Nifty Returns Research Articles

Dynamics of Foreign and Domestic Institutional Investment in India

Foreign and Domestic institutional investment are among the important factors which determine the stock market movements. This paper investigates the short-term relationship among FII, DII and stock market returns. The daily FII, DII net investment and Nifty returns data from April 2008 to March 2017 were used for this study. Granger causality test and Impulse response analysis were used to document the relationship between the institutional investment and stock market returns. Nifty return was used as a proxy for stock market returns. One-way causality was observed from FII to DII net investment. Nifty returns were found to be the cause of FII and DII net investment and not the effect. FII responds positively to an impulse from Nifty returns and DII responds negatively to an impulse from Nifty returns. Hence it can be concluded that FIIs follow positive feedback trading and DIIs follow the exact opposite method.

Dynamic Relationship between Macroeconomic Determinants and Corporate Bonds Performance: The Case of India

Corporate bonds can serve as an important alternative to the more expensive sources of financing and the market requires more exploration for its development. For ensuring the steady growth in this sector, the government can adopt several macroeconomic policy measures which can accelerate the market's progress. The study has been undertaken to analyse the relationship between selected macroeconomic variables and Indian corporate bond market performance. The study used multiple linear regression and Granger causality test to analyse the relationship between the variables. The results found no impact of macro-economic variables on corporate bond performance. Further, study found no causality between macro-economic variables on corporate bond performance. Whereas correlation results clearly shows the bond returns has low degree of negative correlation with Nifty returns and GDP growth rate. The study suggests bond market participants to consider stock market performance before tanking investment decisions.

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.

FX Volatility Impact on Indian Stock Market: An Empirical Investigation

Examining the interrelationship between currency market volatility and stock market volatility will create abundant trading opportunities to the investors irrespective of whether the return of one market is moving up or down. This research work intended to examine how the exchange rate volatility between Indian rupee and foreign currencies, such as US dollar, euro, Japanese yen and British pound, can influence the return and volatility of the Indian stock market. The research data extensively cover daily price observations of foreign currencies as well as Nifty index for 1500 days. The generalized autoregressive conditional heteroskedasticity (GARCH) is used for modelling foreign exchange (FX) rates volatility and its impact across Indian stock market. The mean equation of the model confirms that any appreciation in Indian rupee will lead to channelization of more funds towards stock market. Further, it is validated that the volatility shocks between the stock market and currency market are quite persistent. Besides the model also points that the volatility attributes are very strong between US dollar and Nifty. The Granger causality test wrap up with a finding that the volatility shocks of British pound have a causal relation with Nifty return. The result of this study will help the domestic as well as foreign investors in favour of portfolio diversification decisions. The study also spots that the policymakers can indirectly intervene into stock market through monitory policy measures.

Performance and Tracking Efficiency of Exchange Traded Funds : An Analysis of Indian Equity Index Funds

This research, using daily closing values of five leading Nifty exchange traded funds (ETFs), sought empirical evidence for the efficiency of equity ETFs in India to track their benchmark index. The study computed 'standard deviation of differential returns' and 'standard error in regressing the fund return against the Nifty return' to assess the tracking efficiency of funds. The analysis found underperformance of Nifty ETFs relative to their benchmark index coupled with higher level of tracking errors during market downturns. In contrast, I found significant improvement in tracking ability and most funds outperformed the benchmark with lower tracking errors under reverse market conditions. The research ultimately suggested that preferring ETFs to index portfolio investing offered greater opportunities for arbitrage to the investors of emerging markets like India, particularly during the times of market buoyancy.

An empirical study on the IPO market in India

This paper aims to evaluate a wide range of parameters like IPO listing gain, Nifty return, Sensex return, Nifty abnormal gain and Sensex abnormal gain. These measures reflect the extent of underpricing of Indian IPOs. Two measures of underpricing have been used: “simple” underpricing and “adjusted” underpricing. This paper also aims to analyze the correlation between listing gain, Nifty return, Sensex return, Nifty Abnormal gain and Sensex abnormal gain. For this purpose, Karl Pearson?s Correlation Coefficient has been used. Taking a sample of 33 book-built IPOs that came to the market during 2013 to 2016, it is found that the average simple? underpricing is 10.79%. The average adjusted? underpricing comes out to be 11.77% taking Nifty index and 11.86% taking Sensex index. Thus, underpricing still continues to be an issue of concern.

Can stock market time the FPIs: a study of seasonality

The purpose of the paper is to analyse the presence of the days of the week effect, quarter effect, and month effect on the net investment of foreign portfolio investors (FPI) in India. For this purpose, research has been conducted for the time period from 4th January 2000 till 30th November 2016. FPI's daily net inflows have been analysed for occurrence and existence of month effect along with day of the week and quarter effect. Interaction effect between the quarter, month and nifty returns has been also analysed. Augmented dummy regression with ARIMA and GARCH has been used to analyse presence of seasonal anomalies in FPIs. Results confirmed the presence of calendar effect i.e., December month of the year (December) effect on the FPIs. Also, Friday effect and the Quarter 4 effect were present in the investing pattern of FPIs. Study concluded that FPIs were more biased towards trading more on Fridays and in the month of December in the overall analysis. Also, the presence of quarter effect was seemed to be caused by the presence of month effect along with the nifty returns of those specific months. Hence, there exists the strong interaction effect among the anomalies in the FPI net investment to India.

Can stock market time the FPIs: a study of seasonality

The purpose of the paper is to analyse the presence of the days of the week effect, quarter effect, and month effect on the net investment of foreign portfolio investors (FPI) in India. For this purpose, research has been conducted for the time period from 4th January 2000 till 30th November 2016. FPI's daily net inflows have been analysed for occurrence and existence of month effect along with day of the week and quarter effect. Interaction effect between the quarter, month and nifty returns has been also analysed. Augmented dummy regression with ARIMA and GARCH has been used to analyse presence of seasonal anomalies in FPIs. Results confirmed the presence of calendar effect i.e., December month of the year (December) effect on the FPIs. Also, Friday effect and the Quarter 4 effect were present in the investing pattern of FPIs. Study concluded that FPIs were more biased towards trading more on Fridays and in the month of December in the overall analysis. Also, the presence of quarter effect was seemed to be caused by the presence of month effect along with the nifty returns of those specific months. Hence, there exists the strong interaction effect among the anomalies in the FPI net investment to India.

Integration of Exchange Rate and Stock Market : Evidence from the Indian Stock Market

The stock market represents the financial health of an economy, while the exchange rate represents the country's currency value against the value of international currency. After economic reforms in India post 1991, the economic and overall growth got a boom because of globalization and privatization. These reforms enhanced the imports and exports and brought volatility in the exchange rate. The current research work investigated the rapport among Nifty index returns in India and the Indian exchange rate in consideration with the U.S. dollar. The exchange rate volatility generates volatility in the stock market, which creates confusion in the minds of domestic investors as well as for foreign institutional investors. The main purpose of conducting this research was to find out the impact of change in the exchange rate in India on the trading volume and returns of Nifty index. To examine the relationship between stock market returns and exchange rate, econometric tools like ADF test, correlation, Johansen's co-integration test, and Granger causality test were applied with the help of EVIEWS program. The study was conducted during the period from April 2005 till March 2014. The empirical analysis showed that negative correlation existed among the stock market returns and exchange rate; however, the degree of correlation was not very significant. Co-integration test results showed that there was an absence of co-integration among the variables. The Granger causality test indicated that Nifty stock returns Granger caused the rupee-dollar exchange rates in India. This paper provides suggestions to the investors regarding the strategies that they can opt for trading in volatile stock markets to minimize risk or fear of bearing losses.

Impact of Crude Oil Price and Exchange Rate on Performance of Indian Stock Market

This paper attempts to investigate empirically the dynamic relationship among crude oil price, exchange rate and Indian stock market. Using daily data of Crude oil price, Dollar-Rupee value and Nifty returns from April 2010 to March 2015, correlation, regression and Granger-causality approach in a bi-variate VAR framework has been used to investigate the causality between crude oil and nifty returns; exchange rate and nifty returns. Augmented Dickey Fuller (ADF) test has been used to test whether the data is stationary or not. The outcome of the study was there is a significant negative correlation between nifty returns and exchange rate and significant positive correlation between nifty returns and crude oil, and a unidirectional causality running from nifty returns to exchange rates and crude oil price to nifty returns.

A STUDY OF EXCHANGE RATES MOVEMENT AND STOCK MARKET VOLATILITY

In this paper we have analyzed the relationship between Indian rupess-USdollar exchange rate and Nifty returns. This research is based on dynamic behavior between stock markets movement and volatility of stock market for this purpose; we have applied several statistical tests. .we have taken the data from period of October 2008, to march, 2010.It study has proved that exchange rate and Nifty returns are non-normally disturbed. Unit root tests have proved that Nifty returns and exchange rate are stationary and they are stationary at level form. There is negative relationship between exchange rate and Nifty returns exchange rate. For testing the causal relationship between these variables we have used Granger causality test.This test has shown that there is unidirectional relationship between exchange rate and Nifty returns. This study is trying to attempt that stock market is crucial for the economy. Different researchers have proved from their research that exchange rate is the main determinates of business profitability. This study has provided such type of information, which would favorable for the gaudiness of management decision about the risk and investment. This information will beneficial for government policies. The maintained of foreign exchange would motive the foreign investors.

Foreign Institutional Investment and Stock Market Returns in India: An Empirical Investigation

The Indian financial market has been an eye-catching investment destination for the investors all over the globe. The ever growing contribution of Foreign Institutional Investors (FIIs) surely complements the inland investment but they are extremely volatile compared to other forms of investment flows. Therefore it becomes very much central to have an extensive study on the interconnection between Indian stock market and FIIs investment behaviour. This study considers daily data from April 2001 to March 2015. To ascertain the linkage between FII investments and Stock market returns, the entire study period has been divided into six phases based on the stock market trends. The Correlation Analysis, Granger Causality Test, Variance Decomposition and Impulse Response Function are carried out to know the relationship between FIIs and Indian Stock Market returns. The result indicates that Indian stock market returns Granger cause net FII investments. Nifty returns define FII investments more in all the phases than FII investments defining Nifty returns. It is also observed that Nifty returns define FII investments highly during bear market period than the bull market period.

Dynamic Interactions between Foreign Institutional Investment Flows and Stock Market Returns – The Case of India

There has been a marked increase in the magnitude of Foreign Institutional Investments (FIIs) into India since the 1990s, resulting in increased forex reserves and liquidity and a higher-valued Indian capital market. However, such investment is more volatile than other types of flows, causing disruptive effects in the form of sudden stops (for example, the crash of the Indian stock market on January 21, 2008). This study empirically examines the dynamic relationship between FIIs and Indian stock market returns. It also analyses the effects of FIIs on Indian capital market returns, using data from January, 2004 through September, 2012. The analysis employs a Cross Correlation Function (CCF) approach, a Granger Causality Test and Vector Auto Regression after dividing the data into two parts: Pre Global financial crisis and Post Global financial crisis periods. The results of the CCF suggest bi-directional causality between FIIs and Nifty returns, whereas the Granger Causality Test and the VAR analysis suggest uni-directional causality running Nifty returns to FIIs.

Using Clustering Method to Understand Indian Stock Market Volatility

In this paper we use Clustering Method to understand whether stock market volatility can be predicted at all, and if so, when it can be predicted. The exercise has been performed for the Indian stock market on daily data for two years. For our analysis we map number of clusters against number of variables. We then test for efficiency of clustering. Our contention is that, given a fixed number of variables, one of them being historic volatility of NIFTY returns, if increase in the number of clusters improves clustering efficiency, then volatility cannot be predicted. Volatility then becomes random as, for a given time period, it gets classified in various clusters. On the other hand, if efficiency falls with increase in the number of clusters, then volatility can be predicted as there is some homogeneity in the data. If we fix the number of clusters and then increase the number of variables, this should have some impact on clustering efficiency. Indeed if we can hit upon, in a sense, an optimum number of variables, then if the number of clusters is reasonably small, we can use these variables to predict volatility. The variables that we consider for our study are volatility of NIFTY returns, volatility of gold returns, India VIX, CBOE VIX, volatility of crude oil returns, volatility of DJIA returns, volatility of DAX returns, volatility of Hang Seng returns and volatility of Nikkei returns. We use three clustering algorithms namely Kernel K-Means, Self Organizing Maps and Mixture of Gaussian models and two internal clustering validity measures, Silhouette Index and Dunn Index, to assess the quality of generated clusters.

Forecasting Volatility in Indian Stock Market using Artificial Neural Network with Multiple Inputs and Outputs

Volatility in stock markets has been extensively studied in the applied finance literature. In this paper, Artificial Neural Network models based on various back propagation algorithms have been constructed to predict volatility in the Indian stock market through volatility of NIFTY returns and volatility of gold returns. This model considers India VIX, CBOE VIX, volatility of crude oil returns (CRUDESDR), volatility of DJIA returns (DJIASDR), volatility of DAX returns (DAXSDR), volatility of Hang Seng returns (HANGSDR) and volatility of Nikkei returns (NIKKEISDR) as predictor variables. Three sets of experiments have been performed over three time periods to judge the effectiveness of the approach.

On asymmetric relationship of India volatility index (India VIX) with stock market return and risk management

This study examines the asymmetric relationship between India volatility index (India VIX) and stock market returns, and demonstrates that Nifty returns are negatively related to the changes in India VIX levels, but in case of high upward movements in the market, the returns on the two indices tend to move independently. When the market takes sharp downward turn, the relationship is not as significant for higher quantiles. This property of India VIX makes it a strong candidate for risk management tool whereby derivative products based on the volatility index can be used as a tool for portfolio insurance against worst declines. We also find that India VIX captures stock market volatility better than traditional measures of volatility including ARCH/GARCH class of models. Finally, we test whether changes in India VIX can be used as a signal for switching portfolios. Our analysis of timing strategy based on change in India VIX exhibits that switching to large-cap (mid-cap) portfolio when India VIX increases (decreases) by a certain percentage point can be useful for maintaining positive returns on a portfolio.

Examining Contemporaneous Relationship between Return of Nifty Index and India VIX

The study examines the contemporaneous relationship between Nifty returns and India VIX returns. Literature documents that the relationship between them is negative and asymmetric. Building on this, the study considers the linear and quadratic effect of stock index return (CNX Nifty) and examines the changes in implied volatility index (India VIX). The study finds both linear and quadratic CNX Nifty index returns are significant for changes in the level of India VIX. Findings suggest that India VIX provides insurance both for downside market movement and size of the downside movement.

Re-Examining the Impact of FII Investments on the Indian Markets

This paper examines the impact of daily foreign institutional investors’ (FIIs) investments on the Indian market from January 2010 through January 2014. The motivation for the study is that the past studies on this topic are based only on examining the relationship between FII investments in cash market and any associated effects on stock market returns while ignoring the FII activity in equity derivative markets. The impact of FIIs’ investments on Indian markets is re-examined in this paper by considering FII activity in Nifty index futures that trade on National Stock Exchange of India (NSE) and Singapore Exchange (SGX). The results show that Nifty index returns are statistically influenced by FII investments in Nifty index futures. Further, Nifty index returns effect the FIIs’ investments in Nifty index futures. Prima facie there is evidence of positive feedback trading behavior of FIIs. Hence, FIIs’ index futures activity could be used to predict the Nifty returns. There is a contemporaneous relationship between FIIs investments in cash and index futures market. SGX futures are not found to be influencing Nifty but Nifty is a significant explanatory variable in SGX futures returns.

A Study of Day of the Month Effect in S&P CNX Nifty 50

A curious seasonal anomaly found in finance is the turn of the month effect, where the daily mean return of stock market at the end of a month and beginning of a month is significantly higher than the average daily return of all the days of a month. There have been evidences that certain months in a year deliver significantly higher returns. Similar anomalies are found for week days also, where some days in a week deliver above average returns. Seasonal anomalies for researchers have been a subject of great interest and lot of literature is available worldwide. This paper examines presence of presence of day of the month effect in leading stock indices of India S&P CNX Nifty 50. We have tested the presence of day of the month effect from the year 1994 in the research. We found that the Nifty return data is not normally distributed and skewed. To test the statistical significance of difference in date wise return we have used Kruskal-Wallis Test. To identify the statistically significant days, we have used Z Statistics. We found that the date wise return in Nifty to be statistically different and there exists opportunity to earn excess return due to day of the month anomaly.

Re-Examining the Nifty Returns after the First Decade of Derivative Trading in Indian Capital Market Using Non-Linear Asymmetric GARCH Models

This article examines the Indian stock market for conditional volatility using symmetric and asymmetric GARCH (Generalized Autoregressive Conditional Heteroskedasticity) variants with reference to a comprehensive period of 20 years from July 3, 1990 to November 30, 2010 using S&P CNX Nifty. The impact of future trading on Nifty return and volatility is assessed using dummy variable in total period and using Log (Open Interest of Nifty futures) in post-derivative period. Along with the period of two decades the analysis has also been done on a sub-period of a decade from 1995 to 2005 with NiftyJunior as surrogate index as it had no derivatives during this period. The results show that the PGARCH model is best suited to Indian market conditions.