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The trades the system failed to anticipate were, on average, more closely associated with outperformance, implying that the activity that falls outside routine investment patterns is where most of the value lies. The study's findings imply that the justification for active-management fees increasingly rests on the smaller share of decisions that depart from the predictable template, rather than the majority of portfolio adjustments that can be anticipated by an algorithm.
“If 71% of your decisions can be anticipated by an algorithm, it becomes very hard to justify active-management fees for that portion,” Lauren Cohen, a finance professor at Harvard who co-authored the paper, explained in an email. “Now, the non-routine trades, the ones our model can’t predict, are where genuine alpha lives. But those account for a relatively smaller share of overall activity.”
