https://www.um.edu.mt/library/oar/handle/123456789/11493 2026-05-06T12:04:32Z 2026-05-06T12:04:32Z Khennaoui, Amina-Aicha Ouannas, Adel Bekiros, Stelios Aly, Ayman A. Alotaibi, Ahmed Jahanshahi, Hadi Alsubaie, Hajid https://www.um.edu.mt/library/oar/handle/123456789/146157 2026-05-05T13:14:31Z 2023-01-01T00:00:00Z

Title: Hidden homogeneous extreme multistability of a fractional-order hyperchaotic discrete-time system : chaos, initial offset boosting, amplitude control, control, and synchronization Authors: Khennaoui, Amina-Aicha; Ouannas, Adel; Bekiros, Stelios; Aly, Ayman A.; Alotaibi, Ahmed; Jahanshahi, Hadi; Alsubaie, Hajid Abstract: Fractional order maps are a hot research topic; many new mathematical models are suitable for developing new applications in different areas of science and engineering. In this paper, a new class of a 2D fractional hyperchaotic map is introduced using the Caputo-like difference operator. The hyperchaotic map has no equilibrium and lines of equilibrium points, depending on the values of the system parameters. All of the chaotic attractors generated by the proposed fractional map are hidden. The system dynamics are analyzed via bifurcation diagrams, Lyapunov exponents, and phase portraits for different values of the fractional order. The results show that the fractional map has rich dynamical behavior, including hidden homogeneous multistability and offset boosting. The paper also illustrates a novel theorem, which assures that two hyperchaotic fractional discrete systems achieve synchronized dynamics using very simple linear control laws. Finally, the chaotic dynamics of the proposed system are stabilized at the origin via a suitable controller.

2023-01-01T00:00:00Z Ali, Waris Bekiros, Stelios Hussain, Nazim Khan, Sana Akbar Nguyen, Duc Khuong https://www.um.edu.mt/library/oar/handle/123456789/145954 2026-04-27T13:41:05Z 2024-01-01T00:00:00Z

Title: Determinants and consequences of corporate social responsibility disclosure : a survey of extant literature Authors: Ali, Waris; Bekiros, Stelios; Hussain, Nazim; Khan, Sana Akbar; Nguyen, Duc Khuong Abstract: This paper systematically analyzes and synthesizes the literature on the determinants and consequences of corporate social responsibility (CSR) disclosure. The study is unique in that it synthesizes based on the geographical setting of the original research. We analyzed 135 empirical studies published in Chartered Association of Business Schools (ABS) ranked journals from 1982 to 2020. The results reveal that various global, countryspecific, market-specific, and firm-specific factors are important in determining a firm’s CSR disclosure policies. These factors are consistently relevant in both developed and developing economies. Furthermore, the synthesis shows that companies achieve various CSR disclosure-related benefits in the form of a better reputation, enhanced financial performance, better access to external finances, better stakeholder management, and enhanced corporate accountability. In terms of theories, we observe a high heterogeneity among various studies examining the same empirical phenomenon. Based on the analysis and review results, we identify avenues for future research.

2024-01-01T00:00:00Z Sinha, Avik Bekiros, Stelios Hussain, Nazim Nguyen, Duc Khuong Khan, Sana Akbar https://www.um.edu.mt/library/oar/handle/123456789/144778 2026-03-10T14:35:52Z 2023-01-01T00:00:00Z

Title: How social imbalance and governance quality shape policy directives for energy transition in the OECD countries? Authors: Sinha, Avik; Bekiros, Stelios; Hussain, Nazim; Nguyen, Duc Khuong; Khan, Sana Akbar Abstract: In line with the COP26 Summit objectives, this paper develops a policy framework to achieve energy transition by considering the social imbalances and regulatory effectiveness. A new energy transition index is proposed. It is an output-side indicator based on the energy ladder hypothesis. This index enables to apprehend the energy transition scenario in any country by capturing (a) the transition to a cleaner energy source, and (b) the transition to more energy efficient sources. Using the two-step System GMM approach and data for 37 OECD countries over the 2000–2019 period, the dynamic and extreme marginal impacts of energy transition drivers with respect to estimates of the model parameters are analyzed. The results show that the social imbalance dampens the positive impacts of energy transition drivers, whereas governance quality helps in augmenting those impacts. The outcomes, drawn from a scenario-based policy design approach, are particularly helpful in advancing potential policy discourse. They have important practical implications for the development of the SDG-oriented policy framework, with special focus on the attainment of the SDG 7 and 13.

2023-01-01T00:00:00Z Lahmiri, Salim Bekiros, Stelios Avdoulas, Christos https://www.um.edu.mt/library/oar/handle/123456789/144573 2026-03-04T08:36:01Z 2023-01-01T00:00:00Z

Title: A comparative assessment of machine learning methods for predicting housing prices using Bayesian optimization Authors: Lahmiri, Salim; Bekiros, Stelios; Avdoulas, Christos Abstract: The valuation of house prices is drawing noteworthy attention due to worldwide financial and real estate crises in the last decade. Therefore, there is an immediate need to design more effective predictive systems of house prices. Indeed, investors, creditors, and governments are all interested in such predictive systems to improve their buying and lending decisions and activities. This study explores the application of artificial intelligence, machine learning, and nonlinear statistical models to house price prediction problems. In that order, we use boosting ensemble regression trees, support vector regression, and Gaussian process regression. Bayesian optimization is implemented in a ten-fold cross-validation framework to determine their respective optimal kernels and parameter values. Four performance metrics are used to evaluate the prediction ability of each predictive system. The experimental results showed that boosting ensemble regression trees performed the best, followed by Gaussian process regression and support vector regression. In addition, all three aforementioned predictive systems outperformed artificial neural networks and multi-variate regression employed in recent work on the same data set. Under this perspective, it is concluded that boosting ensemble regression trees are clear candidates to be considered for operational house price prediction in Taiwan.

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