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Isn't this type of trading sort of unique in how easy it is to consume the data? Even in other investing areas, people who just "look at data and make decisions
by burkaman 10y ago
Isn't this type of trading sort of unique in how easy it is to consume the data? Even in other investing areas, people who just "look at data and make decisions" cannot be easily replaced until computers can read and understand natural language and understand the state of the world. I can't immediately think of anything else this type of automation could apply to.
- Retric 10y agoComputers are already making trades based on natural language articles. EX of some research from 2008: http://www.seas.upenn.edu/~cse400/CSE400_2007_2008/DavdaMittal/project.html http://www.seas.upenn.edu/~cse400/CSE400_2007_2008/DavdaMitt... You see real world movement within seconds of the release of some public data. Which is far to soon for humans to read much of anything.
- nosuchthing 10y agoI'd have to wonder what type of safeguards vet against intentional false data released to poison the well of other HFT bots? Maybe it's not too important yet because HFT bots only need to game the slower mass of "dumb (reactionary) money"
- adamiscool8 10y agoNot a whole lot. [1] [1] http://www.theatlantic.com/sponsored/etrade-social-stocks/the-day-social-media-schooled-wall-street/327/ http://www.theatlantic.com/sponsored/etrade-social-stocks/th...
- burkaman 10y agoAbsolutely, but isn't this type of software generally used to augment existing jobs, rather than replace them? The program can make basic inferences like "10 negative GE articles -> Sell GE stock", but you still need a human to make more general decisions about an industry as a whole, related companies, how long-term an issue is, etc. Basically, nobody's job is as simple as reading the news and deciding whether articles are positive or negative. In the case of this Goldman Sachs story, some people's jobs really were as simple as looking at the performance of equities and making decisions based on that alone.
- zardo 10y ago>Absolutely, but isn't this type of software generally used to augment existing jobs, rather than replace them? Is there a difference? If a person does 10x as much you need 1/10th as many of them.
- burkaman 10y agoBut this is about responding faster, not responding more often. It helps you react to a story before everyone else, but there's only so many stories in a day.
- Scea91 10y agoOr you can have 10× productivity.
- zardo 10y agoYou have that either way. The question is do you need 10x the output?
- cm2187 10y agoThese algorithms are only good at front running the reaction of the market, which is fairly predictable in the short term when a relevant piece of news is published. They are nowhere for making long term investment decisions. AI everywhere is as naive as software everywhere. It costs a lot of money to develop and maintain software professionally. It's uneconomical to staff an IT team to replace every routine job there is with software. It will be the same with AI. If you apply enough bright people's mind for long enough, we can possibly automate certain niche jobs, but that's going to be completely uneconomical. You need to pay these AI specialists, and retain them so that you can adapt your algos as the problem moves, and there needs to be more than one in case one goes or is ill. You can easily spend more that the salaries you are going to spare. Other problems are I believe too vast and open ended for AI to come up with anything useful. What company will be successful in 5 years? What is the right level of renumeration for the management of that company? Where is innovation going next? You really think that AI is anywhere near answering that kind of questions? AI will shine in similar areas where software shines. Problems that are simple enough to be understood by computer scientists (and that narrows down this vast world quite a bit), and that are broad enough to justify the economics of making high investments to build data, or where there is already ample of existing and relevant data. Driving a car. Cleaning or building a house. Replicating certain hand movements. Things like that. I doubt an expert on a narrow and specialised domain has much to fear from AI.
- fauigerzigerk 10y agoAh but in 2008 these news articles were still written by humans :-)
- totalZero 10y agoYes. Not to mention that spreads are so tight that the only real challenge for a cash trading desk is how to read the flow, and manage inventory, into events. Computers don't do a great job of interpreting doublespeak and insinuation, which is a substantial part of earnings calls and other releases of information. Derivatives trading has grown as cash trading has shrunk. And the banks haven't figured out how to automate that business yet.
- tomelders 10y agoNot really. The world is data. Anything you need to know about the world is in a table somewhere, and the web has done a great job of making the planets data accessible to machines. Other areas may be more complex, but complexity isn't as big a barrier as it may seem. Also, those areas are probably not as complex as people think they are. I'm reminded of the AI that started making breakthroughs in oncology by looking at the cells around a tumour, despite the scientific consensus that there was nothing to be learned from the cells around a tumor.
- taurath 10y agoSpoken like a true data scientist. Hope there's room for culture in there somewhere.
- pdog 10y agoSorry to burst your bubble, but culture is data too.
- rand_r 10y agoThe problem is understanding data. A human can learn a new word's meaning by reading its definition in a dictionary; a computer can't.
- NikolaeVarius 10y agoIt's a dictionary. I'm pretty sure a computer can look it up faster than a human can.
- rand_r 10y agoAccess to raw text is not the hard part.
- eon1 10y agoEven so, that doesn't stop one human helping the computer to understand, and thus replace x more humans.
- teachrdan 10y agoAnother obvious example is actuaries and any number of other analysts in the insurance industry. According to Google[1], actuaries earn an average of just over $97k/year. While this may not strictly be an "investing area," it's a clear target for automation. 1. https://www.google.com/search?q=actuaries https://www.google.com/search?q=actuaries
- grondilu 10y agoI don't know about actuaries, but I do remember there was an article lately about an insurance company in Japan that replaced hundreds of workers with IBM's Watson. https://www.theguardian.com/technology/2017/jan/05/japanese-company-replaces-office-workers-artificial-intelligence-ai-fukoku-mutual-life-insurance https://www.theguardian.com/technology/2017/jan/05/japanese-...
- api_or_ipa 10y agoThat's really interesting. In my circle of friends, most of us can't point to a single success of Watson, despite it's very publicized capabilities. This may only be a small company, but almost certainly larger insurance agencies & medical foundations must be on to this.
- eru 10y agoActuaries have quite a high regulatory wall protecting them from other human competitors. But probably not from computers. (I don't know about the legalities, but the laws requiring companies to have a actuary sign off on stuff probably can be satisfied by having on part time actuary shared between multiple companies sign off on computer generated analysis.)
- go_prodev 10y agoI know in banking, regulators are satisfied with ML models making decisions about bank capitalisation, so long as the models are annually audited by an external auditor (eg. KPMG, EY)
- 10y ago
- eon1 10y agoComputers don't need to understand natural language, they just need a little guidance towards what the data means. You can train a neural network, or you can just write some solution-specific code. A lot of people are not that hard to replace.
- walshemj 10y agoyeh you can't send a robot of an on site visit to look the management in the eye
- AznHisoka 10y agoi agree 100%. i think the only reason traders can be replaced by machines is because these traders didnt perform better than monkeys shooting darts.