Pub Date : 2018-10-31DOI: 10.3905/JOT.2018.13.4.020
W. Wagner, Mark Edwards, S. Glass
Active investment management is in a fight for competitive survival. Excellent idea generation will succeed only if the process is implemented effectively. The markets are where “the rubber meets the road,” and effective trading forms the foundation for securing the benefits of excellent research and strategy.
{"title":"COMMENTARY: Commentary on “If Best Execution Is a Process, What Does That Process Look Like?”1","authors":"W. Wagner, Mark Edwards, S. Glass","doi":"10.3905/JOT.2018.13.4.020","DOIUrl":"https://doi.org/10.3905/JOT.2018.13.4.020","url":null,"abstract":"Active investment management is in a fight for competitive survival. Excellent idea generation will succeed only if the process is implemented effectively. The markets are where “the rubber meets the road,” and effective trading forms the foundation for securing the benefits of excellent research and strategy.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"100 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":"115543004","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.2018.13.4.099
James P. Selway
The author reviews the original article, “Five Myths about Listed Trading,” published in 2002, and provides three thoughts for consideration to today’s readers.
作者回顾了2002年发表的原文章《上市交易的五个神话》，并提供了三个想法供今天的读者参考。
{"title":"COMMENTARY: Space Unicorns and the Intermarket Trading System: Revisiting Myths","authors":"James P. Selway","doi":"10.3905/jot.2018.13.4.099","DOIUrl":"https://doi.org/10.3905/jot.2018.13.4.099","url":null,"abstract":"The author reviews the original article, “Five Myths about Listed Trading,” published in 2002, and provides three thoughts for consideration to today’s readers.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"25 1 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":"123220376","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.2018.13.4.082
Charles Polk, E. Schulman
Richard Roll observed that continuous markets are more volatile than other market structures. If it is true that continuous markets induce volatility, then unless we change that market structure, we will continue to be plagued with sporadic bursts of nonfunctional, uninformative volatility. This article looks to the underlying reasons and suggests a more serviceable market structure.
{"title":"Market Structure Matters","authors":"Charles Polk, E. Schulman","doi":"10.3905/jot.2018.13.4.082","DOIUrl":"https://doi.org/10.3905/jot.2018.13.4.082","url":null,"abstract":"Richard Roll observed that continuous markets are more volatile than other market structures. If it is true that continuous markets induce volatility, then unless we change that market structure, we will continue to be plagued with sporadic bursts of nonfunctional, uninformative volatility. This article looks to the underlying reasons and suggests a more serviceable market structure.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"33 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":"127329477","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.2018.13.4.130
Jeffrey M. Bacidore
Our paper on Cluster Analysis was inspired by our need to group client data by trading strategy, when the data we were provided did not contain any information on trading strategy whatsoever. We ended up relying on a well-known statistical technique, k-means, which surprisingly had not been used widely in trading applications. At the time, non-quant traders were still reluctant to use quantitative techniques, especially black box applications like k-means. Fortunately, a lot has changed since that time, as quants are now using much more sophisticated techniques, like deep learning. And even more important, non-quant traders and business leaders have become much more accepting of such techniques, making it easier for such advanced techniques to be incorporated into trading applications.
{"title":"COMMENTARY: Reflections on “Cluster Analysis for Evaluating Trading Strategies”","authors":"Jeffrey M. Bacidore","doi":"10.3905/jot.2018.13.4.130","DOIUrl":"https://doi.org/10.3905/jot.2018.13.4.130","url":null,"abstract":"Our paper on Cluster Analysis was inspired by our need to group client data by trading strategy, when the data we were provided did not contain any information on trading strategy whatsoever. We ended up relying on a well-known statistical technique, k-means, which surprisingly had not been used widely in trading applications. At the time, non-quant traders were still reluctant to use quantitative techniques, especially black box applications like k-means. Fortunately, a lot has changed since that time, as quants are now using much more sophisticated techniques, like deep learning. And even more important, non-quant traders and business leaders have become much more accepting of such techniques, making it easier for such advanced techniques to be incorporated into trading applications.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"31 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":"128780080","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.2018.13.4.107
V. Satish, Abhay Saxena, Max Palmer
This article discusses recent techniques and results in the area of forecasting intraday volume and intraday volume percentages. By exploring ways to predict volume, the authors seek to improve the performance of trading algorithms, many of which depend upon the volume that will trade while the order is active. Traditionally, algorithms use historical averages to predict volume over the lifetime of an order. The authors show that improving the prediction of volume boosts the performance of algorithms.
{"title":"Predicting Intraday Trading Volume and Volume Percentages","authors":"V. Satish, Abhay Saxena, Max Palmer","doi":"10.3905/jot.2018.13.4.107","DOIUrl":"https://doi.org/10.3905/jot.2018.13.4.107","url":null,"abstract":"This article discusses recent techniques and results in the area of forecasting intraday volume and intraday volume percentages. By exploring ways to predict volume, the authors seek to improve the performance of trading algorithms, many of which depend upon the volume that will trade while the order is active. Traditionally, algorithms use historical averages to predict volume over the lifetime of an order. The authors show that improving the prediction of volume boosts the performance of algorithms.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"46 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":"122134893","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.2018.13.4.014
Haim A. Mozes, John Launny Steffens
This article introduces a model for forecasting future volatility using fundamental factors, including the extent to which the market’s valuation deviates from its predicted value, the losses reported by companies with negative earnings, projected earnings growth rates, and Treasury bill rates. The main result is that fundamental factors provide significant incremental explanatory power for predicting volatility relative to that provided by past volatility realizations alone. The explanatory power of fundamental factors is greatest when the VIX Index is at moderate rather than extreme levels so there is no expectation of long-term mean reversion for volatility. In addition, the explanatory power of fundamental factors is greatest when the model forecasts an increase in VIX. The overall conclusion of this study is that forecasts of future volatility should incorporate fundamental factors.
{"title":"Using Fundamental Earnings Factors to Forecast Equity Market Volatility","authors":"Haim A. Mozes, John Launny Steffens","doi":"10.3905/jot.2018.13.4.014","DOIUrl":"https://doi.org/10.3905/jot.2018.13.4.014","url":null,"abstract":"This article introduces a model for forecasting future volatility using fundamental factors, including the extent to which the market’s valuation deviates from its predicted value, the losses reported by companies with negative earnings, projected earnings growth rates, and Treasury bill rates. The main result is that fundamental factors provide significant incremental explanatory power for predicting volatility relative to that provided by past volatility realizations alone. The explanatory power of fundamental factors is greatest when the VIX Index is at moderate rather than extreme levels so there is no expectation of long-term mean reversion for volatility. In addition, the explanatory power of fundamental factors is greatest when the model forecasts an increase in VIX. The overall conclusion of this study is that forecasts of future volatility should incorporate fundamental factors.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"1 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":"126146937","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.2018.13.4.027
R. Kissell
In this paper we revisit techniques from “Creating Dynamic Pre-Trade Models: Beyond the Black Box” (Kissell, 2011) which was awarded The Journal of Trading’s Best Paper of the Year Award in 2011. We provide investors a pre-trade of pre-trade modeling technique that can be used to decipher broker and vendor models, and to calibrate a customized investor specific market impact model. We also provide a suite of Excel TCA Add-In functions that can incorporate investor specific market impact parameters and allow investors to perform TCA analysis on their own desktops within Excel, and with the added level of security and comfort that their investment decision process will not be reverse engineered because they do not need to upload or transmit any of their proprietary information and valuable trade information to a third-party website or API for analysis. Techniques in this paper enable investors to create their own customized TCA analyses within Excel to assist with both trading decisions and portfolio analysis and optimization.
在本文中，我们回顾了“创建动态交易前模型:超越黑箱”(Kissell, 2011)中的技术，该论文获得了2011年the Journal of Trading的年度最佳论文奖。我们为投资者提供交易前的交易前建模技术，可用于破译经纪人和供应商模型，并校准定制的投资者特定的市场影响模型。我们还提供了一套Excel TCA插件功能，可以纳入投资者特定的市场影响参数，并允许投资者在Excel中自己的桌面上执行TCA分析，并且增加了安全性和舒适性，他们的投资决策过程不会被逆向工程，因为他们不需要上传或传输任何专有信息和有价值的交易信息到第三方网站或API进行分析。本文中的技术使投资者能够在Excel中创建自己定制的TCA分析，以协助交易决策和投资组合分析和优化。
{"title":"COMMENTARY: Beyond the Black Box Revisited: Algorithmic Trading and TCA Analysis Using Excel","authors":"R. Kissell","doi":"10.3905/jot.2018.13.4.027","DOIUrl":"https://doi.org/10.3905/jot.2018.13.4.027","url":null,"abstract":"In this paper we revisit techniques from “Creating Dynamic Pre-Trade Models: Beyond the Black Box” (Kissell, 2011) which was awarded The Journal of Trading’s Best Paper of the Year Award in 2011. We provide investors a pre-trade of pre-trade modeling technique that can be used to decipher broker and vendor models, and to calibrate a customized investor specific market impact model. We also provide a suite of Excel TCA Add-In functions that can incorporate investor specific market impact parameters and allow investors to perform TCA analysis on their own desktops within Excel, and with the added level of security and comfort that their investment decision process will not be reverse engineered because they do not need to upload or transmit any of their proprietary information and valuable trade information to a third-party website or API for analysis. Techniques in this paper enable investors to create their own customized TCA analyses within Excel to assist with both trading decisions and portfolio analysis and optimization.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"1 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":"121049952","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.2018.13.4.001
Brian R. Bruce
{"title":"Editor’s Letter","authors":"Brian R. Bruce","doi":"10.3905/jot.2018.13.4.001","DOIUrl":"https://doi.org/10.3905/jot.2018.13.4.001","url":null,"abstract":"","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"184 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":"115551543","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.2018.13.4.010
Haim A. Mozes, John Launny Steffens
This paper provides a perspective on volatility forecasting. The basic idea is that a number of factors are leading to volatility having a lower baseline expected value than in prior years. These factors include lower earnings uncertainty, greater market efficiency, better market-marking, and the fact that volatility trading itself tends to reduce volatility.
{"title":"COMMENTARY: Volatility Forecasting","authors":"Haim A. Mozes, John Launny Steffens","doi":"10.3905/jot.2018.13.4.010","DOIUrl":"https://doi.org/10.3905/jot.2018.13.4.010","url":null,"abstract":"This paper provides a perspective on volatility forecasting. The basic idea is that a number of factors are leading to volatility having a lower baseline expected value than in prior years. These factors include lower earnings uncertainty, greater market efficiency, better market-marking, and the fact that volatility trading itself tends to reduce volatility.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"17 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":"131566165","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.2018.13.4.071
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":"COMMENTARY: Dark Pools, Fragmented Markets, and the Quality of Price Discovery: Commentary","authors":"R. A. Schwartz","doi":"10.3905/JOT.2018.13.4.071","DOIUrl":"https://doi.org/10.3905/JOT.2018.13.4.071","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":"60 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":"134531355","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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