Using both a linear regression method and a probability-based method, we find that on average, analysts place larger than efficient weights on (i.e., they overweight) their private information when they forecast corporate earnings. We also find that analysts overweight more when issuing forecasts more favorable than the consensus, and overweight less, and may even underweight, private information when issuing forecasts less favorable than the consensus. Further, the deviation from efficient weighting increases when the benefits from doing so are high or when the costs of doing so are low. These results suggest that analysts' incentives play a larger role in misweighting than their behavioral biases.

Records of over half a million participants in more than 600 401(k) plans indicate that participants tend to allocate their contributions evenly across the funds they use, with the tendency weakening with the number of funds used. The number of funds used, typically between three and four, is not sensitive to the number of funds offered by the plans, which ranges from 4 to 59. A participant's propensity to allocate contributions to equity funds is not very sensitive to the fraction of equity funds among offered funds. The paper also comments on limitations on inferences from experiments and aggregate‐level data analysis.

Using both a linear regression method and a probability-based method, we find that on average, analysts place larger than efficient weights on (i.e., they overweight) their private information when they forecast corporate earnings. We also find that analysts overweight more when issuing forecasts more favorable than the consensus, and overweight less, and may even underweight, private information when issuing forecasts less favorable than the consensus. Further, the deviation from efficient weighting increases when the benefits from doing so are high or when the costs of doing so are low. These results suggest that analysts' incentives play a larger role in misweighting than their behavioral biases.