The impact of different investment goals
First, participants’ likelihood to transfer money from one investment fund to the fund with a different goal was examined to test whether the investment goal conditions successfully induced participants’ mental separation across the different investments. We find that participants are more reluctant to transfer money invested with the prevention-focused goal of repaying a loan (mean: 4.88; standard deviation: 2.51) than to transfer money from the investment fund for the promotion-focused goal of saving for a kitchen (mean 6.15; standard deviation 2.52; difference significant at p < .01). This shows that participants treat the two investments differently, which is in line with the anticipated association with different mental systems across the two goals. Thus, as expected, the regulatory focused-based investment goal manipulation was more effective than the one used in Experiment 1.
Next, we analyzed the effect of the different investment goal conditions on the virtual integration of funds. We anticipated that, with stronger mental separation, participants would exhibit different behaviors depending on the investment goal conditions. However, we find no significant effect of investment goals on participants’ decisions nor do we find a significant interaction with the other conditions, regardless of the degree of virtual integration of the funds; this is similar to the previous two experiments. The investment goal conditions also did not significantly affect the inefficiency of portfolios that participants chose.
The impact of virtual integration and semi-integration with negative correlations
The correlation between the two funds’ returns has a strong impact on the benefits of virtual integration (see Table 3
). In the no-correlation condition, and averaged across the goal conditions, participants chose higher returns and risks in the separate condition (8.3% and 7.2%, respectively) than in the integrated condition (8.0% and 6.6%), replicating the results of Experiment 2. In the negative correlation condition, participant chose lower returns with higher risks in the separate condition (8.1% and 4.9%, respectively) than in the integrated condition (8.4% and 4.5%; all differences are significant at p
< .01). This shows that virtual integration is beneficial and provides individuals with higher expected returns at lower risk levels. This is also reflected in a significant impact of correlation on the loss in returns and excessive risks that participants incurred in the separate choice condition. Participants chose significantly less efficient portfolios in the negative correlation condition (loss in returns 0.62% and excessive risk 0.53%) than in the no-correlation condition (loss in returns 0.16% and excessive risk 0.22%; differences between both aspects are significant at p
Returns and risks of participants’ chosen portfolios in Experiment 3§
Expected returna, c
Riska, b, c
Loss in returnsc
Expected returna, b
Loss in returns
Next, turning to participants’ decisions in the new semi-integrated condition, we find that they selected returns and risks closer to those in the integrated condition than in the separate condition. Specifically, the average returns and risks of the selected portfolios were 8.0% and 6.6% for the no-correlation condition and 8.2% and 5.0% for the negative correlation condition. However, the effect of (fully) integrating the distributions is still beneficial compared to the semi-integrated condition. In fact, participants chose significantly less efficient portfolios when observing the integrated distribution of the two funds rather than the separate distributions. The loss in returns and excessive risk in the semi-integrated, no-correlation condition were 0.22% and 0.30%, respectively, and 0.72% and 0.61% for the semi-integrated, negative correlation condition, which are both larger (p = .00 for the no-correlation; p = .06 for the negative correlation) than in the separate condition. Therefore, helping consumer investors by integrating the distributions, but still requiring them to make separate choices, is not sufficient to enable them to compose fully efficient investment portfolios.
We also conducted a regression analysis to investigate how the inefficiency of investors’ decisions depends on the different experimental conditions. The results replicate our earlier findings in Experiments 1 and 2, proving robustness to the different goals and correlation levels. Participants choose more inefficient portfolios with correlation, both in terms of loss in returns (β = .060, p
< .05) and excessive risks (β = .041, p
< .05). Also, we find no effect of the background variables we measured, including financial literacy, stated risk preference, and cognitive ability, which is similar to Experiments 1 and 2. We provide the full results in Web Appendix 2
. Additionally, we analyzed an order variable indicating whether participants saw the separate condition first or one of the integrated conditions. We find no significant effect of order and the other findings remain unchanged.
Finally, we analyzed whether participants were able to understand the correlation between the fund returns or integrate the distributions themselves, and we studied their satisfaction with the resulting distribution. We find that, even though participants received an explanation of the correlation between the investment returns and also experienced at least 10 samples from the joint return distribution, their correlation perceptions did not vary with the level of correlation. More specifically, when asked about a directional prediction of one investment fund’s return given the return on the second fund, about 60% of participants report a positive relationship between the investment returns, whereas the actual correlations were zero or negative (p = .71 when testing the association with correlation conditions). Moreover, for the questions on the tail probabilities of the joint distribution, the three answers that correspond to no correlation, positive correlation, and negative correlation, were chosen by 34%, 28%, and 22% of participants, respectively. The remaining 16% of participants selected a tail probability that is so large that it is impossible, even with perfectly correlated asset returns. Hence, participants are rather poor in determining the joint distribution, and their answers did not even differ across the two correlation conditions (p = .35).
Regarding decision outcome satisfaction, participants were more satisfied with the resulting investment fund risk–return distribution in the virtually integrated condition (mean: 5.45; standard deviation: 1.04) than in the semi-integrated (mean: 5.40; standard deviation: 1.10; difference with the integrated condition: p < .01) and separate (mean: 5.25; standard deviation: 1.14; difference with the integrated condition: p < .01) conditions. The semi-integrated interface also resulted in higher satisfaction levels than the tool with separate distributions (p < .01).