MATLAB: Merit of feeding Neural Networks data on the certainty of input/output relationship

Question

Hi,

I am working on a stock investing neural network. I have determined some quantitative technical indicators that generally are useful (i.e. correlated) to my output (which at the end of the day is return).

Here is the issue I am grappling with. The indicators sometimes work and sometimes do not work, largely depending on the stock. I am wondering if there is an intelligent way to tell the neural network when to pay more or less attention to the the indicators depending on the stock.

Here is more detail on what I mean. The NN is set up in a way that it accepts a bunch of different inputs and the output is stock return. I only have one NN, so I send it data from different stocks without telling it that there exist different stocks. My idea is to calculate some measure of effectiveness and send it into the NN. Then, hopefully the NN would learn that when effectiveness is low it pay more attention to the quantitative indicators. Of course, this assumes that the trailing effectiveness will help predict the forward effectiveness, which I think is a reasonable assumption and the NN presumably will learn to the extent the effectiveness does tend to stick. Does anybody know if this makes sense to do?

Here is another idea I had. I could set up a NN just for the certain inputs that are known to vary in effectiveness and also feed this NN the effectiveness measures. I could have this NN return expected return, only I train this NN to maximize the return on the top 25% of stocks. Presumably, the return that will come out of this analysis will already consider as lower return those stocks for which effectiveness is bad (since their results are unpredictable).

Finally, perhaps I could do as I suggested above and feed effectiveness, but then because I train the NN to maximize the return of the the top-ranked stocks, the NN will learn to properly use the effectiveness input. The low-effectiveness stocks may still have a high return, but they will be effectively be screened out of my top 25% because of lack of predictability. Does this make sense?

Thanks so much for any help.

Regards, Mike

Answer 1

I have had tremendous success with this simple approach:

Add varying amounts of random noise to the original inputs but keep the original targets.

Piece of cake!

Hope this helps.

Thank you for formally accepting my answer

Greg