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I'm building a project that gathers the maximum amount of historic data about a certain company and try to predict its future market stock values.

I have company's 1 years stock values like open , close, low, high ,volume for each day.

Using these much data, can I predict the stock value?

What can be the approach for this? Is a simple NN with back-propagation training the best I can hope for? Stock data looks like -

https://www.google.com/finance/historical?q=NYSE%3ABME&ei=DSIeVtGMAYrvuATEsZfACA

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  • $\begingroup$ This is a billion dollar question :) I suppose it depends on the particular series. Some are really tricky, but others are more stable. $\endgroup$
    – Felipe Gerard
    Dec 3, 2015 at 6:43
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    $\begingroup$ Of course you can predict the stock values. The relevant question is how accurate your predictions will be. $\endgroup$
    – whuber ♦
    Dec 3, 2015 at 15:26

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Yes, this is possible. You just need better models or better data than the remaining participants.

It is possible to show this using historical data and applying modern algorithms that were not available at that time. This is worked out in more detail in http://dx.doi.org/10.6084/m9.figshare.1287224 .

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  • $\begingroup$ it is only for viewing auto corelation only or for prediction also? $\endgroup$
    – user123
    Dec 4, 2015 at 6:59
  • $\begingroup$ Predictions based on auto correlations. $\endgroup$
    – user36160
    Dec 5, 2015 at 18:20

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