In five experiments ( N = 3,828), we investigate whether people prefer investment decisions to be made by human investment managers rather than by algorithms (“robos”). In all of the studies we investigate morally controversial companies, as it is plausible that a preference for humans as investment managers becomes exacerbated in areas where machines are less competent, such as morality. In Study 1, participants rated the permissibility of an algorithm to autonomously exclude morally controversial stocks from investment portfolios as lower than if a human fund manager did the same; this finding was not different if participants were informed that such exclusions might be financially disadvantageous for them. In Study 2, we show that this robo-investment aversion manifests itself both when considering investment in controversial and non-controversial industries. In Study 3, our findings show that robo-investment aversion is also present when algorithms are given the autonomy to increase investment in controversial stocks. In Studies 4 and 5, we investigate choices between actual humans and an algorithm. In Study 4 –which was incentivized–participants show no robo-investment aversion, but are significantly less likely to choose machines as investment managers for controversial stocks. In contrast, in Study 5 robo-investment aversion is present, but it is not different across controversial and non-controversial stocks. Overall, our findings show a considerable mean effect size for robo-investment aversion ( d = –0.39 [–0.45, –0.32]). This suggests that algorithm aversion extends to the financial realm, supporting the existence of a barrier for the adoption of innovative financial technologies (FinTech).

In three experiments (N = 2380), we show that people find it more permissible when investment decisions concerning controversial (“sin”) stocks are made by human fund managers rather than by computers (“robos”). In Study 1 (N = 466), participants rated the permissibility of a computer (algorithm) to autonomously exclude morally controversial stocks from investment portfolios as lower than if a human fund manager did the same; this finding was not different if participants were informed that such exclusions might be financially disadvantageous for them. In Study 2 (N = 1231), we show that this robo-fund aversion manifests itself both when considering investment in controversial and non-controversial industries. In Study 3 (N = 683), our findings show that robo-fund aversion is also present when algorithms are given the autonomy to increase investment in controversial stocks. Our findings suggest that algorithm aversion extends to the financial realm, supporting the existence of a barrier for the adoption of innovative financial technologies (FinTech), but one that might be overcome by hybrid (human-in-the-loop) solutions.

No abstract

No abstract

The environment in which people make investments is changing substantially. Digitization made financial investing easily accessible to anyone with internet access and some disposable capital. Online investing is often facilitated by social media, where the information of peer top performers is often widely accessible and distributed. We investigate the impact of upward social comparison on risk taking, on trading activity and on investor satisfaction using a tailored experiment with 807 experienced retail investors. We find that investors presented with an upward social comparison take more risk and trade more actively, and they report significantly lower satisfaction with their own performance. These effects are triggered by a cognitive response to social influence, moderated by affect. Our findings demonstrate the pitfalls of modern and growing digital investment platforms with peer information and social trading. The widespread implications of this study also provide guidelines for improving retail investor satisfaction and protection.