Computers and humans are prone to different pitfalls. Humans have far too many biases to count - see for example, the works of Kahneman and Tversky and most of social psychology.
Computers, on the other hand come with a whole host of different problems (perhaps because they're made by humans). The essential advantage a human has is the eye, which is extremely well adapted to picking out patterns. That, and an ability to go beyond the model or completely change it. This is something that computers have difficulty with (unless of course they're programmed for it - i think genetic algorithms claim to do this, but I'm not particularly knowledgeable about those).
Nonetheless, i agree with the thesis that this kind of analysis will invade the rest of the social sciences. In fact, that's one of the reasons I learned to program.
Computers are just the next step, crunching out the patterns until they are unexploitable (below the threshold of trading costs).
The end result is that markets are a random walk - unless you are at the bleeding edge with faster machines, better latency, lower transaction costs, etc.
Of course, an alternative to this is to do true bottom-up analysis, or invest in illiquid companies (like VCs do).
Can you execute your strategy faster than your opponents if you go to a slightly more aggressive (less arbitrage-y) signal?
Should you? (Why bother if you're faster?)
For what situations is it worth "thinking longer"? Some straight arbs require speed beyond what you can do if you want to use your HOT "smart" model.
If you work outwards from the fastest "stupidest" trades, there's a vast array of strategies/opportunities that intersect ML/AI, hardware design, network optimization, and so forth -- I do agree that if you're looking at bad, inaccurately sampled tick data, the opportunities aren't really there anymore. (Because there's increasingly more players correcting relative value mispricings)
I'm not sure that's an advantage. The human eye is so good at discerning patterns that it sees them even where they do not exist. Witness: technical analysis, Eliot Wave Theory, etc.
Quants use math to provide a more rigorous framework for eliminating hocus pocus like that, though they have been known to make less than rigorous assumptions from time to time (good ones like Paul Wilmott have been particularly prescient in calling out that tendancy).
> Nonetheless, i agree with the thesis that this kind of analysis will invade the rest of the social sciences. In fact, that's one of the reasons I learned to program.
Agreed. There's still anachronistic cruft that needs to be exorcised from the field, for example the notion of 'utility' that economists use to evaluate the psychology of decision making (good discussion about that on HN recently, forgot where). CS + X, for (almost) all X, is where the world is heading.
1. http://www.bloomberg.com/apps/news?pid=newsarchive&sid=a...
https://www.quantnet.com/mfe-programs-rankings/
But you should think very carefully about whether starting a new degree like this is worth it. Firstly, you'll notice that the tuition for all of these programs is very high, so there's a strong financial commitment to this.
More importantly, an MFE is extremely specialized; it's not like getting an MBA or an advanced degree in computer science, both of which will open doors for many opportunities. An MFE leads to a very limited set of employers since these programs don't teach the general programming or statistics or even finance skills that are widely applicable to other industries.
Lastly, without any prior experience in the field, a degree alone isn't very attractive to employers. And given the devastation that has occurred to the industry over the past few years, there are plenty of unemployed experienced people that you'll be competing with for jobs.
So you can go into a ton of debt for a degree with limited opportunities that likely won't come anyway.
I wish they'd take a step up the hierarchy of needs from simply trying to find ways to make more money by predicting the future, to instantiating a safer, more robust, more resilient global economic system.
If their funding and earnings need to be guaranteed by governments instead of offered by industry in order to incent and achieve that shift, then so be it. When the entire world and the lives of everyone in it is your lab because it's too complex for isolated simulation and testing, then re-evaluation of your priorities and conflicts of interest is in order.
http://news.ycombinator.com/item?id=3667166
http://news.ycombinator.com/item?id=3685113
In fact, out of curiosity I tried to submit the story once again by munging the fragment ID. And was able to:
4 Days ago and 0 comments. I'd say this 'repost' is warranted, was an interesting read for many people it seems.
I'm constantly underwhelmed by Bloomberg and would love to see more players in news aggregation/analysis space in finance.
it's interesting that it's 80 percent men. it's obviously going to be very lucrative.
Additionally, how are they occasionally destroying the world's economy?
http://www.wired.com/techbiz/it/magazine/17-03/wp_quant?curr...
It's a waste of resources as moving paper around does not contribute to economic growth, whereas say spending time and energy on AI or nano might.
