Frontiers in Business and Finance is a scholarly platform dedicated to advancing research in the fields of business, finance, and related areas. It provides a forum for the publication of cutting-edge studies, empirical research, and theoretical papers that address emerging trends, challenges, and opportunities in these domains. The journal covers a wide range of topics, including but not limited to financial management, investment strategies, corporate governance, business innovation, risk management, and sustainable finance.
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Algorithm of Trust: The Causal Impact of AI Personalization on Consumer Adoption in Mobile Messaging Payments
The convergence of artificial intelligence and mobile messaging platforms has created a new paradigm for digital financial services, yet the mechanisms through which AI-driven personalization influences consumer adoption of payment features remain poorly understood. This paper investigates the causal impact of personalized AI recommendations on user adoption of mobile messaging payment services, with a specific focus on the mediating role of consumer trust. We leverage a natural experiment from a major messaging platform's phased rollout of an AI payment assistant to implement a difference-in-differences research design. Using a panel dataset of fifteen thousand users observed over eighteen months, we estimate the treatment effect of AI personalization on payment adoption rates while accounting for heterogeneous responses across user segments. Our findings reveal that AI personalization significantly increases payment adoption by twenty-three point four percent, with trust-based mechanisms explaining approximately forty-one percent of this total effect. Critically, we uncover substantial heterogeneity in treatment effects: users with prior trust in the platform exhibit adoption increases of thirty-one point two percent, while those with low initial trust show muted responses of only eight point seven percent, suggesting that AI personalization amplifies existing trust rather than building it de novo. We also find that recommendation transparency moderates the effect, with explainable AI features enhancing adoption among skeptical users. These results provide causal evidence for the algorithm of trust hypothesis, demonstrating that AI personalization functions through trust channels and that its effectiveness is conditional on pre-existing user-platform relationships. The findings offer actionable insights for designing AI systems that foster financial inclusion while managing the heterogeneous trust dynamics inherent in digital payment adoption.
Copyright (c) 2026 Yi Han (Author) https://creativecommons.org/licenses/by/4.0/
Dynamic Spillover of Negative Public Opinion on P2P Liquidity Risk Based on Sentiment Analysis
The peer-to-peer (P2P) lending industry has emerged as a significant component of the alternative finance landscape, yet it remains plagued by inherent fragility and information asymmetry. This paper investigates the dynamic spillover effects of negative public opinion on the liquidity risk of P2P platforms, employing advanced sentiment analysis techniques and econometric spillover indices. By constructing a comprehensive daily index of negative sentiment derived from web-scraped data across major investor forums and news outlets, we analyze the transmission mechanisms through which adverse information impacts platform funding flows and withdrawal behaviors. We utilize textual mining to quantify soft information and apply a generalized vector autoregression framework to capture time-varying connectedness. Our findings reveal that negative public opinion exerts a substantial, immediate, and asymmetric impact on liquidity risk, with the intensity of the spillover varying significantly depending on the regulatory environment and the specific nature of the negative news. The results suggest that investor panic, fueled by social contagion, creates a self-fulfilling prophecy that exacerbates liquidity crunches. Furthermore, we identify a distinct lag structure where the volatility transmission from sentiment to liquidity persists even after the initial shock subsides. These insights provide critical implications for platform managers regarding crisis communication and for regulators aiming to stabilize the fintech ecosystem against systemic risks associated with information contagion.
Copyright (c) 2026 Javier Lopez, Matteo Bianchi, Robert Williams, Sarah Davis (Author) https://creativecommons.org/licenses/by-sa/4.0
Capital Scheduling Decisions in Digital Supply Chain Finance based on Multi-Stage Stochastic Linear Programming
The rapid evolution of financial technologies has transformed traditional supply chain management into a digitally integrated ecosystem, enabling real-time visibility and data-driven decision-making. This paper addresses the complex problem of capital scheduling within digital supply chain finance, where liquidity management is subject to significant uncertainties regarding market demand, repayment behaviors, and interest rate fluctuations. Unlike traditional static approaches, we propose a Multi-Stage Stochastic Linear Programming model that dynamically adjusts financing and repayment decisions over multiple time horizons. By leveraging the granular data availability inherent in digital platforms, the proposed framework allows financial planners to optimize working capital allocation while mitigating liquidity risks. We construct a scenario tree to represent the evolution of uncertain parameters and employ a decomposition algorithm to solve the resulting large-scale optimization problem. The study demonstrates that digital integration, when coupled with stochastic optimization, significantly outperforms deterministic planning methods by reducing the cost of capital and improving solvency ratios under volatile market conditions. The findings provide theoretical contributions to the intersection of operations research and financial engineering, offering practical guidelines for liquidity providers and supply chain managers operating in uncertain environments.
Copyright (c) 2026 Aarav Sharma, Sarah E. Thompson (Author) https://creativecommons.org/licenses/by-sa/4.0
Financial Report Entity-Relation Extraction Combining BiLSTM-CRF and FGM Adversarial Training
The rapid digitization of financial markets has resulted in an explosion of unstructured financial data, primarily in the form of textual reports, earnings calls, and regulatory filings. Extracting structured knowledge from these documents is critical for automated risk assessment, quantitative investment strategies, and market surveillance. However, financial texts differ significantly from general domain corpora due to high terminological density, complex nested sentence structures, and subtle semantic dependencies. Traditional named entity recognition and relation extraction models often suffer from overfitting when applied to limited labeled financial datasets, leading to poor generalization on unseen data. This paper proposes a robust entity-relation extraction framework that integrates a Bidirectional Long Short-Term Memory (BiLSTM) network with a Conditional Random Field (CRF) layer, augmented by Fast Gradient Method (FGM) adversarial training. The BiLSTM layer captures long-range semantic dependencies, while the CRF layer ensures the validity of the predicted tag sequences. Crucially, the FGM adversarial training mechanism introduces perturbations to the embedding layer during training, effectively regularizing the model and enhancing its robustness against noise and data sparsity. Experimental results demonstrate that the proposed model achieves superior performance in terms of precision, recall, and F1-score compared to baseline methods, particularly in identifying complex financial entities and their interrelations.
Copyright (c) 2026 Robert J. Miller, Sarah E. Hamilton (Author) https://creativecommons.org/licenses/by-sa/4.0
Reinforcement Learning Based Demand State Modeling and Dynamic Personalization for Digital Retail
Personalization in digital retail involves repeated interventions whose effects unfold over time, with delayed purchase responses and diminishing returns caused by excessive contact. To address this setting, personalized strategy selection is formulated as a Markov decision process in which customer demand evolves across daily decision epochs. Multi-modal behavioral sequences—browsing, add-to-cart, purchase, return, and responses to marketing touchpoints—are encoded into latent demand states using representation learning to obtain compact state vectors from high-dimensional logs. Strategic actions are defined over promotion intensity, content type, and contact frequency, and the learning objective combines conversion revenue with explicit penalties for promotion cost and fatigue-related negative responses. Experiments use a 12-month dataset containing 960,418,327 behavioral records from 2,104,673 customers, trained offline and evaluated through online playback under identical operational constraints across baselines. Relative to rule-based and supervised alternatives, the learned policy improves conversion from 3.72% to 3.88% (+4.3% relative) and increases 30-day LTV from $12.41 to $13.25 (+6.8% relative). Ineffective outreach is reduced, with the share of contacted instances without attributed purchase decreasing from 78.6% to 74.1%. The study support sequence-level optimization of interventions when cost and fatigue must be controlled alongside revenue.
Copyright (c) 2026 Ying Lin, Yao Yao, Chao He (Author) https://creativecommons.org/licenses/by-sa/4.0
Privacy-Enhanced Ad Targeting for Social E-Commerce: A Federated Learning Framework with Zero-Knowledge Verification for Creator Monetization
The convergence of social networking and electronic commerce has given rise to the social e-commerce paradigm, where content creators serve as the primary drivers of consumer engagement and purchase decisions. However, this ecosystem faces a critical tension between the need for high-precision ad targeting to sustain monetization and the increasingly stringent requirements for user privacy preservation. Traditional centralized recommendation systems require the aggregation of massive user behavioral datasets, creating significant risks of data leakage and violating emerging regulatory frameworks. To address this challenge, we propose a novel framework titled Fed-ZKC (Federated Zero-Knowledge Creator). This architecture synergizes Federated Learning (FL) with Zero-Knowledge Proofs (ZKP) to enable privacy-preserving ad targeting while ensuring verifiable monetization attribution for creators. In our system, user preference models are trained locally on edge devices to prevent raw data transmission, while a cryptographic verification layer ensures that ad interactions are genuine without revealing user identities to the platform or the creators. Extensive experiments conducted on large-scale real-world datasets demonstrate that Fed-ZKC achieves recommendation accuracy comparable to centralized baselines while reducing privacy leakage risks by orders of magnitude. Furthermore, the implementation of succinct non-interactive arguments of knowledge (zk-SNARKs) introduces minimal computational overhead, making the protocol feasible for deployment on modern mobile processors.
Copyright (c) 2026 Xiongsheng Yi (Author) https://creativecommons.org/licenses/by-sa/4.0
This article analyses the weakening market position of China’s longan industry in a context where the supply chain remains relatively stable. Over time, longan has relied on an established production base configuration and a supplementary mechanism of cross border trade, thereby forming a comparatively robust system of production and distribution. However, supply stability has not translated into sustained gains in market competitiveness. Instead, amid consumption upgrading and an intensified health orientation, longan has increasingly revealed a lack of alignment with demand side requirements. The challenges facing longan do not stem from insufficient production capacity, but from multiple mismatches triggered by a restructuring of consumer value systems. Within a health discourse that prioritises sugar control and low burden choices, its high sweetness image can readily induce risk avoidance. At the level of functional experience, its perceived value struggles to gain advantage across the dimensions of price, convenience, and perceived eatability. At the social level, the absence of salient category symbolism and a compelling gifting narrative constrains its role in identity expression and scenario based consumption. Meanwhile, existing research has largely focused on supply efficiency and industrial scale, paying insufficient attention to shifts in consumer preferences and meaning construction, and thus overlooking potential medium to long term demand risks. On this basis, this article argues that the longan industry should shift from a scale oriented approach to a value oriented approach. By reconstructing health oriented communication, optimising consumer experience, and reshaping category image, the industry can re establish fit with contemporary consumption logic.
Copyright (c) 2026 Xiao Mengfei, Gan Weixiang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Multi-Granularity Dependency Modeling for Automated Fault Triage in High-Throughput Financial Transaction Systems
High-throughput financial transaction systems constitute critical infrastructure in modern banking and financial services, processing millions of transactions per second while maintaining stringent requirements for reliability, consistency, and availability. The inherent complexity of these distributed systems, characterized by intricate interdependencies among microservices, databases, message queues, and external interfaces, presents substantial challenges in fault diagnosis and root cause analysis. This research proposes a novel multi-granularity dependency modeling framework that captures system behaviors across service-level, transaction-level, and resource-level abstractions to enable automated fault triage. The framework integrates real-time telemetry data including metrics, distributed traces, and transaction logs to construct dynamic dependency graphs that reflect evolving system states. A hierarchical fault propagation model is developed to distinguish between root causes and cascading failures, leveraging causal inference techniques and temporal correlation analysis. The proposed approach employs machine learning-based anomaly detection coupled with graph-based root cause localization algorithms to identify fault origins within seconds of symptom manifestation. Experimental evaluation on a production-scale financial transaction processing platform demonstrates that the multi-granularity approach achieves superior diagnostic accuracy compared to single-level analysis methods, reducing mean time to resolution by approximately 67% while maintaining a precision rate exceeding 92% for root cause identification. The framework provides actionable insights for automated incident response systems and contributes to the broader discourse on reliability engineering in mission-critical financial infrastructure.
Copyright (c) 2026 Tianyu Fang, Qiming Zhou, Ethan Brooks (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Machine Learning Algorithms for Credit Risk Assessment in Financial Markets: A Comparative Study of Gradient Boosting and Neural Networks
The accurate assessment of credit risk remains a cornerstone of financial stability and profitability for lending institutions globally. As the volume of transactional data expands and the complexity of financial behaviors increases, traditional statistical methods such as logistic regression often fail to capture the non-linear intricacies inherent in modern credit datasets. This paper presents a comparative analysis of two dominant machine learning paradigms: Gradient Boosting Machines, specifically the XGBoost implementation, and Artificial Neural Networks. Utilizing a comprehensive dataset of consumer loans, we evaluate these models based on predictive accuracy, computational efficiency, and interpretability. Our findings indicate that while both methodologies significantly outperform traditional baselines, they exhibit distinct advantages depending on the operational constraints. Gradient boosting demonstrates superior performance on tabular data with faster training times and greater interpretability through feature importance analysis. Conversely, neural networks show potential for capturing highly complex, high-dimensional interactions, albeit at a higher computational cost. The study concludes that the choice between these algorithms should be dictated by the specific requirements of the financial institution regarding the trade-off between predictive precision and model transparency.
Copyright (c) 2026 Léa Thomas , David Bowman, Elizabeth Shaw (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Counterfactual Explanations for AML Risk Scores Under Causal Constraints
This study introduces a counterfactual-explanation framework for anti-money-laundering (AML) risk scoring. The method distinguishes actionable from non-actionable variables within a causal graph and generates minimal valid changes needed to shift a case below a risk threshold. Experiments used 42.7 million transactions and 3.1 million customer profiles from a nationwide financial dataset. The system generated valid counterfactuals for 91.5% of high-risk cases, requiring an average of 2.4 feature changes per case. In a user evaluation with 26 compliance analysts, counterfactual explanations reduced review time by 22.6% and increased decision consistency from 0.62 to 0.75 (Cohen’s kappa). Causal constraints eliminated unrealistic recommendations in 98.6% of generated explanations. The method enhances model transparency while maintaining detection performance.
Copyright (c) 2026 Ethan Morales, Sophia Bennett, Michael Turner (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Driving Rural Governance Modernization through New Quality Productive Forces: A Theoretical Framework and Empirical Evidence from China
In the grand historical process of comprehensively promoting Chinese modernization, the modernization of rural governance serves not only as a crucial cornerstone of the national governance system and governance capacity modernization but also as a key support for realizing the rural revitalization strategy. Currently, with the deepening development of the new round of scientific and technological revolution and industrial transformation, New Quality Productive Forces (NQPF)—characterized by high technology, high efficiency, and high quality—are profoundly reshaping traditional modes of production and social structures. This paper aims to construct a theoretical analysis framework based on the three-dimensional synergy of technology-industry-institution to deeply analyze the internal mechanism by which NQPF drives the modernization of rural governance. The paper first reviews the evolution of productivity theory, pointing out that NQPF overcomes the dilemmas of information asymmetry, resource fragmentation, and subject atomization in traditional rural governance through the three dimensions of technological penetration, industrial upgrading, and institutional reshaping. Subsequently, based on panel data from 38 districts and counties in Chongqing, China (2021-2023) and in-depth field research cases, this paper empirically tests the governance effectiveness of NQPF using fixed effects models, mediation effect models, and heterogeneity analysis methods. The study finds that: first, NQPF significantly improves rural governance efficiency, and this conclusion remains valid after various robustness tests; second, the degree of information openness and the degree of multi-subject synergy are key mediating mechanisms; third, there is significant heterogeneity between the main urban metropolitan area and the mountainous areas of northeast/southeast Chongqing, where differences in industrial foundation and digital infrastructure determine the boundaries of the enabling effect. This study not only enriches the micro-application of Marxist productivity theory in the digital age but also provides empirical evidence and policy implications for various regions to utilize NQPF to solve governance problems according to local conditions.
Copyright (c) 2026 Tingchen Kou, Xin Gao, Yuduo Lu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
ESG Performance, Debt Maturity Structure and Corporate Resilience
This paper explores the relationship between corporate ESG performance and corporate resilience through an empirical analysis of data from China A-share listed companies from 2014 to 2023. The study finds that strong ESG performance significantly enhances corporate resilience and improves the ability to cope with risks. Further analysis indicates that the debt maturity structure plays a crucial moderating role, where an increase in the proportion of long-term debt strengthens the positive impact of ESG performance on corporate resilience, while short-term debt weakens this effect. Additionally, there is heterogeneity in the impact of ESG performance on corporate resilience across firms with different ownership structures and internal control levels. Based on these findings, the paper recommends that governments improve relevant policies and regulations, and that companies adopt forward-looking strategies and enhance organizational learning mechanisms to strengthen corporate resilience and achieve sustainable development.
Copyright (c) 2026 Zhang Kaixua (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Entrepreneurial Ecosystem and the Growth of Technology Startups: A Case Study of Regional Innovation Clusters
This study explores the interaction mechanism between entrepreneurial ecosystems and the growth of technology startups, with a focus on fintech innovation clusters in Asia. Adopting a case-based comparative analysis method, three typical Asian fintech clusters—Shanghai Lujiazui, Singapore, and Bangalore—are selected as research cases. Based on the theoretical framework constructed by integrating Dynamic Capability Theory and Innovation Cluster Theory, this study examines the core supporting mechanisms of entrepreneurial ecosystems (resource mobilization, network collaboration, and institutional support) and their impacts on startup growth. The research identifies three common characteristics of Asian fintech entrepreneurial ecosystems: guiding policy support, industry-university-research integration, and multi-level capital guarantees, while also revealing differentiated features driven by regional advantages such as financial resources, internationalization, and talent cost advantage. Finally, optimization paths for enhancing ecosystem effectiveness are proposed. This study enriches the theoretical framework of entrepreneurial ecosystems and provides practical guidance for stakeholders to foster regional innovation clusters.
Copyright (c) 2026 DU YING, Murphy Choy, Wang Ziqian (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Optimizing Digital Adjudication through Social Network Analysis: An Empirical Study of Credit Card Disputes in Beijing
Amid the rapid digitalization of judicial systems, the integration of big data into adjudication remains underexplored, particularly in uncovering the structural logic of legal applications. This study bridges this gap by employing social network analysis (SNA) to examine credit card disputes involving personal information protection adjudicated in Beijing (2022–2024). By constructing a legal citation network, we reveal the latent patterns of substantive and procedural law application. The findings demonstrate that SNA can effectively identify core legal norms and typify cases, offering a robust methodological framework for optimizing 'Digital Court' systems. These insights provide practical pathways for enhancing judicial efficiency and consistency through data-driven case retrieval and holistic judicial information networks.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Privacy-Preserving Federated Learning for Cross-Institution Anti-Money-Laundering Models
The proliferation of digital financial transactions has precipitated a commensurate rise in sophisticated financial crimes, specifically money laundering, which imposes significant stability risks on the global economic framework. Traditional Anti-Money Laundering (AML) systems, predominantly relying on rule-based engines or isolated machine learning models within single institutions, fail to capture the complex, cross-institutional topology of modern laundering networks. While collaborative learning offers a theoretical solution, strict data privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) inhibit the centralized aggregation of sensitive transaction data. This paper presents a comprehensive framework for Privacy-Preserving Federated Learning (PPFL) tailored specifically for AML applications. We propose a novel architecture that integrates Differential Privacy (DP) with Secure Multi-Party Computation (SMPC) to enable financial institutions to collaboratively train robust Deep Neural Networks (DNNs) without sharing raw transaction ledgers. Furthermore, we address the challenge of non-Independent and Identically Distributed (non-IID) data, a characteristic inherent to the heterogeneous customer bases of different banks. Our experimental results demonstrate that the proposed framework achieves detection rates comparable to centralized training baselines while mathematically guaranteeing data privacy, thereby resolving the dilemma between regulatory compliance and effective financial crime detection.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
A Volatility-Aware Temporal Transformer for Intraday Risk Forecasting with Market Microstructure Signals
The accurate forecasting of intraday financial risk is a cornerstone of modern algorithmic trading and systemic stability analysis. Traditional econometric models often fail to capture the nonlinear dependencies and rapid regime shifts characteristic of high-frequency limit order book (LOB) data, while standard deep learning architectures frequently struggle with the extremely low signal-to-noise ratio inherent in microstructure signals. This paper introduces the Volatility-Aware Temporal Transformer (VATT), a novel deep learning architecture designed specifically for intraday realized volatility forecasting. Unlike canonical Transformers, VATT incorporates a specialized Volatility Gating Module (VGM) that dynamically modulates the attention mechanism based on the prevailing market regime, allowing the model to distinguish between transient noise and structural volatility shifts. We leverage granular microstructure signals, including Order Flow Imbalance (OFI) and depth-weighted spread, to enhance the feature space beyond simple price history. Extensive experiments conducted on tick-level data for major equity indices demonstrate that VATT significantly outperforms GARCH-family baselines and standard Long Short-Term Memory (LSTM) networks in terms of Mean Absolute Error and Quasi-Likelihood loss. The results suggest that integrating volatility-specific inductive biases into the Transformer architecture is crucial for robust risk forecasting in high-frequency domains.
Beyond Automation in Tax Compliance Through Artificial Intelligence and Professional Judgment
Tax compliance has evolved significantly with the integration of artificial intelligence (AI) technologies, transforming traditional manual processes into sophisticated automated systems. This review examines the intersection of AI automation and professional judgment in tax compliance, exploring how these elements complement rather than replace each other. The paper analyzes various AI applications including machine learning (ML), natural language processing (NLP), robotic process automation (RPA), and blockchain technology in tax administration and compliance functions. While AI demonstrates remarkable capabilities in processing vast amounts of tax data, identifying patterns, and automating routine tasks, professional judgment remains indispensable for interpreting complex tax regulations, assessing unique circumstances, and making ethical decisions. This review synthesizes recent research on intelligent automation systems, predictive analytics for tax risk assessment, and cognitive technologies that support tax professionals. The analysis reveals that optimal tax compliance outcomes emerge from hybrid approaches that leverage AI efficiency alongside human expertise. Key challenges include algorithmic transparency, regulatory adaptation, data quality, and the preservation of professional skepticism in automated environments. The findings suggest that successful tax compliance in the digital era requires reimagining professional roles, developing new competencies, and establishing governance frameworks that ensure responsible AI deployment. This comprehensive examination provides insights into the future of tax compliance where technology augments rather than diminishes the critical role of professional judgment.
Multi-Touch Attribution and Media Mix Modeling for Marketing ROI Optimization in E-Commerce Platforms
The exponential growth of digital marketing channels has created unprecedented complexity in understanding customer journeys and optimizing marketing investments in e-commerce platforms. Multi-touch attribution (MTA) and media mix modeling (MMM) have emerged as complementary approaches for measuring marketing effectiveness and maximizing return on investment (ROI). This review examines the theoretical foundations, methodological developments, and practical applications of MTA and MMM in e-commerce contexts from 2019 onwards. Multi-touch attribution enables granular tracking of individual customer touchpoints across digital channels, while media mix modeling provides aggregate-level insights into marketing effectiveness through econometric analysis. Machine learning (ML) and artificial intelligence (AI) have revolutionized both approaches, enabling more accurate attribution modeling and predictive optimization. Recent advances integrate unified measurement frameworks that combine the strengths of MTA and MMM to overcome their individual limitations. This paper synthesizes current research on data integration challenges, algorithmic innovations, privacy considerations, and implementation strategies. The review highlights how modern attribution systems leverage deep learning (DL), Bayesian methods, and causal inference techniques to navigate the increasingly complex digital marketing ecosystem. Emerging trends include privacy-preserving measurement, cross-device attribution, and real-time optimization algorithms that adapt to dynamic market conditions. The synthesis reveals that successful ROI optimization requires not only sophisticated analytical techniques but also organizational alignment, data infrastructure investment, and continuous model validation against business outcomes.
From Motivation to Performance: An Empirical Analysis of Human Resource Management in Zhengzhou's Manufacturing SMEs
This study examines the impact of integrated human resource management practices—specifically employee motivation, retention strategies, and well-being initiatives—on firm performance in manufacturing SMEs in Zhengzhou. Using a cross-sectional design, data were collected via structured questionnaires and supplemented by semi-structured interviews with HR managers and employees. Quantitative analysis, including descriptive statistics, correlation analysis, and multiple regression, reveals that all three HR factors are positively associated with firm performance, with employee motivation emerging as the most significant predictor. The findings underscore the importance of comprehensive HR strategies in fostering a motivated, committed workforce that drives competitive advantage in dynamic industrial settings. Although the study is limited by its cross-sectional design and reliance on self-reported data, it provides valuable insights for both scholars and practitioners seeking to enhance HR practices in manufacturing SMEs.
Copyright (c) 2025 Liu Siyuan, Mohd Dan Jantan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Industry-linked stock volatility prediction based on graph neural networks
This paper develops a simple hybrid model that combines Graph Neural Networks (GNN) with Light Gradient Boosting Machine (LightGBM) to improve stock market volatility prediction by using links between industries. The model builds an industry correlation graph to extract relationship features through GNN, and these features are then used by LightGBM for volatility forecasting. Based on data from major U.S. market sectors, the proposed model increases R2R^2R2 by 6.5% compared with the baseline LightGBM model and shows lower prediction error during highly volatile periods. The findings show that using industry connections helps capture cross-sector risk transmission and improves both accuracy and stability. This approach can be applied to market monitoring and investment risk control. However, the current version uses a fixed correlation graph and daily data, which limits its ability to adapt to fast market changes. Future studies should focus on building adaptive graphs and combining real-time data sources for better short-term prediction
China's Rise and the Transformation of the Global Governance System: A Study from the Perspective of Southeast Asian Countries
With China's continuous rise, the global governance system is undergoing profound changes. As the world's second-largest economy and an emerging international responsibility power, China plays a significant role in shaping new international rules and promoting the trend of multipolarization. Southeast Asia, as an important region connecting China with the world, plays a key role in this transformation. This paper takes the major countries in Southeast Asia as the research object, explores the impact of China's rise on regional countries and their response strategies, and analyzes the new pattern of regional cooperation and competition. The research suggests that under the background of China's expanding influence, Southeast Asian countries adopt diverse strategies to seek their own interests, and regional cooperation is gradually entering a new stage of multilateral governance. Finally, it is proposed that regional countries should enhance their autonomy, promote regional integration, and achieve greater say in the global governance system.
Copyright (c) 2025 Luo Kun (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Evolution of Corporate Innovation Networks and Its Impact on Firm Valuation: An Empirical Study Based on Dynamic Graph Neural Networks
In an increasingly interconnected and knowledge-driven economy, corporate innovation networks have become critical conduits for competitive advantage. However, prior research has predominantly relied on static or comparatively static analyses, failing to capture the dynamic nature of these inter-firm relationships. This study addresses this gap by investigating how the temporal evolution of a firm's position within its innovation network influences its market valuation. We construct a dynamic network of strategic alliances among U.S. publicly traded firms from 1995 to 2020, using data from the SDC Platinum database, and link it to financial data from Compustat and CRSP. To model the complex, path-dependent nature of network evolution, we employ a Dynamic Graph Neural Network (DGNN), specifically the EvolveGCN architecture. Our empirical results demonstrate that a firm's network trajectory contains significant predictive power for its future valuation, over and above traditional financial controls and static network metrics. Specifically, trajectories characterized by increasing centrality and brokerage capabilities are positively associated with higher firm valuation, as measured by Tobin's Q. These findings contribute to the Knowledge-Based View and network theory by highlighting the strategic importance of dynamic network management capabilities. Methodologically, this study showcases the utility of DGNNs for addressing complex, time-varying relational questions in strategic management and finance.
Copyright (c) 2025 Ming Guo (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Transformer-Based Architecture for Real-Time Option Greeks Estimation Under Extreme Market Conditions
The accurate and timely estimation of option Greeks remains a critical challenge in financial risk management, particularly during periods of extreme market volatility when traditional computational methods encounter severe limitations in both speed and reliability. This paper presents a novel application of Transformer-based deep learning architectures to the problem of real-time option Greeks estimation under extreme market conditions, addressing fundamental challenges that have constrained conventional approaches including computational bottlenecks, numerical instability, and inadequate handling of long-range temporal dependencies in volatility dynamics. We develop a specialized attention mechanism that exploits the structural properties of option surfaces while maintaining computational efficiency through strategic architectural design incorporating multi-head self-attention, gated neural network mechanisms that enforce economic rationality constraints, and positional encoding adapted for financial time series exhibiting non-stationary behavior. The empirical investigation employs comprehensive datasets spanning multiple market regimes including the 2008 financial crisis characterized by VIX levels exceeding 80 percent as documented in detailed intraday records, the August 2015 volatility spike reaching 53 percent, and the March 2020 COVID-19 pandemic market disruption with VIX peaking at 89.53 percent, providing robust assessment across diverse stress scenarios that reveal the catastrophic failure modes of traditional methods. Our Transformer-based approach achieves Delta estimation accuracy with Mean Absolute Error below 0.001 for at-the-money options during normal market conditions and maintains stable performance with MAE below 0.002 during extreme volatility events where traditional finite difference methods exhibit errors exceeding 0.05, representing more than twentyfivefold improvement in accuracy under stress conditions. The architecture leverages a stratified training strategy that oversamples extreme volatility regimes by factors exceeding thirteen times their natural occurrence frequency, ensuring robust generalization to crisis scenarios despite their rarity in historical data comprising less than two percent of trading days. Furthermore, the architecture delivers inference latency below 100 microseconds per option contract on modern GPU hardware, enabling genuine real-time Greeks calculation for large portfolios containing thousands of positions that require continuous hedging adjustments as volatility surfaces shift rapidly during market stress. This research establishes Transformer models as a transformative methodology for derivatives risk management, offering practitioners a robust tool for maintaining accurate hedge ratios and risk metrics even during the most turbulent market periods when precise Greeks estimation proves most critical for portfolio survival.
Building and Researching a Machine Learning Model for Identifying Corporate Tax Avoidance
Corporate tax avoidance poses significant challenges to public finance and economic equity, yet its detection remains complex due to the nuanced nature of financial and non-financial corporate data. This study aims to develop and evaluate a machine learning model capable of accurately identifying corporate tax avoidance behaviors using a multi-dimensional dataset. The methodology incorporates financial ratios, ownership structures, and industry characteristics as predictive features, employing gradient boosting algorithms to classify firms based on their likelihood of engaging in tax avoidance. The model was trained and validated on a global dataset comprising over 10,000 publicly listed companies from 2000 to 2020. Key findings indicate that the model achieves an F1-score of 0.87, significantly outperforming traditional logistic regression benchmarks. Additionally, feature importance analysis reveals that profitability metrics, subsidiary networks, and jurisdictional attributes are among the most influential predictors. These results underscore the potential of machine learning to enhance regulatory oversight and inform policy-making by enabling early and precise identification of tax avoidance practices.
Copyright (c) 2025 Zhipeng Wang, Yufei Chen (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
AI-enabled Forecasting, Risk Assessment, and Strategic Decision Making in Finance
The integration of artificial intelligence (AI) into financial services has fundamentally transformed how institutions approach forecasting, risk assessment, and strategic decision making. This review examines recent developments in AI-enabled financial applications, with particular emphasis on machine learning (ML) and deep learning (DL) methodologies. Financial forecasting has evolved from traditional statistical models to sophisticated neural network architectures capable of processing vast amounts of structured and unstructured data. Risk assessment frameworks now incorporate advanced AI algorithms that can identify complex patterns and anomalies in real-time, significantly enhancing predictive accuracy and regulatory compliance. Strategic decision making has been revolutionized through the deployment of reinforcement learning (RL) and natural language processing (NLP) systems that analyze market sentiment, optimize portfolio allocation, and generate actionable insights. This paper synthesizes current research on AI applications across these three critical domains, examining the methodologies, performance benchmarks, and practical implementations. The review also addresses persistent challenges including model interpretability, data quality requirements, regulatory constraints, and computational complexity. Through comprehensive analysis of recent literature, this study identifies emerging trends such as explainable AI (XAI) in finance, hybrid modeling approaches, and the integration of alternative data sources. The findings suggest that while AI technologies offer substantial improvements in accuracy and efficiency, successful implementation requires careful consideration of domain-specific constraints, ethical implications, and the balance between automation and human expertise. This review provides researchers and practitioners with a structured understanding of the current state and future trajectory of AI in financial forecasting, risk management, and strategic decision processes.
Usage-Based and Personalized Insurance Enabled by AI and Telematics
The insurance industry is undergoing a fundamental transformation driven by artificial intelligence (AI) and telematics technologies, enabling the shift from traditional risk pooling models to usage-based insurance (UBI) and personalized coverage frameworks. This paper examines how AI algorithms, including machine learning (ML) and deep learning (DL), combined with telematics data collection systems, are revolutionizing insurance pricing, risk assessment, and customer engagement. The integration of Internet of Things (IoT) devices, connected vehicles, and wearable sensors provides insurers with granular, real-time behavioral data that enables dynamic premium adjustment and individualized policy customization. Through comprehensive analysis of current implementations and emerging applications, this study explores the technical architecture of AI-enabled UBI systems, examines the transformation of actuarial practices through predictive analytics, and evaluates the implications for stakeholders across the insurance ecosystem. The findings reveal that AI-driven telematics solutions significantly enhance pricing accuracy, reduce adverse selection, improve customer satisfaction through fairness perceptions, and create new opportunities for preventive risk management. However, challenges persist regarding data privacy, algorithmic transparency, regulatory compliance, and equitable access to technology-enabled insurance products. This paper provides insights into how insurers can leverage AI and telematics to create sustainable competitive advantages while addressing ethical considerations and ensuring consumer protection in the evolving landscape of personalized insurance.
A Knowledge Graph-Enhanced Multimodal AI Framework for Intelligent Tax Data Integration and Compliance Enhancement
Tax administration in the era of digital transformation confronts substantial challenges in integrating and interpreting heterogeneous, multimodal data at enterprise scale. This study proposes a knowledge graph-based multimodal artificial intelligence framework that combines computer vision, natural language processing, and tabular modeling to deliver end-to-end analysis of invoice images, financial texts, and transactional records. At its core, the framework builds a tax-specific knowledge graph that connects transaction entities, voucher attributes, and regulatory terms, and applies graph neural networks for semantic alignment and reasoning. To address privacy and governance constraints, the framework integrates federated learning and differential privacy to protect sensitive financial data while enabling collaborative model improvement. Explainable AI methods generate warning signals and traceable evidence chains for auditors and accountants. We validate the framework using a simulated audit scenario of a medium-sized manufacturing enterprise in a remote, underdeveloped region of the United States that processes over 100,000 invoices annually. The results demonstrate automated linkage of invoice-level details to relevant regulations (for example, value-added tax deduction rules where applicable), extraction of textual anomalies such as duplicate deductions, detection of approximately 15 percent potential compliance risks under privacy protection, and visualized evidence traceability. Preliminary experiments show a 70 percent reduction in processing time and an error rate below 2 percent, indicating significant audit efficiency gains and the potential to reduce labor costs and disputes. The approach offers scalable implications for national tax administration and can be adapted to other industries to advance intelligent tax governance.
Early Warning of Financial Distress in Listed Companies: A Machine Learning Approach
Financial distress prediction is critical for maintaining market stability and protecting investor interests. Traditional statistical models often fall short in capturing complex, non-linear patterns in financial data. This study aims to develop an early warning system for financial distress in listed companies by leveraging machine learning algorithms. Using a dataset of financial ratios and market performance indicators from publicly traded firms over a ten-year period, we applied several classification techniques, including logistic regression, support vector machines, and gradient boosting. The gradient boosting model demonstrated superior predictive accuracy, achieving an F1-score of 0.92, significantly outperforming traditional models. Key predictors identified include cash flow volatility, debt-to-equity ratio, and operating margin trends. The findings underscore the potential of machine learning in enhancing the timeliness and reliability of financial distress alerts, offering valuable insights for regulators, investors, and corporate management to take proactive measures.
Copyright (c) 2025 Yuchen Chen , Rui Li, Xiaolong Wang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Machine Learning Forecasting of Stock Market Indices Based on Macroeconomic Indicators
The accurate forecasting of stock market indices remains a significant challenge in financial research, given the complex interplay of economic variables and market sentiment. This study investigates the predictive power of macroeconomic indicators on major stock market indices using advanced machine learning (ML) techniques. The primary objective is to develop a robust forecasting model that leverages historical data on key macroeconomic variables—such as inflation rates, interest rates, GDP growth, and unemployment figures—to predict future index movements. Using a dataset spanning two decades, we trained and evaluated multiple ML algorithms, including Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) networks. The results demonstrate that ensemble methods, particularly Gradient Boosting, achieve superior predictive accuracy compared to traditional time-series models. Notably, inflation and interest rates emerged as the most influential predictors. These findings underscore the potential of ML-driven approaches to enhance financial decision-making and risk management strategies for investors and policymakers.
Copyright (c) 2025 Xiaowei Chen , Yating Wang , Minghao Li (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Cointegration and Dynamic Interactions: A Study on Trade Openness and Economic Growth in China
This research examines the long-term equilibrium relationship between trade openness and China's economic growth, drawing on quarterly data spanning 2002 to 2021. The analysis applies a VAR framework alongside established econometric approaches—Johansen cointegration testing, Granger causality analysis, impulse response functions, and variance decomposition—to systematically assess dynamic interactions among GDP, export-import volumes, and exchange rate fluctuations. Findings empirically confirm three significant cointegration relationships, supporting a stable long-run equilibrium between trade expansion and economic development. The Granger causality tests identify mutual causality between GDP and exchange rate movements, alongside bidirectional effects between exports and the exchange rate. Meanwhile, a unidirectional causal link runs from exports to imports. Variance decomposition further reveals that both exports and imports increasingly account for economic variability over time, with imports demonstrating stronger explanatory power in the extended horizon. These results inform several policy recommendations focused on promoting export growth, refining industrial and trade configurations, and enhancing exchange rate risk frameworks to support sustained economic progress.
Copyright (c) 2025 Wanting Xu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0