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Good informative assortment of statistics about Indian financial system. I enjoyed going through it. We have to still go a good distance. Percentage of our revenue to world’s complete income needs to be no less than equal to proportion of our population. Actually our country is way richer in assets and human capital so it must be far more. That means we now have to increase our earnings even in ppp terms not less than four instances. Hopefully we will be significantly better of in next four-5 years. Only our authorities should go away enterprise and infra construction building to business managers, industrialists and establishments. Let us hope it happens.
In Figure 5 , we show that the correlations between (i) monetary indices and complete entity occurrences and (ii) monetary indices and the NCI calculated using all documents are very low round R < 0.15.="" on="" the="" opposite="" hand,="" the="" nci-financial="" displays="" a="" lot="" higher="" correlation="" with="" financial="" indices,="" with="" r=""> 0.7 for the implied volatility of the S&P 500 measured by the VIX index. The NCI-monetary correlations with financial market volatility indices are much stronger in comparison with the GSQ classes correlations with volatility measures with R < 0.3.="" in="" contrast="" with="" the="" nci-monetary,="" the="" gsq="" classes="" exhibit="" stronger="" correlations="" with="" inventory="" market="" volumes="" (zero.three="">< r=""><>
It is sometimes interesting to perform an in depth evaluation of which teams of entities or documents contribute essentially the most to the general cohesiveness. For this goal, we will divide entities or documents into groups utilizing any appropriate semantic standards and calculate the cohesiveness for every group separately or between pairs of teams. Semantic partitions in the entity projection are created by way of grouping of entities in mutually disjoint groups, which are defined by their taxonomic labels (therefore, this sort of partition is known as a semantic interpretation). Conversely, semantic partitions within the document projection will be created by grouping documents by their publication date. Figure 2 illustrates the idea of partitioning within the context of various projections.
We base our analyses on a newly developed textual content processing pipeline, New-Stream, which was designed and applied inside the scope of the EU FP7 tasks FIRST ( -/ ) and FOC ( ). NewStream constantly downloads articles from more than 200 worldwide news sources, resembling , , and It extracts the content material, stores full texts of articles and extracts finance-associated entities. It is a website-unbiased knowledge acquisition pipeline but is biased in the direction of finance by the choice of information sources and the taxonomy of entities which might be related to finance.