MATLAB: Neural network with two objective functions

Question

Hi,

I am considering building a neural network with two similar but different objective functions. I have read about genetic optimization with more than one objective function. Is there similar functionality in Matlab for NNs?

In other words, is there a way to train the NN to reach some kind of "pareto" optimal solution for two objective functions?

In case curious, the idea is that one function is the error in forecast return (the NN's output) of stocks and actual return (would like to minimize). The other function is the return (or inverse of it), of the top say 15-25% ranked stocks based on the NN's output. I need to optimize both functions because (1) what I really care about is the best stocks coming out on top and (2) I want to have a forecast return metric so I can combine this with other analysis I am doing. Obviously more accurate return forecast will beget a more accurate stock ranking, but by using ranking the optimization will focus more on accuracy of the best stocks…I think.

Thanks in advance for any help.

Best, Mike

Answer 1

Greg, thanks for the comment. I'm still focused on this idea that what needs to be optimized is the portfolio return and not the accuracy of any particular stock's return prediction. I think this because my feeling is that if I only really want to own 20% of the market over the duration, and my strategy is to own 25% of this 20% (5% of the market), then why worry a lot about the accuracy of the return prediction on the 80% I never own? The other 80% only needs to be accurate enough so that it sorts to the bottom of the list and not the top. But I totally understand it might not work.

The system should naturally glom on to what IS both predictable and high return, not necessarily what is high return because of requiring the portfolio return to be high. What can't be predicted will end up sorting lower, because it's lack of predictability will end up making it less attractive of a portfolio position than a stock with lower expected return but where the model has been very accurate (at least, that is what I am hoping for).

I am really more of a fundamental investor than a quant guy (though I studied EE), so this is in line with the way I invest as a portfolio manager. I look mostly for the ~20% of stocks that have characteristics that would allow them to be owned, and then work to make accurate predictions on these, with the prediction accuracy increasing as a function of (1) the probability of it going into the portfolio at some time and (2) if it is in the portfolio, the size of the position.