In the realm of financial stability, accurate credit default discrimination models are crucial for policy-making and risk management. This paper introduces a robust model that enhances credit default discrimination through a sophisticated integration of a filter-wrapper feature selection strategy, instance selection, and an updated version of majority voting. We present a novel approach that combines individual and ensemble classifiers, rigorously tested on datasets from Chinese listed companies and the German credit market. The results highlight significant improvements over traditional models, offering policymakers and financial institutions a more reliable tool for assessing credit risks. The paper not only demonstrates the effectiveness of our model through extensive comparisons but also discusses its implications for regulatory practices and the potential for adoption in broader financial applications.

We construct the China Crude Oil Volatility Index (CNOVX)—the first model-free, optionimplied measure of forward-looking oil price risk for China—using INE crude oil options from 2021 to 2024 and an adapted CBOE methodology that accounts for sparse strike availability via smooth interpolation and extrapolation. Our results show that CNOVX increases with trading activity in the futures market, declines with option volume, and is strongly predicted by the 30-day realized variance of the SC crude oil futures contract. External shocks, including the Russia–Ukraine conflict and the Geopolitical Risk Index, significantly elevate CNOVX levels. During the COVID-19 pandemic, mortality risk intensifies the volatility-amplifying role of futures trading and strengthens the volatility-dampening effect of options, while confirmed case counts have weaker influence. We further document a pronounced asymmetric leverage effect: negative futures returns raise CNOVX more than positive returns of equal size. However, volatility feedback effects are negligible, as changes in implied volatility respond primarily to contemporaneous market conditions. Overall, CNOVX serves as a timely and informative benchmark for monitoring risk in China’s evolving crude oil derivatives market, with valuable implications for investors, hedgers, and policymakers.

The Fibonacci sequence is created by the recurrence of Fn = Fn−1 + Fn−2 ( n ≥ 2; F0 = 0; F1=1) from which the nearly 38.2% or 61.8% is derived for revenue increase or decrease. It has been increasingly and widely studied in research on options market trading. The high volatility of the options market makes the option premium greatly affected by the growing emotional involvement of buyers and sellers before the position is closed. The efficient affective computing and measures may provide traders a rough guide to working out the route to a profit. Based on the practical application of Fibonacci-like sequence and affective computing of option trading data in China SSE (Shanghai Stock Exchange) 50ETF options, we concluded that profit statistically changes around 38.2% or 61.8% increase line once call options flood in the market and bring the rapid price acceleration. On the contrary, 38.2% or 61.8% is considered another temporary decrease line when the price quickly falls from the balance point of price under the influence of huge put options. The mixed emotions of greed and fear make the option premium commonly fluctuate in cycles. The Fibonacci-like wavelet analysis is only one of the options volatility strategies, and it does not change the nature of market uncertainty.

February 9, 2025 marks the 10th anniversary of the establishment of China's ETF options market. To celebrate this anniversary, China will open the ETF options market to qualified foreign investors on October 9, 2025. This is both an opportunity and a challenge. This is the first time in a decade that China has decided to open its ETF options market. The challenge is that foreign investors will face competition from China's 1.08 million options investors. This article will discuss the basic rules and requirements for options trading in China. In addition, we will introduce the application of Confusion Quotient sentiment index in options trading, and analyze how options contract premiums fluctuated significantly after the Fed cut interest rates by 50 basis points on September 18, 2024. Within a month, the Fed's interest rate cut triggered a sharp rise in call options contracts in China's options market, with a maximum profit of 3507.32%, and put option contracts suffered huge losses, with a maximum loss of 99.91%. Our findings prove that China's ETF options market is highly volatile, presenting both opportunities and challenges for foreign investors. Options trading is a double-edged sword, and you need to be cautious when entering the market.

This study investigates the impact of RegTech adoption in the M&A regulatory review process on deal performance. Leveraging the staggered implementation of the SOEs Online Supervision System (SOSS) by China’s State-Owned Assets Supervision and Administration Commission (SASAC) across its central and 31 provincial offices from 2018 to 2021, we find that SOSS directly enhances SASAC’s decision-making efficiency and improves its capacity to screen and approve higher-quality M&A deals. More importantly, SOE-led M&A transactions exhibit higher announcement returns as well as improved long-run stock and operating performance following the system’s implementation. The positive impact of SOSS is more pronounced for acquirers with stronger technological infrastructure, in transactions characterized by low transparency and weak governance, and in provinces with more stringent external scrutiny. Overall, by addressing regulator-firm information asymmetry and reinforcing managerial accountability, SOSS improves regulatory effectiveness in overseeing major investment activities among SOEs.

In this paper, we investigate alternative one-factor and two-factor continuous-time models with both affine and non-affine variance dynamics for the Chinese options market. Through extensive empirical analysis of the option panel fit and diagnostics, we find that it is necessary to include both the non-affine feature and the multi-factor structure. For performance evaluation, we examine various measures from both aggregate and dynamic perspectives. Our results are statistically significant.

Prefabricated buildings(PBs) have significant advantages in improving construction efficiency, saving resources, and reducing environmental pollution. They have become an important direction for transforming and upgrading the global construction industry. However, the high construction costs have severely restricted their large-scale adoption. To systematically explore the key influencing factors and the mechanism of the construction cost of PBs, this study uses the method of combining interpretative structural model (ISM) and structural equation model (SEM), identifies the main influencing factors by synthesizing literature and data analysis, analyze hierarchical relationships between these factors via ISM, and quantifies the influence intensity and mechanism of the construction cost by SEM method. The results show that the driving factors of the construction cost of PBs can be divided into several levels. The core factors, such as the assembly rate, the production scale of prefabricated components, the integration of design management, the technical level of designers, and the specialization of prefabricated components in the factory, play a crucial role in cost optimization. In conclusion, this study deeply reveals the impact mechanism of the construction cost of PBs, offers practical guidance for reducing construction costs and optimizing resource allocation, and provides a scientific basis for government policy-making and enterprise strategic decision-making.

This study identifies a significant settlement effect in China’s equity options market, where price decline and pre-settlement return momentum exists on the settlement Friday (T+2) due to a temporal misalignment between option expiration (T) and the T+1 trading rule for the underlying asset. We attribute this phenomenon to three distinct behavioral channels: closing pressure from put option unwinding, momentum-generating predatory trading by futures-spot arbitrageurs exploiting liquidity fragility, and an announcement effect that attenuates the anomaly by adjusting spot speculators' expectations. Robust empirical analysis identifies predatory trading as the primary driver of the settlement effect.These findings offer critical insights for market microstructure theory and the design of physically-delivered derivatives.

We empirically examine how institutional investors react to adjustments in environmental policies in China. We observe a seemingly counterintuitive phenomenon: when environmental policies intensify, fund managers do not increase their holdings in high ESG-rated firms as might typically be expected; instead, they significantly divest from these firms. This behavior stems from the fact that, under stringent environmental policies, maintaining a high level of ESG investing leads to financial losses and fund outflows, especially in the short term, which impair fund managers’ compensation and raise career concerns. Further, within the context of environmental policy adjustments, our heterogeneity analysis tries to disentangle the true motivations behind institutional investors' ESG adoptions. We demonstrate that both pro-social preferences and financial incentives play pivotal roles, and that fund managers do not tolerate unlimited financial losses when ESG investing underperform. Our findings reveal the economic impact of environmental policies on institutional investors and shed light on the contentious and complex nature of the ESG concepts.

Public emergencies reduce social welfare but may paradoxically stimulate corporate innovation through crisis-driven technological adoption. This study establishes a theoretical framework demonstrating that exogenous shocks create asymmetric innovation incentives, with digitally disadvantaged firms exhibiting stronger technological upgrading responses. Empirically, we construct a firm-level digital transformation index through textual analysis using a multi-source media database in China to show that digital transformation can endow firm resilience by boosting capital market performance during public emergencies, especially for those medium-sized enterprises due to the costs and need for digital transformation. This research adds to the evidence that public emergencies can leverage advanced technology adoption.