111–120 of 232 posts
Earlier quoted context omitted.
The difference is that chess, go, etc are all essentially rules based. Finance has very few rules that do not break over time. Just look at QE. Arguably the financial market represents the collective intelligence of a huge amount of very clever people. Machines are only just starting to challenge a single human at a rules-based activity. We're very far from beating a brutally darwinian, impressively adaptive, human h…
QE?
because traders are used to seeing such predictions fail. Reuters was trading FX electronically since the early 1990s. At the tier one IB I worked for the IT budget was 500m USD a year (across products), and that was in 1997! Huge resources were thrown at automation. However, to this day, large trades in FX (> 10m USD notional) are still almost exclusively performed by humans over a telephone or over the bloomberg me…
And this is why AI/ML will never be very disruptive in law.
The low-dow is: It's not about the facts, it's a social status game.
Everybody feels their job is safe from automation. Its the same way planes crash but not mine . Believing "I am special", is just built into us.
Regarding your airplane example, it's actually the opposite: plane crashes are so infrequent that you have to believe "I am special" (with a negative twist) if you think you are likely to be in a crash.
Although per passenger mile, it's true that air is very safe overall.
Earlier quoted context omitted.
It looks like the question title was changed, in order to aggregate similar questions/answers. Most of the answers answer the question in the current title. I agree that trading and IB is a big distinction, and traders are 1.5 feet out the door already.
There's also a length limit to submission titles. I don't know what it is, but I suspect the original title exceeds it.
Well, an equally good question is why do programmers feel their jobs are immune from AI, etc.?
Interesting, I never thought about that. It seems like something very far off, though. Does anyone know about any research currently being done in this field?
So any advance in AI/ML/NNs can be seen as advances in the field of automating computer programmers. We don't tend to think of it like that because these techniques have to be applied, currently, by computer programmers, and the software the techniques "write" was generally infeasible to write by hand anyway: it's been so far purely additive and solving problems that hand-written software either couldn't do at all or where it sucked and progress was very slow.
But watch out. The tech is getting better scarily fast. Neural networks have been able to learn how to do basic algorithms like sorting given only examples. The primary limit is still the amount of data they need to work with, but R&D is driving that down too.
I can see a time when many tasks that today automatically require skilled programmers are instead done by domain experts who simply shovel data into an advanced neural network and discover it gets results that are good enough. Perhaps hand-written software made by a skilled programmer would be better, but the machine is a lot cheaper ...
http://www.i-programmer.info/news/105-artificial-intelligenc...
because traders are used to seeing such predictions fail. Reuters was trading FX electronically since the early 1990s. At the tier one IB I worked for the IT budget was 500m USD a year (across products), and that was in 1997! Huge resources were thrown at automation. However, to this day, large trades in FX (> 10m USD notional) are still almost exclusively performed by humans over a telephone or over the bloomberg me…
There was a joke from the 80s: soon the whole trading floor will be replaced by a computer, a man and a dog. The man presses the button to turn on the computer every morning. The computer operates all of the transactions and settlements automatically. And the dog is there to bite the man if he touches any other button.
30 years later, still no dog on the floor!
Earlier quoted context omitted.
Machine learning is "learning from data." It is not the assumption that there are no dynamics, and that the future will simply be a repetition of the past. To the extent that the future is predictable, learning from data is the best that can be done. The reality is that speech recognition, language translation, face recognition, object classification and detection, semantic segmentation, speech and image synthesis ha…
Would Alpha go beat the same human on 17x17 or 21x21? Or even 13x13? Let's not get started about self-driving cars…
Here's what I have seen. For more "liquid" markets - like delta-one blue chip equities, IR swaps, spot FX - jobs ARE moving quickly. People are not complacent. Competition comes from firms that are much faster in execution, having automated most of the process - and presumably having lower hedging costs by being able to move quickly. Or just basing their decisions "better" on history.
Having said that, you have to remember that: a) Size of balance sheet matters. A large bunch of clients of such firms trade with them due to "synergies". Large shops are one-stop shops. They don't just sell you a swap. Its usually a loan/funding + an option to hedge it + some tax advice thrown in. Its hard to break it down into components and trade individually, as most other players may not be even willing to face such counterparts. And shopping for each component separately might mean larger time and friction costs. Here being a large bank helps massively. Basically - capital is hard to be automated, decision making is easier.
b) There are a large number of markets that cannot be automated anytime soon. These are "illiquid" or "exotic" markets. The data on them is scarce and trades happen in a lot less frequency, then say swaps or delta-one equity. Throw in some options, and your model might say here's a price, but there's no guarantee on it. Then you have to balance it compared to your book size, and the price is not discoverable, or even same across firms (for good reason - my cost of cash liquidity might be different to yours). Sometimes counterparts will trade with you even if you have a worse price, because there's lower risk of you falling down in 10 years on a 20 year cross currency trade or inflation swap.
c) Good traders are very defensive and paranoid. They don't work under the paradigm that history will repeat itself. They will try to look for risks that might seem to have nothing to do with their market. I know some traders who have been making good money every year in these exotic markets, regardless of crisis or not. Some of the sharpest people I know - and they love their job. Not just the money aspect. They just love the whole high pressure environment and everything it entails. Now they are being drowned in regulation. Don't get me wrong - regulation is good, but right now we have a mess. I won't try to convince you - and unless you work in a bank, you will not agree with this.
d) Management is old. Might seem obvious, but most of the people at the top have made their money the old-style way. After a certain amount of time, you tend to trust your own instincts more than other things, especially if you've been successful (which may have just been a random process to start with). So they think this is just another fad and it will pass. These things have come and gone - we're still here. This is a prime reason why some banks fail suddenly. The top guys just didn't see it coming.
because traders are used to seeing such predictions fail. Reuters was trading FX electronically since the early 1990s. At the tier one IB I worked for the IT budget was 500m USD a year (across products), and that was in 1997! Huge resources were thrown at automation. However, to this day, large trades in FX (> 10m USD notional) are still almost exclusively performed by humans over a telephone or over the bloomberg me…
Machine learning is "learning from data." It is not the assumption that there are no dynamics, and that the future will simply be a repetition of the past. To the extent that the future is predictable, learning from data is the best that can be done. The reality is that speech recognition, language translation, face recognition, object classification and detection, semantic segmentation, speech and image synthesis ha…
And even if you could, acting on that knowledge would change the future.