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.

This paper investigates the price overreaction during the initial continuous trading period of the Chinese corn futures market. Using a dynamic modeling algorithm, we identify the overreaction behavior of intraday high-frequency (1 min and 3 min) prices during the first session of daytime trading. The results indicate that the overreaction hypothesis is confirmed for the daytime prices of the Chinese corn futures market. We also find a noticeable reduction in overreaction following the introduction of night trading and this decline appears to diminish over time. Furthermore, this paper conducts an overreaction trading strategy to assess traders’ returns, revealing a slight decline in average return after the introduction of night trading. This study provides valuable insights and recommendations for exchanges and regulators in monitoring overreaction and formulating effective policies to address it.

Cultural capital plays a crucial role in influencing entrepreneurial entry, yet the regulatory and supportive role of digital finance in this context remains unclear. Based on Chinese Family Panel Studies (CFPS) and data from the Chinese Intangible Cultural Heritage List, this paper examines the significant regulatory role of digital finance in driving entrepreneurial entry through cultural capital. The research findings indicate that cultural capital, represented by the Intangible Cultural Heritage List, significantly enhances the probability of entrepreneurial entry. The support of digital finance effectively amplifies this promoting effect, as validated by multiple robustness tests. Further heterogeneity analysis reveals that the regulatory impact of digital finance support is more pronounced among urban populations, low-income groups, and individuals with high internet usage frequency.

Estimates of canonical affine term structure model parameters are highly sensitive to sample periods. For example, depending on whether the sample starts in 1961 or 1981, the 5-5 forward risk-neutral rate for September 1981 differs by 4.6 percentage points or 98% of the latter. The estimated response of this rate to high-frequency monetary policy shocks differs by a factor of three, even within a fixed sample for the monetary policy transmission regression. We suggest that a shifting endpoint model can mitigate these issues. Additionally, we provide new estimates of the effects of monetary policy shocks on long-term risk-neutral rates.

We study when CEOs with legal expertise are valuable for firms. In general, lawyer CEOs are negatively associated with frequency and severity in employment civil rights, contract, labor, personal injury, and securities litigation. This effect is partly induced by the CEO’s man- agement of litigation risk and reduction in other risky policies. Lawyer CEOs are further associated with an increase in gatekeepers providing additional legal oversight and a decrease in innovative activities with high litigation risk. Lawyer CEOs are more valuable during periods of enhanced compliance requirements and regulatory pressure and in indus- tries with high litigation risk or better growth opportunities.

Investors' previous experiences with a stock affect their willingness to repurchase it. Using Chinese investor-level brokerage data, we find that investors are less likely to repurchase stocks that have increased in value since they were sold. We then construct a novel measure of Regret to capture investors' repurchase regret and investigate its asset pricing implications. Stocks with higher Regret experience lower buying pressure from retail investors in the future, leading to lower future returns. In terms of economic magnitude, portfolios with low Regret generate 12% more annualized abnormal returns. Further analyses show that the pricing effect of Regret is more pronounced among lottery-like stocks and those in which investors have previously gained profit. The results are robust to alternative estimations.

This paper examines the effect of financial shared service centers (FSSCs) on corporate misconduct. Using a sample of Chinese public companies with hand-collected FSSC data, we find that the adoption of FSSCs is negatively associated with the likelihood and frequency of corporate misconduct. The results hold to a battery of robustness tests. Moreover, we show that the negative association between FSSCs and corporate misconduct is more pronounced in firms that have no management equity ownership, disclose internal control weaknesses, and have more subsidiaries. Additional analyses indicate that FSSCs can help mitigate both disclosure-related and nondisclosure-related misconduct.

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.

In the digital era, the information value of online prices, characterized by weak price stickiness and high sensitivity to economic shocks, deserves more attention. This paper integrates the high-frequency online inflation rate into the dynamic Nelson-Siegel (DNS) model to explore its relationship with the term structure of interest rates. The empirical results show that the weekly online inflation can significantly predict the yield curve, particularly the slope factor, while the monthly official inflation is predicted by yield curve factors. The mechanism analyses indicate that, due to low price stickiness, online inflation is more responsive to short-term economic conditions and better reflects money market liquidity, thereby having predictive power for the yield curve. Specifically, online inflation for non-durable goods and on weekdays shows stronger predictive power for the slope factor. The heterogeneity in price stickiness across these categories explains the varying impacts on the yield curve.

Based on high-frequency data of China's commodity market from 2017 to 2022, this article examines the intraday momentum effect. The results indicate that China's commodity futures and options have significant intraday reversal effects, and the overnight opening factor and opening to last half hour factor are more significant. These effects are driven, in part, by liquidity factors. This trend aligns with market makers' behavior, passively accepting orders during low liquidity and actively closing positions amid high liquidity. Furthermore, our examination of cross-predictive ability shows strong futures-to-options predictability, while the reverse is weaker. We posit options traders' Vega hedging as a key factor in this phenomenon, our study finds futures volatility changes can predict options’ return.