Credit risk management (CRM) is a fundamental pillar of financial systems, attracting attention from practitioners and researchers. Traditional credit assessment methods have limitations in today's complex, fast-changing financial environment. Advances in machine learning (ML) and behavioral data analytics offer new possibilities for improving CRM through better models and performance. This paper provides a systematic review of ML applications in credit default prediction and early warning systems, critically synthesizing recent literature. It discusses three major dimensions: the evolution of ensemble learning algorithms, the use and issues of behavioral data in feature engineering, and advances in model explainability (XAI). The paper shows that ensemble learning models have superior predictive power and argues that behavioral data complement traditional datasets for underbanked populations, such as "credit invisibles." It makes a strong case for XAI being essential for model transparency, combating bias, and meeting regulatory requirements. The review also addresses class imbalance, data privacy, and ethical issues, with mitigation strategies. The review offers theoretical guidance and practical implications for financial institutions to improve risk control and build reliable early warning systems, outlining directions for future research.

Entertainment industry marketing has emerged as one of the most dynamic research domains in the digital era. Based on a systematic review of 14 frontier studies published between 2023 and 2025, this article constructs a three-dimensional analytical framework that integrates emotional mechanisms, content production models, and brand ecosystems. The findings reveal that: (1) emotional mechanisms serve as the core explanatory variable spanning three research pathways—fan economy, social media marketing, and brand crossover collaboration; (2) entertainment products, characterized by six fundamental features (hedonic, narrative, cultural, creative, innovative, and digital), demand distinctive marketing paradigms distinct from conventional goods; (3) content production is undergoing a structural shift from individual creativity toward systematic, industrialized, full-process control models; and (4) brand crossover collaboration has evolved from simple IP licensing into a complex mechanism encompassing cultural symbol translation, consumer identity construction, and ecosystem coordination. At the theoretical level, this article proposes an integrative framework built upon Behrens et al.'s six-feature model, synthesizing emotional capitalization, content industrialization, and brand ecologization. At the practical level, it reveals the timing effects of emotional appeals, the decisive role of content quality, and the boundary conditions of the fan economy efficacy. Research trends indicate that generative AI, immersive technologies, and cross-platform integration will become key growth areas, while the negative effects of fan economy, cross-platform comparative research, and longitudinal tracking constitute critical research gaps.

Valuation Adjustment Mechanisms (VAMs) come with many questions as to their enforceability, risk allocation and means of dispute-resolution under the current regulatory frameworks for private equity investments. The study dissects how the risk is allocated between the investors and the portfolio companies, key contractual features and maps the practical dispute-resolution routes by analysing selected case studies and a relevant regulatory guidance. The paper also recommends clause designs for the actionable sections of the paper and due diligence checklist for equity investments for better risk management and compliance. The results shed light on the design decisions of VAMs in the context of regulatory development, point out some of the typical challenges in terms of enforceability, and provide practical recommendations to practitioners, investors and regulators. The contribution is in the idea and the execution of connecting theory and practice, turning regulatory understanding into drafting guidance and due-diligence processes, and thus improving clarity, predictability and resilience in PE transactions.

Loan approval prediction is central to financial risk management, where lenders need models that are both accurate and interpretable. We compared five machine learning classifiers on a loan approval dataset: Random Forest, XGBoost, LightGBM, Logistic Regression, and Support Vector Machine. The original 45,000-sample dataset was reduced to 20,000 for training due to computational constraints. We applied SHAP TreeExplainer to interpret the best-performing model. XGBoost achieved the highest AUC (0.9747) and accuracy (0.931). SHAP identified previous loan status, personal income, loan percentage, and loan interest rate as the top four features by importance. The analysis also traces how each feature shifts individual predictions toward approval or rejection. These findings give practitioners evidence for model selection in loan approval settings and produce explanations that meet regulatory transparency requirements.

This paper examines whether technical information can generate economically meaningful stock portfolio returns under transaction cost constraints. Using daily data for CSI 300 constituent stocks from April 2, 2019, to December 30, 2025, this paper compare basic price-volume variables, traditional technical indicators, market-state variables, StockJump and VPIN microstructure variables, OHLC image CNN, and Raw OHLCV CNN models. Instead of relying mainly on classification metrics such as AUC and Accuracy, this paper evaluate models through out-of-sample quintile portfolios, focusing on long-short returns, Sharpe ratios, turnover, and transaction-cost-adjusted performance. The results show that traditional technical indicators, market-state variables, and microstructure variables provide limited incremental value beyond basic price-volume information. The 1-day prediction task exhibits some cross-sectional sorting ability, but its returns are largely eroded by high turnover after 10 bps transaction costs. In contrast, the 5-day OHLC image CNN retains a 9.34% long-short net annualized return and a 0.761 net Sharpe after costs, outperforming structured feature models and Raw OHLCV CNN. These findings suggest that technical information should be evaluated by portfolio-level economic value, and that image-based price-path representation better captures nonlinear information over longer horizons.

In modern corporate finance, the weighted average cost of capital is an important indicator for evaluating financing decisions, investment projects and enterprise valuation. With changes in market interest rates, investor expectations and operating risks, enterprises need to understand how their capital structure affects the overall cost of capital. This study examines how capital structure affects weighted average cost of capital (WACC) and corporate valuation using Costco Wholesale Corporation as a case. Based on Costco's FY2024 financial data and market inputs around September 2024, the study estimates the cost of equity, after-tax cost of debt, and market value weights. The result shows that Costco's FY2024 WACC is 7.539%, WACC provides a practical framework for evaluating enterprise financing decisions and company valuation. The analysis of Costco Wholesale Corporation shows that maintaining a balance between debt and equity helps improve financing efficiency and supports long-term value creation. The findings in this paper suggest that Costco maintains a conservative capital structure and that WACC is useful for evaluating financing decisions and long-term firm value.

The construction industry is characterized by long project cycles, substantial upfront capital requirements, slow cash inflows, and high cash flow volatility, making working capital management a critical factor affecting the survival and development of construction enterprises. This paper focuses on working capital management in the construction sector, systematically analyzing its fundamental concepts, industry-specific features, major challenges, and optimization strategies. Employing literature review and inductive analysis methods, the study delves into common issues such as delayed payments, inefficient accounts receivable and payable management, excessive capital tied up in inventory and work-in-progress and inaccurate financial forecasting. These challenges can be addressed through improved cash flow forecasting and monitoring, optimized accounts receivable and payable processes, enhanced inventory and supply chain coordination, and strengthened policy support. Research shows that the problems in the operational capital management of the construction industry are interrelated and mutually influential. To enhance the efficiency of operational capital, it requires the joint efforts of internal improvement within the enterprise and external cooperation.

The COVID-19 pandemic, geopolitical conflicts and extreme climate events have disrupted global supply chains, exposing the fragility of the traditional efficiency-first model. Taking political, economic risks and natural risks as research objects, this paper reviews risk identification theories and deconstructs two cases—Apple's friendshoring and Toyota's response to the Great East Japan Earthquake—to extract behavioral logic and decision-making mechanisms against different risks. The study finds that firms need to shift from passive emergency response to active defense, establishing a four-stage framework "Prediction–Protection–Buffering Recovery", with comprehensive implementation of strategies covering four dimensions: multi-sourcing, dynamic buffer inventory, digital monitoring and optimization of supply chain network structure. This paper points out that current research still lacks sufficient discussion on the interactive mechanism between enterprises' micro-level dynamic capabilities and risk management. Future research can further explore how emerging technologies reshape the practical paradigm of supply chain risk management. This study offers theoretical insights and practical implications for risk management decision-making in multinational corporations amid intensifying uncertainties in global supply chains.

Artificial intelligence investment lies at the core of enterprise digital transformation, and its influencing mechanism on green innovation efficiency urgently needs to be explored. Taking China's A-share listed companies from 2007 to 2023 as samples, this paper measures the development level of enterprise artificial intelligence from two dimensions: disclosure frequency of artificial intelligence-related words and investment level, and uses the random-effects panel Tobit model to examine its impact on green R&D efficiency and green achievement transformation efficiency. The findings are as follows: First, each logarithmic unit increase in the frequency of artificial intelligence words raises green innovation efficiency by approximately 0.042 units (about 8%), and a one-standard-deviation increase in investment level improves green innovation efficiency by about 4%, both of which are significant at the 1% level. Second, the conclusions still hold after robustness tests using the high-dimensional fixed-effects model and the DID policy shock of the National New-Generation Artificial Intelligence Innovation and Development Pilot Zones, and pass the parallel trend test. Third, the asset-liability ratio significantly positively moderates this effect, highlighting the key role of financing capacity. Fourth, the promotion effect is more significant in state-owned enterprises, large-scale enterprises and manufacturing enterprises. This paper provides empirical evidence for optimizing enterprise artificial intelligence investment strategies and promoting green transformation.

Consumer electronics firms have faced relentless margin compression over the past decade, yet Apple's gross profit has remained above 38% despite increasingly incremental hardware upgrades. This discrepancy demonstrates that product design serves not only as a production factor but also as a strategic marketing asset, whose financial benefits cannot be fully reflected under traditional accounting standards. By integrating design thinking, brand equity, and ecosystem theories, this paper traces how Apple's retail architecture, packaging rituals, and proprietary interface protocols generate quantifiable financial outcomes. Empirical analysis of Apple's 2015-2024 trajectory reveals three findings. First, ecosystem lock-in and material design choices sustain a gross margin of roughly 45%, representing 15-25 percentage points above that of Samsung or Google hardware. Second, brand equity derived from these design investments contributes an estimated 23-28% to the firm's $3 trillion market capitalization. Third, the 90% customer retention rate-against an Android benchmark near 70%-raises Customer Lifetime Value by approximately 50%, underpinning shareholder returns exceeding $700 billion over the period. The paper closes with a replicable framework for high-tech firms to isolate and evaluate the financial contribution of design expenditure.