Liquidnet, a global institutional investment network owned by the TP ICAP Group, has hired James Rubinstein as Head of Execution and Quantitative Services (EQS) for the Americas.
Rubinstein, who brings over two decades of experience to the role, will be responsible for setting the strategic direction and leading the effort for Liquidnet’s EQS offerings in the US and the Americas.
The former BNP Paribas’s Head of Electronic Equities Product, Americas, is based in New York, and will report to Rob Laible, Liquidnet’s Global Head of Equities.
James Rubinstein, in reaction to his appointment, expressed confidence in the company’s ability to provide institutional liquidity to its clients. “Liquidnet’s equity franchise has huge potential. Its deep and diverse pool of institutional liquidity
Liquidity
Liquidity is at the core of every broker’s offering. It is a basic characteristic of every financial asset – be it a currency, stock, bond, commodity or real estate. The more liquid an asset is, the easier it is to sell and buy on the open market. Foreign exchange is considered to be the most liquid asset class.Brokers can source liquidity from a single or multiple source, thereby delivering to their clients enough market depth for their orders to get filled. The main characteristic of liquidity is its depth, which will determine how quickly and how big of an order can be executed via the trading platform.Understanding LiquidityLiquidity can be internal or external depending on the size and the book of the broker. Companies which are large enough and have material client flows consistently are creating their own liquidity pools from the order flow of their clients, thereby internalizing flows and saving on costs to send customer orders to the interbank market. By doing that however they are exposing themselves to carry the risk on the trade.Liquidity providers can be prime brokers, prime of primes, other brokers or the broker’s book itself. Traditionally brokers are split between internalizing flows and offloading trades of their clients to different liquidity providers.Generally, retail brokers and their clients prefer more liquid assets which lead to better fill rates and less slippage. When there is lack of liquidity on a certain market, slippage can occur – the order is executed at a price which is the closest available to the one requested by the client.
Liquidity is at the core of every broker’s offering. It is a basic characteristic of every financial asset – be it a currency, stock, bond, commodity or real estate. The more liquid an asset is, the easier it is to sell and buy on the open market. Foreign exchange is considered to be the most liquid asset class.Brokers can source liquidity from a single or multiple source, thereby delivering to their clients enough market depth for their orders to get filled. The main characteristic of liquidity is its depth, which will determine how quickly and how big of an order can be executed via the trading platform.Understanding LiquidityLiquidity can be internal or external depending on the size and the book of the broker. Companies which are large enough and have material client flows consistently are creating their own liquidity pools from the order flow of their clients, thereby internalizing flows and saving on costs to send customer orders to the interbank market. By doing that however they are exposing themselves to carry the risk on the trade.Liquidity providers can be prime brokers, prime of primes, other brokers or the broker’s book itself. Traditionally brokers are split between internalizing flows and offloading trades of their clients to different liquidity providers.Generally, retail brokers and their clients prefer more liquid assets which lead to better fill rates and less slippage. When there is lack of liquidity on a certain market, slippage can occur – the order is executed at a price which is the closest available to the one requested by the client.
Read this Term, combined with its technology, talent and trusted brand mean that we are well placed to meet our Members’ evolving needs,” he said.
Meanwhile, Laible described Rubinstein as “another step forward in enhancing our EQS offering in the US. His deep technical knowledge and track record of delivering innovative, market-driven solutions position us well to drive growth,” the equities head added.
Before joining BNP Paribas, Rubinstein spent three years with Deutsche Bank as Head of Electronic Equities, Americas. At the German multinational investment bank, Rubinstein was responsible for algorithmic development, quantitative research, transaction cost analysis, client consulting and liquidity strategy.
He joined Deutsche Bank from UBS where he spent twelve years as the Americas Head of Algorithms and 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.
Liquidnet’s Focus on Fixed Income
Liquidnet in November last year recruited Nicholas Stephan as the Global Head of Fixed Income to cultivate the company’s fixed income offering across primary and secondary markets. Stephan was charged with advancing new trading protocols, governance and access to Liquidity.
In December, the private trading operator announced that it was enhancing the coverage of its services in continental Europe by deploying equities and fixed income specialists in Paris, Madrid, Frankfurt and Copenhagen.
However, earlier in September, the company launched a protocol that allows its members to trade new issues in bond markets across Europe and the United States.
Liquidnet, a global institutional investment network owned by the TP ICAP Group, has hired James Rubinstein as Head of Execution and Quantitative Services (EQS) for the Americas.
Rubinstein, who brings over two decades of experience to the role, will be responsible for setting the strategic direction and leading the effort for Liquidnet’s EQS offerings in the US and the Americas.
The former BNP Paribas’s Head of Electronic Equities Product, Americas, is based in New York, and will report to Rob Laible, Liquidnet’s Global Head of Equities.
James Rubinstein, in reaction to his appointment, expressed confidence in the company’s ability to provide institutional liquidity to its clients. “Liquidnet’s equity franchise has huge potential. Its deep and diverse pool of institutional liquidity
Liquidity
Liquidity is at the core of every broker’s offering. It is a basic characteristic of every financial asset – be it a currency, stock, bond, commodity or real estate. The more liquid an asset is, the easier it is to sell and buy on the open market. Foreign exchange is considered to be the most liquid asset class.Brokers can source liquidity from a single or multiple source, thereby delivering to their clients enough market depth for their orders to get filled. The main characteristic of liquidity is its depth, which will determine how quickly and how big of an order can be executed via the trading platform.Understanding LiquidityLiquidity can be internal or external depending on the size and the book of the broker. Companies which are large enough and have material client flows consistently are creating their own liquidity pools from the order flow of their clients, thereby internalizing flows and saving on costs to send customer orders to the interbank market. By doing that however they are exposing themselves to carry the risk on the trade.Liquidity providers can be prime brokers, prime of primes, other brokers or the broker’s book itself. Traditionally brokers are split between internalizing flows and offloading trades of their clients to different liquidity providers.Generally, retail brokers and their clients prefer more liquid assets which lead to better fill rates and less slippage. When there is lack of liquidity on a certain market, slippage can occur – the order is executed at a price which is the closest available to the one requested by the client.
Liquidity is at the core of every broker’s offering. It is a basic characteristic of every financial asset – be it a currency, stock, bond, commodity or real estate. The more liquid an asset is, the easier it is to sell and buy on the open market. Foreign exchange is considered to be the most liquid asset class.Brokers can source liquidity from a single or multiple source, thereby delivering to their clients enough market depth for their orders to get filled. The main characteristic of liquidity is its depth, which will determine how quickly and how big of an order can be executed via the trading platform.Understanding LiquidityLiquidity can be internal or external depending on the size and the book of the broker. Companies which are large enough and have material client flows consistently are creating their own liquidity pools from the order flow of their clients, thereby internalizing flows and saving on costs to send customer orders to the interbank market. By doing that however they are exposing themselves to carry the risk on the trade.Liquidity providers can be prime brokers, prime of primes, other brokers or the broker’s book itself. Traditionally brokers are split between internalizing flows and offloading trades of their clients to different liquidity providers.Generally, retail brokers and their clients prefer more liquid assets which lead to better fill rates and less slippage. When there is lack of liquidity on a certain market, slippage can occur – the order is executed at a price which is the closest available to the one requested by the client.
Read this Term, combined with its technology, talent and trusted brand mean that we are well placed to meet our Members’ evolving needs,” he said.
Meanwhile, Laible described Rubinstein as “another step forward in enhancing our EQS offering in the US. His deep technical knowledge and track record of delivering innovative, market-driven solutions position us well to drive growth,” the equities head added.
Before joining BNP Paribas, Rubinstein spent three years with Deutsche Bank as Head of Electronic Equities, Americas. At the German multinational investment bank, Rubinstein was responsible for algorithmic development, quantitative research, transaction cost analysis, client consulting and liquidity strategy.
He joined Deutsche Bank from UBS where he spent twelve years as the Americas Head of Algorithms and 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.
Liquidnet’s Focus on Fixed Income
Liquidnet in November last year recruited Nicholas Stephan as the Global Head of Fixed Income to cultivate the company’s fixed income offering across primary and secondary markets. Stephan was charged with advancing new trading protocols, governance and access to Liquidity.
In December, the private trading operator announced that it was enhancing the coverage of its services in continental Europe by deploying equities and fixed income specialists in Paris, Madrid, Frankfurt and Copenhagen.
However, earlier in September, the company launched a protocol that allows its members to trade new issues in bond markets across Europe and the United States.
Source: https://www.financemagnates.com/executives/liquidnet-taps-james-rubinstein-to-enhance-eqs-offerings-in-us-americas/