  In this article we review several techniques to extract information from
stock market data. We discuss recurrence analysis of time series, decomposition
of aggregate correlation matrices to study co-movements in financial data,
stock level partial correlations with market indices, multidimensional scaling
and minimum spanning tree. We apply these techniques to daily return time
series from the Indian stock market. The analysis allows us to construct
networks based on correlation matrices of individual stocks in one hand and on
the other, we discuss dynamics of market indices. Thus both micro level and
macro level dynamics can be analyzed using such tools. We use the
multi-dimensional scaling methods to visualize the sectoral structure of the
stock market, and analyze the comovements among the sectoral stocks. Finally,
we construct a mesoscopic network based on sectoral indices. Minimum spanning
tree technique is seen to be extremely useful in order to separate
technologically related sectors and the mapping corresponds to actual
production relationship to a reasonable extent.
