We examine the patterns of information flows within and across sectors of the two Chinese stock exchanges in Shanghai and Shenzhen during 1994–2001. Using the generalized forecast error variance decomposition, we find a high degree of interdependence, indicating that the sectors are highly integrated and sector prices reflect information from other sectors. Industry is the most influential sector in both exchanges, while Finance in Shenzhen is the least integrated with other sectors. Implications of the findings for investors and policymakers are also discussed.
Stock markets in China have expanded rapidly following the establishment of two stock exchanges in Shanghai and Shenzhen in the early 1990s. As of January 2001, there were 584 and 514 firms listed in the Shanghai Stock Exchange (SHSE) and the Shenzhen Stock Exchange (SZSE), respectively. The combined capitalization of the markets reached $500 billion in 2000, making up 50% of China's GDP. This figure suggests that the stock market activity may have real economic effects. Recognizing the significance of stock markets in China, researchers have studied many different aspects of the markets. In this paper, we study the pattern of information flows at the sector level in Chinese stock market.
The study contributes to the literature in two aspects. First, filling a gap in the literature, we examine the pattern of information flows both across and within the sectors of two Chinese stock exchanges. As reviewed below, most of previous studies (e.g., Chui & Kwok, 1998; Fung, Lee, & Leung, 2000; Long, Payne, & Feng, 1999; Xu & Fung, 2002; Yang, 2003) on the information linkages in Chinese stock markets have focused on the A- and B-shares in Shanghai and Shenzhen, as well as the China-backed securities markets (i.e., H-shares and red chips). A missing link in the literature is how the information transmits across sectors. Such an investigation of the pattern of information flows at the sector level should be important, as individual and institutional investors often use sector indexes as a benchmark to track the performance of actively managed portfolios ( Ewing, 2002; Ewing, Forbes, & Payne, 2003). Examining the relative importance of the sectors in Chinese stock markets also allows a better understanding of the dynamics of financial markets in an economy undergoing significant reforms and regulatory changes such as China.
Second, this study also employs a relatively new technique, the generalized forecast error variance decomposition of Pesaran and Shin (1998) to investigate the pattern of information flows. Different from the traditional orthogonalized forecast error variance decomposition (see Sims, 1980), this technique is able to circumvent the problem of sensitivity of forecast error variance decompositions to the ordering of variables in the system and result in a robust solution. This method has not been commonly applied in financial research, with the recent exceptions of Ewing (2002) and Yang, Min, and Li (2003).
In the next section, we provide a review of the related literature. In Section 3, we outline our empirical methodology. In Section 5, we describe the data used, while we report our empirical results in Section 4. We discuss the policy implications of our findings in the concluding section.
This study explores the dynamic relationship among major sector indexes in China's two stock exchanges. Using daily and monthly returns, we find a high degree of interdependence. A shock to any sector has a significant impact on other sectors, and this result holds for both markets. About 70% of forecast error variance in a sector typically can be attributed to shocks in other sectors at a 10-day horizon. Similar findings hold across the markets. Shocks to a sector in SHSE can explain more than one-third of the variation of the same sector in SZSE, and vice versa. These findings suggest that sector returns reflect information from other sectors, and there are strong information flows, not only within each exchange, but also across both markets.
Our findings have implications for policymakers and investors. The results suggest that financial trouble in one sector could easily spread to others. The transmission of shocks in one sector to others might create financial market instability during a crisis, which could further spread to the production side of the economy. Policymakers could therefore design policies to improve non-influential sectors to prevent the potential negative transmission of shocks from the influential sector to others. On the other hand, it might also be undesirable to directly regulate the influential sector because it is a good source of information for other sectors and it spreads information faster. Similar to Ewing (2002), our finding also sheds light on the direction of the transmission of shocks between sectors and how to determine the most influential sector.
Given the significant linkage across sector returns, our results also suggest that investors could (partly) predict index movements for a given sector using information flows from other sectors. Hence, our findings are useful for institutional and individual investors that are interested in modeling sector movements in Chinese financial markets. Moreover, our evidence suggests that potential diversification benefits from sector-level investment may also be relatively limited, in the light of the significant linkages and high contemporaneous correlations found among sector returns. In this context, the Finance sector in the SZSE offers the best diversification potential within the Chinese stock market since this sector is the least integrated with other sectors.
