Financial Time Series Forecasting with Deep Learning: A Literature Review
arxiv.org
arxiv.org
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.
The collar trade is explained here: https://www.theoptionsguide.com/the-collar-strategy.aspx
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.
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.
Is this because these strategies don't scale well, or is there another reason?
However, that is only one strategy and there are numerous trading strategies which work on the minutes or hours scale and are very achievable by a single person.
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.
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).
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?
Founded by Jim Simons, the "Father" of quantitative research. The firm is famous for their significant year-over-year returns and notorious for only hiring PhDs from mathematics/physics/computer science.
There is an interesting book on how Jim Simons created the company and built his team of academics from the ground up, "The Man Who Solved the Market"
I don't understand why I was downvoted? I don't think there was enough context for anyone (who's not in the field) to know what you meant, and I thought it would be useful for people like myself to get clued in.
Market data services such as Reuters/Morning Star provide incredibly clean data at great convenience for anything you can imagine, even including astronomical data.
Its kind of like the Black-Scholes model. If it really was that simple and straightforward, everybody would be using it to make a ton of money.
Incredibly clean ,that's a very big stretch.
Source: have worked with TR sirca db for almost a decade.
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.
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.