I am learning about neural network and created some small networks in feed forwarding network myself. I was curious about Recurrent Neural Networks (RNN) and read some papers about RNN in trading. The results in those paper using RNN seems promising but I have some general and hypothetical doubts regarding RNN. I learnt that RNN is best used for learning sequential and time series data.
Lets assume I made two models for predicting future price of stocks, one trained in RNN and other in MLP (Multi Layer Perceptron) using 10 years (OHLC) of data from SPY with good accuracy. I then wants to use these trained models to predict another stock's future price (may or may not be correlated with SPY) eg GOOGLE's tomorrows price.
(first question) Which algorithm has more chances to give an accurate prediction?
I know there is a good chance both models will fail to predict future price of a different product (here GOOG) correctly because the model is trained in SPY data.
But the question is; because RNN is used for training model for sequential and time series data. Is RNN have more probability of a failure prediction than MLP because it trained explicitly for SPY?