{"title":"Editor’s Letter","authors":"Brian R. Bruce","doi":"10.3905/jot.2017.12.2.001","DOIUrl":"https://doi.org/10.3905/jot.2017.12.2.001","url":null,"abstract":"","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124785475","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}
{"title":"Comparing Transition Track Records: An Attempt to Create Like-for-Like Comparisons","authors":"Ramon Tol","doi":"10.3905/jot.2017.12.2.044","DOIUrl":"https://doi.org/10.3905/jot.2017.12.2.044","url":null,"abstract":"This article discusses the issues one encounters when comparing transition track records. Different transition benchmarks, different (sub)asset class classifications, fair versus unfair, and differences in pretrade assumptions and client instructions affect transition composites/track records and make reliable comparison difficult. To remove the need to subdivide transitions and simultaneously address the problem of insufficient transition events, one can apply an independent sophisticated post-trade algorithm. Such an algorithm takes into account the differences in market volatility, liquidity, and price movements of each security during the transition. An algorithm basically normalizes the multiple variations of portfolio transitions so they can legitimately be compared against each other in a broader universe.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115939708","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}
{"title":"Conditional Probabilistic Analysis of Trade Ticks in Currency Derivatives Markets","authors":"Gaurav Raizada, S. N. Nageswara Rao","doi":"10.3905/jot.2017.12.2.050","DOIUrl":"https://doi.org/10.3905/jot.2017.12.2.050","url":null,"abstract":"By examining the conditional probability structure of the price returns in USD/INR currency futures and options across Indian exchanges (the National Stock Exchange, the Bombay Stock Exchange, and the Metropolitan Stock Exchange of India), a higher degree of mean reversion is observed for an aggregated trade set of exchanges when compared to individual exchanges. Trade tick data for the exchanges, independently and aggregated, for a period of one month (July 2015) is examined. We show that the probability of mean reversion is higher at the aggregated level than at individual exchanges, and currency options contain valuable information regarding currency futures price movements. The understanding of the price movement is of great importance to high-frequency traders and institutional players. This study provides evidence that any trend analysis, whether reversion to mean or trend continuation, should be examined along with group of related instruments and exchanges.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116416391","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}
{"title":"Microstructure under the Microscope: Tools to Survive and Thrive in the Age of (Too Much) Information","authors":"R. Kashyap","doi":"10.3905/jot.2017.12.2.005","DOIUrl":"https://doi.org/10.3905/jot.2017.12.2.005","url":null,"abstract":"In this age of (too much) information, it is imperative to uncover nuggets of knowledge (signal) from buckets of nonsense (noise). To aid in this effort to extract meaning from chaos and gain a better understanding of the relationships between financial variables, we summarize the application of Kashyap’s 2016 theoretical results to microstructure studies. The central concept rests on a novel methodology based on the marriage between the Bhattacharyya distance, a measure of similarity across distributions, and the Johnson Lindenstrauss lemma, a technique for dimension reduction, providing us with a simple yet powerful tool that allows comparisons between datasets representing any two distributions. We provide an empirical illustration using prices, volumes, and volatilities across seven countries and three continents. The degree to which different markets or subgroups of securities have different measures of their corresponding distributions tells us the extent to which they are different. This can aid investors who are looking for diversification or looking for more of the same thing.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"25 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123246436","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}
{"title":"Editor’s Letter","authors":"Brian R. Bruce","doi":"10.3905/jot.2017.12.1.001","DOIUrl":"https://doi.org/10.3905/jot.2017.12.1.001","url":null,"abstract":"","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122061194","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}
{"title":"Transaction Cost Analysis with Excel and MATLAB","authors":"R. Kissell, Nina Zhang","doi":"10.3905/jot.2017.12.1.076","DOIUrl":"https://doi.org/10.3905/jot.2017.12.1.076","url":null,"abstract":"In this article, the authors present a pretrade model and transaction cost functions that can be run in Excel and MATLAB. Currently, investors who use pretrade transaction cost analytics do so by logging into a broker/dealer or third-party server. These systems require investors to upload their trade data, portfolio holdings, and/or investment ideas for analysis. This process could, however, subject the fund to information leakage and result in higher trading costs and lower returns. Furthermore, these systems do not allow managers to incorporate their proprietary data, such as alpha estimates. The authors’ pretrade analysis functions provide investors with the ability to run analyses on their own desktop, independent of broker/dealer and third-party servers, and allow them to incorporate their own market expectations and alpha return estimates. Their process helps to preserve the funds’ valuable proprietary research and eliminate information leakage.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124818589","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}
P. Gomber, B. Clapham, Martin Haferkorn, Sven Panz, P. Jentsch
{"title":"Ensuring Market Integrity and Stability: Circuit Breakers on International Trading Venues","authors":"P. Gomber, B. Clapham, Martin Haferkorn, Sven Panz, P. Jentsch","doi":"10.3905/jot.2017.12.1.042","DOIUrl":"https://doi.org/10.3905/jot.2017.12.1.042","url":null,"abstract":"Circuit breakers are important mechanisms used to prevent excess short-term volatility and to ensure price continuity. This article presents the results of an international survey on the design and application of circuit breakers on trading venues worldwide. The majority (86%) of the responding trading venues apply circuit breakers and thereby aim to ensure investor protection and increase market integrity and stability. On cash markets, market-wide trading halts and volatility interruptions are the most prevalent types of circuit breakers (72%). On derivatives markets, most exchanges coordinate their circuit breaker with their cash market (40%), followed by marketwide trading halts (20%) and volatility interruptions (13%). Most circuit breakers do not differentiate between upward and downward market movements. There is also support for greater coordination of circuit breakers across venues, and a few exchanges (32%) already coordinate their circuit breakers with other venues.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123962219","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}
{"title":"Unrecognized Odd Lot Liquidity Supply: A Hidden Trading Cost for High Priced Stocks","authors":"Robert H. Battalio, Shane A. Corwin, R. Jennings","doi":"10.3905/jot.2017.12.1.035","DOIUrl":"https://doi.org/10.3905/jot.2017.12.1.035","url":null,"abstract":"Current National Market System rules do not recognize odd lots in the protected intermarket quote. Thus, liquidity demanders can receive worse prices than they would receive if odd lots were protected. The effect of ignoring odd lots is magnified because off-exchange trades (over one-third of total volume) benchmark executions against the protected quote. The authors identify time intervals with an unprotected odd lot limit order at a price better than the protected quote and examine trades during those intervals for 10 high-priced stocks during one week in 2015. They find over 406,000 intervals, representing 37% of sample stock trading time, in which an odd lot order betters the protected quote. Examining trades within these intervals, they find nearly 55,000 cases in which the trade price is worse than the odd lot price. In total, the price disimprovement in their 10 stocks is $554,675 for the week examined. This previously undocumented trading cost is associated with the corporate decision not to split a stock’s price in a market in which odd lot orders are excluded from the protected quote.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125857315","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}
{"title":"London Stock Exchange Midday Auction","authors":"Mainak Sarkar","doi":"10.3905/jot.2017.12.1.022","DOIUrl":"https://doi.org/10.3905/jot.2017.12.1.022","url":null,"abstract":"The author investigates the success of the recently launched intraday auction at the London Stock Exchange (LSE) along various metrics such as liquidity, price deviation, and so on. This initiative is important because of the coming Markets in Financial Instruments Directive (MIFID II) regulations, which will set a limit on dark trading and seek to encourage larger trade sizes. The author finds that this initiative has been successful for finding liquidity (block trading) for midcap stocks but less so for the FTSE 100 stocks. Interestingly, he also finds that alternative venues such as BATS, Chi-X, and Turquoise stop being good sources for liquidity during the auction phase on the LSE.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127742818","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}
{"title":"Determinants of Price Discovery: Dark Trading and Price Improvement","authors":"D. Harris, F. Harris","doi":"10.3905/jot.2017.12.1.055","DOIUrl":"https://doi.org/10.3905/jot.2017.12.1.055","url":null,"abstract":"What role does dark trading play in price dynamics? The authors estimate the permanent information that dark trading contributes as well as the effect of the Investment Industry Regulatory Organization of Canada (IIROC) Notice 12-0130, which mandated price improvement on dark trades. The analysis provides context and a comparative perspective by assessing informational dynamics between Canadian and U.S. cross-listed securities. Dark trading contributes very little permanent information in Canada—only a 7.1% information leadership share. Post–IIROC Notice 12-0130, the authors find a reduction in the information content of dark trades after controlling for the effects of order book quality, informational asymmetry, and transactional efficiency.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"467 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126353768","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}