Morice ha scritto:
E CHE CAZZ ... STM U.S.A scusa ma sai che novità !! Questo spread e i volumi inesistenti su questo titolo sono all' ordine del giorno ,,,,,
un'interprete dei book come te non lo sa ste cose ??!!!
Mi meravigli
, va bene dai a lunedì e vinca il migliore
CIIAAOOO
:smile:
Ti meravigli di me? Io non degli altri...! La finanza ha dei termini specifici che io cerco di usare pertinentemente: se gli altri li usano in maniera "grottesca" e non capiscono quanto vorrei esprimere....non è problema mio...!
Quello che segue è il concetto a cui solitamente ci si riferisce in finanza per misurare la valenza degli spread...e quindi esprimere in termini oggettivi quella che piu propriamente viene definita efficienza o grado di liquidità degli scambi!
E' in inglese ma a quest'ora e di venerdi non ho ne il tempo ne la voglia di tradurlo o sintetizzarlo!
Buona lettura se ne avrai voglia...
Decimal Trading and Market Impact
Sugato Chakravarty
Purdue University, West Lafayette, IN 47907
Tel: (765) 494 6427
E-mail:
[email protected]
Stephen P. Harris
Bridge Information Systems
Tel: (314) 468-8292
E-mail:
[email protected]
Robert A. Wood
University of Memphis, Memphis, TN 38152
Tel: (901) 755-8322
E-mail:
[email protected]
Version 2.1: March 28, 2001
Abstract
Using high-frequency data and a carefully constructed 1-1 matched sample of control (non decimal) stocks, we isolate the effects of decimalization for a sample of NYSE-listed common stocks trading in decimals. We find that decimalization has resulted in significantly lower quoted and effective bid/ask spreads and that the most popular quoted spread is 1 cent. The available depths at the best bid and ask prices are also significantly lower. Together, these spread-depth results deliver a mixed verdict on market liquidity after decimalization. We find that decimalization has resulted in significant increases (decreases) in relatively smaller size (larger size) trades and trading volume. The frequency of quote updates and autoquotes in decimal stocks following decimalization has also increased significantly. Regional stock exchanges also appear to be significantly more active in competing for order flow. Our results have important research and policy implications.
JEL classification:
Keywords: decimalization, bid/ask spread, trading, stock exchange
Acknowledgements: Please direct all comments to Chakravarty. We thank Robert Battalio, Tarun Chordia, Bill Christie, Mike Cooper, Amy Edwards, Gene Finn, Melinda Gigante-Carlson, Michael Goldstein, Robert Jennings, Ananth Madhavan, Tim McCormick, Eric Sirri, George Sofianos, Robert Wood Jr., and Robert Van Ness, for comments on earlier drafts, Wonil Hwang and Dan Zhou for research assistance, and Stephen Harris of Bridge Information Systems for providing some of the data. An earlier version of this paper was titled "The Effect of Decimal Trading on Market Liquidity." Sadly, we alone are responsible for any remaining errors.
Decimal Trading and Market Impact
Abstract
Using high-frequency data and a carefully constructed 1-1 matched sample of control (non decimal) stocks, we isolate the effects of decimalization for a sample of NYSE-listed common stocks trading in decimals. We find that decimalization has resulted in significantly lower quoted and effective bid/ask spreads and that the most popular quoted spread is 1 cent. The available depth at the best bid and offer prices is also significantly lower. These spread-depth results together deliver a mixed verdict on market liquidity after decimalization. We find that decimalization has resulted in significant increases (decreases) in relatively smaller size (larger size) trades and trading volume. The frequency of bid/ask quote updates and autoquotes in decimal stocks following decimalization has also increased significantly. Regional stock exchanges also appear to be significantly more active in competing for order flow. Our results have important research and policy implications.
JEL classification:
Keywords: decimalization, bid/ask spread, trading, stock exchange
"The theory is straightforward: As prices are quoted in smaller and smaller increments, there are more opportunities and less costs for dealers and investors to improve the bid or offer on a security. As more competitive bidding ensues, naturally the spread becomes smaller. And this means better, more efficient prices for investors."
Arthur Levitt, Chairman SEC.
March, 2000.
Introduction
On August 28, 2000, based on the Securities and Exchange Commission (SEC) order of June 13, 2000, Phase I of the decimal trading pilot went into effect. Under this pilot, thirteen securities listed at the New York Stock Exchange (NYSE) and the American Stock Exchange (AMEX) began trading in decimals. Subsequently, in September 25, 2000, under Phase IIA, additional Exchange-Listed Issues were added. At that time another 107 listed securities began trading in decimals. Then on December 4, 2000, Phase IIB went into operation, whereby 94 additional listed securities began trading in decimals. Finally, on January 29, 2001, all stocks trading in NYSE and AMEX began trading in decimals. With this series of seemingly small steps, Wall Street took a giant leap into history by ending its over two hundred year tradition of trading in fractions.
According to the NYSE, the reduction in minimum price increment to sixteenths, in June 1997, was but an interim step in a move toward the decimalization of prices and price increments (Jones and Lipson (2000)). And while it is tempting to think of decimalization as merely a continuation in the process of tick size reduction, it is actually much more than just that. While the move to sixteenths resulted in a doubling of the price points or "ticks", the move to decimals results in a six-fold increase in ticks. Also, a unique, and potentially dangerous, aspect of decimalization is that for as little as 1 cent per share (as compared to about 6.25 cents a share under sixteenths), intermediaries could step in front of public limit orders in what amounts to "front running." If the stock heads down after this purchase, these investors could simply sell to the public limit orders and be none the worse for it. It is, therefore, conceivable that the supply of liquidity from the investing public in the form of public limit orders may also dry up. Conversely, with decimalization it will be easier for institutional investors to jump in front of the queue with limit orders, supplying liquidity rather than demanding liquidity (acting as pseudo-dealers) and, in the process, earning the spread rather than paying it. Overall, whether the supply effect or the demand effect dominates is ultimately an empirical issue.
The NYSE's board approved conversion to decimal pricing in June 1997 with the goal of making prices more easily understood by investors, reducing spreads and bringing the United States into conformity with international practices. The importance of liquid markets is of paramount importance to both suppliers and demanders of capital as well as regulators entrusted with the mandate of maintaining a fair and orderly market. While there is little debate that prices are easily understood under the decimal system where one does not have to pause momentarily and wonder if 5/8 is greater than 9/16, for example, whether or not this has actually impacted market liquidity (and its various facets) is an empirical issue and is a focus of the current paper.
Many stock exchanges have recently reduced their minimum ticks. And despite such changes being studied extensively, both theoretically and empirically, its appropriateness remains an open question. Of particular relevance to the current paper is Bacidore’s (1997) investigation into the impact on market quality associated with Toronto Stock Exchange’s switching to decimal trading on April 15, 1996. He finds that for stocks whose minimum tick was reduced from one-eighth dollar to five cents, such reduction decreased spreads, while liquidity was not adversely affected and trading volume did not increase. For stocks whose minimum tick size declined from 5 cents to 1 cent, the reduction had little impact on market quality. Goldstein and Kavajecz (2000) investigate the impact of reducing the tick size from eighths to sixteenths on the NYSE (on June 24, 1997) on the liquidity of the market. Their main finding is that the quoted spreads and the corresponding depths, as well as depth in the limit order book itself, declined post sixteenths. Finally, Jones and Lipson (2000) investigate the impact of the tick size reduction to sixteenths on execution costs of a sample of institutional trades. They conclude that smaller tick sizes may actually reduce market liquidity.
We use tick-by-tick (TAQ) data from the NYSE to investigate the impact of decimal trading on the various facets of market liquidity. Our investigation period comprises of October 2, 2000, to January 26, 2001 (defined as the decimal period) and of July, 2000 (defined as the pre-decimal benchmark period). The ending date of our analysis corresponds to the last trading date of the decimal pilot program. We also report results from week six onwards from the start of Phase I in August 28, 2000. This is because a preliminary examination of the first five weeks of decimal trading reveals extreme (and unpredictable) patterns in the data. Private communications with market participants confirm that the first few weeks were unusually volatile as traders freely experimented with the new system. Given that significant "learning" was taking place in the first few weeks of commencement of the decimal pilot, we chose to report results from week 6 onwards to give enough time for the markets to have settled down into some equilibrium trading pattern.
Our decimal sample consists of 87 common stocks listed in the NYSE and included in the decimal pilot in any of the three phases before full conversion. To isolate the effects of decimalization independent of market trends, we also construct a portfolio of control stocks matched 1-1 with the decimal stocks in our sample. Essentially, at the time of selection, each control stock was "similar" to its matched decimal stock on a number of dimensions with the important distinction that it was still trading in sixteenths over the decimal period. The details of the selection of the control stocks are provided later.
Our main results for decimal stocks over the decimal period can be summarized as follows.
Regional exchange competition parameters
We find significant increases (of about 14%) in frequency of quote adjustment (or quote flickering) and in autoquotes in decimal stocks among regional stock exchanges after decimalization. The increase in quote adjustments and autoquotes remains significant after controlling for factors other than decimalization through our sample of matched control stocks.
There are significant increases in time during which the regional exchanges possess either the best bid and/or the best offer (BBO) following decimalization in the decimal stocks.
About 29% of the BBO quote changes in decimal stocks occur at a penny and almost 80% of the quote changes occur at a nickel or less. In comparison, about 75% of all (bid or ask) quote changes in control stocks occur at 1/16ths and a further 19% of the quote changes occur at 1/8ths. The NYSE also appears to be leading the way in the greatest number of 1- through 5-cent quote changes (i.e., in the updating of the best bid or the best ask or both) among all exchanges.
Liquidity related parameters
Quoted spreads in the decimal stocks declined (compared to the non event period) an average of 35% compared to about 9% in the control stocks. This decline in decimal stocks remains significant after controlling for factors other than decimalization. Specifically, we find that decimalization itself results in quoted spreads in decimal stocks declining by an average of about 3.8 cents. Also, the most frequently quoted spread among the decimal stocks was 1 cent.
Effective spreads (measured as twice the absolute difference between the trade price and the prevailing BBO midpoint) declined an average of 11% in the decimal stocks compared to a 2% increase in effective spreads in control stocks. Decimalization itself is responsible for a reduction of about 2.7 cents in effective spreads.
Effective spreads also display most significant decline in relatively smaller trade sizes, ranging from a decline of 28% in trade sizes of less than 500 shares, to a 20% decline in trade sizes of 1,000-4,999 shares. Its decline for large size trades is relatively modest. For example, the decline in effective spreads for trade sizes of at least 10,000 shares is about 3.5%. In comparison, the control stocks either show a significantly modest decrease or a significant increase in effective spreads in the corresponding trade size categories. The effect of decimalization itself appears to have had no significant impact on the reduction in effective spreads associated with 10,000-share (or greater) trades.
Bid and ask depths (trade sizes for which the corresponding quotes are guaranteed) in decimal stocks declined an average of about 62% compared to an increase of about 19% in control stocks. Decimalization itself is associated with a decline in depth of about 7,560 shares.
Transaction related parameters
We find that that while both trades and trading volume have increased in relatively lower size trades (less than 1,000 shares) in decimal stocks following decimals, the relatively larger size trades (greater than 1,000 shares) have declined significantly both in frequency and in trading volume.
We find no evidence of any significant changes in volatility in decimal stocks following decimalization.
We find evidence of significant increases in runs (or trends) in BBO quotes and simultaneously fewer quote reversals in decimal stocks, compared to the control stocks.
These results have important policy implications as the debate on decimalization continues. Foremost among them is the concern on available liquidity. Proponents argue that decimals allow for more efficient price discovery without adversely affecting the supply of liquidity. Opponents of decimalization, however, argue that decimals will result in less liquid and high volatility markets. Our results provide a mixed verdict on the issue of liquidity and volatility. Overall, our results suggest that we have moved to a new phase in security transactions – one in which participants will have to learn new rules to play the game effectively. Whether or not they do so, will determine, in large part, the success of decimalization in keeping markets price efficient and liquid. At the very least, information about the available supply and demand schedule outside the BBO will be have to be made available to market participants. There is evidence that the NYSE is already heading in that direction. Other implications of our study are discussed in the concluding section.
The plan for the remainder of the paper is as follows. Section 2 discusses related research. Section 3 provides a brief history of decimal pricing. Section 4 describes the data methodology. Section 5 provides results related to bid/ask spreads. Section 6 provides transactional results. Section 7 provides a discussion of some of the implications of our results. The Appendix provides details of the decimal stocks in our sample and their corresponding matched control stocks.
2. Related Research
Many stock exchanges have recently reduced their minimum ticks. And despite such changes being studied extensively, both theoretically and empirically, its appropriateness remains an open question. On the theoretical front, one stream of research has investigated the issue of the optimal tick size and posits that the minimum tick results from market friction arising from Bertrand competition among the liquidity providers (Anshuman and Kalay (1998) Bernhardt and Hughson (1996), Kandel and Marx (1996)). Another stream models the idea that minimum tick size coordinates negotiation (Brown, Laux and Schachter (1991) and Cordella and Foucault (1996)). A third stream of theoretical literature investigates the relationship between tick size and payment for order flow (Chordia and Subrahmanyam (1995) and Battalio and Holden (1996)). A fourth group of researchers (see, for example, Hart (1993), Peake (1995), O’Connell (1997) and Ricker (1998)) argues that with smaller ticks, competition between liquidity suppliers should reduce the bid/ask spread—thereby increasing liquidity and benefiting liquidity demanders. Finally, a fifth group (Grossman and Miller (1988), Harris (1994, 1997), and Seppi (1997)) makes the opposite argument that a finer tick size would lower spreads but also lower the quoted depth (and, hence, market liquidity) due to the decline in the marginal profitability of supplying liquidity. This may reduce the incentive of liquidity suppliers to supply liquidity in such markets.
The current paper, however, appeals to the empirical research that investigates the effects of actual tick size reduction in financial markets. These include the September 1992 reduction in minimum tick size for stocks priced under five dollars for AMEX stocks (Crack (1994) and Ahn, Cao and Choe (1996)); the April 1996 reduction in tick size from eighths to a nickel in the Toronto Stock Exchange (Bacidore (1997), Huson, Kim and Mehrotra (1997), Porter and Weaver (1997) and Ahn, Cao and Choe (1998)); the May 1997 changeover of AMEX stocks to sixteenths (Ronen and Weaver (1998)); the June 1997 changeover of NYSE stocks to sixteenths (Bollen and Whaley (1998), Ricker (1998), Goldstein and Kavajecz (2000), and Jones and Lipson (2000)). Additionally, Bessembinder (1997) examines Nasdaq stocks that change on an individual basis to (or from) sixteenths. Overall, the focus of almost all of the empirical research is to examine trades, the inside bid/ask quotes (or, the best bid and offer or BBO) and the corresponding bid and offer sizes; and the effective spreads around tick-size reduction. Some have also examined the impact of institutional trades on spreads. The overall conclusion of these studies is that a reduction in tick sizes has caused spreads to decline and has made it costlier for institutions to trade.
3. Brief History of Decimal Pricing
The move toward decimal pricing began in 1996 – significantly before the conversion to sixteenths. At that point, a congressional subcommittee was formed to study the issue. Hearings were held and legislation was proposed that would direct the SEC to require that securities be traded in dollars and cents rather than in traditional fractions. During the 105th Congress, Finance and Hazardous Materials Subcommittee Chairman Mike Oxley proposed H.R. 1053, the Common Cents Stock Pricing Act of 1997. His bill required U.S. exchanges to price stocks in dollars and cents, rather than in government set fractions. This set the tone for the initiatives that followed. The National Association of Securities Dealers (NASD) has been a strong proponent of the marketplace's shift to decimals. The NASD, in cooperation with the Securities Industry Association (SIA), the SEC, and other equity and options markets have been working to ensure a successful transition to decimals. Some of the benefits that decimalization is expected to provide include savings for investors through narrower spreads and easily understood numbers.
Data and Methodology
4.1 The Sample Stocks
The stocks in the decimal pilot were chosen based on several criteria developed by the NYSE, along with a securities-industry committee. These criteria include stocks having varying levels of daily trading activity, that trade on multiple exchanges, are part of an index, are underlying issues for multiply listed options; and may have corporate action pending. To avoid introducing confounding effects in our analysis, we restrict attention to the 87 NYSE-listed common stocks selected for trading in decimals under the three phases of the decimal pilot. These include 4 stocks from Phase I, 39 stocks from Phase IIA and 44 stocks from Phase IIB. To avoid introducing confounding effects, we eliminate all preferred and convertible preferred stocks, closed end funds, ADRs, stocks that may have merged, stocks with abnormally low average daily trading volume over the study period, and stocks with no trading in at least one day over the decimal and/or control periods. We also exclude all common stocks below $5 and above $150, and those stocks in the decimal pilot that moved from Nasdaq to NYSE during the period of the study. Finally, decimal stocks, which were delisted due to merger, or for other reasons, during the study period, were also excluded.
Even though we report results corresponding to NYSE-listed stocks only, we repeated the analysis for the eligible AMEX-listed stocks in the various phases of the decimal pilot, as well. We do not report them here since they do not add any new insights.
We obtain tick-by-tick transaction and quote data for each stock in our sample over October 2, 2000, and January 26, 2001, in the post-decimals phase, and over July 2000 as the non-event benchmark period. It should be noted that while the overall period after decimalization that is studied is from October to January, it is only the phase I and IIA stocks that entered the sample on October 2 and remained till January 26, 2001. Phase IIB stocks entered the sample only on December 11 and remained till January 26, 2001.
The quote data was extensively error filtered and all quotes with missing values, with negative and zero spreads, and with quoted spreads greater than $2, were eliminated. This removed less than 1% of the quotes. Transactions prices were also examined (and filtered) for potential errors. The appendix provides a breakdown of the specific decimal tickers included in our study.
4.2 Selection of Control Stocks
Investigating the impact of decimalization on the pilot stocks after and before the event is valid under the assumption that the market remains constant over the examination period. In the presence of market trends, however, it is impossible to tell if an effect is due to decimalization or due to market trends. One way to isolate the effect of decimalization, independent of market trends, is to examine a matching sample of stocks, identical to the control stocks in every way, except that they do not trade in decimals. We, therefore, compile a matching sample of NYSE-listed control stocks for the decimal stocks in our sample, using the following exhaustive criteria.
Specifically, for each stock in the decimal sample, the selection criterion for a matching stock in the control group is
(option availability). Decimal stocks that have options got matching control stocks that also have options, and vice versa.
(similar price). The price of each control stock had to be no more than 20% above or below the price of its corresponding decimal stock. This limit was increased somewhat for lower priced stocks. For stocks trading under $3, the limit was expanded to 35%. In many cases, a control stock had a price within a dollar or two of its decimal partner.
(similar traded volume). The average traded volume of each control stock was no more than 20% above or below the average traded volume of its corresponding decimal stock. In practice this limit was set lower than 20% and if no satisfactory matches were found it was increased.
(similar volatility). The volatility of the decimalized stocks generally ranged from 20 - 80, with a few outliers. The control stocks were selected to have a volatility within 25% of that of their decimal partners. In a few cases this limit had to be widened to find a suitable match.
(similar market capitalization). The market capitalization of each control stock was selected to be within a 50% range of that of the corresponding decimal stock in most cases. This range had to be expanded in a few cases to find a match.
The three-month relative strength to the S&P 500 Index, between a decimal stock and its chosen control stock, should have been similar.
Additionally, recent price performance was considered. A stock that had been rising strongly in recent months was not given a control stock whose price had been falling sharply and vice versa.
Control stock selected from same industry as decimal stock, if possible.
Each NYSE decimal stock was given a control stock that also traded on the NYSE.
It should be noted, however, that for each phase, control stocks were selected shortly before the date of decimalization. Due to normal stock market fluctuations, the prices, trading volume and volatilities of stocks that were similar when the selection was made could have been much less similar by the end of the study period. They also could have been different during the benchmark period of July 2000. The appendix provides a breakdown of the control stocks matched with the corresponding NYSE-listed decimal stock.
Decimalization and Bid and Ask Quotes
We present the results of our analysis based on the NYSE-listed decimal stocks in our sample and the matched control stock sample, both before decimalization (over July, 2000) and after decimalization (from October, 2, 2000 through January 26, 2001).
5.1. Daily Quote Frequency
From a theoretical standpoint, bid/ask quotes are updated when market conditions change. These may be related to adverse selection, liquidity pressure or a combination of the two. Suffice it to say that frequent updating of quotes would be indicative of a volatile market overall where the search for an efficient price for that security is actively being pursued, compared to a market with relatively infrequent quote adjustments. A market with frequently changing quotes is also a trading venue where the "best" possible price for a trade execution may be harder to determine and, hence, may be open to gaming and other strategic behavior by professional investors for personal gain.
Table 1 presents the daily average quote frequency of the NYSE-listed decimal and control stocks in our sample, both after (panel A) and before (panel B) decimalization, across the five regional stock exchanges in addition to the NYSE and NASDAQ. While the non-event period finds average daily quote adjustment frequencies that are statistically similar between decimal and control stocks in almost all of the regional exchanges, the picture changes significantly over the decimal period. Here we see significant increases in quote adjustment frequencies in all five regional exchanges, both with respect to the control stocks and with respect to the non-event period. Overall, table 1 shows that there was an increase of 14% in quote adjustments in decimal stocks after decimalization relative to a decrease of 11% in quote adjustments in control stocks over the same period.
To focus on the change in quote frequencies in decimal stocks due to the act of decimalization itself, we compute the net difference in daily average quote frequencies by subtracting the daily average (quote frequency) of each control stock from its matched decimal stock in our sample. The average of these differences over each exchange and over each period (pre decimal and post decimal) is then computed. The table reports this difference in the pre-decimal average from the post-decimal average for each exchange. Overall, these numbers confirm that the act of decimalization itself appears to have caused a significant increase in quote frequency in all regional stock exchanges. The main markets like NYSE and NASDAQ, however, do not display this tendency, where quote frequencies after decimalization stay about the same as those before decimals. What these results imply towards the overall market liquidity is unclear and is examined in greater detail in following sections.
An extreme case of quote adjustment is known as quote flickering where quotes changes occur so rapidly that the eye cannot discern the changes. Not surprisingly, some traders are uncomfortable in a market with quote flickering since it is extremely difficult to get a sense of the true value of the stock at a given point in time. Our results (not reported) indicate that quote flickering is likely to be a significant issue in a world of decimal trading.
Prevalence and distribution of the Best Bid and Offer (BBO) among Regional Stock Exchanges
In a competitive dealer market, multiple dealers post quotes simultaneously and there exists at any point in time, a set of bid and ask quotes that represent the highest price to buy and the lowest price to sell. This is known as the best bid and offer (BBO). In a continuous auction market, such as the NYSE and AMEX, a specialist posts quotes in stocks, comprising of a combination of own interest as well as the interest of the public limit orders competing directly with the specialist. Such competition can also come from the regional stock exchanges, posting quotes simultaneously in those stocks. In short, BBOs are generated by public limit orders originating both in the primary market as well in the regional stock exchanges, and dictate which exchange at any point in time has the best bid and offer quotes. The fraction of time that any particular exchange has the BBO is an indication of its dominance in terms of a supplying liquidity as well as price discovery – the two most important roles of a modern financial exchange.
To examine the issue of BBO dominance, we compute the frequency of BBOs using the quoted bids and asks and the corresponding bid and ask depths offered by the exchanges at those prices. If two exchanges have the same bid, for example, but one has a larger quoted size at the bid, it is awarded the BBO. If both have the same bid price and quoted size, then the BBO is awarded to that exchange with the oldest bid quote. Note that this procedure strongly favors the NYSE since by virtue of its dominant position the NYSE almost always has the maximum size offered.
Table 2 presents the frequency of BBO observations and the corresponding % of total trading time that a BBO is in effect across the regional exchanges for decimal and control stocks both during the decimal period and during the pre event period. While the percentage of BBO times held by regional exchanges in decimal stocks increases significantly after decimalization, the percentage of the BBO times held by most regionals in control stocks also increases over the same period, although by a relatively lesser extent. Simultaneously, the percentage of BBO times held by NYSE declines significantly in decimal stocks from 86% to 79% and in control stocks from 87% to 81%.
To isolate the effect of decimalization on BBO times in the regional exchanges, the net difference in decimal stocks is calculated as the difference between the daily average frequency of BBO observations of each decimal stock and its paired control stock. The average of these differences over each exchange and over each (pre decimal and post decimal) period is then computed. The table reports the difference in the pre-decimal average from the post-decimal average. The results indicate that after controlling for other factors, decimalization appears to have significantly increased the ability of most regionals to price-compete with the NYSE – and the effect of this increased price competition is seen both in decimal and control stocks. An important implication of this finding for academics is that research involving spreads, which have traditionally considered only NYSE quotes as a representation of the market as a whole, will henceforth need to consider regional and third-market quotes.
Frequency distribution of changes in the best bids and the best asks (BBOs)
In this section, we investigate the frequency of changes in the best bids and the best offers (BBOs). The purpose is to see if all penny increments are being hit after decimalization or are the BBO quote changes loading up only on certain penny increments. Specifically, we find the number of times the best prevailing bid (for example) changes by 1 penny, 2 pennies, and so on, and simultaneously the number of times the best prevailing ask changes by the same increments, for all the decimal and control stocks in our sample and over the entire sample period.
Table 3 explores the frequency distribution of the sum of changes in both the best bids and the best asks, in cents, and presents the corresponding cumulative %, of all such changes at the bid and at the ask. Note that while about 75% of quote changes in the control stocks occur at 1/16ths and a further 18% at 1/8ths, about 29% of the changes in decimal stocks occur at one penny, while about 80% of the changes occur at 5 cents or less. Clearly, even though the cumulative changes in the bid and offer quotes between decimal and control stocks at an 1/8th or less are almost identical, all penny ticks in between appear to be hit by the decimal stocks and, especially comforting to the supporters of decimal pricing is the fact that, quote increments of 5 cents or less appear to be used actively by the market in the decimal stocks. The latter may also indicate that 1/8 and 1/16 minimum tick sizes may have represented barriers to price competition, which have been ameliorated with decimal pricing.
Distribution of bid and ask quotes and quote changes among regional exchanges
Given the significant usage of increments of 5 cents or less in the BBOs noted in Table 3, a natural question is to ask which regional exchanges are responsible for this narrowing of the BBOs. A related question is to investigate for possible clustering in BBO quotes ending in numbers 0-9. Toward that end, panel A of table 4 provides a frequency distribution of all bid and ask quotes ending in numbers 0-9. We find evidence of clustering in that quotes ending in 0, 4, 5, and 9 appear to be favored over quotes ending in the remaining numbers in all regional and national exchanges. Panel B of table 4 examines 1-5 cents quote changes (at the best bid and at the best ask) by the regional exchanges for NYSE-listed decimal stocks over the sample period. The reported numbers, for each exchange, are computed as the sum of all ask changes and bid changes of 1 through 5 cents expressed as a percentage of all changes at a given cent. From Table 4, we see that NYSE has by far the greatest number of 1-5 cent changes, while Nasdaq is competitive in all five-cent categories. The Pacific comes in third in all except the 3-cent category. The NYSE and NASDAQ also appear to have disproportionately high frequencies of 5-cent quote changes. Overall, it appears that all the regional exchanges are actively contending for quote changes, even though the NYSE is still leading the way.
Quoted and Effective bid/ask spreads
A traditional measure of market liquidity has been the quoted bid-ask spread that captures the ex ante transactions cost (see O’Hara (1995), Huang and Stoll (1996) and Bessembinder (1997)). Peterson and Fialkowski (1994) and others have, however, argued that the quoted bid-ask spread is no longer an accurate measure of transaction costs when trades are executed inside the prevailing quoted spread. They also show that when the posted (quoted) spread widens, only about 10-22% of the increase in posted spreads appears in the effective spread, where the effective bid-ask spread associated with a trade is defined as twice the absolute difference between the transaction rice and the midpoint of the prevailing quoted bid-ask spread (BBO). The effective spread, which measures the ex post transaction cost, is thus favored by some.
We begin our investigation into spreads by examining, in Table 5A, the distribution of all quoted spreads originating from the national and regional exchanges in terms of odd and even ticks for decimal and control stocks, following decimalization. Even ticks are spreads of 2, 4,…cents, while odd ticks are covered by 1,3, 5,…..cent spreads. While decimal stocks show an almost even split between even and odd ticks in every exchange, the control stocks over the same period show a preponderance towards even ticks in Boston, Chicago and Philadelphia and towards odd ticks in Cincinnati and Pacific. About 61% of all quotes from the NASDAQ are at even-ticks while 45% of all quotes at the NYSE are at even-ticks.
Table 5B refines the examination of quoted spreads by investigating the distribution of BBO spreads (in cents) for decimal and control stocks following decimalization. It is notable that decimal stocks have a higher frequency of spreads at six cents or less compared to the control stocks (49.4% versus 44.9%). Even for spreads up to 13 cents, decimal stocks dominate the control stocks (75.3% versus 73.8%). Not surprisingly, the difference (in cumulative frequencies) between decimal and control stocks disappears at spreads of 19 cents or higher. Overall, there is evidence that 1-5 cent spreads are actively being quoted by market participants.
Tables 6A and 6B provide information on the quoted and the effective bid/ask spreads on the NYSE-listed decimal stocks and the corresponding control sample over the sample period and over the non-event benchmark period of July 2000. Both the quoted and effective bid/ask spreads are calculated on the basis of the best bid and offer (BBO) prices available at the time of trade. Thus, autoquotes, which simply bracket an existing BBO, are automatically eliminated from consideration. Such autoquotes, however, have other purposes and are discussed later in section 5.7. We further classify spreads into portfolios comprised of stocks, based on their average daily dollar volume of trade over the benchmark non-event period. Stocks in portfolio 1 (5) comprise the smallest (largest) dollar volume stocks. The reported spreads in Table 6A (and in Panel A of Table 6B) for each dollar volume portfolio are computed by averaging, for a given stock within a given day, by the time they are outstanding, and across stocks, with their non-event daily average dollar trading volume.
Table 6A reveals that the decrease in quoted spreads for decimal stocks is significant across all five dollar volume portfolios, and ranges from 21% for the portfolio 1 to 36% in portfolio 4, while the dollar-volume weighted average over all portfolios is also about 35%. The corresponding changes in the control stock portfolios range from a 0% increase to a 10% decrease, with an average of 9% decrease. Thus, decimalization appears to have significantly reduced quoted spreads, in comparison to control stocks.
To isolate the effect of decimalization on the quoted spreads of decimals stocks, the net difference in quoted spreads in decimal stocks is calculated as the difference between the daily average quoted spread of each decimal stock and its paired control stock in a particular size rank. The average of these differences over each size rank and over each (pre decimal and post decimal) period is then computed. The table reports the difference in the post-decimal and pre-decimal averages. These reveal that decimalization itself may have resulted in quoted spreads declining by an average of about 3.8 cents.
Table 6B Panel A reports the effective spread results of portfolios, once again classified by their average daily non event dollar volume. The reduction in effective spreads in decimal stocks ranges from 4% to 15% among the five portfolios with the greatest reductions occurring in the higher dollar volume portfolios. The overall (dollar volume weighted average) decrease is about 11%. In contrast, the control portfolios show significant increases in effective spreads in almost all portfolio size categories with an overall (dollar volume weighted average) increase of about 1.5%. Once again, a net difference variable is reported to isolate the effect of decimalization on the effective spreads of decimal stocks. These indicate that decimalization itself is responsible for a reduction of about 2.7 cents in effective spreads.
To get a sense of the relationship between effective spreads and trade size, we further compute the effective spreads corresponding to trades in each of five trade size categories: all trades less than 500 shares (small trades), all trades between 500 and 999 shares (medium1 trades), all trades between 1,000 and 4,999 shares (medium2 trades), all trades between 5000 and 9999 shares (medium3 trades), and all trades of 10000 shares or greater (large trades). Panel B of Table 6B presents the average effective spreads (in pennies) in each trade size category, for decimal and control stocks, before and after decimalization.
We see that while the spreads in each trade size category are statistically similar between decimal and control stocks over the pre-decimal period, there are significant declines in effective spreads in every trade size category in decimal stocks after decimalization. Specifically, the spread decreases monotonically from 28% in small trades to about 3% in large size trades. In contrast, within control stocks, effective spreads appear to have increased in the larger trade size categories and show relatively insignificant declines in the two remaining smaller trade size categories, compared to effective spreads associated with corresponding size trades in decimal stocks. The net difference variable indicates that decimalization itself has resulted in reduction of effective spreads ranging from 2.08 – 2.81 cents in all but the largest trade size category. For large size trades, however, the net difference variable is negative but not statistically significant at the 10% level, indicating no significant effect of decimalization on effective spread reduction.
In summary, decimalization appears to have significantly reduced both quoted and effective spreads. Further, the reduction (in both quoted and effective spreads) is greater in higher dollar volume stocks than in lower volume (and, hence, less active) stocks. Our conclusion holds even after accounting for market trends over the decimal pilot period and are consistent with those reported in Goldstein and Kavajecz (2000) and Jones and Lipson (2001) following the conversion to sixteenths. Further, decimalization appears to have enabled relatively smaller size trades to obtain better prices and execute deeper inside the quoted spreads, compared to larger size trades.
5.5 Changes in bid and ask depths
Following Harris (1990), it is now widely accepted that a complete characterization of market liquidity encompasses both the bid/ask spreads and the corresponding bid and ask depths. When liquidity is defined along these two dimensions, it is entirely likely that a reduction in liquidity could occur through a reduction in the bid and/or ask depth even though the bid/ask spread itself remains unchanged. Having examined spreads in the prior section, we now turn to the depths to see if we could say anything conclusive about market liquidity following decimalization.
Related to the magnitude of the quoted bid and ask depths themselves, it is reasonable to expect that quoted depths will reduce, the closer a bid or offer is to the equilibrium price, consistent with the shape of the supply-demand curves. Further, the likelihood of a quote becoming stale increases as the spread narrows, so the option cost implicit in quotes will increase correspondingly. Finally, the advantage of being at the front of the queue decreases as the tick size decreases and the spread narrows.
Table 7 provides information on the bid and ask depths of the decimal and control stocks both before and after decimalization. As in the case of spreads, we report depths based on the five portfolios (1-5) formed on the basis of the average daily dollar volume of trade over the benchmark period. Consistent with the spread table, the reported depths in Table 7 for each dollar volume portfolio are computed by averaging across stocks in a portfolio, by their non-event daily average dollar trading volume.
We see that both the bid and ask depths decreased significantly during the decimal period compared to the pre event period. Specifically, the decline in depths among the five decimal stock portfolios ranges from 15% to 79%. The higher dollar volume stock portfolios appear to experience a greater decline in depths than the less active lower dollar volume stock portfolios. In contrast, the control stock portfolios, over the same period, show an overall increase in depths (by about 19%). There is also no clear trend between depth change and size portfolios. Once again, the net difference variable isolates the impact of decimalization on the depths of decimal stocks following decimalization. From here, we see that decimalization has lead to a reduction in depth of about 7,560 shares.
In summary, after controlling for market trends, there appears to be a significant decline in bid and ask depths at the BBO. Goldstein and Kavajecz (2000), in comparison, report an average quote depth decline of about 48% following conversion to sixteenths. While a decline in quoted and effective spreads indicates an increase in market liquidity, a simultaneous decrease in the corresponding bid and ask depths (i.e., the corresponding order sizes that these quotes are valid for) implies a drop in liquidity. The overall effect is therefore uncertain, although the observation that trading volume in upstairs markets and in automated equity-matching systems like POSIT has increased significantly since decimalization would suggest that institutional trading costs, and the cost of executing large size trades through conventional channels, have, in fact, increased.
Autoquoting behavior of regional exchanges
In this section, we examine the dynamic behavior of quotes originating from the NYSE and the regional stock exchanges between decimal and control stocks, before and after decimalization. The issue of dynamic quote behavior itself has been addressed by various researchers in the context of increased fragmentation of U.S. equity orders and in the context of price discovery (see, for example, Garbade and Silber (1979), Shapiro (1993) and Hasbrouck (1995)). The NYSE and the regional exchanges are electronically linked and all trades and quotes are disseminated by a central transmission authority (the Consolidated Tape Association (CTA)). Although the regional exchanges sometimes establish the BBO, frequently they will choose to extract themselves from active quote competition in listed stocks by programming a computer to intercept all NYSE/AMEX quotes and immediately generate a new quote of their own by adding a delta to the ask and subtracting a delta from the bid, with 100 shares bid and 100 shares offered (the bid and ask depths). The mechanism(s) by which the regional quotes default to the BBOs, widened by a small arbitrary amount, are known as "autoquotes". Thus, an autoquote is effectively a non-quote whereby regional specialists signal that they are willing to supply liquidity but not at the best prices. Trades occur on the regional exchanges during periods of autoquoting, but those trades must match or better the existing BBO. Our algorithm identifies an autoquote as any regional or third-market quote that brackets the existing NYSE or AMEX quote.
With the advent of decimalization it is reasonable to anticipate an increase in posting quotes off the BBO by serious liquidity providers. With the possible elimination of the barrier to price competition that was provided by a minimum tick size of sixteenths, it follows that liquidity providers who wish to earn more than a 1-2 cent spread will be forced to post quotes off the BBO following decimalization. With the thin book that naturally results from such tight spreads, significant buy/sell programs could easily march up/down the book and hit limit orders away from the minimum tick size. Thus, some regional exchange specialists are increasingly autoquoting to supply liquidity with sophisticated algorithms to determine quote prices and sizes. These autoquotes are, however, not for 100 shares bid and 100 shares offered but for quantities greater than 100 shares on either (or both) side. We attempt to capture this behavior by reporting autoquotes over both 100-share depths (the regular autoquotes) and greater than 100-share depths (the smart autoquotes).
Table 8 reports the percentage of time the NYSE-listed decimal and control stocks are (regular and smart) autoquoted before and after decimalization in the various regional exchanges. The reported numbers are a simple percentage of all autoquotes (in each regular or smart autoquote category) from a given regional over all quotes from the regionals and the NYSE. While tests reveal that the percentage of regular autoquoting is statistically similar between decimal and control stocks in the pre event period, post decimalization finds that regular autoquoting has increased significantly in decimal stocks compared to the control stocks in almost all the regional exchanges. Cincinnati, Pacific and Chicago have the highest increases of autoquoting in decimal stocks, while there is decline in autoquoting in the control stocks in two of these exchanges.
To isolate the effect of decimalization itself on autoquoting behavior, we provide the net difference in (regular or smart) autoquotes, calculated as the difference in daily average autoquote frequency between each decimal stock and its matched control stock. The averaging is now done over each exchange and over each (pre decimal and post decimal) period. The table reports the difference in post and pre-decimal average. While Boston and Chicago show evidence of significant increase in autoquotes as a result of decimalization, for the rest, including NYSE and NASDAQ, the increase is statistically insignificant at the 10% level. From Panel B, the net increase in smart autoquotes as a result of decimalization is positive and statistically significant in all but the Pacific and Philadelphia stock exchanges.
Overall, it appears that (with few exceptions), the regional exchanges are ready to participate in quote competition in the decimal stocks following decimalization, as evidenced by overall net increases in both regular and smart autogquoting.
6. Decimalization and Transaction-related Variables
Trades and trading volume
An issue of interest to regulators and practitioners is the relative behavior of institutions compared to individual investors. It is generally perceived that institutions are "smart" or informed traders while individuals are uninformed investors. Evidence that institutional investors are more likely to use larger trade sizes than individuals, appears in Chakravarty (2001). Support that large trade sizes are more likely to be from informed traders than small trade sizes appears in Easley and O’Hara (1987). To investigate the relative activity of large (informed) trades versus small (uninformed) trades around decimalization, we investigate the frequency of trades and trading volume in the five trade size categories described earlier: small, medium1, medium2, medium3 and large.
Table 9 reports results both for decimal and control stocks over the benchmark period and the decimal pilot period. Panel A presents the average daily trading volume results classified by trade size; panel B provides the corresponding daily average trade frequency statistics.
From table 9, we see an increase in average daily trading volume in almost all trade size categories in decimal stocks following decimalization. Interestingly, the control stocks show an even greater increase in every trade size category. Average daily trades show a decline in three of the five categories in the decimal stocks while the control stocks show a significant increase in each trade size category following decimalization.
To isolate the effects of decimalization, we report the net difference in trade volume (or trade frequency) in decimal stocks is calculated as the difference between the daily average volume (or trade frequency) of each decimal stock and its paired control stock in a particular trade size category. The average of these differences over each category and over each (pre decimal and post decimal) period is then computed. The table reports the difference in the post-decimal average and the pre-decimal average. These numbers indicate that while both trades and trading volume have increased in relatively lower size trades (less than 1,000 shares) in decimal stocks following decimals, the relatively larger size trades (greater than 1,000 shares) have declined significantly both in frequency and in trading volume. Decimalization appears to have dampened the market for large size trades.
Volatility
In this section we examine volatility, before and after decimalization, and between decimal and control stocks. Our investigation is motivated by a desire to investigate if market risk in decimal stocks has increased significantly (relative to the control stocks) following decimalization. If it is the case that the aggregate liquidity in the limit order book has shrunk post-decimals, we would expect a greater price impact (or higher volatility) for a trade of a given size.
To study volatility, we use the same five (quintile) portfolios of decimal and control stocks separately, based on the average daily dollar trading volume over the non-event period (and discussed earlier). We form a minute-by-minute return series for the stocks within these portfolios, where each return is weighted by its corresponding share volume. Portfolio returns are then formed by weighting each stock in a portfolio by its pre event average daily dollar volume. Overnight returns are discarded. Volatility for portfolio returns is calculated daily and the average volatility across days is reported.
Table 10 does not reveal a clear pattern of volatility increases or decreases across the dollar volume portfolios. There is, however, evidence that volatility appears to have gone up in the smallest (7.9%) and largest quintile (47%) portfolios of decimal stocks following decimalization. In comparison, the corresponding control stock portfolios show a decrease of about 13% and an increase of about 62%, respectively. The remaining portfolios indicate both increases and decreases for both decimal and control stocks.
As always, the net difference variable attempts to isolate the effects of decimalization on decimal stocks. The results indicate that in four of the five portfolios there is a decline in volatility in decimal stocks following decimalization although none are significant at the 10% level.
In summary, there is no evidence of any significant pattern of volatility changes following decimalization.
6.3 Runs and Reversals in Quote Adjustments
There is concern amongst practitioners in Wall Street that decimal pricing may lead to more runs in price and less price reversals, thereby exposing the limit orders of individual investors to greater stale price risks, which may, in turn, leave them at the mercy of being picked off by professional investors. Thus, an investor could easily step in front (by a penny) of a standing limit order in a rising market or sell into a standing limit order in a falling market (see also footnote 9). In either situation, the liquidity supplier would be faced with losses in her position. If this situation leads some limit order traders to avoid submitting such orders, the overall liquidity and depth of the markets are in jeopardy. Brown and Holden (1999) model "mispricing" risk of limit orders and show how market adjusted (or smart) limit orders can mitigate this risk.
To examine the issue of price runs versus reversals, we define a simple metric to measure such phenomena. Specifically, we examine the prevailing BBO quote midpoint sequence and define an increase in quote midpoint by +1 and a decrease by –1. A simple run of length 1 is defined as (+1+1) or a (-1-1). Similarly, a run of length 2 is defined as (+1+1+1) or (-1-1-1), and so on. A reversal is defined very simply as triples of BBO quote changes, where the direction of the quote midpoint is reversed, as in (+1-1+1) or (-1+1-1). While relatively crude, it nevertheless allows us to provide some kind of framework to study this issue.
Table 11 Panel A documents quote reversals, both in terms of the frequency of quote reversals and the corresponding percentage of all non-zero quote changes, among decimal and control stocks, both in the pre event period and during the decimal pilot. While the frequency of quote reversals between the decimal and control stock sample over the pre event period is statistically similar, about 25% (34%) of all non-zero quote changes were reversals in the decimal (control) stock sample. This difference is statistically distinct at reasonable levels of significance. Thus, quotes appear to be reversed less frequently in decimal stocks following decimalization.
Panel B provides the frequency (and the corresponding cumulative percentage) of runs in quote adjustments between decimal and control stocks before and after decimalization. While the pre-decimal run length frequency at run lengths up to 25 are statistically similar between the decimal and control stocks, following decimalization we find statistically significantly greater run lengths among decimal stocks over the control stock sample for almost all reasonable run lengths reported in the table.
In summary, our results underscore the significant increase in quote trends in the market following decimalization and provide support to the argument that decimalization may have increased stale price risk of the liquidity supplying limit order traders, who stand a greater chance of being picked off for a loss. The broader implications of our finding here are discussed in the conclusion.
7. Discussion of Results
Using tick-by-tick transaction and quote data over the pre-decimal period of July 2000, and the decimal period of October 2, 2000, through January 26, 2001, we study the impact of decimalization on NYSE and AMEX-listed common stocks. To ensure that our results are not influenced by market trends over the decimal period, we carefully construct a sample of control stocks matched 1-1 with each decimal stock in our sample. We study the effects of decimalization, both with respect to the non-event period and with regard to the control sample of stocks.
Even though it is tempting to think of decimalization as just another decrease in minimum tick size, it is, in reality, much more than just that. The mantra is that the minimum tick must make it an economic cost for someone to step up in front of another liquidity supplier. While this was true even up to the conversion to sixteenths, it may not be true anymore where it takes just one penny to step ahead of a standing limit order. And if it is almost costless to step ahead, as one fund manager puts it, "……mutual funds may stop using limit orders and simply pay fees to brokers to buy and sell at the prevailing market price. Most stocks already are traded that way on the nation's exchanges." In this backdrop, we study the impact of decimalization on market liquidity parameters, price competition (through quotes) of regional exchanges vis-à-vis the NYSE and/or AMEX, and transaction related parameters.
We find mixed evidence on market liquidity following decimalization. Specifically, while both the quoted and effective bid/ask spreads show significant decline, the corresponding bid and ask depths also display a significant decline. Thus, while better prices now exist to buy and sell, the quantities that can be purchased or sold at those improved prices have also declined significantly. Our finding of significantly smaller spreads post decimalization bodes well for retail traders trading smaller size trades. Anecdotal evidence, however, suggests that institutional investors are, in fact, experiencing significantly higher costs to execute block trades (in decimal stocks) following decimalization and automated trading systems like POSIT, which let users confidentially find the natural buyer or seller of a trade during the market day, has experienced a tremendous increase in trading volume in recent months.
We find evidence of significantly increased competition coming from the regional exchanges in terms of bid/ask quote adjustment frequencies, BBO times, and percentage of autoquotes. Decimalization may have increased the ability of the regionals and Nasdaq third-market dealers to aggressively price-compete with the main listing exchanges like the NYSE. Interestingly, however, the NYSE appears to be still leading the way in terms of 1-5 cent changes in bids and offers among all exchanges.
Finally, using a simple metric to capture runs and reversals in bid/ask quotes, we find evidence of significantly greater frequency of trending quotes (and significantly lower quote reversals) in decimal stocks. One implication of the latter result is the possibility of the existence of greater stale price or mispricing risk in public limit orders, which could then be picked off by relatively professional investors, thereby inflicting losses on these liquidity suppliers. If this is pervasive, there is concern that placement of limit orders (and, by extension, the supply of liquidity) may begin to dry up in the market. In fact, Bacidore et al. (2001) report that, following decimalization, traders appear to have decrease their use (and size) of traditional limit orders and are canceling orders more frequently.
One way for investors to protect themselves from being "penny-jumped" is to place smart limit orders, which could be adjusted automatically to reflect changing market conditions by a computer algorithm. Brown and Holden (1998) provide a comparative analysis of liquidity supply through market and quote adjusted limit orders to minimize mispricing risk. Fortunately, there is evidence that such mechanisms may already be in place. Recently, a brokerage subsidiary of CS First Boston began offering its clients the opportunity to trade with limit orders that are adjusted automatically by the brokerage to reflect changing market conditions. It is likely that more such opportunities will be available to investors in the near future.
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