
Hedge funds embrace machine learning up to a point - josephby
https://www.economist.com/news/finance-and-economics/21732147-investing-more-artificial-intelligence-need-not-mean-less-human
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TuringNYC
Funds would not share what they are doing because then others would do the
same, narrow spreads and reduce profits for all participants. But you can get
a good idea of what they _might_ be working on by some of their workers'
expertise. Notice how places like RenTech
([https://en.wikipedia.org/wiki/Renaissance_Technologies](https://en.wikipedia.org/wiki/Renaissance_Technologies)
and [https://www.bloomberg.com/news/articles/2016-11-21/how-
renai...](https://www.bloomberg.com/news/articles/2016-11-21/how-renaissance-
s-medallion-fund-became-finance-s-blackest-box)), Two Sigma, and others hire
loads of NLP and machine learning academics.

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QML
Perhaps these newer hedge funds just want to give off the perception that they
know what they’re doing — I’m more sure about a company like RenTech since
they have been through a recession and market crash, and has still come up net
positive. So how much of this is simply the market rising; how much of this is
due to chance? If we can remove these confounding factors then we could maybe
actually evaluate the efficiency of ML-based hedge funds (or regular ones).

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hendzen
Many quant funds are market neutral. For every dollar long they have a dollar
short. Such funds aren’t necessarily benefiting from an upward rise in the
market.

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mrigor
Are you referring to gamma hedging or another strategy?

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zeusk
It's called delta neutral afaik.

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TuringNYC
I worked at a hedge fund for 9 years. Regression models are used for
EVERYTHING. Regression is machine learning. This article has a narrow focus
and doesnt understand what machine learning is.

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kgwgk
Usually « machine learning » is used to talk about things beyond basic
statistical methods that predate computers. On the other hand, basic
statistics are often good enough...

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TuringNYC
Other machine learning techniques _widely used on wall street_ for over a
decade \- Dimensionality Reduction \- Clustering \- Classification techniques
using trees, SVM, bayesian models

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vadimberman
An anecdote from 1990s continental Europe.

A friend of mine, upon reaching a status of a "high-net worth individual", was
invited to invest in a hedge fund associated with a bank of regional
importance (major for that small country). They demonstrated the best of the
tech of the time, and then shifted the tone to, "but we also use more
traditional approaches".

Unusually large portion of their "consulting research staff" was female and
spread Europe-wide. Not because they were progressive or pursuing diversity
agenda; these were call girls. The call girls were paid for timely tips. Even
if they only report that a particular top executive stayed overnight far away
from home, was in a foul mood, and a couple of top-ranking colleagues were
present, this can be used to detect an significant internal event not reported
publicly. If the girl was crafty enough to extract more information, even
better.

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bllguo
I don't think technically sophisticated funds are going to be sharing any
details at all for pieces like these. There is bound to be fancy ML in play at
hedge funds, though - do not underestimate the lengths to which people will go
to get an edge in the markets.

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matchagaucho
_" Share-price time series going back decades still contain far less
information than, say, the image data used to train Facebook’s facial-
recognition algorithms."_

What?

Clearly these ML models aren't trying hard enough. Correlating stock movements
with the weather, geo populations, ... there are potentially infinite patterns
buried in the data that a hedge fund could uncover.

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bfrink
There are about 30,000 institutionally-investable stocks in the world. They
have ~252 closing prices per year. This is the thing you're trying to predict,
so it forms the outputs of your training set (be sure to hold some back for
out-of-sample testing and there are probably distinct regimes you need to
switch between). Yes, there may be lots of features (weather, geo populations,
etc., as you point out), but you're often hard up against the curse of
dimensionality here. Sure, intraday data is more voluminous, but there are
fewer economically reasonable influences on that.

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mathperson
Also commodities, forex...

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lordnacho
That only adds a few dozen items. There's only so many currencies and commods,
bonds. Even if you include really illiquid stuff maybe you get 150 things.

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JumpCrisscross
> _There 's only so many currencies and commods, bonds_

Currencies are traded in pairs. Bonds are numerous, if illiquid off the run.
Not to mention the bursting menagerie of derivatives our species tends to.

~~~
lordnacho
Highly correlated. I mean you can add option surfaces, but to count each
option as separate is a stretch.

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swyx
I like the shoutout to Numerai. I think they are so left field that
traditional hedge funds don't even have the ability to understand how much
better Numerai can be.

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dboreham
Huh? I did my commute with a guy who worked for an automated trading shop when
I worked in London in 1994 and he talked constantly about what sounded just
like machine learning.

