CNX Nifty Index Research Articles

Covid-19-A Baleful Aftermath for the Stocks of Indian Pharmaceutical Companies

Covid-19 catastrophe has not spared any market across the world due to widespread disruptions in its supply chain operations. In today's world, however, stock markets serve as a catalyst for a country's economic and financial development. But, with the development of Covid-19 infection and widespread lockdown in the majority of countries, its stock market has plummeted even further into the depths. Therefore, to determine whether the Covid-19 outbreak has impacted the expected return of Nifty Pharma and stock return of 3 leading pharmaceutical companies Cipla, Dr. Reddy's, and Sun Pharma in the before-Covid-19 period (1 Jan 2019 to 20 Mar 2020) and the after-Covid-19 period (23 Mar 2020 to 20 Mar 2021) this study is being carried out. The study aims to uncertain whether there exists a meaningful link between the S&P CNX Nifty index's forecasted market return and the expected security return of Nifty Pharma before and after Covid-19. To evaluate the relevant hypotheses, descriptive statistics event study technique, non-parametric testing, particularly the Wilcoxon Signed test, and regression analysis were used. It was discovered that the expected returns of Nifty Pharma and the expected security returns of Cipla, Dr. Reddy's, and Sun Pharma did not vary significantly before and throughout the Covid-19 epidemic period. However, for both before and throughout the Covid-19 era, there was a link between the predicted market return of the S&P CNX Nifty index and the security return of Nifty Pharma, implying that the benchmark index S&P CNX Nifty was influenced by Nifty Pharma's performance.

Exploring Herding Behaviour in Indian Equity Market during COVID-19 Pandemic: Impact of Volatility and Government Response

This study investigates the behavioural bias of market-wide herding in the Indian equity market during the spread of COVID-19 pandemic. The study also examines the impact of market volatility and government response on herding during the sample period. We use the measure of cross-sectional absolute deviation and semi-parametric estimator of quantile regression for the period 1 January 2020 till 15 June 2020 for S&P CNX Nifty Index and its 50 constituent companies. The results obtained reveal significant herding in the Indian equity market that is aggravated by market volatility. Further, we find that the government response and control measures implemented are successful in reducing herd behaviour. The research calls for better information disclosure guidelines to promote market efficiency. We further suggest that during exogenous events, investors need to realign their portfolios and formulate trading strategies for better risk-return management.

Forecasting gains by using extreme value theory with realised GARCH filter

Early empirical evidence suggests that the realised generalised autoregressive conditional heteroskedasticity (GARCH) model provides significant forecasting gains over the standard GARCH models in volatility forecasting. We extend this literature in quantile forecasting by implementing conditional extreme value theory (EVT) framework with realised GARCH. We generate one-step-ahead value-at-risk (VaR) and expected shortfall (ES) forecasts for the S&P CNX NIFTY index using 14 standalone GARCH and GARCH-EVT models. In out-of-sample comparisons, the GARCH-EVT specification generally outperforms the standalone GARCH models. In general, the realised-GARCH EVT models provide the best forecasting performance. This finding is robust to the choice of different realised volatility estimators used to estimate realised GARCH.

Co - Movements and Dynamic Linkages between Gold Price and CNX Nifty

This paper an attempt to examine the movement and linkages (relationship) between Gold price and CNX Nifty index during the period from 2000 to 2018. By testing the normality, stationary, movements and linkages of sample variables through the econometric tools like descriptive statistics, ADF, ARMA and granger causality. It is found that the test results of granger causality are statistically significant at 5 % confidence level. Hence, there is a relationship between gold price and stock market index (CNX Nifty). It is find that the individual investors have often trusted gold as a better investment. Therefore our findings suggest that investors may design appropriate investments with gold and stock market are best investment alternatives during the study period.

Do demand curves for stocks slope down - new evidence from a unique event in India

The objective of this study is to examine the price and non-price effects of the constituent stocks following the change of the computation methodology of S&P CNX Nifty index from full market capit...

Macroeconomic Factors as a Predictor of Stock Market: Empirical Evidences from India, U.S. and U.K.

The study investigated the impact of Macroeconomic variables such as: Gross Domestic Product (GDP), The Index of Industrial Production (IIP), Consumer Price Index (CPI), Foreign-exchange reserves (also called forex reserves or FX reserves), International Crude Price (CP) on selected stock market, namely Indian Stock Market (S&P BSE SENSEX (BSE 30) index, S&P CNX Nifty index (NIFTY 50), London Stock Exchange (Financial Times Stock Exchange 100 Index (FTSE 100) and New York Stock Exchange Dow Jones Industrial Average (Dow 30). The data sets of all variables have been considered from April, 2001 to March, 2018 on a monthly basis. The study reveals long run relationship among the variables and the results of Granger Causality test reveals unidirectional, bilateral relation (Feedback) and exogeneity (Independence) among the variables.

Impact of the Changes in the Nifty Index Constituents

The objective of this study is to conduct an empirical examination of the S&P CNX Nifty index reconstitutions, between 2009 and 2018, focusing on both the price and non-price effects and the explanations surrounding them. The event methodology, with multiple abnormal return computational methods, is employed to improve the robustness and reliability of the results. The results show that the Nifty index additions (deletions) are associated with significant positive (negative) permanent abnormal returns. But the evidence of permanent abnormal volume is limited, unlike the developed markets. The evidence in this study favours the downward sloping demand curve hypothesis as the dominant explanation for the permanent abnormal return. This study extends the existing literature to a hitherto unexplored new sample period.

Does Nifty Index Inclusion (still) Convey Information? A Comprehensive Empirical Examination in the Indian Stock Market

The objective of this study is to conduct a comprehensive empirical examination of the S&P CNX Nifty index additions, for the complete period (2000 - 2018), first sub period (2000-2009) and second sub period (2010-2018), using both the price and non-price effects and the explanations surrounding them in the Indian stock market. This event methodology is used with three abnormal return computational methods, around the announcement and inclusion dates of index addition, in order to improve the robustness and reliability of the results. The results show that the Nifty index additions are associated with significant permanent abnormal returns in the complete, first and second period. However, the index effect has diminished in the second period. The information related explanations dominate in the complete and first period. The second period finds support for the downward sloping demand curve hypothesis. I extend the existing literature to a hitherto unexplored new sample period (2010-2018) in order to examine the price and non-price effects around Nifty index additions.

Technical analysis and National Stock Exchange of India: testing the RSI rule using CNX Nifty index

Technical analysis is just a strategy for seeing if it worth purchasing or offering a stock. This review can be made on the premise of information that is produced through the activities of the general population in the market. Technical analysis attempts to quantify the aggregate mind of the speculators which is led by covetousness and dread. The essential starting point under which specialised investigation works is the investigation of interest and supply, past costs and volume in the market and the heading of the value incline thereof later on. This study is significant for investors and traders as it leads to identify the level of price movement that further helps in understanding buying and selling situations in the market by identifying support and resistance levels. The results of the study show that the RSI test is reliable for any investor while trading in stock market.

Technical analysis and National Stock Exchange of India: testing the RSI rule using CNX Nifty index

Technical analysis and National Stock Exchange of India: testing the RSI rule using CNX Nifty index

Testing the Efficiency of Indian Index Options Market by Employing the Box - Spread Strategy : Empirical Evidence from S&P CNX Nifty Index

Testing the Efficiency of Indian Index Options Market by Employing the Box - Spread Strategy : Empirical Evidence from S&P CNX Nifty Index

Index Revisions, Stock Liquidity and the Cost of Equity Capital

This study examines the stock liquidity and cost of equity capital (COEC) effects around the CNX Nifty index revisions during the period 1998–2011. To examine these effects, the inclusion (exclusion) firms are compared with their matching peers. The stock liquidity effect has been examined by using distinct liquidity measures, such as trading volume, turnover rate and illiquidity ratio. The COEC effect has been examined with the help of cost of equity, stock liquidity, firm size, leverage and inclusion (exclusion) dummies. It was found that the stocks included to the CNX NIFTY were less liquid than their matching peers were, whereas the stocks excluded were experiencing more liquidity than their matching peers. Further, the study finds an increase in the COEC for the included firms and a decrease in the COEC for the excluded firms.

Identifying explosive behavioral trace in the CNX Nifty Index: a quantum finance approach

The financial markets are found to be finite Hilbert space, inside which the stocks are displaying their wave-particle duality. The Reynolds number, an age old fluid mechanics theory, has been redefined in investment finance domain to identify possible explosive moments in the stock exchange. CNX Nifty Index, a known index on the National Stock Exchange of India Ltd., has been put to the test under this situation. The Reynolds number (its financial version) has been predicted, as well as connected with plausible behavioral rationale. While predicting, both econometric and machine-learning approaches have been put into use. The primary objective of this paper is to set up an efficient econophysics’ proxy for stock exchange explosion. The secondary objective of the paper is to predict the Reynolds number for the future. Last but not least, this paper aims to trace back the behavioral links as well.

Violations of put-call parity for CNX Nifty index options: a study at National Stock Exchange

Derivatives markets provide a platform for market participants to hedge their risk, and aid price discovery. Correct pricing of derivatives instruments is imperative in the performance of these functions. In the present study, an attempt has been made to examine the valuation of CNX Nifty index options in terms of put-call parity (PCP) relationship, over a ten-year period. Since PCP is a no-arbitrage-based argument, it does not suffer from the limitations that are inevitable while gauging pricing efficiency using model-based approaches. Various error estimates, supported by non-parametric tests have been used validate our results statistically. Our findings indicate frequent violations of PCP, though the magnitude of mispricing is small. Further, the results are charted across moneyness, days-to-expiry and liquidity. The present study can be extremely important for all stakeholders, as violation of PCP leads to risk-free profitable arbitrage, which is an antithesis to efficient markets.

Violations of put-call parity for CNX Nifty index options: a study at National Stock Exchange

Violations of put-call parity for CNX Nifty index options: a study at National Stock Exchange

Derived signals for S & P CNX nifty index futures

BackgroundThe financial futures market in India is relatively new. The major advantage of derivatives as financial products is that their use minimizes the risks associated with securities. However, hedging effectiveness requires understanding key market signals such as trading margins, credit availability, and price discreteness.MethodsThis study considers the Standard & Poor’s CNX Nifty 50 Index futures for data analysis with the application of V-IGARCH (1, 1) two-stage model. The purpose for V-IGARCH (1, 1) is used to observe the positive effects of credit availability on the variance of futures returns. The first stage V-IGARCH (1, 1) endogenous mean and conditional variance returns are measured with exogenous factors from the second stage V-IGARCH (1, 1) models. The second stage V-IGARCH (1, 1) models specify the market participants’ exogenous conditional probabilistic values for returns.ResultsIn the first stage, it was observed that returns and trading margins, as well as credit availability, were cointegrated, thereby indicating a long-term relationship between them. In the first stage of the V-IGARCH (1, 1) model, heteroscedasticity with the mean returns through residuals was observed, where the estimated coefficients were negative. This finding indicated that maximizing returns requires efficient use of trading margins as well as availability of credit positions. From the second stage regression estimation, it was observed that trading prices and total money supply were directly related, and thus had direct effects on returns. The total money supply increased gradually until the last trading hour. In the conditional variance equation, total money supply was related negatively to the availability of credit for market participants. Under these circumstances, the efficient interbank call interest rate was necessary to maintain the trading margin. In effect, efficient Nifty returns would be achieved.ConclusionsThis study found that trading margins, credit availability, and price discreteness affect the variance of returns in the Indian futures markets. The study also found that market participation was inadequate as a result of endogenous and exogenous conditional probabilistic reasons. Efficient trading margins and effective credit availability positions were not realized. Price discreteness had a negative impact on returns, as trading prices and credit availability in each of the trading hours were inversely related. Trading risks, and hence losses, were not minimized by hedging positions. The monopoly power in the Nifty market was 8.9526. Given this monopoly power, returns were less elastic with respect to the existing trading margins, financial resources, and market microstructure (price discreteness) that were available for reinvestment. Therefore, before investing in derivatives (index futures), market investors should evaluate trading margins, credit availability positions, and price discreteness. Through these signals, investors will be able to gain essential market knowledge and participate accordingly in trading for efficient returns.

Price Discovery of Index Futures across Markets

The trading of foreign index futures by the Singapore Exchange (SGX) offers an ideal opportunity to study price discovery and information of trading across different markets. We examine four popular indices - Nikkei 225 Index, MSCI Taiwan Index, CNX Nifty Index and the FTSE China A50 Index traded in SGX and compare them with their home market trading. In contrary to standard theory and evidence, we show that smaller bid-ask spread, lower minimum lots and cheaper transaction cost do not necessary improve information efficiency. These results may shed some light on the usefulness of the role of an international financial centre on the price discovery of foreign indices.

An Empirical Investigation on Spot and Futures Market With Reference to Energy Sector on NSE

The importance of volatility is widespread in the area of financial economics. Equilibrium prices, obtained from asset pricing models, are affected by changes in volatility, investment management lies upon the mean-variance theory, while derivatives valuation hinges upon reliable volatility forecasts. Portfolio managers, risk arbitrageurs, and corporate treasurers closely watch volatility trends, as changes in prices could have a major impact on their investment and risk management decisions. Volatility may be defined as the degree to which asset prices tend to fluctuate. Volatility is the variability or randomness of asset prices. Volatility is often described as the rate and magnitude of changes in prices and in finance often referred to as risk. This study has been carried out on Spot and Futures market with reference to energy sector stocks on NSE. It also analysed the co-integration and Causal relationship between Spot and Futures prices of stocks and index. The study also estimated the impact of Spot market on Futures market. The study has been carried out on volatility of Indian Stock Market represented by S&P CNX Nifty Index along with influence by energy sector stocks.

CO INTEGRATION AND CAUSAL RELATIONSHIP AMONG CRUDE OIL PRICES, EXCHANGE RATE AND STOCK MARKET PERFORMANCE: AN EVIDENCE FROM INDIA

This paper has made an attempt to evaluate the combined impact of crude oil prices and exchange rate on the performance of Indian stock market. As the impact of dollar nominated oil prices on stock prices may not be quite relevant for Indian context. Therefore, in this study WTI Crude oil prices per Dollars along with the USD/Rupee exchange rate would be more meaningful and relevant to understand the impact of oil prices on stock market by using monthly data from 2003 to 2016 for S&P CNX Nifty Index, WTI Crude oil prices per Barrel (Dollars) and Dollar/Rupee Exchange rate. All the series were found to be stationery at First difference. The Granger causality tests revealed that there exists a Bi directional causality between stock prices and exchange rates in the short run i.e. stock prices lead exchange rates in the short run, but result of Johansen cointegration suggested that there is no long run relationship between these two financial variables. The results of the Johansen cointegration test suggest absence of any long term relationship between WTI crude oil price, USD/Rupee exchange rate and stock prices in India. The result of forecast error variances suggested that USD/Rupee exchange rate is influenced by Stock market performance. The forecast error variances of USD/Rupee exchange rate is significantly explained by the value of Nifty. Results also indicate that the values of oil price and exchange rate are comparatively less exogenous than the Indian stock market. Particularly, the contribution of Stock market shocks to the USD/Rupee exchange rate is greater than that of WTI Crude oil price shocks in all the periods.

Risk Free Model for Risky Stock Market: Evidence from Indian Stock Market

The paper examines the return pattern of the Indian stock market and proposes a model for long term investors by maximizing and minimizing the risk. The rolling compounded annual growth rate (CAGR) of the flagship S&P BSE SENSEX index as well as CNX Nifty index is calculated over various investment horizons during the entire history of Indian stock market. It is observed that an investment horizon of more than 10 years will lead to a drastic reduction of loss probability which is almost close to zero. Long term investors with a perspective of more than 10 years can significantly enhance their return by extending the investment horizon by couple of years even if they are faced with huge market risk towards the end of their investment horizon. The study involves devising of trading strategies using metrics like P/E ratio, P/BV ratio, Dividend Yield, 200 DMA and P/E ratio comparisons so that returns can be further enhanced. The robustness of the study is examined by dividing the valuation metrics into various categories based on size.