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In this paper, we explore the detection of clusters of stocks that are insynergy in the Indian Stock Market and understand their behaviour in differentcircumstances. We have based our study on high frequency data for the year2014. This was a year when general elections were held in India, keeping thisin mind our data set was divided into 3 subsets, pre-election period: Jan-Feb2014; election period: Mar-May 2014 and :post-election period: Jun-Dec 2014. Onanalysing the spectrum of the correlation matrix, quite a few deviations wereobserved from RMT indicating a correlation across all the stocks. We then usedmutual information to capture the non-linearity of the data and compared ourresults with widely used correlation technique using minimum spanning treemethod. With a larger value of power law exponent {\alpha}, corresponding todistribution of degrees in a network, the nonlinear method of mutualinformation succeeds in establishing effective network in comparison to thecorrelation method. Of the two prominent clusters detected by our analysis, onecorresponds to the financial sector and another to the energy sector. Thefinancial sector emerged as an isolated, standalone cluster, which remainunaffected even during the election periods.

Uncovering networks amongst stocks returns by studying nonlinear  interactions in high frequency data of the Indian Stock Market using mutual  information