The paper considered the sensitivity of unstructured network data to external shocks in the financial system, based on HMM applied in the traditional financial indicator system to construct the new composite index. We integrated economic statistical structure data and internet information, to capture the internal correlation and external shocks to financial markets. There appeared to be some evidence that the new index was superior at measuring the systemic financial risk. In addition, according to the new composite index we constructed, China's systemic financial risk was at the medium high level. It is an important task to prevent the systemic financial risk and maintain the stability of macro-economy. INDEX TERMSSystemic financial risk, Baidu index, hidden Markov model, text mining

Investor behavior is one of the important factors that affects market liquidity. It is very interesting to find out how investor behavior affects stock market liquidity. The Investor sentiment changes and information cognitive ability affect not only their expected returns but also market liquidity through short-selling restrained market behavior. This paper gives a comprehensive index of investor sentiment based on the entropy method. According to the empirical analysis based on evidence from China, we obtain the following results: The investor sentiment has a positive impact on market liquidity; the development of margin trading has curbed the positive impact of investor sentiment on market liquidity; the information cognitive ability has a negative impact on market liquidity; the explosive information volume enhances the market liquidity in the bull, weakens the market liquidity in the bear, and has no significant impact while shocked.

Volatility has been the focus in the financial field in recent decades. It can be used to measure the uncertainties of yield and represent the risk of assets. In this paper, GARCH models and Markov switching models are used to fit the volatility of the Chinese stock market. Results illustrate that Markov switching models take the regime-switch as an endogenous variable and a random process, which enable it to describe all the remarkable structural change in one united model and help to forecast price. Therefore, it is superior to GARCH models. 490