China’s markets provide strong support for the size effect but little support for the price-to-book effect. In the context of monetary policy, the size effect is stronger when monetary policy is restrictive. An examination of herding behavior between 2002 and 2012 reveals significant effects that decrease after 2006, which suggests that information asymmetries in China’s markets are diminishing. Herding and monetary policy add significant information to a four-factor conditional pricing model.
What’s Inside?
The authors’ objective is to study the size and price-to-book ratio (P/B) effects in China’s stock markets and to examine whether these effects are influenced by monetary policy. The authors add herding and monetary policy factors as information variables in a four-factor conditional beta model. The primary contribution of the research is in identifying herding as a possible information variable in a conditional asset pricing model.
How Is This Research Useful to Practitioners?
The authors find evidence that the size effect is present across all levels of systematic risk, whereas evidence does not support the P/B effect in China’s markets. Some research suggests that the size and value effects are simply risk factors that compensate for the probability of distress. Given that many of China’s stocks, especially those listed in Shanghai, are state-owned enterprises (SOEs) that might be protected from bankruptcy, the value premium does not apply; these stocks may not require a risk premium for distress. China’s markets are dissimilar to US markets in that the size effect in the former is consistent and significant only under restrictive monetary policy conditions. The authors suggest that their findings on the size and P/B effect differences between Chinese and US markets are the result of higher informational asymmetries and a high level of state ownership in China’s companies.
Retail investors are the dominant participants in China’s markets, trading over 80% of volume. These investors are characterized as having limited knowledge of the stock markets, which creates favorable conditions for herding behavior.
The conclusions the authors make on the size and P/B effects and the existence of herding have two important inferences for China’s markets. First, investors in China do not seem to demand higher returns for small companies and value stocks during periods of expansive monetary policy. Second, the size and P/B effects in China’s markets can be affected not only by monetary policy but also by investor herding, which may moderate these effects.
The authors’ four-factor conditional beta model, which uses herding and monetary policy as information variables, provides evidence that herding and monetary policy affect conditional betas of the market risk premium as well as size, value, and momentum risk factors.
Because of regulations, China’s investors generally have investment opportunities limited to China’s stocks, real estate, and bank deposits. If regulations restrict investments in real estate, fund flows to the equity market may increase. To address the differing study results on investor herding in China’s markets, the authors exclude the possibility that investor herding is triggered by changes in the flow of funds caused by government regulations in real estate markets and find that herding cannot be credited to these actions.
How Did the Authors Conduct This Research?
The sample includes all companies listed on the Shanghai and Shenzhen stock exchanges from 1999 to 2012. With only A-shares and companies with complete data included, the study sample consists of 732–1,266 companies for each year.
The authors form and analyze 100 portfolios ranked by beta–size and beta–P/Bs. Portfolio performance is evaluated by calculating equally weighted returns for each month from July of year t to June of year t + 1 for each beta–size and beta–P/B portfolio. For the 10-year period, this process results in 120 monthly returns for each portfolio. The discount rate set by the People’s Bank of China is used to classify the monetary environment as restrictive or expansionary but only in the month when the discount rate is changed.
To examine whether herding behavior is present in China’s markets, the selected methodology assumes herding occurs over the entire distribution of market returns. The authors study the 2002–12 time period and then divide the sample into two time periods to capture whether important regulations concerning the securities markets have any impact on herding behavior. Although herding still exists after 2006, it seems to have diminished as China’s market regulations have encouraged greater information flow to equity market investors.
To evaluate whether herding and monetary policy can be used as information variables in a conditional asset pricing model, the authors use a four-factor model and assume that the betas are linear functions of time-varying herding and monetary policy variables. Size, value, and momentum factors are estimated for the Shanghai and Shenzhen stock exchanges, and portfolios are formed to calculate monthly factors. Herding and monetary policy factors are included in the asset pricing model.
Abstractor’s Viewpoint
The authors’ research, in my opinion, is an important contribution to the existing literature comparing China’s markets with such developed markets as those in the United States. The academic and practitioner communities should be particularly interested in the identification of herding as a potential information variable in a conditional asset pricing model. This important contribution warrants further examination and study.