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  • Impact Of Working Capital Management On Profitability In The Listed Firms In The Retail Sector In The Saudi Stock Exchange

    Raheem, Mohamed Mahees; AlHashiem, Yasmin; Department Collaboration; NA; 1; 0; Finance; 0; AlHashiem, Yasmin (PAL.ARCH, 2021-05-09)
    Working capital management is an important issue in financial management, considering its large role on the profitability and liquidity of firms. This paper aims to explore the effects of working capital management on profitability in 14 Saudi retail companies that are listed in the Saudi Stock Exchange (Tadawul) over the period of 2011-2014. This study will employ panel data regression analysis using Pooled OLS to test the relationship between working capital components, which are the cash conversion cycle (CCC), current assets to total assets (CATAR), current assets to current liabilities ratio (CACLR), current liabilities to total assets ratio (CLTAR) and debt to total assets ratio (DTAR) and profitability measured by return on assets (ROA) and return on invested capital (ROIC). The working capital management components represent the independent variables, while the profitability variables represent the dependent variables used in the model. The results show no significant relationship between working capital management components and ROA and ROIC in all the companies and the ones who predominantly deal with services, except for companies that deal with mainly goods, where there is a significant negative relationship between CACLR and ROA.
  • Assessing the speculative dynamics and determinants of residential apartment rentals in Mogadishu, Somalia: A hybrid modeling approach

    Raheem, Mohamed Mahees; Nor, Mohamed Ibrahim; External Collaboration; NA; 0; 0; Finance; 0; Nor, Mohamed Ibrahim (Elsevier, 2024-01-12)
    There has been relatively rapid growth in Somalia's real estate sector recently, resulting in property market booms in major cities of the country. Despite this sector's importance, very little scientific research has been conducted. Thus, this study aims to model the rental value of residential apartments in Mogadishu, Somalia. A hybrid modeling approach is utilized, in which a hedonic regression model was used in the first phase and an Artificial Neural Network (ANN) in the second. After analysis, the study established that an apartment's age, size, value, view, number of toilets, air quality, proximity to CBD, proximity to a university and probability of explosion are key factors determining apartment rental rates in Mogadishu, while floor level, parking space, and school proximity do not have a significant impact on apartment rental rates. Because of speculation, there is a higher likelihood of overvaluation than undervaluation in the housing market, which has profound policy and practical implications. Rather than being driven by real demand, Mogadishu's real estate market is driven by speculation, as overvaluation signals are more evident than undervaluation signals. As one of the world's poorest nations, where 70% of the population lives below the international poverty line, and about 80% of jobs are in the informal sector, extreme speculative activities adversely affect Somalia's economic quality and the living conditions of its citizens. Somali citizens need government policies that facilitate the accessibility, affordability, and adequate availability of decent housing. The study recommends protecting Somalia's real estate sector to attract more investors and boost the country's post-conflict development initiatives. A vital contribution of the study is that it is the first to examine the rental value of residential apartments in Mogadishu systematically. This study contributes significantly to housing economics and real estate development literature.
  • Impact of FinTech Growth on Bank Performance in GCC Region

    Litimi, Houda; Bensaida, Ahmed; Raheem, Mohamed Mahees; Department Collaboration; External Collaboration; NA; 0; 0; Finance; 0; et al. (SAGE Journals, 2023-12-29)
    This article investigates the effect of financial technology (FinTech) growth on bank performance in the Gulf Cooperation Council (GCC) region. The application is conducted on a panel dataset containing annual observations of banks from 2012 to 2021 using the generalized method of moments (GMM) method. FinTech growth is set as an explanatory variable on three proxies of bank performance, namely, return on assets (ROA), return on equity (ROE), and net interest margin (NIM). Moreover, several control variables are added to the model, including bank-specific and macroeconomic variables. The results are significant as all the proxies of bank performance are negatively affected by the growth of FinTech startups. Consequently, banks are urged to proactively invest in FinTech startups and engage in partnerships to avoid the risk of disruption.
  • Digital Twin for Neurology: An Introduction to a New Frontier in Healthcare

    Raheem, Mohamed Mahees; Chishti, Mohammad Ahsan; Ashraf, Taniya; External Collaboration; NA; 0; 0; Finance; 0; Ashraf, Taniya (IEEE, 2024-03-21)
    A digital twin (DT) is a virtual representation of a real thing that features two-way interactive links connecting the physical object and its digital twin. Significant improvements in healthcare, notably in the area of neurology, are being made because of DT technology. Clinicians can mimic therapies and track disease progression in real-time using DTs to build a virtual duplicate of the brain and neural systems. Digital twins can also aid scientists and medical professionals in comprehending the progression and onset of neurological illnesses, which can lead to the development of more efficient treatments for ailments. The development of digital twins can help healthcare professionals manage large amounts of patient data by standardizing the integration of data from various sources, implementing individualized clinical pathways, fostering physician-patient communication, and promoting shared decision-making. This paper explores the concept of Digital Twin technology and its key features, as well as the services it offers for healthcare. The focus then shifts to specific applications of DTs in neurology, including Mild Cognitive Impairment and Alzheimer’s Disease, multiple sclerosis, stroke, migraine, and epilepsy. The paper also discusses the challenges of implementing digital twins in healthcare, such as data privacy and security, data management, and ethical concerns as well as the potential future direction of DT in neurology to enhance diagnosis and treatment plans.
  • Deep Learning Meets Graph Theory: A Novel Approach to Generating Embeddings for Recommender Systems

    Raheem, Mohamed Mahees; Din, Aafaq Mohi Ud; Qureshi, Shaima; External Collaboration; NA; 0; 0; Finance; 0; Din, Aafaq Mohi Ud (IEEE, 2024-01-15)
    Graph-based models have emerged as a promising approach for capturing intricate relationships in various real-world applications [1], including recommendation systems. A novel approach called P-GNN (Partitioned-Graph Neural Network) has been proposed in this work, which utilizes the bipartite graph structure commonly used in recommendation systems. The approach generates separate embeddings for users and items, allowing highly discriminative recommendations. A significant advantage of this approach is its ability to address the oversmoothing and cold-start problem by computing separate subgraphs for users and items. This feature enables the generation of embeddings even for new users or items. The effectiveness of the proposed approach has been evaluated on multiple benchmark datasets and compared against state-of-the-art methods. The results demonstrate that Partitioned-GNN outperforms existing approaches, highlighting the potential of graph-based methods for recommendation systems.
  • Realizing the Pristine Islamic Economics and Social System Through Gold-Based Productive Waqf: Introducing the Ubaid Waqf Economy Model for Empowering Businesses and Individuals

    Raheem, Mohamed Mahees; Meera, Ahamed Kameel Mydin; Kemin, Abu Ubaidah; External Collaboration; Islamic Finance Studies Unit; 0; 0; Finance; 0; meera, Ahamed Kameel Mydin (AIJR Publisher, 2024-02-09)
    It is an ardent desire of many Muslims to witness the realization of a true Islamic economy that is benevolent to the community. The growth and advancement of Islamic banking and finance within the present riba-based monetary system, is indeed a step towards that vision. However, the success of the industry has not translated into uplifting the society’s socioeconomic standards. In addition, the current financial systems have rendered economies and environment unsustainable. The need arises to look beyond traditional financial institutions and economic models to seek a sustainable, equitable system that will benefit all sections of the community. Towards this end, this paper proposes an innovative Waqf Economy model that is being practiced in South East Asia, which in our reasoning is capable of rendering that pristine Islamic Economics and Social system that is aspired.
  • Improving the security of Internet of Things (IoT) using Intrusion Detection System (IDS)

    Raheem, Mohamed Mahees; Rahim, Rahila; Chishti, Mohammad Ahsan; External Collaboration; NA; 0; 0; Finance; 0; Rahim, Rahila (IEEE, 2024-03-21)
    The potential of both current and future IoT applications is substantial in improving user comfort, productivity, and automation. However, to continue implementing this technology on a larger scale and across various devices, it is necessary to have high levels of security, privacy, authentication, and resilience against attacks. With the development of many IoT technologies that rely on the internet, there is a constant threat of cyberattacks that can wreak havoc globally. Denial of service attacks can disrupt network security and result in illegal access, misuse, or alteration. Using IoT in real-world environments requires security and privacy considerations. Increasing cyberattacks and security breaches have made safeguarding IoT infrastructure and networks a top priority in today’s computing landscape. This paper examines the latest solution for IoT security using Intrusion detection systems (IDSs) to achieve secure end-to-end environments. This paper explores the profound impact of IDSs on IoT security such as vulnerability detection, Anomaly detection, real-time alerts, incidence response, etc. Furthermore, the paper focuses on various challenges regarding the applicability and implementation of IDS technology in real-world scenarios.
  • Physics-based and data-driven approaches for lifetime estimation under variable conditions: Application to organic light-emitting diodes

    Helal, Sara; BenSaïda, Ahmed; Abed, Fidaa; El-Amin, Mohamed F.; Abdul Majid, Mohammed; Kittaneh, Omar; Department Collaboration; College collaboration; University Collaboration; External Collaboration; et al. (Wiley, 2024-03-04)
    The prognosis of organic light-emitting diodes (OLEDs) not only requires early detection of a bearing defect, but also the capability to predict their life data under all operational scenarios. The use of sophisticated machine learning (ML) algorithms is undoubtedly becoming an increasingly exciting research direction, as these algorithms can yield high predictive models with minimal domain expertise. The central question of this perspective is: how well can ML models advance our ability to forecast the lifetime of OLEDs compared to the physics based models? In this paper, data-driven methods, feed-forward neural networks (FFNN), support vector machines (SVMs), k-nearest neighbors (KNNs), partial least squares regression (PLSR), and decision trees (DTs), are used to predict the lifetime and reliability of OLEDs through analyzing the lumen degradation data collected from the accelerated lifetime test. The final predicted results indicate that both the data-driven and our physics-based OLED lifetime models fit well the experimental data. The main drawback of the former method is that their efficacy is highly contingent on the quantity and quality of the operational dataset. Among all these methods, much more reliability information (time to failure) and the highest prediction accuracy can be achieved by FFNN.
  • Impact of FinTech growth on bank performance in GCC region

    BenSaïda, Ahmed; Litimi, Houda; Raheem, Mohamed Mahees; Department Collaboration; External Collaboration; NA; 0; 0; Finance; 0; et al. (Sage, 2024-01)
    This article investigates the effect of financial technology (FinTech) growth on bank performance in the Gulf Cooperation Council (GCC) region. The application is conducted on a panel dataset containing annual observations of banks from 2012 to 2021 using the generalized method of moments (GMM) method. FinTech growth is set as an explanatory variable on three proxies of bank performance, namely, return on assets (ROA), return on equity (ROE), and net interest margin (NIM). Moreover, several control variables are added to the model, including bank-specific and macroeconomic variables. The results are significant as all the proxies of bank performance are negatively affected by the growth of FinTech startups. Consequently, banks are urged to proactively invest in FinTech startups and engage in partnerships to avoid the risk of disruption.
  • Resilience amidst turmoil: a multi-resolution analysis of portfolio diversification in emerging markets during global financial and health crises

    Smolo, Edib; Nagayev, Ruslan; Jahangir, Rashed; Tarazi, Christo S. C.; External Collaboration; NA; 0; 0; Finance; 0; et al. (2023-10-11)
    Using Wavelet Coherence, Frequency Interconnectedness and Spillover methodologies, this study investigates the dynamic comovements and spillover effects between emerging markets (BRICS and Türkiye) with a specific emphasis on the effects of the GFC and COVID-19 pandemic. It aims to compare the impact of these events on portfolio diversification in the equity markets from the perspective of international equity investors. The results indicate that the stock markets are positively interlinked and depend on the time and frequency of returns. Significant correlations among the equity markets and a spike in overall spillover are also evident in the recent period due to the COVID-19 pandemic. These findings can be useful for policymakers and investors in their decision making.
  • The efficiency of Participation Banking Sector in Turkey: A DEA Approach

    Smolo, Edib; Iqbal, Mohamed Ashraf; Akdemir, Ayşe; Finance (Springer, 2024-02-15)
    There are six participation banks in Turkey, and they are expected to contribute significantly to the economy in the future. The aim of this study is to assess the efficiency of these participation banks based on financial data from 2010 to 2020. Data envelopment analysis (DEA), a non-parametric efficiency approach, is used in this study with three input variables (profit share expenses, personnel expenses, and total funds), and two output variables (profit share income and net profit). While Kuveyt Türk, Ziraat Katılım, and Türkiye Finans are found to be the most efficient, Albaraka Türk and Vakıf Katılım are inefficient – at least when the most recent observations (2018-2020) and the four-year moving average efficiency score for both outputs and inputs are used. As for the overall average scores, Türkiye Finans is the most efficient participation bank, followed by Ziraat Katılım and Vakıf Katılım. The least efficient participation banks are Kuveyt Türk and Albaraka Türk. For participation banks to gain a larger share of the market and to continue contributing to overall economic growth, policymakers must pay attention to these underperforming banks.
  • Asymmetric Impact of Microfinance on Economic Growth: Evidence from Bosnia and Herzegovina

    Smolo, Edib; No Collaboration; NA; 0; 0; Finance; 0; Smolo, Edib (2023-12)
    This study explores the correlation between microfinance loans (MFL) and economic growth in Bosnia and Herzegovina (Bosnia). It utilizes the non-linear Autoregressive Distributed Lag (NARDL) method to examine cointegration and short-run dynamics by analyzing quarterly data spanning from 2010 to 2022. The findings underscore the link between MFL shocks and long-term economic growth. The study unveils the unique effects of both positive and negative MFL shocks on growth, suggesting a non-linear relationship between microfinance loans and economic growth in Bosnia. However, the study concludes that the impact of MFL on Bosnia's GDP is adverse. Short-term fluctuations in MFL show no substantial influence on Bosnian economic growth. The coefficient of the error correction model is both negative and significant indicating the stability of the long-term relationship. This implies a rapid correction, with 46.4 % of the previous quarter's imbalance rectified within the current quarter. While our results are based on a single country, they align with recent criticisms of microfinance practices. Furthermore, our study offers a novel approach as it represents the first examination of the asymmetric relationship between MFL and GDP in Bosnia, providing valuable policy recommendations.
  • A Bayesian Approach to Analyse the Nexus Between the Environmental and Financial Factors to Affect Energy Efficiency in the GCC Region

    Shaheen, Rozina; No Collaboration; Islamic Finance Studies Unit; 0; 0; Finance; 0; Shaheen, Rozina (2023-02-04)
    Energy, being a significant input in the production process is considered an important element in the socio- economic development of a country. In this context, global economy is concentrating on attaining financial stability through increased energy efficiency and plummeting the energy intensity. Efficient use of energy enables the countries to improve their trade balance whereas it helps to reduce operating costs at micro level. Using energy-efficient techniques can reduce the environmental risks posed by the higher level of economic activity. Similarly, development of financial sector stimulates economic efficiency through the expansion in the financial activities (Sadorsky, 2010). Financial development helps to reduce cost of borrowing and financial risk, thereby creating transparency between lenders and borrowers. In addition, financial sector development facilitates the acquisition of sophisticated energy efficiency products and technology. However, with the rapid economic development, the environmental degradation has become a focus point for the nations across the globe. The feedback effects from environment to economic growth have stimulated the researchers to investigate the reasons for environmental dilapidation and determine the solutions for environmental conservation. Many of these research studies are encircled around the environmental Kuznets curve (EKC) hypothesis (Grossman &Krueger, 1991) which elaborates the linkages between the environmental degradation and economic growth. Middle Eastern economies are heavily reliant on the energy-based revenues and any curb on the energy production directly affects their economic growth in the region. Furthermore, financial system in the Middle Eastern region is technologically advanced with strong regulatory frameworks related to data protection, consumer protection, cybersecurity, and anti-money laundering. These regional characteristics provide a strong base to assess the existence of environmental Kuznets curve in the region and to examine the nexus between the financial sector development, economic growth, energy efficiency and Co2 emission in the region. The relationship between the environmental quality and economic growth is explored by various studies such as Jaeger et al. (1995), Tucker (1995), Barbier (1997), Horvath (1997), Ansuategi et al. (1998), List and Gallet (1999), Stern and Common (2001), Roca (2003), Dinda and Coondoo (2006), Coondoo and Dinda (2008) and Akbostanci et al. (2009). However, this research is unique as it is first to examine the environmental Kuznets curve hypothesis for the oil based Middle Eastern economies while using a conditionally homogenous autoregressive model for the panel of Middle Eastern economies. This model considers homogeneity across the cross-sectional units with identical structural characteristics. The panel conditionally homogenous vector autoregressive specification permits the heterogeneity in the dynamic panel data set and evaluates the relationship between the observed heterogeneity across the units and their structural characteristics
  • Gender, entrepreneurship and the digital divide: a global perspective

    Visvizi, Anna; Lytras, Miltiadis; Chuman, Merwat; Sarirete, Akila; Kozłowski, Krzysztof; University Collaboration; Urban Design Lab; 0; 0; Entrepreneurship; et al. (Emerald Publishing, 2023-10-11)
    The concepts and terms defining the thrust of this special issue, i.e. gender, entrepreneurship (Berger et al., 2021), the digital divide (Bowen and Morris, 2019; Millan et al., 2021; Satalkina et al., 2021), Global South (Simaan, 2020) and Global North, are very well established in the literature. Nevertheless, relatively little has been written about (1) the gendered dimension of the digital divide, (2) the digital divide and the gendered dimension of entrepreneurship (Elliott et al., 2021); and finally, (3) the specificity of these topics as they are in the Global South and Global North's peripheries (Ojediran and Anderson, 2020; Wood et al., 2021; Althalathini et al., 2020). Even if research on each of these individual domains exists, relatively little research on the intersection of these three areas exists (but cf. Visvizi et al., 2023, and earlier Kasusse, 2005; Alden, 2003). Notably, given the pace and the pervasive impact of digital transformation globally, and their diverse political, social and economic manifestations, it is necessary that the mechanisms underlying these interconnected issues and developments are identified and explored. This special issue sought to encourage this kind of conversation, always in context of the United Nation's (UN) Sustainable Development Goals (SDGs).
  • Financial sector development and testing the environmental Kuznets curve (EKC) hypothesis through a PCHVAR specification for the Middle Eastern region

    Shaheen, Rozina; No Collaboration; Islamic Finance Studies Unit; 0; 0; Finance; 0; Shaheen, Rozina (Springer Berlin Heidelberg, 2023-08)
  • Testing Monetary Policy Trilemma for Middle Eastern Economies Under a Bayesian Panel VAR Specification

    Shaheen, Rozina; Almaktoom, Abdulaziz; College collaboration; Islamic Finance Studies Unit; 0; 0; Finance; 0; Shaheen, Rozina (2023-08-27)
    Purpose: This research aims to investigate the presence of monetary policy trilemma in the Middle Eastern region and evaluate the spillover effects of US monetary policy on the region. Theoretical framework: Middle Eastern economies follow the dollar pegged exchange rate policy with open capital account and this poses a question about the autonomy of the monetary policy stance adopted by the regional central banks. In this context, current research considers variables such as domestic interbank interest rate, domestic liquidity, oil price and federal fund rate to test the monetary policy trilemma in the region Design/methodology/approach: To investigate the presence of monetary policy trilemma in the Middle Eastern region, this research employs the time - varying Bayesian panel vector autoregression approach and selects a panel of five Middle Eastern countries which include Saudi Arabia, United Arab Emirates, Qatar, Oman and Kuwait while considering the monthly data for the sample period 2009m10 until 2021m12. Findings: This research finds that a positive shock in US federal fund rate increases the domestic interest rates in the Middle Eastern economies. In addition, this research finds a negative relationship between oil price shocks and domestic interest rates. Whereas a positive shock in US federal fund rates induces a reduction in the oil price. Research, Practical & Social implications: Current research provides insights for policy makers to determine the autonomy of domestic monetary policy stance to achieve its broader macroeconomic objectives. Originality/value: This research is unique as it examines the monetary policy trilemma while considering oil price as a control variable in the system under a time varying Bayesian panel vector autoregression specification.
  • Making Housing Affordable Through Solar Energy: A Proposal for Malaysia

    Raheem, Mohamed Mahees; External Collaboration; Islamic Finance Studies Unit; 0; 0; Finance; 0; Meera, Ahmed Kameel Mydin (Al Qasimia University Journal of Islamic Economics, 2022-06-22)
    Globally, housing affordability is becoming an increasing concern, particularly to the low and middle-income group. Housing priceshavebeen steadily growing over theyears, therebymake it even more burdensome,particularly for fresh graduates and low-income earners (B40),to own a home. The constant price increaseis mainly due to inflation in the property sector brought about by the very nature of the modern monetary system, particularly the fractional reserve banking andcompound interest charges, leading to housing and property bubbles. Accordingly, governments have generally failed in successfully solving the housing problem. In this regard, this paper provides an affordable housing solution through solar energy harvesting combined with Islamic financing provisions for the case of Malaysia. In our proposed model, housing can be provided in a very affordable manner regardless of the credit-worthiness of buyers with the possibilityof free-energy at the end of the tenure.
  • Safe haven property of gold and cryptocurrencies during COVID-19 and Russia-Ukraine conflict

    BenSaïda, Ahmed; No Collaboration; NA; 0; 0; Finance; 0; BenSaïda, Ahmed (2023-08-12)
    During recent years the world has witnessed several unprecedented crises that affected the international financial markets. Indeed, the COVID-19 pandemic and the Russia–Ukraine conflict caused major perturbations that slowed down the economic and financial development around the globe. International investors are switching their attention to more reliable assets as a refuge to their portfolios. This paper investigates the hedge and safe haven properties of gold and major cryptocurrencies, mainly Bitcoin and Ethereum. Empirical analysis is conducted on main fiat currencies using the multivariate asymmetric dynamical conditional correlation model. Results show that gold has a superior hedging effectiveness compared to cryptocurrencies. Moreover, the precious metal and the digital currencies are safe havens for almost all fiat currencies.
  • Extending the concept of financial literacy: A step toward a sustainable society

    Smolo, Edib; Knezović, Emil; Aydin, Šejma; Finance (Association "Research and Development Academy", 2023-05-22)
    This study analyzes financial literacy in Bosnia and Herzegovina by considering three areas: interest, inflation, and diversification, with financial literacy as a multi-dimensional construct consisting of financial knowledge and financial skills. Using a cross-sectional questionnaire-based survey, 638 valid responses were collected from working-age individuals (18-65 years old). Financial knowledge and skills were analyzed through a prism of several demographic factors, including age, education, household income, and gender. Welch's F tests, ANOVA with Brown-Forsythe, LSD post hoc tests, and Welch's t-tests were performed to test the hypotheses. The findings provide evidence of moderate financial literacy. Similarly to previous studies, financial knowledge and skills partially depend on the respondent's age, education, household income, and gender. The study contributes to the current literature by taking a much-needed non-functional approach to examining financial literacy, focusing not only on financial knowledge but also on often neglected financial skills and providing insight into the unique context of Bosnia and Herzegovina.
  • Dependence and risk management of portfolios of metals and agricultural commodity futures

    BenSaïda, Ahmed; Hanif, Waqas; Mensi, Walid; Vo, Xuan Vinh; Hernandez, Jose Arreola; Kang, Sang Hoon; External Collaboration; NA; 0; 0; et al. (Elsevier, 2023-05-12)
    This paper examines the dependence structure and the portfolio allocation characteristics of a main industrial portfolio metals (gold, platinum, palladium, aluminum, silver, copper, zinc, lead, and nickel), and of an agricultural commodities portfolio (wheat, corn, soybeans, coffee, sugar cane, sugar beets, cocoa, cotton, and lumber). Our methodology is based on regular vine copulas and the conditional Value-at-Risk. The motivation to investigate the dependence structure and connectedness between agricultural, and metal commodities is to identify ways in which agricultural and metal commodities can hedge each other and to explore the possibilities of parallel investments. The results indicate that the dependence dynamics of the main metals portfolio are characterized by symmetric features. However, the dependence dynamics of the agricultural commodities portfolio are characterized by symmetric and asymmetric features; symmetric dynamics are predominant. Finally, the metal commodities portfolio is observed to be less risky for financial resource allocation during the global financial crisis.

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