It has been well documented that cognitive biases — meaning systematic deviations from rational behavior — lead to inferior investment performance. We measured five biases: 1) local bias, which describes angel investors’ tendency to make investments that are in close geographic proximity to themselves; 2) loss aversion, meaning angel investors’ tendency to be more sensitive to potential losses than to potential gains; 3) overconfidence, when investors “overcommitted” and spent significantly more money on one startup that they usually would; 4) gender bias; and 5) racial bias. Our data shows that all biases were present among the angel investors with overconfidence — which 91% fell prey to at least once — being the most frequent and strongest bias to affect investment returns.
Because cognitive biases cause investors to make irrational investment decisions, it is not surprising that our investment algorithm outperformed the human average. While the algorithm achieved an average internal rate of return (IRR) of 7.26%, the 255 angel investors — on average — yielded IRRs of 2.56%. Put another way, the algorithm produced an increase of more than 184% over the human average.
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