  The paper presents the comparative study of the nature of stock markets in
short-term and long-term time scales with and without structural break in the
stock data. Structural break point has been identified by applying Zivot and
Andrews structural trend break model to break the original time series (TSO)
into time series before structural break (TSB) and time series after structural
break (TSA). The empirical mode decomposition based Hurst exponent and variance
techniques have been applied to the TSO, TSB and TSA to identify the time
scales in short-term and long-term from the decomposed intrinsic mode
functions. We found that for TSO, TSB and TSA the short-term time scales and
long-term time scales are within the range of few days to 3 months and greater
than 5 months respectively, which indicates that the short-term and long-term
time scales are present in the stock market. The Hurst exponent is $\sim 0.5$
and $\geq 0.75$ for TSO, TSB and TSA in short-term and long-term respectively,
which indicates that the market is random in short-term and strongly correlated
in long-term. The identification of time scales at short-term and long-term
investment horizon will be useful for investors to design investment and
trading strategies.
