Thomson Reuters (NYSE:TRI) has teamed up with independent order management system (OMS) provider, Eze Software Group, to help optimize a number of workflows via the Eikon Server API, according to a Thomson Reuters statement.
The partnership will aim to help Buy-Side
Buy-Side
The buy-side is comprised of firms in the financial industry that purchase securities and are accompanied by account investment managers, pension funds, and hedge funds.The buy-side is composed of those that buy and invest large sums of securities with the intention of generating a lucrative return or have their funds managed. The Buy-Side ExplainedIn terms of Wall Street, the buy-side includes investment institutions that purchase securities, stocks, or other financial instruments with the aim of satisfying their client’s portfolio demands. Through the analysis and acquisition of underpriced assets, buy-side entities purchase these assets with the prediction that they will appreciate. Moreover, the largest buy-side participants include firms such as BlackRock, The Vanguard Group, and UBS Group to name a few. It is important to note that firms such as BlackRock are able to influence market prices as a result of placing large investments under single entities while the Securities and Exchange Commission (SEC) requires a quarterly 13-F filing for all holdings bought or sold by buy-side managers. What differentiates buy-side investors from other traders would be the advantages that are yielded to them. Buy-side investors not only have access to a much broader range of trading resources and market insight but also tend to possess decreased trading costs through large lot acquisitions. To sum up, firms work with buy-side analysts to provide research recommendations that are kept exclusive to those participants of the firm while all analysts are overseen by regulations set forth by the International Organization of Securities Commissions (IOSCO).
The buy-side is comprised of firms in the financial industry that purchase securities and are accompanied by account investment managers, pension funds, and hedge funds.The buy-side is composed of those that buy and invest large sums of securities with the intention of generating a lucrative return or have their funds managed. The Buy-Side ExplainedIn terms of Wall Street, the buy-side includes investment institutions that purchase securities, stocks, or other financial instruments with the aim of satisfying their client’s portfolio demands. Through the analysis and acquisition of underpriced assets, buy-side entities purchase these assets with the prediction that they will appreciate. Moreover, the largest buy-side participants include firms such as BlackRock, The Vanguard Group, and UBS Group to name a few. It is important to note that firms such as BlackRock are able to influence market prices as a result of placing large investments under single entities while the Securities and Exchange Commission (SEC) requires a quarterly 13-F filing for all holdings bought or sold by buy-side managers. What differentiates buy-side investors from other traders would be the advantages that are yielded to them. Buy-side investors not only have access to a much broader range of trading resources and market insight but also tend to possess decreased trading costs through large lot acquisitions. To sum up, firms work with buy-side analysts to provide research recommendations that are kept exclusive to those participants of the firm while all analysts are overseen by regulations set forth by the International Organization of Securities Commissions (IOSCO).
Read this Term clients undergo a number of improvements to their workflows and costs. More specifically, customers will gain easier access to Eikon’s content and data through Eze OMS’ overlay onto a singular unified display.
The Eikon solution is one of Thomson Reuters’ flagship products that helps aggregate and process real-time and historical data, which collectively enable financial markets transactions and connectivity with the financial markets community.
One of the impetuses for the initiative was to eliminate the plausibility of data inconsistencies, which can occur when third-party applications for trade and order management are often fed through different or disjointed data sources. Subsequently, Eikon’s Server API will help mitigate these data discrepancies by ensuring congruent data is used across multiple applications.
According to Michael Chin, Global Head of Equities at Thomson Reuters, in a recent statement on the partnership, “Integrated workflow is very important to our buy-side customers as it improves efficiency and reduces errors and costs. However, at the same time, they need the flexibility to work with the trading tools of their choice. By being open to working with third-party EMS/OMS providers to fully integrate Eikon data, Thomson Reuters is supporting that choice.”
“Thomson Reuters has a long history of being committed to open standards and partnership. With this latest development our customers benefit from the integration of Thomson Reuters Eikon’s powerful data Analytics
Analytics
Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability.
Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability.
Read this Term directly into their order management workflow,” he reiterated.
“Integrating Thomson Reuters Eikon content and data into Eze OMS is consistent with our strategy of providing high-quality, easy-to-access data to our customers seamlessly within their workflow. We are excited to partner with Thomson Reuters in the quest to improve operational efficiencies for our buy-side clients,” added Rob Keller, CFA, Executive Managing Director of Product Management and Development, Eze Software Group, in an accompanying statement.
Earlier this week, Thomson Reuters (NYSE:TSI) expanded its Eikon platform to include a more comprehensive commodities focus, focusing on supply chain data to help better pinpoint financial markets behavior. As a result, Thomson Reuters extended its focus to include additional commodities, including liquid petroleum gas, iron ore, and coal in the near future.
Thomson Reuters (NYSE:TRI) has teamed up with independent order management system (OMS) provider, Eze Software Group, to help optimize a number of workflows via the Eikon Server API, according to a Thomson Reuters statement.
The partnership will aim to help Buy-Side
Buy-Side
The buy-side is comprised of firms in the financial industry that purchase securities and are accompanied by account investment managers, pension funds, and hedge funds.The buy-side is composed of those that buy and invest large sums of securities with the intention of generating a lucrative return or have their funds managed. The Buy-Side ExplainedIn terms of Wall Street, the buy-side includes investment institutions that purchase securities, stocks, or other financial instruments with the aim of satisfying their client’s portfolio demands. Through the analysis and acquisition of underpriced assets, buy-side entities purchase these assets with the prediction that they will appreciate. Moreover, the largest buy-side participants include firms such as BlackRock, The Vanguard Group, and UBS Group to name a few. It is important to note that firms such as BlackRock are able to influence market prices as a result of placing large investments under single entities while the Securities and Exchange Commission (SEC) requires a quarterly 13-F filing for all holdings bought or sold by buy-side managers. What differentiates buy-side investors from other traders would be the advantages that are yielded to them. Buy-side investors not only have access to a much broader range of trading resources and market insight but also tend to possess decreased trading costs through large lot acquisitions. To sum up, firms work with buy-side analysts to provide research recommendations that are kept exclusive to those participants of the firm while all analysts are overseen by regulations set forth by the International Organization of Securities Commissions (IOSCO).
The buy-side is comprised of firms in the financial industry that purchase securities and are accompanied by account investment managers, pension funds, and hedge funds.The buy-side is composed of those that buy and invest large sums of securities with the intention of generating a lucrative return or have their funds managed. The Buy-Side ExplainedIn terms of Wall Street, the buy-side includes investment institutions that purchase securities, stocks, or other financial instruments with the aim of satisfying their client’s portfolio demands. Through the analysis and acquisition of underpriced assets, buy-side entities purchase these assets with the prediction that they will appreciate. Moreover, the largest buy-side participants include firms such as BlackRock, The Vanguard Group, and UBS Group to name a few. It is important to note that firms such as BlackRock are able to influence market prices as a result of placing large investments under single entities while the Securities and Exchange Commission (SEC) requires a quarterly 13-F filing for all holdings bought or sold by buy-side managers. What differentiates buy-side investors from other traders would be the advantages that are yielded to them. Buy-side investors not only have access to a much broader range of trading resources and market insight but also tend to possess decreased trading costs through large lot acquisitions. To sum up, firms work with buy-side analysts to provide research recommendations that are kept exclusive to those participants of the firm while all analysts are overseen by regulations set forth by the International Organization of Securities Commissions (IOSCO).
Read this Term clients undergo a number of improvements to their workflows and costs. More specifically, customers will gain easier access to Eikon’s content and data through Eze OMS’ overlay onto a singular unified display.
The Eikon solution is one of Thomson Reuters’ flagship products that helps aggregate and process real-time and historical data, which collectively enable financial markets transactions and connectivity with the financial markets community.
One of the impetuses for the initiative was to eliminate the plausibility of data inconsistencies, which can occur when third-party applications for trade and order management are often fed through different or disjointed data sources. Subsequently, Eikon’s Server API will help mitigate these data discrepancies by ensuring congruent data is used across multiple applications.
According to Michael Chin, Global Head of Equities at Thomson Reuters, in a recent statement on the partnership, “Integrated workflow is very important to our buy-side customers as it improves efficiency and reduces errors and costs. However, at the same time, they need the flexibility to work with the trading tools of their choice. By being open to working with third-party EMS/OMS providers to fully integrate Eikon data, Thomson Reuters is supporting that choice.”
“Thomson Reuters has a long history of being committed to open standards and partnership. With this latest development our customers benefit from the integration of Thomson Reuters Eikon’s powerful data Analytics
Analytics
Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability.
Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability.
Read this Term directly into their order management workflow,” he reiterated.
“Integrating Thomson Reuters Eikon content and data into Eze OMS is consistent with our strategy of providing high-quality, easy-to-access data to our customers seamlessly within their workflow. We are excited to partner with Thomson Reuters in the quest to improve operational efficiencies for our buy-side clients,” added Rob Keller, CFA, Executive Managing Director of Product Management and Development, Eze Software Group, in an accompanying statement.
Earlier this week, Thomson Reuters (NYSE:TSI) expanded its Eikon platform to include a more comprehensive commodities focus, focusing on supply chain data to help better pinpoint financial markets behavior. As a result, Thomson Reuters extended its focus to include additional commodities, including liquid petroleum gas, iron ore, and coal in the near future.
