Special Issue "Soft Computing in Economics, Finance and Management"

A special issue of Axioms (ISSN 2075-1680).

Deadline for manuscript submissions: closed (30 November 2020).

Special Issue Editors

Dr. M. Glòria Barberà-Mariné
Website
Guest Editor
Department of Business Management, Faculty of Business and Economics, Universitat Rovira i Virgili, 43204 Reus, Spain
Interests: finance; decision making; fuzzy aplications
Dr. Cristina Ponsiglione
Website
Guest Editor
Department of Industrial Engineering, Università degli Studi di Napoli Federico II, 40-80143 Napoli, Italy
Interests: agent-based modelling; complex-adaptive systems; geographical clusters and territorial ecosystems
Dr. Maria Teresa Sorrosal-Forradellas
Website
Guest Editor
Department of Business Management, Faculty of Business and Economics, Universitat Rovira i Virgili, 43204 Reus, Spain
Interests: financial markets; financial risk management; memory; artificial neural networks; self-organizing maps; uncertainty
Special Issues and Collections in MDPI journals
Prof. Dr. Giuseppe Zollo
Website
Guest Editor
Department of Industrial Engineering, Università degli Studi di Napoli Federico II, 80143 Napoli, Italy
Interests: aesthetics and management, fuzzy logic; complexity science; knowledge and competence management

Special Issue Information

Dear Colleagues,

We are launching a Special Issue of Axioms on “Soft computing in economics, finance, and management”. The focus of this Special Issue will be the application of soft computing methods to analyze or solve issues in the different domains of economics and business. This will provide an opportunity to study and discuss how these methodologies offer effective solutions to cope with the increasing complexity of markets and organizations. Among the topics that this Special Issue will address, we may consider the following non-exhaustive list, with its applications to economics, finance and management:

  • Agent-Based Modelling and Simulation
  • Chaos Theory
  • Competencies Management and Soft Computing Methods
  • Complex Adaptive Systems
  • Complexity Science
  • Data Mining
  • Decision Making Systems
  • Evolutionary Modelling
  • Fuzzy Expert Systems
  • Fuzzy Games Theory
  • Fuzzy Logic and Expert Systems
  • Fuzzy Multicriteria Methods
  • Knowledge Management and Soft Computing Methods
  • Linguistic Information and Decision Making
  • Management Intelligent Systems
  • Management Methods based on Artificial Intelligence
  • Modeling of Processes in Uncertainty
  • Modeling Aggregate Behaviour
  • Multi-agent systems
  • Network Economics
  • Neural Networks and GAs in Hybrid Systems
  • Neuro–Fuzzy Models
  • Pattern Recognition
  • Soft Computing in Management Control

Needless to say, the Special Issue is open to receiving further ideas, apart from the aforementioned topics. Submissions for this Special Issue could enjoy a Special Discount on the Article Processing Charge (APC). For details, please contact the guest editors or the editorial office.

In the hopes that this initiative will be of interest, we encourage you to submit your current research to be included in the Special Issue.

Dr. M. Glòria Barberà-Mariné
Dr. Cristina Ponsiglione
Dr. M. Teresa Sorrosal-Forradellas
Prof. Dr. Giuseppe Zollo
Guest Editors

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 papers will be 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. Axioms is an international peer-reviewed open access quarterly 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 1000 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

  • Agent-Based Modelling and Simulation
  • Artificial Neural Networks
  • Complex Adaptive Systems
  • Complexity
  • Economics
  • Evolutionary Algorithms
  • Fuzzy Logic
  • Finance
  • Fuzzy Set-Theory
  • Fuzzy-Set Qualitative Comparative Analysis
  • Machine Learning
  • Management
  • Multi-Criteria Methods
  • Network Analysis
  • Social Network Theory
  • Soft Computing

Published Papers (6 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Deep Reinforcement Learning Agent for S&P 500 Stock Selection
Axioms 2020, 9(4), 130; https://doi.org/10.3390/axioms9040130 - 10 Nov 2020
Abstract
This study investigated the performance of a trading agent based on a convolutional neural network model in portfolio management. The results showed that with real-world data the agent could produce relevant trading results, while the agent’s behavior corresponded to that of a high-risk [...] Read more.
This study investigated the performance of a trading agent based on a convolutional neural network model in portfolio management. The results showed that with real-world data the agent could produce relevant trading results, while the agent’s behavior corresponded to that of a high-risk taker. The data used were wide in comparison with earlier reported research and was based on the full set of the S&P 500 stock data for twenty-one years supplemented with selected financial ratios. The results presented are new in terms of the size of the data set used and with regards to the model used. The results provide direction and offer insight into how deep learning methods may be used in constructing automatic trading systems. Full article
(This article belongs to the Special Issue Soft Computing in Economics, Finance and Management)
Show Figures

Figure 1

Open AccessArticle
Value at Risk Based on Fuzzy Numbers
Axioms 2020, 9(3), 98; https://doi.org/10.3390/axioms9030098 - 12 Aug 2020
Abstract
Value at Risk (VaR) has become a crucial measure for decision making in risk management over the last thirty years and many estimation methodologies address the finding of the best performing measure at taking into account unremovable uncertainty of real financial markets. One [...] Read more.
Value at Risk (VaR) has become a crucial measure for decision making in risk management over the last thirty years and many estimation methodologies address the finding of the best performing measure at taking into account unremovable uncertainty of real financial markets. One possible and promising way to include uncertainty is to refer to the mathematics of fuzzy numbers and to its rigorous methodologies which offer flexible ways to read and to interpret properties of real data which may arise in many areas. The paper aims to show the effectiveness of two distinguished models to account for uncertainty in VaR computation; initially, following a non parametric approach, we apply the Fuzzy-transform approximation function to smooth data by capturing fundamental patterns before computing VaR. As a second model, we apply the Average Cumulative Function (ACF) to deduce the quantile function at point p as the potential loss VaRp for a fixed time horizon for the 100p% of the values. In both cases a comparison is conducted with respect to the identification of VaR through historical simulation: twelve years of daily S&P500 index returns are considered and a back testing procedure is applied to verify the number of bad VaR forecasting in each methodology. Despite the preliminary nature of the research, we point out that VaR estimation, when modelling uncertainty through fuzzy numbers, outperforms the traditional VaR in the sense that it is the closest to the right amount of capital to allocate in order to cover future losses in normal market conditions. Full article
(This article belongs to the Special Issue Soft Computing in Economics, Finance and Management)
Show Figures

Figure 1

Open AccessArticle
Can the SOM Analysis Predict Business Failure Using Capital Structure Theory? Evidence from the Subprime Crisis in Spain
Axioms 2020, 9(2), 46; https://doi.org/10.3390/axioms9020046 - 27 Apr 2020
Abstract
The paper aims to identify which variables related to capital structure theory predict business failure in the Spanish construction sector during the subprime crisis. An artificial neural network (ANN) approach based on Self-Organizing Maps (SOM) is proposed, which allows one to cluster between [...] Read more.
The paper aims to identify which variables related to capital structure theory predict business failure in the Spanish construction sector during the subprime crisis. An artificial neural network (ANN) approach based on Self-Organizing Maps (SOM) is proposed, which allows one to cluster between default and active firms’ groups. The similarities and differences between the main features in each group determine the variables that explain the capacities of failure of the analyzed firms. The network tests whether the factors that explain leverage, such as profitability, growth opportunities, size of the company, risk, asset structure, and age of the firm, can be suitable to predict business failure. The sample is formed by 152 construction firms (76 default and 76 active) in the Spanish market. The results show that the SOM correctly predicts 97.4% of firms in the construction sector and classifies the firms in five groups with clear similarities inside the clusters. The study proves the suitability of the SOM for predicting business bankruptcy situations using variables related to capital structure theory and financial crises. Full article
(This article belongs to the Special Issue Soft Computing in Economics, Finance and Management)
Show Figures

Figure 1

Open AccessArticle
Vague Expert Information/Recommendation in Portfolio Optimization-An Empirical Study
Axioms 2020, 9(2), 38; https://doi.org/10.3390/axioms9020038 - 10 Apr 2020
Abstract
In a real market, the quantity of information and recommendations is constantly increasing. However, recommendations are often in linguistic form and no one recommendation is based on a single piece of information. Predictions of individuals and their confidence can vary greatly. Thus, a [...] Read more.
In a real market, the quantity of information and recommendations is constantly increasing. However, recommendations are often in linguistic form and no one recommendation is based on a single piece of information. Predictions of individuals and their confidence can vary greatly. Thus, a problem arises concerning different (disjointed or partially coherent) vague opinions of various experts or information from multiple sources. In this paper, we introduce extensions of the Black—Litterman model with linguistic expressed views from different experts/many sources. The study focuses on empirical analysis of proposed fuzzy approach results. In the presented modification every expert presents its opinion about particular assets according to intervals, and then an experton for each asset is built. In the portfolio optimization, we use aggregated views presented by interval, which is the mean value of the experton built on particular views. In an empirical study, we built and tested 10,000 portfolios based on recommendation from EquityRT, which was made by 14–49 experts monthly between November 2017 and June 2019 for the 29 biggest companies from the US market and different sectors. The annual average return from portfolios is 9.5–11.8%, depending on the width of the intervals and additional constraints. This approach allows people to formulate intuitive views and view the opinions of a group of experts. Full article
(This article belongs to the Special Issue Soft Computing in Economics, Finance and Management)
Show Figures

Figure 1

Open AccessArticle
A Comparative Assessment of Graphic and 0–10 Rating Scales Used to Measure Entrepreneurial Competences
Axioms 2020, 9(1), 21; https://doi.org/10.3390/axioms9010021 - 13 Feb 2020
Cited by 1
Abstract
This article presents an empirical comparative assessment of the measurement quality of two instruments commonly used to measure fuzzy characteristics in computer-assisted questionnaires: a graphic scale (a line production scale using a slider bar) and an endecanary scale (a 0–10 rating scale using [...] Read more.
This article presents an empirical comparative assessment of the measurement quality of two instruments commonly used to measure fuzzy characteristics in computer-assisted questionnaires: a graphic scale (a line production scale using a slider bar) and an endecanary scale (a 0–10 rating scale using radio buttons). Data are analyzed by means of multitrait–multimethod models estimated as structural equation models with a mean and covariance structure. For the first time in such research, the results include bias, valid variance, method variance, and random error variance. The data are taken from a program that assesses entrepreneurial competences in undergraduate Economics and Business students by means of questionnaires administered on desktop computers. Neither of the measurement instruments was found to be biased with respect to the other, meaning that their scores are comparable. While both instruments achieve valid and reliable measurements, the reliability and validity are higher for the endecanary scale. This study contributes to the still scarce literature on fuzzy measurement instruments and on the comparability and relative merits of graphic and discrete rating scales on computer-assisted questionnaires. Full article
(This article belongs to the Special Issue Soft Computing in Economics, Finance and Management)
Show Figures

Figure 1

Open AccessArticle
Optimal Saving by Expected Utility Operators
Axioms 2020, 9(1), 17; https://doi.org/10.3390/axioms9010017 - 09 Feb 2020
Abstract
This paper studies an optimal saving model in which risk is represented by a fuzzy number and the total utility function of the model is defined by an expected utility operator. This model generalizes some existing possibilistic saving models and from them, by [...] Read more.
This paper studies an optimal saving model in which risk is represented by a fuzzy number and the total utility function of the model is defined by an expected utility operator. This model generalizes some existing possibilistic saving models and from them, by a particularization, one can obtain new saving models. In the paper, sufficient conditions are set for the presence of potential risk to increase optimal saving levels and an approximation formula for optimal saving is demonstrated. Particular models for a few concrete expected utility operators are analyzed for triangular fuzzy numbers and CRRA-utility functions. Full article
(This article belongs to the Special Issue Soft Computing in Economics, Finance and Management)
Back to TopTop