Pub Date : 2016-03-31DOI: 10.3905/jot.2016.11.2.049
Francesco Ceccon, Lovjit Thukral, Pedro Vergel Eleuterio
Given the increase in the popularity of algorithmic trading resulting from an increase in market participants, more considerations are now required to prototype a profitable trading strategy. Trading strategies, which require optimization of parameters based on linear or nonlinear relationships, cause an increase in complexity, which in turn increases computational run time. We find that C provides the best performance for prototyping quantitative trading strategies; however, it is the most time-consuming to implement. Among the languages that allow for faster development times, the difference between Cython and Julia is relatively small, so choice between them comes down to user preference and other factors. We find Julia to be the standout programming language due to its simplicity and high performance.
{"title":"Momentum Strategies: Comparison of Programming Language Performance","authors":"Francesco Ceccon, Lovjit Thukral, Pedro Vergel Eleuterio","doi":"10.3905/jot.2016.11.2.049","DOIUrl":"https://doi.org/10.3905/jot.2016.11.2.049","url":null,"abstract":"Given the increase in the popularity of algorithmic trading resulting from an increase in market participants, more considerations are now required to prototype a profitable trading strategy. Trading strategies, which require optimization of parameters based on linear or nonlinear relationships, cause an increase in complexity, which in turn increases computational run time. We find that C provides the best performance for prototyping quantitative trading strategies; however, it is the most time-consuming to implement. Among the languages that allow for faster development times, the difference between Cython and Julia is relatively small, so choice between them comes down to user preference and other factors. We find Julia to be the standout programming language due to its simplicity and high performance.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125367556","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 : 2016-03-31DOI: 10.3905/jot.2016.11.2.011
Grace E Chung, R. Kissell
We propose a transaction cost analysis (TCA) portfolio optimization procedure that incorporates transaction costs directly into the problem of the objective function of portfolio optimization. The results show that a fund achieves considerably higher net returns with TCA optimization than with traditional quadratic programming methods that do not directly consider transactions costs. For a large-cap, 50-stock portfolio, the improvement in net returns was on average +4.5 bp to +8.2 bp and as high as +7.6 bp to +13.5 bp. For a large-cap, 100-stock portfolio, the improvement in net returns was on average +3.2bp to +7.0 bp and as high as +5.0 bp to +10.2 bp. These results show that a manager can start with a seemingly suboptimal or inefficient ex ante portfolio in traditional mean variance space and earn higher ex post net returns after accounting for transaction costs.
{"title":"An Application of Transaction Cost in the Portfolio Optimization Process","authors":"Grace E Chung, R. Kissell","doi":"10.3905/jot.2016.11.2.011","DOIUrl":"https://doi.org/10.3905/jot.2016.11.2.011","url":null,"abstract":"We propose a transaction cost analysis (TCA) portfolio optimization procedure that incorporates transaction costs directly into the problem of the objective function of portfolio optimization. The results show that a fund achieves considerably higher net returns with TCA optimization than with traditional quadratic programming methods that do not directly consider transactions costs. For a large-cap, 50-stock portfolio, the improvement in net returns was on average +4.5 bp to +8.2 bp and as high as +7.6 bp to +13.5 bp. For a large-cap, 100-stock portfolio, the improvement in net returns was on average +3.2bp to +7.0 bp and as high as +5.0 bp to +10.2 bp. These results show that a manager can start with a seemingly suboptimal or inefficient ex ante portfolio in traditional mean variance space and earn higher ex post net returns after accounting for transaction costs.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122593906","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 : 2016-03-31DOI: 10.3905/jot.2016.11.2.021
Philip Sommer, S. Pasquali
Despite its importance, there currently exists no universally agreed upon and adopted measure or model that adequately captures cost and time to liquidation in bond (over-thecounter) markets. To fill this gap, we reviewed 40 years’ worth of research and summarize our findings in this article. We claim that the lack of concurrence on a definition can be attributed to the lack of consistent methodology. Connecting the dots within the vast body of literature, we find the key ingredients of such a novel measure: Taking market impact models as a natural starting point and adding the necessary math to quantify the inherent uncertainty of such a measure. We further suggest that machine learning methods are a natural candidate to overcome the main obstacles in this process, as they can help extract useful information from the extremely sparse data that form the main difference between equity and bond markets.
{"title":"Liquidity—How to Capture a Multidimensional Beast","authors":"Philip Sommer, S. Pasquali","doi":"10.3905/jot.2016.11.2.021","DOIUrl":"https://doi.org/10.3905/jot.2016.11.2.021","url":null,"abstract":"Despite its importance, there currently exists no universally agreed upon and adopted measure or model that adequately captures cost and time to liquidation in bond (over-thecounter) markets. To fill this gap, we reviewed 40 years’ worth of research and summarize our findings in this article. We claim that the lack of concurrence on a definition can be attributed to the lack of consistent methodology. Connecting the dots within the vast body of literature, we find the key ingredients of such a novel measure: Taking market impact models as a natural starting point and adding the necessary math to quantify the inherent uncertainty of such a measure. We further suggest that machine learning methods are a natural candidate to overcome the main obstacles in this process, as they can help extract useful information from the extremely sparse data that form the main difference between equity and bond markets.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125516159","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 : 2016-03-03DOI: 10.3905/jot.2016.11.3.081
R. Kashyap
In this article, the author utilizes a fundamentally different model of trading costs to look at the effect of the opening of the Hong Kong Shanghai Connect, linking the stock exchanges in the two cities. The author designs a novel methodology that compensates for the lack of data on trading costs in China. He estimates trading costs across similar positions on the dual listed set of securities in Hong Kong and China and then compares actual and estimated trading costs on a sample of real orders across the Hong Kong securities in the dual-listed pair to establish the accuracy of his measurements. The primary question the article seeks to answer is, “Which market would be better to trade to gain exposure to the same (or similar) set of securities or sectors?” The author finds that trading costs on the Shanghai exchange, which might have been lower than on the Hong Kong exchange, seem to have become higher leading up to the Connect. It remains to be seen whether this increase in trading costs is a temporary equilibrium due to the frenzy to gain exposure to Chinese securities or whether it will persist as the two markets become more tightly coupled. Future study should examine whether this pioneering policy will lead to security exchanges across the globe linking up, creating a trade anything, anywhere, and anytime marketplace. Looking beyond mere trading costs, such studies can be used to gather evidence the effects mode of governance and other aspects of life in one country have on another country once they start linking their financial markets.
{"title":"Hong Kong–Shanghai Connect/Hong Kong–Beijing Disconnect? Scaling the Great Wall of Chinese Securities Trading Costs","authors":"R. Kashyap","doi":"10.3905/jot.2016.11.3.081","DOIUrl":"https://doi.org/10.3905/jot.2016.11.3.081","url":null,"abstract":"In this article, the author utilizes a fundamentally different model of trading costs to look at the effect of the opening of the Hong Kong Shanghai Connect, linking the stock exchanges in the two cities. The author designs a novel methodology that compensates for the lack of data on trading costs in China. He estimates trading costs across similar positions on the dual listed set of securities in Hong Kong and China and then compares actual and estimated trading costs on a sample of real orders across the Hong Kong securities in the dual-listed pair to establish the accuracy of his measurements. The primary question the article seeks to answer is, “Which market would be better to trade to gain exposure to the same (or similar) set of securities or sectors?” The author finds that trading costs on the Shanghai exchange, which might have been lower than on the Hong Kong exchange, seem to have become higher leading up to the Connect. It remains to be seen whether this increase in trading costs is a temporary equilibrium due to the frenzy to gain exposure to Chinese securities or whether it will persist as the two markets become more tightly coupled. Future study should examine whether this pioneering policy will lead to security exchanges across the globe linking up, creating a trade anything, anywhere, and anytime marketplace. Looking beyond mere trading costs, such studies can be used to gather evidence the effects mode of governance and other aspects of life in one country have on another country once they start linking their financial markets.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129494311","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 : 2016-02-26DOI: 10.3905/jot.2016.11.3.053
Vinesh Jha
Many managers of long-horizon quantitative stock selection portfolios do not use short-horizon alpha signals because of the fast decay of these signals. The author demonstrates a simple tactical trade timing strategy that allows a long-horizon manager to take advantage of short-horizon alphas without incurring additional transaction costs. He shows that the strategy’s value added is consistent across time and capitalization groups and does not affect the portfolio’s risk exposures.
{"title":"Timing Equity Quant Positions with Short-Horizon Alphas","authors":"Vinesh Jha","doi":"10.3905/jot.2016.11.3.053","DOIUrl":"https://doi.org/10.3905/jot.2016.11.3.053","url":null,"abstract":"Many managers of long-horizon quantitative stock selection portfolios do not use short-horizon alpha signals because of the fast decay of these signals. The author demonstrates a simple tactical trade timing strategy that allows a long-horizon manager to take advantage of short-horizon alphas without incurring additional transaction costs. He shows that the strategy’s value added is consistent across time and capitalization groups and does not affect the portfolio’s risk exposures.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"52 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132973785","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 : 2016-01-08DOI: 10.3905/jot.2016.11.2.071
A. Kumiega, Greg Sterijevski, Ben Van Vliet
Extreme events in financial markets can arise from fundamental information, but they can also arise from latent hazards embedded in the market design. This concept is known as systemic risk, and someone must bear it. Extreme events add to risk, and their probability and severity must be accounted for by market participants. This article shows how this risk fits into the finance literature and that, from an engineering perspective, this risk in markets has never been lower. The industry is evolving to mitigate this risk. This article presents an overview of the complexity of the automated market network and describes how market participants interact through the exchange mechanism. It defines new terms and a new framework for understanding the risk of extreme market moves from a reliability and safety perspective.
{"title":"Beyond the Flash Crash:Systemic Risk, Reliability, and High Frequency Financial Markets","authors":"A. Kumiega, Greg Sterijevski, Ben Van Vliet","doi":"10.3905/jot.2016.11.2.071","DOIUrl":"https://doi.org/10.3905/jot.2016.11.2.071","url":null,"abstract":"Extreme events in financial markets can arise from fundamental information, but they can also arise from latent hazards embedded in the market design. This concept is known as systemic risk, and someone must bear it. Extreme events add to risk, and their probability and severity must be accounted for by market participants. This article shows how this risk fits into the finance literature and that, from an engineering perspective, this risk in markets has never been lower. The industry is evolving to mitigate this risk. This article presents an overview of the complexity of the automated market network and describes how market participants interact through the exchange mechanism. It defines new terms and a new framework for understanding the risk of extreme market moves from a reliability and safety perspective.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"74 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127183583","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 : 2015-12-31DOI: 10.3905/jot.2016.11.1.038
A. Raudys, Skirmantė Matkėnaitė
Order execution methods using various combinations of limit and market orders in U.S. and European futures markets are investigated in this article. Similar to smart order routing in stocks, smart order execution can noticeably reduce futures trading costs. This is important because, in more frequent trading cases, transaction costs can add up to 50% of fund performance. There is much speculation and very little scientific research on whether algos (order execution tactics/methods) can produce the smallest slippage. We try to fill this gap in the literature by doing a simulation study using 0.4 trillion ticksized real world data. We obtained the tick data from a systematic trading firm and simulated various execution tactics aiming to reduce average slippage per contract. We generated trades uniformly and investigated the best tactics to execute them. The research concludes that the best tactic overall is the limit then market tactic, in which we place a limit order on the last seen price, hold fort seconds, and then convert to market order. Transaction costs can be reduced up to 70% for some markets in comparison to the benchmark. In some specific illiquid markets like platinum and palladium, however, this method increases slippage. We note that different markets vary in terms of the best tactics to use, and the methods we have discovered may not hold for large orders, as these orders may start to infl uence the market.
{"title":"Analysis of Execution Methods in U.S. and European Futures","authors":"A. Raudys, Skirmantė Matkėnaitė","doi":"10.3905/jot.2016.11.1.038","DOIUrl":"https://doi.org/10.3905/jot.2016.11.1.038","url":null,"abstract":"Order execution methods using various combinations of limit and market orders in U.S. and European futures markets are investigated in this article. Similar to smart order routing in stocks, smart order execution can noticeably reduce futures trading costs. This is important because, in more frequent trading cases, transaction costs can add up to 50% of fund performance. There is much speculation and very little scientific research on whether algos (order execution tactics/methods) can produce the smallest slippage. We try to fill this gap in the literature by doing a simulation study using 0.4 trillion ticksized real world data. We obtained the tick data from a systematic trading firm and simulated various execution tactics aiming to reduce average slippage per contract. We generated trades uniformly and investigated the best tactics to execute them. The research concludes that the best tactic overall is the limit then market tactic, in which we place a limit order on the last seen price, hold fort seconds, and then convert to market order. Transaction costs can be reduced up to 70% for some markets in comparison to the benchmark. In some specific illiquid markets like platinum and palladium, however, this method increases slippage. We note that different markets vary in terms of the best tactics to use, and the methods we have discovered may not hold for large orders, as these orders may start to infl uence the market.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115908785","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 : 2015-12-31DOI: 10.3905/jot.2016.11.1.013
M. Borkovec, K. Tyurin
This article summarizes results of an extensive empirical study motivated by the intuitively appealing statement that institutional clients’ average transaction costs are sensitive to market conditions. Using a comprehensive sample of client execution data covering two years of trading, we confirm that the average cost of institutional trades varies considerably and systematically with volatility, volume, and trade imbalance surprises. For the overwhelming majority of buy-side institutions, implementation shortfall is higher than normal when volatility and volume exceed their historical average values. However, the deviations of trading volume in excess of the values typically observed in high volatility conditions dampen the effect of a high volatility environment on the execution costs of institutional orders. We document a strong dependence of transaction costs on contemporaneous trade imbalances, which is amplified by higher than normal contemporaneous volatility. We observe that cost curves are more sensitive to order size in times of less favorable buy-sell trade imbalances, reflecting the role played by directional market pressure indicators. In summary, buy-side institutions should not neglect market conditions monitoring, as failure to adjust promptly to market conditions may result in deteriorated performance and missed cost savings opportunities.
{"title":"Is Volatility Your Nemesis or Best Friend? It Depends on Who You Ask","authors":"M. Borkovec, K. Tyurin","doi":"10.3905/jot.2016.11.1.013","DOIUrl":"https://doi.org/10.3905/jot.2016.11.1.013","url":null,"abstract":"This article summarizes results of an extensive empirical study motivated by the intuitively appealing statement that institutional clients’ average transaction costs are sensitive to market conditions. Using a comprehensive sample of client execution data covering two years of trading, we confirm that the average cost of institutional trades varies considerably and systematically with volatility, volume, and trade imbalance surprises. For the overwhelming majority of buy-side institutions, implementation shortfall is higher than normal when volatility and volume exceed their historical average values. However, the deviations of trading volume in excess of the values typically observed in high volatility conditions dampen the effect of a high volatility environment on the execution costs of institutional orders. We document a strong dependence of transaction costs on contemporaneous trade imbalances, which is amplified by higher than normal contemporaneous volatility. We observe that cost curves are more sensitive to order size in times of less favorable buy-sell trade imbalances, reflecting the role played by directional market pressure indicators. In summary, buy-side institutions should not neglect market conditions monitoring, as failure to adjust promptly to market conditions may result in deteriorated performance and missed cost savings opportunities.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132753306","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 : 2015-12-31DOI: 10.3905/jot.2016.11.1.001
Brian R. Bruce
{"title":"Editor’s Letter","authors":"Brian R. Bruce","doi":"10.3905/jot.2016.11.1.001","DOIUrl":"https://doi.org/10.3905/jot.2016.11.1.001","url":null,"abstract":"","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116477992","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 : 2015-12-31DOI: 10.3905/jot.2016.11.1.026
I. Grynkiv, K. Russell
Industry reports on the intraday volume profile of U.S. equities, known to many as the volume smile, have noted that volumes have shifted toward closing auctions. In this article, we study how volume profiles have evolved over the last three years for different groups of U.S. equities. We analyze how the percent of daily volume has changed in three parts of the trading day: opening auctions; the last 15 minutes of continuous trading; and closing auctions. Although much of the trade press tends to treat volume trends as universal across U.S. equities, we conclude that volume shifts can vary considerably across different securities. Our results have practical implications for algorithmic trading strategies, which highlights the importance of using volume forecast models that are specific to symbols or to a group of securities with similar liquidity characteristics and account for the fact that intraday volume profiles change over time.
{"title":"A Look Inside the Shifting Volume Smile for U.S. Equities","authors":"I. Grynkiv, K. Russell","doi":"10.3905/jot.2016.11.1.026","DOIUrl":"https://doi.org/10.3905/jot.2016.11.1.026","url":null,"abstract":"Industry reports on the intraday volume profile of U.S. equities, known to many as the volume smile, have noted that volumes have shifted toward closing auctions. In this article, we study how volume profiles have evolved over the last three years for different groups of U.S. equities. We analyze how the percent of daily volume has changed in three parts of the trading day: opening auctions; the last 15 minutes of continuous trading; and closing auctions. Although much of the trade press tends to treat volume trends as universal across U.S. equities, we conclude that volume shifts can vary considerably across different securities. Our results have practical implications for algorithmic trading strategies, which highlights the importance of using volume forecast models that are specific to symbols or to a group of securities with similar liquidity characteristics and account for the fact that intraday volume profiles change over time.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129838411","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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