Summary:
According to the behavioral finance theory of limited attention, individuals effectively pay attention only to limited information (Kahneman, 1973). Investors' ability to collect, process, and analyze stock market information has a heavy cost. Thus, firms' stock prices respond slowly to the disclosure of new information about related firms.Firms' previous stock returns can predict the future stock returns of similar firms. These information spillovers in the US stock market are validated in the literature using a range of measures of the economic links between firms. Compared with the US stock market, China's A-share stock market has lower market validity, more retail investors, and higher turnover rates. Therefore, China's stock market displays different characteristics to developed markets.It's important to understand the pricing mechanism for information from economically linked companies in China's stock market. However, studies of China's stock market mainly focus on the momentum and reversal effects of individual stocks and rarely consider the momentum spillover effect among various economically linked companies. Unanswered empirical questions include whether economic momentum, such as industry, geographic, supply chain, and technological momenta, exist in China's stock market, whether such momentum has predictive power and the period of such prediction. We exploit economic momentum, including industry, geographic, supply chain, and technological momenta, to compare their predictive power and prediction periods. We use data for China's A-share listed companies from 2008 to 2017 to construct these four types of economic factors and use Cohen and Frazzini's (2008) regression model to empirically test the return prediction ability of these economic factors. We further select technological momentum, which presents unique characteristic of China's stock market, to explore the internal mechanism. First, we find that the prediction period for these economic factors in China's stock market is shorter. Only industry momentum is significant at the monthly level, while the other economic correlation factors cannot predict the monthly return. The technological, geographic, and customer momenta are significant at the weekly level, but when all economic factors are controlled simultaneously, technological momentum shows the most significant predictive power. Compared with the results of Lee et al. (2019), the technological momentum in China's stock market has a shorter prediction period. Next, we explore this unique characteristic of technological momentum further and find that it can predict 1-3 weeks of the focal firm's future stock returns. A long-short strategy based on this effect yields a weekly excess return of 0.16% (yearly, 8.67%). The current fundamentals (SUE) of technologically linked firms also predicts the focal firm's future fundamentals (SUE), which suggests that the technological spillover effect exists in Chinese firms. A mechanism test shows that the short forecast period for technological factor in China's stock market may be attributed to a large number of retail investors with a gambling mentality, who tend to “buy the winners” and “sell the losers,” and thus accelerate the process of incorporating technological information into firms' stock prices. Third, by selecting the proxy variables of limited attention and market friction, we further reveal the internal mechanism for technological momentum and prove that it emerges from investors' mispricing behavior. Finally, we find that technological momentum is stronger in state-owned enterprises and after the promulgation of China's National Patent Development Strategy (2011-2020) in 2010. Our results contribute to the literature in three ways. First, we contribute to the Chinese asset pricing literature by observing four types of economic momentum in China's stock market and comparing their predictive power and prediction period on weekly and monthly bases. Second, we explore the mechanisms underlying China's stock market momentum and find that retail investors may be the main driver, which contributes to the literature on the momentum mechanism. Finally, we enrich the empirical literature on limited attention. Previous studies of limited attention have mainly explored the diffusion of industry and individual stock information. We use patent information to directly test the predictive power of information complexity on stock prices, which helps explain the theory of limited attention.
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