Millennium Management Hires Quant Trader Derrick Li as Risk Manager
- In his new position, Li will interview prospective portfolio managers and evaluate their trading strategies.
Millennium Management, a global investment management firm, has hired Derrick Li, a quantitative analysis (quant) trader and researcher, as a risk manager this month. According to his LinkedIn profile, Li has been tasked with an interesting responsibility in his new role.
On top of the typical responsibilities of a risk manager, such as overseeing coverage of the equity derivatives market risk, Li will also interview prospective portfolio managers and evaluate their trading strategies.
It’s not clear if Millennium Management is one of the first hedge funds to include risk managers in the hiring process but it's not known to be a common practice. As a risk manager, Li will also be responsible for building in-house quantitative risk 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 for cross-asset Volatility Volatility In finance, volatility refers to the amount of change in the rate of a financial instrument, such as commodities, currencies, stocks, over a given time period. Essentially, volatility describes the nature of an instrument’s fluctuation; a highly volatile security equates to large fluctuations in price, and a low volatile security equates to timid fluctuations in price. Volatility is an important statistical indicator used by financial traders to assist them in developing trading systems. Traders can be successful in both low and high volatile environments, but the strategies employed are often different depending upon volatility. Why Too Much Volatility is a ProblemIn the FX space, lower volatile currency pairs offer less surprises, and are suited to position traders.High volatile pairs are attractive for many day traders, due to quick and strong movements, offering the potential for higher profits, although the risk associated with such volatile pairs are many. Overall, a look at previous volatility tells us how likely price will fluctuate in the future, although it has nothing to do with direction.All a trader can gather from this is the understanding that the probability of a volatile pair to increase or decrease an X amount in a Y period of time, is more than the probability of a non-volatile pair. Another important factor is, volatility can and does change over time, and there can be periods when even highly volatile instruments show signs of flatness, with price not really making headway in either direction. Too little volatility is just as problematic for markets as too much, we uncertainty in excess can create panic and problems of liquidity. This was evident during Black Swan events or other crisis that have historically roiled currency and equity markets. In finance, volatility refers to the amount of change in the rate of a financial instrument, such as commodities, currencies, stocks, over a given time period. Essentially, volatility describes the nature of an instrument’s fluctuation; a highly volatile security equates to large fluctuations in price, and a low volatile security equates to timid fluctuations in price. Volatility is an important statistical indicator used by financial traders to assist them in developing trading systems. Traders can be successful in both low and high volatile environments, but the strategies employed are often different depending upon volatility. Why Too Much Volatility is a ProblemIn the FX space, lower volatile currency pairs offer less surprises, and are suited to position traders.High volatile pairs are attractive for many day traders, due to quick and strong movements, offering the potential for higher profits, although the risk associated with such volatile pairs are many. Overall, a look at previous volatility tells us how likely price will fluctuate in the future, although it has nothing to do with direction.All a trader can gather from this is the understanding that the probability of a volatile pair to increase or decrease an X amount in a Y period of time, is more than the probability of a non-volatile pair. Another important factor is, volatility can and does change over time, and there can be periods when even highly volatile instruments show signs of flatness, with price not really making headway in either direction. Too little volatility is just as problematic for markets as too much, we uncertainty in excess can create panic and problems of liquidity. This was evident during Black Swan events or other crisis that have historically roiled currency and equity markets. Read this Term.
With this move, it seems as if the investment management firm is looking to mitigate trading risk before even hiring an employee. Earlier this year, the Financial Stability Board (FSB) made some recommendations to financial firms to eliminate risk. One of its suggestions included utilising risk/compliance department employees for hiring new employees. However, whether or not Li's hire is in response to this is unclear.
Derrick Li’s career
Most recently, Li was a quant researcher and trader at Nomura. Here he researched and built systematic volatility strategies. These were primarily in VIX and equity, rates, credit and commodities ETFs. He held this position from March 2016 until August 2018.
Before this, from March 2010 up until March 2016, Li worked in equity derivatives quant at Barclays Capital. According to his LinkedIn profile, during the six years that he worked at the multinational investment bank, he built pricing models and trading tools, as well as providing coverage to the flow and structure volatility desks.
Li also worked at Lehman Brothers as a software developer. He studied at Carnegie Mellon University - Tepper School of Business and Tulane University.
Millennium Management, a global investment management firm, has hired Derrick Li, a quantitative analysis (quant) trader and researcher, as a risk manager this month. According to his LinkedIn profile, Li has been tasked with an interesting responsibility in his new role.
On top of the typical responsibilities of a risk manager, such as overseeing coverage of the equity derivatives market risk, Li will also interview prospective portfolio managers and evaluate their trading strategies.
It’s not clear if Millennium Management is one of the first hedge funds to include risk managers in the hiring process but it's not known to be a common practice. As a risk manager, Li will also be responsible for building in-house quantitative risk 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 for cross-asset Volatility Volatility In finance, volatility refers to the amount of change in the rate of a financial instrument, such as commodities, currencies, stocks, over a given time period. Essentially, volatility describes the nature of an instrument’s fluctuation; a highly volatile security equates to large fluctuations in price, and a low volatile security equates to timid fluctuations in price. Volatility is an important statistical indicator used by financial traders to assist them in developing trading systems. Traders can be successful in both low and high volatile environments, but the strategies employed are often different depending upon volatility. Why Too Much Volatility is a ProblemIn the FX space, lower volatile currency pairs offer less surprises, and are suited to position traders.High volatile pairs are attractive for many day traders, due to quick and strong movements, offering the potential for higher profits, although the risk associated with such volatile pairs are many. Overall, a look at previous volatility tells us how likely price will fluctuate in the future, although it has nothing to do with direction.All a trader can gather from this is the understanding that the probability of a volatile pair to increase or decrease an X amount in a Y period of time, is more than the probability of a non-volatile pair. Another important factor is, volatility can and does change over time, and there can be periods when even highly volatile instruments show signs of flatness, with price not really making headway in either direction. Too little volatility is just as problematic for markets as too much, we uncertainty in excess can create panic and problems of liquidity. This was evident during Black Swan events or other crisis that have historically roiled currency and equity markets. In finance, volatility refers to the amount of change in the rate of a financial instrument, such as commodities, currencies, stocks, over a given time period. Essentially, volatility describes the nature of an instrument’s fluctuation; a highly volatile security equates to large fluctuations in price, and a low volatile security equates to timid fluctuations in price. Volatility is an important statistical indicator used by financial traders to assist them in developing trading systems. Traders can be successful in both low and high volatile environments, but the strategies employed are often different depending upon volatility. Why Too Much Volatility is a ProblemIn the FX space, lower volatile currency pairs offer less surprises, and are suited to position traders.High volatile pairs are attractive for many day traders, due to quick and strong movements, offering the potential for higher profits, although the risk associated with such volatile pairs are many. Overall, a look at previous volatility tells us how likely price will fluctuate in the future, although it has nothing to do with direction.All a trader can gather from this is the understanding that the probability of a volatile pair to increase or decrease an X amount in a Y period of time, is more than the probability of a non-volatile pair. Another important factor is, volatility can and does change over time, and there can be periods when even highly volatile instruments show signs of flatness, with price not really making headway in either direction. Too little volatility is just as problematic for markets as too much, we uncertainty in excess can create panic and problems of liquidity. This was evident during Black Swan events or other crisis that have historically roiled currency and equity markets. Read this Term.
With this move, it seems as if the investment management firm is looking to mitigate trading risk before even hiring an employee. Earlier this year, the Financial Stability Board (FSB) made some recommendations to financial firms to eliminate risk. One of its suggestions included utilising risk/compliance department employees for hiring new employees. However, whether or not Li's hire is in response to this is unclear.
Derrick Li’s career
Most recently, Li was a quant researcher and trader at Nomura. Here he researched and built systematic volatility strategies. These were primarily in VIX and equity, rates, credit and commodities ETFs. He held this position from March 2016 until August 2018.
Before this, from March 2010 up until March 2016, Li worked in equity derivatives quant at Barclays Capital. According to his LinkedIn profile, during the six years that he worked at the multinational investment bank, he built pricing models and trading tools, as well as providing coverage to the flow and structure volatility desks.
Li also worked at Lehman Brothers as a software developer. He studied at Carnegie Mellon University - Tepper School of Business and Tulane University.