Identification of shortterm and longterm time scales in stock markets and effect of structural break
Abstract
The paper presents the comparative study of the nature of stock markets in shortterm and longterm time scales (τ) 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 two time series: time series before structural break (TSB) and time series after structural break (TSA). In order to identify the τ of shortterm and longterm market, the Hurst exponent (H) technique has been applied on the intrinsic mode functions (IMF) obtained from the TSO , TSB and TSA by using empirical mode decomposition method. H ≈ 0 . 5 for all the IMFs of TSO , TSB and TSA having τ in the range of few days (D) to 3 months (M) , and H ≥ 0 . 75 for all the IMFs of TSO , TSB and TSA having τ ≥ 5 M . Based on the value of H, the market has been divided into two time horizons: shortterm market having 3 D ≥ τ ≥ 3 M and H ≈ 0 . 5 , and longterm market having τ ≥ 5 M and H ≥ 0 . 75 . As H ≈ 0 . 5 in shortterm and H ≥ 0 . 75 in longterm, the market is random in shortterm and has longrange correlation in longterm. Robustness of the results has also been verified by using detrended fluctuation exponent (ν) analysis and normalised variance (NV) techniques. We obtained ν ≈ 0 . 5 for reconstructed shortterm time series and ν ≈ 1 . 68 for longterm reconstructed time series. Separation of shortterm and longterm market are also identified using NV technique. The time scales for shortterm and longterm markets are independent of structural break happened due to extreme event. The τ obtained using the proposed method for shortterm and longterm market may be useful for investors to identify the investment time horizon, and hence to design the investment and trading strategies.
 Publication:

Physica A Statistical Mechanics and its Applications
 Pub Date:
 May 2020
 DOI:
 10.1016/j.physa.2019.123612
 arXiv:
 arXiv:1907.03009
 Bibcode:
 2020PhyA..54523612M
 Keywords:

 Structural break;
 Empirical mode decomposition;
 Hurst exponent;
 Detrended fluctuation analysis;
 Shortterm time scale;
 Longterm time scale;
 Quantitative Finance  Statistical Finance
 EPrint:
 Physica A: Statistical Mechanics and its Applications, Volume 545, 1 May 2020, 123612