The declining costs of technology have led to its widespread adoption through-out financial industries. The resulting technological change has revolutionized financial markets and the way financial assets are traded. Many institutions now trade via algorithms, and we study whether AT at the NYSE improves liq-uidity. In the 5 years following decimalization, AT has increased, and markets
20See “Ahead of the tape—algorithmic trading,” Economist, June 23, 2007.
have become more liquid. To establish causality we use the staggered intro-duction of autoquoting as an instrumental variable for AT. We demonstrate that increased AT lowers adverse selection and decreases the amount of price discovery that is correlated with trading. Our results suggest that AT lowers the costs of trading and increases the informativeness of quotes. Surprisingly, the revenues to liquidity suppliers also increase with AT, though this effect appears to be temporary.
While we have not studied it here, it seems likely that AT can also improve linkages between markets, generating positive spillover effects in these other markets. For example, when computer-driven trading is made easier, stock index futures and underlying share prices are likely to track each other more closely. Similarly, liquidity and price efficiency in equity options probably im-prove as the underlying share price becomes more informative.
A couple of caveats are in order, however. Our overall sample period covers a period of generally rising stock prices, and stock markets are fairly quiescent during the 2003 introduction of autoquote. While we do control for share price levels and volatility in our empirical work, it remains an open question whether AT and algorithmic liquidity supply are equally beneficial in more turbulent or declining markets. Like NASDAQ market makers refusing to answer their phones during the 1987 stock market crash, algorithmic liquidity suppliers may simply turn off their machines when markets spike downward. With access to the right data, the 2007 and 2008 stock markets could prove to be a useful laboratory for such an investigation.
A second caveat relates to trading by large institutions. Some market par-ticipants complain that the decline in depth has hampered investors’ ability to trade large amounts without substantial costs. While the rise of AT has contributed to the decline in depth, we are optimistic that other technological innovations can offset some of these effects. For instance, some “dark pools”
such as LiquidNet and Pipeline represent a modern version of an upstairs market, allowing traders with large orders to electronically search for counter-parties without revealing their trading interest (see, e.g., Bessembinder and Venkataraman (2004)).
Finally, our results have important implications for both regulators and de-signers of trading platforms. For example, the U.S. Securities and Exchange Commission’s Regulation NMS (SEC (2005)) is designed to increase competi-tion among liquidity suppliers. Our results highlight the importance of algo-rithmic liquidity suppliers and the benefits of ensuring vigorous competition among them. Of course, markets need not leave this problem to the regulator.
Trading venues can attract these algorithms by lowering development and im-plementation costs. For example, exchanges and other trading platforms can calculate useful information and metrics to be fed into algorithms, distributing them at low cost. A market can also allow algorithmic traders to co-locate their servers in the market’s data center. Finally, offering additional order types, such as pegged orders, can lessen the infrastructure pressures that algorithms impose.
32 The Journal of FinanceR REFERENCES
Almgren, Robert, and Neil Chriss, 2000, Optimal execution of portfolio transactions, Journal of Risk 3, 5–39.
Arellano, Manuel, 2003, Panel Data Econometrics (Oxford University Press, New York).
Arellano, Manuel, and Stephen R. Bond, 1991, Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations, Review of Economic Studies 58, 277–297.
Barclay, Michael J., and Terrence Hendershott, 2003, Price discovery and after trading hours, Review of Financial Studies 16, 1041–1073.
Barclay, Michael J., and Terrence Hendershott, 2004, Liquidity externalities and adverse selection:
Evidence from trading after hours, Journal of Finance 59, 681–710.
Bertsimas, Dimitris, and Andrew W. Lo, 1998, Optimal control of execution costs, Journal of Financial Markets 1, 1–50.
Bessembinder, Hendrik, 2003, Issues in assessing trade execution costs, Journal of Financial Markets 6, 233–257.
Bessembinder, Hendrik, and Kumar Venkataraman, 2004, Does an electronic stock exchange need an upstairs market? Journal of Financial Economics 73, 3–36.
Biais, Bruno, David Martimort, and Jean-Charles Rochet, 2000, Competing mechanisms in a common value environment, Econometrica 68, 799–837.
Biais, Bruno, and Pierre-Olivier Weill, 2009, Liquidity shocks and order book dynamics, Working paper, Toulouse University, IDEI.
Black, Fischer, 1995, Equilibrium exchanges, Financial Analysts Journal 51, 23–29.
Boulatov, Alex, and Thomas J. George, 2007, Securities trading when liquidity providers are informed, Working paper, University of Houston.
Bound, John, David A. Jaeger, and Regina M. Baker, 1995, Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak, Journal of the American Statistical Association 90, 443–
450.
Chaboud, Alain, Benjamin Chiquoine, Erik Hjalmarsson, and Clara Vega, 2009, Rise of the ma-chines: Algorithmic trading in the foreign exchange market, Working paper, Federal Reserve Board.
Chan, Louis K.C., and Josef Lakonishok, 1995, The behavior of stock prices around institutional trades, Journal of Finance 50, 1147–1174.
Cohen, Kalman, Steven Maier, Robert Schwartz, and David Whitcomb, 1981, Transaction costs, order placement strategy and existence of the bid-ask spread, Journal of Political Economy 89, 287–305.
Comerton-Forde, Carole, Terrence Hendershott, Charles M. Jones, Mark S. Seasholes, and Pamela C. Moulton, 2010, Time variation in liquidity: The role of market maker inventories and revenues, Journal of Finance 65, 295–331.
Copeland, Thomas E., and Dan Galai, 1983, Information effects on the bid-ask spread, Journal of Finance 38, 1457–1469.
Domowitz, Ian, and Henry Yegerman, 2005, The cost of algorithmic trading: A first look at compar-ative performance, in Brian R. Bruce, ed.: Algorithmic Trading: Precision, Control, Execution (Institutional Investor London).
Engle, Robert F., Jeffrey R. Russell, and Robert Ferstenberg, 2007, Measuring and modeling execution cost and risk, Working paper, New York University.
Foucault, Thierry, Ohad Kadan, and Eugene Kandel, 2010, Liquidity cycles and make/take fees in electronic markets, Working paper, HEC Paris.
Foucault, Thierry, and Albert J. Menkveld, 2008, Competition for order flow and smart order routing systems, Journal of Finance 63, 119–158.
Foucault, Thierry, Ailsa Ro¨ell, and Patrik Sandas, 2003, Market making with costly monitoring:
An analysis of the SOES controversy, Review of Financial Studies 16, 345–384.
Glosten, Lawrence R., 1987, Components of the bid ask spread and the statistical properties of transaction prices, Journal of Finance 42, 1293–1307.