One of my machine learning project involves the use of adjusted close prices (from Yahoo Finance, for better or worse) to determine the label – if a stock's adjusted close price increases by more than 10% in the subsequent year, it is labelled as a '1', otherwise it is a '0'.
I have been trying to make the backtests more rigorous, and one aspect of this involves a careful inspection to remove common pitfalls like the lookahead bias.
I would not have thought that using adjusted closes is a problem (in fact it seems like a necessity), but calculating the adjusted close does involve "data from the future", in the sense that the adjusted close price in 2010 has been adjusted for future splits/dividends.