Previous studies (e.g. Benston and Hagerman, 1974, Bagehot, 1971 and Stoll, 1978) suggest that the bid-ask spread consists of three components: asymmetric information cost, inventory holding cost, and order processing cost. Other literature (e.g. Brock and Kleidon, 1992, Hef-lin et al, 2007, and McInish and Van Ness, 2002) reports that the bid-ask spread varies during a trading day following a U-shaped pattern. One explanation for this observation is that it is the result of changes in information asymmetry costs over the trading hours, assuming the other costs are fixed. However, no empirical study directly measures how information asym-metry changes over the trading day. We explore how this measure relates to the spread as well as the quote depth.<p> Our research divides a trading day into 13 half-hour trading intervals and measures in-formation asymmetry during each interval following the model developed by Madhavan and Smidt (1991) and Noronha et al (1996). Their model can directly estimate the level of infor-mation asymmetry in each interval. This enables us to observe the intraday pattern of infor-mation asymmetry directly and compare it to the patterns of the spread and the quote depth. Furthermore, we test the relationship between the spread and the information asymmetry and the relationship between the depth and the information asymmetry in a dynamic context to see how market makers manage information risk over trading hours.<p> We find that the risk of information asymmetry varies significantly during the trading day. There is a large drop over the first interval, and another large drop over the last interval, with smaller fluctuations over the remaining intervals. Moreover, we show that the spread is consistent with an L-shaped pattern as opposed to the U-shaped pattern proposed by previous studies while the depth is increasing throughout the 13 trading intervals. Furthermore, we ob-serve that the variations of the spread and the depth are respectively positively and negatively related to the intraday variations in the degree of information asymmetry across the trading intervals. In particular, a large decline in information asymmetry at the beginning of the day is associated with a large reduction in the spread, whereas a large decline in information asymmetry at the end of the day is associated with a large increase in the quote depth. This emphasises the importance of studying both measures of liquidity simultaneously.

Previous studies (e.g. Benston and Hagerman, 1974, Bagehot, 1971 and Stoll, 1978) suggest that the bid-ask spread consists of three components: asymmetric information cost, inventory holding cost, and order processing cost. Other literature (e.g. Brock and Kleidon, 1992, Hef-lin et al, 2007, and McInish and Van Ness, 2002) reports that the bid-ask spread varies during a trading day following a U-shaped pattern. One explanation for this observation is that it is the result of changes in information asymmetry costs over the trading hours, assuming the other costs are fixed. However, no empirical study directly measures how information asym-metry changes over the trading day. We explore how this measure relates to the spread as well as the quote depth.<p> Our research divides a trading day into 13 half-hour trading intervals and measures in-formation asymmetry during each interval following the model developed by Madhavan and Smidt (1991) and Noronha et al (1996). Their model can directly estimate the level of infor-mation asymmetry in each interval. This enables us to observe the intraday pattern of infor-mation asymmetry directly and compare it to the patterns of the spread and the quote depth. Furthermore, we test the relationship between the spread and the information asymmetry and the relationship between the depth and the information asymmetry in a dynamic context to see how market makers manage information risk over trading hours.<p> We find that the risk of information asymmetry varies significantly during the trading day. There is a large drop over the first interval, and another large drop over the last interval, with smaller fluctuations over the remaining intervals. Moreover, we show that the spread is consistent with an L-shaped pattern as opposed to the U-shaped pattern proposed by previous studies while the depth is increasing throughout the 13 trading intervals. Furthermore, we ob-serve that the variations of the spread and the depth are respectively positively and negatively related to the intraday variations in the degree of information asymmetry across the trading intervals. In particular, a large decline in information asymmetry at the beginning of the day is associated with a large reduction in the spread, whereas a large decline in information asymmetry at the end of the day is associated with a large increase in the quote depth. This emphasises the importance of studying both measures of liquidity simultaneously.

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This paper firstly constructed an order-driven market probability model of informed trading to analyze the correlation between informed trade and return of assets and the trade-price effect. Secondly, using the probability model of informed trading, we constructed a probability model of arbitrage trading in order-driven call market, which could analyze the stabilization fund and the arbitrage trade, to investigate whether the government¡¦s interference measures were necessary and whether the intervened timepoints conformed to the set-up spirit of the stabilization fund¡Xto intervene while falling and not to while rising. Finally, we set up a ratio empirical model of informed trading which could analyze the intraday trade scale of each trade section of informed traders and uninformed traders, to analyze the change of intraday trade scale of each type of investors while trade frequency changed to explore the factors of market performance. The main results are as follows respectively: Regarding the correlation analysis of informed trading and return of assets and trade-price effect, we found that (1) in the short-term (intraday, day) there was no relationship between probability of informed trading and return of assets, whereas in the mid-term probability of informed trading was correlated with return of assets although the influence impact was not as high as prior researches (Hasbrouck (1991a, b), Glosten and Harris (1988)) expected. (2) The intraday probability of informed trading of good news days was obviously higher than that of bad news days, which indicated that unbalanced buy-sell informed trade phenomenon existed in the market. Regarding the investigation of whether the intervened timepoints of stabilization fund conformed to the set-up spirit of the stabilization fund¡Xto intervene while falling and not to while rising, the main results are: (1) the individual stocks intervened by the stabilization fund had slightly smaller volatility, slightly worse efficiency, better returns and significantly larger liquidity. (2) There was no significant difference in the probability of arbitrage trading between the targets intervened by the stabilization fund and the other companies, nor in the performance (including volatility, efficiency, liquidity and return) between both. (3) The stabilization fund and arbitragers tended to conduct transactions in the opening period, which corresponds with the proposition of Schwartz (1988). (4) We also found that compared with other arbitrage trade, the trade of the stabilization fund was more correlated with the price up-down of the market, but not with that of individual stocks. In the analysis of the intraday trade scale change of each type of investors while trade frequency changed, the main findings are: (1) the slowdown of trade frequency caused smaller intraday trade ratio and worse performance in the opening, but it increased the intraday trade ratio and performance of the closing period, which was especially significant in the high-liquidity companies. (2) The increase of trade frequency could raise the liquidity of the high-liquidity and middle-liquidity companies. As to the low-liquidity companies, although the increase of trade frequency increased the liquidity, it raised their volatility and decreased their price finding speed. The main contributions of this paper¡¦s models are indicated as follows. Regarding a probability model of informed trade: first, it improves the prior ones by bringing the order-driven call market model; second, the addition of informed traders¡¦ possibility to use limit order in the model set-up better corresponds to the real market; third, the model can calculate the probability of informed trading of intraday trade section and thus can analyze the intraday and intraweek behavior or phenomenon of informed traders and the market; fourth, the model estimates the probability of informed trading using trade data, not order data, and thus avoids the probability of informed trade estimation error caused by order trade risk; fifth, the model calculates the probability of informed trade of individual stock after separating good and bad news and thus can analyze buy-sell informed trade behavior. Regarding the probability model of arbitrage trading, it provides a method to analyze whether self-stabilization mechanism-arbitrage trade exists in the market to investigate on the necessity of the stabilization fund and its intraday trade behavior. Finally, regarding the ratio empirical model of informed trading, since this paper calculated the section informed and uninformed trade ratio by simulating uninformed traders¡¦ intraday trade strategy and by extracting the ratio of the trade volume variation of intraday trade section explained by uninformed traders¡¦ intraday behavior variation using regression analysis, it can avoid the deficiency that every trade volume was regarded as from a single trader in the prior order empirical model of informed trading.