主要加密货币的结构多分形缩放:使用可自我解释的机器学习进行检验

IF 3.4 3区 经济学 Q1 ECONOMICS
Foued Saâdaoui, Hana Rabbouch
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引用次数: 0

摘要

本文介绍了一种称为分段去趋势多分形波动分析(SMF-DFA)的新型统计测试技术,用于分析金融回报的结构缩放特性,并预测金融市场的长期记忆。所提出的方法适用于评估主要加密货币的效率,通过纳入通过变化点检测测试确定的不同波动机制,对传统方法进行了扩展。采用单因素模型来描述影响缩放行为的内生因素,从而开发出一种用于价格预测的不言自明的机器学习方法。使用从 2017 年 4 月到 2022 年 12 月的三种主要加密货币的每日数据,对所提出的方法进行了评估。分析旨在确定近年来数字市场是否经历了重大变化,并评估这是否导致了结构化的多分形行为。研究确定了三种价格之间共同的局部缩放期,2018 年后观察到多分形明显减少。此外,还对洗牌数据和代用数据进行了补充测试,以探索其分布、线性相关和非线性结构,在一定程度上揭示了结构化多分形的解释。此外,基于神经网络和多分叉数据的预测实验证明了这种新的自解释算法对于寻求更准确和可解释预测的决策者和投资者的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structured multifractal scaling of the principal cryptocurrencies: Examination using a self-explainable machine learning

This paper introduces a novel statistical testing technique known as segmented detrended multifractal fluctuation analysis (SMF-DFA) to analyze the structured scaling properties of financial returns and predict the long-term memory of financial markets. The proposed methodology is applied to assess the efficiency of major cryptocurrencies, expanding upon conventional approaches by incorporating different fluctuation regimes identified through a change-point detection test. A single-factor model is employed to characterize the endogenous factors influencing scaling behavior, leading to the development of a self-explanatory machine learning approach for price forecasting. The proposed method is evaluated using daily data from three major cryptocurrencies spanning from April 2017 to December 2022. The analysis aims to determine whether the digital market has experienced significant changes in recent years and assess whether this has resulted in structured multifractal behavior. The study identifies common periods of local scaling among the three prices, with a noticeable decrease in multifractality observed after 2018. Furthermore, complementary tests on shuffled and surrogate data are conducted to explore the distribution, linear correlation, and nonlinear structure, shedding light on the explanation of structured multifractality to some extent. Additionally, prediction experiments based on neural networks fed with multi-fractionally differentiated data demonstrate the utility of this new self-explanatory algorithm for decision-makers and investors seeking more accurate and interpretable forecasts.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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