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Advanced Techniques and Modeling in Business and Economics
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Department of Financial Economics and Accounting, University of Murcia, Campus de Espinardo, 30100 Murcia, Spain
Accounting & Management Department, University of Málaga, C. Ejido, 6, 29013 Málaga, Spain
Department of Economics and Business, University of Almería, La Cañada de San Urbano, 04120 Almería, Spain
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Advanced Techniques and Modeling in Business and Economics

Abstract submission deadline
30 June 2025
Manuscript submission deadline
30 September 2025
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12159

Topic Information

Dear Colleagues,

The integration of advanced techniques, including artificial intelligence (AI), computational economics and big data analytics, into the economy and business sectors has become increasingly significant. These technologies possess capabilities to process vast amounts of data, recognize intricate patterns, and provide precise predictions, thereby revolutionizing our approach to economic and business issues. This shift has transformed our understanding and methods of addressing challenges within these landscapes.

Businesses leverage these advanced techniques to optimize their processes, streamline operations, and achieve higher levels of productivity and cost-effectiveness. Similarly, policymakers are recognizing the potential of these tools in formulating more informed and targeted economic policies. By harnessing the analytical power of such advanced techniques, governments and organizations can make data-driven decisions that lead to better outcomes and increased competitiveness in the global market.

This Topic endeavors to delve into the multifaceted application of advanced techniques in business and economics. We welcome contributions that explore the innovative use of AI, blockchain, big data analytics, computational economics, trend forecasting, and other emerging technologies. We encourage a broad range of topics, including but not limited to economic trend forecasting, optimization of business processes, meticulous analysis of financial risks, intricate modeling of consumer behaviors, formulation of impactful policies, innovative applications within the realms of finance and banking, scrutiny of its effects on the labor market dynamics, as well as its potential contributions to fostering Corporate Social Responsibility (CSR) practices.

We invite scholars and researchers to submit their original research, empirical studies, theoretical frameworks, and case studies that illuminate the myriad dimensions of integrating advanced techniques into the economic and business landscapes.

Prof. Dr. José Manuel Santos Jaén
Dr. Ana León-Gomez
Prof. Dr. María del Carmen Valls Martínez
Topic Editors

Keywords

  • advanced techniques
  • business optimization
  • economic modeling
  • AI
  • big data
  • machine learning
  • predictive analytics

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
AI
ai 		3.1 7.2 2020 17.6 Days CHF 1600 Submit
Data
data 		2.2 4.3 2016 27.7 Days CHF 1600 Submit
Economies
economies 		2.1 4.0 2013 21.7 Days CHF 1800 Submit
Mathematics
mathematics 		2.3 4.0 2013 17.1 Days CHF 2600 Submit
Risks
risks 		2.0 3.8 2013 18.7 Days CHF 1800 Submit

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Published Papers (6 papers)

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17 pages, 7514 KiB  
Article
Predicting Mutual Fund Stress Levels Utilizing SEBI’s Stress Test Parameters in MidCap and SmallCap Funds Using Deep Learning Models
by Suneel Maheshwari and Deepak Raghava Naik
Risks 2024, 12(11), 179; https://doi.org/10.3390/risks12110179 - 13 Nov 2024
Viewed by 595
Abstract
Abstract: The Association of Mutual Funds of India (AMFI), under the direction of the Securities and Exchange Board of India (SEBI), provided open access to various risk parameters with respect to MidCap and SmallCap funds for the first time from February 2024. Our [...] Read more.
Abstract: The Association of Mutual Funds of India (AMFI), under the direction of the Securities and Exchange Board of India (SEBI), provided open access to various risk parameters with respect to MidCap and SmallCap funds for the first time from February 2024. Our study utilizes AMFI datasets from February 2024 to September 2024 which consisted of 14 variables. Among these, the primary variable identified in grading mutual funds is the stress test parameter, expressed as number of days required to liquidate between 50% and 25% of the portfolio, respectively, on a pro-rata basis under stress conditions as a response variable. The objective of our paper is to build and test various neural network models which can help in predicting stress levels with the highest accuracy and specificity in MidCap and SmallCap mutual funds based on AMFI’s 14 parameters as predictors. The results suggest that the simpler neural network model architectures show higher accuracy. We used Artificial Neural Networks (ANN) over other machine learning methods due to its ability to analyze the impact of dynamic interrelationships among 14 variables on the dependent variable, independent of the statistical distribution of parameters considered. Predicting stress levels with the highest accuracy in MidCap and SmallCap mutual funds will benefit investors by reducing information asymmetry while allocating investments based on their risk tolerance. It will help policy makers in designing controls to protect smaller investors and provide warnings for funds with unusually high risk. Full article
(This article belongs to the Topic Advanced Techniques and Modeling in Business and Economics)
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15 pages, 512 KiB  
Article
Polynomial Moving Regression Band Stocks Trading System
by Gil Cohen
Risks 2024, 12(10), 166; https://doi.org/10.3390/risks12100166 - 18 Oct 2024
Viewed by 1018
Abstract
In this research, we attempted to fit a trading system based on polynomial moving regression bands (MRB) to Nasdaq100 stocks from 2017 till the end of March 2024. Since stocks movement does not follow a linear behavior, we used multiple degree polynomial regression [...] Read more.
In this research, we attempted to fit a trading system based on polynomial moving regression bands (MRB) to Nasdaq100 stocks from 2017 till the end of March 2024. Since stocks movement does not follow a linear behavior, we used multiple degree polynomial regression models to identify the stocks’ trends and two standard deviations from the regression model to generate the trading signals. This way, the MRB was transformed into a momentum indicator designed to identify strong uptrends that can be used by a fully automated trading system. Our results indicate that the behavior of Nasdaq100 stocks can be tracked using all three examined polynomial models and can be traded profitably using fully automated systems based on those models. The best performing model was the model that used a four-degree polynomial MRB achieving the highest average net profit (USD 162.73). Regarding the risks involved, the third model has the lowest loss in dollar value (USD −95.52), and the highest minimum percent of profitable trades (41.51%) and profit factor (0.55) that indicates that this strategy is relatively less risky than the other two strategies. Full article
(This article belongs to the Topic Advanced Techniques and Modeling in Business and Economics)
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25 pages, 1003 KiB  
Article
Multi-Factor Cost Adjustment for Enhanced Export-Oriented Production Capacity in Manufacturing Firms
by Ashraf Mishrif and Mohamed A. Hammad
Economies 2024, 12(8), 219; https://doi.org/10.3390/economies12080219 - 22 Aug 2024
Viewed by 822
Abstract
Many manufacturing firms face considerable difficulties in building export capacity and selling their products in international markets. These firms often struggle with unpredictable cost changes, logistical problems along the supply chain, and rising labor expenses that could threaten the competitive edge of manufacturing [...] Read more.
Many manufacturing firms face considerable difficulties in building export capacity and selling their products in international markets. These firms often struggle with unpredictable cost changes, logistical problems along the supply chain, and rising labor expenses that could threaten the competitive edge of manufacturing operations. As there is also a clear absence of practical export models tailored to the unique needs of industrial firms, our study aims to offer a more holistic approach to assessing the impact of cost components on enhancing export-oriented production capacity (EOPC), a perspective not comprehensively provided by the comparative advantage theory, the Heckscher–Ohlin model, or the resource-based theory. While offering a comprehensive analysis of cost components in production, we argue that adjusting the resources, managing the costs, and enhancing production efficiency can significantly improve the EOPC of the manufacturing firms. Using primary data collected from 200 manufacturing firms in Oman during the period 2012–2016, multiple regression analysis followed by descriptive statistical analysis together with a correlation matrix indicates strong positive relationships between the EOPC and factors such as the raw material cost (RMC), labor wages (LW), labor force (LF), and R&D costs (RND). Multicollinearity assessment shows VIF values below the threshold, suggesting reliable estimates. Interaction terms and market conditions were integrated into the model, enhancing its predictive accuracy. Preliminary multiple regression analysis confirms the significant impact of the RMC, LW, LF, and R&D on the EOPC, while highlighting the importance of market conditions in moderating these effects. The model’s adjusted R2 value indicates a strong fit, showing that the independent variables account for a substantial proportion of the variance in the EOPC. Each variable’s importance is reflected in its coefficient, while p-values assess the statistical significance, highlighting which factors are crucial for enhancing export capabilities. Specifically, low p-values for cost components, labor force size, and wages confirm their significant influence, and varying market conditions further modulate these effects, demonstrating the accurate interplay between internal and external factors. Adjustments in cost components under varying market scenarios were analyzed, indicating optimal strategies for increasing the EOPC. Of the five scenarios proposed to distribute the cost either among some variables while keeping others constant or among all the factors, the best-case scenario adjusted all variables together, resulting in a 20% increment in exports. We conclude with some practical and policy implications for governments to support industries in accessing cheap resources through tax reductions on imported raw materials and efficient supply chains, while promoting innovation, technology adoption, and R&D investment at the firm level. Full article
(This article belongs to the Topic Advanced Techniques and Modeling in Business and Economics)
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20 pages, 846 KiB  
Article
Efficiency Analysis of Human Capital Investments at Micro and Large-Sized Enterprises in the Manufacturing Sector Using Data Envelopment Analysis
by Rafael Bernardo Carmona-Benítez and Aldebarán Rosales-Córdova
Economies 2024, 12(8), 213; https://doi.org/10.3390/economies12080213 - 21 Aug 2024
Viewed by 768
Abstract
Micro and large-sized enterprises are important elements to enhance the economic growth of any country, and even more so for developing countries such as Mexico. These enterprises highly contribute to job generation, competitiveness, and gross domestic product, factors that are important for the [...] Read more.
Micro and large-sized enterprises are important elements to enhance the economic growth of any country, and even more so for developing countries such as Mexico. These enterprises highly contribute to job generation, competitiveness, and gross domestic product, factors that are important for the developing of a nation. The aim of this paper is to study the impact of human capital investments in the efficiency of the 21 economic activity subsectors for micro and large-sized enterprises in the Mexican manufacturing industry between 2009–2021. The database come from Mexico Annual Manufacturing Industry Survey. Four Data Envelopment Analysis models are developed to study the relationship between annual average working days, annual average wages, and annual average investment in training with average sales per year. Data indicate that, most of the micro-sized enterprises of the Mexican manufacturing sector do not invest in human capital training, contrary to their large-sized enterprises. The results show that investing in human capital training increase sales and wages in micro-sized enterprises of the Mexican manufacturing industry, but it is not evident in large-size enterprises of the Mexican manufacturing industry. The calculation of the economic activity subsectors efficiencies using the developed Data Envelopment Analysis models indicate that all the economic activity subsectors with scale efficiency equal to one optimally invest, and the average amount of investments in human capital training needed to increase the global and pure technical efficiencies of the others are calculated with the developed Data Envelopment Analysis models. In the three main economic activity subsectors of the Mexican manufacturing industry, a significant increase—in 83.33% of cases—in wages and salaries is seen in both micro and large-sized enterprises. Particularly, the results indicate that the Chemical industry economic activity subsectors show the highest efficiency in both micro and large-sized enterprises when the human capital training variable is present. This paper demonstrates the importance of investing in human capital to enhance the efficiency of micro and large-sized enterprises. Full article
(This article belongs to the Topic Advanced Techniques and Modeling in Business and Economics)
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23 pages, 4667 KiB  
Article
Study of Flexibility Transformation in Thermal Power Enterprises under Multi-Factor Drivers: Application of Complex-Network Evolutionary Game Theory
by Lefeng Cheng, Pan Peng, Wentian Lu, Pengrong Huang and Yang Chen
Mathematics 2024, 12(16), 2537; https://doi.org/10.3390/math12162537 - 16 Aug 2024
Cited by 2 | Viewed by 780
Abstract
With the increasing share of renewable energy in the grid and the enhanced flexibility of the future power system, it is imperative for thermal power companies to explore alternative strategies. The flexible transformation of thermal power units is an effective strategy to address [...] Read more.
With the increasing share of renewable energy in the grid and the enhanced flexibility of the future power system, it is imperative for thermal power companies to explore alternative strategies. The flexible transformation of thermal power units is an effective strategy to address the previously mentioned challenges; however, the factors influencing the diffusion of this technology merit further investigation, yet they have been seldom examined by scholars. To address this gap, this issue is examined using an evolutionary game model of multi-agent complex networks, and a more realistic group structure is established through heterogeneous group differentiation. With factors such as group relationships, diffusion paths, compensation electricity prices, and subsidy intensities as variables, several diffusion scenarios are developed for research purposes. The results indicate that when upper-level enterprises influence the decision-making of lower-level enterprises, technology diffusion is significantly accelerated, and enhanced communication among thermal power enterprises further promotes diffusion. Among thermal power enterprises, leveraging large and medium-sized enterprises to promote the flexibility transformation of units proves to be an effective strategy. With regard to factors like the compensation price for depth peak shaving, the initial application ratio of groups, and the intensity of government subsidies, the compensation price emerges as the key factor. Only with a high compensation price can the other two factors effectively contribute to promoting technology diffusion. Full article
(This article belongs to the Topic Advanced Techniques and Modeling in Business and Economics)
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20 pages, 1659 KiB  
Article
A Fuzzy Entropy Approach for Portfolio Selection
by Milena Bonacic, Héctor López-Ospina, Cristián Bravo and Juan Pérez
Mathematics 2024, 12(13), 1921; https://doi.org/10.3390/math12131921 - 21 Jun 2024
Viewed by 805
Abstract
Portfolio management typically aims to achieve better returns per unit of risk by building efficient portfolios. The Markowitz framework is the classic approach used when decision-makers know the expected returns and covariance matrix of assets. However, the theory does not always apply when [...] Read more.
Portfolio management typically aims to achieve better returns per unit of risk by building efficient portfolios. The Markowitz framework is the classic approach used when decision-makers know the expected returns and covariance matrix of assets. However, the theory does not always apply when the time horizon of investments is short; the realized return and covariance of different assets are usually far from the expected values, and considering additional factors, such as diversification and information ambiguity, can lead to better portfolios. This study proposes models for constructing efficient portfolios using fuzzy parameters like entropy, return, variance, and entropy membership functions in multi-criteria optimization models. Our approach leverages aspects related to multi-criteria optimization and Shannon entropy to deal with diversification, and fuzzy and fuzzy entropy variants provide a better representation of the ambiguity of the information according to the investors’ deadline. We compare 418 optimal portfolios for different objectives (return, variance, and entropy), using data from 2003 to 2023 of indexes from the USA, EU, China, and Japan. We use the Sharpe index as a decision variable, in addition to the multi-criteria decision analysis method TOPSIS. Our models provided high-efficiency portfolios, particularly those considering fuzzy entropy membership functions for return and variance. Full article
(This article belongs to the Topic Advanced Techniques and Modeling in Business and Economics)
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