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Fractal Fract
Special Issues
Fractal Dynamics and Machine Learning in Financial Markets
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Fractal Dynamics and Machine Learning in Financial Markets

A special issue of Fractal and Fractional (ISSN 2504-3110). This special issue belongs to the section "Numerical and Computational Methods".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 3351

Special Issue Editor

Dr. David Alaminos

E-Mail Website
Guest Editor
Department of Business, University of Barcelona, Av. Diagonal, 690, 08034 Barcelona, Spain
Interests: fractal analysis; fractional dynamics; computational finance; financial markets, speculation and corporate finance

Special Issue Information

Dear Colleagues,

In the dynamic landscape of financial markets and speculation, a unique blend of classical and quantum physics, statistical physics, and mathematics, including fractional calculus, has emerged as a promising approach. This interplay is marked by the recognition that many phenomena exhibit long-range correlations in both time and space, memory effects, fractality, and power–law dynamics. These inherent complexities underscore the relevance of fractional analysis and its application to understanding the behavior of assets, speculation, and corporate finance in financial markets.

This Special Issue aims to explore the interplay between complex, fractal, and fractional dynamics in financial markets and speculation, and how these techniques can provide valuable insights into asset pricing, market behavior, risk management and corporate finance. We invite rigorous, original contributions that align with the scope of the journal. Authors are encouraged to delve into various aspects, including (but not limited to):

  • Memory models and their applications in financial markets, both univariate and multivariate.
  • The use of complex and fractional modeling techniques to analyze and predict asset prices and market dynamics.
  • The application of complex and fractional approaches in econophysics, with a focus on speculative behaviors.
  • The utilization of complex and fractional methods to model and understand the intricacies of financial markets.
  • Exploring the mathematical underpinnings of fractals and fractal–fractional order mathematical models within financial markets.
  • Investigating the role of fractional non-linear dynamics and chaos in speculation.
  • Leveraging big data for complex and fractional analysis to enhance trading strategies and corporate financial decision making.
  • The integration of fractional order advanced control systems, including machine learning in high-frequency trading, financial markets, speculation, and corporate finance.

We invite you to contribute to this Special Issue where we aim to enrich our understanding of the role of fractal analysis and complex dynamics in the realm of financial markets, speculation, and corporate finance. Your contributions will illuminate the potential and challenges of these tools in modeling and enhancing the study of financial markets, ultimately shaping the future of financial decision making, including corporate finance.

Dr. David Alaminos
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fractal and Fractional is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • financial markets
  • speculation
  • fractal analysis
  • fractional dynamics
  • asset pricing
  • machine learning
  • risk management
  • volatility
  • algorithmic trading
  • corporate finance

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

8 pages, 407 KiB  
Article
Seasonal Long Memory in Retail Sales in the G7 Countries
by Luis Alberiko Gil-Alana and Carlos Poza
Fractal Fract. 2024, 8(11), 650; https://doi.org/10.3390/fractalfract8110650 - 7 Nov 2024
Viewed by 1140
Abstract
This article examines the seasonal patterns of retail sales in the G7 nations, a key component of private consumption. Using seasonal fractional integration, we assess whether shocks present a lasting or temporary effect on retail sales trends, considering the high seasonal component. We [...] Read more.
This article examines the seasonal patterns of retail sales in the G7 nations, a key component of private consumption. Using seasonal fractional integration, we assess whether shocks present a lasting or temporary effect on retail sales trends, considering the high seasonal component. We observe mean reversion in France, Germany, Italy, Japan, and the UK, and permanent effects in the cases of Canada and USA. However, these outcomes vary based on the error term model applied. These results offer valuable information for policymakers seeking to boost consumption depending on the seasonal long memory property of the G7 countries. Full article
(This article belongs to the Special Issue Fractal Dynamics and Machine Learning in Financial Markets)
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38 pages, 11580 KiB  
Article
Deep Learning-Based Anomaly Detection in Occupational Accident Data Using Fractional Dimensions
by Ömer Akgüller, Larissa M. Batrancea, Mehmet Ali Balcı, Gökhan Tuna and Anca Nichita
Fractal Fract. 2024, 8(10), 604; https://doi.org/10.3390/fractalfract8100604 - 17 Oct 2024
Cited by 1 | Viewed by 1133
Abstract
This study examines the effectiveness of Convolutional Autoencoder (CAE) and Variational Autoencoder (VAE) models in detecting anomalies within occupational accident data from the Mining of Coal and Lignite (NACE05), Manufacture of Other Transport Equipment (NACE30), and Manufacture of Basic Metals (NACE24) sectors. By [...] Read more.
This study examines the effectiveness of Convolutional Autoencoder (CAE) and Variational Autoencoder (VAE) models in detecting anomalies within occupational accident data from the Mining of Coal and Lignite (NACE05), Manufacture of Other Transport Equipment (NACE30), and Manufacture of Basic Metals (NACE24) sectors. By applying fractional dimension methods—Box Counting, Hall–Wood, Genton, and Wavelet—we aim to uncover hidden risks and complex patterns that traditional time series analyses often overlook. The results demonstrate that the VAE model consistently detects a broader range of anomalies, particularly in sectors with complex operational processes like NACE05 and NACE30. In contrast, the CAE model tends to focus on more specific, moderate anomalies. Among the fractional dimension methods, Genton and Hall–Wood reveal the most significant differences in anomaly detection performance between the models, while Box Counting and Wavelet yield more consistent outcomes across sectors. These findings suggest that integrating VAE models with appropriate fractional dimension methods can significantly enhance proactive risk management in high-risk industries by identifying a wider spectrum of safety-related anomalies. This approach offers practical insights for improving safety monitoring systems and contributes to the advancement of data-driven occupational safety practices. By enabling earlier detection of potential hazards, the study supports the development of more effective safety policies, and could lead to substantial improvements in workplace safety outcomes. Full article
(This article belongs to the Special Issue Fractal Dynamics and Machine Learning in Financial Markets)
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