Tracing the regional dual value chains: Measurement on the production position and evidence from China

When there is insufficient internal and external impetus for developing countries’ economy, building a domestic and international dual circulation is conducive to promoting the regional industrial growth. On the basis of regional embedded international input-output tables, this paper extends the measurement framework of production position and proposes the concept of the dual value chain, which measure production position that unifies the national and global value chains from forward and backward industrial linkages. We decompose the national and global value chains into three categories and investigate the production position characteristics of China with a multi-dimensional perspective. Consistent evidence shows that a feasible path of technological progress in optimizing the production in value chains in which technological progress plays a crucial role on the pure national value chain across the high-tech manufacturing sector, the eastern and central regions. Their posterior probabilities are 0.96, 0.21and 0.86, respectively. Moreover, the impact on the dual production is nonnegligible that the posterior probability of technological progress on the eastern and central regions is 0.40 and 0.92. In addition, the impact of the Chinese economic stimulus program and technological progress on the economic crisis has a certain moderating effect. Our proposed evaluation framework sheds new light that national value chain production can boost economic growth，and further promote the coordinated of regional industries for developing countries.     

The association between quarter length,forecast errors,and firms’ voluntary disclosures

Approximately 60 percent of adjacent fiscal quarters contain a different number of calendar days. In preliminary analyses, we find the change in quarter length is significantly associated with the changes in sales and earnings and that analysts condition on the prior quarter's results when making their forecasts. These results indicate that it is important for analysts to adjust for changes in quarter length when making forecasts. However, we find the quarterly change in days is positively associated with analysts’ sales and earnings forecasts errors, where forecast error equals the actual earnings minus the forecasted earnings. These results indicate that analysts systematically underestimate (overestimate) performance when quarter length increases (decreases). We find evidence indicating investors make similar errors as returns around earnings announcements are positively associated with the change in quarter length, but only when changes in firm performance is more sensitive to changes in quarter length. Corroborating these findings, managers are more (less) likely to discuss quarter length during conference calls when quarter length decreases (increases). These results are consistent with managers’ strategic disclosure incentives. In summary, our evidence suggests analysts and investors fail to fully take account of the quasi-mechanical effect that quarter length has on firm performance and managers strategically alter their voluntary disclosures to take advantage of these failures.     

Multilayer networks in the frequency domain: Measuring extreme risk connectedness of Chinese financial institutions

We propose multilayer networks in the frequency domain, including the short-term, medium-term, and long-term layers, to investigate the extreme risk connectedness among financial institutions. Using the conditional autoregressive value at risk (CAViaR) tool to measure the extreme risk of financial institutions, we construct extreme risk networks and inter-sector extreme risk networks of 36 Chinese financial institutions through the proposed approach. We observe that the extreme risk connectedness across financial institutions is heterogeneous in the short-, medium-, and long-term. In general, the long-term connectedness among financial institutions rises sharply during times of financial stress, such as the 2015 Chinese stock market turbulence and the 2020 COVID-19 pandemic. Moreover, we note that the insurers are key players in driving the inter-sector extreme risk networks, because the inter-sector systemic importance of insurance institutions is dominant. Finally, our conclusions provide valuable information for regulators to prevent systemic risk.