Automated equity trading: Theevolution of market structure and its

effect on volatility and liquidity

David Weisberger and Paul Rosa

Two Sigma Securities∗

david.weisberger@tssecurities.com

2013.06.19

Abstract

An oft-repeated maxim is that volatility in the US equity markets has increased overthe past several years and that the rising use of automation is the cause. In contrast,we believe that the issues that investors often attribute to automation are due todifferent factors. Firstly, we show that, despite this widely held perception, there islittle numerical evidence that overall market volatility has increased materially overthe past decade. Secondly, we demonstrate that changes in market structure createdsome of the vulnerabilities in the equity market—specifically, that regulation haslowered the financial incentives for capital commitment. Thus, oversized individualorders which are larger than the immediately available liquidity can create sharpprice movements. Such orders normally result from manual order entry but can alsooccur when algorithms have insufficient control parameters. Fortunately, more recentregulation such as circuit breakers and the new Limit Up / Limit Down regimeshould help to address these risks.

∗Two Sigma Securities, LLC (“TSS”) is a broker-dealer that makes markets in over 7,000 securities, and is a member of the FinancialIndustry Regulatory Authority, Inc. and 11 equity U.S. exchanges. The views expressed herein represent only the opinions of the authorsand not necessarily the views of TSS or any of TSS’s affiliates.

CONFIDENTIAL - NOT FOR REDISTRIBUTION. THIS REPORT IS PREPARED AND CIRCULATED FOR INFORMATIONAL AND

EDUCATIONAL PURPOSES ONLY. PLEASE SEE THE FINAL PAGE OF THIS DOCUMENT FOR IMPORTANT DISCLAIMER ANDDISCLOSURE INFORMATION.

1 Introduction

There is little doubt that the amount of automated trading in the equity markets hasincreased rapidly in the past one to two decades. Near the end of the 1990s, almostall trades were executed individually by traders or via the specialists at the NYSE.Automation was beginning to gain traction, but was used for order delivery (DOT), ordergrouping (program trading) and order matching (primarily ECNs such as Island andInstinet). More recently, however, automation has come to dominate smart order routing,the handling of institutional orders, and market making. After multiple discussions withindustry executives, we estimate that the majority of institutional trades are now handledelectronically and most retail trades are handled via automated market making.

Public opinion regarding automation is largely negative. There is pervasive thinking thatautomation is responsible for higher volatility in the markets and that this is indicativeof a game that is “rigged” against retail investors. The frequency of news stories onaberrant intraday price fluctuations seems to have increased, and policy makers aredevoting significantly more attention to the issue (e.g., SEC roundtable on. . . ). However,we maintain that these beliefs have received an unwarranted amount of media attentionand are not supported by statistical evidence.

By two different volatility measures that we discuss in the next section, there is littledata to support the assertion of increased systemic volatility over the past decade (withthe exception of the 2008 Financial Crisis, which was unrelated to market automation).We agree that the current market structure is vulnerable to large price dislocations insingle stocks, but we believe that this weakness developed as a side effect of RegulationNMS, which was implemented in 2007.1 This regulation was instrumental in encouragingthe growth of automation by enhancing competition among market centers, but it alsoreduced both the incentive and the means for market participants to commit capital thatwould provide a buffer against oversized orders.

Overall, we believe that automation has been positive for the markets. Lower average bid/ offer spreads indicate that market liquidity is better now than it was in the early 2000s,prior to the rapid growth of automated trading. Trading costs are also dramatically lower.For example, according to a recent report by Credit Suisse, trading costs for institutionalinvestors decreased 30% between 2005 through 2012 [1]. Furthermore, according to datapublished by Thomson Reuters, retail trading costs have declined even more. The widely-

1For more information on Regulation NMS, see the SEC release at http://www.sec.gov/rules/final/34-51808.pdf (accessed 2013.05.10).

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used metric of effective spread divided by quoted spread2 indicates that costs have fallenby more than 35% between January 2006 and January 2013.

2 A numerical analysis of volatility

In order to evaluate whether overall market volatility has increased in recent years, welooked at two different measures: intraday price swings of individual stocks and longer-term market volatility, as measured by the CBOE Volatility Index (VIX). We realize thatthere are other metrics that one could use, but these two (particularly intraday volatility)are most closely related to the market structure changes we discuss in Section 3.

2.1 Intraday volatility

As a proxy for intraday volatility of individual stocks, we analyzed the average dailyprice moves of the securities in the S&P 500 Index.3 With the exception of 2008 and2009, there are no major differences in measured volatility over the time period (in fact,intraday volatility in 2012 was equivalent to the levels seen in 2004 through 2006 andapproximately 50 basis points below levels observed in 2007):

Figure 1: Average intraday volatility for all S&P 500 stocks, measured as the difference between astock’s high and low price over the open price. The high, mid and low points of the boxes represent 75th,50th and 25th percentiles of volatility, respectively. The ends of the whiskers mark the 5th and 95thpercentiles

2This measure is also known as EOQ, where effective spread is two times the difference from the midspread to the executed price, while quoted spread is the full spread at the time an order is received. Itis published for all market makers pursuant to SEC Rule 605.

3We define intraday volatility as HighPrice - LowPriceOpenPrice . To limit selection bias, when we calculated the daily

average volatility, we included all stocks that were part of the index on the given day.

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To determine whether the averages of the entire universe concealed large moves amongthe most volatile stocks, we performed a separate analysis of the top 5% and top 1% mostvolatile daily moves over the period.4 Once again, we were unable to detect any notableshifts in volatility:

Figure 2: Average daily volatility for the top 1% of moves on each day

Figure 8 in the Appendix shows a similar result for the top 5% of moves.

Lastly, in order to control for market effects, we analyzed the volatility of the idiosyncraticreturn5 measured against the S&P 500 Index (SPY). Like the unresidualized volatility,periods outside of 2008 and 2009 are comparable:

4We created these new datasets by culling the original selection of 500 S&P stocks to the top 5% and1% (25 and 5 stocks, respectively) with the largest percentage swings on each day.

5We define idiosyncratic return as the difference between a given stock’s intraday price move and theprice move of SPY on that day. Thus, if stock XYZ moved 10% during the day and SPY moved 7%,the idiosyncratic return would be 3%. It is this 3% value that we measure across the period, ratherthan the 10% value as we would have in the previous dataset.

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Figure 3: Average daily residual volatility for all S&P 500 stocks

The same analysis for the top 1% and 5% can be found in the Appendix.

2.2 VIX

A commonly cited gauge for overall market volatility is the VIX, which measures theimplied forward volatility of options on the S&P 500 Index. Figure 4 shows the dailyprice of the VIX from 2002 to 2012. From this plot it is quite clear that volatility hasfluctuated significantly over the period, but the 2008 Financial Crisis and subsequentflare-ups in Europe in 2010 and 2011 dominate the graph. The past 6 months have seenthe VIX settle to levels near historical lows despite ever-increasing automation:6

6The two spikes following the 2008 Financial Crisis correspond to May 2010 and August 2011. May6, 2010 marked a nationwide strike in Greece against proposed government austerity bills, the firstof several major protests against these programs [2]. In the US, this was also the date of the FlashCrash in the equity markets. August 2011 reflected the downgrade of the United States’s credit ratingfrom AAA by S&P and the market’s subsequent anxiety over the political discussions to raise thecountry’s debt ceiling [3].

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Figure 4: VIX daily price from 2002 to 2012. The line represents the historical low of 9.31 on 1993.12.22

3 Vulnerability in the current market

structure

While some concerns of increasing systemic volatility over the past few years are notsupported by numerical evidence, we do believe that there is a flaw in the current marketstructure that makes it vulnerable to large price dislocations in single stocks. We maintainthat some discussions of increasing market volatility mistakenly equate these single-stockprice dislocations with overall volatility, but we have shown that they have not impactedour chosen systemic statistical measures. Furthermore, when such disturbances occur,they are likely to be caused by oversized orders entering the marketplace in error (frommanual order entry or poorly designed or controlled algorithms) rather than automatedtrading.

The current equity market structure, while highly automated and efficient, has engendereda market that is vulnerable to large, price-insensitive orders that exceed the size of theavailable liquidity in the first few levels of the order book. In response to the “tradethrough” protection of Regulation NMS, market makers now have considerable financialincentives to concentrate available liquidity very close to the current price of an instrumentand have financial disincentives to provide liquidity away from the quote. Furthermore,this protection applies only to automated quotations. As a result of these developments,market making has shifted away from block trading and towards automated tradingalgorithms.

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We demonstrate below that oversized orders now have the potential to create large, rapidmovements in the price of an instrument in a way that they could not prior to theimplementation of Regulation NMS. For example, the “fast quote” provision forced theNYSE to severely restrict the ability of exchange specialists to manually execute trades,thereby reducing their ability to moderate the entry of large orders directly into themarket.7

3.1 Market structure before Regulation NMS

The market structure prior to the implementation of Regulation NMS was relativelyinsensitive to block trading. To illustrate why, we examine Figure 5, which shows what thehypothetical liquidity density curve for a stock would have looked like before RegulationNMS. It charts the total volume available for trading (both displayed and undisplayed)at various bid / offer levels away from the current quote. We see that specialist firmsand bulge-bracket market makers were willing—and, more importantly, permitted—toevaluate and transact block trading opportunities, either outside of the market (in thecase of NASDAQ) or on an arranged basis (such as on the floor of the NYSE), at priceshigher or lower than the market when asked to do so by their institutional clients:

Figure 5: Theoretical chart of volume available from block positioners in reaction to an institutionalorder prior to Regulation NMS

While there is no statistical method for evaluating this data directly (since the actionableliquidity was not displayed), we can estimate the natural liquidity profile of market

7The trade through requirement of Regulation NMS was designed to improve posted liquidity on NationalSecurities Exchanges, with the goal of tightening spreads and lowering trading costs. It did not,however, include a block trading exemption.

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makers. A reasonable estimation can be found in representations of marginal utility curvespresented in academic research. Consider the following excerpt from Robert Almgren’s2001 paper [4]:

Figure 6: Excerpted from Nonlinear Optimal Execution

This graph represents the price-to-liquidity function from the perspective of a block trader.Note that the slope of h(v) is relatively flat for small block sizes but increases quickly asthe trade size grows. We believe that this is indicative of the theoretical model used byblock desks to price liquidity.

As an example, consider how an institution might have executed a large order prior toRegulation NMS. Assume that a manager would like to sell 1 million shares of stock XYZ,with 25,000 shares displayed at the market. Since the order far exceeds the displayedvolume of the stock, the institution would likely have wanted to obscure the trade so as toprevent information leakage and to do so they would have used a market maker to provideblock liquidity for all or part of the order. Assuming that the price was bid at $17.32 andoffered at $17.33, the market maker may have offered it several possible options:

1. Sell all 1 million shares to the market maker at $17.27 plus commission (typicallyaround 6 cents in 2002)

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2. Sell 500,000 shares at $17.30 to the market maker and allow the market maker towork the balance (500,000) on an agency basis directly in the market, charging the6 cents commission on the whole block

3. Sell 200,000 shares at $17.32 to the market maker and allow the market maker towork the balance (800,000) on an agency basis directly in the market, charging the6 cents commission on the whole block

This demonstrates the pricing flexibility that the market maker would have had, as theliquidity provider was able to adjust its financial incentives according to the amount ofrisk that it would be absorbing. In addition, this structure made it extremely rare thatlarge trades would be entered directly into the market.8

3.2 Market structure now (post-Regulation NMS)

Regulation NMS specifically disadvantaged market makers from pricing and trading blockliquidity as shown in Figure 6 above by requiring that resting orders at the top of thebook on all protected markets be given trade through protection, regardless of the sizeof the order. In most cases, this has the effect of forcing market makers to sweep thedisplayed composite market liquidity all the way to the price that they want to trade. Forthe XYZ example above, the market maker would likely have been forced to sell sharesdown to the price of $17.28 (1 cent above the institution’s bid price in the first option)before buying the stock from the client at $17.27. For any traders watching the market,this pattern would signal a large seller currently engaging, significantly raising the risk forthe market maker providing the liquidity.

Regulation NMS was also the direct catalyst for changes to the NYSE market structure.Due to the provision that allowed other market participants to essentially ignore “slow”quotes, the ability of the specialists (renamed to Designated Market Makers, or DMMs,after the regulation) to evaluate incoming orders and survey the floor for liquidity wasessentially eliminated. The NYSE became almost fully electronic, with human interventionlimited to the opening and closing auctions.

Electronic trading by nature involves less human oversight, which means that “fat finger”mistakes and other trading errors are less likely to be noticed before being executed. It isworth pointing out that, before Regulation NMS, specialists and NASDAQ market makers

8If a large trade did pass to the market, it was usually the result of a poorly constructed program tradeor a “fat finger” type of error. In any case, the specialist on the exchange would hold up the orderuntil necessary liquidity was assembled to fill it.

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would sometimes detect trading mistakes and intervene to minimize market disruptions.The regulation made it less profitable or simply impractical to employ people to scrutinizeorders, due to the combination of reduced financial incentives for block trading and theremoval of protection for “slow” quotes (i.e., the times when a trader would intervene toslow down an order). Thus, the markets lost a layer of protection.

As a result, institutions have adapted by dramatically increasing their use of algorithmictrading to implement their trading decisions. Algorithms are typically designed withcontrols to detect orders that are too large for the market at the time of the trade, but thiswill not always detect errors as there can be flaws in the controls and not every order isentered via an algorithm. Since there are still orders that are entered to exchanges directlyby participants and these orders have much less oversight now than they did prior toRegulation NMS, the fact that the average liquidity in today’s market is disproportionatelyskewed towards the current price of each stock constitutes a potential vulnerability.

To illustrate, consider the actual depth-of-book pattern in Figure 7 below. Similar to thehypothetical example in Figure 5, it charts the volume available for trading (including alldisplayed orders on protected exchanges) on the Y axis and the price of the instrument(as a ratio of the spread) on the X axis:9

9To produce this graph, we gathered daily depth-of-book data for up to 10 price levels away from thequote for all the constituents in the S&P 500 during 2012. For each level n, we calculated: (1) thespread ratio, where: spread ration = Askn−Ask0

Ask0−Bid0(or Bidn−Bid0

Ask0−Bid0for the opposite side of the book); and,

(2) the total number of shares displayed at that level. Next, we aggregated the same multiples acrossthe time period—so if Day 1 indicated 100 shares at 1x the displayed spread and Day 2 indicated 200shares at 1x the displayed spread, the total of the two days would be 300 shares at 1x the displayedspread. Lastly, for smoothing, we rounded decimals to their nearest whole multiple (values less thanX.5 were rounded to X, values greater than or equal to X.5 were rounded to X+1).

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Figure 7: Actual dept-of-book liquidity for S&P 500 stocks in 2012

This liquidity profile, coupled with market makers’ decreased incentives to “buffer” largeorders, is indicative of a market where an order that is larger than the displayed size canhave a significant impact on price. Note that the representation of liquidity presented byAlmgren in Figure 6 still holds in this market, but very few market makers are willing toact on it directly due to the disincentives discussed earlier.

The main outcome is that modern market makers place orders at prices where they arelikely to be executed. In the current environment, orders placed far from the markethave a number of disadvantages compared to those placed close to the current bid / offer,including:

• The order may only get filled at a time when the security’s price would continue tomove against the order (this is typically referred to as adverse selection)

• All open orders are considered by firms’ risk control systems when monitoringtheir net capital, so leaving multiple unexecuted orders at different price levels isdisadvantageous to capital

• Market makers have reputational incentives to have a high execution percentage,due to negative connotations associated with a low executions-to-orders ratio

Whether the market maker is employing a human or an algorithm to trade, the orderswill operate on these same basic risk principles.

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3.3 2010 Flash Crash

The market-wide Flash Crash on May 6, 2010 was a real-world example of the structuraldeficiency that Regulation NMS may engender in the equities market. The futures marketalready has many of the structural characteristics that Regulation NMS has created forequities, including trade through protection. In the CFTC-SEC report on the crash, theanalysis concluded that a trader placed into a trading algorithm a SPY futures order thatwas significantly larger than the available liquidity in the market at the time and thealgorithm did not account for these conditions. As a result, the trading algorithm placedorders that rapidly depleted the posted liquidity, resulting in a large downward movein the market. Index arbitrageurs then sold the stocks in the underlying index (mostlyusing market orders) as they provided liquidity to the ETF seller. This, in turn, triggeredextreme movements in several stocks in the S&P 500 Index as the orders outstrippedavailable liquidity. After that, stop loss market orders, which by their very nature aredesigned to trigger when there are large price moves, continued to drive stocks downward,several of them to the minimum price in the order book [5].

This was fundamentally a liquidity issue, albeit an extraordinary example with thebackdrop of rumors of a Greek default and riots in the streets of Athens. We do, however,believe that the liquidity deficit in individual stocks was exacerbated in part by theprotection given to resting orders at the top of the book and the resulting lack of liquidityaway from the original quote.

4 Conclusion

We do not see evidence of increased systemic volatility to the degree that is being assertedin media reports. However, we have described a structural weakness in the U.S. equitymarket that makes single stocks susceptible to rapid price swings due to large orders. Thevulnerability manifests itself whenever market participants demand more liquidity than isinstantly available. In such cases, the price of the security can move dramatically due tothe concentration of orders close to the current price. We believe that some discussions of“volatility” are referring to these one-off events, rather than overall market volatility.

Recently, regulators have introduced several corrective remedies to help address thisweakness. Subsequent to the 2010 Flash Crash, regulators tightened the bands on market-wide circuit breakers, which will help to contain damage if a similar scenario repeats.They also introduced single-stock circuit breakers to try to limit the size of moves that

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result from individual stock liquidity disruptions. Furthermore, the implementation of theLimit Up / Limit Down (LULD) rule10 will hold the magnitude of intraday price swingsto more acceptable levels. The rule also includes built-in time delays so that after tradingis halted due to large price swings, liquidity providers will have sufficient time to becomere-engaged subsequent to the re-opening auction.11

Finally, we do not believe that increasing automation is the root cause of this liquidityissue, but rather the logical outgrowth of market makers responding to specific economicincentives. We also do not believe that a trade-off between automation and liquidityis necessary. To the contrary, the transition to increased automation has been positiveon an aggregate basis, as automation can continue to deliver increased liquidity, tighterbid / offer spreads and improved overall execution quality. However, it is incumbenton market participants to build protections into their algorithms against outstrippingavailable liquidity. We believe this to be an essential component of the algorithmicdevelopment process. As the majority of participants enhance their algorithms to besensitive to liquidity, the market’s resilience to large orders will improve.

References

[1] Mackintosh, Phil. How Much is Market Structure Hurting Investors?. Credit SuisseTrading Strategy, 2013.03.13.

[2] Bilefsky, Dan. “Greek Parliament Passes Austerity Measures” The New York Times.New York Times, 2010.05.10 (accessed 2013.02.28).

[3] Nazareth, Rita. “U.S. Stocks Fall as S&P 500 Has Biggest Slump Since November ‘08”Bloomberg. Bloomberg, 2011.08.08 (accessed 2013.02.28).

[4] Almgren, Robert. Nonlinear Optimal Execution. University of Toronto, Departments ofMathematics and Computer Science. http://www.math.nyu.edu/~almgren/papers/nonlin.pdf

[5] CFTC-SEC. Findings Regarding The Market Events Of May 6, 2010: Report OfThe Staffs Of The CFTC And SEC To The Joint Advisory Committee On EmergingRegulatory Issues. 2010.09.10 (accessed 2013.03.01).

10The LULD rule prevents the trading of equity securities outside of specified price bands, established asa percentage level above and below the average price of the security for the previous five minutes oftrading.

11For more information on the circuit breakers and the LULD rule, see the SEC’s press release athttp://www.sec.gov/news/press/2012/2012-107.htm (accessed 2013.03.01).

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Appendices

A Additional volatility graphs

Figure 8: Average daily volatility for the top 5% of moves on each day

Figure 9: Average daily residual volatility for the top 1% of moves on each day

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Figure 10: Average daily residual volatility for the top 5% of moves on each day

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