Search Results (565)

Quantum finance, as a key application scenario of quantum computing, showcases multiple significant advantages of quantum machine learning over traditional machine learning methods. This paper first aims to overcome the limitations of the hidden quantum Markov model (HQMM) in handling continuous data and proposes an innovative method to convert continuous data into discrete-time sequence data. Second, a hybrid quantum computing model is developed to forecast stock market trends. The model was used to predict 15 stock indices from the Shanghai and Shenzhen Stock Exchanges between June 2018 and June 2021. Experimental results demonstrate that the proposed quantum model outperforms classical algorithmic models in handling higher complexity, achieving improved efficiency, reduced computation time, and superior predictive performance. This validation of quantum advantage in financial forecasting enables the practical deployment of quantum-inspired prediction models by investors and institutions in trading environments. This quantum-enhanced model empowers investors to predict market regimes (bullish/bearish/range-bound) using real-time data, enabling dynamic portfolio adjustments, optimized risk controls, and data-driven allocation shifts. Full article

The introduction of mobile banking is essential in today’s financial sector, where technological innovation plays a critical role. To remain competitive in the current market, businesses must analyze client attitudes and perspectives, as these influence long-term demand and overall profitability. While previous studies have explored general adoption behaviors, limited research has examined how individual factors such as social influence, lifestyle compatibility, financial technology self-efficacy, and perceived usage cost affect mobile banking adoption among specific generational cohorts. This study addresses that gap by offering insights into these variables, contributing to the growing literature on mobile banking adoption, and presenting actionable recommendations for financial institutions targeting younger market segments. Using a structured questionnaire survey, data were collected from both users and non-users of mobile banking among the Gen Z population in the United States. The regression model significantly predicts mobile banking adoption, with an intercept of $-0.548$ ($p < 0.001$). Among the independent variables, perceived cost of usage has the strongest positive effect on adoption ($B=0.857$, $\beta =0.722$, $p < 0.001$), suggesting that adoption increases when mobile banking is perceived as more affordable. Social influence also has a significant positive impact ($B=0.642$, $\beta =0.643$, $p < 0.001$), indicating that peer influence is a central driver of adoption decisions. However, self-efficacy shows a significant negative relationship ($B=-0.343$, $\beta =-0.339$, $p < 0.001$), and lifestyle compatibility was found to be statistically insignificant ($p=0.615$). These findings suggest that reducing perceived costs, through lower fees, data bundling, or clearer communication about affordability, can directly enhance adoption among Gen Z consumers. Furthermore, leveraging peer influence via referral rewards, Partnerships with influencers, and in-app social features can increase user adoption. Since digital self-efficacy presents a barrier for some, banks should prioritize simplifying user interfaces and offering guided assistance, such as tutorials or chat-based support. Future research may employ longitudinal designs or analyze real-life transaction data for a more objective understanding of behavior. Additional variables like trust, perceived risk, and regulatory policies, not included in this study, should be integrated into future models to offer a more comprehensive analysis. Full article

The accurate estimation of urban property values is a key challenge for appraisers, market participants, financial institutions, and urban planners. In recent years, machine learning (ML) techniques have emerged as promising tools for price forecasting due to their ability to model complex relationships among variables. However, their application raises two main critical issues: (i) the risk of overfitting, especially with small datasets or with noisy data; (ii) the interpretive issues associated with the “black box” nature of many models. Within this framework, this paper proposes a methodological approach that addresses both these issues, comparing the predictive performance of three ML algorithms—k-Nearest Neighbors (kNN), Random Forest (RF), and the Artificial Neural Network (ANN)—applied to the housing market in the city of Salerno, Italy. For each model, overfitting is preliminarily assessed to ensure predictive robustness. Subsequently, the results are interpreted using explainability techniques, such as SHapley Additive exPlanations (SHAPs) and Permutation Feature Importance (PFI). This analysis reveals that the Random Forest offers the best balance between predictive accuracy and transparency, with features such as area and proximity to the train station identified as the main drivers of property prices. kNN and the ANN are viable alternatives that are particularly robust in terms of generalization. The results demonstrate how the defined methodological framework successfully balances predictive effectiveness and interpretability, supporting the informed and transparent use of ML in real estate valuation. Full article

Forestry carbon sinks play a crucial role in mitigating climate change and protecting ecosystems, significantly contributing to the development of carbon trading systems. Remote sensing technology has become increasingly important for monitoring carbon sinks, as it allows for precise measurement of carbon storage and ecological changes, which are vital for forecasting carbon prices. Carbon prices fluctuate due to the interaction of various factors, exhibiting non-stationary characteristics and inherent uncertainties, making accurate predictions particularly challenging. To address these complexities, this study proposes a method for predicting carbon trading prices influenced by multiple factors. We introduce a Decomposition (DECOMP) module that separates carbon price data and its influencing factors into trend and cyclical components. To manage non-stationarity, we propose the KAN with Multi-Domain Diffusion (KAN-MD) module, which efficiently extracts relevant features. Furthermore, a Wave-MH attention module, based on wavelet transformation, is introduced to minimize interference from uncertainties, thereby enhancing the robustness of the model. Empirical research using data from the Hubei carbon trading market demonstrates that our model achieves superior predictive accuracy and resilience to fluctuations compared to other benchmark methods, with an MSE of 0.204% and an MAE of 0.0277. These results provide reliable support for pricing carbon financial derivatives and managing associated risks. Full article

In this paper, we aim to provide a new algorithm for managing financial risk in portfolios containing multiple high-volatility assets. We assess the variability of volatility with the Volterra model, and we construct an estimator of the Value-at-Risk (VaR) function using quantile regression. Because of its long-memory property, the Volterra model is particularly useful in this domain of financial time series data analysis. It constitutes a good alternative to the standard approach of Black–Scholes models. From the weighted asymmetric loss function, we construct a new estimator of the VaR function usable in Multi-Functional Volterra Time Series Model (MFVTSM). The constructed estimator highlights the multi-functional nature of the Volterra–Gaussian process. Mathematically, we derive the asymptotic consistency of the estimator through the precision of the leading term of its convergence rate. Through an empirical experiment, we examine the applicability of the proposed algorithm. We further demonstrate the effectiveness of the estimator through an application to real financial data. Full article

Background/Objectives: Given the heterogeneous nature of long COVID, its treatment and management remain challenging. This study aimed to investigate whether poor pre-pandemic sleep quality, its deterioration during the peak of the pandemic, and circadian preference increase the risk of long COVID symptoms. Methods: An online survey was conducted between 9 October and 12 December 2022, with 384 participants who had recovered from COVID-19 at least three months prior to data collection. Participants were categorized based on the presence of at least one long COVID symptom. Logistic regression models assessed associations between sleep-related variables and long COVID symptoms. Results: Participants with long COVID symptoms reported significantly poorer sleep quality, higher perceived stress, greater somatic and cognitive pre-sleep arousal, and elevated levels of post-traumatic stress symptoms, anxiety, depression, and aggression. Fatigue (39.8%) and memory problems (37.0%) were the most common long COVID symptoms. Sleep deterioration during the pandemic peak was reported by 34.6% of respondents. Pre-pandemic poor sleep quality, its deterioration during the pandemic, and poor sleep at the time of the survey were all significantly associated with long COVID. An extreme morning chronotype consistently predicted long COVID symptoms across all models, while an extreme evening chronotype was predictive only when accounting for sleep quality changes during the pandemic. COVID-19 frequency, severity, financial impact, and somatic pre-sleep arousal were significant predictors in all models. Conclusions: Poor sleep quality before the pandemic and its worsening during the pandemic peak are associated with a higher likelihood of long COVID symptoms. These findings underscore the need to monitor sleep health during pandemics and similar global events to help identify at-risk individuals and mitigate long-term health consequences, with important clinical and societal implications. Full article

The development of stock markets is pivotal for economic growth, particularly through the mobilization of idle resources into productive investments. Despite recent reforms to enhance Uzbekistan’s capital market, public engagement remains limited. This study examines the behavioral determinants of stock market investment intention and participation among university students, employing the Theory of Planned Behavior (TPB) and Partial Least Squares Structural Equation Modeling (PLS-SEM). The model investigates the influence of digital literacy, financial literacy, social interaction, herding behavior, overconfidence bias, risk tolerance, and financial well-being on investment intention and behavior. A survey of 369 university students was conducted to assess the proposed relationships. The results reveal that risk tolerance, overconfidence bias, and herding behavior significantly and positively affect investment intention, while digital literacy demonstrates a notable negative effect, suggesting caution in assuming technology readiness automatically translates to investment readiness. Investment intention, in turn, strongly predicts actual participation and mediates several of these effects. Conversely, financial literacy, financial well-being, and social interaction showed no significant direct or mediating influence. Additionally, differences according to gender and academic background were observed in how intention translates into behavior. The findings underscore the need for integrated financial and behavioral education to enhance market participation and contribute to policy discourse on youth financial engagement in emerging economies. Full article

As investment portfolios become increasingly diversified and financial asset risks grow more complex, accurately forecasting the risk of multiple asset classes through mathematical modeling and identifying their heterogeneity has emerged as a critical topic in financial research. This study examines the volatility and tail risk of gold, crude oil, Bitcoin, and selected stock markets. Methodologically, we propose two improved Value at Risk (VaR) forecasting models that combine the autoregressive (AR) model, Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, Extreme Value Theory (EVT), skewed heavy-tailed distributions, and a rolling window estimation approach. The model’s performance is evaluated using the Kupiec test and the Christoffersen test, both of which indicate that traditional VaR models have become inadequate under current complex risk conditions. The proposed models demonstrate superior accuracy in predicting VaR and are applicable to a wide range of financial assets. Empirical results reveal that Bitcoin and the Chinese stock market exhibit no leverage effect, indicating distinct risk profiles. Among the assets analyzed, Bitcoin and crude oil are associated with the highest levels of risk, gold with the lowest, and stock markets occupy an intermediate position. The findings offer practical implications for asset allocation and policy design. Full article

Background/Objective: Frontline healthcare staff who contend diseases and mitigate their transmission were repeatedly exposed to high-risk conditions during the COVID-19 pandemic. They were at risk of mental health issues, in particular, psychological stress, depression, anxiety, financial stress, and/or burnout. This study aimed to investigate and evaluate the occupational stress of medical doctors, nurses, pharmacists, physiotherapists, and other hospital support crew during the COVID-19 pandemic in Saudi Arabia. Methods: We collected both qualitative and quantitative data from a survey given to public and private hospitals using methods like correspondence analysis, cluster analysis, and structural equation models to investigate the work-related stress (WRS) and anxiety of the staff. Since health-related factors are unclear and uncertain, a fuzzy association rule mining (FARM) method was created to address these problems and find out the levels of work-related stress (WRS) and anxiety. The statistical results and K-means clustering method were used to find the best number of fuzzy rules and the level of fuzziness in clusters to create the FARM approach and to predict the work-related stress and anxiety of healthcare staff. This innovative approach allows for a more nuanced appraisal of the factors contributing to work-related stress and anxiety, ultimately enabling healthcare organizations to implement targeted interventions. By leveraging these insights, management can foster a healthier work environment that supports staff well-being and enhances overall productivity. This study also aimed to identify the relevant health factors that are the root causes of work-related stress and anxiety to facilitate better preparation and motivation of the staff for reorganizing resources and equipment. Results: The results and findings show that when the financial burden (FIN) of healthcare staff increased, WRS and anxiety increased. Similarly, a rise in psychological stress caused an increase in WRS and anxiety. The psychological impact (PCG) ratio and financial impact (FIN) were the most influential factors for the staff’s anxiety. The FARM results and findings revealed that improving the financial situation of healthcare staff alone was not sufficient during the COVID-19 pandemic. Conclusions: This study found that while the impact of PCG was significant, its combined effect with FIN was more influential on staff’s work-related stress and anxiety. This difference was due to the mutual effects of PCG and FIN on the staff’s motivation. The findings will help healthcare managers make decisions to reduce or eliminate the WRS and anxiety experienced by healthcare staff in the future. Full article

This paper explores the effectiveness of an innovative FinTech risk-scoring model to predict the risk-appropriate return for short-term credit sales. The risk score serves to mitigate the information asymmetry between the seller of receivables (“Seller”) and the purchaser (“Funder”), at the same time providing an opportunity for the Funder to earn returns as well as to diversify its portfolio on a risk-appropriate basis. Selling receivables/credit to potential Funders at a risk-appropriate discount also helps Sellers to maintain their short-term financial liquidity and provide the necessary cash flow for operations and other immediate financial needs. We use 18,304 short-term credit-sale transactions between 23 April 2020 and 30 September 2022 from the private FinTech startup Crowdz and its Sustainability, Underwriting, Risk & Financial (SURF) risk-scoring system to analyze the risk/return relationship. The data includes risk scores for both Sellers of receivables (e.g., invoices) along with the Obligors (firms purchasing goods and services from the Seller) on those receivables and provides, as outputs, the mutual gains by the Sellers and the financial institutions or other investors funding the receivables (i.e., the Funders). Our analysis shows that the SURF Score is instrumental in mitigating the information asymmetry between the Sellers and the Funders and provides risk-appropriate periodic returns to the Funders across industries. A comparative analysis shows that the use of SURF technology generates higher risk-appropriate annualized internal rates of return (IRR) as compared to nonuse of the SURF Score risk-scoring system in these transactions. While Sellers and Funders enter into a win-win relationship (in the absence of a default), Sellers of credit instruments are not often scored based on the potential diversification by industry classification. Crowdz’s SURF technology does so and provides Funders with diversification opportunities through numerous invoices of differing amounts and SURF Scores in a wide range of industries. The analysis also shows that Sellers generally have lower financing stability as compared to the Obligors (payers on receivables), a fact captured in the SURF Scores. Full article

Accurate crop yield prediction is essential for stabilizing food supply chains and reducing the uncertainties in financial risks related to agricultural production. Yet, it is even more essential to understand how crop yield models make predictions depending on their relationship to Earth Observation (EO) indicators. This study presents a state-of-the-art explainable artificial intelligence (XAI) method to estimate corn yield prediction over the Corn Belt in the continental United States (CONUS). We utilize the recently introduced Kolmogorov–Arnold Network (KAN) architecture, which offers an interpretable alternative to the traditional Multi-Layer Perceptron (MLP) approach by utilizing learnable spline-based activation functions instead of fixed ones. By including a KAN in our crop yield prediction framework, we are able to achieve high prediction accuracy and identify the temporal drivers behind crop yield variability. We create a multi-source dataset that includes biophysical parameters along the crop phenology, as well as meteorological, topographic, and soil parameters to perform end-of-season and in-season predictions of county-level corn yields between 2016–2023. The performance of the KAN model is compared with the commonly used traditional machine learning (ML) models and its architecture-wise equivalent MLP. The KAN-based crop yield model outperforms the other models, achieving an R² of 0.85, an RMSE of 0.84 t/ha, and an MAE of 0.62 t/ha (compared to MLP: R² = 0.81, RMSE = 0.95 t/ha, and MAE = 0.71 t/ha). In addition to end-of-season predictions, the KAN model also proves effective for in-season yield forecasting. Notably, even three months prior to harvest, the KAN model demonstrates strong performance in in-season yield forecasting, achieving an R² of 0.82, an MAE of 0.74 t/ha, and an RMSE of 0.98 t/ha. These results indicate that the model maintains a high level of explanatory power relative to its final performance. Overall, these findings highlight the potential of the KAN model as a reliable tool for early yield estimation, offering valuable insights for agricultural planning and decision-making. Full article

Modern financial practices introduce complex risks, which in turn force financial institutions to rely increasingly on computational risk analytics (CRA). The purpose of our research is to attempt to systematically explore the evolution and intellectual structure of CRA in banking using a detailed bibliometric analysis of the literature sourced from Web of Science from 2000 to 2025. A comprehensive search in the Web of Science (WoS) Core Collection yielded 1083 peer-reviewed publications, which we analyzed using analytical tools like VOSviewer 1.6.20 and Bibliometrix (Biblioshiny 5.0) so as to examine the dataset and uncover bibliometric characteristics like citation patterns, keyword occurrences, and thematic clustering. Our initial analysis results uncover the presence of key research clusters focusing on bankruptcy prediction, AI integration in financial services, and advanced deep learning applications. Furthermore, our findings note a transition of CRA from an emerging to an expanding domain, especially after 2019, with terms like machine learning (ML), artificial intelligence (AI), and deep learning (DL) being identified as prominent keywords and a recent shift towards blockchain, explainability, and financial stability being present. We believe that this study tries to address the need for an updated mapping of CRA, providing valuable insights for future academic inquiry and practical financial risk management applications. Full article

The agriculture sector is increasingly challenged to maintain productivity and sustainability amidst environmental, marketplace, and geopolitical pressures. While precision agriculture enhances physical production, the financial resilience of agricultural firms has been understudied. In this study, machine learning (ML) methods, including logistic regression (LR), decision trees (DTs), and artificial neural networks (ANNs), are employed to predict the bankruptcy risk for Central and Eastern European (CEE) farming firms. All models consistently showed high performance, with AUC values exceeding 0.95. DTs had the highest overall accuracy (95.72%) and F1 score (0.9768), LR had the highest recall (0.9923), and ANNs had the highest discrimination power (AUC = 0.960). Visegrad, Balkan, Baltic, and Eastern Europe subregional models featured economic and structural heterogeneity, reflecting the need for local financial risk surveillance. The results support the development of AI-based early warning systems for agricultural finance, enabling smarter decision-making, regional adaptation, and enhanced sustainability in the sector. Full article

Loan default prediction is a critical task for financial institutions, directly influencing risk management, loan approval decisions, and profitability. This study evaluates the effectiveness of machine learning models, specifically XGBoost, Gradient Boosting, Random Forest, and LightGBM, in predicting loan defaults. The research investigates the following question: How effective are machine learning models in predicting loan defaults compared to traditional approaches? A structured machine learning pipeline is developed, including data preprocessing, feature engineering, class imbalance handling (SMOTE and class weighting), model training, hyperparameter tuning, and evaluation. Models are assessed using accuracy, F1-score, ROC AUC, precision–recall curves, and confusion matrices. The results show that Gradient Boosting achieves the highest overall classification performance (accuracy = 0.8887, F1-score = 0.8084, recall = 0.8021), making it the most effective model for identifying defaulters. XGBoost exhibits superior discriminatory power with the highest ROC AUC (0.9714). A cost-sensitive threshold-tuning procedure is embedded to align predictions with regulatory loss weights to support audit requirements. Full article

This study conducts a comprehensive bibliometric analysis and predictive modeling of financial risk research from 2015 to 2024, integrating conceptual, knowledge, and collaboration perspectives. Utilizing the PRISMA framework for literature screening, the study identifies publications, research areas, and research institutions. A co-citation network approach reveals the intellectual structure and milestone works, while emergent keyword detection highlights cutting-edge topics such as economic policy uncertainty, climate risk, and green innovation. Furthermore, the study proposes a novel semantic forecasting model, SEF-ACLSTM (Semantic Evolution Forecasting with Aligned Clustered LSTM), to predict the evolution of research themes through 2030. The results identify three major thematic clusters: methodological innovation, traditional risk management, and green finance. The predictive analysis indicates a growing emphasis on methodological and sustainability-oriented topics, suggesting a paradigmatic shift in financial risk research. The findings offer theoretical insights and strategic guidance for future academic inquiry and policy formulation. Full article