Summary
The fractal nature of financial data has been investigated through literature. The aim of this paper is to use the information given by the detection of the fractal measure of data in order to provide support for trading decisions when dealing with technical analysis signals that can be used to trigger buy/sell orders. Trendlines are considered as a case study.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ausloos M, Ivanova K (1999) Low q-moment multifractal analysis of Gold price, Dow Jones Industrial Average and BLG-USD exchange rate. Eur. Phys. J. B 8: 665–669
Borodin A N, Salminen P (1996) Handbook of Brownian Motion-Facts and Formulae. Birkhäuser Verlag Basel
Dębicki K, Michna Z, Rolski T (1998) On the supremum from Gaussian processes over infinite horizon. Probability and Mathematical Statistics 18
Johansen A, Sornette D (2004) Endogenous versus Exogenous Crashes in Financial Markets. Contemporary Issues in International Finance. In press
Lo A W (1991) Long memory in stock market prices. Econometrica 59:1279–1313
Mandelbrot B B, Van Ness J W (1968) Fractional brownian Motions, fractional Noises and Applications. SIAM Review 10:422–437
Mantegna R N, Stanley H E (2000) Introduction to econophysics. Cambridge University Press, Cambridge
Menna M, Rotundo G, Tirozzi B (2002) Distinguishing between chaotic and stochastic systems in financial time series. Int. J. Mod. Phys. C 13:31–39
Murphy J J (1986) Technical analysis of the Futures Markets. Prentice Hall, New York
Rotundo G (2004) Logistic function in large financial crashes. Preprint
Samorodnitsky G, Taqqu, M S (1994), “Stable Non-Gaussian Random Processes”, Chapman & Hall, London
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Tokyo
About this paper
Cite this paper
Rotundo, G. (2006). The Hurst’s exponent in technical analysis signals. In: Takayasu, H. (eds) Practical Fruits of Econophysics. Springer, Tokyo. https://doi.org/10.1007/4-431-28915-1_21
Download citation
DOI: https://doi.org/10.1007/4-431-28915-1_21
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-28914-2
Online ISBN: 978-4-431-28915-9
eBook Packages: Business and EconomicsEconomics and Finance (R0)