Pub Date : 2018-10-31DOI: 10.3905/jot.2018.13.4.119
J. Blocher, Ricky Cooper, J. Seddon, Ben Van Vliet
This article examines every NASDAQ ITCH feed message for S&P 500 Index stocks for 2012 and identifies clusters of extremely high and extremely low limit-order cancellation activity. The authors find results consistent with the idea that cancel clusters are the result of high-frequency traders jockeying for queue position and reacting to information to establish a new price level. Furthermore, few trades seem to be executed during cancel clusters or even immediately after them. Low cancellation activity seems to be markedly different, with many level changes all caused by executions. The results are consistent with high-frequency trading firms behaving as agents who bring efficiency to the market without the need to have executions at intermediate prices. The authors also discuss the misconception that investors and low-frequency traders are synonymous and its implications for policy given these results.
{"title":"Phantom Liquidity and High-Frequency Quoting","authors":"J. Blocher, Ricky Cooper, J. Seddon, Ben Van Vliet","doi":"10.3905/jot.2018.13.4.119","DOIUrl":"https://doi.org/10.3905/jot.2018.13.4.119","url":null,"abstract":"This article examines every NASDAQ ITCH feed message for S&P 500 Index stocks for 2012 and identifies clusters of extremely high and extremely low limit-order cancellation activity. The authors find results consistent with the idea that cancel clusters are the result of high-frequency traders jockeying for queue position and reacting to information to establish a new price level. Furthermore, few trades seem to be executed during cancel clusters or even immediately after them. Low cancellation activity seems to be markedly different, with many level changes all caused by executions. The results are consistent with high-frequency trading firms behaving as agents who bring efficiency to the market without the need to have executions at intermediate prices. The authors also discuss the misconception that investors and low-frequency traders are synonymous and its implications for policy given these results.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114818739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-31DOI: 10.3905/jot.2018.13.4.074
R. A. Schwartz
This commentary is on a paper published in 2010. Few would wish to roll the markets back to where they were eight years ago, but have the issues that were debated then been adequately resolved? Are today’s markets acceptably efficient? Can we relax about market quality? My answer to each of these is “no.” What I wrote in 2010, I stand by now. Along with revisiting my previous discussion on dark pools, fragmentation, price discovery, and liquidity, this commentary presents my newer thoughts concerning the definition of the term “liquidity,” and the existence of an illiquidity premium.
{"title":"Dark Pools, Fragmented Markets, and the Quality of Price Discovery","authors":"R. A. Schwartz","doi":"10.3905/jot.2018.13.4.074","DOIUrl":"https://doi.org/10.3905/jot.2018.13.4.074","url":null,"abstract":"This commentary is on a paper published in 2010. Few would wish to roll the markets back to where they were eight years ago, but have the issues that were debated then been adequately resolved? Are today’s markets acceptably efficient? Can we relax about market quality? My answer to each of these is “no.” What I wrote in 2010, I stand by now. Along with revisiting my previous discussion on dark pools, fragmentation, price discovery, and liquidity, this commentary presents my newer thoughts concerning the definition of the term “liquidity,” and the existence of an illiquidity premium.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130436566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-31DOI: 10.3905/JOT.2018.13.4.085
Vlad Rashkovich
Since the introduction of Trader Alpha Frontier, this framework has been adopted by asset managers of all sizes, to monitor their trading performance. The next logical step is for Chief Investment Officers to incorporate Trader Alpha Frontier into their main view of portfolio returns. The author visualizes how CIOs can get a full insight in all alpha sources throughout the investment value chain including Analysts, Portfolio Managers, Traders, and Brokers.
{"title":"COMMENTARY: Chief Investment Officer (CIO) View of Trader Alpha Frontier (TAF)","authors":"Vlad Rashkovich","doi":"10.3905/JOT.2018.13.4.085","DOIUrl":"https://doi.org/10.3905/JOT.2018.13.4.085","url":null,"abstract":"Since the introduction of Trader Alpha Frontier, this framework has been adopted by asset managers of all sizes, to monitor their trading performance. The next logical step is for Chief Investment Officers to incorporate Trader Alpha Frontier into their main view of portfolio returns. The author visualizes how CIOs can get a full insight in all alpha sources throughout the investment value chain including Analysts, Portfolio Managers, Traders, and Brokers.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130340608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-31DOI: 10.3905/JOT.2018.13.4.080
Charles Polk, E. Schulman
Trading “these” securities for “those” (portfolio trades) can be expensive if done through our current continuous markets. This article compares a broker-implemented blind bid solution to this problem in a continuous market setting versus a combined value computerized call market that maximizes available liquidity to create balanced trades between such lists. The technology is known: combined value markets are in use today servicing markets in logistics contracts, emissions permits, spectrum licenses, and aerospace procurement. Should not financial concerns, such as custodial banks, be currently offering such services to their clients?
{"title":"COMMENTARY: A Market Structure That Fits the Needs of Portfolio Managers","authors":"Charles Polk, E. Schulman","doi":"10.3905/JOT.2018.13.4.080","DOIUrl":"https://doi.org/10.3905/JOT.2018.13.4.080","url":null,"abstract":"Trading “these” securities for “those” (portfolio trades) can be expensive if done through our current continuous markets. This article compares a broker-implemented blind bid solution to this problem in a continuous market setting versus a combined value computerized call market that maximizes available liquidity to create balanced trades between such lists. The technology is known: combined value markets are in use today servicing markets in logistics contracts, emissions permits, spectrum licenses, and aerospace procurement. Should not financial concerns, such as custodial banks, be currently offering such services to their clients?","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"45 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120819969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-31DOI: 10.3905/jot.2018.13.4.138
R. Kissell, Jungsun “Sunny” Bae
In this paper we present a machine learning technique that can be used in conjunction with multi-period trade schedule optimization used in program trading. The technique is based on an artificial neural network (ANN) model that determines a better starting solution for the non-linear optimization routine. This technique provides calculation time improvements that are 30% faster for small baskets (n = 10 stocks), 50% faster for baskets of (n = 100 stocks) and up to 70% faster for large baskets (n ≥ 300 stocks). Unlike many of the industry approaches that use heuristics and numerical approximation, our machine learning approach solves for the exact problem and provides a dramatic improvement in calculation time.
{"title":"Machine Learning for Algorithmic Trading and Trade Schedule Optimization","authors":"R. Kissell, Jungsun “Sunny” Bae","doi":"10.3905/jot.2018.13.4.138","DOIUrl":"https://doi.org/10.3905/jot.2018.13.4.138","url":null,"abstract":"In this paper we present a machine learning technique that can be used in conjunction with multi-period trade schedule optimization used in program trading. The technique is based on an artificial neural network (ANN) model that determines a better starting solution for the non-linear optimization routine. This technique provides calculation time improvements that are 30% faster for small baskets (n = 10 stocks), 50% faster for baskets of (n = 100 stocks) and up to 70% faster for large baskets (n ≥ 300 stocks). Unlike many of the industry approaches that use heuristics and numerical approximation, our machine learning approach solves for the exact problem and provides a dramatic improvement in calculation time.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"38 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117007538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-31DOI: 10.3905/JOT.2018.13.4.117
J. Blocher, Ricky Cooper, J. Seddon, Ben Van Vliet
In this paper we take a retrospective look at our paper “Phantom Liquidity and High-Frequency Quoting” and discuss the context of the research in light of our broader inquiry into the nature of the high-frequency trading industry. The data presented in this paper appear to show that limit order cancellations of high-frequency traders are associated with price discovery and liquidity provision, rather than some manner of systematic taking advantage of other market participants. These firms are acting as rational, profit-seeking businesses, and we believe time has shown this view to be correct. In the years since publication, HFT has matured, and consolidated into fewer, lower-cost providers of efficiency and liquidity services, much like we would expect in any other industry.
{"title":"COMMENTARY: A Retrospective Look: Phantom Liquidity and High-Frequency Quoting","authors":"J. Blocher, Ricky Cooper, J. Seddon, Ben Van Vliet","doi":"10.3905/JOT.2018.13.4.117","DOIUrl":"https://doi.org/10.3905/JOT.2018.13.4.117","url":null,"abstract":"In this paper we take a retrospective look at our paper “Phantom Liquidity and High-Frequency Quoting” and discuss the context of the research in light of our broader inquiry into the nature of the high-frequency trading industry. The data presented in this paper appear to show that limit order cancellations of high-frequency traders are associated with price discovery and liquidity provision, rather than some manner of systematic taking advantage of other market participants. These firms are acting as rational, profit-seeking businesses, and we believe time has shown this view to be correct. In the years since publication, HFT has matured, and consolidated into fewer, lower-cost providers of efficiency and liquidity services, much like we would expect in any other industry.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130920317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-31DOI: 10.3905/jot.2018.13.4.049
Rune Tevasvold Aune, Adam Krellenstein, Maureen O'Hara, Ouziel Slama
This article examines information leakage when trading in distributed ledgers. We show how the lack of time priority in the period between the publication of a transaction and its validation by miners or designated participants can expose a transaction’s footprint to the market, resulting in potential front-running and manipulation. We propose a cryptographic approach for solving information leakage problems in distributed ledgers that relies on using a hash (or fingerprint) to secure time priority, followed by a second communication that reveals more features of the underlying market transaction—in effect using a transaction’s fingerprint to hide its footprint. Solving the information leakage problem greatly expands the potential applications of private distributed ledger technology to include trading.
{"title":"Footprints on a Blockchain: Trading and Information Leakage in Distributed Ledgers","authors":"Rune Tevasvold Aune, Adam Krellenstein, Maureen O'Hara, Ouziel Slama","doi":"10.3905/jot.2018.13.4.049","DOIUrl":"https://doi.org/10.3905/jot.2018.13.4.049","url":null,"abstract":"This article examines information leakage when trading in distributed ledgers. We show how the lack of time priority in the period between the publication of a transaction and its validation by miners or designated participants can expose a transaction’s footprint to the market, resulting in potential front-running and manipulation. We propose a cryptographic approach for solving information leakage problems in distributed ledgers that relies on using a hash (or fingerprint) to secure time priority, followed by a second communication that reveals more features of the underlying market transaction—in effect using a transaction’s fingerprint to hide its footprint. Solving the information leakage problem greatly expands the potential applications of private distributed ledger technology to include trading.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"69 5-6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132364500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The authors examine their 2014 publication “Predicting Intraday Trading Volume and Volume Percentages” and discuss subsequent changes in trading that validated the models outlined in the paper and prompted updates. The original models accommodate the general shift to passive investing and the trend toward ETF investing. Analyzing imbalance information has become more important to institutional traders as relative participation in closing auctions has increased. The authors discuss the evolution of analytical software platforms since the paper and outline expected trends in both volume forecasting and trading analytics. A major application of enhanced volume forecasts relates to the trend of buy-side clients performing scientific experiments to select algorithms and inform parameter selection. Specifically, volume profile error, a metric examined in the paper, provides context to compare broker algorithm performance and real-time volume forecasts can be used in algorithm routing decisions.
{"title":"COMMENTARY: Trends in Volume Forecasting: Developments and Applications","authors":"V. Satish, Max Palmer, Abhay Saxena","doi":"10.3905/JOT.2018.1.066","DOIUrl":"https://doi.org/10.3905/JOT.2018.1.066","url":null,"abstract":"The authors examine their 2014 publication “Predicting Intraday Trading Volume and Volume Percentages” and discuss subsequent changes in trading that validated the models outlined in the paper and prompted updates. The original models accommodate the general shift to passive investing and the trend toward ETF investing. Analyzing imbalance information has become more important to institutional traders as relative participation in closing auctions has increased. The authors discuss the evolution of analytical software platforms since the paper and outline expected trends in both volume forecasting and trading analytics. A major application of enhanced volume forecasts relates to the trend of buy-side clients performing scientific experiments to select algorithms and inform parameter selection. Specifically, volume profile error, a metric examined in the paper, provides context to compare broker algorithm performance and real-time volume forecasts can be used in algorithm routing decisions.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130807365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In our original JOT paper, we described a logical approach to developing and implementing an intraday intrinsic value estimate. The approach is “bottoms up” or bond-by-bond, based on adjustments to previous quotes or trade prices for subsequent movements in the individual bond’s yield curve plus an adjustment for changes in the credit spread. Adding in accrued interest and the fund’s cash, we can then derive a portfolio level estimate of the fund’s value. In this retrospective piece, we (1) provide some new evidence about the applications of our approach; and (2) further examine the possibility that the industry coalesce around improving iNAV to reach an industry standard calculation for ETF Intrinsic Value that adjusts for staleness, as proposed in our Journal of Trading article.
{"title":"COMMENTARY: Retrospective: “Toward Greater Transparency and Efficiency in Trading Fixed-Income ETF Portfolios”","authors":"Ananth Madhavan, Stephen Laipply, A. Sobczyk","doi":"10.3905/jot.2018.1.065","DOIUrl":"https://doi.org/10.3905/jot.2018.1.065","url":null,"abstract":"In our original JOT paper, we described a logical approach to developing and implementing an intraday intrinsic value estimate. The approach is “bottoms up” or bond-by-bond, based on adjustments to previous quotes or trade prices for subsequent movements in the individual bond’s yield curve plus an adjustment for changes in the credit spread. Adding in accrued interest and the fund’s cash, we can then derive a portfolio level estimate of the fund’s value. In this retrospective piece, we (1) provide some new evidence about the applications of our approach; and (2) further examine the possibility that the industry coalesce around improving iNAV to reach an industry standard calculation for ETF Intrinsic Value that adjusts for staleness, as proposed in our Journal of Trading article.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128626942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The CFA Institute defines Best Execution for securities trading as a process, not an outcome. For many, this is a disquieting definition, for it does not lead to easy yes/no answers. Instead, it leads to an active modeling and analysis of what goes into trading. We apply the work of Peter Drucker to the execution process, with an emphasis on: 1] establishing goals (typically to increase returns by lowering costs), 2] defining the process (including the roles of the portfolio manager, broker, and commission directing clients), 3] analyzing the data (measuring costs, but with context) to identify problems, 4] proposing solutions. This is not a simple exercise, for the process is both complex and filled with nuance. But it takes the random element out of the measurement of best execution. More importantly, it also leads to improved results over time.
{"title":"If Best Execution Is a Process, What Does That Process Look Like?","authors":"W. Wagner, Mark Edwards","doi":"10.3905/JOT.V14I4.5157","DOIUrl":"https://doi.org/10.3905/JOT.V14I4.5157","url":null,"abstract":"The CFA Institute defines Best Execution for securities trading as a process, not an outcome. For many, this is a disquieting definition, for it does not lead to easy yes/no answers. Instead, it leads to an active modeling and analysis of what goes into trading. We apply the work of Peter Drucker to the execution process, with an emphasis on: 1] establishing goals (typically to increase returns by lowering costs), 2] defining the process (including the roles of the portfolio manager, broker, and commission directing clients), 3] analyzing the data (measuring costs, but with context) to identify problems, 4] proposing solutions. This is not a simple exercise, for the process is both complex and filled with nuance. But it takes the random element out of the measurement of best execution. More importantly, it also leads to improved results over time.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114797294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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