سرعت همگرایی تا رسیدن به کارایی بازار: نقش ECN ها
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|13111||2012||19 صفحه PDF||سفارش دهید||16905 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Empirical Finance, Volume 19, Issue 5, December 2012, Pages 702–720
Chordia, Roll and Subrahmanyam (2005, CRS) estimate the speed of convergence to market efficiency based on short-horizon return predictability of the 150 largest NYSE firms. We extend CRS to a broad panel of NYSE stocks and are the first to examine the relation between electronic communication networks (ECNs) and the corresponding informational efficiency of prices. Overall, we confirm CRS's result that price adjustments to new information occur on average within 5–15 min for large NYSE stocks. We further show that it takes about 20 min longer for smaller firms to incorporate information into prices. Most importantly, we demonstrate that the speed of convergence to market efficiency is significantly related to the type of trading platform where orders are executed, even after controlling for relative order flows, trading costs, volatility, informational effects, trading conditions, market quality, institutional trading activity, and other firm-specific characteristics. Our findings provide direct answers and insights to issues raised in a recent SEC concept release document.
Modern trading technology increasingly affects the way how orders are entered, routed, and executed. Competition from alternative electronic markets (i.e. electronic communication networks, hereafter ECNs), regional exchanges, and regulatory pressures are forcing traditional exchanges to react and adapt. As ECNs began competing for order flows from major U.S. exchanges, NASDAQ and NYSE acquired some of the emerging ECNs in order to remain competitive.1 NYSE Euronext, the world's largest cash equities market, now trades more than one-third of the world's cash equity volume and offers its clients alternate trading platforms with trading models from what NYSE Euronext describes as a “high tech/high touch” trading floor system to a fully electronic system. One of the most successful ECNs is Euronext's NYSE Arca (hereafter Arca), an all-electronic trading platform with distinct market structure and certain advantages over the traditional NYSE floor trading (e.g. deeper liquidity, after hours trading, increased transparency, and efficient electronic execution in multiple U.S. market centers through its smart order routing algorithm). As of March 2007, Arca accounted for approximately one sixth of all the shares traded on the U.S. financial markets. For NYSE-listed securities, Arca accounted for over 10% of the shares traded, a rapid increase from less about 3% in 2004 (Stoll, 2006). Given the increasing importance of ECNs as alternate trading platforms, our main objective is to study the informational efficiency of prices of NYSE stocks whose orders are also routed and executed through the ECN Arca platform, and to determine whether Arca trading affects the speed off convergence to market efficiency.2 Prior literature provides mixed theoretical predictions on the informational efficiency of prices on ECNs compared to traditional exchanges.3 On one hand, some researchers propose that all-electronic trading should improve the efficiency of stock prices. Stoll (2006) argues that ECNs not only reduce the cost of providing liquidity, but also increase the accuracy of price signals. Lower trading costs and higher volume improve liquidity, which allows rational traders (arbitrageurs) to keep stock prices closer to their equilibrium values. On the other hand, other researchers find that trading on ECNs has a greater permanent price impact, and therefore is more likely to carry informed trades than the traditional markets (Barclay et al., 2003 and Huang, 2002). There is also evidence in the literature that periods with more information asymmetry are associated with higher short-horizon return predictability and that trading volume is most strongly associated with market efficiency (Chung and Hrazdil, 2010a). Further, those who believe that markets are dominated by uninformed or noise traders argue that the low cost of trading and high turnover on ECNs will lead to excessive uninformed trading driving stock prices away from their fundamental values (Shleifer and Summers, 1990). A third possibility also exists (the null hypothesis), that the efficiency of information processing will be the same between orders executed through an ECN and orders executed through a traditional trading platform. If NYSE provides sufficient liquidity (as is most likely the case for large, actively traded stocks) and enhances arbitrageurs' ability to take advantage of any mispricing, then the additional liquidity obtained through the ECN should not have incremental effect on increasing market efficiency.4 Therefore, whether and to what extent ECNs impact the informational efficiency of prices is an empirical issue, which is a main focus of this study. Previous research on ECNs concentrates primarily on the trading of NASDAQ stocks and provides evidence on the efficiency of ECNs in terms of competition for order flow, volume growth, liquidity, market quality, and information asymmetry (i.e. Barclay et al., 2003, Fink et al., 2006, Huang, 2002, Rakowski and Beardsley, 2008, Simaan et al., 2003, Tse and Hackard, 2004 and Weston, 2002 among others). The general consensus among these studies is that ECNs are efficient in competing for order flow (most studies find that ECNs get at least 20% of the order flow of NASDAQ-listed securities) and are not detrimental to market quality. To the best of our knowledge, only two studies examine the effect of ECN activity on NYSE-listed stocks. First, Lipson (2004) analyzes competition between various market centers for NYSE stocks and reports that marketable limit orders routed to Archipelago (now NYSE Arca) are typically more informed than those routed through the NYSE. Second, Nguyen et al. (2005) examine the Archipelago's change to becoming a stand-alone exchange and the impact of this change on the execution quality and the exchange's ability to compete for order flow in NYSE and NASDAQ stocks. Nguyen et al. (2005) find that while the effect of the change is positive on the execution quality of NYSE stocks, the effect for NASDAQ stocks is negative. In our study, we take an exploratory approach; we concentrate on the NYSE stocks and focus our attention on the impact that the ECN Arca trading platform has on the price efficiency of these stocks. We directly measure whether and to what extent order execution through different trading venues results in different speeds of convergence to market efficiency between the Arca and the NYSE trades. Recent developments in market microstructure give us a basis to explore the price formation process and study how fast information is incorporated into security prices. We rely on seminal approach developed by Chordia et al. (2005, hereafter CRS) who estimate the speed of convergence to market efficiency based on the short-horizon return predictability from past order flows of 150 largest, actively traded NYSE firms. CRS estimate the amount of time it takes for market participants to observe and extract information from order flows, ascertain whether there is new relevant information about values, take advantage of any predictable price movements, and in the process eliminate any serial return dependence remaining after prices adjust to their new equilibrium levels. CRS measure the speed of convergence as the time that the market requires to achieve weak-form market efficiency and on the basis of the time interval over which historical returns and order imbalances are no longer significant in explaining short-horizon return predictability.5 In their subsequent work, Chordia et al. (2008) confirm that the short-horizon predictability of stock returns from past order flows alone is sufficient for and can be used as an inverse indicator of market efficiency. Chordia et al. (2008) further encourage additional research and that “future investigation should extend the analysis to smaller firms and other years, exchanges, and countries” (p. 252). In our study, we utilize the speed of convergence to market efficiency measure developed by CRS to evaluate the informational efficiency of prices across the Arca and the traditional NYSE platforms.6 Our study makes three specific contributions to the literature. First, we complement CRS by using more recent data and extend CRS to a broad sample of NYSE stocks. Compared to CRS's sample of 150 companies, we cover a total of 2041 firms with shares that were traded simultaneously on Arca and NYSE during the first six months of 2008. Second, unlike Boehmer and Kelley (2009) and Chung and Hrazdil (2011) who utilize levels of market efficiency (based on deviations from random walk and short-horizon return predictability, respectively), we focus on the actual length of time that it takes for the trading of a stock to achieve market efficiency. With this focus, we extend CRS (2005) and Visaltanachoti and Yang (2010) by using finer and higher frequency time intervals and by modifying the speed of convergence to market efficiency estimation to control for possible volatility effects not considered in prior literature. This refinement allows us to better compare the speed between large and small firms, control for possible confounding effects, and study the impact of routing orders through Arca versus the traditional NYSE platform on the corresponding informational efficiency of prices. Third, we extend prior literature (i.e. Boehmer and Kelley, 2009) and examine how informational effects (probability of informed trading, volatility, and order arrival rate of informed and uninformed traders), trading conditions and market quality (effective spread, realized spread, price impact, order traffic, and flow toxicity) affect the speed of convergence to market efficiency. Through this analysis, we attempt to control for potential endogeneity due to selection bias (choice of traders to route their trades through the Arca versus NYSE). Our results provide new insights into understanding of the price formation process. We first corroborate CRS's finding that price adjustments to information occur on average in less than 15 min for large NYSE stocks regardless of whether the order is routed through Arca or NYSE. We further show that, for smaller stocks, it takes on average at least 15 min longer on NYSE and 20 min longer on Arca to fully incorporate information into prices. In bivariate setting, we find that trading volume has the strongest impact on reducing the time required to achieve market efficiency and that smaller stocks with orders executed through Arca take longer to incorporate information into prices compared to the same stocks with orders that are executed through NYSE. However, after controlling for volume, and other firm and exchange specific effects, our results show that the Arca platform by itself is associated with significantly faster speed of convergence to market efficiency. Finally, we provide evidence that the speed of convergence to market efficiency is significantly associated with measures of investor sophistication and revenue to liquidity providers, and significantly slower when there are more uninformed traders, stronger adverse selection, and heavier order traffic in the market. Our results also show that the faster speed of convergence on the Arca platform can be explained by decreasing activities of uninformed traders, decreasing order traffic, increasing volatility, and increasing trading volume on this platform relative to the NYSE platform. The analysis of the speed of convergence to market efficiency is of interest not only to market microstructure researchers, but also to investors, listed firms, regulators, and competing stock exchanges. Studying the returns to financial assets and the process through which markets become efficient is fundamental to understanding how economies work in allocating goods and services (O'Hara, 1997). Examining how alternative trading platforms affect the price discovery process is an important step towards exploring the process through which markets become efficient. Stock exchanges are also interested in enhancing price discovery. As the CEO of NYSE Euronext points out, building investor confidence in the equity markets is important and stock exchanges “must enhance transparency, price discovery and accountability across the marketplace” (Niederauer, 2010). Furthermore, in a recent SEC concept release document, the commission asks questions such as: “Are there useful metrics for assessing the quality of price discovery in equity markets, such as how efficiently prices respond to new information?” and “What is the best approach for assessing whether the secondary markets are appropriately supporting the capital-raising function for companies of all sizes?” (Securities and Exchange Commission, 2010). Results of our study provide direct answers and additional insights for addressing issues raised in these questions. We demonstrate that the speed of convergence can be a useful measure to assess how efficiently prices respond to new information. Our findings are consistent with the theoretical framework that information about future returns is contained in past order flows (Subrahmanyam, 2008), and that it may take some time for prices to reflect fully the impact of new information (Chan et al., 1996 and Hillmer and Yu, 1979). Our results confirm that trading volume has the strongest impact on improving the speed of convergence to market efficiency for companies of all sizes. However, further analysis suggests that the effects of other factors such as investor sophistication are not uniform across the large and the small firms. Overall, our results show that an ECN platform, such as Arca, can play a significant role in the price formation process by further affecting the speed of price adjustment to new information for both the large and the small firms. The rest of this paper is organized as follows. We provide overview of order processing through Arca in Section 2. We describe the data and methods of analysis in Section 3. Empirical results and robustness tests are presented in Section 4. We summarize and conclude the paper in Section 5.
نتیجه گیری انگلیسی
Chordia et al. (2005, CRS) estimate the speed of convergence to market efficiency based on short-horizon return predictability by examining 150 of the largest and actively traded NYSE companies. We extend this analysis to a much bigger sample, consisting of 2041 NYSE firms that were traded simultaneously on the Arca and NYSE traditional trading platforms during the first six months of 2008. We are the first to explore the relation between the trading venue of electronic communication networks (ECNs) and the corresponding informational efficiency of prices in terms of the amount of time required for prices to achieve efficiency. We first corroborate CRS's results and provide evidence that price adjustments to new information occur in less than 15 min for large NYSE stocks, regardless of whether a trade is routed through NYSE's Arca electronic platform or its traditional floor trading platform. We then extent CRS and show that, for smaller stocks, it takes on average over 15 min longer on NYSE and over 20 min longer on Arca to incorporate information into prices. We show, in a bivariate setting, that smaller stocks whose orders are executed through NYSE are priced efficiently at a faster speed compared to orders that are executed through Arca. We further extend our study to a multivariate setting, where we examine various proxies for trading costs, volatility, informational effects, trading conditions, market quality, and institutional trading activity and their impact on the speed of convergence required to achieve market efficiency. Most importantly, after controlling for and documenting the effects of these variables, we provide evidence that the Arca ECN platform is associated with significantly faster speed of convergence to market efficiency. Our results also show that while higher trading volume and volatility speed up the convergence process, and increased activities of uninformed traders and heavier order traffic have the counteracting effects of lengthening the time of convergence, the faster speed on the Arca platform is also associated with decreasing activities of uninformed traders, decreasing order traffic, increasing volatility, and increasing trading volume on this platform relative to the NYSE platform. These results have important implications for investors, listed companies, regulators and stock exchanges. Our findings provide direct answers and insights for addressing issues raised in the recent Securities and Exchange Commission (2010) concept release document. We demonstrate that the speed of convergence can be a useful measure for assessing how efficiently prices respond to new information. Our results also show that the ECN platform can play a significant role and contribute positively in the price discovery process by further enhancing the speed of adjustment to new information for both large and small firms. Whether the microstructure estimates of speed to achieve market efficiency can help evaluate market efficiency of other trading platforms, and whether there are other cross-platform effects remain subjects for future research.