بهره وری قیمت در آینده و تجارت لحظه ای: نقش فن آوری اطلاعات
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|4190||2010||10 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Electronic Commerce Research and Applications, Volume 9, Issue 5, September–October 2010, Pages 400–409
During the last years information technology has had a profound impact on financial markets. The speed of trading and the amount of available information has increased substantially. Nearly all exchanges have upgraded their trading systems to meet the demand of investors and enhance their competitive position. However, the impact on liquidity and price efficiency remains unclear. In this paper we present an event study to examine the effects of an infrastructure change at the Deutsche Börse in Germany. On April 23, 2007, Deutsche Börse released an upgraded version of their electronic trading system Xetra. We study the impact that this upgrade had on the efficiency of prices, measured as the pricing gaps between the observed futures prices and their theoretical values based on the underlying cash market. Our results suggest that the system upgrade reduced the pricing gapand thus improved price efficiency.
Securities exchanges are becoming increasingly automated. Stock and derivative exchanges around the world have introduced fully automated and transparent electronic trading systems, replacing their trading floors on which brokers manually match orders using an open-outcry system. For example, Lucas et al. (2009) provide a detailed analysis of how information and communication technologies changed trading on the New York Stock Exchange (NYSE). In 1980 a trade execution on the NYSE trading floor took 3–5 min. Since 2006 it has become possible to bypass the trading floor completely. Electronic trading systems today allow for order times of less than 10 ms. Over the course of the past 5 years most major exchanges have further upgraded their trading infrastructure to accommodate the increase in algorithmic trading (Hendershott et al. forthcoming). Algorithmic trading is commonly defined as the use of computer algorithms to manage the trading process. The increase in algorithmic trading is the reason most often cited for the corresponding increased trading volume in securities (Hasbrouck and Saar 2009). Interestingly, few if any studies in the information systems (IS) and finance literature have focused on the effect of system upgrades on transaction costs (spreads, brokerage, and commissions paid by investors), transparency, and price discovery. This study focuses on the effect an infrastructure upgrade in one market – the Xetra stock market – has on the pricing discrepancies with the Eurex derivatives market. In modern markets, algorithms are used to identify deviations of prices from fair values and implement the trading decisions. Assuming that an index future is priced too high compared to its theoretical price, an algorithmic trader could exploit the divergence while selling the stock index future and simultaneously buying the underlying stocks of that index. As profit of the arbitrage strategy, she gains the temporarily inflated pricing gap between these two baskets. Since the cost of trading in the spot and futures markets differs, the amount of information impounded may differ. As a result, the predictions of the efficient market hypothesis (Fama 1970) may not always hold. Our study provides new evidence that reduced latency1 of electronic trading systems improves the price discovery process, thus producing more efficient prices. The efficient market hypothesis states that asset prices should reflect all available information. It further breaks efficiency into three forms: weak, semi-strong, and strong. The most important form of efficiency, in the context of this study, is the weak form. Weak form efficiency states that future prices cannot be predicted with past realization of public information including trades, quotes, and volumes. The infrastructure upgrade we study effectively improves the provision of public information in one market over another. By providing more timely prices, the market prices should deviate less from the efficient price. We implicitly test this relationship below. The two markets under analysis are both owned and operated by Deutsche Börse AG. Xetra and Eurex are linked in two important ways: they both operate on a similar underlying trading system, and the derivatives listed on Eurex have Xetra traded securities as their underlying financial instruments. Due to this unique market structure we can measure the exact pricing gap between the two markets. This feature is a contrast to other studies of spot and futures index deviations in electronic markets. Specifically, we study the effect of the upgrade of the Xetra system on the pricing gap between the theoretical futures prices based on the price of the German blue chip stock index DAX and observed futures prices on this index. The DAX is calculated using real-time trade prices in the underlying stocks. Moreover, we use trade prices of the most liquid DAX futures. In theory the observed futures prices should always be equal to the theoretical futures prices. Empirically, we often observe discrepancies in the pricing relationship between these two prices. We call these discrepancies pricing gaps and measure the effect of an IT-infrastructure upgrade on these. Due to faster, lower-risk arbitrage, we expect that the infrastructure upgrade should reduce these gaps. The paper is organized as follows: In Section 2 we discuss related work in the IS and finance literature. Section 3 covers the market microstructure that is relevant for our study. The data and methodology are presented in Sections 4 and 5, respectively. We discuss and interpret the results in Section 6 and conclude with a summary and provide an outlook for further research.
نتیجه گیری انگلیسی
We present evidence that a pure IT-infrastructure upgrade reduces the pricing gap between the observed and the theoretical futures price in two highly liquid electronic markets. The gap is reduced even after controlling for variables that are strongly correlated with these deviations like volume, liquidity, and volatility. This demonstrates the robustness of the results to alternative explanations. This study has important implications for other established exchanges. By allowing market participants faster access to the trading platform, prices better reflect supply and demand. This improved price efficiency will induce more trade. Increasing trade on electronic platforms is beneficial in that investors will be able to execute more trades. Platform operators also benefit because they typically profit per transaction. In addition, faster access to trade is likely to provide a competitive advantage in attracting algorithmic traders. Investors’ willingness to pay more is also related to their ability to reduce search and monitoring costs. Still, while the costs of 2.0 million euros for the Xetra Release 8.0 can be seen in Deutsche Börse’s 2007 annual report, the assessment of the social value of this infrastructure upgrade cannot be determined given the data available and remains a topic for further analyses. There are numerous related future research topics in this area. It would be interesting to get access to participant level futures trading level data to study how trading was actually affected by this upgrade. An interesting question here is whether there was a broad change in trading or if this effect is driven by a small number of participants. We expect algorithmic traders to react faster and bring a higher amount of information into their spot market price discovery and trading decisions after the infrastructure upgrade. However, we cannot address this directly because we are not able to identify the quotes of algorithmic traders in our data set. Future research can also explore which kinds of infrastructure upgrades are associated with more efficient prices. It would also be interesting to extend this type of research to other linked markets, including the spot and options markets.