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.

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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.

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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.

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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.

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