Decimalization of the U.S. stock markets has attracted considerable contemporaneous research attention. Chakravarty, Wood, and Van Ness (2004) use high- frequency data and a carefully constructed matched sample of control (non decimal) stocks, and isolate the effects of decimalization for a sample of NYSE- listed common stocks trading in decimals.
They find that both quoted and effective bid-ask spreads and depths have declined significantly following decimalization. Both trades and trading volume have declined significantly in all trade size, as well as in all stock size, categories. Stock return volatilities display an initial increase but a decline over the longer term ─ probably as traders become more comfortable in their new milieu. Finally, although there is some evidence of increased presence among regional stock exchanges in the wake of decimalization, the NYSE still appears to be very much in the lead in all categories.
Furfine (2003) examine the impact of decimalization on the liquidity of NYSE stocks.
Analyzing transaction data for a sample of 1,339 stocks listed on the NYSE over a five-week period. He found that decimalization led to a narrowing of average bid-ask spreads. The largest declines in spreads were found for the most actively traded stocks,
where the average decline in spread was over 35 percent. The decline in depth was also most pronounced for the most actively traded stocks. Because previous findings suggest that decimalization had an ambiguous impact on market liquidity using spreads and depth as proxies for liquidity, Furfine estimated the price impact of a trade for each stock in his sample and then found that actively traded socks generally experienced an increase in liquidity following decimalization.
Harris (1994), using data from a time when the minimum tick was 1/8, fits a regression model estimating the frequency at which spreads are at the minimum. Using this relationship, Harris estimates that the impact of reducing the minimum tick size to 1/16 would be accompanied by both lower bid-ask spreads and lower quoted depth. His results are therefore also consistent with the notion that optimal tick size is related to the size of a trade. He indicates that small traders would almost certainly benefit from smaller tick sizes, but that large traders might be hurt if the depth of the market were to fall sufficiently.
Unlike Harris (1994), Chakravarty, Panchapagesan and Wood (2003) examine the effect of decimalization on institutional investors using proprietary data. They find no evidence that decimalization has increased trading costs for institutions. In fact, institutional trading costs appear to have declined by about 23 basis points (or, roughly 5 cents per share) after decimalization. In economic terms, this decrease roughly translates to an average monthly saving of $133 million in institutional trading costs. Estimations involving robust multivariate techniques that condition on order, manager and market characteristics yield roughly similar reductions as well. Their result are surprising in light of an oft-repeated, and increasingly louder, complaint among professional traders that liquidity is hard and expensive to find in a post-decimal treading milieu. Though there is significant changes in order routing practices overall, they find an increase usage of alternate brokers (represented by ECNs and crossing networks such as Instinet) for
easy-to- fill (i.e., smaller) orders and full service and independent research brokers for orders that are difficult to fill (i.e., larger size orders).
Chakravarty, Van Ness and Van Ness (2003) examine adverse selection costs around decimalization and relationship between adverse selection costs and trade size by using a sample of NYSE stocks around the implementation of complete decimalization and tick-by-tick trade and quote data. They find a significant reduction in adverse selection costs following complete decimalization on the NYSE. This decline in adverse selection costs is associated with all stocks in their sample except the very smallest. They further try to understand the source of this decrease in adverse selection costs. They find that both the number of trades and trading volume in medium and large size trades fell significantly following complete decimalization on the NYSE while those in small size trades increased significantly. O n estimating the adverse selection components by trade size classes, they find a decline in adverse selection costs in trades of all sizes, with the strongest evidence coming from medium size trades, following by small and large size trades. O ne implication of their findings is that there appears to be less stealth trading following complete decimalization and less institutional trading overall.
Goldstein and Kavajecz (2000) analyze the NYSE’s reduction in tick size from 1/8 to 1/16 and address the relationship between minimum tick size, bid-ask spread, and market liquidity. What is unique about this study is that these authors not only look at the depth reported at the best bid and ask prices, they also collect data on liquidity available at some distance away from the best bid and ask prices. This complete collection of prices and available depth is called the limit order book. They find that not only did depth at the best bid and ask decline, but cumulative depth similarly declined throughout the limit order book following the NYSE’s previous reduction in minimum tick size. Using implied average price of a trade of a given derived from the limit order book, these authors find that large traders were not made better off by the smaller tick sizes and were made worse
off for infrequently traded stock.
Bessembinder (2003) assesses trade execution costs and market quality for NYSE and NASDAQ stocks before and after the 2001 change to decimal pricing. Q uoted bid-ask spreads declined substantially on each market, with the largest declines for heavily traded stocks. The percentage of shares receiving price improvement increased on the NYSE, but not on NASDASQ. However, those trades completed at prices within or outside the quotes were improved or disimproved by smaller amounts after decimalization, and trades completed outside the quotes saw the largest reductions in trade execution costs, as a class.
Effective bid-ask spreads as a percentage of share price, arguably the most relevant measure of execution costs for smaller trades, averaged 0.33% on a volume-weighted basis after decimalization for both NYSE and NASDAQ stocks. There is no evidence of systematic intraday reversals of quote changes on either market, as would be expected if decimalization had damaged liquidity supply.
Bollen and Busse (2003) measure changes in equity mutual fund trading costs following two changes in tick size on NASDAQ and NYSE: the switch from eighths to sixteenths and the switch from sixteenths to decimals. They estimate trading costs by comparing a mutual fund ’s daily returns to the daily returns of a synthetic benchmark portfolio that matches the fund ’s holdings but has zero trading costs by construction. They find that index fund performance is unaffected by the switch to pennies. In contrast, actively managed funds under perform their benchmark by an additional one percent of fund assets per year after decimalization.
Henker and Martens (2004) find that market efficiency increased and the arbitrage link between index-futures and the stock market strengthened after Jane 24, 1997, by examine the impact of the New York Stock Exchange reduced the minimum change for stock prices and quotes from an eighth to sixteenth of a dollar. After the change they find a substantial increase in the number of arbitrage trades reported to the Securities and Exchange
Commission. The average number of stocks traded and the average dollar amount underlying each arbitrage trade increase and decrease respectively. The average index-futures mispricing error that triggers arbitrage is lower and reverts to zero more quickly.