Marcos Lopez de Prado seems to be a well known and renowned machine learning expert in the field of finance. I am very far from his level, as have not yet finished my PhD in economics, and only have an applied level statistical knowledge. I have encountered a much cited paper of Lopez de Prado. I can not say I completely understand all the mathematical parts. But there are some claims in the paper, which seem to completely contradict things, which I have learned on statistics and economics thus far, or at least seem to be illogical for me. For a specific example, the paper under the section Pitfall #4 and Solution #4 suggests, that by differencing time series to make them stationary for classical statistical models (ARIMA etc.) removes the memory of the series and thus makes them lose the predictive power:
The conclusion is that, for decades, most empirical studies have worked with series where memory has been unnecessarily wiped-out. The reason this is a dangerous practice is that fitting a memory-less series will likely lead to a spurious pattern, a false discovery. Incidentally, this over-differentiation of time series may explain why the Efficient Markets Hypothesis is still so prevalent among academic circles: Without memory, series will not be predictive, and researchers may draw the false conclusion that markets are unpredictable.
In economics there really is a simplified theoretical model on equity returns, which posits, that it is a memory-less white noise, and the prices (the integrated returns) follow a random walk. But from an empirical perspective, as far as I understand the memory-less attribute of returns pertains only to the individual data points themselves, not the series as a whole. A differentiated series still should have a "collective" memory put together, and it has almost the same information as the integrated version, only lacks a constant value. So it should have the same predictive power as well, should it not? Or it is me, who has a lack of understanding?