Algorithmic trading (AT) has increased sharply over the past decade. Does it improve market quality, and should it be encouraged? We provide the first analysis of this question. The New York Stock Exchange automated quote dissemination in 2003, and we use this change in market structure that increases AT as an exogenous instrument to measure the causal effect of AT on liquidity. For large stocks in particular, AT narrows spreads, reduces adverse selection, and reduces trade-related price discovery. The findings indicate that AT improves liquidity and enhances the informativeness of quotes.TECHNOLOGICAL CHANGE HAS REVOLUTIONIZED the way financial assets are traded. Every step of the trading process, from order entry to trading venue to back office, is now highly automated, dramatically reducing the costs incurred by intermediaries. By reducing the frictions and costs of trading, technology has the potential to enable more efficient risk sharing, facilitate hedging, improve liquidity, and make prices more efficient. This could ultimately reduce firms' cost of capital.Algorithmic trading (AT) is a dramatic example of this far-reaching technological change. Many market participants now employ AT, commonly defined as the use of computer algorithms to automatically make certain trading decisions, submit orders, and manage those orders after submission. From a starting point near zero in the mid-1990s, AT is thought to be responsible for as much as 73 percent of trading volume in the United States in 2009. 1 There are many different algorithms, used by many different types of market participants. Some hedge funds and broker-dealers supply liquidity * Hendershott is at Haas School of Business, University of California Berkeley. Jones is at Columbia Business School. Menkveld is at VU University Amsterdam. We thank Mark van Achter, Hank Bessembinder, Bruno Biais, Alex Boulatov, Thierry Foucault, Maureen O'Hara, Sébastien Pouget, Patrik Sandas, Kumar Venkataraman, the NASDAQ Economic Advisory Board, and seminar participants at the University of Amsterdam, Babson College, Bank of Canada, CFTC, HEC Paris, IDEI Toulouse, Southern Methodist University, University of Miami, the 2007 MTS Conference, NYSE, the 2008 NYU-Courant algorithmic trading conference, University of Utah, the 2008 Western Finance Association meetings, and Yale University. We thank the NYSE for providing system order data. Hendershott gratefully acknowledges support from the National Science Foundation, the Net Institute, the Ewing Marion Kauffman Foundation, and the Lester Center for Entrepreneurship and Innovation at the Haas School at UC Berkeley. Menkveld gratefully acknowledges the College van Bestuur of VU University Amsterdam for a VU talent grant.1 See "SEC runs eye over high-speed trading," Financial Times, July 29, 2009. The 73% is an estimate for high-frequency trading, which, as discussed later, is a subset of AT. 2The Journal of Finance R using algorithms, competing with designated market-makers and other liquidity suppliers (e.g., ...

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may ABSTRACTWe examine empirically the role of high-frequency traders (HFTs) in price discovery and price efficiency. Based on our methodology, we find overall that HFTs facilitate price efficiency by trading in the direction of permanent price changes and in the opposite direction of transitory pricing errors, both on average and on the highest volatility days. This is done through their liquidity demanding orders. In contrast, HFTs' liquidity supplying orders are adversely selected.The direction of buying and selling by HFTs predicts price changes over short horizons measured in seconds. The direction of HFTs' trading is correlated with public information, such as macro news announcements, market-wide price movements, and limit order book imbalances. To obtain our results we follow approach, and use a state space model to decompose price movements into permanent and temporary components and to relate changes in both to HFTs. The permanent component is normally interpreted as information and the transitory component as pricing errors, also referred to as transitory volatility or noise. Transitory price movements, also called noise or short-term volatility make it difficult for unsophisticated investors to determine the true price. This may cause them to buy when they should be selling or sell when they should be buying. HFTs appear to reduce this risk. The state space model incorporates the interrelated concepts of price discovery (how information is impounded into prices) and price efficiency (the informativeness of prices). We also find that HFTs' trading is correlated with public information, such as macro news announcements, market-wide price movements, and limit order book imbalances. Keywords 3Our results have implications for policy makers that are contemplating the introduction of measures to curb HFT. Our research suggests, within the confines of our methodological approach, that HFT provide a useful service to markets. They reduce the noise component of prices and acquire and trade on different types of information, making prices more efficient overall. Introducing measures to curb their activities without corresponding measures to that support price discovery and market efficiency improving activities could result in less efficient markets.HFTs are a type of intermediary by standing ready to buy or sell securities. When thinking about the role HFTs play in markets it is natural to compare the new market st...

We examine the effects of trading after hours on the amount and timing of price discovery over the 24-hour day. A high volume of liquidity trade facilitates price discovery. Thus prices are more efficient and more information is revealed per hour during the trading day than after hours. However, the low trading volume after hours generates significant, albeit inefficient, price discovery. Individual trades contain more information after hours than during the day. Because information asymmetry declines over the day, price changes are larger, reflect more private information, and are less noisy before the open than after the close. We thank Maureen O'Hara (the editor), an anonymous referee,

This paper explores the competition between two trading venues, Electronic Communication Networks (ECNs) and Nasdaq market makers. ECNs o¡er the advantages of anonymity and speed of execution, which attract informed traders. Thus, trades are more likely to occur on ECNs when information asymmetry is greater and when trading volume and stock-return volatility are high. ECN trades have greater permanent price impacts and more private information is revealed through ECN trades than though market-maker trades. However, ECN trades have higher ex ante trading costs because market makers can preference or internalize the less informed trades and o¡er them better executions.TECHNOLOGICAL INNOVATIONS THAT ENABLE HIGH-SPEED, low-cost electronic trading systems are dramatically changing the structure of ¢nancial markets. Exchanges and markets around the world are merging or forming alliances to improve liquidity and reduce costs in the face of increased competition from each other and from these computerized trading systems. Trading volume on Electronic Communications Networks (ECNs) has grown rapidly over the past several years. ECNs are now involved in more than a third of Nasdaq trading volume and are attempting to increase their market share in NYSE-listed

This paper studies the interaction between dealer markets and a relatively new form of exchange, passive crossing networks, where buyers and sellers trade directly with one another. We find that the crossing network is characterized by both positive~"liquidity"! and negative~"crowding"! externalities, and we analyze the effects of its introduction on the dealer market. Traders who use the dealer market as a "market of last resort" can induce dealers to widen their spread and can lead to more efficient subsequent prices, but traders who only use the crossing network can provide a counterbalancing effect by reducing adverse selection and inventory holding costs.COMPETITION BETWEEN EXCHANGES for order f low is a growing phenomenon in financial markets. From London to Paris to Tel Aviv, exchanges and trading systems are introducing new trading mechanisms that compete for order f low. In the United States, the SEC promulgated new rules that redefine the regulation of Alternative Trading Systems and intensify the competition between existing exchanges and new electronic markets. Indeed, new electronic trading venues are cited as the reason for a decline in the value of seats on major exchanges, even though trading volumes are growing rapidly.What will be the impact of new trading mechanisms on market participants and on existing dealer markets~DMs!? In this paper we study the effect of introducing a passive call market that competes with an existing traditional DM. The DM is based on competing market makers as in Nasdaq, the London Stock Exchange, the Foreign Exchange market, and the U.S. government securities market. An important benefit provided by the DM is the assurance of immediate execution. An important disadvantage is the cost: the bid-ask spread, which can be substantial. Traders who do not place a high value on immediacy and assured execution can try to reduce their trading costs by searching for counterparties on their own. As computing and communication costs have declined, electronic communication networks have been increasingly deployed to reduce search costs, making it less costly for traders to find one another and reducing the demand for DMs.

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