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Derived signals for S&P CNX nifty index futures

Financial Innovation 2017年第1期3卷 281-302页

作者： B.Prasanna Kumar Department of Economics Davangere University P.G.CentreGuddadarangavvanhalliChitradurgaKarnataka 577502India

Background:The 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.Methods:This 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.Results:In 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.Conclusions:This study found that trading margins,credit availability,and price discreteness affect the variance of returns in the Indian futures markets

关键词： Signals Credit Margin Discreteness nifty Returns

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Stock market prediction using deep learning algorithms

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 82-94页

作者： Somenath Mukherjee Bikash Sadhukhan Nairita Sarkar Debajyoti Roy Soumil De Nazrul Center of Social and Cultural Studies Kazi Nazrul UniversityAsansolIndia Department of Computer Science and Engineering Techno International New Town(Techno India College of Technology)KolkataIndia

The Stock Market is one of the most active research areas,and predicting its nature is an epic necessity nowadays.Predicting the Stock Market is quite challenging,and it requires intensive study of the pattern of data.Specific statistical models and artificially intelligent algorithms are needed to meet this challenge and arrive at an appropriate solution.Various machine learning and deep learning algorithms can make a firm prediction with minimised error possibilities.The Artificial Neural Network(ANN)or Deep Feedforward Neural Network and the Convolutional Neural Network(CNN)are the two network models that have been used extensively to predict the stock market prices.The models have been used to predict upcoming days'data values from the last few days'data values.This process keeps on repeating recursively as long as the dataset is valid.An endeavour has been taken to optimise this prediction using deep learning,and it has given substantial results.The ANN model achieved an accuracy of 97.66%,whereas the CNN model achieved an accuracy of 98.92%.The CNN model used 2-D histograms generated out of the quantised dataset within a particular time frame,and prediction is made on that data.This approach has not been implemented earlier for the analysis of such datasets.As a case study,the model has been tested on the recent COVID-19 pandemic,which caused a sudden downfall of the stock market.The results obtained from this study was decent enough as it produced an accuracy of 91%.

关键词： artificial neural network convolutional neural network nifty stock market

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Nature of Volatility Patterns of Futures and Options on nifty Index

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Journal of Modern Accounting and Auditing 2020年第10期16卷 436-445页

作者： Divya Verma Guru Gobind Singh Indraprastha University New DelhiIndia

Derivatives were introduced in Indian financial market to reduce volatility in the spot market.The present study attempts to study the impact of derivatives on stock market volatility.In the present study,data have been taken for nifty Index for a period from 01-01-1996 to 05-02-2016.For analyzing the impact of introduction of derivatives on nifty Index Volatility,we have taken proxy variable of nifty Junior Index and Standard&Poor’s 500(S&P 500)Index returns.The data have also been classified into pre-futures(introduced on 12-06-2000)and post-futures and pre-options(introduced on 04-06-2001)and post-options period.The results show that volatility has reduced after introduction of futures and options.

关键词： derivatives volatility nifty futures stock market efficiency

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Existence Of Certain Anomalies In Indian Stock Market

Existence Of Certain Anomalies In Indian Stock Market

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2011 International Conference on Economics and Finance Research（ICEFR2011）

作者： D.S.SELVAKUMAR School of social science VIT UNIVERSITY

Since the pioneering works of Fama （1965） and Cross （1973） there have been many anomalies documented concerning the behaviour of security price returns. The most prevalent of these anomalies appear to be weekend effect, where stocks exhibit significantly lower returns over the period between Friday’s close and Monday’s close （French 1980, Lakonishok and Levi 1982, keim and Stambaugh 1984）;a January effect, where returns are unusually high during the month of January as compared to any other month （Rozeff and Kinney 1976, Reinganum 1983）;and a Holiday effect, where returns are much higher on trading days immediately prior to holidays （Ariel 1990, Kim and Park 1994）. The other irregularities include monthly effect （Ariel 1987）, turn-of-the month effect （Lakonishok and Smidt 1988） and relatively less documented Friday-the-thirteenth effect （Kolb and Rodriguez 1987）. The anomalies mentioned above have originally been documented for the US market and its empirical confirmation and extension has now taken place to other capital markets mostly in developed world. In spite of these phenomena appearing in different markets around the world, there still remain people who believe that some of the irregularities are artifacts caused by institutional factors such as ex-dividend, tax and liquidity effects （Bowers and Dimson 1988）. Hence, one’s belief regarding the true market anomalies would be strengthened if it is known to occur also in other capital markets of developing economies which are characterized by separate cultures and institutional arrangements. Thus, it is of interest to search whether such anomalies exist for a developing market such as one in India. In fact very little empirical research on calendar anomalies has been undertaken on security market using Indian data. Chaudhury （1991） studied BSE Sensex between June 1988 and January 1990 and found that average return on Monday is negative and highest returns are on Friday. Poshakwale （1996） studied BSE National Index and found that mean return except for Monday

关键词： Anomoly Indian Stock Market Sensex nifty

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