We introduce a real-time dispersion measure based on cross-sectional stock returns explicitly designed to capture retail-driven speculative episodes. Elevated return dispersion effectively identifies periods characterized by intensified retail investor trading behaviors, driven by salience, diagnostic expectations, and extrapolative beliefs. During these high-dispersion states, momentum strategies collapse, and short-term reversals become dominant. Conditioning momentum strategies on our dispersion measure resolves the longstanding puzzle of missing momentum in retail-intensive markets such as China, substantially enhancing profitability. A dynamic rotation strategy between momentum and short-term reversal portfolios guided by dispersion states achieves annualized Sharpe ratios nearly double those of static approaches. Extending our analysis internationally, we employ Google search trends as proxies for retail investor attention, confirming that dispersion robustly predicts momentum and reversal returns globally. Our findings underscore the behavioral channel through which retail-driven speculation conditions momentum dynamics, providing clear implications for dynamic portfolio management strategies.

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.

Background Economic stability depends on the ability to foresee financial risk, particularly in markets that are extremely volatile. Unbalanced financial data is difficult for traditional Support Vector Machine (SVM) models to handle, which results in subpar crisis detection capabilities. In order to improve financial risk early warning models, this study combines Gaussian SVM with stochastic gradient descent (SGD) optimisation (SGD-GSVM). Methods The suggested model was developed and assessed using a dataset from China's financial market that included more than 2,000 trading days (January 2022–February 2024). Missing value management, Min-Max scaling for normalising numerical characteristics, and ADASYN oversampling for class imbalance were all part of the data pretreatment process. Key evaluation metrics, such as accuracy, recall, F1-score, G-Mean, AUC-PR, and training time, were used to train and evaluate the SGD-GSVM model to Standard GSVM, SMOTE-SVM, CS-SVM, and Random Forest. Results Standard GSVM (76% accuracy, 1,200s training time) and CS-SVM (81% accuracy, 1,300s training time) were greatly outperformed by the suggested SGD-GSVM model, which obtained the greatest accuracy of 92% with a training time of just 180 seconds. Additionally, it showed excellent recall (90%) and precision (82%), making it the most effective and efficient model for predicting financial risk. Conclusion This work offers a new method for early warning of financial risk by combining SGD optimisation with Gaussian SVM and employing adaptive oversampling for data balancing. The findings show that SGD-GSVM is the best model because it strikes a balance between high accuracy and computational economy. Financial organisations can create real-time risk management plans with the help of the suggested technique. For additional performance improvements, hybrid deep learning approaches might be investigated in future studies.

This study examines whether and how minimum wage hikes affect workers’ strikes in the context of China. We show that minimum wage significantly increases strikes at the city-level, and this effect is mainly motivated by demands for unpaid wages and severance pay. Mechanism analysis reveals that workers’ strikes are caused by inevitable involuntary unemployment arising from wage hikes. In addition, the increase in workers’ strike activities is more significant in tertiary industries, which require a larger share of low-wage workers and in regions with a higher degree of digital economy and innovation. Our findings provide clear policy implications for policymakers concerned with minimum wage and unemployment.

本文提出了基础资产为无红利分配股票的美式看跌期权的第一个闭合计算公式。美式看跌期权赋予其持有人在期权存续期的任一时刻、以约定价格出售股票的权利但非义务。在过去的几十年中，特别是在Black-Scholes模型给出欧式期权的定价公式后，人们在美式期权的定价方面做了大量探索，提出了不少方法，但尚无闭合公式求法。本文提出了一个美式看跌期权提前行权的最优策略，即当且仅当一个美式看跌期权被提前行权时的收益大于其对应的欧式看跌期权的价值时，该美式看跌期权才会被提前行权。基于这一策略，本文提出了一系列紧密关联的定理并最终推出了一个闭合计算公式。另外，基于该闭合公式得出的结论，本文还指出了Merton（1973）有关永久美式看跌期权(perpetual American put option)的模型是不妥的，明确指出永久美式看跌期权（股票无红利）的价格等于该期权的执行价格。This paper proposes a closed form solution for pricing an American put option on a non-dividend paying stock. An American put option grants its holder rights, but not obligation to sell a stock in a fixed price at any time up until maturity. In the past decades, there is no closed form solution for pricing American options although many people made great efforts. In this paper, an optimally early exercise strategy of an American put option on a non-dividend paying stock is set up. That is, an American put option should be early-exercised when the maximum option premium of early exercise is no less than the value of its European counterpart; otherwise, it should not be early-exercised. Based on this strategy, a series of lemmas is proposed and a closed form formula is drawn. Also, this paper shows that Merton (1973)’s formula does not do a good job for pricing perpetual American put options and shows the price of a perpetual American put option on a non-dividend paying stock is equal to the strike price.

This paper proposes a closed form solution for pricing an American put option on a non-dividend paying stock. An American put option grants its holder rights, but not obligation to sell a stock in a fixed price at any time up until maturity. In the past decades, there is no closed form solution for pricing American options although many people made great efforts. In this paper, an optimally early exercise strategy of an American put option on a non-dividend paying stock is set up. That is, an American put option should be early-exercised when the maximum option premium of early exercise is no less than the value of its European counterpart; otherwise, it should not be early-exercised. Based on this strategy, a series of lemmas is proposed and a closed form formula is drawn. Also, this paper shows that Merton (1973)’s formula does not do a good job for pricing perpetual American put options and shows the price of a perpetual American put option on a non-dividend paying stock is equal to the strike price.