Investment management in venture capital underscores the significance of post-investment management (PIM) as a core mechanism for fostering startups’ sustainable development through non-financial value-added services. Such PIM activities typically include monitoring, strategic support, resource provision, and exit planning. This paper aims to analyze how the sustainability of starups reflects a balance between survivability and scalability. The study focuses on startups funded by venture capital (VC) firms globally. Specifically, methods involve qualitative synthesis of theoretical frameworks and quantitative analysis of survival rates, revenue growth, and innovation metrics. Research findings reveal that PIM enhances startups’ survival rates and accelerates revenue growth. However, excessive intervention is correlated with a reduction in research and development investment. Moreover, governance quality is positively linked to long-term performance. Findings validate that a balanced portfolio investment management approach, which prioritizes resource empowerment rather than stringent control, maximizes sustainable development outcomes. Future research should further examine ESG integration mechanisms and cultural dynamics within emerging-market contexts.

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From the 1990s to the 2000s, Japan, the United States, and Spain have all experienced real estate bubbles triggered by prolonged loose monetary policies. Unlike Japan and Spain, which fell into prolonged economic recessions after the bubbles burst, the U.S. economy recovered quickly after a brief decline in asset values. Therefore, this paper finds that whether it was after Japan signed the Plaza Accord or after Spain joined the European Monetary Union, the loose monetary policies and low interest rates implemented by both governments led to excessive investment in the real estate industry. During the asset boom period, both Japan and Spain lacked adequate financial regulation, which resulted in the accumulation of systemic risks. Following the burst of the bubble, the surge in non-performing loan ratios led to credit tightening and significant wealth losses, ultimately plunging both economies into a deep recession. Before the subprime crisis erupted, the Federal Reserve had already assumed the responsibility of regulating financial institutions. The macroprudential management tools it employed, which targeted both the temporal dimension (countercyclical measures) and the structural dimension (risk correlation), effectively curbed the spread of the crisis across markets following the asset bubble burst. This paper suggests that developing countries should begin to establish a “two-pillar” policy before a crisis to smooth economic fluctuations and coordinate the implementation of macroprudential and microprudential policies.

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This paper explores the real-world applicability of capital structure theories by comparing two representative Chinese firms: JD.com and Alibaba. Using financial indicators and theoretical alignment, the study examines how the Trade-off Theory and Pecking Order Theory explain each firm’s financial behavior under varying corporate conditions. The comparative analysis focuses on debt structure, interest coverage, cash flow patterns, and governance models. Findings show that JD.com’s stable cash flow, asset intensity, and centralized governance align with the Trade-off Theory, while Alibaba's strong internal cash flow, asset-light operations, and sensitivity to control dilution reflect the logic of the Pecking Order Theory. These results highlight that capital structure decisions are highly context-specific and must correspond to a firm’s financial profile, risk tolerance, and strategic priorities. The study emphasizes that no single capital structure theory is universally applicable; rather, firms should adopt a contextual approach that balances cost efficiency with organizational stability. These insights contribute to a more nuanced understanding of corporate financing strategy and offer practical guidance for companies facing diverse financial and governance environments.

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Reinforcement learning (RL) has demonstrated significant potential in optimizing sequential decision-making within financial markets' highly dynamic and uncertain environments, offering distinct advantages over traditional trading approaches. This literature review investigates the use of RL in developing and improving trading strategies by integrating the findings of ten recent studies published between 2018 and 2025, selected for their focus on RL applications in different financial domains. These studies employ a range of RL techniques, such as Q-learning, and Proximal Policy Optimization (PPO), across a variety of financial markets, including stocks, Forex, Bitcoin, and derivatives. The review shows that RL-based strategies, which often use innovations such as multi-agent systems, ensemble learning, and sentiment analysis, demonstrate superiority such as better adaptability to non-dynamic stationary market conditions, enhanced risk-adjusted returns, and capability to learn complex relationships directly from market data, thus outperforming conventional methods and market benchmarks. Challenges hindering the practical application of reinforcement learning in trading include sample efficiency, training stability, market complexity, and the necessity for accurate market assumptions. These are areas requiring further examination and enhancement.

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