EconPapers    
Economics at your fingertips  
 

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

Ajit Mahata, Debi Prasad Bal and Md Nurujjaman

Physica A: Statistical Mechanics and its Applications, 2020, vol. 545, issue C

Abstract: The paper presents the comparative study of the nature of stock markets in short-term and long-term 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 short-term and long-term 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≥5M. Based on the value of H, the market has been divided into two time horizons: short-term market having 3D≥τ≥3M and H≈0.5, and long-term market having τ≥5M and H≥0.75. As H≈0.5 in short-term and H≥0.75 in long-term, the market is random in short-term and has long-range correlation in long-term. 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 short-term time series and ν≈1.68 for long-term reconstructed time series. Separation of short-term and long-term market are also identified using NV technique. The time scales for short-term and long-term markets are independent of structural break happened due to extreme event. The τ obtained using the proposed method for short-term and long-term market may be useful for investors to identify the investment time horizon, and hence to design the investment and trading strategies.

Keywords: Structural break; Empirical mode decomposition; Hurst exponent; Detrended fluctuation analysis; Short-term time scale; Long-term time scale (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S037843711932014X
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:545:y:2020:i:c:s037843711932014x

DOI: 10.1016/j.physa.2019.123612

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-03-19
Handle: RePEc:eee:phsmap:v:545:y:2020:i:c:s037843711932014x