中文摘要：

针对已有流动性深度日内模式的研究数据受异常事件污染的局限,基于马尔科夫调制泊松过程,构建了交易量分离模型,该模型可分离交易量中的异常交易量.进一步以交易量为流动性深度的代理指标,研究了中国股市的流动性深度日内模式.研究发现：异常交易量确实对我国股市的深度日内模式有影响;由于对信息的敏感程度不同,不同规模股票的深度日内模式不同,对信息敏感的中、小市值股票具有W型模式,而对信息相对不敏感的大市值股票呈U型模式;由于不同市场走势下,投资者对市场信息的敏感程度不同,我国股市深度日内模式受市场走势的影响,牛市时,深度日内模式呈U型,而熊市时,深度日内模式呈W型.

英文摘要：

The data of the studies about the patterns of the liquidity depth in the stock markets is limited by abnormal e- vents. To overcome this shortcoming, this paper suggests a disjunctive model about trading volume based on Markov modu- lated Poisson process, which separates the abnormal trading volume from the trading volume. Furthermore, it defined the trading volume to be the proxy variable of the liquidity depth and then empirically tested the intraday patterns of the liquidi- ty depth in Chinese stock markets . The empirical results showed： the intraday patterns of the liquidity depth in Chinese stock markets are really affected by the abnormal trading volume. Due to the sensitivity of different information, different si- zes of stock differs from each other in the depths of intraday patterns. Middle and small-sized stocks, sensitive to the infor- mation, show a shape of W, while large-sized stocks, unsensitive to the information, show a shape of U. Because the trad- ers react differently when the information arrives in bull and bear markets. The intraday patterns of the liquidity depth are affected by the market trend in China： in bull market, it shows a shape of U. However in bear market, it shows a shape of W.