@article{Bill,
author = "Fern{\'a}ndez Mart{\'i}nez, Manuel and M.A. S{\'a}nchez-Granero and M.J. Mu{\~n}oz Torrecillas and Bill McKelvey",
abstract = "Since the pioneer contributions due to Vandewalle and Ausloos, the Hurst exponent has been applied by econophysicists as a useful indicator to deal with investment strategies when such a value is above or below 0.5
0
.
5
, the Hurst exponent of a Brownian motion. In this paper, we hypothesize that the self-similarity exponent of financial time series provides a reliable indicator for herding behavior (HB) in the following sense: if there is HB, then the higher the price, the more the people will buy. This will generate persistence in the stocks which we shall measure by their self-similarity exponents. Along this work, we shall explore whether there is some connections between the self-similarity exponent of a stock (as a HB indicator) and the stock’s future performance under the assumption that the HB will last for some time. With this aim, three approaches to calculate the self-similarity exponent of a time series are compared in order to determine which performs best to identify the transition from random efficient market behavior to HB and hence, to detect the beginning of a bubble. Generalized Hurst Exponent, Detrended Fluctuation Analysis, and GM2 algorithms have been tested. Traditionally, researchers have focused on identifying the beginning of a crash. We study the beginning of the transition from efficient market behavior to a market bubble, instead. Our empirical results support that the higher (respectively the lower) the self-similarity index, the higher (respectively the lower) the mean of the price change, and hence, the better (respectively the worse) the performance of the corresponding stock. This would imply, as a consequence, that the transition process from random efficient market to HB has started. For experimentation purposes, S{\&}P500 stock Index constituted our main data source.",
doi = "10.1142/S0218348X17500062",
journal = "Fractals",
keywords = "Hurst Exponent; Market Bubble; Herding Behavior; Forecasting; SP500",
month = "February",
number = "1",
pages = "1-10",
title = "{A} comparison of three {H}urst exponent approaches to predict nascent bubbles in {S}{\&}{P}500 stocks",
url = "http://www.worldscientific.com/doi/abs/10.1142/S0218348X17500062",
volume = "25",
year = "2017",
}