# Identification of short-term and long-term time scales in stock markets and effect of structural break

## TL;DR

This study analyzes short-term and long-term time scales in stock markets, considering structural breaks, using empirical mode decomposition and Hurst exponent analysis to inform investment strategies.

## Contribution

It introduces a method to identify short-term and long-term time scales in stock data with structural breaks using empirical mode decomposition and Hurst exponent analysis.

## Key findings

- Short-term time scales are within a few days to 3 months.
- Long-term time scales are greater than 5 months.
- Market is random in short-term and strongly correlated in long-term.

## Abstract

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.