Does Algorithmic Trading Improve Liquidity

Does Algorithmic Trading Improve Liquidity? Algorithmic trading has sharply increased over the past decade. Equity market liquidity has improved as well. Are the two trends related? For a recent five-year panel of New York Stock Exchange (NYSE) stocks, we use a normalized measure of electronic message traffic as a proxy for algorithmic liquidity supply and trace the associations between liquidity and message traffic. Based on within-stock variation, we find that algorithmic trading and liquidity are positively related. To sort out causality, we use the start of autoquoting on the NYSE as an exogenous instrument for algorithmic trading. Previously, specialists were responsible for manually disseminating the inside quote. As stocks were phased in gradually during early 2003, the manual quote was replaced by a new automated quote whenever there was a change to the NYSE limit order book. This market structure change provides quicker feedback to traders and algorithms and results in more message traffic. For large-cap stocks in particular, quoted and effective spreads narrow under autoquote and adverse selection declines, indicating that algorithmic trading does causally improve liquidity. Technological change has revolutionized the way financial assets are traded. Back office improvements can support vastly increased trading volume. Retail investors place orders via computer rather than speaking to a broker on the phone. Trading floors have largely been replaced by electronic trading platforms (Jain (2005)). The nature of order execution has changed dramatically as well, as many market participants now employ algorithmic trading (AT), commonly defined as the use of computer algorithms to manage the trading process. From a starting point near zero about ten years ago, AT is now thought to be responsible for 1 3 of trading volume in the U.S and is expected to account for perhaps half of trading volume by 2010 (Economist (2007a)). The intense activity generated by algorithms threatens to overwhelm exchanges and market data providers (Economist (2007b)), forcing significant upgrades to their infrastructures (Economist (2007a)). Before algorithmic trading took hold, a pension fund manager who wanted to buy 30,000 shares of IBM might hire a broker-dealer to search for a counterparty to execute the entire quantity at once in a block trade. Alternatively, that institutional investor might have hired a New York Stock Exchange (NYSE) floor broker to go stand at the IBM post and quietly “work” the order, using his judgment and discretion to buy a little bit here and there over the course of the trading day to keep from driving the IBM share price up too far. As trading became more electronic, it became easier and cheaper to replicate that floor trader with a computer program doing algorithmic trading (see Hendershott and Moulton (2007) for evidence on the decline in NYSE floor broker activity). Now virtually every large brokerdealer offers a suite of algorithms to its institutional customers to help them execute orders in a single stock, in pairs of stocks, or in baskets of stocks. Algorithms typically determine the timing, price, and quantity of orders, dynamically monitoring market conditions across different securities and trading venues, reducing market impact by optimally (and possibly randomly) breaking large orders into smaller pieces, and closely tracking benchmarks such as the volume-weighted average price (VWAP) over the execution interval. Many observers think of algorithms from the standpoint of this institutional buyside investor. But there are other important users of algorithms. Some hedge funds and broker-dealers supply liquidity using algorithms, competing with designated market-makers and other liquidity suppliers. For assets that trade on multiple venues, liquidity demanders often use smart order routers to determine where to send a marketable order. All of these are also forms of algorithmic trading. See, for example, Domowitz and Yegerman (2005). Algorithms can also be used to formulate trading decisions and strategies as well as implement them.