Advanced Techniques and Modeling in Business and Economics

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	5.0	6.9	2020	19.2 Days	CHF 1800
Data data	2.0	5.0	2016	25 Days	CHF 1600
Economies economies	2.1	4.7	2013	23.1 Days	CHF 1800
Mathematics mathematics	2.2	4.6	2013	17.3 Days	CHF 2600
Risks risks	1.5	5.0	2013	20 Days	CHF 1800

22 pages, 689 KB

Open AccessArticle

Artificial Intelligence and the Emergence of New Quality Productive Forces: A Machine Learning Perspective

by Lei Tan, Xiaobing Lai, Yuxin Zhao and Yuan Zhong

Mathematics 2026, 14(1), 135; https://doi.org/10.3390/math14010135 - 29 Dec 2025

Viewed by 475

In the era of the digital economy, AI technology is regarded as a key driver in promoting the development of new quality productive forces of enterprises. Based on the theories of creative destruction and resource allocation, this study selects Chinese enterprise-level data from [...] Read more.

In the era of the digital economy, AI technology is regarded as a key driver in promoting the development of new quality productive forces of enterprises. Based on the theories of creative destruction and resource allocation, this study selects Chinese enterprise-level data from 2009 to 2022 as the research sample, constructs enterprise new quality productivity indicators through text analysis and machine learning methods, and explores the impact of artificial intelligence on new quality productivity. The study results show that AI technology significantly improves the new quality productivity of enterprises. Further research found that enterprise director background, digital industry agglomeration and financial agglomeration positively moderated the relationship between AI and new quality productivity. Heterogeneity analysis shows that the enabling effect of AI technology on new quality productivity is more significant in high-tech enterprises, state-owned enterprises and enterprises with strong policy support. Through empirical analysis, this study verifies the facilitating effect of AI technological innovation on enterprises’ new quality productivity, which provides important insights for enterprises in emerging economies to achieve the development of new quality productive forces in digital transformation. Full article

(This article belongs to the Topic Advanced Techniques and Modeling in Business and Economics)

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33 pages, 6744 KB

Open AccessArticle

Local Attention and ASEAN-5 Connectedness: A TVP-VAR and GARCH-MIDAS Analysis

by Faten Chibani and Jamel Eddine Henchiri

Risks 2025, 13(12), 251; https://doi.org/10.3390/risks13120251 - 15 Dec 2025

Viewed by 611

Abstract

We show that financial integration in emerging Asia is state-dependent in the sense that cross-market linkages vary systematically across regimes of global uncertainty and market stress. Focusing on Indonesia, Malaysia, Singapore, Thailand, and Vietnam, this study combines a time-varying parameter VAR (TVP–VAR) with [...] Read more.

We show that financial integration in emerging Asia is state-dependent in the sense that cross-market linkages vary systematically across regimes of global uncertainty and market stress. Focusing on Indonesia, Malaysia, Singapore, Thailand, and Vietnam, this study combines a time-varying parameter VAR (TVP–VAR) with a GARCH–MIDAS volatility model to link short-run transmission to long-run behavioural effects. We construct a regional investor-sentiment (IS) index from Google search data on five macro-financial topics using principal component analysis and analyse it together with global benchmarks (MSCI EM, S&P 500), gold, clean-energy equities, and macro-uncertainty indicators. The TVP–VAR maps dynamic spillovers among the ASEAN-5 and external nodes, while the GARCH–MIDAS relates the slow component of variance to investor attention. The evidence indicates that connectedness tightens in stress regimes, with global benchmarks and policy uncertainty acting as transmitters and ASEAN equities absorbing incoming shocks. In the volatility block, the Google-based IS factor exerts a negative and economically meaningful influence on the long-run component over and above global uncertainty, supporting the view that attention and uncertainty function as complementary channels of risk propagation. The integrated framework is parsimonious and replicable, and it offers actionable insights for regime-aware risk management, policy communication, and the timing of green-finance issuance in emerging markets. Full article

(This article belongs to the Topic Advanced Techniques and Modeling in Business and Economics)

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24 pages, 9423 KB

Open AccessArticle

Operator Learning with Branch–Trunk Factorization for Macroscopic Short-Term Speed Forecasting

by Bin Yu, Yong Chen, Dawei Luo and Joonsoo Bae

Data 2025, 10(12), 207; https://doi.org/10.3390/data10120207 - 12 Dec 2025

Viewed by 592

Abstract

Logistics operations demand real-time visibility and rapid response, yet minute-level traffic speed forecasting remains challenging due to heterogeneous data sources and frequent distribution shifts. This paper proposes a Deep Operator Network (DeepONet)-based framework that treats traffic prediction as learning a mapping from historical [...] Read more.

Logistics operations demand real-time visibility and rapid response, yet minute-level traffic speed forecasting remains challenging due to heterogeneous data sources and frequent distribution shifts. This paper proposes a Deep Operator Network (DeepONet)-based framework that treats traffic prediction as learning a mapping from historical states and boundary conditions to future speed states, enabling robust forecasting under changing scenarios. We project logistics demand onto a road network to generate diverse congestion scenarios and employ a branch–trunk architecture to decouple historical dynamics from exogenous contexts. Experiments on both a controlled simulation dataset and the real-world Metropolitan Los Angeles (METR-LA) benchmark demonstrate that the proposed method outperforms classical regression and deep learning baselines in cross-scenario generalization. Specifically, the operator learning approach effectively adapts to unseen boundary conditions without retraining, establishing a promising direction for resilient and adaptive logistics forecasting. Full article

(This article belongs to the Topic Advanced Techniques and Modeling in Business and Economics)

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27 pages, 3182 KB

Open AccessArticle

From Digital Transformation to Circular Growth: The Role of Economic Complexity and Eco-Innovation in Africa’s Sustainable Future

by Hela Mili

Economies 2025, 13(12), 339; https://doi.org/10.3390/economies13120339 - 22 Nov 2025

Viewed by 609

Abstract

This study investigates how eco-innovation, digitalization, and economic complexity drive the circular economy in African economies between 2000 and 2024. Using the Dumitrescu–Hurlin causality test with POLS, DKSE, and MMQR estimators, we identify determinants of CE growth under varying conditions of development. Our [...] Read more.

This study investigates how eco-innovation, digitalization, and economic complexity drive the circular economy in African economies between 2000 and 2024. Using the Dumitrescu–Hurlin causality test with POLS, DKSE, and MMQR estimators, we identify determinants of CE growth under varying conditions of development. Our results show the strongest positive effect on CE is induced by digitalization, followed by eco-innovation and economic complexity. In addition, digitalization equally enhances CE at all quantiles, while for eco-innovation, its contribution towards CE growth is greater in low-performing CE countries. The evidence suggests that in the course of its circular transformation, Africa needs to embark on digital infrastructure, innovation capacity, and productive diversification. Accordingly, such regional strategies will be contributing to SDG 9 on industry and innovation, SDG 12 on responsible consumption, and to the African Union Agenda 2063 for sustainable growth. Full article

(This article belongs to the Topic Advanced Techniques and Modeling in Business and Economics)

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16 pages, 2198 KB

Open AccessArticle

Dynamic Interactions Between Oil Price Volatility and Fiscal Policy: Empirical Evidence from GCC Countries

by Tarek Sadraoui and Hela Mili

Economies 2025, 13(11), 334; https://doi.org/10.3390/economies13110334 - 18 Nov 2025

Viewed by 1747

Abstract

This article examines how the volatile oil price affected Saudi Arabia’s and the other GCC countries’ fiscal policy dynamics from 2000 and 2024. The region’s fiscal systems are vulnerable to external shocks due to their substantial reliance on petroleum profits, even with ongoing [...] Read more.

This article examines how the volatile oil price affected Saudi Arabia’s and the other GCC countries’ fiscal policy dynamics from 2000 and 2024. The region’s fiscal systems are vulnerable to external shocks due to their substantial reliance on petroleum profits, even with ongoing efforts to diversify. The dynamic links between government spending, budget balances and oil prices are examined using a panel vector Autoregression (PVAR) model. We also estimate different fiscal reaction functions for Saudi Arabia and several of its neighbors with somewhat more diversified income systems, like Kuwait and the United Arab Emirates. This study’s primary contribution is its direct comparative analysis of GCC fiscal responses using a long panel (2000–2024) that encompasses major oil price cycles, and the application of a dynamic PVAR framework to quantify the persistence and heterogeneity of these effects across countries. The data shows that different countries have different fiscal responses. For example, Saudi Arabia reacts to oil shocks more slowly than other nations with more diverse revenue sources, who have better consolidation mechanisms. These results highlight the necessity of strong fiscal frameworks that build resilience to fluctuations in commodity prices and offer insightful guidance to policymakers seeking sustainable fiscal management in countries that rely heavily on natural resources. Full article

(This article belongs to the Topic Advanced Techniques and Modeling in Business and Economics)

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16 pages, 679 KB

Open AccessArticle

Deep Reinforcement Learning in a Search-Matching Model of Labor Market Fluctuations

by Ruxin Chen

Economies 2025, 13(10), 302; https://doi.org/10.3390/economies13100302 - 20 Oct 2025

Cited by 1 | Viewed by 1271

Abstract

Shimer documents that the search-and-matching model driven by productivity shocks explains only a small share of the observed volatility of unemployment and vacancies, which is known as the Shimer puzzle. We revisit this evidence by replacing the representative firm’s optimization with a deep [...] Read more.

Shimer documents that the search-and-matching model driven by productivity shocks explains only a small share of the observed volatility of unemployment and vacancies, which is known as the Shimer puzzle. We revisit this evidence by replacing the representative firm’s optimization with a deep reinforcement learning (DRL) agent that learns its vacancy-posting policy through interaction in a Diamond–Mortensen–Pissarides (DMP) model. Comparing the learning economy with a conventional log-linearized DSGE solution under the same parameters, we find that while both frameworks preserve a downward-sloping Beveridge curve, learning-based economy produces much higher volatility in key labor market variables and returns to a steady state more slowly after shocks. These results point to bounded rationality and endogenous learning as mechanisms for labor market fluctuations and suggest that reinforcement learning can serve as a useful complement to standard macroeconomic analysis. Full article

(This article belongs to the Topic Advanced Techniques and Modeling in Business and Economics)

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22 pages, 2721 KB

Open AccessArticle

Multimodal Livestock Operations Analysis Using Business Process Modeling: A Case Study of Romanian Black Sea Ports

by Catalin Popa, Ovidiu Stefanov and Ionela Goia

Economies 2025, 13(3), 69; https://doi.org/10.3390/economies13030069 - 7 Mar 2025

Cited by 2 | Viewed by 1902

Abstract

In spite of its strong increase and relevant position in the evolution of international maritime routes, the global livestock trade is still a poorly treated topic in the maritime business domain of research. Aiming to cover this gap, the authors are focused on [...] Read more.

In spite of its strong increase and relevant position in the evolution of international maritime routes, the global livestock trade is still a poorly treated topic in the maritime business domain of research. Aiming to cover this gap, the authors are focused on revealing the livestock logistics technology in intermodal transports, approaching both equipment reliability and operation flow design, applying the business processes modeling method to map the most relevant stages in animals’ port operation, transfer, and maritime transportation. This paper examines the intricate logistics of maritime livestock transportation through a case study on the Port of Midia, administrated by the Constanța Maritime Port Administration, one of Romania’s primary export hubs for livestock operations, using BPM software, seeking to identify the most important deficiencies and alternatives in improving the technical and technological effectiveness. Key findings indicate that improving ramp availability, automating document verification, and implementing RFID-based animal tracking systems could significantly enhance operational efficiency. By integrating workflow models, real-time monitoring, and simulation-based optimization, the study offers a comprehensive framework for streamlining multimodal livestock transportation. The implications extend to policymakers, port authorities, and logistics operators, emphasizing the necessity of digital transformation, regulatory harmonization, and technological integration in livestock maritime transportation. This research contributes to the expansion of intermodal transportation studies, providing practical recommendations for enhancing livestock logistics efficiency while ensuring compliance with European animal welfare regulations. The findings pave the way for further research into AI-driven risk assessments, smart logistics solutions, and sustainable livestock transportation alternatives. Full article

(This article belongs to the Topic Advanced Techniques and Modeling in Business and Economics)

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18 pages, 3309 KB

Open AccessFeature PaperArticle

A Study of the Colombian Stock Market with Multivariate Functional Data Analysis (FDA)

by Deivis Rodríguez Cuadro, Sonia Pérez-Plaza, Antonia Castaño-Martínez and Fernando Fernández-Palacín

Mathematics 2025, 13(5), 858; https://doi.org/10.3390/math13050858 - 5 Mar 2025

Cited by 2 | Viewed by 2771

Abstract

In this work, Functional Data Analysis (FDA) is used to detect behavioral patterns in the Bolsa de Valores de Colombia (BVC) in reaction to the global crises caused by COVID-19 and the war in Ukraine. The oil price fluctuation curve is considered a [...] Read more.

In this work, Functional Data Analysis (FDA) is used to detect behavioral patterns in the Bolsa de Valores de Colombia (BVC) in reaction to the global crises caused by COVID-19 and the war in Ukraine. The oil price fluctuation curve is considered a covariate. The FDA’s distinctive ability is to represent stock values as smooth curves that evolve over time and provide new insights into the dynamics of the BVC. The methodology makes use of functional multivariate techniques applied to the smoothed curves of the closing prices of the main stocks of the BVC. The results show that the correlations of the oil curve with the average market curve change from almost null or low in the global period to extremely significant in time windows immediately after the beginnings of COVID-19 and the war in Ukraine, respectively. On the other hand, the velocity curves, which are used to evaluate the stock market volatility, show a pattern of synchronization of companies in the crisis periods. Furthermore, in these crisis periods, the companies in BVC showed a high synchronization with the Brent crude oil price. In conclusion, this work shows the usefulness of the FDA as a complement to time series analysis in the study of stock markets. The results of this research could be of interest to academic researchers, financial analysts, or institutions. Full article

(This article belongs to the Topic Advanced Techniques and Modeling in Business and Economics)

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17 pages, 7514 KB

Open AccessArticle

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 4483

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 KB

Open AccessArticle

Polynomial Moving Regression Band Stocks Trading System

by Gil Cohen

Risks 2024, 12(10), 166; https://doi.org/10.3390/risks12100166 - 18 Oct 2024

Cited by 1 | Viewed by 5048

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 KB

Open AccessArticle

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

Cited by 3 | Viewed by 3706

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 KB

Open AccessArticle

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

Cited by 1 | Viewed by 3093

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 KB

Open AccessArticle

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 13 | Viewed by 1777

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 KB

Open AccessArticle

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

Cited by 1 | Viewed by 2439

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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