
Financial Time Series Forecasting with Deep Learning: A Literature Review - Bostonian
https://arxiv.org/abs/1911.13288
======
nl
I've discovered a very interesting thing, which apparently most in the machine
learning space don't realise (I didn't until recently). Trading isn't what
most people think it is at all. I've spent a week or two doing a bit of
reading into what it actually _is_.

Three things that have jumped out at me:

1) Trading is _not_ investing. I was extremely doubtful about a lot of the
"models" people throw around in this area because to me they seemed closer to
numerology than anything related to investment. Then someone pointed out that
_trading is not investing_ , and suddenly I looked at these models in a new
light.

2) There's lots of interesting trading you can do even without having any idea
about what the price is going to do. Do you think volatility is going to
increase? You can make money from that - read about the Collar Trade Strategy
(This is just an example).

3) There's lots of strategies which don't make enough money for companies to
be interested in, but are viable for an individual.

~~~
lordgrenville
> Trading is _not_ investing

Keynes' famous metaphor is a competition where you have to pick the most
beautiful women from a page of photos. The point is not to guess your honest
opinion, but to try to predict the aggregate of people's opinions - the face
with the most mainstream attractiveness. A speculator needs to guess what
other people think, and which way the price will go. By contrast investing is
putting money in something that seems to _you_ to have inherent value.

[https://en.wikipedia.org/wiki/Keynesian_beauty_contest](https://en.wikipedia.org/wiki/Keynesian_beauty_contest)

~~~
nl
This is what I thought, but it _understates_ the difference a huge amount.

In trading you are looking for signals like "resistance" \- which is basically
that someone(s) has a incomplete "buy" order at a particular price. Trading is
all about exploiting these signals.

It's like the difference in architecture and coding, or selecting a sports
team and individual ball skills.

To make it clear - there are a number of trading strategies where you don't
need to know which way the price is moving at all.

------
rocqua
This is mostly a list counting how popular certain topics are, with lots of
references.

I am missing any actual 'review' about which methods had more success, which
methods show promise, and which 'subjects' seem more amenable to Deep Learning
that others.

~~~
scottlocklin
Several of the papers listed there are known (by me anyway) to be complete
bullshit. This seems to be "here are search results on google scholar."

I actually don't know anyone using deep learning the hedge fund business,
other than for screwing around. It's a terrible tool for that sort of thing.
And as someone pointed out below; predicting the future is only a small part
of what a trading strategy is (for some trading strategies, forecasting is
actually the null set).

------
roenxi
On page 18, 36 and 37 this paper is talking about time horizons of a few
seconds to about a month. Over that time horizon the market is going to be
behaving much more like a casino than a weighing machine and the people who
make money are presumably those who move quickly or know something others
don't.

I suppose it seems implausible to me that something on arxiv is going to
secure an actual advantage over other traders or reliably deduce knowledge
that isn't public. Statistics can sometimes seem like magic but it can't do
the impossible. Notwithstanding that this topic is interesting, is there any
reason to think that these models are valuable in practice?

~~~
phyalow
If they were valuable, why would someone give them away for free!

------
thedudeabides5
Seems like a pretty big list of prior papers, which is a nice reference, but
some notion of how successful these various efforts were / purport to be would
be more helpful.

~~~
throwlaplace
though there's some comparative analysis the point of survey papers is to
trace to the history/lineage of current state of the art (it's for researchers
after all).

~~~
thedudeabides5
Yeah I guess, still seems a little odd. Like imagine a 'survey' of health care
experiments designed to cure cancer, but no mention anywhere in the paper if
any of the experiments worked!

------
w1ntermute
The most important factor for financial time series forecasting is undoubtedly
access to clean data. This is what sets Renaissance apart. There’s no need for
particularly sophisticated math - they’ve been doing it for 3 decades.

~~~
throwawaymath
That's not what sets Renaissance apart. Please don't add to the baseless
speculation that gets tossed around about RenTech so often. Are you aware of
just how many trading firms have access to phenomenally clean data?

It drives me nuts when I see words like "undoubtedly" thrown around so
confidently this way. A new book comes out about Simons and Renaissance, it
enters the financial zeitgeist for a little while, and now everyone is
apparently an expert on the firm's differentiating competency.

For what it's worth, what you're saying is contradicted by Nick Patterson. He
did not say that Renaissance had access to clean data no one else did. What he
said is that in the early days, they spent almost all their time _cleaning_
the data. In any case, that's table stakes these days. All successful quant
firms spend time sourcing exceptional data and ensuring it's as polished as
possible.

~~~
throwlaplace
>It drives me nuts when I see words like "undoubtedly" thrown around so
confidently this way

I see this very often on Reddit and here; someone reads a story and then
behaves like they're suddenly a prophet whose job it is to inform the rest of
us. On Reddit (because the threads are much bigger) I see this kind of
repetition promulgation in the same thread. Here it's typically across threads
(as you've noticed). I'll never understand what people get out seeming like an
authority figure in a completely anonymous forum.

