Derived signals for S&P CNX nifty index futures

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.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.

Nature of Volatility Patterns of Futures and Options on Nifty Index

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.

Stock market prediction using deep learning algorithms

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%.

NIFTI格式医学图像显示方法研究

探讨NIFTI格式图像的多平面显示、功能和结构像的融合显示、基于三维重建的脑效应连接网显示方法,并分析各种显示方法的优缺点。仿真结果表明,多平面显示方法实现简单,能从多角度和多层次显示脑功能和结构的影像,但不能直接定位脑功能和结构区的空间位置;功能和结构像的融合显示方法能够对脑功能区进行高分辨率定位,有利于脑功能研究,但不能显示功能区间的因果联系;基于三维重建的脑效应连接网显示方法不但能显示因果连接网的节点拓扑属性,也能显示脑区间相互联系的因果关系,有利于疾病的影像学机理研究,但该显示方法实现复杂。NIFTI格式图像的各种显示方法,各有优缺点,在脑功能和结构研究中具有重要应用。

Robust Watermarking Scheme for NIfTI Medical Images

Computed Tomography(CT)scan and Magnetic Resonance Imaging(MRI)technologies are widely used in medical field.Within the last few months,due to the increased use of CT scans,millions of patients have had their CT scans done.So,as a result,images showing the Corona Virus for diagnostic purposes were digitally transmitted over the internet.The major problem for the world health care system is a multitude of attacks that affect copyright protection and other ethical issues as images are transmitted over the internet.As a result,it is important to apply a robust and secure watermarking technique to these images.Notably,watermarking schemes have been developed for various image formats,including.jpg,.bmp,and.png,but their impact on NIfTI(Neuroimaging Informatics Technology Initiative)images is not noteworthy.A watermarking scheme based on the Lifting Wavelet Transform(LWT)and QR factorization is presented in this paper.When LWT and QR are combined,the NIfTI image maintains its inherent sensitivity and mitigates the watermarking scheme’s robustness.Multiple watermarks are added to the host image in this approach.Measuring the performance of the graphics card is done by using PSNR,SSIM,Q(a formula which measures image quality),SNR,and Normalized correlation.The watermarking scheme withstands a variety of noise attacks and conversions,including image compression and decompression.