Indian Stock Market Index Research Articles

Testing the Long-Memory Features in Return and Volatility of NSE Index

Long-term memory of stock markets is a topic that has not received its due attention from academics. Posting the assertion made by Fama, 1970 [1] about markets being efficient, no one can consistently outrun it for a longer duration. Handful of papers checked the efficiency in emerging markets to see if the efficiency proposition held true. Furthering the literature in this study we test for the long-term memory of National Stock Exchange (NSE) index, Nifty and NSE_500 which are a collection of 50 and 500 listed firms respectively in India. The duration of the data for study is roughly eight years over the period from 2006-06-29 to 2012-09-13, a total of 1545 observations. We observe that long-term memory does exist in the context of Indian stock market index.

A Complex Dynamical Analysis of the Indian Stock Market

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.

A hybrid FLANN and adaptive differential evolution model for forecasting of stock market indices

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-stationar

Impact of International Financial Flows on Indian Stock Markets – an Empirical Study

FDI and FII have become instruments of international economic integration and stimulation. Fast growing economies like Singapore, China, Korea etc have registered incredible growth at onset of FDI. Though US captures most of the FDI inflows, developing countries still account for significant growth of FDI and rise in FII. FDI not only gives access to foreign capital but also provides domestic countries with cutting edge technology, desired skill sets, tools of innovation and other complementary skills. The policies drafted to stimulate the flow of foreign capital in to India provided much needed impetus for India to emerge as an attractive destination for foreign investors. External factors such as global economic cues, FDI & FII, Exchange rate and Internal factors such as demand and supply, market cap, EPS generally drive and dictates the Indian stock market. The current paper makes an attempt to study the relationship and impact of FDI & FII on Indian stock market using statistical measures correlation coefficient and multi regression for 12 years data starting from 2001 to 2012. Sensex and Nifty were considered as the representative of stock market as they are the most popular Indian stock market indices.

Foreign Institutional Investment and Stock Market Returns: Evidence from Indian Capital Market

This study investigated the effect of foreign institutional investment (FII) on the returns of Indian stock market using monthly data collected over the period from January 1993 to May 2012. By applying augmented Dickey-Fuller unit root test, Johansen cointegration test, Granger causality test and vector error correction model (VECM), this study found that FII and Sensex are level and first difference stationary series, respectively. It also found positive long-run equilibrium relationship between stock market index and FII. The study also revealed bidirectional causality between stock market index and FII. This study formulated VECM, which can be used for the estimation purpose. The major implication that this study derived is that foreign investments play a beneficial role in the development of the Indian economy and, therefore, the Indian government should try to maximise net foreign investment. The results obtained in this study were based on the monthly data of Sensex and FII; therefore, one needs to be cautious before generalising the results. For future research, one may consider other Indian stock market indices, sector indices and individual companies. Even more robust analysis can be done by reducing the frequency of data, which will help in validating the result of this study.

Interaction of Macroeconomic Factors and Stock Market Index: Empirical Evidence from Indian Data

The study investigates the relationships between the Indian stock market index (BSE Sensex) and five macroeconomic variables, namely, industrial production index, wholesale price index, money supply, treasury bills rates and exchange rates over the period 1994:04–2011:04. Johansen’s co-integration and vector error correction model have been applied to explore the long-run equilibrium relationship between stock market index and abovementioned macroeconomic variables. The analysis reveals that macroeconomic variables and the stock market index are co-integrated and, hence, a long-run equilibrium relationship exists between them. It is observed that the stock prices positively relate to the money supply and industrial production but negatively relate to inflation. The exchange rate and the short-term interest rate are found to be insignificant in determining stock prices. In the Granger causality sense, macroeconomic variable causes the stock prices in the long-run as well as in the short-run.

Out-Performance of a Top 50 Equal Weighted Index over Nifty Market Index: Study of Indian Stock Market Indices

This report looks at the performance of a portfolio of Top 50 Indian stocks by market capitalisation with equal weights against the performance of the Nifty Index which are the Top 50 Indian stocks weighted by their market capitalisation. The equal weight portfolio shows substantial out performance with lower volatility especially in bull markets. This report also discusses the difference in portfolio characteristics which explains some of this out performance.The following few pages are a brief synopsis of some of my proprietary research from early 2009 and have been updated with results till Jan 2012. Current report only covers last 5 years from Jan 2007 to Jan 2012 (Original research covers 15 years), if you are an academic or an industry professional and want to discuss this research and get free access to rest of the article, please contact using my email shown at the end of this report.

Short-term under/overreaction, anticipation or uncertainty avoidance? Evidence from India

We examine the short-term price behaviour of three, size-conditioned Indian stock market indices, in response to informational shocks. A standard mean-adjusted returns model as well as the GJR-GARCH specification point towards underreaction to negative events in the medium and small capitalization indices. Also, the pre-event coefficients are generally negative and statistically significant, regardless of the sign of the shock, thus ruling out information leaks. We uncover a stable abnormal volatility pattern which increases monotonically a few days before the shock before suddenly decreasing in magnitude on the event day and beyond. We suggest uncertainty avoidance as a potential explanation of these features. The results are fairly robust across alternative event selection procedures, time, and size-conditioned shocks.

A search for long-range dependence and chaotic structure in Indian stock market

This study tests for the presence of nonlinear dependence and deterministic chaos in the rate of returns series for six Indian stock market indices. The overall result of our analysis suggests that the returns series do not follow a random walk process. Rather it appears that the daily increments in stock returns are serially correlated and the estimated Hurst exponents are indicative of marginal persistence in equity returns. Result from the test of independence on filtered residuals suggests that the existence of nonlinear dependence, at least to some extent, can be attributed to the presence of conditional heteroskedasticity. It appears, therefore, that low order GARCH-type models can adequately explain some, but not all, of the observed nonlinear dependence in the data. Further, we find very little evidence to support the proposition that returns are generated by a chaotic system. Only in two out of six cases the results are supportive of sensitive dependence on initial condition, which indicates chaos. Presence of chaos in market indices implies that profitable nonlinearity based trading rules may exist at least in the short-run. Finally, fairly contrary to the findings of previous studies, rejection of random walk hypothesis offers some possibility of returns predictability.

Testing the Random Walk Model in Indian Stock Markets

The present study attempts to examine the random movements in stock indices in the Indian equity market. It tests the random walk hypotheses in daily, weekly and monthly returns of six Indian stock market indices from January 2000 to October 2009. The indices considered for the purpose of the study include Nifty, CNX Nifty Junior, NSE 500, SENSEX, BSE 100 and BSE 500. The study uses Jarque-Bera (JB) Test for testing normality in return series. It also applies Box Pierce Q-Statistics and Ljung-Box (LB) statistics, and Augmented Dickey-Fuller (ADF) test to test whether return series follow random walk or not. The results indicate that there are no random movements in share indices. However, when we apply Lo and MacKinlay (1988) variance ratio test under the assumptions of both homoskedasticity and heteroskedasticity, we observe contradictory results. It is also found that sometimes heteroskedasticity is the source of non-random behavior in share indices.

Neural Networks for the Prediction of Stock Market

Predicting stock data with traditional time series analysis has proved to be difficult. An artificial neural network may be more suitable for the task. The reason is that no assumption about a suitable mathematical model has to be made prior to forecasting. Furthermore, a neural network has the ability to extract useful information from large sets of data, which often is required for a satisfying description of a financial time series. This paper discusses the various conventional analysis methods and neural network methodology to forecast a stock market series. This paper also provides the effect of various topological parameters on the accuracy and training time of neural networks. A topology of neural network is proposed for the prediction of Indian stock market index S&P CNX NIFTY.

Integration of Indian Capital Markets with Global Markets: An Empirical Study

From 1997 onwards, the effect of globalization is becoming evident in the Indian capital markets. The stock prices of Indian companies and the stock market indices have been driven not just by the macro and micro factors of the Indian economy. Events in other parts of the world have also increasingly started having an impact. This is in stark contrast to the situation in the insular days prior to 1991, when major policy changes were made by the Indian government to open up the economy. The increased volatility of stock markets and reduction in controls over capital movements across borders has reflected in the stock prices in India. This paper aims at validating that indeed globalization has found its way into the Indian capital markets. It estimates the extent of correlation between the major world stock markets in USA, UK, Japan and Hong Kong with the Indian Stock Market Index like the BSE Sensex and also how portfolio fund flows have affected its movement. The study restricts itself to the period between January 1997 and June 2000. In brief, this paper seeks to: establish the relationship between the Bombay 30 Stock Sensitivity Index (SENSEX) and the global indices mentioned above like Dow Jones Industrial Average, NASDAQ Composite of USA, FTSE100 of UK, Nikkei 225 of Japan and the Hang Seng of Hong Kong. look at how portfolio funds flows have been affecting the Indian stock market.