Effects of Speed Bump on NYSE American

Naresh Neupane BSc in Finance, Southeastern Louisiana University, 2016

An Extended Essay Submitted in Partial Fulfillment of the Requirements for the Degree of

MASTER OF ARTS in the Department of Economics

We accept this extended essay as conforming to the required standard

Dr. Ke Xu, Supervisor (Department of Economics)

Dr. Pascal Courty, Member (Department of Economics)

 Naresh Neupane, 2020 University of Victoria

The objective of this research project is to analyze effects of speed bump on market liquidity after it was introduced in the New York Stock Exchange (NYSE) American on July 24, 2017. The project aims to evaluate the changes in liquidity by utilizing high-frequency intra-day NYSE Trade and Quote (TAQ) dataset from June 22, 2017 to August 18, 2017, spanning 20 trading days each before and after the speed bump. I compare various liquidity measures of the NYSE American with corresponding measures of the Chicago Stock Exchange in the same 40-day period. I find that the percentage time displayed at the National Best Bid and Offer (NBBO) for the NYSE American did not increase after the speed bump. I use difference-in-differences approach and find that effective spread, realized spread, adverse selection, and quoted spread all increase for the NYSE American following the speed bump. The results suggest that the speed bump did not result in tighter spread or lower adverse selection.

1 List of Figures

1 Number of Traded Stocks for NYSE American (A) and CHX (M) ...... 41 2 Number of Stocks Displayed in NBB for NYSE American (A) and CHX (M) 42 3 Number of Stocks Displayed in NBO for NYSE American (A) and CHX (M) 43 4 Percentage Time at NBBO for NYSE American (A) and CHX (M) . . . . . 44 5 Share of Time-weighted Quoted Spread for NYSE American (A) and CHX (M) 45 6 Share of Time-weighted Quoted Depth for NYSE American (A) and CHX (M) 46 7 Daily Average Eﬀective Spread of NYSE American (A) and CHX (M) in Matched Sample ...... 47 8 Daily Average Quoted Spread of NYSE American (A) and CHX (M) in Matched Sample ...... 48

List of Tables

1 Share of Exchange Venues by Number of Transactions ...... 39 2 Share of Exchange Venues by Traded Shares ...... 39 3 Balance for Matched Data ...... 40 4 Summary of Balance and Balance Improvement ...... 40 5 Number of Traded Stocks ...... 41 6 Number of Stocks Displayed in NBB ...... 42 7 Number of Stocks Displayed in NBO ...... 43 8 Percentage Time at NBBO ...... 44 9 Time-weighted Quoted Spread ...... 45 10 Time-weighted Quoted Depth ...... 46 11 Diﬀerence in Pre- and Post-Speed Bump in Eﬀective Spread, Realised Spread and Adverse Selection ...... 47

2 12 Means (in Percentage) of Dependent Variables Before the Speed Bump on July 24, 2017 ...... 48

13 Estimation of DD1 and Other Relevant Coeﬃcients ...... 49

14 Estimation of DD1 and Other Relevant Coeﬃcients: Fixed Eﬀects ...... 50

3 Acknowledgments

I am thankful to my supervisor Dr. Ke Xu for constant guidance and feedback in providing me the topic of research, revising manuscript multiple times, and encouraging me to test ideas for this essay. Dr. Xu also helped me access the dataset and constantly guided me in the productive use of the dataset for this essay. I would like to thank Dr. Pascal Courty for comments and feedback for revising this essay. I am thankful to all of my professors at the Department of Economics in the University of Victoria for teaching course materials this essay has largely beneﬁted. I am also thankful to staﬀs at the department for various kinds of assistance throughout my enrollment in the University of Victoria. I am deeply grateful to my family members and friends for encouraging and inspiring me to perform better in academic works.

4 Contents

List of Figures 2

List of Tables 2

1 Introduction 7

2 Literature Review 10

3 Institutional Details 15 3.1 Main US Trading Venues ...... 15 3.2 NYSE American ...... 16 3.3 Chicago Stock Exchange ...... 16

4 Data 18 4.1 NBBO Dataset ...... 19 4.2 Trade Dataset ...... 20

5 Methodology 20 5.1 Summary Statistics ...... 21 5.2 Propensity Score Matching and Matched Sample ...... 23 5.3 Difference-in-Differences (DD) Approach ...... 25 5.4 Panel Data and Fixed Effects ...... 25 5.5 Regression Specification ...... 26 5.5.1 Dependent Variables ...... 26

6 Results 29 6.1 Summary Statistics ...... 29 6.2 Pre-period Means and Regression Coeﬃcients ...... 31

7 Limitations 33

5 8 Conclusion 34

Bibliography 36

A Appendix 51 A.1 NBBO Dataset Characteristics ...... 51 A.2 Filters to Format the NBBO Dataset ...... 51 A.3 Trade Dataset Characteristics ...... 54 A.4 BBO and NBBO: Definition ...... 55 A.4.1 Best Bid and Offer (BBO) ...... 55 A.4.2 National Best Bid and Offer (NBBO) ...... 55 A.5 Code for MatchIt Package in R ...... 56

6 1 Introduction

The speed of trading financial securities in various exchanges around the world has increased spectacularly in the past decade. Most stock trading in recent years happens electronically and in sub-millisecond level, requiring measurement standards as small as a nano-second, one-billionth of a second (O’Hara, 2015). A large portion of transactions of securities such as stocks, bonds, and derivatives increasingly rely on algorithms customized for particular needs of investors, regulators, market makers and other stakeholders in various phases of trading financial securities. Security trading happening in sub-millisecond speed with increasing reliance of algorithms generated and implemented by computers is known as high-frequency trading (HFT). Liquidity providers in security markets generally seek speed advantage in order to minimize adverse selection and increase realized spread (Chen et al., 2017). The key regulator of securities traded in the United States, Securities and Exchange Commission (SEC), notes that HFT, a subset of algorithmic trading (AT), likely exceeded over 50% of all equities traded in the United States. It recognizes the five criteria of “use of extraordinarily high speed and sophisticated programs for generating, routing, and ex- ecuting orders; use of co-location services and individual data feeds offered by exchanges and others to minimize network and other latencies; very short time-frames for establishing and liquidating positions; submission of numerous orders that are cancelled shortly after submission; and ending the trading day in as close to a flat position as possible (that is, not carrying significant, unhedged positions overnight)”, as key features of HFT, which are not necessarily the same features of AT (SEC, 2014).1 Increasing trading speed and competition among exchanges in introducing various trading criteria based on speed have resulted in new trading behaviors as well as new mechanism of

1The Security and Exchange Commission (SEC) also notes that many HFT proxies that are algorithmic and computer-assisted in nature cannot be classiﬁed as HFT. As such: “Examples of such HFT proxies derived from market-wide data include high message rates, bursts of order cancellations and modiﬁcations, high order-to-trade ratios, small trade sizes, and increases in trading speed. These market-wide proxies are associated with the broader phenomena of algorithmic trading and computer-assisted trading in all their forms. HFT represents a large subset, but by no means all, of algorithmic and computer-assisted trading” (SEC, 2014).

7 regulatory monitoring and compliance (Woodward, 2018). Although some research inquiries such as by Angel et al. (2011) have found that increasingly sophisticated technological in- novation in trading and communications networks increases welfare for exchanges as well as brokers and dealers, causing transaction costs to drop significantly, not all find the new scenario optimistic. Instead, some believe that the competition in speed and its selective access have resulted in new kinds of grievances among investors as well as new mechanism by exchanges and regulators to address them. One key concern is levelling the playing field among the investors with disproportionate access to computing power, which accrues large benefits with speed advantage in a fraction of a second and even leads to marketwide inefficiencies and distortion (Pagnotta and Philippon, 2011). This had led to some exchanges all over the world, including the USA and Canada, to introduce the feature of speed bump as a key barrier for preventing large benefits to institutional investors solely due to access to trading speed. Speed bump is an intentional slowing down of market or limit orders. A key reason exchanges tout this feature is to prevent potentially predatory trading of large and institutional investors with huge computing power, most likely by reducing arbitrage happening at a sub-millisecond level. As the gap between retail investors and institutional investors keeps growing in terms of access to speed, such features like speed bump intended to correct informational and price efficiencies are also getting prioritized by exchanges and regulators (Woodward, 2018). Speed bump is intended to correct market liquidity and efficiency by reducing opportunistic trades by those with access to higher speeds. Many venues around the world are implementing speed bump, but the design and structure of speed bumps vary.2

2TSX Alpha in Canada implemented randomised speed bump of intentionally slowing down of trades on September 21st, 2015. The randomised speed bump between one to three seconds also entailed that some investors could pay higher fees and cancel their limit orders without getting aﬀected by the delay (Chen et al., 2017). Investors Exchange (IEX), which is designed from its inception with 350-microsecond delay of all market orders, got approval from Securities and Exchange Commission (SEC) in the United States to run as an exchange on June 17, 2016 and got approval for listing from SEC on October 24, 2017 in the US. The Commodity Futures Trading Commission, the chief regulator of futures market in the US, approved Intercontinental Exchange (ICE) for speed bump of three milliseconds to be implemented on ICE’s Gold Daily and Silver Daily futures.

8 The New York Stock Exchange American (NYSE American), a trading venue owned and operated by the NYSE Group and mainly catering to small- and medium-sized companies, implemented a speed bump of 350 microseconds on July 24, 2017. This duration of 350 microseconds is the same duration of speed bump repeatedly pursued by its rival Chicago Stock Exchange (CHX) (Bullock, 2017) but not approved by the SEC. In this essay, I analyze the main implication of speed bump in the NYSE American before and after the speed bump by comparing various liquidity measures of the NYSE American and the Chicago Stock Exchange (CHX). In this essay, I elaborate on how implementing the speed bump did not result in increase in the percentage time at the National Best Bid and Offer (NBBO) despite large increases in the number of stocks traded and quoted in the NYSE American in the observed period of study of 40 trading days, with 20 trading days from June 22 to July 21 without speed bump and 20 trading days from July 24 to August 18 with speed bump. I also provide context of increased spreads and higher adverse selection observed after the implementation of the speed bump at the NYSE American. The overall trade size in the NYSE American increases following the speed bump, resulting in over twenty times increase in the number of stocks traded in the NYSE American. I find that the number of times quotes in the NYSE American show up in the NBBO rises after the speed bump, resulting in more than ten times increase in the number of stocks showing up in the NBBO. I also compare the NYSE American with the Chicago Stock Exchange (CHX) on various liquidity measures given that both exchanges are similar in their size of overall trades and generally cater to investors in small-cap and medium-cap companies. No significant increase or decrease happens in the number of stocks traded in the CHX following the speed bump in the NYSE American. Likewise, the speed bump in the NYSE American doesn’t significantly alter the frequency of quotes originating from the CHX or the number of CHX stocks showing up in the NBBO. In the matched sample of 160 stocks (consisting of 80 stocks traded only in the NYSE American and 80 stocks traded only in the CHX) for the same 40 days, I use difference-in-

9 differences approach to factor in the pre-existing differences in the treated group of the NYSE American stocks and the control group of the CHX stocks. This approach yields that the speed bump results in higher effective spread, higher realized spread, higher adverse selection, and higher quoted spread in the NYSE American, contrary to the expected direction of these measures under the speed bump, as predicted by empirical research on similar topics as speed bump. The implication is that the speed bump did not achieve its intended goals, at least in the short run within the four trading weeks, in achieving tighter spreads or lower adverse selection. If spreads on liquidity do not improve, which also likely increases adverse selection, the high frequency market reflects increase in both price inefficiencies and market distortion. I discuss possible reasons underlying these results later in this essay. The rest of the essay proceeds as follows. Section 2 discusses the literature on high- frequency trading with focus on its key characteristic of speed. Section 3 outlines the trading landscape currently prevailing in the United States in the context of the NYSE American and the Chicago Stock Exchange. Section 4 discusses data relied for this essay, focusing on various characteristics of the NBBO and Trade data obtained from intra-day NYSE Trade and Quote (TAQ) datasets. Section 5 outlines the methodological approaches employed in this essay and mainly discusses difference-in-differences approach utilized in the matched sample of equal number of stocks obtained from each of the NYSE American and the Chicago Stock Exchange. Section 6 presents results primarily focusing on results obtained from the difference-in-differences approach in a linear regression model. Section 7 discusses possible limitations of approaches in this essay. Section 8 concludes, while Appendices A.1-A.5 elaborate details on various sections of the essay.

2 Literature Review

High-frequency trading (HFT) and its key attributes, including superfast trading speed, have garnered a sizable attention in ﬁnance and economics literature in recent years. Although

10 speed bump has been implemented in a few venues around the world only in the last few years, several theoretical and empirical contributions are specifically focused on the speed bump aspect of HFT. This essay has also taken inspiration from several research papers on the trading speed in the setting of high-frequency trading that have not explicitly accounted the speed bump. Woodward (2018) elaborates on characteristics of high-frequency trading and provides important details on the first speed bump introduced by the Investors Exchange (IEX) in the United States. The paper also introduces the new landscape of trading, quoting, and regulations in the high-frequency market by keeping the rationale of speed bump at the center of discussion. The paper also describes and weighs rationale of other arrangements similar to speed bump implemented until 2018, including the NYSE American speed bump, the CHX LEAD, and the NASDAQ Extended Life. Hendershott and Moulton (2011) make an empirical investigation of the automation caused by the introduction of the Hybrid market in the New York Stock Exchange (NYSE) and its effects on stock market trading in the NYSE. Towards the end of 2006, the New York Stock Exchange (NYSE) introduced Hybrid market, which led to increased automation as execution time for market orders reduced from 10 seconds to a fraction of a second. Working on data from June 1, 2006 to May 31, 2007, roughly four months prior and post to the activation of Hybrid in NYSE and focusing on a sample of 400 NYSE stocks that went Hybrid with a matched sample of 400 Nasdaq-listed stocks through propensity score matching to control for market quality changes, the authors find that the Hybrid mechanism increased standard bid-ask spread measures of effective spread and quoted spread. My essay has utilized several liquidity measures explained in their research. Their research would suggest that the NYSE American speed bump, which is opposite of the NYSE Hybrid mechanism in terms of automation, would ideally result in decreased bid-ask measures. In my research, I find that the speed bump did not decrease quoted spread and effective spread. The authors also find that adverse selection, as a measure of difference between effective spread and realized spread, increases by the Hybrid mechanism. They argue that increased

11 adverse selection results in higher quoted spread to properly compensate liquidity suppliers. A key distinction between their work and this essay is the choice of venue: Hybrid was introduced by the NYSE, a highly liquid and much relied upon venue in the United States for all kinds of large and small companies in terms of market capitalization, while speed bump was introduced by the NYSE American, a comparatively minor player at the time of introduction of speed bump mostly catering to less liquid medium and small companies by market capitalization. In my essay, I do not model liquidity compensation in relation to particular stocks due to several complications arising from venue fragmentation, complex rebates and fees structure across venues, and most importantly, due to stocks simultaneously listed and traded across multiple venues. In another related paper, Hendershott et al. (2011) find that algorithmic trading causes tighter bid-ask spreads, reduced adverse selection and reduced trade-related price discovery. My essay has drawn inspiration from the empirical research by Chen et al. (2017) that examines the effect of randomized speed bump of 1-3 milliseconds (0.001-0.003 seconds) introduced in the TSX Alpha on September 21, 2015. Due to rise in algorithmic and high frequency trading, traders who routinely involve in automated and large trades and who could bear upfront costs in computing power tend to have a definitive speed advantage over retail investors. Increased computing power allows more information and faster analysis and trade execution. Human judgment is not as fast as modern computers in quickly decipher- ing information in quotes and trades happening in a nanosecond. This has led to some trading venues getting concerned that algorithmic traders have largely benefited from more information, faster judgment, better price options, and faster quote cancellation or revision. Although my essay is related to the empirical work by Chen et al. (2017), the speed bump introduced in the NYSE American was deterministic and applied to almost all incoming order, quite a contrast to a randomized speed bump with a fee structure for choosing trades not subject to speed bump. The speed bump at the NYSE American is fixed at 350 microseconds (0.350 milliseconds), shorter than the 1-3 milliseconds introduced in the TSX

12 Alpha. Jungsuk et al. (2014) propose a model of high frequency trading market making where market makers can cancel their limit orders after receiving an adverse information. They hy- pothesize that such scenario induces low-frequency market makers to widen bid-ask spreads. The authors propose a level playing field for both high-frequency and low-frequency market makers to prevent market distortion and price inefficiency. My research aims to understand the effects of adjusting the speed premium available to large and institutional investors and to discuss the results in light of price efficiency resulting from such adjustment. In their model, high frequency traders can update their quotes based on the publicly available information in the incoming order flow while low frequency traders cannot, resulting an adverse selection problem. Suppose the market has only low frequency traders. With no high-frequency traders present, the adverse selection doesn’t increase based on order cancellation because all traders can update or cancel quotes with equal ease with no information discrepancy. The adverse selection also vanishes in the same manner with only high-frequency traders present. The authors argue that whenever the probability of high-frequency trader is strictly greater than 0 or strictly less than one, the bid-ask spread is higher than when the probability is either zero or one. My essay doesn’t specifically account for the ‘high-frequency’ and ‘low-frequency’ traders in an HFT setting. However, based on the theoretical formulation of Jungsuk et al. (2014), having only set of participants (either high-frequency traders or low-frequency ones) instead of both implies that participants have equal access of speed and thus the same access to all publicly available information. Therefore, equal access to trading speed would result in efficient markets and optimal liquidity measures. A key suggestion from their study is that leveling a playing field in terms of access to information is necessary to achieve tighter bid- ask spreads and improved market efficiency. However, if inefficiencies and distortions are solely due to differential availability of public information, deteriorated liquidity measures,

13 as obtained in the results in this essay, would inform that that the speed bump was not successful in its intended goal of minimizing the different level of accessing public information by all kinds of traders. Baruch and Glosten (2013) provide a game-theoretic foundation of flickering quotes that are rapidly modified or canceled in a strategic interaction environment of high frequency trading. Such new environment of interactions in super-fast trading is crucial in explaining the motivation of exchanges in introducing and customizing various features of speed bump. Although my approach in this essay in not game-theoretic, the model is implicitly related with the information gap existing among retail investors and institutional investors, which informs discussion on market access for modifying or canceling ‘flickering’ quotes. Their model argues that flickering quotes may not always be a part of predatory pricing and unfair trading; instead, it could be a common trading behavior expected in the high frequency trading landscape. In their model, quote flickering occurs frequently as traders try to adjust their quote to new information, although actual trading is not as frequent. With speed bump, the playing field among traders in adjusting their quote to new information narrows. Although my essay is concerned mostly with liquidity measures without directly taking two sets of traders in a game-theoretic setting, I make several inferences relevant to the essay from the model in Baruch and Glosten (2013). Low-frequency traders could be understood as ‘noisy’ retail traders before the implementation of speed bump, while high-frequency traders could be understood as ’informed’ traders after the speed bump. The authors find that a sequence of randomized strategy equilibria generally exist that eventually converge to competitive Limit Order Book equilibrium in the setting of both informed and noisy (uninformed) traders. Although I do not discuss my research in terms of ‘equilibria’ attained in a game-theoretic setting, it provides some discussions on the motivations of various traders, exchanges, and regulators for or against the speed bump as part of their strategic advantage. Conrad et al. (2015) investigate the full longitudinal data of stock prices in the United States from 2009 to 2011 and 300 largest stocks in the Tokyo Stock Exchange from 2010

14 to 2011 regarding quoting behavior of stocks in an HFT setting. The authors elaborate how quotes don’t necessarily reflect trading patterns and provide an empirical background on pricing behavior of trades in relation to quoting. One implication of their finding in relation to this essay would be that quotes obtained from the NBBO dataset, usually more frequent than trades, reflect only the intentions underlying strategies of investors, while trades obtained from the Trade dataset represent the final decision based on but not the same as the intentions of investors. The large draw-downs across multiple venues due to high frequency trading specifically reflected in the NBBO instead of the actual trade doesn’t cause exceptional damage to pricing information. This essay has accounted pricing information through the lens of several liquidity measures and bid-ask spread. In this essay, I try to explain the efficacy of the speed bump implemented in the NYSE American in preventing damage in pricing information, assuming that this is due to the reduced probability of large draw-downs in a setting of deterministic and all-binding NYSE American speed bump. The authors find that reduced latency and colocation of servers due to a major technological change in Tokyo Stock exchange also reflects pricing behavior that resemble random walk with cost of trading going down. In my essay, I do not discuss efficiency in terms of random walk behavior of the stock prices. Instead, this essay is more concerned about inefficiencies wrought by reduced latency and colocation of servers reflected in changes in liquidity measures utilizing both the NBBO and the Trade datasets.

3 Institutional Details

3.1 Main US Trading Venues

Based on the NYSE Daily TAQ ﬁle, I take the size of transactions for June 22, 2017 (20 trading days before the speed bump) and August 18, 2017 (20 trading days after the speed bump) to obtain the summary on the size of NYSE American’s size with respect to its

15 competitors. The dates of June 22 and August 18 for Table 1 and 2 are the ﬁrst date and the last date of the period of study. This also helps to roughly assess changes wrought by the speed bump in the NYSE American and its key competitors.

3.2 NYSE American

The NYSE American is a stock (equity) exchange based in New York mainly catering to small-cap and mid-cap companies. The NYSE American is owned by the Intercontinental Exchange (ICE), which also owns the New York Stock Exchange (NYSE) renowned for featuring many companies across the globe. Previously known as the NYSE Market, the NYSE American got its recent name after implementing the speed bump on July 24, 2017. A company listed in NYSE American is assigned electronic Designated Market Maker (e-DMM) that includes quoting obligations (Business Insider, 2017). e-DMM is seen as an incentive towards large-size and institutional trading of equities of these companies. The trading hours of the NYSE American is split into four sessions: 1) Pre-opening session that last from 6:30 to until 7:00, 2) Early Trading Session that lasts from 7:30-9:00 3) Core Trading Session that lasts from 9:30 to 4:00, and 4) Late Trading Session that lasts from 4:00 PM to 8:00 PM ( ICE , 2017). NYSE American also charges fees to trades that remove liquidity and provides rebates and credits to those trades that add liquidity, along with other kinds of fees.

3.3 Chicago Stock Exchange

In order to empirically test various liquidity measures related to the stocks quoted and traded in the NYSE American, it is relevant to compare measures with a comparable stock exchange, mainly catering to small-cap and mid-cap companies, without a speed bump throughout the period of study. The Chicago Stock Exchange (CHX), an equity exchange located in Chicago, Illinois in the United States, was denied its application to the SEC for speed bump and hence did not feature speed bump during the period of study. In terms of volume of

16 trade and market capitalization of traded stocks, Chicago Stock Exchange comes closest as demonstrated in Table 1 and Table 2. For instance, on June 22, 2017 (20 trading days earlier than the application of speed bump in NYSE American), the number of transactions in the NYSE Amercian and the CHX stands at 34,003 and 9,736, respectively. As the number of transactions increases to 103,877 for NYSE American on August 18 (20 trading days after the speed bump), the number of transactions for the CHX has not altered signiﬁcantly at 9,948. The NYSE American and the CHX have traded volume of 18.53 million and 31.04 million, respectively, on June 22, 2017. This ﬁgure stands at 26.93 and 41.75 millions for the NYSE American and the CHX on August 18, 2017, respectively. No other stock exchange is comparable in terms of number of transactions and traded volume as well as lacking the attribute of speed bump so as to contrast with and control for the NYSE American. Founded in 1882, the Chicago Stock Exchange evolved in its present form through mergers with important exchanges in the past such as the New Orleans Stock Exchange. As man- dated by the SEC rules, the CHX allows trading stocks listed on other exchanges without simultaneously getting listed in the CHX. The exchange utilizes the CHX Matching Sys- tem which allows full electronic trade matching. In securities priced above $1 or above, the exchange charges fees for removing liquidity while provides rebates for providing liquidity. The CHX allows a security trading feature called the CHX SNAP (Sub-second Non- displayed Auction Process) introduced in 2016 designed to mitigate advantages of the institutional and algorithmic traders. This facilitates bulk trading of securities on a lit market and minimizes the information advantage accrued to large and institutional traders. The appeal of the CHX for a randomized and asymmetric speed bump did not receive approval from the SEC. The initially proposed Liquidity Taking Access Delay (LTAD) would require all incoming orders to a 350 microseconds software delay. This was withdrawn by the CHX and replaced by another proposal for Liquidity Enhancing Access Delay (LEAD), which would subject all market participants except some important market makers paying a fee to the exchange. However, the proposal for LEAD was not approved by the SEC. The

17 SEC instead approved 2-year pilot program on October 20, 2017, whereby the CHX would be required to report various statistics related to the speed bump.

4 Data

The dataset I relied on was extracted as Daily TAQ (Trade and Quote), which contains files of all trades and relevant quotes of “all issues listed and traded on US regulated exchanges for a single trading day” (Intercontinental Exchange, Inc., 2017). The files are obtained from the output of Consolidated Tape Association (CTA) and Unlisted Trading Privileges’ (UTP) feeds of Security Information Processor (SIP), and the tapes are named A, B and C. Every trading day, the SIP data is published between 4:00 AM to 8:00 AM. The archive of Daily TAQ is also maintained by the NYSE Group that contains historical data available in its website. According to Regulation National Market System, also abbreviated as Reg NMS, a doc- ument published by the SEC as a set of rules applicable to all exchanges in the United States, a bid price and an offer price indicate “bid price or the offer price communicated by a member of a national securities exchange or member of a national securities association to any broker or dealer, or to any customer, at which it is willing to buy or sell one or more round lots of an NMS security, as either principal or agent, but shall not include indications of interest ” (SEC, 2005). The Trade and Quote (TAQ) file includes the Best Bid And Offer (BBO) datasets, the National Best Bid and Offer (NBBO) datasets, and the Trade datasets separately for each day. The BBO file records every bid or ask attempts made at a particular exchange without denoting whether it is the best among all available bid or ask for a particular stock in terms of price or size among all venues at that time. At a given trading time, the best bid is the maximum of all bids and the best offer is the minimum of all offers, taking all exchanges. Among all the best bid (offer) price, size or exchange, national best bid (offer) price,

18 size or exchange represents the best among those prevailing ones across all exchanges in the United States.

4.1 NBBO Dataset

The National Best Bid and Offer (NBBO) dataset records quoting characteristics related to the best bids and the best offers among all venues in the US. The timestamp recorded in this data is in a nanosecond. Each day’s data is included in a single dataset arranged alphabetically by stocks and each stock arranged by timestamp. The dataset contains 30 quoting attributes. The details are provided in the Appendix A.1. The NBBO dataset is a subset of the BBO dataset that records every bid or ask attempts made at a particular exchange, no matter whether it is the best among all available bid or ask among all venues for a particular stock at that time. The National Best Bid (NBB) is the one that is the highest among all prevailing best bids while the National Best Offer (NBO) is the lowest among all prevailing best offer prices. Therefore, the dataset for the NBBO is relatively smaller than the BBO. The NBBO dataset consists of both the bid price and the best bid price for a given quote. Likewise, it also consists of the bid size and the best bid size for that quote. The best bid price and the best bid size are the most optimal price and the size among all existing optimal ones in each exchange. The same logic applies for choosing the best offer price and the best offer size instead of an offer price and an offer size, respectively, corresponding to a given quote. Similar reasoning applies to distinguish between the best bid exchange and the best offer exchange instead of bid exchange and offer exchange, respectively. I format the original NBBO dataset as it records various revised and cancelled quotes. The details of filters applied as formatting strategies are provided in the Appendix A.2.

19 4.2 Trade Dataset

All trades happening across all equity exchanges in the USA in a particular day is recorded as a single dataset. This is also arranged in the same manner as the NBBO dataset: First by stocks in alphabetical order and then by respective timestamp. The details of the dataset, which consists of 26 trading characteristics, are provided in the Appendix A.3. The timestamp of a trade, denoted by Time, is not the same as Participant Timestamp that is not necessarily in an increasing order. Relying on Participant Timestamp doesn’t provide a consistent positive values for a duration any given quote is alive. This is of particular interest in calculating several summary statistics measures discussed in details later in the essay. Both the NBBO and the Trade datasets for a specific date are merged so that each trade of a particular stocks at a given timestamp has both the immediately preceding and the immediately succeeding National Best Bid as well as the National Best Offer quotes. Combining both datasets is necessary to capture various liquidity spreads such as effective spread, realized spread and adverse selection.

5 Methodology

The first methodological approach employed in this essay are various summary statistics, such as the percentage time at the NBBO, time-weighted quoted spread and time-weighted quoted depth. The next approach is an econometric application of the difference-in-differences (DID) to assess various liquidity measures of stocks traded at the NYSE American controlling for corresponding measures at the Chicago Stock Exchange for the daily data from June 22, 2017 to August 18, 2017. Both approaches are described in details below.

20 5.1 Summary Statistics

All summary statistics are taken for both the NYSE American and the CHX. The first summary statistics measure is the number of traded stocks that accounts for stocks transacted in a given exchange without accounting for trade prices or trade sizes. Increase in the number of stocks traded in an exchange, assuming no change in liquidity, increases the number of transactions as well. However, even with an increase in the number of stocks traded, if liquidity deteriorates substantially, causing significantly lower transactions, then the overall number of transactions can decrease. For a given venue, the number of stocks in the NBBO quoted as the National Best Bid Exchange or the National Best Offer Exchange generally fluctuates each trading day. This statistic helps if the number of stocks in the NBBO quoting the NYSE American and its key rival CHX as either the best bid exchange or the best offer exchange fluctuated significantly without accounting for the percentage of time each exchange is displayed in the NBBO. This statistic also doesn’t account the best bid or offer prices and sizes. Percentage Time at the NBBO indicates the interval a given venue is represented in the NBBO taken as the average of the percentage of time as the best bid exchange and the percentage of time as the best offer exchange in the NBBO. Increase in a venue percentage time at the NBBO generally suggests its higher market liquidity and more trades. For a given venue, its percentage time at the NBBO for a given day is

Pn Pn k=1 Iv,nbb × Alivek + k=1 Iv,nbo × Alivek Perc Time at the NBBOv = Pn (1) 2 × k=1 Alivek Another set of summary statistics in this essay are quoted spread and time-weighted quoted spread. These measures can be calculated exclusively from the NBBO datasets. Assume a record i on venue v at the NBBO. Then,

Best Oﬀer Price − Best Bid Price Quoted Spread in Percentage = i,v i,v (2) i,v Mid-quote Price

21 where

Mid-quote Pricei,v = (Best Oﬀer Pricei,v + Best Bid Pricei,v)/2

As the best bid exchange doesn’t necessarily match the best offer exchange, the quoted spread could only be calculated if the quotes are first segregated by the best bid exchange and the best offer exchange. Therefore, I calculate the share of quoted spread in each venue first by the best bid venue bbv and then by the best offer venue bov with k representing the number of venues. Taking the average results in the quoted spread for a stock i and venue v. Taking the average of the the best bid and offer prices is technically challenging as each quote of a given stocks lasts for different duration. Therefore, it is prudent to weight bid-ask spread of a quote by the time interval that particular quote is alive. Time-weighted quoted spread measures the quoted spread at the NBBO based on the duration the quote is reflected as either the National Best Bid (NBB) or the National Best Offer (NBO).

n X Time-weighted QSi,v = (Alivek × Quoted Spreadi,v) k=1

n ! X Best Offer Pricei,v − Best Bid Pricei,v = Alive × (3) k Mid-quote Price k=1 i,v Time-weighted quoted depth of a venue measures the quoted depth at the venue based on the duration the quote is reflected as either National Best Bid (NBB) or National Best Offer (NBO). The quoted depth of a single quote is the average size of best bid size and the best offer size. The quoted depth of a venue is the average of all available quoted depth. Calculating quoted depth, as in quoted spread, requires quotes segregated by the best bid exchange and the best offer exchange because the best bid exchange doesn’t necessarily match the best offer exchange. Therefore, I take the share of quoted depth by taking the average of best bid size and the best offer size relative to the average of the total of best bid

22 size (BBS) and the best oﬀer size (BOS).

Pn k=1 Ii,v,k(BBSi,v,k + BOSi,v,k) Quoted Depth i,v = Pn (4) 2 × k=1 Ii,v,k

n X Time-weighted Quoted Depthi,v,k = (Alivek × Quoted Depthi,v,k) (5) k=1

5.2 Propensity Score Matching and Matched Sample

Propensity score is a probability score assigned to treated units considering baseline characteristics. In a non-randomized study, assigning propensity score helps design and analyze a study resembling a randomized controlled trial. As the US National Institute of Health mentions it: “In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects” (Austin, 2011). Propensity score matching (PSM) is a matching technique utilized to estimate the effect of a treatment, policy intervention or other interventions by properly addressing the covariates predicting the treatment. This technique is relied upon to reduce existing bias by comparing the outcomes of the treated units and the control units. This would ideally filter out the bias due to confounding variables in an estimate of treatment effect. In randomized experiments, randomization automatically entails unbiased estimation of treatment effects due to the law of large numbers balancing out the treated groups. The assignment of treated groups are not necessarily random in observational studies. PSM facilitates in obtaining a matched sample for each treated groups by mimicking randomization and by reducing the bias in assigning treatment (Ho et al., 2007). The PSM technique used in this essay relies on market capitalization and shares price of stocks based on the nearest neighborhood matching. Given the availability of matched sample and the data characteristics, I believe that the difference-in-differences method would

23 be the best among suite of econometric approaches to examine the speed bump, where the stocks in the the NYSE American could be assumed as the treated group and the stocks in the Chicago Stock Exchange (CHX) as the control group. Our matched sample consists of 80 stocks traded in the NYSE American and 80 stocks traded in the Chicago Stock Exchange, resulting in the the total of 160 stocks, controlling for shares price and market capitalization,following closely on the suggestion of Davies and Kim (2009). The market capitalization used as a covariate for each stock listed in the NYSE American and the Chicago Stock Exchange is based on their trading price in the opening of June 22, 2017, the first day of our study. Before performing matching, all stocks traded both in the NYSE American and the Chicago Stock Exchange during our period of study of 40 trading days are removed. Based on share price and market capitalization and equal weights of one- on-one matching, the Matchit package in R generates a matched sample, eventually resulting in the 80 stocks in the NYSE American matched with the 80 stocks in the Chicago Stock Exchange. The MatchIt package is specifically developed for selecting a well-matched sample of the treated and control groups. Among a suite of options, I use the nearest method with the ratio of 1. This helps us get 1:1 optimal ratio matching based on the propensity score values obtained from the logistic regression. Nearest (neighbor) matching involves either a “greedy” algorithm, which first tries to come up with the potential matches and retains the closest unmatched option to match each time. Otherwise, it involves a more involved form of “optimal matching” that minimizes global balance over all matches by involving certain calculations. As Davies and Kim (2009) point,“tests based on one-to-one nearest-neighbor matching have comparable power and less size distortion than alternatives that place more weight on distant firms.” The results of pre-matched sample and matched sample along with balance improvement using MatchIt package in R is provided in the Table 4.

24 5.3 Diﬀerence-in-Diﬀerences (DD) Approach

The difference-in-differences (DD) approach is a commonly utlized econometric technique in measuring the true effect of treatment (an event, such as a change in policy) by comparing a treated group with a control group. The period before the event is called the pre-period while the period after the event, including the treatment date, is called the post-period. A dummy variable (also called an indicator variable) separates the control group from the treated group (conventionally by assigning ‘0’ for the control group and ‘1’ for the treated group). Another dummy variable separates the pre-period from the post-period (conventionally by assigning ‘0’ for pre-period and ‘1’ for post period). Another dummy variable captures both the control group versus treated group and pre-period versus post- period by multiplying these two dummy variables. This leads to ‘0’ assigned to the pre- period control group, the pre-period treated group, and the post-period control group and ‘1’ assigned to only the post-period treated group. The coefficient obtained for post-period treated group is the difference-in-differences estimation. Effectively, this separates the true effect of a treatment by factoring already existing differences between the treated group and the control group. Some common issues are problematic to the DD approach in a linear model estimation. A few of these include the treatment and control participation owing to differences in outcomes before the time of intervention; non-linear and other forms of functional form relationship; comparison of groups with irregular intensity (one group with high treatment intensity while another with low treatment intensity); and autocorrelation (Gertler et al., 2016).

5.4 Panel Data and Fixed Eﬀects

In the panel data setting of this essay where the cross-sectional or longitudinal units are stocks in the sample while the temporal units are indexed by day, a key issue is to address confounders (confounding variables) so that the regression estimation is not spurious. This is an important issue in a panel data that gives rise to two kinds of confounders: 1) time-

25 invariant but cross-section varying, and 2) cross-section invariant but time-varying. In the first case, there could be differences among cross-section units. To properly capture the effects of such confounders requires adjusting an intercept form for each cross-section units. In the second case, differences in each unit of time indexed in a given regression specification are adjusted by an intercept form for each indexed temporal unit. However, one chief difficulty in estimating panel data in a linear regression model even after adjusting for fixed effects is that in the treatment condition for both treated and control units could switch in and out at various periods of time (Imai and Kim, 2020). In order to restrict this effect, the sample selection relied in this essay excludes those stocks that are traded in both the NYSE American and the CHX even for a single day during the period of study.

5.5 Regression Speciﬁcation

5.5.1 Dependent Variables

The deﬁnitions of various spreads on liquidity utilized as dependent variables in the regression speciﬁcation are elaborated in this section. In each measure of stock i in an exchange j at

time t, pi,j,t represents the trading price (to be found in the trade data) and mi,j,t is the midquote prevailing at or immediately before the trade execution (to be found in the NBBO data). Also, let’s assume mi,j,t+5 represents the midquote prevailing ﬁve minutes or before the trade execution (also to be found in the NBBO data). Ii,j,t represents an indicator variable which equals ‘1’ if it is buyer-initiated trade while ‘-1’ is it is seller-initiated trade.

1. Eﬀective Spread:

The effective spread captures the net effect of adverse selection and competition chan- nel. Effective spread constitutes two components: permanent component (changes in adverse selection) and temporary component (profits obtained by the liquidity provider).

26 The eﬀective spread for a given stock i in venue j at time t is given by

(pi,j,t − mi,j,t) ESpreadi,j,t = 2Ii,j,t × (6) mi,j,t

In order to reflect whether the trade is a buy-side or a sell-side one, it is important to follow the Lee and Ready (1991) standard approach to trade-signing. The ‘tick- test’ of this standard approach classifies trades into four categories: uptick, downtick, zero-uptick, and zero-downtick. A trade is an uptick (downtick) if its price is higher (lower) than the previous trade. It is classified as a zero-uptick (zero down-tick) if its price does not change compared to its previous trade while the previous trade is an uptick (downtick). A trade is a buyer-initiated is it is an uptick or zero-uptick; it is a seller-initiated if it is a downtick or zero-downtick. Therefore, the indicator variable

Ii,j,t is 1 if it is buyer-initiated trade and hence uptick or zero upticke; if a trade is

seller-initiated (downtick or zero-downtick), the indicator variable Ii,j,t is negative.

2. Realized Spread: Realized spread measures the temporary eﬀect by capturing the premium obtained by the liquidity providers. The realized spread for a given stock i in venue j at day t is given by

(pi,j,t − mi,j,t+5) RSpreadi,j,t = 2 × (7) mi,j,t

3. Adverse Selection:

The percentage adverse selection for each trade on stock measures the permanent eﬀect. The adverse selection for a given stock i in venue j at day t is given by

AdverseSelectioni,j,t = ESpreadi,j,t − RSpreadi,j,t (8)

In the regression speciﬁcation below, the dependent variable yi,t could be eﬀective spread,

27 realized spread, adverse selection, quoted spread or quoted depth.

1. Without Fixed Eﬀects:

yi,t = γ + β1Exci,t + β2SpdBumpi,t + β3SpdBump Exci,t + bXi,t + εi,t (9)

2. With Fixed Eﬀects:

yi,t = γ + αi + δt + β1Exci,t + β2SpdBumpi,t + β3SpdBump Exci,t + bXi,t + εi,t (10)

The coefficient β3 estimates the difference-in-differences measure. The independent variables used in the above the regression specification with fixed effects or without fixed effects are:

1. SpdBumpit: A binary indicator variable that is assigned ‘1’ for an observation after the speed bump and ‘0’ for those before the speed bump.

2. Excit: A binary indicator variable assigned ‘1’ for those stocks in the NYSE American and ‘0’ for those in the Chicago Stock Exchange.

3. SpdBump Exc: This is simply a cross term obtained as SpdBumpit × Excit: A binary indicator variable assigned ‘1’ for stocks traded after the speed bump in the NYSE American and ‘0’ for stocks: 1) trade in the NYSE American before the speed bump, 2) traded in CHX before the speed bump, and 3) traded in CHX after the speed bump.

4. Xit: a set of control variables described under a separate section later.

5. γ: a constant

Following are the terms specifically only employed in the regression specification with fixed effects:

28 (a) αi: a term representing individual (stock-variant but time-invariant) ﬁxed-eﬀects

(b) δt: a term representing the daily (time-variant but stock invariant) ﬁxed eﬀects

The following control variables are used in the regression speciﬁcation:

1. VIX Score: The volatility index (VIX) was created by the Chicago Board Options Exchange (CBOE). This is a real-time market index that represents forward-looking volatility of 30-day market based on the current expectation of the market. The regression results utilize the daily average VIX score.

2. Trade Volume: The daily traded volume of a given stock is quite ﬂuctuating driven by a host of market factors. Trade volume can be quite disparate across companies.

3. Market Capitalization: This measure is a valuation measure calculated by the number of share outstanding times the current stock price. This value provides a perspective on how ‘large’ a company is currently valued, reﬂected in its share price at a given number of shares outstanding.

6 Results

6.1 Summary Statistics

As demonstrated in the Figure 1 and Table 5, the number of stocks traded in the NYSE American rose steeply following the speed bump, marking an almost over 20 times increase in the number of stocks traded daily. However, there is only a slight decrease in the number of stocks traded in the CHX following the speed bump at the NYSE American. Increase in traded stocks suggests that the speed bump incentivized investors to trade more stocks, primarily due to expectation of increased returns, in the NYSE American due to the introduced feature of speed bump.

29 As displayed in Figure 2 and Table 6, the number of stocks at the NYSE American quoted as the national best bid (NBB) in the National Best Bid and Offer (NBBO) increases significantly after the introduction of speed bump, while the number of stocks at the Chicago Stock Exchange quoted as the NBB in the NBBO barely changes throughout the period of study. The likely reason for the increase multiple times at the NYSE American is the increased quoting activities, which puts an upward pressure on the quotes prevailing in the exchanges to also display in the National Best Bid and Offer (NBBO). The same reason likely holds for the daily number of stocks at the NYSE American quoted as the national best offer (NBO) in the NBBO, which increases almost by the same magnitude following the speed bump, as displayed in the Figure 3 and Table 7. The same figure and table also demonstrate no such noticeable change in the CHX. Fair access to speed mostly likely induced investors to place their bids or offers with increased frequency in the NYSE American, resulting in higher quotes in the exchange itself as well as those quotes appearing in the NBBO. Ideally, the speed bump would have attracted more retail investors to trade stocks in the NYSE American, which would likely increase the exchange’s overall percentage time displayed at the NBBO. There is hardly a noticeable trend as demonstrated in Figure 4 and Table 8 on the percentage time of NBBO displayed by either the NYSE American or the CHX. Despite increase in the number of stocks in the NBBO related to the NYSE American, some quotes are only marginally quoted, thus making no such significant change in the percentage time at the NBBO. Figure 5 and Table 9 demonstrate that the time-weighted quoted spread for the CHX is quite large compared to the corresponding measure for the NYSE American. The trend in quoted spread for the NYSE American is low and stable, while that of the CHX is fluctuating and slightly downwards after the speed bump on NYSE American. Throughout the period of observation, the daily average quoted spread for the CHX is still multiple times that of the NYSE American. Quoted depth accounts for the average size of quotes from the perspective of both bids

30 and oﬀers. Figure 6 and Table 10 demonstrate that the time-weighted quoted depth in both the NYSE American and the CHX decreased following the introduction of speed bump. This could be due to increased appetite of retail investors for less bulkier purchases in both exchanges. Figure 7 plots daily average eﬀective spreads of stocks in the NYSE American and the CHX in the matched sample consisting of 160 stocks (80 stocks from the NYSE American and 80 stocks from the CHX) obtained by propensity score matching. Figure 8 plots daily average quoted spreads of stocks in the NYSE American and the CHX in the same matched sample.

6.2 Pre-period Means and Regression Coeﬃcients

Table 12 displays pre-period means of daily average effective spread, realized spread, adverse selection, and quoted spread in percentages. It also displays pre-period means of the daily average quoted depth. This calculation is necessary to know the magnitude and direction of impacts caused by the speed bump in case the coefficient estimates obtained in the regression specification are statistically significant. In our regression specification with effective spread as a dependent variable, the the coefficient estimates on the cross term representing DD as shown in Table 13 is SpdBump Exc, is 0.053, implying that the speed bump resulted in an increase of effective spread in the NYSE American by 5.3 basis points, controlling for its pre-existing differences with the

0.053 Chicago Stock Exchange. This represents 0.70893 ×100% = 7.48% increase in effective spread following the speed bump. The result is statistically significant at 10% significance level. The coefficient estimate on the cross term SpdBump Exc with realized spread as a dependent variable is -0.016, which would have made intuitive sense as literature would predict, but it is not statistically even at 10% significance level. This suggests that the evidence of the effect of speed bump on realized spread is not conclusive. The coefficient estimate on the cross term SpdBump Exc with adverse selection as a dependent variable is 0.068, implying that the speed

31 0.068 bump increased adverse selection by 6.8 basis points, representing 0.28647 × 100% = 23.74% increase in adverse selection following the speed bump. The coeﬃcient estimate on the cross term SpdBump Exc with quoted spread as a dependent variable, shown in Table 13, is 0.14, implying that the speed bump increased average

0.14 quoted spread in the NYSE American by 14 basis points, representing 0.89236 × 100% = 15.69% increase in quoted spread following the speed bump. The coeﬃcient estimate on the cross term SpdBump Exc with quoted depth (average per quote) as a dependent variable is 7.903, implying that the speed bump increased average quoted depth in the NYSE American

7.903 by 7.903 shares per quote, representing 103.1297 × 100% = 7.663% increase in average quoted depth following the speed bump. The regression coefficient that accounts for fixed effects excludes exchange and market capitalization in order to remove collinearity problems. These coefficient estimates with fixed effects, as shown in Table 14, are remarkably close to those obtained without using fixed effects in Table 13, including the statistical significance for each coefficient estimates. Therefore, I skip the interpretation of coefficient estimates in the regression specification accounting for fixed effects. The above results demonstrate that the speed bump feature did not improve the liquidity measures in the NYSE American. Perhaps the exchange was successful in luring investors who believed that the speed advantage accrued to them would increase their market per- formance. However, with wider spread due to increased adverse selection, the speed bump instead deteriorates market quality and may likely cause price distortions. This is in contrast to the theoretical underpinnings of Baruch and Glosten (2013), which predicts that leveling information access among all kinds of investors would result in consistent quote flickering, resulting in a well-behaved market equilibrium. An important concern is whether the duration of 350 microseconds is optimal as an ideally designed speed bump. If this duration is significantly less or more than an optimal one, then speed bump would result in reduced market quality. My results are also in contrast with those obtained by Hendershott and

32 Moulton (2011). In their empirical work, increase in automation brought by the activation of Hybrid in the NYSE causes effective spread and quoted spread to increase. However, the design of speed bump is not just reversal of automation. Instead, it was designed not to decrease the level of automation but to prevent adverse selection caused by speed arbitrage. Another key issue quite related to the results I obtain but not explicitly dealt in my model is the behavior of large and institutional investors following the speed bump. If such investors drove out of the NYSE American in significant numbers, then our analysis on adverse selection faced by the new set of participants in the venue would be distorted. The increase in adverse selection then would suggest more about the information divergence among new set of participants after the large and institutional investors, most likely those with access to huge computing power, left the venue for other venues lacking the speed bump. Our results regarding wider spreads or larger adverse selection, thus lower aggregate market quality, is consistent with arguments brought forth by Chen et al. (2017). They find that the speed bump causes profits accrued to the fast liquidity providers compared to other liquidity providers and aggregate market quality. The authors also report concentration of negative effects in stocks more exposed to immediate adverse selection before the speed bump. However, the speed bump at the NYSE American, unlike the one in the TSX Alpha, is not randomized and cannot be bypassed for an extra fee.

7 Limitations

Difference-in-differences (DD) approach in an ordinary least squares (OLS) framework of linear regression relies on a common trend assumption that corrects for the underlying differences between the treated group (stocks on the NYSE American) and the control group (stocks on the CHX) in order to obtain true effect of treatment (Speed Bump). However, the methodological assessment of establishing the similar trend in absence of treatment both groups is empirically challenging (Wing et al., 2018).

33 For such empirical investigation, the dataset could cover a longer time frame of several months, possibly over six months before and six months after the speed bump, to discern noticeable pattern and trend in various liquidity and market measures. Factoring only 20 trading days before the speed bump and 20 trading days with the speed bump may not fully capture the magnitude of effects that of speed bump on measures such as effective spread, realized spread, adverse selection, quoted spread and quoted depth. Some important variables omitted in the regression specification that could have alter causal estimation are market-maker rebates and market-taker fees charged by various venues, including the two exchanges considered in the model. Other variables could relate with the regulatory changes in big trading platform such as dark pools that can significantly affect relatively smaller venues like the NYSE American and the Chicago Stock Exchange.

8 Conclusion

Ideally any mechanism such as speed bump put in practice to improve the distortion caused by differential access to speed in a given exchange would improve market indicators reflecting tighter spread, less adverse selection and improved efficiency in terms of access by retail and big investors (Hendershott and Moulton, 2011). The empirical results in this essay suggest that the speed bump of 350 microseconds introduced to the NYSE American attracted investors to trade various stocks in the NYSE American as demonstrated by the increased number of traded stocks and stocks displayed by the NYSE American in best bid exchange and best offer exchange following the speed bump. Due to its composition of small-cap and medium-cap companies, the NYSE American tends to have lower liquidity relative to exchanges such as the NYSE and the NASDAQ. Therefore, implementing a speed bump with the goal of removing unfair advantages to fast high-frequency investors would be an incentive towards more trading by retail investors (Woodward, 2018). The speed bump did not increase the duration displayed in the NBBO despite slight increase in the volume of

34 trade at the NYSE American. Applying the regression specification implementing difference-in-differences setting to control for intertemporal differences across treated group of the NYSE American stocks and control group of the Chicago Stock Exchange stocks, I find that the introduction of speed bump increased effective spread and adverse selection. Although counterintuitive, a key reason for this result could be the new realities of the high frequency world in which “setting up specialized microstructure to attract particular trading clienteles” could cause the end result of trading that is “both fragmented and extremely fluid” (O’Hara, 2015). This may also explain significant discrepancy in quoted spread compared with effective and realized spreads. Another possible reason for such results can be venues differentiation arising from various kinds of competing speed bump arrangement across several venues, which eventually leads to welfare losses through misallocation of assets among investors and also suboptimal venues due to misallocated investors (Pagnotta and Philippon, 2011). This could also be the result of liquidity measurement problems caused by withdrawn quotes and record of timestamps updated and consolidated in slightly different ways in Trade and NBBO files (Holden and Jacobsen, 2014). The result on realized spread is inconclusive due to statistical insignificance. Although this study considers some important covariates like VIX score for the volatility measure, market capitalization, and traded volume, it could further consider other measures related to regulatory changes in the context of high-frequency trading. Therefore, further checks on market measures require additional empirical investigation.

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38 Table 1: Share of Exchange Venues by Number of Transactions June 22 August 18 Exchange Venues No. of Trans- Share No. of Trans- Share actions of Total actions of Total Transac- Transac- tions tions NYSE American 34,003 0.11 % 103,877 0.34 % NASDAQ OMX 1,828,921 6.04 % 1,921,487 6.25 % FINRA ADF (D) 6,875,602 22.72 % 6,783,396 22.05 % Bats EDGA 854,370 2.82 % 771,925 2.51 % Bats EDGX 2,414,433 7.98 % 2,384,048 7.75 % CHX 9,736 0.03 % 9,948 0.03 % NYSE 2,481,844 8.2 % 2,559,793 8.32 % NYSE Arca 3,161,441 10.45 % 3,394,960 11.04 % NASDAQ (C) 2,904,769 9.6 % 2,878,363 9.36 % NASDAQ (A, B) 3,106,307 10.27 % 3,427,957 11.14 % IEX 885,704 2.93 % 761,818 2.48 % NASDAQ OMX 356,896 1.18 % 333,158 1.08 % Bats BYX 2,452,076 8.1 % 2,487,403 8.09 % Bats BZX 2,891,516 9.56 % 2,940,709 9.56 % Total 30,257,618 30,758,842

Table 2: Share of Exchange Venues by Traded Shares June 22 August 18 Exchange Venues Traded Vol- Share of To- Traded Vol- Share of To- ume (in mil- tal Traded ume (in mil- tal Traded lions) Volume lions) Volume NYSE American 18.53 0.26 % 26.93 0.36 % NASDAQ OMX BX 203.01 2.81 % 218.70 2.96 % FINRA ADF 2,633.49 36.43 % 2,464.61 33.38 % Bats EDGA 112.45 1.56 % 115.67 1.57 % Bats EDGX 445.02 6.16 % 449.78 6.09 % CHX 31.04 0.43 % 41.75 0.57 % NYSE 1,050.10 14.53 % 1,139.93 15.44 % NYSE Arca 787.26 10.89 % 819.70 11.1 % NASDAQ (Tape C) 601.70 8.32 % 670.41 9.08 % NASDAQ (Tape A, B) 400.60 5.54 % 471.88 6.39 % IEX 155.45 2.15 % 126.61 1.71 % NASDAQ OMX PSX 51.05 0.71 % 55.49 0.75 % Bats BYX 325.55 4.5 % 337.74 4.57 % Bats BZX 413.09 5.71 % 444.18 6.02 % Total 7,228.33 7,383.37

39 Table 3: Balance for Matched Data

Control Treated All 80 228 Matched 80 80 Unmatched 0 148 Discarded 0 0

Notes: The data consists of the New York Stock Exchange American (NYSE Amer- ican) stocks and the Chicago Stock Exchange (CHX) stocks over the period from June 22, 2017 to August 18, 2017 (40 trading days). A total of 20 trading days from July 22, Thursday until July 21, Friday did not have speed bump while a total of 20 trading days from July 24, 2017 to August 18, 2017 had speed bump.

Table 4: Summary of Balance and Balance Improvement

Summary of Balance for All Data Means Means SD Con- Mean Diff eQQ Me- eQQ eQQ Treated Control trol dian Mean Max Distance 0.87 0.32 0.38 0.56 0.81 0.55 0.89 Shares out 93.7 835.85 1,772.82 -742.15 178.18 727.34 7,839.65 Market Cap 536.3 75,727.37 160,735.44 -75,191.07 16,619.1 75,096.05 1,029,874.02 Summary of Balance for Matched Data Means Means SD Con- Mean Diff eQQ Me- eQQ eQQ Treated Control trol dian Mean Max Distance 0.9 0.32 0.38 0.58 0.89 0.58 0.9 Shares Out 21.9 835.85 1,772.82 -813.95 194.1 813.97 9,859.08 Market Cap 35.29 75,727.37 160,735.44 -75,692.08 16,702.42 75,692.08 1,056,178 Summary of Balance Improvement Mean Diff. eQQ Med eQQ Mean eQQ Max Distance -4.71 -9.27 -5.66 -0.58 Shares Out -9.67 -8.93 -11.91 -25.76 Market Cap -0.67 -0.5 -0.79 -2.55

40 Figure 1: Number of Traded Stocks for NYSE American (A) and CHX (M)

Table 5: Number of Traded Stocks

A M Before After Diﬀerence After Before Diﬀerence Average 363.81 7,907 7,543.19 1,379.14 1,186.5 -192.64 Median 363 7,905.5 7,542.5 1,393 1,213.5 -179.5 Maximum 378 7,969 7,591 1,566 1,360 -206 Minimum 355 7,838 7,483 1,221 1,060 -161 Std Ev 6.67 81.68 75.01 106.28 81.68 -24.6

41 Figure 2: Number of Stocks Displayed in NBB for NYSE American (A) and CHX (M)

Table 6: Number of Stocks Displayed in NBB

A M Before for After for A Diﬀerence Before for After for M Diﬀerence A M Average 353.5 2,438.9 2,085.4 1,943.95 1,879.5 -64.45 Median 355.5 2,492.5 2,137 1,937 1,881.5 -55.5 Maximum 365 2,914 2,549 2,156 1,997 -159 Minimum 294 1,388 1,094 1,663 1,759 96 Std Ev 14.84 327.77 312.93 127.37 61.09 -66.28

42 Figure 3: Number of Stocks Displayed in NBO for NYSE American (A) and CHX (M)

Table 7: Number of Stocks Displayed in NBO

A M Before for After for A Diﬀerence Before for After for M Diﬀerence A M Average 357 2,154.35 1,797.35 1,958.5 1,867.2 -91.3 Median 359 2,256.5 1,897.5 1,968 1,875 -93 Maximum 371 2,506 2,135 2,210 1,964 -246 Minimum 298 1,310 1,012 1,589 1,725 136 Std Ev 14.53 296.01 281.48 144.55 72 -72.55

43 Figure 4: Percentage Time at NBBO for NYSE American (A) and CHX (M)

Table 8: Percentage Time at NBBO

A M Before for After for A Diﬀerence Before for After for M Diﬀerence A M Average 1.09 % 1.10 % 0.01 % 0.33 % 0.31 % -0.02 % Median 1.10 % 1.10 % 0.00 % 0.34 % 0.31 % -0.03 % Maximum 1.15 % 1.20 % 0.05 % 0.38 % 0.35 % -0.02 % Minimum 1.01 % 0.93 % -0.08 % 0.22 % 0.23 % 0.01 % Std Ev 0.04 % 0.07 % 0.02 % 0.04 % 0.03 % -0.01 %

44 Figure 5: Share of Time-weighted Quoted Spread for NYSE American (A) and CHX (M)

Notes: NYSE American and CHX are represented by A and M, respectively. A total of 20 trading days from July 22, Thursday until July 21, Friday did not have speed bump while a total of 20 trading days from July 24, 2017 to August 18, 2017 had speed bump.

Table 9: Time-weighted Quoted Spread

A M Before for After for A Diﬀerence Before for After for M Diﬀerence A M Average 2.76% 2.45% -0.31% 0.12% 0.13% 0.01% Median 2.79% 2.26% -0.53% 0.12% 0.13% 0.01% Maximum 2.99% 3.61% 0.62% 0.16% 0.17% 0% Minimum 2.33% 1.88% -0.45% 0.08% 0.1% 0.02% Std Ev 0.17% 0.6% 0.43% 0.03% 0.02% -0.01%

45 Figure 6: Share of Time-weighted Quoted Depth for NYSE American (A) and CHX (M)

Table 10: Time-weighted Quoted Depth

A M Before for After for A Diﬀerence Before for After for M Diﬀerence A M Average 1.38 % 0.92 % -0.45 % 1.07 % 0.96 % -0.1 % Median 1.39 % 0.88 % -0.5 % 1.06 % 1.01 % -0.05 % Maximum 2 % 1.78 % -0.22 % 1.4 % 1.14 % -0.26 % Minimum 1.16 % 0.62 % -0.53 % 0.82 % 0.3 % -0.51 % Std Ev 0.19 % 0.23 % 0.05 % 0.13 % 0.18 % 0.05 %

46 Figure 7: Daily Average Eﬀective Spread of NYSE American (A) and CHX (M) in Matched Sample

Notes: The values for the CHX (M) are plotted in the primary axis in the left, while the values for the NYSE American (A) are plotted in the secondary axis in the right.

Table 11: Diﬀerence in Pre- and Post-Speed Bump in Eﬀective Spread, Realised Spread and Adverse Selection

ESpread RSpread AdSelection A M A M A M Mean 0.045 % -0.001 % -0.021 % 0.000 % 0.066 % 0.000 % SD 0.135 % -0.019 % 0.026 % -0.028 % 0.129 % 0.001 % Min 0.036 % 0.000 % 0.000 % 0.000 % 0.001 % 0.000 % Max 1.553 % -1.11 % -0.406 % -2.178 % 1.992 % 0.641 % Med -0.003 % -0.051 % -0.027 % 0.003 % 0.02 % -0.001 %

Notes: ESpread, RSpread, and AdSelection stand for eﬀective spread, realized spread, and adverse selection, respectively. The sample consists of the 80 New York Stock Exchange Amer- ican (NYSE American) stocks and the 80 matched Chicago Stock Exchange (CHX) stocks over the period from June 22, 2017 to August 18, 2017 (40 trading days). A total of 20 trading days from July 22, Thursday until July 21, Friday did not have speed bump while a total of 20 trading days from July 24, 2017 to August 18, 2017 had speed bump.

47 Figure 8: Daily Average Quoted Spread of NYSE American (A) and CHX (M) in Matched Sample

Notes: The values for the NYSE American (A) are plotted in the primary axis in the left, while the values for the CHX are plotted in the secondary axis in the right.

Table 12: Means (in Percentage) of Dependent Variables Before the Speed Bump on July 24, 2017

ESpread RSpread AdSelection QSpread QDepth

Pre-period 0.70893 0.42247 0.28647 .89236 103.1297 Mean

Notes: 1) ESpread is eﬀective spread, 2) RSpread is realized spread, 3) AdSelection is adverse selection (price impact), 4) QSpread is quoted spread, and 5) QDdepth is quoted depth. The sample consists of the 80 New York Stock Exchange American (NYSE Amer- ican) stocks and the 80 matched Chicago Stock Exchange (CHX) stocks over the period from June 22, 2017 to August 18, 2017 (40 trading days). A total of 20 trading days from July 22, Thursday until July 21, Friday did not have speed bump while a total of 20 trading days from July 24, 2017 to August 18, 2017 had speed bump.

48 Table 13: Estimation of DD1 and Other Relevant Coeﬃcients

Dep. Var → ESpread RSpread AdSelection QSpread QDepth Regressors ↓ SpdBump -0.017 -0.011 -0.0056 -0.0002 -5.813*** (0.022) (0.018) (0.014) (0.0002) (1.972)

Exc 1.012*** 0.608*** 0.404*** 0.016*** -207.062*** (0.105) (0.065) (0.044) (0.001) (57.021)

SpdBump Exc 0.053* -0.016 0.068*** 0.14*** 7.903*** (0.03) (0.024) (0.019) (0.0003) (2.742)

VIX Score 0.019*** 0.0126*** 0.006* 0.0003*** 0.184 (0.005) (0.004) (0.003) (0.0001) (0.484)

Mrkt Cap -0.1008 -0.0601 -0.04059 -0.0011 -344.771 (0.42) (0.2580) (0.173) (0.0056) (231.354)

Trade Vol 5.701 15.935 -10.7 -0.241 10,074.73*** (33.78) (27.25) (20.951) (0.345) (3,094.567)

Cons 0.008 -0.012 0.02 -0.0022* 216.408*** (0.096) (0.066) (0.048) (0.001) (42.597)

R-Squared 0.2752 0.1978 0.2 0.4094 0.0717

No. of Obs 6,400 6,400 6,400 6,400 6,400

Notes: The regressors used in the regression are: 1) SpdBump is a dummy for Speed Bump, 2) Exc is a dummy for the exchange (venue) with the speed bump, i.e., NYSE American, 3) SpdBump Exc is a cross-term dummy for both Speed Bump and Exchange, 4) VIX Score is daily average CBOE Volatility Index, 5) Mrkt cap is market capitalization in billions of US dollars, and 6) Trade Vol is trade volume in millions shares. The sample consists of the 80 New York Stock Exchange American (NYSE American) stocks and the 80 matched Chicago Stock Exchange (CHX) stocks over the period from June 22, 2017 to August 18, 2017 (40 trading days), making altogether 6,251 observations. A total of 20 trading days from July 22, Thursday until July 21, Friday did not have speed bump while a total of 20 trading days from July 24, 2017 to August 18, 2017 had speed bump.

49 Table 14: Estimation of DD1 and Other Relevant Coeﬃcients: Fixed Eﬀects

Dep. Var → ESpread RSpread AdSelection QSpread QDepth Regressors ↓ SpdBump -0.017 -0.011 -0.006 -0.0002 -5.812*** (0.022) (0.018) (0.014) (0.0002) (1.97)

SpdBump Exc 0.053* -0.015 0.068*** 0.14*** 7.9*** (0.03) (0.024) (0.019) (0.0003) (2.74)

VIX Score 0.019*** 0.013*** 0.0064* 0.0003*** 0.184 (0.005) (0.004) (0.003) (0.0001) (0.484)

Trade Vol 6.266 16.708 -10.442 -0.2391 9,858.67*** (33.955) (27.493) (21.203) (0.346) (3,091.71)

Cons 0.503*** 0.285*** 0.218*** 0.006*** 100.94*** (0.057) (0.046) (0.036) (0.001) (5.226)

R-squared 0.0487 0.0056 0.0845 0.1222 0.0003

No. of Obs 6,400 6,400 6,400 6,400 6,400

Notes: The regressors used in the regression are: 1) SpdBump is a dummy for Speed Bump, 2)SpdBump Exc is a cross-term dummy for both Speed Bump and Exchange, 3) VIX Score is daily average CBOE Volatility Index, 4) Trade Vol is trade volume in millions shares. The sample consists of the 80 New York Stock Exchange American (NYSE American) stocks and the 80 matched Chicago Stock Exchange (CHX) stocks over the period from June 22, 2017 to August 18, 2017 (40 trading days). A total of 20 trading days from July 22, Thursday until July 21, Friday did not have speed bump while a total of 20 trading days from July 24, 2017 to August 18, 2017 had speed bump.

50 A Appendix

A.1 NBBO Dataset Characteristics

In the raw data format of NBBO, the 30 columns (variables) are: ‘Time’,‘Exchange’,‘Symbol’,‘Bid Price’,‘Bid Size’,‘Offer Price’,‘Offer Size’,‘Quote Condition’,‘Sequence Number’,‘National BBO Ind’,‘FINRA BBO Indicator’,‘FINRA ADF MPID Indicator’,‘Quote Cancel Correction’,‘Source Of Quote’,‘NBBO Quote Condition’,‘Best Bid Exchange’,‘Best Bid Price’,‘Best Bid Size’,‘Best Bid FINRA Market Maker ID’,‘Best Offer Exchange’,‘Best Offer Price’,‘Best Offer Size’,‘Best Offer FINRA Market Maker ID’,‘LULD Indicator’,‘LULD NBBO Indicator’,‘SIP Generated Message Identifier’,‘Participant Timestamp’,‘FINRA ADF Timestamp.’

A.2 Filters to Format the NBBO Dataset

The following set of ﬁlters are applied to clean the raw data format:

1. The last row, which is not actually an observation, is deleted.

2. The regular trading hours start at 9:30 AM and last until 4:00 PM Eastern Time. Therefore, I don’t consider any quotes that fall outside of this time, which are a really small fraction of those happening in the regular trading hours hours.

3. Any quote with zero best bid price, zero best bid size, zero best oﬀer price or zero best oﬀer size is dropped. Although it is theoretically possible that this is the case when prices are rounded, including such observations could distort our empirical investigation.

4. The column Quote Cancel/Correction represents whether a given quote is a correction of the previous quote or the cancellation of its own. If it is blank, it is not a ’Cancel Quote’. If the attribute is ’B’, it is, according to the Quote speciﬁcation ﬁle, is “Cancel

51 quote, Cancel Price Indication, or Cancel Trading Range Indication”. If the attribute is ‘C’, it is “Corrected Price Indication.” Therefore, I drop all those with attributes ‘B’ and ‘C’.

5. The column Luld BBO Indicator represents if the participant quote can be executed due to price bands for the quote. If it is blank, it means that the LULD is not applicable. If it is ‘A’, then it means the bid price is above the “Upper Limit Price Band” so that the bid is not executatble. If the attribute is ‘B’, it means that the offer price is below “Lower Limit Price Band” implying that the offer is not executable. If the attribute is ‘C’, then the bid and the offer are outside price band and the quote is not executable. Therefore, I drop quotes with attributes ‘A’,‘B’ and ‘C’.

6. The NBBO LULD Indicator represents the eﬀect that LULD Limit Price Band has on the NBB and the NBO. If it is blank, it means LULD is not applicable. If it is ‘A’, then NBBO and/or NBO are executable. If it is ‘B’, then NBB is below Lower Band, implying that NBB cannot be executed. If it is ’C’, then NBO is above Upper Band and NBO cannot be executed. If it is ‘D’ then NBB is below lower bound and NBO is above upper band, and both NBB and NBO cannot be executed. If it is ‘E’, then NBB is in Limit State as it equals the Upper Band. Likewise, if it is ‘F’, NBO is in Limit State as it equals Lower Band. If it is ‘G’, NBB is in limit state and NBO is non-execuatble because NBB equals Upper Band and NBO is above Upper Bound. If it is ‘H’, then NBB is non-execuatble and NBO is in limit state because NBB is below Lower Band and NBO equal Lower Band. If it is I, then NBB equals Upper Band and NBO equal the Lower Band, which implies it is ”crossed’ and not in the Limit State.

7. The quotes are originated either from CTA or UTP. In the Source of Quote column, ‘C’ indicates that the originating SIP for this message is CTA. Otherwise, if it is ‘N’, then it is UTP. If it is CTA, the column is quote condition have the following properties:

(a) If there is Space, then the quote condition does not apply.

52 (b) A implies that the quote is slow on the oﬀer side

(d) C implies that the quote is closing

(e) E implies that the quote is a ‘Slow Quote’ on the Bid due to LRP or GAP quote

(f) F implies that the quote is a ‘Slow Quote’ on the oﬀer befcause it is a LRP or GAP quote.

(g) H implies that the quote is a ’Slow Quote’ on both the Bid and the oﬀer sides

(h) L implies that the quote is a coled Market Maker in FINRA

(i) N implies that the quote is a Non-ﬁrm quote

(j) O implies that the quote is an opeing quote

(k) R rimplies that the quote is a two-sided open quote, and hence is a regular quote

(l) U plies that the quote is slow on the Bid and the Oﬀer Sides due to Liquidity Replenishment Point (LRP), or GAP quote

(m) W implies that the quoate is a Dlow quote due to a Set Slow list on the both the Bid and the Oﬀer sides

(n) 4 implies that the quote is on Demand Intra-Day Auction.

If the SIP-generated message originates from UTP, the quote condition are given by:

(a) A implies that Ask is manual while the Bid is automated.

(b) B implies that Bis is manual while the Ask is automated.

(d) H implies Manual Bid and Ask

(e) I implies Order imbalance

(f) L implies NBBO Closed Market Maker

53 (g) N implies that the quote is non-ﬁrm

(h) O implies that the opening quote is automated

(i) R implies NBBO is regular, two-sided quote

(j) U implies that the quote is manual Bid and manual Ask, that is, non-ﬁrm

(k) X implies that the quote is order inﬂux

(l) Y implies that the quote is NBBO regular, one-sided quote

(m) Z is non-open, no-resume quote 4 implies the quote is Intraday auction.

In light of the above explanation, I allow the ﬁlter of combination of 1) Source of Quote = C and Quote Condition = A, 2) Source of Quote = C and Quote Condition B 3) Source of Quote = C and Quote Condition H 4) Source of Quote = C and Quote Condition R 5)Source of Quote = C and Quote Condition W 6) Source of Quote = N and Quote Condition A 7)Source of Quote = N and Quote Condition B 8) Source of Quote = N and Quote Condition F 9) Source of Quote = N and Quote Condition H 10) Source of Quote = N and Quote Condition R 11) Source of Quote = N and Quote Condition O.

8. If the best bid price is greater than 150% or less than 50% of the previous bid price, I also drop the quote. The reason is that if the bid price sequence is abrupt, then it suggests some external intervention.

9. If the quoted spread is more than 25% of the previous quote, I also drop that. Quoted spread above 25% implies an unusual spread and hence would result in distorted calculation.

A.3 Trade Dataset Characteristics

In the raw data format of NBBO, the 15 columns (variables) are: ‘Time’,‘Exchange’,‘Symbol’,‘Sale Condition’,‘Trade Volume’,‘Trade Price’,‘Trade Stop Stock Indicator’,‘Trade Correction Indi-

54 cator’,‘Sequence Number’,‘Trade ID’,‘Source of Trade’,‘Trade Reporting Facility’,‘Participant Timestamp’,‘Trade Reporting Facility (TRF) Timestamp’, and ‘Trade Through Exempt In- dicator’. Since this dataset only reports all prevailing trades, I did not apply ﬁlters applied to the NBBO dataset.

A.4 BBO and NBBO: Deﬁnition

A.4.1 Best Bid and Oﬀer (BBO)

BBO stands for Best Bid and Offer. The best bid at a given time is the maximum of all bid across all exchanges. This persists until a new maximum value arises by comparing bids across all venues. According to Reg NMS: “Best bid and best offer mean the highest priced bid and the lowest priced offer” (SEC, 2005).

A.4.2 National Best Bid and Oﬀer (NBBO)

NBBO stands for National Best Bid and Offer. That is, of all bid or offer attempt, this represents the best among those prevailing ones. The relation between BBO and NBBO is that all entries in NBBO are subset of BBO as the meaning of ‘best’ underpins a comparison of different fields. This could be “as narrow as a single trading venue, but we are usually interested in a comprehensive set consisting of all venues where quotes are posted. In this connection, U.S. participants often refer to the National Best Bid and Offer (NBBO)” (Has- brouck, 2010). Regulation NMS also takes a similar position, where the national best bid and best offer are first “calculated and disseminated on a current and continuing basis by a plan processor pursuant to an effective national market system plan” (SEC, 2005). When two or more market centers or venues transmit to the plan processor according to such plan identical bids or offers for an NMS security, then the best bid or best offer is determined by first ranking all identical bids or offers “first by size (giving the highest ranking to the bid or offer associated with the largest size), and then by time (giving the highest ranking to the

55 bid or oﬀer received ﬁrst in time)” (SEC, 2005).

A.5 Code for MatchIt Package in R

This package is built upon the theoretical foundations of the paper by Ho et al. (2007). Following the code I employed using “nearest” neighborhood matching, a speciﬁc form of propensity score matching: install.packages(“MatchIt”) library(MatchIt) library(readxl) **The dataset mydata is imported.** m.out = matchit(x ∼ market cap+stock price,data = mydata, method = “nearest”, ratio = 1) View(m.out) summary(m.out) m.data <- match.data(m.out) write csv(m.data, path = “matched.txt”)