The very concept of ‘singularity’ is borrowed from the world of artificial intelligence (AI) research, and refers to a scenario in which AI systems are able to create future superior generations, exponentially faster, ultimately surpassing human intelligence.
Visiting two industry conferences in London, one has something of a mixed image; while fintech advocates hail new technologies and allude to their role in creating a more efficient market, financial singularity is keeping key participants “awake at night”.
Speaking at Battle of the Quants in London, Paul Netherwood, Partner at systematic investment fund Beach Horizon, noted that the financial singularity could become a reality in the world of trading systems.
You could potentially build AIs that are capable of also creating other trading systems
“If you are able to extract rules, and your understanding of how you create rules...how you adapt those systems, you could potentially build AIs that are capable of also creating other trading systems,” said Netherwood. “(These) will be even more efficient at extracting the inefficiencies of the markets, and will be able to do it at such speeds that humans are not able to compete.”
Presently, significant volumes of US and European exchanges are dominated by high-frequency trading systems, which are fast becoming a global phenomenon. Meanwhile, regulators have taken their time in catching up to the new technical reality of the global markets.
“Those sorts of lagged reactions are perhaps because the debate hasn’t started,” Netherwood said. That debate, he added, should not be about whether a singularity will occur or not, rather to try and understand what the result would be and where it could lead to. Perhaps the industry needs to consider whether systems should have ethical guidelines built in, for example.
Does AI Dream of Electric Returns?
Though technologies associated with high-frequency trading are now widely adopted by the industry, the use of artificial intelligence and machine learning in finance is only just starting to catch on within the mainstream.
The essence of machine learning, said Marco Fasoli, Co-Founder and Managing Partner of QuantBridge, a fully automated program that derives several of its strategies from AI systems, is to empower the computer to determine optimal rules at each prediction point.
“You need really to have systems that have the ability to fine tune their own structure as a function of the data that they see,” he added.
On the predictive side of its business, QuantBridge’s systems do this every 24 hours with 600 systems for each of the markets traded in a given strategy, including futures markets across equities, bonds and commodities.
“They have the freedom to identify the hidden relationships between past and future price,” Fasoli noted.
There is also a second layer of machine learning supervising the systems as well, functioning as a kind of ‘head trader’ in charge of 600 individual traders.
Such technology receives more than its fair share of cynicism. Critics say that funds claiming to be at the cutting edge of machine learning are constantly going out of business.
However, the reality is that some funds are little more than a Mechanical Turk, or are not using the right AI technologies. Funds that have stood out are notoriously secretive, with firms like Renaissance Technologies emerging as highly successful actors. Only recently, massive fund houses like Bridgewater have announced big commitments to AI research and development.
Speaking to Finance Magnates on the sidelines of the Battle of the Quants conference, Fasoli said that one of the fundamental problems is that there needs to be a ‘scientific engineering mind set’.
“If you just try to adapt what has been done in the quantitative space, from the more traditional finance background-type approach, you are going to have difficulty. This is really an engineering, scientific problem,” he said.
Positively Disrupted
It’s hardly surprising that asset managers are wary of new technology, particularly if it seems to have sharp elbows and threatens to disintermediate some level of decision-making. But there is a group of vendors ready to help firms come around to the inevitable changes buffeting the industry.
Global managed services provider, Perseus, is one of the actors making markets efficient. Jock Percy, Founder and CEO of the firm, said that the financial singularity is one of those things keeping him “awake at night”, and that there is a point when stat arbitrage opportunities should be at zero.
Technology-driven market makers, which form a significant portion of Perseus’ clients, are part of driving that reality, largely at the expense of legacy systems, he added.
In terms of major disruptive trends, two in particular were highlighted at Finance Magnates’ London Summit: one is the democratization of investing via algorithmic trading, robo-advisory services, and social trading, and another is process externalization to deal with, for example, extra reporting procedures due to regulations.
Presenting at the Summit, Susanne Chishti, CEO of the European angel network, Fintech Circle, said that asset management is being disrupted by Fintech in a ‘very positive’ way.
“What we can see (is) companies developing solutions which really help asset managers to be more productive, to help lower their cost base, achieve their customers in a more easier, automated way,” she said. “Which I think will be better for the whole industry.”
She pointed to examples of firms that are offering such services as advanced predictive 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. Analyt
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. Analyt
Read this Term, matching trades in the illiquid corporate bond market, and cloud-based solutions for mandatory reporting functions.
Special FX
Behind equities, the FX market is lockstep in terms of high-frequency trading (HFT) disruption to trading practices. However, FX may actually be ahead in terms of the financial singularity.
Speaking to Finance Magnates at the London Summit, Nicolas Vitale, Founder and Director of Alpha Novae, said that, perhaps surprisingly, FX exhibits more features associated with the theory than other asset classes.
“When you have news – ECB news, rate decisions – and the prices jumping...then everything moves very fast, readjusts, which is more or less what would happen with a financial singularity, where you think the market will move only when there is an event that needs to be priced,” Vitale said.
A singularity will not exist if you have people who get information at different speeds
Alpha Novae is a consulting firm and technology provider specialized in systematic, automated and algorithmic trading. Vitale noted that algo trading is definitely one of the technologies moving the markets towards a ‘zero alpha’ environment.
“On the sell side, market making used to be dealing rooms and that is going away. Investment banks that were lagging behind on the automation of market making have to now progress on that,” he said. On the buy side, there is more and more automation, though the FX market has “a lot of room to progress”.
But Vitale does not believe that alpha will go all the way to zero, adding that there are risks associated with singularity, such as massive 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
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
Read this Term related to some events, with the Switzerland central bank’s decision to unpeg the franc from the euro as a case in point.
Moreover, “Singularity will not exist if you have people who get information at different speeds,” he added.
How the financial industry will face its own singularity moment remains to be seen, but one thing does seem to be clear: the same disruptive forces shaking up traditional business models have come home to roost, and all sides of the industry are wondering where the alpha will reside.
The very concept of ‘singularity’ is borrowed from the world of artificial intelligence (AI) research, and refers to a scenario in which AI systems are able to create future superior generations, exponentially faster, ultimately surpassing human intelligence.
Visiting two industry conferences in London, one has something of a mixed image; while fintech advocates hail new technologies and allude to their role in creating a more efficient market, financial singularity is keeping key participants “awake at night”.
Speaking at Battle of the Quants in London, Paul Netherwood, Partner at systematic investment fund Beach Horizon, noted that the financial singularity could become a reality in the world of trading systems.
You could potentially build AIs that are capable of also creating other trading systems
“If you are able to extract rules, and your understanding of how you create rules...how you adapt those systems, you could potentially build AIs that are capable of also creating other trading systems,” said Netherwood. “(These) will be even more efficient at extracting the inefficiencies of the markets, and will be able to do it at such speeds that humans are not able to compete.”
Presently, significant volumes of US and European exchanges are dominated by high-frequency trading systems, which are fast becoming a global phenomenon. Meanwhile, regulators have taken their time in catching up to the new technical reality of the global markets.
“Those sorts of lagged reactions are perhaps because the debate hasn’t started,” Netherwood said. That debate, he added, should not be about whether a singularity will occur or not, rather to try and understand what the result would be and where it could lead to. Perhaps the industry needs to consider whether systems should have ethical guidelines built in, for example.
Does AI Dream of Electric Returns?
Though technologies associated with high-frequency trading are now widely adopted by the industry, the use of artificial intelligence and machine learning in finance is only just starting to catch on within the mainstream.
The essence of machine learning, said Marco Fasoli, Co-Founder and Managing Partner of QuantBridge, a fully automated program that derives several of its strategies from AI systems, is to empower the computer to determine optimal rules at each prediction point.
“You need really to have systems that have the ability to fine tune their own structure as a function of the data that they see,” he added.
On the predictive side of its business, QuantBridge’s systems do this every 24 hours with 600 systems for each of the markets traded in a given strategy, including futures markets across equities, bonds and commodities.
“They have the freedom to identify the hidden relationships between past and future price,” Fasoli noted.
There is also a second layer of machine learning supervising the systems as well, functioning as a kind of ‘head trader’ in charge of 600 individual traders.
Such technology receives more than its fair share of cynicism. Critics say that funds claiming to be at the cutting edge of machine learning are constantly going out of business.
However, the reality is that some funds are little more than a Mechanical Turk, or are not using the right AI technologies. Funds that have stood out are notoriously secretive, with firms like Renaissance Technologies emerging as highly successful actors. Only recently, massive fund houses like Bridgewater have announced big commitments to AI research and development.
Speaking to Finance Magnates on the sidelines of the Battle of the Quants conference, Fasoli said that one of the fundamental problems is that there needs to be a ‘scientific engineering mind set’.
“If you just try to adapt what has been done in the quantitative space, from the more traditional finance background-type approach, you are going to have difficulty. This is really an engineering, scientific problem,” he said.
Positively Disrupted
It’s hardly surprising that asset managers are wary of new technology, particularly if it seems to have sharp elbows and threatens to disintermediate some level of decision-making. But there is a group of vendors ready to help firms come around to the inevitable changes buffeting the industry.
Global managed services provider, Perseus, is one of the actors making markets efficient. Jock Percy, Founder and CEO of the firm, said that the financial singularity is one of those things keeping him “awake at night”, and that there is a point when stat arbitrage opportunities should be at zero.
Technology-driven market makers, which form a significant portion of Perseus’ clients, are part of driving that reality, largely at the expense of legacy systems, he added.
In terms of major disruptive trends, two in particular were highlighted at Finance Magnates’ London Summit: one is the democratization of investing via algorithmic trading, robo-advisory services, and social trading, and another is process externalization to deal with, for example, extra reporting procedures due to regulations.
Presenting at the Summit, Susanne Chishti, CEO of the European angel network, Fintech Circle, said that asset management is being disrupted by Fintech in a ‘very positive’ way.
“What we can see (is) companies developing solutions which really help asset managers to be more productive, to help lower their cost base, achieve their customers in a more easier, automated way,” she said. “Which I think will be better for the whole industry.”
She pointed to examples of firms that are offering such services as advanced predictive 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. Analyt
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. Analyt
Read this Term, matching trades in the illiquid corporate bond market, and cloud-based solutions for mandatory reporting functions.
Special FX
Behind equities, the FX market is lockstep in terms of high-frequency trading (HFT) disruption to trading practices. However, FX may actually be ahead in terms of the financial singularity.
Speaking to Finance Magnates at the London Summit, Nicolas Vitale, Founder and Director of Alpha Novae, said that, perhaps surprisingly, FX exhibits more features associated with the theory than other asset classes.
“When you have news – ECB news, rate decisions – and the prices jumping...then everything moves very fast, readjusts, which is more or less what would happen with a financial singularity, where you think the market will move only when there is an event that needs to be priced,” Vitale said.
A singularity will not exist if you have people who get information at different speeds
Alpha Novae is a consulting firm and technology provider specialized in systematic, automated and algorithmic trading. Vitale noted that algo trading is definitely one of the technologies moving the markets towards a ‘zero alpha’ environment.
“On the sell side, market making used to be dealing rooms and that is going away. Investment banks that were lagging behind on the automation of market making have to now progress on that,” he said. On the buy side, there is more and more automation, though the FX market has “a lot of room to progress”.
But Vitale does not believe that alpha will go all the way to zero, adding that there are risks associated with singularity, such as massive 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
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
Read this Term related to some events, with the Switzerland central bank’s decision to unpeg the franc from the euro as a case in point.
Moreover, “Singularity will not exist if you have people who get information at different speeds,” he added.
How the financial industry will face its own singularity moment remains to be seen, but one thing does seem to be clear: the same disruptive forces shaking up traditional business models have come home to roost, and all sides of the industry are wondering where the alpha will reside.
