Independent regulatory reporting provider, Lombard Risk Management
Risk Management
One of the most common terms utilized by brokers, risk management refers to the practice of identifying potential risks in advance. Most commonly, this also involves the analysis of risk and the undertaking of precautionary steps to both mitigate and prevent for such risk.Such efforts are essential for brokers and venues in the finance industry, given the potential for fallout in the face of unforeseen events or crises. Given a more tightly regulated environment across nearly every asset class, most brokers employ a risk management department tasked with analyzing the data and flow of the broker to mitigate the firm’s exposure to financial markets moves. Why Risk Management is a Fixture Among BrokersTraditionally the company is employing a risk management team that is monitoring the exposure of the brokerage and the performance of select clients which it deems risky for the business. Common financial risks also come in the form of high inflation, volatility across capital markets, recession, bankruptcy, and others.As a countermeasure to these issues, brokers have looked to minimize and control the exposure of investment to such risks.In the modern hybrid mode of operation, brokers are sending out the flows from the most profitable clients to liquidity providers and internalize the flows from customers.This is deemed less risky and are likely to incur losses on their positions.This in turn allowing the broker to increase its revenue capture. Several software solutions exist to assist brokers to manage risk more efficiently and as of 2018, most connectivity/bridge providers are integrating a risk-management module into their offerings. This aspect of running a brokerage is also one of the most crucial ones when it comes to employing the right kind of talent.
One of the most common terms utilized by brokers, risk management refers to the practice of identifying potential risks in advance. Most commonly, this also involves the analysis of risk and the undertaking of precautionary steps to both mitigate and prevent for such risk.Such efforts are essential for brokers and venues in the finance industry, given the potential for fallout in the face of unforeseen events or crises. Given a more tightly regulated environment across nearly every asset class, most brokers employ a risk management department tasked with analyzing the data and flow of the broker to mitigate the firm’s exposure to financial markets moves. Why Risk Management is a Fixture Among BrokersTraditionally the company is employing a risk management team that is monitoring the exposure of the brokerage and the performance of select clients which it deems risky for the business. Common financial risks also come in the form of high inflation, volatility across capital markets, recession, bankruptcy, and others.As a countermeasure to these issues, brokers have looked to minimize and control the exposure of investment to such risks.In the modern hybrid mode of operation, brokers are sending out the flows from the most profitable clients to liquidity providers and internalize the flows from customers.This is deemed less risky and are likely to incur losses on their positions.This in turn allowing the broker to increase its revenue capture. Several software solutions exist to assist brokers to manage risk more efficiently and as of 2018, most connectivity/bridge providers are integrating a risk-management module into their offerings. This aspect of running a brokerage is also one of the most crucial ones when it comes to employing the right kind of talent.
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Mr. Groetch joined Lombard Risk back in April 2014 as its Regional Sales Director, where he was tasked with helping manage and mentor the organization’s structure and development – just five months later, he is now being promoted to spearhead operations in the Americas.
According to Mr. Groetch in a recent statement on the promotion, "It has been an honor to work with such a dynamic team. I am thrilled to manage the company's strategic drive in the region, executing upon our ambitious growth plans, both in staffing and revenues."
"We are delighted to have John Groetch oversee our Americas region. With his over two decades of experience, he is well-equipped for this role. We expect the Americas to continue to be a fast-growing market for us and are particularly keen to be able to capture more of the regional regulatory, collateral management and compliance opportunities that exist," added John Wisbey, Lombard Risk CEO.
Prior to working at Lombard Risk, Mr. Groetch has worked in a variety of firms in senior level roles, including Markit 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.
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Independent regulatory reporting provider, Lombard Risk Management
Risk Management
One of the most common terms utilized by brokers, risk management refers to the practice of identifying potential risks in advance. Most commonly, this also involves the analysis of risk and the undertaking of precautionary steps to both mitigate and prevent for such risk.Such efforts are essential for brokers and venues in the finance industry, given the potential for fallout in the face of unforeseen events or crises. Given a more tightly regulated environment across nearly every asset class, most brokers employ a risk management department tasked with analyzing the data and flow of the broker to mitigate the firm’s exposure to financial markets moves. Why Risk Management is a Fixture Among BrokersTraditionally the company is employing a risk management team that is monitoring the exposure of the brokerage and the performance of select clients which it deems risky for the business. Common financial risks also come in the form of high inflation, volatility across capital markets, recession, bankruptcy, and others.As a countermeasure to these issues, brokers have looked to minimize and control the exposure of investment to such risks.In the modern hybrid mode of operation, brokers are sending out the flows from the most profitable clients to liquidity providers and internalize the flows from customers.This is deemed less risky and are likely to incur losses on their positions.This in turn allowing the broker to increase its revenue capture. Several software solutions exist to assist brokers to manage risk more efficiently and as of 2018, most connectivity/bridge providers are integrating a risk-management module into their offerings. This aspect of running a brokerage is also one of the most crucial ones when it comes to employing the right kind of talent.
One of the most common terms utilized by brokers, risk management refers to the practice of identifying potential risks in advance. Most commonly, this also involves the analysis of risk and the undertaking of precautionary steps to both mitigate and prevent for such risk.Such efforts are essential for brokers and venues in the finance industry, given the potential for fallout in the face of unforeseen events or crises. Given a more tightly regulated environment across nearly every asset class, most brokers employ a risk management department tasked with analyzing the data and flow of the broker to mitigate the firm’s exposure to financial markets moves. Why Risk Management is a Fixture Among BrokersTraditionally the company is employing a risk management team that is monitoring the exposure of the brokerage and the performance of select clients which it deems risky for the business. Common financial risks also come in the form of high inflation, volatility across capital markets, recession, bankruptcy, and others.As a countermeasure to these issues, brokers have looked to minimize and control the exposure of investment to such risks.In the modern hybrid mode of operation, brokers are sending out the flows from the most profitable clients to liquidity providers and internalize the flows from customers.This is deemed less risky and are likely to incur losses on their positions.This in turn allowing the broker to increase its revenue capture. Several software solutions exist to assist brokers to manage risk more efficiently and as of 2018, most connectivity/bridge providers are integrating a risk-management module into their offerings. This aspect of running a brokerage is also one of the most crucial ones when it comes to employing the right kind of talent.
Read this Term
Mr. Groetch joined Lombard Risk back in April 2014 as its Regional Sales Director, where he was tasked with helping manage and mentor the organization’s structure and development – just five months later, he is now being promoted to spearhead operations in the Americas.
According to Mr. Groetch in a recent statement on the promotion, "It has been an honor to work with such a dynamic team. I am thrilled to manage the company's strategic drive in the region, executing upon our ambitious growth plans, both in staffing and revenues."
"We are delighted to have John Groetch oversee our Americas region. With his over two decades of experience, he is well-equipped for this role. We expect the Americas to continue to be a fast-growing market for us and are particularly keen to be able to capture more of the regional regulatory, collateral management and compliance opportunities that exist," added John Wisbey, Lombard Risk CEO.
Prior to working at Lombard Risk, Mr. Groetch has worked in a variety of firms in senior level roles, including Markit 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.
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