Are neural networks and genetic algorithms interesting research fields for financial modelling next time?
Sorry for intruding, I just saw this topic and since I am teaching about both and doing research in finance with genetic algorithms maybe I can say something. About neural networks in general they are good and work when there is nothing known about the process. They are very useful to learn about what happens in black boxes. They do predict and actually it is very easy to obtain predictions given the inputs. When applied to financial data they are not very good even compared with something really simple such as a linear time series. The reason is simple we know more and can include more about the structure of the data then the nn can possibly learn. I however do believe that nn can be used for complex things that are hard to study otherwise. For example, what triggers change in behavior by studying shifts using a large number of assets. The data has to be selected carefully and clearly the nn can only learn about types of shifts already encountered but it should be possible.
Finally, about the genetic algorithms I am using something like that (a particle filtering technique) to detect shifts in volatility. That actually works really well. So yes, I do believe they have a future but one needs to understand how they work and their limitations before applying them.
you might be looking for something like MATLAB with its ANN/Global Optimization toolkit. It’s straightforward, includes several different algorithms and structures, and has built-in GUI tutorials to get you started. MATLAB has a big brand name, and you can switch to command-line code if you like after you get up to speed
NNs were experimented with by many of the big houses during a fashionable period. None of them that I am aware of are still using them. Are there any successful funds out there using NNs?
I am fairly new in comparison to these distinguished gentlemen, but so far I have found GA’s to be most effective for the same reasons as above. However I caution you need a HUGE data sample to test on, and only accept those which generate a significant number of trades with a reasonable equity curve growth to prevent the overfitting. Even then, it’s hard for me to find good systems without having multiple entry and exit rules staggered with the moment of trigger of each rule, and each type of trigger varying the next rule to tip an entry or ramp-up or change type of exit depending on behaviour, which gets very time-consuming computationally, as well, there may be some research there on how to cut down the search space for the time problem, good luck
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