"That 71% of mutual fund managers' trade directions can be predicted in the absence of the agent making a single trade. For some managers, this increases to nearly all of their trades in a given quarter. Further, we find that manager behavior is more predictable and replicable for managers who have a longer history of trading and are in less competitive categories."
"The paper finds that the larger the ownership stake of the manager in the fund, the less predictable their behavior. It also found less predictable managers strongly outperform their peers, while the most predictable managers significantly underperform."
"Those stock positions that are more difficult to predict strongly outperform those that are easier to predict, suggesting that unpredictability in investment decisions correlates with superior performance outcomes."
A Harvard Business School study analyzing trading data from 1990 to 2023 demonstrates that artificial intelligence can predict approximately 71% of mutual fund managers' trade directions without executing any trades themselves. For some managers, particularly those with longer trading histories in less competitive categories, predictability reaches nearly 100% for quarterly trades. However, the research reveals an important inverse relationship: managers with larger personal ownership stakes show less predictable behavior, and these less predictable managers significantly outperform their more predictable counterparts. Within individual portfolios, harder-to-predict stock positions substantially outperform easily predictable ones, suggesting that unpredictability correlates with superior investment performance.
#artificial-intelligence-in-finance #mutual-fund-management #predictive-analytics #investment-performance #machine-learning-applications
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