Frontiers in Business and Finance
type: channel
description: 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.
publisher: INTERNATIONAL STUDY COUNSELOR (PRIVATE) LIMITED
language: en
publication name: Frontiers in Business and Finance
issn: 3079-9325
items: Industry-linked stock volatility prediction based on graph neural networks
items: China's Rise and the Transformation of the Global Governance System: A Study from the Perspective of Southeast Asian Countries
items: The Evolution of Corporate Innovation Networks and Its Impact on Firm Valuation: An Empirical Study Based on Dynamic Graph Neural Networks
items: Transformer-Based Architecture for Real-Time Option Greeks Estimation Under Extreme Market Conditions
items: Building and Researching a Machine Learning Model for Identifying Corporate Tax Avoidance
items: AI-enabled Forecasting, Risk Assessment, and Strategic Decision Making in Finance
items: Usage-Based and Personalized Insurance Enabled by AI and Telematics
items: A Knowledge Graph-Enhanced Multimodal AI Framework for Intelligent Tax Data Integration and Compliance Enhancement
items: Early Warning of Financial Distress in Listed Companies: A Machine Learning Approach
items: Machine Learning Forecasting of Stock Market Indices Based on Macroeconomic Indicators
items: Cointegration and Dynamic Interactions: A Study on Trade Openness and Economic Growth in China
items: The Driving Factors of China’s Foreign Trade under Macroeconomic Fluctuations: An Empirical Analysis Based on Bilateral Linkages
items: Dual-Stage Graph Contrastive Learning for Robust E-Commerce Fraud Detection
items: Adversarially Robust Temporal Graph Contrastive Learning for Financial Fraud Detection
items: Blockchain-Enabled FinTech Platforms for Tokenizing Carbon Capture Outputs in Mechanical Systems
items: Real-Time Fraud Detection Using Reinforcement Learning with Dynamic Feature Selection
items: Assessing the Impact of Enhanced Capital Adequacy, Elevated Liquidity Ratios, Segregation of Banking Activities, and Restrictions on Bankers' Bonuses on Risk Management in Banks
items: Resilience Evaluation of Supply Chain Configuration in Export-Oriented Enterprises: Indicator Construction and Rebound Prediction Based on Transaction Data
items: Intelligent Classification of Residential Property Tax Levels Based on Temporal Clustering and Federated Learning
items: Machine Learning for Identifying and Classifying Uncertain Tax Positions in Corporate Filings
items: AUBIQ: A Generative AI-Powered Framework for Automating Business Intelligence Requirements in Resource-Constrained Enterprises
items: Autonomous Supply Strategy and Substitution Modeling for Critical Components in Complex Supply Chains
items: The Impact of Short Video Platform Marketing Strategies on the Acceleration of the Consumer Decision-Making Cycle
items: The Impact of Hotel Companies' ESG Activities on Corporate Image, Trust and Customer Loyalty
items: Adaptive Marketing Campaigns Using Deep Reinforcement Learning: A Customer-Centric Approach
items: A Deep Reinforcement Learning Framework for End-to-End Retail Supply Chain Optimization
items: Blockchain-Based Title Confirmation and Transaction Mechanism for Commercial Real Estate Tokenization
items: Design and Evaluation of Quantitative Investment Strategies Using Reinforcement Learning and Multi-Factor Models
items: Carbon Credit Evaluation for Green Finance Based on Multimodal Data and Dynamic Ratings
items: The Future of Crypto currency: Opportunities and Risks in a Digital Economy

Industry-linked stock volatility prediction based on graph neural networks
type: item
description: 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
subject: Articles
  topic: section
subject: graph neural network
  topic: keywords
subject: volatility prediction
  topic: keywords
subject: industry relation
  topic: keywords
subject: LightGBM
  topic: keywords
subject: financial market
  topic: keywords
subject: risk modeling
  topic: keywords
creator: Liam J. Thompson
creator: Sofia Martinez
creator: Ethan K. Wong
creator: Amelia R. Clark
creator: Oliver B. Reid
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-30
publication date: 2025-11-30
starting page: 366
ending page: 372
doi: 10.71465/fbf452

China's Rise and the Transformation of the Global Governance System: A Study from the Perspective of Southeast Asian Countries
type: item
description: 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.
subject: Articles
  topic: section
subject: China's rise
  topic: keywords
subject: Global governance system
  topic: keywords
subject: Southeast Asia
  topic: keywords
subject: Regional and country-specific studies
  topic: keywords
creator: Luo Kun
rights: Copyright (c) 2025 Luo Kun (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-15
publication date: 2025-08-15
starting page: 355
ending page: 365
doi: 10.71465/fbf429

The Evolution of Corporate Innovation Networks and Its Impact on Firm Valuation: An Empirical Study Based on Dynamic Graph Neural Networks
type: item
description: 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.
subject: Articles
  topic: section
subject: Corporate Innovation Networks
  topic: keywords
subject: Firm Valuation
  topic: keywords
subject: Dynamic Graph Neural Networks (DGNNs)
  topic: keywords
subject: Knowledge-Based View
  topic: keywords
creator: Ming Guo
rights: Copyright (c) 2025 Ming Guo (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-02
publication date: 2025-11-02
starting page: 339
ending page: 354
doi: 10.71465/fbf425

Transformer-Based Architecture for Real-Time Option Greeks Estimation Under Extreme Market Conditions
type: item
description: 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 stra…
subject: Articles
  topic: section
subject: Transformer Architecture
  topic: keywords
subject: Attention Mechanism
  topic: keywords
subject: Option Greeks
  topic: keywords
subject: Delta Hedging
  topic: keywords
subject: Volatility Surface
  topic: keywords
subject: Extreme Market Conditions
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Real-Time Computation
  topic: keywords
subject: Financial Risk Management
  topic: keywords
subject: VIX Index
  topic: keywords
subject: Market Crisis
  topic: keywords
subject: Gated Neural Networks
  topic: keywords
subject: Stratified Sampling
  topic: keywords
creator: Liang Zhao
creator: Mei Lin
creator: Hiroshi Tanaka
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-25
publication date: 2025-10-25
starting page: 311
ending page: 338
doi: 10.71465/fbf420

Building and Researching a Machine Learning Model for Identifying Corporate Tax Avoidance
type: item
description: 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.
subject: Articles
  topic: section
subject: Tax Avoidance Detection
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Corporate Governance
  topic: keywords
subject: Predictive Modeling
  topic: keywords
creator: Zhipeng Wang
creator: Yufei Chen
rights: Copyright (c) 2025 Zhipeng Wang, Yufei Chen (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-23
publication date: 2025-10-23
starting page: 296
ending page: 310
doi: 10.71465/fbf399

AI-enabled Forecasting, Risk Assessment, and Strategic Decision Making in Finance
type: item
description: 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 strat…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Financial Forecasting
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Fintech
  topic: keywords
creator: Shiyang Chen
creator: Shaochen Ren
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-19
publication date: 2025-10-19
starting page: 274
ending page: 295
doi: 10.71465/fbf397

Usage-Based and Personalized Insurance Enabled by AI and Telematics
type: item
description: 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.
subject: Articles
  topic: section
subject: Usage-based insurance
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Telematics
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Personalized insurance
  topic: keywords
subject: Risk assessment
  topic: keywords
subject: IoT
  topic: keywords
subject: Predictive analytics
  topic: keywords
subject: Insurance technology
  topic: keywords
creator: Tiejiang Sun
creator: Mengdie Wang
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-19
publication date: 2025-10-19
starting page: 262
ending page: 273
doi: 10.71465/fbf396

A Knowledge Graph-Enhanced Multimodal AI Framework for Intelligent Tax Data Integration and Compliance Enhancement
type: item
description: 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.
subject: Articles
  topic: section
subject: knowledge graph
  topic: keywords
subject: multimodal AI
  topic: keywords
subject: tax compliance
  topic: keywords
subject: differential privacy
  topic: keywords
creator: Tiantian Zhang
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-14
publication date: 2025-09-14
starting page: 247
ending page: 261
doi: 10.71465/fbf384

Early Warning of Financial Distress in Listed Companies: A Machine Learning Approach
type: item
description: 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.
subject: Articles
  topic: section
subject: Financial Distress Prediction
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Early Warning System
  topic: keywords
subject: Corporate Finance
  topic: keywords
creator: Yuchen Chen
creator: Rui Li
creator: Xiaolong Wang
rights: Copyright (c) 2025 Yuchen Chen , Rui Li, Xiaolong Wang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-04
publication date: 2025-10-04
starting page: 230
ending page: 246
doi: 10.71465/fbf378

Machine Learning Forecasting of Stock Market Indices Based on Macroeconomic Indicators
type: item
description: 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.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Stock Market Forecasting
  topic: keywords
subject: Macroeconomic Indicators
  topic: keywords
subject: Financial Prediction
  topic: keywords
creator: Xiaowei Chen
creator: Yating Wang
creator: Minghao Li
rights: Copyright (c) 2025 Xiaowei Chen , Yating Wang , Minghao Li (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-04
publication date: 2025-10-04
starting page: 212
ending page: 229
doi: 10.71465/fbf376

Cointegration and Dynamic Interactions: A Study on Trade Openness and Economic Growth in China
type: item
description: 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.
subject: Articles
  topic: section
subject: Trade Openness
  topic: keywords
subject: Economic Growth
  topic: keywords
subject: VAR Model
  topic: keywords
subject: Cointegration Analysis
  topic: keywords
subject: Impulse Response Function
  topic: keywords
creator: Wanting Xu
rights: Copyright (c) 2025 Wanting Xu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-01
publication date: 2025-10-01
starting page: 206
ending page: 211
doi: 10.71465/fbf365

The Driving Factors of China’s Foreign Trade under Macroeconomic Fluctuations: An Empirical Analysis Based on Bilateral Linkages
type: item
description: Since the launch of the reform and opening-up policy, China has steadily advanced its level of openness to the outside world, and its ties with the global economy have grown increasingly close. During this process, import and export trade has displayed the typical characteristics of market-oriented development. The country has reaped significant benefits from international trade, yet it has also faced adverse shocks from the external environment. With the deepening of global economic integration and China’s accession to the World Trade Organization, the interconnection between domestic and international markets has become stronger. This closer relationship has given rise to new conditions and contradictions, which are intertwined and exert multifaceted effects on national economic development. The specific manifestations include a persistent trade surplus, rising incidents of trade frictions, large inflows of international capital, and upward pressure on the renminbi exchange rate.
subject: Articles
  topic: section
subject: Trade Surplus
  topic: keywords
subject: Trade Frictions
  topic: keywords
subject: International Capital Inflows
  topic: keywords
subject: RMB Exchange Rate
  topic: keywords
creator: Wanting Xu
rights: Copyright (c) 2025 Wanting Xu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-01
publication date: 2025-10-01
starting page: 199
ending page: 205
doi: 10.71465/fbf364

Dual-Stage Graph Contrastive Learning for Robust E-Commerce Fraud Detection
type: item
description: E-commerce fraud detection represents one of the most challenging problems in modern digital commerce, requiring sophisticated approaches that can capture complex relational patterns while maintaining robustness against evolving fraud schemes. This paper proposes a novel Dual-Stage Graph Contrastive Learning (DSGCL) framework that leverages graph convolutional networks and advanced contrastive learning techniques to achieve robust fraud detection in e-commerce environments. Our approach employs a two-stage architecture where the first stage utilizes graph neural networks with multi-hop neighborhood aggregation to capture local structural patterns, while the second stage implements contrastive learning with multiple view generation to learn discriminative representations that are robust to fraudulent behavior variations. The framework integrates message-passing mechanisms for effective feature aggregation across user-merchant-transaction networks and employs sophisticated view augmentation strategies to enhance the model's ability to distinguish between legitimate and fraudulent activities. Experimental results on large-scale e-commerce datasets demonstrate that DSGCL achieves 94.7% accuracy with 92.3% F1-score, representing a 7.8% improvement over state-of-the-art baselines while maintaining exceptional robustness against adversarial attacks and novel fraud patterns.
subject: Articles
  topic: section
subject: graph contrastive learning
  topic: keywords
subject: fraud detection
  topic: keywords
subject: e-commerce security
  topic: keywords
subject: graph neural networks
  topic: keywords
subject: representation learning
  topic: keywords
creator: Carlos Ramirez
creator: Holly Peterson
rights: Copyright (c) 2025 Carlos Ramirez , Holly Peterson (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-19
publication date: 2025-09-19
starting page: 184
ending page: 198
doi: 10.71465/fbf359

Adversarially Robust Temporal Graph Contrastive Learning for Financial Fraud Detection
type: item
description: Financial fraud detection has become increasingly challenging due to the sophisticated nature of modern fraudulent activities and the dynamic evolution of transaction patterns. This paper proposes a novel Adversarially Robust Temporal Graph Contrastive Learning (ARTGCL) framework that combines temporal graph neural networks with contrastive learning mechanisms to enhance fraud detection capabilities while maintaining robustness against adversarial attacks. Our approach leverages the temporal dynamics of financial transactions represented as evolving graph structures, where nodes represent entities and edges capture transaction relationships over time. The contrastive learning component learns discriminative representations by maximizing agreement between augmented views of the same temporal graph while minimizing similarity with different graphs. To address the vulnerability of graph neural networks to adversarial perturbations, we integrate adversarial training techniques that expose the model to carefully crafted perturbations during training, thereby improving its robustness in real-world scenarios. Extensive experiments on three large-scale financial datasets demonstrate that ARTGCL achieves superior performance compared to state-of-the-art methods, with improvements of 8.5% in F1-score and 12.3% in Area Under the Curve (AUC) while maintaining computational efficiency. The adversarial robustness evaluation shows that our Hybrid Machine Learning Framework (HMLF) sustains robust accuracy improvements from 45% to 85% and reduces attack success rates from 35% to 5% under various attack scenarios, significantly outperforming baseline approaches.
subject: Articles
  topic: section
subject: Temporal graph neural networks
  topic: keywords
subject: contrastive learning
  topic: keywords
subject: financial fraud detection
  topic: keywords
subject: adversarial robustness
  topic: keywords
subject: graph representation learning
  topic: keywords
subject: anti-money laundering
  topic: keywords
creator: Matthew O’Connor
rights: Copyright (c) 2025 Matthew O’Connor (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-19
publication date: 2025-09-19
starting page: 172
ending page: 183
doi: 10.71465/fbf352

Blockchain-Enabled FinTech Platforms for Tokenizing Carbon Capture Outputs in Mechanical Systems
type: item
description: The escalating urgency of climate change mitigation has created unprecedented demand for innovative financial instruments that can effectively monetize carbon capture and storage (CCS) technologies while ensuring transparent, verifiable, and tradeable environmental credits. This research presents a novel blockchain-enabled Financial Technology (FinTech) platform specifically designed for tokenizing carbon capture outputs from mechanical systems, addressing critical gaps in current carbon credit markets through distributed ledger technology (DLT), smart contract automation, and real-time monitoring integration. The proposed platform utilizes Ethereum-compatible blockchain infrastructure to create fungible carbon tokens representing verified CO2 sequestration quantities from industrial mechanical systems including Direct Air Capture (DAC) units, industrial scrubbers, and enhanced weathering reactors. Through comprehensive analysis of 47 pilot implementations across diverse industrial sectors, our platform demonstrates remarkable improvements in carbon credit verification speed by 78%, transaction cost reduction of 45%, and fraud prevention mechanisms that achieve 99.2% accuracy in detecting false carbon capture claims. The system integrates Internet of Things (IoT) sensors with smart contracts to provide automated verification of carbon capture quantities, eliminating manual auditing processes that traditionally required 3-6 months for credit issuance. Real-time tokenization capabilities enable immediate liquidity for carbon credits, with average settlement times reduced from weeks to minutes while maintaining full regulatory compliance with emerging carbon accounting standards. The platform's decentralized architecture supports global carbon market integration, processing over 2.3 million tons of verified carbon capture data across 12 countries with transaction throughput exceeding 10,000 operations per second. Economic analysis reveals substantial market efficiency …
subject: Articles
  topic: section
subject: Blockchain Technology
  topic: keywords
subject: FinTech Platforms
  topic: keywords
subject: Carbon Capture Tokenization
  topic: keywords
subject: Smart Contracts
  topic: keywords
subject: Distributed Ledger Technology
  topic: keywords
subject: Environmental Credits
  topic: keywords
subject: Mechanical Systems
  topic: keywords
subject: IoT Integration
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
creator: Yao Ge
rights: Copyright (c) 2025 Yao Ge (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-19
publication date: 2025-09-19
starting page: 158
ending page: 171
doi: 10.71465/fbf66

Real-Time Fraud Detection Using Reinforcement Learning with Dynamic Feature Selection
type: item
description: Real-time fraud detection systems require sophisticated approaches capable of adapting to evolving fraud patterns while maintaining high accuracy and minimal false positive rates under strict latency constraints. Traditional fraud detection methods rely on static feature sets and rule-based systems that cannot adapt effectively to new fraud techniques or changing transaction patterns. The challenge lies in developing systems that can continuously learn optimal feature selection strategies while processing high-volume transaction streams in real-time environments where detection decisions must be made within milliseconds. This study proposes a novel Dynamic Feature Selection Reinforcement Learning (DFS-RL) framework that integrates reinforcement learning algorithms with adaptive feature selection mechanisms to enable real-time fraud detection with continuously evolving feature importance patterns. The framework employs Deep Q-Network (DQN) agents to learn optimal feature selection policies while utilizing ensemble detection models that adapt to selected feature subsets dynamically. The integrated approach enables real-time processing of transaction streams while maintaining detection accuracy through intelligent feature adaptation that responds to changing fraud patterns and system performance feedback. Experimental evaluation using large-scale financial transaction datasets demonstrates that the proposed framework achieves 43% improvement in fraud detection accuracy compared to traditional static feature approaches. The DFS-RL method results in 37% reduction in false positive rates while maintaining average transaction processing latency under 50 milliseconds for real-time requirements. The framework successfully combines adaptive feature selection with high-performance fraud detection, achieving 41% better adaptation to new fraud patterns while supporting real-time transaction processing at scale.
subject: Articles
  topic: section
subject: Real-Time Fraud Detection
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Dynamic Feature Selection
  topic: keywords
subject: Deep Q-Network
  topic: keywords
subject: Financial Transaction Processing
  topic: keywords
subject: Adaptive Systems
  topic: keywords
subject: Machine Learning Security
  topic: keywords
subject: Stream Processing
  topic: keywords
creator: Rajat Ghosh
creator: Linh Truong
rights: Copyright (c) 2025 Rajat Ghosh, Linh Truong (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-13
publication date: 2025-08-13
starting page: 131
ending page: 143
doi: 10.71465/fbf326

Assessing the Impact of Enhanced Capital Adequacy, Elevated Liquidity Ratios, Segregation of Banking Activities, and Restrictions on Bankers' Bonuses on Risk Management in Banks
type: item
description: This study examines the effectiveness of four key regulatory measures—enhanced capital adequacy requirements, elevated liquidity ratios, separation of banking activities, and restrictions on bankers’ bonuses—in mitigating excessive risk-taking and strengthening risk management within the banking sector. Drawing on case studies such as Northern Rock, Bear Stearns, the Glass-Steagall framework, and post-crisis bonus regulations, the paper evaluates both the benefits and limitations of each approach. The findings indicate that while these measures contribute to financial stability by improving loss absorption capacity, reducing systemic risk, and promoting prudent behavior, they also entail potential downsides, including constrained lending, reduced profitability, and unintended risk migration to less-regulated sectors. The analysis underscores that no single policy is sufficient in isolation; rather, a comprehensive, balanced regulatory framework—integrating macro- and micro-prudential measures—is essential to safeguard financial stability without unduly hindering innovation and economic growth.
subject: Articles
  topic: section
subject: Capital adequacy ratio
  topic: keywords
subject: liquidity ratio
  topic: keywords
subject: separation of banking activities
  topic: keywords
subject: bankers’ bonuses
  topic: keywords
subject: risk management
  topic: keywords
subject: financial stability
  topic: keywords
subject: systemic risk
  topic: keywords
subject: banking regulation
  topic: keywords
creator: Yifei Lyu
creator: Liping Wang
rights: Copyright (c) 2025 Yifei Lyu, Liping Wang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-06
publication date: 2025-08-06
starting page: 123
ending page: 130
doi: 10.71465/fbf292

Resilience Evaluation of Supply Chain Configuration in Export-Oriented Enterprises: Indicator Construction and Rebound Prediction Based on Transaction Data
type: item
description: Under the complex and dynamic international market environment, export-oriented enterprises are increasingly exposed to external disruptions, highlighting the critical role of supply chain configuration resilience. This study proposes a data-driven evaluation framework for supply chain resilience, constructed based on actual transaction records. Utilizing Principal Component Analysis (PCA) and Bayesian Structural Time Series (BSTS) models, the framework builds a set of indicators across four core dimensions: procurement concentration, order fulfillment cycle, contract breach response, and customer stickiness. The modeling focuses on two key aspects: the “rebound cycle” and the “configuration flexibility.” Through retrospective testing on order data from 537 export enterprises in the sectors of machinery manufacturing, consumer electronics, and light food processing from 2020 to 2023, the findings show that enterprises with high resilience levels recover from disruptions within an average of 17.4 days—significantly shorter than the industry average of 25.6 days. Furthermore, during periods of market volatility, these enterprises maintained a fulfillment reliability rate exceeding 92.3%. The proposed framework also supports dynamic stratification of supply chain configuration strategies based on rebound speed, offering practical guidance for differentiated strategy optimization. This contributes to enhancing the overall supply chain resilience of export-dominant enterprises.
subject: Articles
  topic: section
subject: Supply chain resilience
  topic: keywords
subject: transaction data analysis
  topic: keywords
subject: rebound cycle prediction
  topic: keywords
subject: configuration flexibility
  topic: keywords
subject: export fulfillment strategy
  topic: keywords
creator: Aiden Foster
creator: Lily Bennett
creator: Carter Hayes
creator: Zoe Mitchell
creator: Julian Rivera
rights: Copyright (c) 2025 Aiden Foster , Lily Bennett , Carter Hayes, Zoe Mitchell, Julian Rivera (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-06
publication date: 2025-08-06
starting page: 106
ending page: 114
doi: 10.71465/fbf291

Intelligent Classification of Residential Property Tax Levels Based on Temporal Clustering and Federated Learning
type: item
description: To address the problem of inconsistent and highly sensitive property tax level classifications across multiple U.S. states, this study proposes a distributed intelligent classification model based on temporal feature clustering and federated learning. The method first uses time windows to analyze property transaction histories and tax records and constructs typical property patterns. Then, a federated framework is used to jointly train classifiers across multiple state governments, avoiding data leakage. The model achieves an average accuracy of 87.5% on validation sets from seven states and can automatically adjust tax level ranges based on land price trends.
subject: Articles
  topic: section
subject: Property tax classification
  topic: keywords
subject: Temporal clustering
  topic: keywords
subject: Federated learning
  topic: keywords
subject: Privacy protection
  topic: keywords
subject: Intelligent assessment
  topic: keywords
creator: James Whitmore
creator: Priya Mehra
creator: David R. Kim
creator: Emily Linford
rights: Copyright (c) 2025 James Whitmore, Priya Mehra, David R. Kim, Emily Linford (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-06
publication date: 2025-08-06
starting page: 95
ending page: 105
doi: 10.71465/fbf290

Machine Learning for Identifying and Classifying Uncertain Tax Positions in Corporate Filings
type: item
description: Uncertain tax positions (UTPs) present significant challenges for both corporate entities and regulatory bodies due to their complexity, subjectivity, and the lack of standardized disclosure practices. With the growing volume and sophistication of corporate financial reports, traditional audit and compliance methods struggle to efficiently detect and classify UTPs. This study proposes a machine learning-based approach to identify and classify UTPs by analyzing linguistic patterns, contextual cues, and financial metrics in corporate filings, particularly in Form 10-K disclosures. Leveraging natural language processing (NLP) and supervised learning algorithms, we develop a predictive framework capable of flagging potential UTPs with high accuracy and interpretability. The model is trained and validated on a labeled dataset of SEC filings with annotated tax disclosures. Our results demonstrate the effectiveness of ensemble learning and deep learning models in automating UTP identification and offer insights into the key features contributing to model predictions. This research aims to assist auditors, tax professionals, and regulators by enhancing transparency and enabling more proactive tax risk management.
subject: Articles
  topic: section
subject: Uncertain tax positions
  topic: keywords
subject: machine learning
  topic: keywords
subject: SEC filings
  topic: keywords
subject: natural language processing
  topic: keywords
subject: corporate taxation
  topic: keywords
subject: tax risk
  topic: keywords
subject: financial disclosures
  topic: keywords
subject: classification models
  topic: keywords
subject: explainable AI
  topic: keywords
creator: Zoe Mitchell
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-06-13
publication date: 2025-06-13
starting page: 87
ending page: 94
doi: 10.71465/fbf267

AUBIQ: A Generative AI-Powered Framework for Automating Business Intelligence Requirements in Resource-Constrained Enterprises
type: item
description: The rapid evolution of Business Intelligence (BI) systems has necessitated a paradigm shift from static reporting to dynamic, self-service analytics. However, this transition presents significant challenges for Small and Medium-sized Enterprises (SMEs), where the paucity of technical expertise and the absence of dedicated data engineering teams create a "translation gap" between business intent and technical implementation. This paper introduces AUBIQ (Automated BI Query framework), a novel generative AI-powered system designed to automate the requirements engineering process for BI in resource-constrained environments. By leveraging Large Language Models (LLMs) with a retrieval-augmented generation (RAG) architecture, AUBIQ translates natural language business queries into executable BI specifications and SQL logic without requiring human intervention. This study adopts a Design Science Research methodology to conceptualize, build, and evaluate the framework. The findings indicate that AUBIQ significantly reduces the latency between query formulation and insight generation, achieving a 92.5% accuracy rate in requirement parsing compared to traditional manual methods. Furthermore, the framework demonstrates a marked improvement in user satisfaction metrics among non-technical stakeholders. These results suggest that Generative AI can democratize access to advanced analytics in SMEs, mitigating the dependency on scarce technical resources and enabling more agile decision-making processes.
subject: Articles
  topic: section
subject: Generative AI
  topic: keywords
subject: Business Intelligence
  topic: keywords
subject: Requirements Engineering
  topic: keywords
subject: SMEs
  topic: keywords
subject: Natural Language Processing
  topic: keywords
creator: Ruolin Qi
rights: Copyright (c) 2025 Ruolin Qi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-06-03
publication date: 2025-06-03
starting page: 66
ending page: 86
doi: 10.71465/fbf262

Autonomous Supply Strategy and Substitution Modeling for Critical Components in Complex Supply Chains
type: item
description: Amidst the current global landscape characterized by complex and volatile supply structures, the uncertainty in the supply of critical components has emerged as a significant constraint on enterprise development. This study focuses on constructing an innovative evaluation framework that integrates system dynamics (SD) modeling with stage-based substitution strategy simulation, with the objective of accurately identifying effective pathways for achieving autonomous supply of critical components. Through detailed analysis and simulation of three key stages—collaborative development, small-batch prototyping, and large-scale substitution—the study systematically investigates the substitution timeline and capacity transition patterns of critical components. Using 23 categories of heavy industrial equipment parts as case examples, relevant parameters were comprehensively collected for model construction. Simulation results reveal marked differences under various implementation pathways and strategy combinations. The proposed model demonstrates potential for integration with enterprise ERP systems, and is expected to provide robust quantitative early-warning support for the localization and substitution processes of essential materials.
subject: Articles
  topic: section
subject: component substitution
  topic: keywords
subject: autonomous supply strategy
  topic: keywords
subject: system dynamics modeling
  topic: keywords
subject: stage-based simulation
  topic: keywords
subject: supply transition efficiency
  topic: keywords
creator: Zihao Liu
creator: Cecelia Costa
creator: Ying Wu
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-06-06
publication date: 2025-06-06
starting page: 57
ending page: 65
doi: 10.71465/fbf260

The Impact of Short Video Platform Marketing Strategies on the Acceleration of the Consumer Decision-Making Cycle
type: item
description: This paper explores how short video platform marketing strategies influence the acceleration of the consumer decision-making cycle. It integrates classical decision-making models with modern frameworks such as the Information Adoption Model (IAM), the Stimulus–Organism–Response (S-O-R) framework, and heuristic processing theory to analyze the mechanisms of decision compression. Through an examination of influencer persuasion, emotional content design, algorithmic personalization, and social proof, the study identifies how short video platforms create emotionally engaging, trust-based, and algorithmically targeted environments that encourage fast consumer responses. This research contributes to updated theoretical models of consumer behavior and provides actionable strategies for marketers seeking to optimize short video campaigns.
subject: Articles
  topic: section
subject: short video platforms
  topic: keywords
subject: consumer decision-making
  topic: keywords
subject: digital marketing
  topic: keywords
subject: influencer strategy
  topic: keywords
subject: algorithmic targeting
  topic: keywords
creator: Bowen Shi
creator: Hyoungtae Kim
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-06-06
publication date: 2025-06-06
starting page: 50
ending page: 56
doi: 10.71465/fbf258

The Impact of Hotel Companies' ESG Activities on Corporate Image, Trust and Customer Loyalty
type: item
description: This study investigates the effects of hotel corporations’ ESG (Environmental, Social, and Governance) activities on corporate image, corporate trust, and customer loyalty. The results show that environmental and social dimensions of ESG significantly and positively influence corporate image, corporate trust, and customer loyalty. Governance practices, while positively affecting corporate image, had no significant impact on trust or loyalty. Corporate image was found to significantly enhance both trust and loyalty, and corporate trust also positively influenced customer loyalty. These findings suggest that ESG initiatives—particularly those related to environmental protection and social contribution—are effective strategies for enhancing hotel brand image, building customer trust, and fostering long-term loyalty. The limited effect of governance may stem from its low visibility or relevance from the customer’s perspective.
subject: Articles
  topic: section
subject: ESG
  topic: keywords
subject: corporate image
  topic: keywords
subject: corporate trust
  topic: keywords
subject: customer loyalty
  topic: keywords
subject: hotel industry
  topic: keywords
subject: Chinese market
  topic: keywords
creator: Yang Fan
creator: Hyoungtae Kim
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-06-06
publication date: 2025-06-06
starting page: 42
ending page: 49
doi: 10.71465/fbf256

Adaptive Marketing Campaigns Using Deep Reinforcement Learning: A Customer-Centric Approach
type: item
description: In an era of data-driven decision making, traditional static marketing campaigns fall short in responding to rapidly evolving customer behaviors and preferences. This paper proposes a deep reinforcement learning (DRL) framework to enable adaptive, personalized marketing strategies that continuously evolve based on customer interactions. The proposed model treats marketing as a sequential decision-making process, optimizing campaign strategies through trial-and-error interactions with dynamic customer segments. The DRL agent learns to tailor content, timing, and channels of engagement to maximize long-term customer lifetime value (CLV), rather than short-term conversion metrics. Empirical experiments using simulated and real-world e-commerce datasets demonstrate that the DRL-based approach significantly outperforms rule-based and supervised learning baselines in retention rate, click-through rate (CTR), and cumulative revenue. The findings support the potential of DRL to drive customer-centric, self-optimizing marketing systems in digital commerce.
subject: Articles
  topic: section
subject: Adaptive Marketing
  topic: keywords
subject: Deep Reinforcement Learning
  topic: keywords
subject: Customer Lifetime Value
  topic: keywords
subject: Personalization
  topic: keywords
subject: Customer Engagement
  topic: keywords
subject: Sequential Decision Making
  topic: keywords
subject: E-commerce Campaign Optimization
  topic: keywords
creator: Amelia Reyes
creator: Steven Cho
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-06-03
publication date: 2025-06-03
starting page: 33
ending page: 41
doi: 10.71465/fbf254

A Deep Reinforcement Learning Framework for End-to-End Retail Supply Chain Optimization
type: item
description: Retail supply chains are increasingly challenged by volatility in consumer demand, supplier uncertainties, logistics constraints, and global disruptions. Traditional supply chain management approaches, often relying on deterministic planning or shallow learning-based heuristics, struggle to adapt dynamically to changing conditions. This paper proposes a novel end-to-end optimization framework leveraging Deep Reinforcement Learning (DRL) to improve supply chain decision-making across procurement, inventory, warehousing, and distribution. Our proposed architecture models the entire retail supply chain as a Markov Decision Process (MDP), where each node (e.g., warehouse, store, supplier) acts as an agent interacting with a stochastic environment. The DRL framework employs a centralized actor-critic algorithm to learn optimal joint policies for multiple supply chain functions, aiming to minimize operational costs while maximizing service levels. The model is trained in a simulated environment constructed from historical retail transaction and logistics data. Experimental results demonstrate that the DRL-based policy outperforms traditional rule-based and forecast-driven methods in terms of inventory turnover, fulfillment rate, and response to demand shocks. This study contributes to the literature by integrating dynamic learning and real-time adaptation into holistic supply chain operations, offering a promising approach to scalable, intelligent retail logistics.
subject: Articles
  topic: section
subject: Retail Supply Chain
  topic: keywords
subject: Deep Reinforcement Learning
  topic: keywords
subject: End-to-End Optimization
  topic: keywords
subject: Inventory Management
  topic: keywords
subject: Dynamic Decision-Making
  topic: keywords
subject: Actor-Critic
  topic: keywords
subject: Markov Decision Process
  topic: keywords
subject: Intelligent Logistics
  topic: keywords
creator: Sofia Petrova
creator: James Lee
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-06-03
publication date: 2025-06-03
starting page: 24
ending page: 32
doi: 10.71465/fbf253

Blockchain-Based Title Confirmation and Transaction Mechanism for Commercial Real Estate Tokenization
type: item
description: Amid the rapid development of Web3.0 technologies and blockchain infrastructures, the commercial real estate industry is experiencing a significant shift toward digitalization. This study proposes a tokenization framework for commercial real estate assets, grounded in the ERC-1400 standard. The system enables precise asset share registration via smart contracts and ensures regulatory compliance through on-chain KYC authentication and identity mapping mechanisms. To address liquidity challenges, an off-chain valuation oracle and a decentralized finance (DeFi) collateralization model are integrated into the architecture, enhancing the tradability of tokenized real estate assets. Simulation experiments and empirical analyses were conducted to evaluate title confirmation efficiency, asset liquidity, and operational controllability. The results demonstrate that, compared to conventional methods, the proposed system improved title confirmation efficiency by 99.6% (t = 327.4, p < 0.001), increased average daily transaction volume by 327% (χ² = 158.6, p < 0.001), achieved 100% transaction accuracy in 1,500 simulations, and successfully identified and intercepted 47 abnormal transactions via the KYC mechanism. These findings provide both a viable technical approach and theoretical basis for implementing real estate asset tokenization in practice, contributing to the secure and scalable integration of traditional assets into decentralized ecosystems.
subject: Articles
  topic: section
subject: Commercial Real Estate
  topic: keywords
subject: Asset Tokenization
  topic: keywords
subject: Blockchain
  topic: keywords
subject: Web3.0
  topic: keywords
subject: Smart Contracts
  topic: keywords
subject: Decentralized Finance
  topic: keywords
subject: Distributed Ledger
  topic: keywords
subject: Consensus Mechanism
  topic: keywords
creator: Minh T. Le
creator: Oliver Harris
creator: Charlotte Bennett
creator: Fiona Greene
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-30
publication date: 2025-05-30
starting page: 16
ending page: 23
doi: 10.71465/fbf247

Design and Evaluation of Quantitative Investment Strategies Using Reinforcement Learning and Multi-Factor Models
type: item
description: Traditional quantitative investment models often suffer from limited adaptability in volatile market environments. To overcome this constraint, this study proposes a reinforcement learning-based framework, RL-Quant, which integrates technical indicators, sentiment signals, and fundamental variables into a multi-factor state representation. The agent is trained using the Proximal Policy Optimization (PPO) algorithm, with a customized reward function incorporating dynamic risk control parameters to constrain maximum drawdown and return volatility. Empirical backtesting is conducted on the CSI 300 and S&P 500 indices from 2014 to 2023. The proposed framework achieves an annualized return of 19.6%, a maximum drawdown of 9.2%, and a Sharpe ratio of 1.87, consistently outperforming benchmark ETFs and equal-weighted portfolios. Notably, the model demonstrates robust downside protection during periods of heightened market stress, including the March 2020 downturn. These results suggest that reinforcement learning can enhance the responsiveness and stability of quantitative strategies under dynamic market conditions.
subject: Articles
  topic: section
subject: Reinforcement Learning
  topic: keywords
subject: Quantitative Investment
  topic: keywords
subject: PPO Algorithm
  topic: keywords
subject: Multi-Factor Modeling
  topic: keywords
subject: Risk Control
  topic: keywords
creator: Wing-Yee Lam
creator: Ka-Yan Cheung
creator: Tsz-Hin Lau
creator: Man-Kit Leung
creator: Ho-Lam Chan
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-10
publication date: 2025-05-10
starting page: 9
ending page: 15
doi: 10.71465/

Carbon Credit Evaluation for Green Finance Based on Multimodal Data and Dynamic Ratings
type: item
description: This study aims to construct a carbon emission credit evaluation system that meets the development requirements of green finance and ensures high scientific validity and accuracy. By integrating both structured and unstructured multimodal data—including corporate Environmental, Social, and Governance (ESG) reports, financial information, policy response data and news sentiment—this study adopts a joint modeling approach based on Graph Convolutional Networks (GCN) and Light Gradient Boosting Machine (LightGBM) to assess the carbon emission credit levels of enterprises. The dataset is obtained from the China Carbon Exchange, the Green Bond Public Information Platform, and the Wind ESG Rating Database, covering data from 1,824 enterprises between 2017 and 2022. The experimental results show that the proposed method achieves an accuracy of 86.1% in predicting the probability of corporate carbon violations, representing an improvement of 12.7% over the traditional logistic regression model. Moreover, this system can dynamically generate highly interpretable rating reports at the industry level, showing broad application prospects in key areas of green finance, such as green credit approval and the design of carbon asset securitization products.
subject: Articles
  topic: section
subject: Green finance
  topic: keywords
subject: Carbon credit rating
  topic: keywords
subject: Multimodal data
  topic: keywords
subject: Graph Convolutional Network
  topic: keywords
subject: ESG indicators
  topic: keywords
creator: Thabo Dlamini
creator: Naledi Khumalo
creator: Kabelo Mthembu
creator: Ayanda Mokoena
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-10
publication date: 2025-05-10
starting page: 1
ending page: 8
doi: 10.71465/

The Future of Crypto currency: Opportunities and Risks in a Digital Economy
type: item
description: The future of crypto currency presents a dynamic landscape characterized by substantial opportunities and inherent risks. As digital currencies continue to evolve, their impact on the global economy, financial systems, and regulatory environments is becoming increasingly significant. This paper explores the emerging trends in crypto currency, assesses the potential benefits such as financial inclusion, enhanced transaction efficiency, and investment opportunities, while also examining the risks including regulatory challenges, security vulnerabilities, and market volatility. By analyzing current developments and forecasting future scenarios, this study aims to provide a comprehensive understanding of how crypto currency may reshape the digital economy.
subject: Articles
  topic: section
subject: Crypto currency
  topic: keywords
subject: Digital Economy
  topic: keywords
subject: Blockchain Technology
  topic: keywords
subject: Financial Inclusion
  topic: keywords
subject: Market Volatility
  topic: keywords
subject: Regulatory Challenges
  topic: keywords
subject: Investment Opportunities
  topic: keywords
subject: Digital Currencies
  topic: keywords
subject: Security Risks
  topic: keywords
subject: Future Trends
  topic: keywords
creator: Dr. Faisal Ahmed
rights: Copyright (c) 2024 Frontiers in Business and Finance
date: 2024-12-31
publication date: 2024-12-31
starting page: 218
ending page: 231