Empirical Study On The Relationship Beteen Herd Behavior And Idiosyncratic Volatility In China Equity Market

Since 2015, turmoil in China’s stock market is growing. “Thousand shares limit” appeared 16 times in 2015, and “hundred shares limit”are too numerous. Absolutely the classical asset pricing model cannot accurately reflect the situation in the stock price and the price fluctuations in China due to imperfect regulation, asymmetric information and other issues in China’s stock market. The academic raised concerns about the impact of irrational factors on the market. "Herding or Herd behavior ", which is the emerging field of finance has been a focus on behavioral finance research in recent years. Herding reflects the impact of irrational factors of investors on the market, which give us a direct understanding of the difference between the reality of the financial markets and the efficient market hypothesis.Idiosyncratic volatility is a measure of idiosyncratic risk. The capital market equilibrium pricing model based on incomplete information which is found by Merton(1987), assumes that the company which cannot be sufficiently dispersed nature of the risk should have a higher risk premium. This means that there is a significantly positive correlation between the stock idiosyncratic volatility and expected return. But, there was a significant negative correlation between stocks idiosyncratic volatility and expected return from abroad as some of the empirical literature, and so far there is not a phenomenon of asset pricing theory can make a good explanation for this. So the academic community called this “the idiosyncratic volatility puzzle”.On the one hand, the herd behavior is caused by private investors who are affect by irrational factors ignored specific information and decision-making. On the other hand, idiosyncratic volatility index is a measure of idiosyncratic information. Based on the definition of herd behavior and idiosyncratic volatility, we do empirical research aimed at the relationship between idiosyncratic volatility and herd behavior. Firstly, using the five-factor model estimates stocks’ idiosyncratic volatility. Then using an asymmetrical CCK model of idiosyncratic volatility researched the herd behavior. The study found that stocks combination with high idiosyncratic volatility showed a significant rise in herding regardless of their shares prices. The stocks combination of low idiosyncratic volatility showed no significant herding. Then the paper different herd behavior on different market group. Respectively, it was found that Shanghai Stock showed obvious herding, while the GEM stocks did not reflect a significant herding on in the medium term(the above study based on monthly data). In order to study the relationship within the monthly cycle and the cycle of day between herding and idiosyncratic volatility and investor sentiment, we also construct structural vector auto regression model(SVAR model) with herding factor, idiosyncratic volatility factor coupled with investor sentiment factor. It was found that there was a significant interaction relationship between herding factor and idiosyncratic volatility in the monthly cycle of Shanghai stocks. Herd behavior significant increasing will make stocks which have high idiosyncratic volatility reduces obtain risk premiums. When the high idiosyncratic volatility obtained higher risk premium, herding increase significantly in the next cycle. Thereby the risk premium of high volatility stocks will reduce. It also reflects that herding factor and idiosyncratic volatility factor memory have a very significant impact on mutual relations in GEM market in the day cycle. The risk premium of high idiosyncratic volatility portfolio led the herding to be not significant in the period, but will strengthen herding after a day or two. Then the high volatility risk premium will be reduced. Finally, we carried out in two parts theoretical analysis based on empirical research, and gave some advice for different investment on stock market.