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【求助】一个公司Nadaq上市了，我的朋友想卖出股份，请问选择那间broker好？ -- AttorneyAtLaw - (227 Byte) 2007-10-10 周三, 14:02 (1954 reads) |
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作者：AttorneyAtLaw 在 海归商务 发贴, 来自【海归网】 http://www.haiguinet.com
Disclosure of information on order execution practices of market centers: How can investors utilize it?
Financial Services Review, Summer 2004 by Saraoglu, Hakan, Ascioglu, N Asli
Abstract
The U.S. Securities and Exchange Commission has recently adopted Rule 11Ac1-5 that requires market centers to disclose statistical information regarding their order execution practices. The rule enables investors to assess the quality of execution for different types and sizes of orders in market centers. This paper develops a framework for comparing order execution quality across competing market centers by utilizing the data set made available as a result of the new rule. Different investors may have different preferences related to the execution quality of their orders. Our framework allows investors to incorporate their preferences as well as order types and sixes into the measurement of execution quality. © 2004 Academy of Financial Services. All rights reserved.
Jel classification; G10; G18; 020
Keywords: Order execution quality; SEC Rule 11Ac1-5; Market microstructure; Market centers; Analytic hierarchy process
1. Introduction
The comparison of order execution quality across different securities market centers has been a central issue in market microstructure studies and in a recent rule of the U.S. Securities and Exchange Commission (SEC). In today's markets, investors demand the best execution of their orders from competing market centers, and a key service of market centers is to provide the highest quality for trade executions. Therefore, execution quality directly reflects market quality.
Execution quality for investors is commonly measured by execution costs. The difference between the ask and bid prices (dollar spread), and the difference between the execution price and the midpoint of the ask and bid prices are common measures of execution costs. Realized spread, measured by the transaction price relative to the midpoint of the bid and ask prices at some time subsequent to the trade, is also commonly used as a proxy for execution costs. Realized spread impounds the price movements after the trade and measures the potential loss to a dealer or a trader taking the other side of the order. Execution quality also has other dimensions such as speed of transaction, which is the difference between the time a broker receives an order from a customer and the time the order is executed, and the extent to which an order is executed for the full size at the best available price. A proper comparison of trading across market centers is dependent on the availability of standardized data on various measures of execution quality. Acknowledging this fact, the SEC introduced Rule 11Ac1-5 to increase the transparency of order execution practices of market centers (see the SEC Final Rule: Disclosure of Order Routing and Execution Practices, SEC, 2001). Rule 11Ac1-5 became effective on January 30, 2001, and market centers began publishing monthly data on execution quality in June 2001.
In this study, we provide a framework for the comparison of execution quality among different market centers using the data made available as a result of Rule 11Ac1-5. Our framework highlights the Analytic Hierarchy Process (AHP), which is a widely used tool for solving multi-attribute decision problems. Given that the assessment of execution quality involves measurements based on multiple criteria, it can apparently be difficult for an individual investor to decide which market provides the most desirable execution of an order. By using the AHP framework, investors can rank market centers based on the relative importance they assign to each execution quality criterion. More importantly, the AHP ensures consistency in the determination of the relative importance of the criteria. This is the first study that provides an analytical decision process for the comparison of market centers.
In the following section, we include a literature review for the execution quality of markets and discuss Rule 11Ac1-5. We provide an overview of the AHP in Section 3. In Section 4, we present an example in which we recommend a suitable order type and a market center to a hypothetical investor using the AHP. We provide a summary in Section 5.
2. Comparison of execution quality in market centers and Rule 11Ac1-5
Studies that analyze the quality of market centers primarily focus on measures of execution price quality such as percentage spread and effective spread, and report significant differences in these measures across market centers. Christie and Schultz (1994) find that National Association of Securities Dealers Automated Quotation System (Nasdaq) dealers avoided odd-eight quotes in 70 of the 100 largest Nasdaq stocks in 1991, leading to higher percentage spreads for those stocks. Using the Trades, Orders, Reports, and Quotes (TORQ) Database that covers 144 randomly selected NYSE stocks from November 1990 through January 1991, Chung et al., (2001) also show that the average Nasdaq spread was significantly larger than the average New York Stock Exchange (NYSE) specialist's spread because of a higher use of even-eighth quotes of Nasdaq market makers. Moreover, Christie and Huang (1994) and Barclay (1997) report that spreads decrease when stocks move from Nasdaq to the NYSE. Bessembinder (1999) finds that spreads on Nasdaq are greater than those of the NYSE after the 1997 Nasdaq market reforms. Heidle and Huang (2002) suggest that informed traders who want to be anonymous prefer to trade in dealer markets rather than in an auction environment, and this leads to wider spreads in dealer markets such as Nasdaq. The SEC's Report on the Practice of Preferencing (SEC, 1997) finds significant differences in execution quality among the NYSE and the regional exchanges.
based on these findings, it is important for investors to be able to compare market centers for the quality of execution of their orders. Rule 11Ac1-5 is intended to increase access to information about how securities transactions are executed, hence enhancing investors' ability to make choices on the basis of execution criteria important to their particular needs. Rule 11Ac1-5 states that "market centers that trade national market system securities (specialists, over-the-counter "OTC" market makers, and ATSs) would be required to make available to the public monthly electronic reports that include uniform statistical measures of execution quality on a security-by-security basis." As a result of Rule 11Ac1-5, investors now have access to uniform statistics of order execution quality provided by each market center. Although the availability of such information is very valuable, it is difficult especially for individual investors to act on it without a proper framework. We suggest that information on the performance of competing market centers can be utilized in a multi-attribute evaluation framework in which investors can rank various market centers based on a set of performance criteria. Full-service brokerage firms, on-line brokers, and financial planners can offer a service that uses the AHP to help investors identify a market center and an order type that are suitable to their personal needs and preferences. Such a service would be useful not only as an evaluation framework but also as a reliable repository of the disclosure data.
3. Using the AHP for evaluating the execution quality of market centers
The AHP, which was developed by Saaty (1977, 1980), is a tool that helps decision-makers solve complex multi-attribute problems. It is commonly used to rank competing alternatives based on a set of evaluation criteria, and it has been applied to a variety of problems in finance, such as determining investor suitability (Bolster et al., 1995), selecting mutual funds (Saraoglu & Detzler, 2002), assigning sovereign debt ratings (Johnson et al., 1990), selecting a life insurance contract (Puelz, 1991), and determining an optimum portfolio mix (Khaksari et al., 1989).
Comparison of execution quality in markets centers and making choices on the basis of execution criteria are typical multi-attribute decision-making problems that can be solved using the AHP framework. In the following section, we present an example in which we rank market center-order type pairs based on the execution quality of small orders of the Nasdaq 100 Trust Series I (QQQ).
4. An example: evaluating the execution quality of market centers for QQQ
The first step in the AHP is to represent a given decision problem in a hierarchical structure, which typically includes three levels: the overall objective of the decision, the assessment criteria, and the competing alternatives. Fig. 1 presents the hierarchy of evaluating the order execution quality in different market centers for a hypothetical investor. In this case, the overall objective is to recommend to an investor a suitable order type and market center for the execution of a small order of QQQ. The assessment criteria are the different component measures of order execution quality for the investor, and the 25 market center-order type pairs are the competing alternatives. The hierarchy in Fig. 1 is kept simple for illustration purposes, and can be easily modified to include additional assessment criteria and market centers.
We use QQQ in our example because it has been the most actively traded issue among all the stocks and exchange traded funds listed on the American Stock Exchange (AMEX) since its inception on March 10, 1999. Both small individual investors as well as institutional investors heavily trade QQQ because it provides significant portfolio diversification benefits. The other characteristic of QQQ that makes it a good example for our study is that it is traded on all five market centers that we focus on: the AMEX, the NYSE, Nasdaq, Electronic Communication Networks (ECNs), and regional exchanges.
We suggest potential loss to a dealer or a trader taking the other side of an order (from now on referred to as potential loss), likelihood of execution, and speed of execution as the evaluation criteria of order execution quality. Importance weight of each criterion may be different for each investor based on his or her preferences and constraints. Therefore, the investor must first determine the relative importance of the execution quality criteria through pairwise comparisons. Table 1 presents a pairwise comparison scale typically used in the AHP. A financial advisor can guide the investor in the process of pairwise comparisons through a questionnaire, which can be prepared for a specific order size. With three criteria, the investor has to make three pairwise comparisons. A sample questionnaire and responses from a hypothetical investor are presented in the Appendix. Table 2 Panel A presents the preferences of the hypothetical investor in a matrix format. For example, comparing potential loss to speed of execution (row 1, column 3 of the matrix), the investor assigns a preference score of seven, indicating that he or she puts more emphasis on potential loss as an execution quality criterion. The investor also sees potential loss as more important compared to likelihood of execution, and assigns it a preference score of three.
weights, we refer the reader to Saaty (1977, 1980). Table 2 Panel B presents the relative importance weights of the execution quality criteria as determined by the hypothetical investor. In this example, the investor sees potential loss as the most important criterion for measuring execution quality with a weight of 0.6693. This investor also considers likelihood of execution more important than speed of execution.
Rule 11 Ac1-5 requires market centers to report their execution quality statistics for four size categories and five order types. Order size categories are 100-499 shares, 500-1999 shares, 2000-4999 shares, and 5000 shares or higher. Order types are market orders, marketable limit orders, inside-the-quote limit orders, at-the-quote limit orders, and nearthe-quote limit orders.1 We use these order types and the NYSE, the AMEX, Nasdaq, ECNs, and regional exchanges to form the market center-order type pairs. Regional exchanges in our sample are Chicago Stock Exchange, Boston Stock Exchange, Philadelphia Stock Exchange, and Cincinnati Stock Exchange. We include Bernad L. Madoff Investment Securities, Knight Securities, Salomon Smith Barney, and Trimark Securities in our sample of Nasdaq market makers. ECNs are Archipelago Exchange, Brut LLC, Island, and Instinet.2 A list containing the codes and the titles of market centers as well as the codes and the titles of member firms is provided in Table 3.
We obtain the execution quality statistics of orders that are in the small size category (100-499 shares) for each market center for the month of October 2002 using the data mandated by Rule 11 Ac 1-5. Panels A and B of Table 4 include these statistics for market orders and marketable limit orders, respectively.3 Table 5 shows field names for the execution quality statistics and their definitions. We use a weighted average based on the number of orders submitted to a member firm to calculate the composite execution quality statistics for the NYSE, the AMEX, and Nasdaq. We calculate a weighted average based on the number of orders submitted to each ECN to obtain the composite execution quality statistics for the ECNs. We use a similar approach for regional exchanges. For each regional exchange, we first calculate a weighted average based on the number of orders submitted to each member firm, then, we obtain a weighted average across all regional exchanges in our sample to compute the composite execution quality statistics of regional exchanges.
We compare the market center-order type pairs based on three execution quality criteria: potential loss, likelihood of execution, and speed of execution. We use realized spread (RSPR) as a proxy for potential loss. Under Rule 11 Ac1-5, realized spread is defined as twice the difference between the execution price and the midpoint of the consolidated best bid and ask prices five minutes after the time of order execution for buy orders, and twice the difference between the midpoint and the execution price for sell orders. A positive realized spread indicates a price increase (decrease) five minutes after the execution of a sell (buy) order. Therefore, it reflects a potential loss for an individual trader and a potential profit for a dealer or a trader taking the other side of the order. The larger the positive realized spread, the worse off the individual trader is. When realized spread is negative, it implies that the other side of the trade is losing money.
We use fill rate (FR) to represent the likelihood of execution.4 Fill rate is calculated using the following equation.
Table 6 shows the values of RSPR, FR, and ES that we calculate for each market
center-order-type pair. We find that realized spread is negative for inside-the-quote limit orders and near-the-quote limit orders in the AMEX, the NYSE, ECNs, and Nasdaq. Nasdaq also has a large negative realized spread (-0.0174) for marketable limit orders, which implies that market makers or other liquidity providers on Nasdaq lose money on average when they take the other side of marketable limit orders. When dealers have serious inventory imbalances that need to be adjusted in a short time, they demand liquidity and pay the spread rather than earning the spread. As is apparent in Table 6, the only market center that docs not report negative realized spreads for any of the order types is regional exchanges. All other market centers have negative realized spreads for at least two types of orders. This indicates that it is difficult for dealers or specialists to make short-term profits by taking the other side of the small orders of QQQ.
We show that orders submitted to the NYSE, AMEX, Nasdaq, and regional exchanges are executed almost with the full-submitted size for all order types except near-thc-quote limit orders. Because these limit orders are submitted outside the best bid price and best ask price, they might expire without execution as the prices move away from the submitted limit price. ECNs have very high oil rates that are close to one for all types of orders except market orders. ECNs' fill rate is only 0.4263 for market orders. The ECNs that we include in our analysis route more than half of the market orders to other market centers. As a result, orders submitted to ECNs take the highest execution time among all the market centers with 16.3748 seconds for market orders. For each market center, marketable limit orders take more time to execute than market orders except ECNs. NYSE and Nasdaq have relatively low execution times of 6.3536 seconds and 6.8370 seconds for market orders, respectively. Regional exchanges and AMEX execute market orders at around 10.4404 seconds and 13.9382 seconds, respectively. At-the-quote limit orders and near-the-quote limit orders wait longer than other order types to be executed on each market center, since those orders will take their place behind the marketable limit orders and inside-the-quote limit orders on the limit order book.
After we calculate the values of RSPR, FR, and ES, we transform them so that they map onto the interval ranging from one to nine, where the minimum value and the maximum value are transformed to one and nine, respectively. The variables for which the decisionmaker requires smaller values are transformed so that the maximum value corresponds to one and the minimum value corresponds to nine. Then, we normalize the transformed values to calculate the relative strength weights of the market center-order type pairs in terms of the execution quality criteria. Similar normalization methods are used in studies that incorporate quantitative data to the AHP (see Week et al., 1997; and Yu et al., 2000). Table 7 shows the relative strength weights for market center-order type pairs based on the values of the execution quality criteria. For example, given its realized spread value, NYSE-inside-the-quote limit order pair has the highest strength weight of 0.0681, indicating that an inside-the-quote limit order executed at the NYSE is the best choice for the investor under the potential loss criterion. After determining the relative importance of the execution quality criteria and the strength of the market center-order type pairs under each criterion, we combine them to determine the relative suitability of market center-order type pairs for executing a small order of QQQ for the hypothetical investor in our example. The relative strength weights of market center-order type pairs under the execution quality criteria form a 25 × 3 matrix. Each row of the matrix represents a market center-order type pair and each column represents a criterion. The relative importance weights of the execution quality criteria for the investor form a 3 × 1 vector. We multiply the relative strength matrix of the market center-order type pairs by the relative importance vector of the execution quality criteria to obtain a 25 X 1 vector, which reflects the relative suitability of the market center-order type pairs for the investor. Table 8 shows the elements of this vector as well as the suitability rankings of the market center-order type pairs.
In our example, the most important criterion for the hypothetical investor, who submits a small order for QQQ, is potential loss, followed by likelihood of execution and speed of execution. The NYSE-inside-the-quote limit order pair ranks first based on these criteria with a suitability weight of 5.9814%. It is followed by the NYSE-near-the-quote limit order pair with a suitability weight of 5.3063%. For all order types, regional exchanges rank very low in the list. The reason for this is that potential loss, measured by realized spread, is positive and large for most of the order types on regional exchanges, indicating a large potential loss by the hypothetical investor. The rankings we obtain reflect the relative importance of each execution quality criterion for the hypothetical investor in our example.
5. Summary
In this paper, we used the AHP to develop a framework for evaluating market center-order type pairs based on a set of execution quality criteria. The AHP allows an investor to incorporate his or her particular preferences and constraints into the comparison of execution quality in market centers. The main contributions of this paper are its introduction to the market microstructure literature of a methodology that is widely used in multi-attribute decision-making, and the development of a framework that allows investors to utilize the data available as a result of Rule 11Ac1-5.
Notes
1. In Rule 11Ac1-5, the terms inside-the-quote limit order, at-the-quote limit order, and near-the-quote limit order arc described as non-marketable buy (sell) orders with limit prices that are, respectively, higher (lower) than, equal to, and lower (higher) by $0.10 or less than the consolidated best bid (offer) at the time of order receipt. A marketable limit order is any buy (sell) order with a limit price equal to or higher (lower) than the consolidated best offer (bid) at the time of order receipt.
2. In July 2000, the Pacific Stock Exchange signed an agreement with Archipelago, an ECN, to create the Archipelago Exchange. On October 25, 2001, the Pacific Exchange received approval from the SEC to launch the "Archipelago Exchange" enabling all buyers and sellers to meet electronically.
3. For brevity purposes, we only include market orders and marketable limit orders in Table 4.
4. Low fill rates could be desirable for a trader who would like a better ability to quickly cancel limit orders. If the decision hierarchy is set up by a trader that considers low fill rate as an advantage, then the questionnaire would include "ability to quickly cancel an order" as an evaluation criterion.
Acknowledgments The authors thank Editor Conrad Ciccotello, an anonymous reviewer, Bob Wood, Murat Aydogdu, Lynn Kugele, and the participants at the 2002 Eastern Finance Association Annual Meeting and the 2003 Academy of Financial Services Annual Meeting.
References
Barclay, M. (1997). Bid-ask spreads and the avoidance of odd-eighth quotes on Nasdaq: An examination of exchange listings. Journal of Financial Economics, 45, 35-60.
Bessembinder, H. (1999). Trade execution costs on Nasdaq and the NYSE: A post-reform comparison. Journal of Financial and Quantitative Analysis, 34, 387-408.
Bolster, P. J., Janjigian, V., & Trahan, E. A. (1995). Determining investor suitability using the analytic hierarchy process. Financial Analysts Journal, 51, 63-75.
Christie W. G., & Huang, R. D. (1994). Market structures and liquidity: A transactions data study of exchange listings. Journal of Financial Intermediation, 3, 300-326.
Christie, W., & Schultz, P. (1994). Why do NASDAQ market makers avoid odd-eighth quotes? Journal of Finance, 49, 1813-1840.
Chung, K., Van Ness, B., & Van Ness, R. (2001). Can the treatment of limit orders reconcile the differences in trading costs between NYSE and Nasdaq issues? Journal of Financial and Quantitative Analysis, 36, 267-286.
Heidle, H. G., & Huang, R. (2002). Information-based trading in dealer and auction markets: An analysis of exchange listings. Journal of Financial and Quantitative Analysis, 37, 391-425.
Johnson, R. A., Srinivasan, V., & Bolster, P. J. (1990). Sovereign debt ratings: A judgmental model based on the Analytic Hierarchy Process. Journal of International Business Studies, 21, 95-117.
Khaksari, S., Kamath, R., & Grieves, R. (1989). A new approach to determining optimum portfolio mix. Journal of Portfolio Management, 15, 43-49.
Puelz, R. (1991). A process for selecting a life insurance contract. Journal of Risk and Insurance, 58, 138-146.
Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15, 234-281.
Saaty, T. L. (1980). The Analytic Hierarchy Process. New York: McGraw-Hill.
Saraoglu, H., & Detzler, M. (2002). A sensible mutual fund selection model. Financial Analysts Journal, 58, 60-72.
U.S. Securities and Exchange Commission. (1997). Report on the Practice of Preferencing. Available at: https://www.see.gov/news/studies/prefrep.htm (accessed February 19, 2004).
U. S. Securities and Exchange Commission. (2001). Final Rule: Disclosure of Order Routing and Execution Practices. Available at: https://www.sec.gov/rules/final/34-43590.htm (accessed December 08, 2002).
Weck, M., Klocke, F., Schell, H., & Ruenauver, E. (1997). Evaluating alternative production cycles using the extended fuzzy AHP method. European Journal of Operatioanal Research, 100, 351-366.
Yu, Y., Jin, K., Zhang, H. C., Ling, F. F., & Barnes, D. (2000). A decision-making model for materials management of end-of-life electronic products. Journal of Manufacturing Systems, 19, 94-107.
Hakan Saraoglu*, N. Asli Ascioglu
Department of finance, Bryant College, Smithfield, RI 02917-1284, USA
Received March 2004; accepted 19 April 2004
* Corresponding author. Tel.: +1-401-232-6450; fax: +1-401-232-6319.
E-mail address: [email protected] (H. Saraoglu).
Copyright Academy of Financial Services Summer 2004
Provided by ProQuest Information and Learning Company. All rights Reserved
作者：AttorneyAtLaw 在 海归商务 发贴, 来自【海归网】 http://www.haiguinet.com
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