This study constructs a three-dimensional space model based on fund investment styles, using a sample of open-end equity and mixed funds from 2005 to 2021 to measure the degree of style convergence. The research explores how style convergence impacts fund managers’ window dressing behavior. The results indicate that, after accounting for the effects of fund performance, style convergence exacerbates window dressing behavior among fund managers. Specifically, this is reflected in fund managers increasing their holdings in winning stocks and selling off losing stocks, which indirectly highlights the intense competition within China’s open-end fund industry. The findings remain robust after a series of endogeneity and robustness tests. Further analysis reveals that style convergence contributes to the risk of client attrition, thereby intensifying the agency problem within the fund industry. The window dressing effect due to style convergence is particularly pronounced in funds managed by individuals with lower educational backgrounds, lower investment skills, smaller family sizes, and lower institutional investor ownership. The paper offers valuable insights into the agency problems arising from investment style convergence and provides guidance for mitigating fund managers' self-interested behavior.

This paper investigates how the Global Supply Chain Pressure Index (GSCPI) affects long-term stock–bond correlations in China, employing mixed-frequency data from April 2005 to June 2025 in a DCC-MIDAS-X framework. Results show that higher GSCPI significantly reduces long-term stock–bond correlations, thereby enhancing the hedging property of bonds. This effect is both state-dependent and asymmetric, remaining significant in low-volatility regimes and following negative shocks, while becoming largely muted during high-volatility periods or after positive shocks. However, the impact of GSCPI weakens substantially after China’s 2014 financial liberalization, as global financial factors increasingly drive cross-asset dynamics. Moreover, GSCPI provides incremental information that enhances portfolio diversification and hedging performance.

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.

The co-holding of mutually interdependent pairs of heterogeneous blocks can provide firms with stable financial support. The interdependence of heterogeneous blockholders’ investment decision has become an important frontier in the financial literature on large shareholders. In this paper, we study the interdependence of heterogeneous blocks in China. We find significant positive interdependence among blockholders of the same type. In heterogeneous block pairs, the financial–private pair shows positive interdependence. The findings are in contrast to those observed in the US. Under the mixed-ownership reform in China, our findings suggest the potential for cooperation among multiple blocks of the same type rather than between heterogeneous strategic partners.

This paper investigates the contribution of futures market information to enhancing the predictive accuracy of macroeconomic forecasts, using data from China. We employ three cat-egories of predictors: monthly macroeconomic factors, daily commodity futures factors, and daily financial futures variables. Principal component analysis is applied to extract key fac-tors from large data sets of monthly macroeconomic indicators and daily commodity futures contracts. To address the challenge of mixed sampling frequencies, these predictors are incor-porated into factor-MIDAS models for both nowcasting and long-term forecasting of critical macroeconomic variables. The empirical results indicate that financial futures data provide modest improvements in forecasting secondary and tertiary GDP, whereas commodity futures factors significantly improve the accuracy of PPI forecasts. Interestingly, for PMI forecast-ing, models relying exclusively on futures market data, without incorporating macroeconomic factors, achieve superior predictive performance. Our findings underscore the significance of futures market information as a valuable input to macroeconomic forecasting.

Despite nearly twenty years of privatization, mixed-ownership reform has been the mainstay of SOE reform in China in recent years. This raises the question of whether the financial performance of mixed-ownership firms (Mixed firms) is better than private-owned enterprises (POEs). Although Mixed firms suffer more from government intervention, unclear property rights, and interest conflicts between state shareholders and private shareholders, they can also benefit from the external resources controlled by the state. Therefore, the performance of Mixed firms is still unclear. Collecting data from the Chinese A-share listed market, we divide the firms into POEs, Mixed firms controlled by the state (MixedSOEs), and Mixed firms controlled by the private sectors (MixedPOEs). Measuring profitability using ROA and ROE, we find that on average, POEs perform better than Mixed firms, and MixedPOEs have a higher profitability than MixedSOEs. Within Mixed firms, more state shares are related to lower profitability, and more private shares are related to higher profitability. Using the NBS survey data, we further find that on average, SOEs exhibit the lowest profitability, with MixedSOEs and MixedPOEs in the middle, and POEs have the highest profitability. We try to address the endogeneity challenge in several ways and get similar results. Overall, our analysis provides new evidence on the financial performance of mixed-ownership firms.

This paper investigates the high-frequency dynamics of macroeconomic expectations and disagreement among professional forecasters. We propose a novel mixed-frequency estimation approach that integrates daily asset returns with quarterly expectation data from the Survey of Professional Forecasters. Our findings indicate that consensus forecasts are updated efficiently according to Bayes' rule, independent of prior forecasts. By employing "representative forecasters" as proxies for real-world agents, we derive a simple yet intuitive evolution equation for disagreement, revealing that changes in disagreement are primarily driven by different interpretations of new information. Furthermore, we reconstruct daily series of expectations and disagreement concerning macroeconomic growth, achieving impressive R2 values of 93.3% and 84.5% against the true quarterly series.

Despite nearly twenty years of privatization, mixed-ownership reform has been the mainstay of SOE reform in China in recent years. This raises the question of whether the financial performance of mixed-ownership firms (Mixed firms) is better than private-owned enterprises (POEs). Although Mixed firms suffer more from government intervention, unclear property rights, and interest conflicts between state shareholders and private shareholders, they can also benefit from the external resources controlled by the state. Therefore, the performance of Mixed firms is still unclear. Collecting data from the Chinese A-share listed market, we divide the firms into POEs, Mixed firms controlled by the state (MixedSOEs), and Mixed firms controlled by the private sectors (MixedPOEs). Measuring profitability using ROA and ROE, we find that on average, POEs perform better than Mixed firms, and MixedPOEs have a higher profitability than MixedSOEs. Within Mixed firms, more state shares are related to lower profitability, and more private shares are related to higher profitability. Using the NBS survey data, we further find that on average, SOEs exhibit the lowest profitability, with MixedSOEs and MixedPOEs in the middle, and POEs have the highest profitability. We try to address the endogeneity challenge in several ways and get similar results. Overall, our analysis provides new evidence on the financial performance of mixed-ownership firms.

There is mixed evidence of a positive relationship between the stock market risk and return. We reexamine this critical implication of asset pricing theory using fresh data from China's stock market, which is largely segmented from the rest of the global financial market. Using formal variable selection methods and a comprehensive set of predictor variables, we identify conditional market variance, scaled market prices, and inflation as crucial determinants of equity premiums. The estimated simple risk-return relationship exhibits downward omitted variable bias, which underlines the importance of considering multiple factors to explain the variation in equity premiums. We cannot wholly attribute the three-factor conditional equity premium model to data mining, as Guo, Sanni, and Yu (2022) select the same model for the U.S. stock market. These findings challenge existing asset pricing models and provide valuable guidance for future theoretical research.

In the era of digital economy, although the production (labor) tools of enterprises are digitalized, intelligent, and networked, and new characteristics and scenarios have emerged in enterprise operation and labor methods, the human capital possessed by workers has become the driving force for the sustainable development of entrepreneurial enterprises. Workers who master digital technology play a decisive role in the sustainable and healthy development of entrepreneurial enterprises. The article points out that in the era of digital economy, human capital is a key factor for economic growth and development. In the fields of mixed ownership and private economy, the employment system will gradually "retire", and modern partnership systems will prevail; The modern partnership system can motivate partners to collaborate and innovate, which is an upgrade of the manager system; The article briefly introduces the advantages and disadvantages of the dual ownership structure and the dynamic equity distribution mechanism of start-up companies; And the achievements made by Xiaomi Group, Huawei Company, and Midea Group in implementing a business partnership system.