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Using a Keras Long Short-Term Memory Model to Predict Stock Prices

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Re: Using a Keras Long Short-Term Memory Model to Predict Stock Prices

#131

Post by Loughla »

goodside wrote: post #93

This is pure nonsense. This isn’t even the right way to begin thinking about this as a forecasting task — the target series should be log-normal returns, not raw asset price. The performance of this model is laughably bad, which is probably why he spends zero time evaluating its effectiveness. You could trivially get better forecasts than this by naively repeating the last-observed price . This isn’t ML. It’s cargo-c…

As someone outside the tech sphere on either coast, that's all ML seems to be.

What I've seen from companies marketing to Higher Education is - we have a lot of data, you set arbitrary flags to the data that you believe indicate 'x' (or even better, they have pre-built data expectations) and you will get 'y' outcome.

And none of it is actually based on anything real. It's all anecdotal applied to extreme amounts of actual data.

And when I read about ML on here, it seems to confirm my experiences.

Re: Using a Keras Long Short-Term Memory Model to Predict Stock Prices

#132

Post by throwawaymath »

JumpCrisscross wrote: post #111
ttobbaybbob wrote: post #91

Earlier quoted context omitted.

The NDA/non-compete may not enforceable, but if Renaissance believes employees with valuable IP are being poached, they will sue and make it very expensive to hire, then defend, ex-rentec employees. https://www.marketwatch.com/story/renaissance-millennium-set...

They’re totally up front about this requirement, too, when making offers. (Source: I turned one down.) You are to move to Long Island, buy a house, buy into the employees-only fund and work there for the rest of your financial career.

That's interesting. Did you apply, or did they find you? I could see that offer being attractive.

Re: Using a Keras Long Short-Term Memory Model to Predict Stock Prices

#133

Post by Nalta »

goodside wrote: post #93

This is pure nonsense. This isn’t even the right way to begin thinking about this as a forecasting task — the target series should be log-normal returns, not raw asset price. The performance of this model is laughably bad, which is probably why he spends zero time evaluating its effectiveness. You could trivially get better forecasts than this by naively repeating the last-observed price . This isn’t ML. It’s cargo-c…

Clearly one LSTM didn't work. Lets try FOUR!

- edit: miscounted number of LSTMs

Re: Using a Keras Long Short-Term Memory Model to Predict Stock Prices

#134

Post by JumpCrisscross »

WrtCdEvrydy wrote: post #115
JumpCrisscross wrote: post #111

Earlier quoted context omitted.

They’re totally up front about this requirement, too, when making offers. (Source: I turned one down.) You are to move to Long Island, buy a house, buy into the employees-only fund and work there for the rest of your financial career.

LOL, No. How do you even enforce that? If I get a job somewhere else, there's no reason for me to disclose where I'm going.

> How do you even enforce that?

By suing your new employer for $20 million [1].

(On the other side of the coin, they offer like a month of paid vacation a year, plus longer sabbaticals every few years, plus tons of benefits...if Long Island and math are your thing, it's a very nice place to work.)

[1] https://www.marketwatch.com/story/renaissance-millennium-set...

Re: Using a Keras Long Short-Term Memory Model to Predict Stock Prices

#135

Post by samfisher83 »

mmq wrote: post #119
samfisher83 wrote: post #58

All these LSTM Stock Market tutorials seem to be a variation of this guys tutorial which he did a while ago: https://github.com/jaungiers/LSTM-Neural-Network-for-Time-Se... Except Jakob explains the folly of the method. Whereas the author of this piece says the following: From the plot we can see that the real stock price went up while our model also predicted that the price of the stock will go up. This clearly show…

I made this code https://github.com/mouradmourafiq/tensorflow-lstm-regression at least 6 months earlier than the first push in the github repo you mentioned, and I am sure many people did some version before that. A lot of people sometimes just need a reason to play with some technology. I assume that OP used stock market to learn about LSTMs.

The guy I posted uses Keras which is what this guys code looks like.

I am sure plenty of people have done lstm on the stock market but the form of the code looked similar between the two.

Re: Using a Keras Long Short-Term Memory Model to Predict Stock Prices

#137

Post by nradov »

derefr wrote: post #105
aggronn wrote: post #74

Earlier quoted context omitted.

The problem I see with that hypothesis is that the asset pricing must eventually be tied to actual performance--while it may be self-reinforcing to some extend, if it _is_ fundamentally wrong, there will be a reckoning and an adjustment after a high value stock goes bankrupt, for example.

But the stock market isn’t just a bunch of people placing speculative bets about company performance; it’s a bunch of people placing speculative bets about company performance by buying shares of the companies . If the market is irrational, it can actually prop up companies that would otherwise go bankrupt.

That effect doesn't go very far. It can slightly reduce a company's cost of capital but won't enable them to be profitable over the long term.

Re: Using a Keras Long Short-Term Memory Model to Predict Stock Prices

#138

Post by [deleted] »

ak39 wrote: post #129
jpeterson wrote: post #114

Earlier quoted context omitted.

Investment markets operate on relative gain, not absolute gain. E.g.: if you invest in a stock and it gains $5, this would be a great return for a $1 stock but a poor one for a $1000 stock, so the absolute gain doesn't mean anything on its own. A 5% return always means that you've gained 5% on your investment.

Would it be the same (valid) with percentage returns?

[deleted]

Re: Using a Keras Long Short-Term Memory Model to Predict Stock Prices

#139

Post by lvs »

Loughla wrote: post #131
goodside wrote: post #93

This is pure nonsense. This isn’t even the right way to begin thinking about this as a forecasting task — the target series should be log-normal returns, not raw asset price. The performance of this model is laughably bad, which is probably why he spends zero time evaluating its effectiveness. You could trivially get better forecasts than this by naively repeating the last-observed price . This isn’t ML. It’s cargo-c…

As someone outside the tech sphere on either coast, that's all ML seems to be. What I've seen from companies marketing to Higher Education is - we have a lot of data, you set arbitrary flags to the data that you believe indicate 'x' (or even better, they have pre-built data expectations) and you will get 'y' outcome. And none of it is actually based on anything real. It's all anecdotal applied to extreme amounts of a…

It's so much worse to see ML applied to problems that could surely be studied and understood mechanistically, but people are sold on using ML instead in deference to buzzwords alone. The result is that they may get some model of a phenomenon, but they'll never learn a goddamn thing about why it works. What good is that?

Re: Using a Keras Long Short-Term Memory Model to Predict Stock Prices

#140

Post by goodside »

Loughla wrote: post #131
goodside wrote: post #93

This is pure nonsense. This isn’t even the right way to begin thinking about this as a forecasting task — the target series should be log-normal returns, not raw asset price. The performance of this model is laughably bad, which is probably why he spends zero time evaluating its effectiveness. You could trivially get better forecasts than this by naively repeating the last-observed price . This isn’t ML. It’s cargo-c…

As someone outside the tech sphere on either coast, that's all ML seems to be. What I've seen from companies marketing to Higher Education is - we have a lot of data, you set arbitrary flags to the data that you believe indicate 'x' (or even better, they have pre-built data expectations) and you will get 'y' outcome. And none of it is actually based on anything real. It's all anecdotal applied to extreme amounts of a…

The problem isn’t that ML has gotten worse. It’s just as rigorous and far more powerful than it ever was. The problem is ML is hard, it hasn’t gotten orders-of-magnitude easier to understand, and there’s enormous incentive now to pass off amateur understanding as complete. The real ML still happens — it’s just drowned out.
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