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Carbon Futures Trading and Short-Term Price Prediction: An Analysis Using the Fractal Market Hypothesis and Evolutionary Computing
 
 
Review

A Review of the Fractal Market Hypothesis for Trading and Market Price Prediction

by 1,2,3,4,5,6,7,8,* and 7,8,9
1
Science Foundation Ireland, Three Park Place, Hatch Street Upper, D02 FX65 Dublin, Ireland
2
Centre for Advanced Studies, Warsaw University of Technology, Plac Politechniki 1, 00-661 Warsaw, Poland
3
Department of Computer Science, University of Western Cape, Robert Sobukwe Rd., Bellville, Cape Town 7535, South Africa
4
Faculty of Arts, Science and Technology, Wrexham Glyndŵr University of Wales, Mold Rd., Wrexham LL11 2AW, UK
5
School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, University Rd., Westville, Durban 3629, South Africa
6
Smart Data and Assets Limited, 86-90 Paul Street, London EC2A 4NE, UK
7
School of Electrical and Electronic Engineering, Central Quad, Grangegorman Campus, Technological University Dublin, D07 EWV4 Dublin, Ireland
8
Dublin Energy Laboratory, Technological University Dublin, D07 ADY7 Dublin, Ireland
9
Mace Group, The Masonry, 151 Thomas Street, D08 PY5E Dublin, Ireland
*
Author to whom correspondence should be addressed.
Academic Editor: Anatoliy Swishchuk
Mathematics 2022, 10(1), 117; https://doi.org/10.3390/math10010117
Received: 28 November 2021 / Revised: 19 December 2021 / Accepted: 21 December 2021 / Published: 31 December 2021
(This article belongs to the Special Issue Fractal Market Hypothesis, Trend Analysis and Future Price Prediction)
This paper provides a review of the Fractal Market Hypothesis (FMH) focusing on financial times series analysis. In order to put the FMH into a broader perspective, the Random Walk and Efficient Market Hypotheses are considered together with the basic principles of fractal geometry. After exploring the historical developments associated with different financial hypotheses, an overview of the basic mathematical modelling is provided. The principal goal of this paper is to consider the intrinsic scaling properties that are characteristic for each hypothesis. In regard to the FMH, it is explained why a financial time series can be taken to be characterised by a 1/t11/γ scaling law, where γ>0 is the Lévy index, which is able to quantify the likelihood of extreme changes in price differences occurring (or otherwise). In this context, the paper explores how the Lévy index, coupled with other metrics, such as the Lyapunov Exponent and the Volatility, can be combined to provide long-term forecasts. Using these forecasts as a quantification for risk assessment, short-term price predictions are considered using a machine learning approach to evolve a nonlinear formula that simulates price values. A short case study is presented which reports on the use of this approach to forecast Bitcoin exchange rate values. View Full-Text
Keywords: fractal geometry; financial time series analysis; random walk hypothesis; Efficient Market Hypothesis; fractal market hypothesis; future price prediction; machine learning fractal geometry; financial time series analysis; random walk hypothesis; Efficient Market Hypothesis; fractal market hypothesis; future price prediction; machine learning
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MDPI and ACS Style

Blackledge, J.; Lamphiere, M. A Review of the Fractal Market Hypothesis for Trading and Market Price Prediction. Mathematics 2022, 10, 117. https://doi.org/10.3390/math10010117

AMA Style

Blackledge J, Lamphiere M. A Review of the Fractal Market Hypothesis for Trading and Market Price Prediction. Mathematics. 2022; 10(1):117. https://doi.org/10.3390/math10010117

Chicago/Turabian Style

Blackledge, Jonathan, and Marc Lamphiere. 2022. "A Review of the Fractal Market Hypothesis for Trading and Market Price Prediction" Mathematics 10, no. 1: 117. https://doi.org/10.3390/math10010117

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