An Intraday Examination of the Components of the Bid–Ask Spread

Abstract

Using transactions data for a sample of NYSE stocks, we decompose the bid-ask spread (BAS) into order-processing (OP) and asymmetric information (AI) components using the techniques of George, Kaul, and Nimalendran (1991) and Madhavan, Richardson, and Roomans (1997). McInish and Wood (1992) demonstrate that the intraday behavior of BASs can be explained by variables measuring activity, competition, risk, and information. We investigate whether these variables explain the behavior of the OP and AI components of the spread over the trading day. We conclude that, on balance, the variables that determine the aggregate BAS also determine its intraday components.

Tick Size Reduction and the Components of the Bid-Ask Spread on the Taiwan Stock Exchange

“…The evidence of the inverse relationship between the magnitude of reduced order-processing costs and trade size also agreed with that of Chan and Lakonishok (1995) and Keim and Madliavan (1997). The current results indicating that increments of proportional asymmetric-information cost after tick size reduction have an inverse relationship with trade size agree with those of McInish and Van Ness (2002), in that a smaller tick size might discourage information collection and reduce adverse-selection costs.…”

“…The Wilcoxon rank-sum test confirmed the significant results derived from parametric t tests. The evidence agreed with that of McInish and Van Ness (2002) that liquidity providers of high-volume stocks can experience a large reduction in economic rent after tick-size conversion. By contrast, Panel B of Table 4 shows that no unanimous conclusions can be made regarding the asymmetric-information components of high-volume and low-volume stocks after tick-size changes occur.…”

“…Hasbrouck (1988) concluded that low trade-volume stocks involve higher-order processing costs because these stocks rely on the matching mechanism offered by market specialists. Copland and Stoll (1990), Lin et al (1995), andVan Ness (2002) have confirmed the inverse relationship between order-processing costs and trading activities. They reported a positive association between asymmetric-information costs and the proxy of the number of trades.…”

“…The following provides a brief description of the models, and the details are described in McInish and Van Ness (2002) and Gibson et al (2003). (Note 1) George, Kaul, and Nimalendran (1991) GKN demonstrated that the difference between transaction returns and quote midpoint returns can be used to filter out the serial dependence in returns.…”

“…We further analyzed the cross-sectional idiosyncratic attributes of trading activity, competition, and information structure documented in McInish and Van Ness (2002) and Ascioglu, Comerton-Forde, and McInish (2010) using regression analysis on the effect of reduced tick size as follows: George, Kaul, and Nimalendran (1991); HS represents Huang and Stoll (1997); and MRR represents Madhavan, Richardson, and Roomans (1997). The percentage changes in the time-series average order-processing component are regressed on the ratio of the new tick size to the original tick size (Ratio), the natural log of the time-series average stock price (LogPrice pre ), the natural log of the time-series average daily number of trades (LogANumtrade pre ), the natural log of the time-series average daily mean trade size (LogATrsize pre ), the inverse of the time-series average standard deviation of returns (1/stdev pre ), and the natural log of turnover (LogTurnover pre ), which were all estimated using data from the 3 months prior to the tick-size conversion.…”