http://www.financialnews-us.com/?page=ushome&contentid=2348859489
Ohhh the poor quants....all those billions down the drain.
Ohhh the poor quants....all those billions down the drain.
Algorithmic Trading Gets Smarter After Quant Upset
- Thread starter archon
- Start date
David Viniar, chief financial officer of Goldman Sachs â whose flagship quantitative multi-strategy Global Alpha hedge fund is down 32.9% this year to mid-September, and in August plunged 22.5% â termed Augustâs market moves as a 25-standard deviation event, something that would normally only occur once every 100,000 years. In September, the fund was valued at $6bn (â¬4.3bn), down from $10bn last year.
Is this true, the correction was 25 standard deviations?
Is this true, the correction was 25 standard deviations?
Quote from kinggyppo:
David Viniar, chief financial officer of Goldman Sachs â whose flagship quantitative multi-strategy Global Alpha hedge fund is down 32.9% this year to mid-September, and in August plunged 22.5% â termed Augustâs market moves as a 25-standard deviation event, something that would normally only occur once every 100,000 years. In September, the fund was valued at $6bn (â¬4.3bn), down from $10bn last year.
Is this true, the correction was 25 standard deviations?
Wtf?? 25 standard deviations? What a total and utter load of shit.
This should give hope to any beginners out there. These high-end quants turn out to be shit traders.
what i dont understand is this.
these guys who run these quant based funds and systems are clearly intelligent and the guys who program it all in and look after the systems are intelligent as well.
so when we keep getting these rare events every 12 months that wipe a load of them out dont they kind of look at it and go you know what maybe we aint that great after all.
trading and making money is not a science its an art.
these funds are all similiar because they have the same taught staff with the same mentality and education of quant maths so they are all positioned the same way.
now im no genius here but when this happens of course they are going to screwed every time they all try to get out.
what is so clever about that.
these guys should take a long look at themselves and just admit that they dont have a clue what they are doing and lot of it is down to leverage,size,low round trip fees and who they know.
there does not seem to be any skill at all in programming an algo that loses a boat load every 12 months then blaming it on a fucking black swan.
theres seems to be more black swans than white ones.
these guys who run these quant based funds and systems are clearly intelligent and the guys who program it all in and look after the systems are intelligent as well.
so when we keep getting these rare events every 12 months that wipe a load of them out dont they kind of look at it and go you know what maybe we aint that great after all.
trading and making money is not a science its an art.
these funds are all similiar because they have the same taught staff with the same mentality and education of quant maths so they are all positioned the same way.
now im no genius here but when this happens of course they are going to screwed every time they all try to get out.
what is so clever about that.
these guys should take a long look at themselves and just admit that they dont have a clue what they are doing and lot of it is down to leverage,size,low round trip fees and who they know.
there does not seem to be any skill at all in programming an algo that loses a boat load every 12 months then blaming it on a fucking black swan.
theres seems to be more black swans than white ones.
<i>"The sad thing is they hide behind BS statistics and "25 sigma events" when they know exactly that financial markets do not follow normal distributions."</i>
100% agreed. I'm going to assume that quant guy has never measured charts from the summer of 2002 as part of his comparison. What would that period be, a 100 sigma event? Five years ago.
100% agreed. I'm going to assume that quant guy has never measured charts from the summer of 2002 as part of his comparison. What would that period be, a 100 sigma event? Five years ago.
But here comes the "GOOD" news =>
Algorithmic trading gets smarter after quant upset
...Indeed, the algorithms that underpin the strategies of these funds â deleveraging their positions in the tightest of market conditions â have been blamed for contributing to the increased equity market volatility....
While managers have posited various explanations, few have blamed their own models for the losses. The growing popularity of quant strategies is partly to blame, since they do not necessarily require huge resources, just technical know-how and a computer.
This inevitably means funds are sharing similar positions and strategies. The nature of the high-frequency trades, which rely on speed to arbitrage minuscule mispricings, coupled with overcrowding in the market, means many funds are trying to escape the same trades at the same moment....
The difficulty is factoring in liquidity squeezes. It is not as easy as it looks, according to John Edge, European head of electronic client solutions at JP Morgan in London. He said: âOne of the key things in this episode was the challenge of extreme events, which suddenly compromise previously performing models.
âRepeatedly adjusting a model and altering previously well-thought-of positions in such a short time frame, especially in leveraged positions, is a difficult and sometimes costly exercise.â...
But some tweaks to the underlying algorithms will be going on to ensure they are more sensitive to market patterns, according to John Bates, founder and vice-president of Apama Products at Progress Software. Smarter algorithms are being developed.
Bates said: âYou need to have an algorithm that can deal with any circumstance, including extreme events. Many of the institutions havenât been prepared for the extreme events seen in recent months.â
Bates expects quant trading models to increasingly incorporate risk rules alongside trading rules. This would allow, for example, an algorithm to automatically trade on a change in a particular risk measure, such as value at risk, as well as trading on arbitrage opportunities, said Bates.
He added: âWe are seeing algorithmic trading spread across asset classes â from equities to futures and options and foreign exchange â as people look at cross-asset trading to hedge a position. We are also seeing real-time compliance being incorporated into the underlying model.â
http://www.financialnews-us.com/?page=ushome&contentid=2348859489
Ha, ha, ha...the game is not over...it just recalibrates...
Algorithmic trading gets smarter after quant upset
...Indeed, the algorithms that underpin the strategies of these funds â deleveraging their positions in the tightest of market conditions â have been blamed for contributing to the increased equity market volatility....
While managers have posited various explanations, few have blamed their own models for the losses. The growing popularity of quant strategies is partly to blame, since they do not necessarily require huge resources, just technical know-how and a computer.
This inevitably means funds are sharing similar positions and strategies. The nature of the high-frequency trades, which rely on speed to arbitrage minuscule mispricings, coupled with overcrowding in the market, means many funds are trying to escape the same trades at the same moment....
The difficulty is factoring in liquidity squeezes. It is not as easy as it looks, according to John Edge, European head of electronic client solutions at JP Morgan in London. He said: âOne of the key things in this episode was the challenge of extreme events, which suddenly compromise previously performing models.
âRepeatedly adjusting a model and altering previously well-thought-of positions in such a short time frame, especially in leveraged positions, is a difficult and sometimes costly exercise.â...
But some tweaks to the underlying algorithms will be going on to ensure they are more sensitive to market patterns, according to John Bates, founder and vice-president of Apama Products at Progress Software. Smarter algorithms are being developed.
Bates said: âYou need to have an algorithm that can deal with any circumstance, including extreme events. Many of the institutions havenât been prepared for the extreme events seen in recent months.â
Bates expects quant trading models to increasingly incorporate risk rules alongside trading rules. This would allow, for example, an algorithm to automatically trade on a change in a particular risk measure, such as value at risk, as well as trading on arbitrage opportunities, said Bates.
He added: âWe are seeing algorithmic trading spread across asset classes â from equities to futures and options and foreign exchange â as people look at cross-asset trading to hedge a position. We are also seeing real-time compliance being incorporated into the underlying model.â
http://www.financialnews-us.com/?page=ushome&contentid=2348859489
Ha, ha, ha...the game is not over...it just recalibrates...