{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101451"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101451","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Essays on stock exchanges speed competition, designs and high-frequency trading","abstract":"The first chapter shows that a key driver of stock exchanges' competition on order-processing speeds is the Order Protection Rule, which requires an exchange to route its customers' orders to other exchanges with better prices. Faster exchanges attract more price-improving limit orders because the probability of being bypassed by trades with inferior prices on other exchanges is reduced. When all exchanges speed up, this probability can increase, potentially harming the welfare of investors. In contrast, increasing connection speeds between exchanges raises investor welfare by reducing this probability. Nevertheless, no exchange wants to improve connection speeds because this will reduce its trading volume. I provide empirical evidence showing that slow exchanges lose trading volume to fast exchanges as the latter attract more price-improving orders. I first show that a slow exchange's (IEX) market share of trading volume in stocks with a five-cent tick, the minimum price movement, increases by 13 percent relative to one-cent tick stocks after the introduction of Tick Size Pilot Program in 2016, because price improving is less likely with larger tick size. I then show that after switching from a dark pool to a public exchange, IEX attracts more trading volume in stocks that are more likely to have one tick bid-ask spread as price improving is impossible with binding spread. To reduce high-frequency trader's speed advantage, new stock exchange designs such as frequent batch auctions and several order delay designs have been proposed to slow down trading speed and eliminate the speed arms race among high-frequency traders. In the second chapter, I investigate how newly designed exchanges with these speed bump features would compete against traditional exchanges. I find that among order delay proposals, the most effective design is to delay only liquidity taking orders as proposed by the Chicago Stock Exchange. Frequent batch auctions are shown not to improve liquidity when the degree of private information is high enough. Moreover, when frequent batch auctions are implemented, exchanges have incentives to compete on the frequency at which batch auctions take place. Finally, I show that even when sniping is a significant problem for some stocks, exchanges with large market share of total trading volume may lack incentives to implement frequent batch auctions or order delays even when these innovative designs could improve long-term investor welfare. Therefore, the interests of exchanges may not be aligned with those of long-term investors with regard to how they value designs that alleviate sniping. In the last chapter, I incorporate discrete tick size and allow non-high-frequency traders (non-HFTs) to supply liquidity in the framework of Budish et al. (2015). When adverse selection risk is low or tick size is large, the bid-ask spread is typically below one tick, and HFTs dominate liquidity supply. In other situations, non-HFTs dominate liquidity supply by undercutting HFTs, because supplying liquidity to HFTs is always less costly than demanding liquidity from HFTs. A small tick size improves liquidity, but also leads to more mini-flash crashes. The cancellation-to-trade ratio, a popular proxy for HFTs, can have a negative correlation with HFTs' activity.","abstract_html":"The first chapter shows that a key driver of stock exchanges' competition on order-processing speeds is the Order Protection Rule, which requires an exchange to route its customers' orders to other exchanges with better prices. Faster exchanges attract more price-improving limit orders because the probability of being bypassed by trades with inferior prices on other exchanges is reduced. When all exchanges speed up, this probability can increase, potentially harming the welfare of investors. In contrast, increasing connection speeds between exchanges raises investor welfare by reducing this probability. Nevertheless, no exchange wants to improve connection speeds because this will reduce its trading volume. I provide empirical evidence showing that slow exchanges lose trading volume to fast exchanges as the latter attract more price-improving orders. I first show that a slow exchange's (IEX) market share of trading volume in stocks with a five-cent tick, the minimum price movement, increases by 13 percent relative to one-cent tick stocks after the introduction of Tick Size Pilot Program in 2016, because price improving is less likely with larger tick size. I then show that after switching from a dark pool to a public exchange, IEX attracts more trading volume in stocks that are more likely to have one tick bid-ask spread as price improving is impossible with binding spread. To reduce high-frequency trader's speed advantage, new stock exchange designs such as frequent batch auctions and several order delay designs have been proposed to slow down trading speed and eliminate the speed arms race among high-frequency traders. In the second chapter, I investigate how newly designed exchanges with these speed bump features would compete against traditional exchanges. I find that among order delay proposals, the most effective design is to delay only liquidity taking orders as proposed by the Chicago Stock Exchange. Frequent batch auctions are shown not to improve liquidity when the degree of private information is high enough. Moreover, when frequent batch auctions are implemented, exchanges have incentives to compete on the frequency at which batch auctions take place. Finally, I show that even when sniping is a significant problem for some stocks, exchanges with large market share of total trading volume may lack incentives to implement frequent batch auctions or order delays even when these innovative designs could improve long-term investor welfare. Therefore, the interests of exchanges may not be aligned with those of long-term investors with regard to how they value designs that alleviate sniping. In the last chapter, I incorporate discrete tick size and allow non-high-frequency traders (non-HFTs) to supply liquidity in the framework of Budish et al. (2015). When adverse selection risk is low or tick size is large, the bid-ask spread is typically below one tick, and HFTs dominate liquidity supply. In other situations, non-HFTs dominate liquidity supply by undercutting HFTs, because supplying liquidity to HFTs is always less costly than demanding liquidity from HFTs. A small tick size improves liquidity, but also leads to more mini-flash crashes. The cancellation-to-trade ratio, a popular proxy for HFTs, can have a negative correlation with HFTs' activity.","abstract_has_math":false,"creators":["Wang, Xin"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Economics","degree_department":null,"school":null,"contributors":["Bernhardt, Dan","Ye, Mao","Pearson, Neil","Kahn, Charles"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-27T16:11:05Z","date_published":"2018-09-27T16:11:05Z","updated_at":"2026-07-22T22:24:40Z","subjects":["Exchange Speed","High-frequency Trading","Order Protection Rule","Frequent Batch Auctions","Order Delay"],"languages":["en"],"rights":["Copyright 2018 Xin Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101451","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bernhardt, Dan","Ye, Mao","Pearson, Neil","Kahn, Charles"]},{"key":"dc:creator","label":"Author","values":["Wang, Xin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-27T16:11:05Z","2018-04-18","2018-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Economics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Exchange Speed","High-frequency Trading","Order Protection Rule","Frequent Batch Auctions","Order Delay"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Xin Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101451"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The first chapter shows that a key driver of stock exchanges' competition on order-processing speeds is the Order Protection Rule, which requires an exchange to route its customers' orders to other exchanges with better prices. Faster exchanges attract more price-improving limit orders because the probability of being bypassed by trades with inferior prices on other exchanges is reduced. When all exchanges speed up, this probability can increase, potentially harming the welfare of investors. In contrast, increasing connection speeds between exchanges raises investor welfare by reducing this probability. Nevertheless, no exchange wants to improve connection speeds because this will reduce its trading volume. I provide empirical evidence showing that slow exchanges lose trading volume to fast exchanges as the latter attract more price-improving orders. I first show that a slow exchange's (IEX) market share of trading volume in stocks with a five-cent tick, the minimum price movement, increases by 13 percent relative to one-cent tick stocks after the introduction of Tick Size Pilot Program in 2016, because price improving is less likely with larger tick size. I then show that after switching from a dark pool to a public exchange, IEX attracts more trading volume in stocks that are more likely to have one tick bid-ask spread as price improving is impossible with binding spread. To reduce high-frequency trader's speed advantage, new stock exchange designs such as frequent batch auctions and several order delay designs have been proposed to slow down trading speed and eliminate the speed arms race among high-frequency traders. In the second chapter, I investigate how newly designed exchanges with these speed bump features would compete against traditional exchanges. I find that among order delay proposals, the most effective design is to delay only liquidity taking orders as proposed by the Chicago Stock Exchange. Frequent batch auctions are shown not to improve liquidity when the degree of private information is high enough. Moreover, when frequent batch auctions are implemented, exchanges have incentives to compete on the frequency at which batch auctions take place. Finally, I show that even when sniping is a significant problem for some stocks, exchanges with large market share of total trading volume may lack incentives to implement frequent batch auctions or order delays even when these innovative designs could improve long-term investor welfare. Therefore, the interests of exchanges may not be aligned with those of long-term investors with regard to how they value designs that alleviate sniping. In the last chapter, I incorporate discrete tick size and allow non-high-frequency traders (non-HFTs) to supply liquidity in the framework of Budish et al. (2015). When adverse selection risk is low or tick size is large, the bid-ask spread is typically below one tick, and HFTs dominate liquidity supply. In other situations, non-HFTs dominate liquidity supply by undercutting HFTs, because supplying liquidity to HFTs is always less costly than demanding liquidity from HFTs. A small tick size improves liquidity, but also leads to more mini-flash crashes. The cancellation-to-trade ratio, a popular proxy for HFTs, can have a negative correlation with HFTs' activity.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-09-27 without embargo terms","The student, Xin Wang, accepted the attached license on 2018-04-18 at 06:17.","The student, Xin Wang, submitted this Dissertation for approval on 2018-04-18 at 06:43.","This Dissertation was approved for publication on 2018-04-18 at 14:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12333 on 2018-09-27 at 10:42:53","Made available in DSpace on 2018-09-27T16:11:05Z (GMT). No. of bitstreams: 2 WANG-DISSERTATION-2018.pdf: 1814179 bytes, checksum: e54657cd2e9ce0fa9154eb0b5e7023c5 (MD5) LICENSE.txt: 4205 bytes, checksum: fe3a482fac7baedb47661f6b26d4dc5f (MD5) Previous issue date: 2018-04-18"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Essays on stock exchanges speed competition, designs and high-frequency trading"]}]}],"canonical_facts":{"dc:contributor":["Bernhardt, Dan","Ye, Mao","Pearson, Neil","Kahn, Charles"],"dc:creator":["Wang, Xin"],"dc:date":["2018-09-27T16:11:05Z","2018-04-18","2018-08"],"dc:description":["The first chapter shows that a key driver of stock exchanges' competition on order-processing speeds is the Order Protection Rule, which requires an exchange to route its customers' orders to other exchanges with better prices. Faster exchanges attract more price-improving limit orders because the probability of being bypassed by trades with inferior prices on other exchanges is reduced. When all exchanges speed up, this probability can increase, potentially harming the welfare of investors. In contrast, increasing connection speeds between exchanges raises investor welfare by reducing this probability. Nevertheless, no exchange wants to improve connection speeds because this will reduce its trading volume. I provide empirical evidence showing that slow exchanges lose trading volume to fast exchanges as the latter attract more price-improving orders. I first show that a slow exchange's (IEX) market share of trading volume in stocks with a five-cent tick, the minimum price movement, increases by 13 percent relative to one-cent tick stocks after the introduction of Tick Size Pilot Program in 2016, because price improving is less likely with larger tick size. I then show that after switching from a dark pool to a public exchange, IEX attracts more trading volume in stocks that are more likely to have one tick bid-ask spread as price improving is impossible with binding spread. To reduce high-frequency trader's speed advantage, new stock exchange designs such as frequent batch auctions and several order delay designs have been proposed to slow down trading speed and eliminate the speed arms race among high-frequency traders. In the second chapter, I investigate how newly designed exchanges with these speed bump features would compete against traditional exchanges. I find that among order delay proposals, the most effective design is to delay only liquidity taking orders as proposed by the Chicago Stock Exchange. Frequent batch auctions are shown not to improve liquidity when the degree of private information is high enough. Moreover, when frequent batch auctions are implemented, exchanges have incentives to compete on the frequency at which batch auctions take place. Finally, I show that even when sniping is a significant problem for some stocks, exchanges with large market share of total trading volume may lack incentives to implement frequent batch auctions or order delays even when these innovative designs could improve long-term investor welfare. Therefore, the interests of exchanges may not be aligned with those of long-term investors with regard to how they value designs that alleviate sniping. In the last chapter, I incorporate discrete tick size and allow non-high-frequency traders (non-HFTs) to supply liquidity in the framework of Budish et al. (2015). When adverse selection risk is low or tick size is large, the bid-ask spread is typically below one tick, and HFTs dominate liquidity supply. In other situations, non-HFTs dominate liquidity supply by undercutting HFTs, because supplying liquidity to HFTs is always less costly than demanding liquidity from HFTs. A small tick size improves liquidity, but also leads to more mini-flash crashes. The cancellation-to-trade ratio, a popular proxy for HFTs, can have a negative correlation with HFTs' activity.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-09-27 without embargo terms","The student, Xin Wang, accepted the attached license on 2018-04-18 at 06:17.","The student, Xin Wang, submitted this Dissertation for approval on 2018-04-18 at 06:43.","This Dissertation was approved for publication on 2018-04-18 at 14:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12333 on 2018-09-27 at 10:42:53","Made available in DSpace on 2018-09-27T16:11:05Z (GMT). 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