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., ...

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We use a long, recent panel of proprietary system order data from the New York Stock Exchange to examine the incidence and information content of various kinds of short sale orders. On average, at least 12.9% of NYSE volume involves a short seller. As a group, these short sellers are extremely well-informed. Stocks with relatively heavy shorting underperform lightly shorted stocks by a risk-adjusted average of 1.07% in the following 20 days of trading (over 14% on an annualized basis). Large short sale orders are the most informative. In contrast, when more of the short sales are small (less than 500 shares), stocks tend to rise in the following month, indicating that these orders are uninformed. We partition short sales by account type: individual, institutional, member-firm proprietary, and other, and we can distinguish between program and non-program short sales. Institutional non-program short sales are the most informative. Compared to stocks that are lightly shorted by institutions, a portfolio of stocks most heavily shorted by institutions on a given day underperforms by a risk-adjusted average of 1.36% in the next month (over 18% annualized). These alphas do not account for the cost of shorting, and they cannot be achieved by outsiders, because the internal NYSE data that we use are not generally available to market participants. But these findings indicate that institutional short sellers have identified and acted on important value-relevant information that has not yet been impounded into price. The results are strongly consistent with the emerging consensus in financial economics that short sellers possess important information, and their trades are important contributors to more efficient stock prices. WHICH SHORTS ARE INFORMED?A number of theoretical models, beginning with Miller (1977) and Harrison and Kreps (1978), show that when short selling is difficult or expensive, stocks can become overvalued as long as investors agree to disagree on valuations. There is a horde of much more recent empirical evidence which uniformly supports this proposition. There is now a consensus, at least in the financial economics literature if not on Main Street, that short sellers occupy a fairly exalted position in the pantheon of investors for their role in keeping prices in line.But there is surprisingly little direct evidence that short sellers know what they are doing, that they are any different from or better informed about fundamentals than other investors.There is plenty of indirect evidence. For example, Aitken et al. (1998) show that in Australia, where short sales are immediately disclosed to the public, the reporting of a short sale causes prices to decline immediately. Jones and Lamont (2002) show that when the price of shorting rises (indicating either an increase in shorting demand or a decline in the supply of lendable shares), stock prices soon fall. Cohen, Diether, and Malloy (2005) cleverly separate these two and show that it is the increase in shorting demand that is associated with...

We test whether the reaction of international stock markets to oil shocks can be justified by current and future changes in real cash flows and/or changes in expected returns. We find that in the postwar period, the reaction of United States and Canadian stock prices to oil shocks can be completely accounted for by the impact of these shocks on real cash flows alone. In contrast, in both the United Kingdom and Japan, innovations in oil prices appear to cause larger changes in stock prices than can be justified by subsequent changes in real cash flows or by changing expected returns.

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