Long-range correlation and predictability of Chinese stock prices

Lei Wang and Lutao Liu

Physica A: Statistical Mechanics and its Applications, 2020, vol. 549, issue C

Abstract: It is well known that the price time series of an efficient market display a Brownian motion. In such a case the best prediction of a future price is the last-known price thus no arbitrage is possible. A real market is, however, possibly not perfectly efficient. In this paper, we apply an error back-propagation neural network to tick-by-tick high-frequency time series of Chinese A-share stock prices and try to predict their future values. The performance of the predictions varies largely for different stocks. In order to seek for the dependence of the performance, we study the long-range correlation (LRC) of the data by the conventional detrended fluctuation analysis (DFA) and its generalization, the multifractal detrended fluctuation analysis (MFDFA). Clear relevance between the performance and the LRC is observed. The prediction is particularly good when the time series are strongly anti-persistent.

Keywords: Detrended fluctuation analysis; Artificial neural network; Chinese stock market; High-frequency time series; Multiscale analysis (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S037843712030145X
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:549:y:2020:i:c:s037843712030145x

DOI: 10.1016/j.physa.2020.124384

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 ().