I came across a paper that uses Support Vector Machines to classify a
buy/sell/hold decision each hour at the $\pm$0.5% threshold. The paper can bee seen here. The paper yielded impressive predictive power as well as high returns. It was noted that during the training phase they created the
[1, 0, -1] labels by computing the percentage rate of change on log hourly returns.
I have looked at answers provided here Why should we use log returns? Log normality, however this wasn't in the context of label creation for an ML classification problem.
I was wondering if this technique offers predictive insight that using normal percentage returns? And if so, why does this work?