfolio optimization model is developed. • Coherent fuzzy numbers are used to model the returns and the investor attitude. • Credibilistic optimization model is solved through a real-coded genetic algorithm. • The models' performance is tested using real-time return data of assets. In this paper, we use an extension of fuzzy numbers, called coherent fuzzy numbers, to model asset returns and an investor's perception of the stock market (pessimistic, optimistic, or neutral) simultaneously. Two multi-period multi-objective portfolio optimization models are formulated using mean absolute semi-deviation and Conditional Value-at-Risk (CVaR) as risk measures, respectively. We aim to provide more flexibility to the investor in specifying the risk tolerance and devise optimum investment plans for different investment horizons. The proposed models also incorporate bound, cardinality, and skewness constraints for each investment period to capture various stock market scenarios. A real-coded genetic algorithm is employed to solve the resultant models. Two real-life case studies involving 20 assets of the National Stock Exchange (NSE), India, and another involving 50 assets listed in the S&P 500 and NASDAQ-100 indexes have been provided to illustrate the efficacy and advantages of the models. An in-sample and out-of-sample analysis have been done for both the models to analyze the performance in the real-world scenario. The conclusion drawn from the analysis strongly emphasizes on accurately assessing the current stock market prospects, i.e., adopting the right attitude (pessimistic, optimistic, or neutral), is of paramount importance and must be included in the portfolio optimization problem. [ABSTRACT FROM AUTHOR]

We analyze daily prices of 42 stocks listed in the Nifty50 index of National Stock Exchange of India from 2006 to 2019. The Nifty50 is one of the leading indexes of Indian the stock market which comprises 50 dominant companies. We use well established techniques such as random matrix theory, complex network analysis, hierarchical clustering, diffusion entropy, and conditional entropy to study the correlation among stocks and topological properties of the Indian stock market. We perform the static and dynamic analysis of the Nifty50 stocks to extract useful information from them. In the static period analysis, we consider three sub periods i.e. before, during and the subprime crisis of 2008. In dynamic period analysis, we consider overlapped and non-overlapped time windows of one year with varying shifts. We study the effect of 'global events' (such as the sub-prime crisis of 2008 and the Chinese crisis) and 'local events' (such as General elections 2009, Assembly elections in 7 states, General elections 2014 and Demonetization in India). We find that global and local extreme events affect the correlations among Nifty50 stocks of the Indian stock market. The analysis performed in this paper may be helpful in the development of different indicators of systemic risks in India. • We study 42 companies of the Nifty50 index of Indian stock market from 2006 to 2019. • Correlations and complexity of this market is explored by using different techniques. • Network theory has been used to study changes in the networks in response to crisis. • We observe bull market effects before and after crisis in diffusion entropy analysis. • Global and local extreme events significantly change the correlations and networks. [ABSTRACT FROM AUTHOR]

In this paper, we apply the methods, Multifractal Detrended Fluctuation Analysis (MF-DFA) and Multifractal Detrended Cross-Correlation Analysis (MF-DXA) to study the auto correlation/cross-correlation behaviour and multifractal characteristics on twenty three stock market indices of Department of Public Enterprises (DPE), India. The auto correlation and cross-correlation have been measured from the Hurst scaling exponents and the singularity spectrum quantitatively. From the calculated power law scaling exponents we observe the volume and price-volume change time series possess strong anti-persistent behaviour. The price change of different time series shows persistent, stochastic and anti-persistent behaviour. We also observe the existence of multifractal characteristics in all price, volume and price-volume change time series. The price changes in the time series shows high complexity compared to volume and cross correlated price-volume. • We have studied the auto-correlation and cross-correlation on DPE, India. • The MF-DFA and MF-DXA methods applied on price and volume change time series. • We found the existence of multifractal behaviour in all time series. • Complexity parameters were used to find the strength of the multifractality. [ABSTRACT FROM AUTHOR]