

"Artificial Intelligence" Gains Fans Among Investors - ctkrohn
http://online.wsj.com/article/SB10001424052748703834604575365310813948080.html?mod=WSJ_hps_MIDDLESecondNews

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dododo
it seems people call these techniques machine learning/AI simply because it
sounds cooler: a lot of the techniques have a 30+ year history in statistics.
this is the _same_ story as neural networks: neural networks are just nested
logistic regression, a technique with much history in statistics.

a lot of high frequency trading is simply linear/logistic regression (on the
right features of course). anything more complicated is too slow.

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jimbokun
<http://www-stat.stanford.edu/~tibs/stat315a/glossary.pdf>

Note entries for "large grant" and "nice place to have a meeting."

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T_S_
Market prediction is one of the hardest areas for machine
learning/statistics/AI to prove their value. What the system _can_ do is
monitor more inputs than a single trader, so the screening process can be
scaled up--provided you have relevant inputs. With more signals becoming
available electronically it is possible to automatically incorporate non-
market prices into trading decisions. All this will require lots of non-ML
engineering to supply the data (just like Google search). When monthly-issued
government economic statistic stop moving the market you'll know we are there.

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HSO
The reason for this may be that the market is made up of interacting learning
machines (if you subsume humans among them). Thus, every algorithm introduced
changes the basis on whcih the algorithm was selected. Talk about moving
targets...

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T_S_
I agree, and the word I would use is _competing_ machines. Every time me (or
my algo) bid up a security price I remove that "cheapness" signal from a
competitor, increasing the noise they have to deal with and vice versa. ML
hates noisy data. Returns are very noisy.

ML has a much easier time with data generated from situations that are lacking
competition, such as data from any process inside a company, e.g. consumer
behavior. These are problems that _want_ to be solved.

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Vivtek
Oh, great. AI will be over-hyped again, just like in the 80's, and we'll have
to call it something else for a decade when our families ask us what we're
doing lately.

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wheaties
Yes, it's true but

1) there will be a renewed interest in the science with more funding leading
to more discoveries which could, potentially benefit man. Who knows?

2) it may also be derided much like in the 80's when there were many funds
employing the same "fail safe" measures to guard against loss.

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tkahnoski
Hypothetical: What if an AI driven investment fund significantly out performed
other funds?

The focus on number crunching seems contrary to advice given by Warren Buffet
and other successful investors about investing in what you know.

i.e. "I think the market is under-estimating the impact of Product X, I'll
invest in that company" versus "Analyze market cap, yearly revenue...."

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icey
I've always wondered if there was a way to make some money in the market by
performing lexical analysis of news articles about stocks and gauging the
performance of those stocks based on the contents of the article.

So, you'd train your software by analyzing every news article you could find
over the past N years for every company in the market you're targeting. It
would look for words or phrases that occurred with higher frequency a few days
before stocks made significant moves in either direction.

I suppose you'd probably want to weight the source as well, articles sourced
from the WSJ might earn a better reputation than ones from the NY Post for
example.

At some point there would hopefully be enough confidence in the data that you
could test it out with $1000 or something small; news-driven automation...
There are enough people who buy and sell on news and opinion pieces that there
may be enough fat there to make some profits.

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tkahnoski
There was actually an interesting study done on quarterly earnings releases
that had some interesting implications as far as "soft data" versus hard
numbers: <http://ideas.repec.org/p/fip/fedgif/951.html>

There was a company "Quant the News" that launched a product that did
sentiment analysis on company related news, but their website no longer
exists. <http://www.crunchbase.com/company/quant-the-news>

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captaincrunch
I have my hand at this now with <http://www.algxchange.com> \- its really
cool, but training is a nightmare. I have 4 situations that required training,
and an actual decision model that needed training on what situation model to
use.

So many factors, Bear market, Bull market, etc...

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apu
Hello, submarine!

<http://www.paulgraham.com/submarine.html>

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Tichy
Thinking about it, if an investment fund has a strategy, maybe it should
really be possible to put it into code after all. If not, then what is the
basis of the investment fund - the gut feeling of it's managers?

You could also still add the gut feeling of the managers as one input variable
for the system. Probably even then there would be lots of things left to
automate. Just because it is AI doesn't mean it has to do everything by
itself.

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HSO
Yes in principle. Except that those who have a gut feeling for markets and
economics usually are not the same as those with technical acumen.

Usually, those with technical acumen find manipulating economic and financial
data by hand, and doing so repeatedly, boring. Which, I think, is necessary to
gain intuition. On the other hand, those who are willing to do this and
perhaps even enjoy studying balance sheets and cash flow statements and
product markets spend so much time doing this that they wouldn't have time to
learn math and science and AI.

In other words, you will always have either/or guys. Those who understand the
algos and those who understand the markets.

Which, by the way, doesn't keep me from trying ;)

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cageface
Isn't the underlying assumption behind all these machine learning models that
an incomprehensibly complex system can be modeled with a far simpler
approximation of that system? I only have limited experience with ML
techniques but my impression was that they're all essentially statistical and
the essence of a statistical approach is that you don't really model the
underlying mechanics.

I'm not saying that this approach can't work, but just that an insanely
complex macro-system like the market is never _really_ going to be reducible
to a tractable statistical model.

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reader5000
The complexity of your statistical model is arbitrary. For example agent-based
models can consist of hundreds of thousands of heterogenous agents interacting
in an economy (fitted to real data). The only requirement for a predictive
model is it computes faster than reality.

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cageface
Sure but that's still a fairly coarse approximation of the actual problem
domain in the case of the market, right? You're making an educated guess about
how much of that raw information you can ignore. You probably can ignore a lot
of it but I wouldn't be surprised if a lot of the serious left-turns in the
market in the last century wouldn't have been predicted by the same models
that could track day-to-day trends well.

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motters
Having tried to do this sort of thing in the past, I'm fairly skeptical. Maybe
if you're trading in large volumes over short spaces of time you can make
money with AI, but at least in my simulated runs with small scale investment
the results didn't look encouraging.

Also it should be borne in mind that this kind of activity isn't really making
any constructive or lasting contribution to society. It's really just moving
money around slightly more efficiently than previously. If you really want to
change the world, do something else.

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vecter
_Also it should be borne in mind that this kind of activity isn't really
making any constructive or lasting contribution to society. It's really just
moving money around slightly more efficiently than previously._

I disagree. Efficient allocation of capital is what allowed modern society to
advance as far as it did. Capital markets are a tremendous innovation and add
tons of value to society. Almost every startup turned major corporation out
there was capitalized by the financial markets. You trivialize this value when
you say investing is simply "moving money around more efficiently". That money
NEEDS to be moved around more efficiently so the most productive members and
organizations in society are (a) given the resources to expand their business
and (b) rewarded for the value they add to society.

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jey
He isn't disputing the fact that it has net-positive value creation. He's just
saying that it's a tiny lever in terms of impact. You'll immeasurably increase
market efficiency, but you'll still get a lot of money because finance is an
area with a very high money-to-value ratio.

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reader5000
And of course as with all AI and investing, there is huge incentive for the
stuff that works to remain secret.

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sethg
If human beings can’t understand the rationale that an AI-driven hedge fund is
using to make its trades, then how can humans tell the difference between an
AI that has actually figured out a strategy to beat the market and an AI that
is just having a run of good luck?

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vecter
When you have lots of samples, you're usually pretty certain.

