NDLI: Predicting drastic drop in Chinese stock market with local Hurst exponent

Content Provider	IEEE Xplore Digital Library
Author	Xu Shao-jun Jin Xue-jun
Copyright Year	2009
Description	Author affiliation: College of Economics, Zhejiang University, China, 310027 (Xu Shao-jun; Jin Xue-jun)
Abstract	In this paper we employ detrended fluctuation analysis (DFA) technique to estimate the local Hurst exponent of Shanghai Stock Exchange Composite (SSEC) index, which is used to predict the crash or other drastic decreases in Chinese stock market. We assume the local Hurst exponent can be used to as a measurement of actual excitation or nervous state of the market before the displaying of market index. The empirical result shows that a very clear decreasing trend from greater than 0.5 to less than 0.5 in local Hurst exponent is visible, preceding the market drastic drop point. In-sample vs. out-of-sample tests confirm above viewpoint, showing that local Hurst exponent below 0.5 can be used as an important signal of stock return when stock index is going to face drastic drop, and it has more significant predict power than historical average return forecasting method. So we find efficient market hypothesis (EMH) doesn't receive support in Chinese stock market, and explore a new method for investment beyond the traditional technical analysis.
Starting Page	1309
Ending Page	1315
File Size	306561
Page Count	7
File Format	PDF
ISBN	9781424439706
DOI	10.1109/ICMSE.2009.5318022
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-14
Publisher Place	Russia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	out-of-sample test Fluctuations Doped fiber amplifiers Power generation economics drastic drop Conference management Educational institutions Computer crashes Chinese stock market DFA method Engineering management local Hurst exponent Economic forecasting Autocorrelation Stock markets
Content Type	Text
Resource Type	Article

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