At National Stock Exchange (NSE), the largest by market capitalization and the most liquid stocks, which form the majority of free float market capitalization of the exchange, have traded futures contracts. The equity stock futures segment in India has recorded very high growth in trading volume and turnover for more than one decade where majority trade happens on NSE. The primary objective of this study is to investigate the dynamic linkages between equity stock futures and their underlying spot markets. The article examines market efficiency and the causal relationship between single-stock futures and underlying stocks traded at NSE, by employing Johansen cointegration test, a test for autoregressive (AR) order of basis, vector error correction model (VECM) and impulse response functions. The result shows the existence of long-run equilibrium relationship between equity stock futures and their underlying stocks. The spot market is found to play a lead role in correction of any short-run disequilibrium towards long-run equilibrium, for the majority of stocks. Both spot and future markets contribute in price discovery and neither of the markets display considerably higher information efficiency compared to the other. In contrast, the study also reveals possibilities of arbitrage opportunity between the equity stock futures market and the underlying spot market in absence of transaction costs possibly due to faster correction of short-run disequilibrium by spot prices.

BackgroundPrediction or forecasting is both an art as well as science. The process and outcome of forecasting have long been a matter of research and still are in its childhood state. We can devise numerous ways of modeling a phenomenon and predict its outcome, but there are no universal methods using which we can model every phenomena. Modeling of linear systems is comparatively simpler than dynamical systems. Stock markets are completely chaotic and dynamic systems which are both time and sentiment driven. The time series generated through stock market data can only represent a financial time series of prices but cannot represent the overall sentiment of the market players who trade and invest in the stock markets. Hence modeling of stock market data is one of the toughest as it should incorporate not only data but market sentiment also. The stock market data are a series of prices that are observed in a series of certain time intervals (minutes, hours, days, or weeks etc.). Data mining is a very effective tool using which the Abstract Information retrieval systems are generally used to find documents that are most appropriate according to some query that comes dynamically from the users. In this paper, a novel fuzzy document-based information retrieval scheme (FDIRS) is proposed for the purpose of Stock Market Index forecasting. The novelty of the proposed approach is the use of a modified tf-idf scoring scheme to predict the future trend of the stock market index. The contribution of this paper has two dimensions: (1) In the proposed system, the simple daily time series data are converted to an enriched fuzzy linguistic time series with a unique approach of incorporating information about the manner in which the OHLC (open, high, low, and close) price formation took place at every instance of the time series, and (2) A unique approach is followed while modeling the information retrieval (IR) system which converts a simple IR system into a forecasting system. The modified IR system provides us with a trend forecast and after which a crisp value is generated that becomes the forecast value that can be achieved in next few trading sessions. From the performance comparison of FDIRS with standard benchmark models, it can be affirmed that the proposed model has a potential of becoming a good forecasting model. Transaction data of CNX NIFTY-50 index of National Stock Exchange of India are used to experiment and validate the proposed model. Roy Appl Inform (2016) past behavior of the price movement can be modeled to predict the future. Fuzzy logic is a very effective tool using which the market sentiment can be captured and modeled. By adopting a hybrid approach of combining time series, data mining, and fuzzy logic, an effective system can be built to model the stock market price data that can not only give information about price but also the market sentiment or the mood of the market participants. Keywords RESEARCHThe stock market gives facilities to gain both from rising prices as well as from falling prices....

This article proposes a novel idea for creating a sentiment-based stock market index forecasting model by amalgamating price and sentiment data. The design guidelines are provided using which researchers can explore and create prediction or expert systems that can help in profitably trading the stock markets. A modular design approach is proposed for developing the prediction system. The article demonstrates steps using which the prediction system model can be built. A prototype of the model is also been developed whose performance results are provided. It is observed that there is an ample avenue for improvements that can done by the researchers for tuning up the performance of the proposed model. The model performs a unique modification to the traditional tf-idf technique and converts it into a forecasting tool. The model is simple and easy to implement with very nominal memory requirements, compared to other types of models. Nifty-50 index values were used to analyze the performance of the proposed model. This model is being developed for research purposes only.

Family members can observe VDU parameters to own display the use of the internet on their own mobile thanks to the research's vital platform for conversations and decision-support regarding the patient's health when viewed online at home. Clinicians might give an update on the patient's status and care during these encounters. The ICU VDU display parameters will all be available for family members to view online from their mobile device. These gatherings serve as a forum for information sharing as well as a source of emotional support for families. Support in making decisions and explicit recognition of feelings. Three resources that may be utilized to help family members and ICU patients communicate effectively are presented. Here, we offer a largely accurate monitoring tool that identifies the actual therapy and avoid the fabrication of patient therapy by ensuring that their patient is receiving the proper care.

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