With the growing importance of climate change and environmental sustainability in the world, businesses are now understood to play a significant part in reducing emissions and becoming more sustainable. Carbon information disclosure is a major way for companies to disclose their carbon emissions and climate risk, which ultimately affects the cost of capital. This paper examine the association between carbon information disclosure quality and corporate financing costs, based on a sample of listed firms in the Asia-Pacific region. Using fixed-effects models, the study investigates how a carbon disclosure affects financial costs and also examines the nonlinear form of this relationship with regulating factors. The findings show that a high quality level of carbon disclosure generally reduces financing costs but non-linearly, following an inverted U-shape. In particular, over-disclosure may be associated with greater financing costs. Second, the study shows that institutional context is an important moderating factor; stricter environmental disclosure requirements lead to a greater negative relationship between carbon disclosure quality and financing costs.

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With the increasing volatility of global capital markets, particularly in the stock market, efficiently forecasting price fluctuations has become a core issue in financial technology. The traditional AutoRegressive Integrated Moving Average (ARIMA) model performs well in fitting linear relationships but struggles to capture complex nonlinear behaviors. In contrast, the deep learning method Long Short-Term Memory (LSTM) is capable of handling nonlinear dependencies and long-sequence features. Based on the assumption that stock market data exhibit both linear and nonlinear characteristics, this study develops a hybrid LSTM-ARIMA model that integrates the strengths of both approaches: ARIMA is first employed to extract linear trends and generate residuals, which are then combined with technical indicators such as Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) and fed into LSTM to capture nonlinear fluctuations. Experiments are conducted using 30-minute frequency data of the Standard & Poor's (S&P) 500 index, adopting two integration strategies: dynamic weighting and stacking. The results indicate that during the Federal Reserve's interest rate cut cycle, the model outperforms the single model in terms of accuracy and stability in short-term volatility prediction. Specifically, stacking demonstrates stronger adaptability during policy shock periods by correcting residuals, whereas dynamic weighting, which relies on historical Mean Squared Error (MSE), proves slightly insufficient under regime shifts. This study provides empirical evidence and quantitative insights for financial time series forecasting and volatility management during interest rate cut cycles.

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In April 2020, Yuan You Bao, a retail financial derivative product launched by Bank of China, encountered unprecedented losses when the NYMEX WTI May futures contracts settled its price at -$37.63 per barrel. This incident reflects the cruel reality about China’s retail finance ecosystem by revealing the structural weakness in product design, disclosure, and risk controls. This paper aims to apply a qualitative method, combining regulatory documents, media reports, and comparative international frameworks on investor protection and product governance, to analyze. Problems addressed in this research are purposed to conclude why a commodity derivative reached mass-market shares, and how institutional risk management malfunctioned, as well as what measures of reforms are worth considering to align retail-facing derivative products with long-term stability. Methods applied include timeline reconstruction, stakeholder analysis, and comparison to EU MiFID II product-governance and PRIIPs disclosure regimes and U.S. Title VII (Dodd-Frank) conduct standards. The research finds there is a mismatch between product structures and investor capacity, insufficient margining and position-limit safeguards, delayed rollover safety protocols, and limited updated risk communication. From the macro-perspective, this incident indicates that the financial market in China requires further regulatory methods, including robust product-approval committees, negative-price logistics, suitability gating, and pre-contractual information disclosures. The paper draws a conclusion that the financial crisis should catalyze governance and regulation upgrades across Chinese banks and accelerate rule-making that balances financial innovation for protecting investors’ stakes.

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In recent years, with the development of e-commerce platforms and the proliferation of short videos and the internet, live-streaming commerce has emerged as a new marketing approach. Generation Z has also moved beyond purely functional consumption, increasingly gravitating towards personalized spending that delivers positive experiences and emotional fulfilment. Based on this, this paper employs the concept of emotional value to explore how the language used in live-streamed sales influences consumer decision-making. This study, grounded in emotional consumption theory and incorporating the customer value model, analyses consumers' purchasing behaviour, habits, and preferences. It aids in understanding the relationship between emotional value and consumer decision-making, while also contributing to practical applications within the live-streaming e-commerce sector. On this basis, it can be concluded that live-streamed sales are fundamentally driven by emotional value, employing multiple methods to create immersive experiences that prompt rapid user decision-making. Furthermore, emotional value influences both consumers' immediate and future purchasing behaviour. This provides a theoretical foundation for relevant e-commerce models and indicates that corporate marketing must balance rational information with emotional experiences. This approach assists brands in building consumer loyalty and securing a differentiated competitive advantage.

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