The Effect of Algorithmic Trading on Liquidity in the Options Market

Algorithmic trading consistently reduces the bid-ask spread in options markets, regardless of firm size, option strike price, call or put option, or volatility in the markets. However, the effect on depth depends on the categorization of the data. The examination of the introduction of penny quotes provides a successful robustness test for the importance of algorithmic trading on liquidity. Overall, this study provides a controlled analysis of options with different levels of activity and different types of market participants across strikes/calls/puts/underlying stocks. Our findings also contribute to the extant literature on the characteristics of the liquidity of options markets during the growth period of algorithmic trading. During the past several years the widespread development of automated order execution systems (algorithmic or algo trading) has transformed the financial markets. In particular, the promulgation of Order Protection Rule 611 under Regulation NMS in 2005 promoted the use of electronic trading and subsequently computerized algorithms. According to Rule 611, limit orders that are “immediately and automatically accessible” via an “Immediate or Cancel” (IOC) order have their prices protected from another trade execution at an inferior price. Consequently, Regulation NMS leveled the playing field across all U.S. exchanges regarding order executions. 1 These rule changes caused exchanges to compete based on trading fees, the speed of order handling, and the quality of execution in order to obtain a greater share of trading volume (Palmer, 2009). Because of the proliferation of electronic trading across all exchanges, the use of algorithms became indispensable for the trading process of institutions, market makers, and 1 On April 6, 2005, the Securities and Exchange Commission adopted Regulation NMS, a series of initiatives designed to modernize and strengthen the national market system for equities. Regulation NMS was published in Securities Exchange Act Release No. 51808 (Jun. 9, 2005), 70 FR 37496 (Jun. 29, 2005) (“NMS Release”). These initiatives include: (1) Rule 610, which addresses the access to markets; (2) Rule 611, which provides inter-market price priority for displayed and accessible quotations; (3) Rule 612, which establishes minimum pricing increments; and (4) amendments to the joint-industry plans and rules governing the dissemination of market data. Rule 611, among other things, requires a trading center to establish, maintain, and enforce written policies and procedures reasonably designed to prevent “trade-throughs” – the execution of trades at prices inferior to protected quotations displayed by other trading centers. In order to be protected a quotation must be immediately and automatically accessible. (See Palmer (2009)). 3 professional traders. This resulted in algorithmic trading taking over the market making function for smaller size trades in the stock market due to its speed and cost advantages (see Hendershott and Moulton (2007)). More generally, Hendershott, Jones and Menkveld (2011) explain the use of algorithmic trading as follows: Algorithms are used to supply as well as to demand liquidity. For example, liquidity demanders use smart order routers (SORs) to decide the placement of a liquidity order, whereas liquidity suppliers such as hedge funds and broker-dealers use algorithms to supply liquidity. Overall, as trading became more electronic, it became easier and cheaper to replicate a floor trader’s activity with a computer program doing algorithmic trading. The growth of algorithmic trading has spurred interest in its potential effects on market dynamics (Hendershott and Riordan, 2009). In particular, such mechanized trading systems potentially could both reduce liquidity and increase volatility, particularly in times of market stress. 2 Two sides to the argument exist concerning the use of algorithmic trading. On the one hand, algos can increase competition and result in an increase in liquidity, thereby lowering the cost of immediacy. On the other hand, liquidity could decrease if algorithmic trades are used mainly to demand liquidity. For example, whereas limit order submitters supply liquidity by granting a trading option to others, liquidity demanders attempt to identify and pick-off beneficial trading opportunities by increasing the cost of submitting limit orders by causing spreads to widen. An example of liquidity demanders are algo traders executing large institutional blocks in short periods of time. Empirically, Hendershott et al. (2011) and Hendershott and Riordan (2009) find that the net effect of algo trading is to reduce bid-ask spreads and aid in the pricing efficiency in the stock market. 2 The Flash Crash of May 6 th , 2010 is an example of how algorithmic trading may have led to extreme volatility and the disappearance of liquidity. This potential liability of algorithmic trading has caused critics to support curbs to be placed on such trading. More recently, algorithmic trading also was criticized because of its “unfair” advantage over non-computerized traders, since algos possess a sub-second timing advantage in placing quotes and the related potential of front running of larger block orders. Here we concentrate on the effect of algorithmic trading on options market pricing for market scenarios other than the Flash Crash. 4 We extend the pioneering work of Hendershott et al. (2011) on the effects of algorithmic trading in the stock market to options. The importance of algorithmic trading for options on the demand side is found in the “Smart Routing” of options and the algorithmic execution of price improving multi-leg orders, as well as spread enhancing market-making activities across strikes, expirations, calls/puts, and on as many as seven options exchanges at once. Alternatively, the multitude of options challenges the ability of this market mechanism to generate liquidity for supply side activities. Supply side traders require execution of positions at current bid/ask prices such that the bid-ask spread widens and depth declines. Large supply side option orders challenge the ability of a potentially think market (such as options with many strikes, expirations, and exchanges) to consistently provide liquidity. Preliminary evidence on the extent of algorithmic trading in the options markets is found in Figure 1, which shows the growth of OPRA message traffic from 2006 to 2008. Such activity is clearly visible in 2007 and increases in 2008. We examine the relation between algorithmic trading and liquidity by analyzing the bid-ask spread and the best bid-ask depth values for the Options Price Reporting Authority (OPRA) data feed for the flow of option messages as a proxy of algo trading. We differentiate between “call” and “put” options, and among “in-”, “near-” and “out-of-the-money” options, as well as providing separate results by market capitalization, volume, and volatility quintiles. Given the liquidity differences among the various options groupings, we have the advantage of analyzing the effect of algo trading on liquidity for a wide range of instrumental characteristics. These results provide more definitive conclusions than stocks concerning the ability of algo trading to supply liquidity effectively across a wide range of different characteristics (option strikes/expirations/calls-puts), thereby determining to what extent bid-ask spreads and depth responds to non-human intervention. Such results and