Is it better to use a MinMax or a Log Return normalization to predict stock price movements?

I am trying to use a LSTM model to predict d+2 and d+3 closing prices. I am not sure whether I should normalize the data

• with a MixMax scaler (-1,+1)

• using the log return

• (P(n)-P(0))/P(0) for each sample

I have tried quite a lot of source code from Github and they don't seem to converge on any technique.

1. Log returns are symmetric compared to percentage change. Log(a/b)=log(b/a) and this (less skewness), in theory, leads to better results for most models (linear regression, neural networks).