A common method in technical analysis is the construction of moving averages along time series of stock prices. We show that they present a practical interest for physicists, and raise new questions on fundamental ground. Indeed, self-affine signals characterized by a defined roughness exponent H can be investigated through moving averages. The density ρ of crossing points between two moving averages is shown to be a measure of long-range power-law correlations in a signal. Finally, we present a specific transform with which various structures in a signal, e.g. trends, cycles, noise, etc. can be investigated in a systematic way.
%0 Journal Article
%1 vandewalle99
%A Vandewalle, N
%A Ausloos, M
%A Boveroux, Ph
%D 1999
%J Physica A: Statistical Mechanics and its Applications
%K finance moving.average stock
%N 1
%P 170--176
%R 10.1016/S0378-4371(99)00090-4
%T The Moving Averages Demystified
%V 269
%X A common method in technical analysis is the construction of moving averages along time series of stock prices. We show that they present a practical interest for physicists, and raise new questions on fundamental ground. Indeed, self-affine signals characterized by a defined roughness exponent H can be investigated through moving averages. The density ρ of crossing points between two moving averages is shown to be a measure of long-range power-law correlations in a signal. Finally, we present a specific transform with which various structures in a signal, e.g. trends, cycles, noise, etc. can be investigated in a systematic way.
@article{vandewalle99,
abstract = {A common method in technical analysis is the construction of moving averages along time series of stock prices. We show that they present a practical interest for physicists, and raise new questions on fundamental ground. Indeed, self-affine signals characterized by a defined roughness exponent H can be investigated through moving averages. The density ρ of crossing points between two moving averages is shown to be a measure of long-range power-law correlations in a signal. Finally, we present a specific transform with which various structures in a signal, e.g. trends, cycles, noise, etc. can be investigated in a systematic way. },
added-at = {2016-06-10T09:30:35.000+0200},
author = {Vandewalle, N and Ausloos, M and Boveroux, Ph},
biburl = {https://www.bibsonomy.org/bibtex/2382c9705f70ea84c1159f40b236ac07d/ytyoun},
doi = {10.1016/S0378-4371(99)00090-4},
interhash = {ec7a8ebd171d465a25e1f4e343e92cbf},
intrahash = {382c9705f70ea84c1159f40b236ac07d},
issn = {0378-4371},
journal = {Physica A: Statistical Mechanics and its Applications },
keywords = {finance moving.average stock},
number = 1,
pages = {170--176},
timestamp = {2016-08-20T11:04:13.000+0200},
title = {The Moving Averages Demystified },
volume = 269,
year = 1999
}