We conclude that a change to a higher risk level for the second task is mostly caused by the subjects performance in the first task. Next, we examine the relation of a change in the degree of diversification and the hormones in Panel C of Table 6. Specifically, we employ the change in the sum of squared allocations as the dependent variable to measure diversification. The results for cortisol are negative and significant with cortisol levels and previous return from diversification task 1 being able to capture 59% of the change in the sum of squared allocations in the model. Overall, the results show that higher levels of stress are associated with changes to more diversified (less concentrated) portfolios. Lastly, we examine the influence of testosterone and cortisol on the propensity for extrapolation bias (i.e., trend following). The GROW asset has the highest expected return and in

In the second task, all 23 Electronic copy available at: the assets have the same return distribution as in the first task. However, 21 out of 39 subjects increased their allocation to GROW for the second trial in an attempt to increase the returns. Panel D of Table 6 shows the regressions for the change in allocation to GROW. The results show slight gender differences as females with high testosterone to cortisol ratios are more likely to increase their allocation to the Grow asset providing similar results as Panel A. 6. Rebalancing Task Analysis

6.1. Trading Decisions In the first two trials, subjects completed set it and forget it investment tasks. That is, they made asset allocation decisions and then witnessed the 20-year simulation results. In the last trial, the subjects had three opportunities to rebalance their portfolios. Specifically, subjects set their initial allocations exactly as they did in the first two trials. The software then simulates the asset returns for years 15. After the fifth year, the subjects are able to buy and sell assets in order to modify their portfolios to take into consideration their progress to date (REBAL1), even though the expected return and standard deviation for the next years are still the same. The software then simulates the returns for years 610. The subjects subsequently are able to rebalance again (REBAL2). Finally, the subjects rebalance after the returns are known for years 1115 (REBAL3). The final results are known after the simulation is completed for years 1620.

Examination of the rebalancing task reveals several interesting facts. First, two of the subjects always rebalanced back to their initial percentage asset allocations. The rest rebalanced to different allocations. Figure 1 shows the return dynamics at each stage of the rebalancing task. The overall allocation decisions required subjects to obtain a 5.65% compounded return over the 20-year period. The portfolios the subjects selected at the outset were expected to earn an average 24