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Robo-Advisors in Fintech-Challenges and Solutions
Get PDFThe banking sector has experienced substantial transformations as a result of the proliferation of financial technology, commonly known as FinTech. The introduction of these breakthroughs has caused a transition from conventional banking to digital services, bringing forth a range of developments like AI platforms, Blockchain technology, virtual currencies, Robo-advisors, and chatbots. Despite the speedy and easy services offered by FinTech, there are still difficulties regarding trust, security, and data privacy, particularly in nations such as Pakistan. This study explores the integration of Robo-advisors within Pakistan's FinTech sector, focusing on the challenges of trust, security, and data privacy. Chatbots, playing a crucial role in the banking and telecom industries, encounter challenges such as customer skepticism and the potential for cybersecurity threats. Using Grounded Theory and Social Representation Theory (SRT), the study comprehensively examines how chatbots and Robo-advisors in the Pakistani FinTech industry address these challenges. The findings indicate that establishing trust, guaranteeing data security, and enhancing user experience are of utmost importance. Transparent communication, robust security measures, and user-centric design have been identified as critical for building trust and driving adoption. Collaboration with financial professionals, continuous innovation, and user education emerge as crucial answers. The study highlights the significance of transparency and adherence to regulations, while proposing future research avenues to investigate the psychological aspects influencing the adoption of FinTech and the effects of developing technologies. These insights contribute to the growing discourse on FinTech innovation and its potential to enhance financial inclusion in developing economies
Supply–demand price decoupling in European-type day-ahead electricity markets
No full textIn this paper, we consider the possibility of supply–demand price decoupling in European-type day-ahead electricity markets, considering also the possibility of the supply price exceeding the demand price for some periods. Using a simple market model and an illustrative example, we show that this approach can resolve the paradoxical rejection of block orders and thus potentially increase the total social welfare and surplus of bidders. However, it has additional implications, which must be considered in a potential application. The first is the non-uniqueness of the decoupled market-clearing prices, while the second is that price decoupling affects the relation between the sum of individual bid surpluses and the total social welfare, as these values may no longer be equal, and the approach may imply a nonzero income for the auctioneer. To tackle the issue of non-uniqueness of market-clearing prices, we propose an iterative three-step clearing method. In the second part of the paper, we consider realistic-sized examples, analyze how the proposed approach affects the market outcome. We show that the proposed method reduces the number of paradoxically rejected block bids by 34%–42% and slightly increases the total welfare. In addition, we define a measure (opportunity cost of paradox rejection) to characterize the level of paradox rejection in a clearing solution. We show that the proposed price decoupling-based clearing method may significantly (34%–44%) decrease the value of this measure compared to the conventional clearing approach. We also study the computational demand of the proposed method. © 2025 The Author
Optimizing short food supply chain logistics to lower carbon emissions and enhance operational efficiency for small-scale rural producers
No full textFood hubs serve as platforms that aggregate products from small-scale food producers and facilitate their delivery to final consumers, which can enhance their profit margins and foster local economic development. However, the logistics involved in operating food hubs can be particularly costly. The research aims to show the possibilities of improving the environmental and operational efficiency of food hubs by developing a new mathematical model. A Mixed- Integer Linear Programming (MILP) model addresses the ‘producer-to-hub-to-customer’ transport problem, drawing on comprehensive real-world data. Computational experiments demonstrate that enhancing cooperation among producers when delivering goods to the hub can lead to a reduction in logistics costs and carbon emissions. To bolster environmental outcomes, the study presents empirical evidence indicating that transitioning from conventional to electric vehicles can reduce transport costs by nearly one-third and diminish carbon emissions by as much as 70%
Heat, health, and habitats : analyzing the intersecting risks of climate and demographic shifts in Austrian districts
Get PDFThe impact of hot weather on health outcomes of a population is mediated by a variety of factors, including its age profile and local green infrastructure. The combination of warming due to climate change and demographic aging suggests that heat-related health outcomes will deteriorate in the coming decades. Here, we measure the relationship between weekly all-cause mortality and heat days in Austrian districts using a panel data set covering 2015-2022 . An additional day reaching 30 \,^\circ \textrm{C} is associated with a 2.4\% increase in mortality per 1, 000 inhabitants during summer. This association is increased by approximately 50\% in districts with a two standard deviation above average share of the population over 65. Using Representative Concentration Pathways (RCP) projections of heat days and demographics in 2050, we observe that districts will have elderly populations and heat days 2-5 standard deviations above the current mean in just 25 years. This predicts a drastic increase in heat-related mortality. At the same time, district green scores, measured using 10\times 10 meter resolution satellite images of residential areas, significantly moderate the relationship between heat and mortality. Thus, although local policies likely cannot reverse warming or demographic trends, they can take measures to mediate the health consequences of these growing risks, which are highly heterogeneous across regions, even in Austria
Rethinking Economics: Embracing Sustainability and Human Well-Being – Review of the Book The New Economics: A Bigger Picture by David Boyle and Andrew Simm
No full textTaking an insightful and thought-provoking look at alternative economic systems, the book "The New Economics: A Bigger Picture", published by David Boyle and Andrew Simms, is highly recommended. The writers question the traditional way of thinking about economics. Instead, they would champion a system that puts a lot more emphasis on the well-being of humans and the sustainability of the environment than it does merely on profit. I find this book very exceptional and enjoyable to read because it helps me understand the problems with the standard or traditional economics and the chance for a more fair and long-lasting future
A Magyarországon elérhető ESG minősített befektetési alapok versenyképességének vizsgálata
No full textA tanulmány célja annak vizsgálata, hogy milyen tényezők befolyásolják a Magyarországon működő részvényalapok éves hozamait és teljes költségmutatóját (TER), különös tekintettel az ESG-minősítéssel rendelkező alapokra. A vizsgálat a 2021 és 2023 közötti időszak adatain alapul és a nyilvános részvénybefektetési alapokat célozza meg. Az elemzéshez OLS és kvantilis regressziós módszereket alkalmaztunk, figyelembe véve a szélsőséges inflációs környezet és a COVID-19, valamint az orosz-ukrán háború okozta gazdasági változásokat. Az eredmények azt mutatják, hogy az ESG-minősítés szignifikánsan negatív hatással van az éves hozamokra az OLS modell szerint, míg a kvantilis regresszió alapján ez a negatív hatás különösen a hozamok felső kvantiliseiben jelentkezik. A teljes költségmutatóra gyakorolt hatás ezzel szemben nem szignifikáns, bár a magasabb kvantilisekben a feltörekvő piacokra fókuszáló alapok szignifikánsan magasabb költségszinteket mutatnak. Emellett megállapítható, hogy a nagyobb nettó eszközértékű alapok alacsonyabb költséggel működnek, ami a méretgazdaságosság érvényesülésére utal. Az alapok életkora pozitív kapcsolatot mutat a teljes költség mutatóval, jelezve, hogy a régebbi alapok jellemzően magasabb működési költségekkel rendelkeznek. Eredményeink hozzájárulnak az ESG-alapok teljesítményének és költségszerkezetének jobb megértéséhez, és rámutatnak arra, hogy a fenntarthatósági szempontokat a befektetési döntések során a hozam és költség szempontjából is differenciáltan kell értékelni
Nem elég büntetni, megelőzni kell – az integritás kultúrája
No full textBáger Gusztáv: Korrupció: büntetés, integritás, kompetencia.Budapest, Akadémiai Kiadó, 201
Selected African Studies in Memory of Zsuzsánna Biedermann, Edited by Judit Kiss and István Tarrósy, 2024, Cambridge Scholars Publishing, Newcastle upon Tyne, ISBN (10): 1-0364-0445-5.
No full textStudying African issues in Central and Eastern Europe (CEE) is important for several compelling reasons. Many countries in the region have historical ties with Africa, which shape current political and social dynamics. Understanding migration patterns from Africa helps address integration and multicultural challenges in these societies. Furthermore, Africa’s growing economies present new trade and investment opportunities for CEE countries. Consequently, engaging with African issues promotes a broader understanding of global interconnectedness and challenges. Furthermore, African studies highlight human rights and social justice movements, fostering empathy and advocacy for marginalized groups, relevant for CEE countries as well. It also enriches cultural understanding, leading to greater intercultural dialogue and inclusivity. Incorporating African studies in education encourages critical thinking and prepares students for a globalized world. Understanding Africa’s role in global geopolitics is crucial for informed foreign policy
Machine Learning-Based Analysis of Technology Acceptance in FinTech : A Behavioral Study Using Digital Wallet Data
No full textThe rapid growth of FinTech services, particularly robo-advisors, has transformed how individuals engage with digital financial platforms. Understanding the behavioral drivers of technology acceptance in this context is critical for enhancing adoption and designing more effective user experiences. This study investigates whether user-level behavioral and transactional data can be leveraged to predict technology acceptance, operationalized through daily app usage. Grounded in the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT), the study uses behavioral proxies such as customer satisfaction, loyalty points, and lifetime value to reflect constructs like perceived usefulness, performance expectancy, and facilitating conditions. Using a real-world dataset of 7000 FinTech users sourced from Kaggle, we applied four machine learning algorithms, Logistic Regression, Support Vector Machine, Random Forest, and XGBoost, to classify users into high and low acceptance categories. Results revealed that ensemble models, particularly XGBoost, outperformed linear classifiers, achieving moderate improvements in precision and recall for the high-acceptance class. However, overall predictive performance remained constrained by class imbalance and overlapping behavioral patterns. These findings suggest that while machine learning can reveal patterns linked to technology acceptance, predictive precision remains limited without richer temporal and psychographic features. The study contributes to the evolving discourse on FinTech adoption by offering a data-driven lens to complement intention-based models and inform adaptive engagement strategies
A Trend Factor for the Cross Section of Cryptocurrency Returns
No full textWe propose CTREND, a new trend factor for cryptocurrency returns, which aggregates price and volume information across different time horizons. Using data on more than 3,000 coins, we employ machine learning methods to exploit information from various technical indicators. The resulting signal reliably predicts cryptocurrency returns. The effect cannot be subsumed by known factors and remains robust across different subperiods, market states, and alternative research designs. Moreover, it survives the impact of transaction costs and persists in big and liquid coins. Finally, an asset pricing model that incorporates CTREND outperforms competing factor models, providing a superior explanation of cryptocurrency returns