I want to make sure I'm correct in my assumptions. I'm predicting financial returns by using different ML models. There are 4500 values in each dataset. The density plots of all models are shown like this:
I clipped the x-axis at [-0.025, 0.025]. Now I wanted to see if the predicted values of the models have the same distribution as my observed values from the "sp500". I used z-normalization on the datasets to make them comparable.
For my Non-NN models, these QQ plots were created:
Imho, it seems that all Non-NN models have the same distribution as the observed dataset.
For my NN-models, these QQ plot were created:
Here it seems that both, especially the LSTM model, have distributions that differ from the observed dataset.
Is it correct to assume that the NN models have less predictive value than the Non-NN models as their distributions differ from the observed dataset?