The Chinese University of Hong Kong issued a study that calls for a second look at the effectiveness of generating returns from investment strategies based on machine learning. The study found that when applying several well-established deep learning methods to the broader markets, superior value-weighted, risk-adjusted returns could be generated – 0.75-1.87 But in the event of basic exclusions that weigh down benchmark performance – such as microcaps or distressed firms – performance weakens. When microcaps were excluded, adjusted returns fell 62 percent, attributable to small capitalizations and a higher likelihood of low liquidity. Performance also declined when exclusions involved non-rated firms (68 percent) and distressed firms (80 percent). Exclusions aside, the study also highlighted the need to be able to stomach high transaction costs at levels that may not be applicable to most retail investors.