Robo-investment aversion

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)

Robo-investment aversion

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).ISSN:1932-620

Will the use of machines to identify negative activist investments backfire? Experimental evidence

Current research does not resolve how people will judge harmful activist investments if machines (machine learning algorithms) are involved in the investment process as advisors, and not the ones “pulling the trigger”. On the one hand, machines might diffuse responsibility for making a socially responsible, but harmful investment. On the other hand, machines could exacerbate the blame that is assigned to the investment fund, which can be penalized for outsourcing part of the decision process to an algorithm. We attempted to resolve this issue experimentally. In our experiment (N = 956), participants judge either a human research team or a machine learning algorithm as the source of advice to the investment team to short-sell a company that they suspect of committing financial fraud. Results suggest that investment funds will be similarly blameworthy for an error regardless of using human or machine intelligence to support their decision to short-sell a company. This finding highlights a novel and relevant circumstance in which reliance on algorithms does not backfire by making the final decision-maker (e.g., an investment fund) more blameworthy. Nor does it lessen the perceived blameworthiness of the final decision-maker, by making algorithms into “electronic scapegoats” for providing well-intended but harmful advice

Zurich Trading Simulator (ZTS) — A dynamic trading experimental tool for oTree

Recent literature on the intersection of economics, finance and psychology indicates the benefits of simulated experience in tools measuring decision making. Here, we present the Zurich Trading Simulator (ZTS) first used by Andraszewicz et al. (2022) to test the impact of upward social comparison on trading activity. ZTS is a free application for oTree designed to create dynamic investment experiments suitable for measuring trading activity and risk taking. The software is ready-to-use applying the default settings. It can also be freely adapted in the source code depending on the experimenter's needs. Price paths developed for experiments with the ZTS are freely available online. We also outline recommendations and possibilities for future studies and extensions.ISSN:2214-6350ISSN:2214-636

The Influence of Upward Social Comparison on Retail Trading Behavior

Online investing is often facilitated by digital platforms, where the information of peer top performers can be widely accessible and distributed. However, the influence of such information on retail investors’ psychology, their trading behaviour and potential risks they may be prone to is poorly understood. We investigate the impact of upward social comparison on risk-taking, trading activity and investor satisfaction using a tailored experiment with 807 experienced retail investors trading on a dynamically evolving simulated stock market, designed to systematically measure various facets of trading activity. 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. Our findings demonstrate the pitfalls of modern investment platforms with peer information and social trading. The broad implications of this study also provide guidelines for improving retail investor satisfaction and protection

The influence of upward social comparison on retail trading behaviour

Abstract Online investing is often facilitated by digital platforms, where the information of peer top performers can be widely accessible and distributed. However, the influence of such information on retail investors’ psychology, their trading behaviour and potential risks they may be prone to is poorly understood. We investigate the impact of upward social comparison on risk-taking, trading activity and investor satisfaction using a tailored experiment with 807 experienced retail investors trading on a dynamically evolving simulated stock market, designed to systematically measure various facets of trading activity. 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. Our findings demonstrate the pitfalls of modern investment platforms with peer information and social trading. The broad implications of this study also provide guidelines for improving retail investor satisfaction and protection

Skin conductance predicts earnings in a market bubble-and-crash scenario

In financial markets, profit is usually associated with risk-taking, as those who take risks, use the opportunities that markets present. However, during market bubbles, risk-taking might lead to losses, whereas risk aversion can lead to more profit. Emotion-based warning signals might play a role here by helping to recognize when risk aversion is preferable. To study this, we used a trading simulator, where 27 male participants traded on a historical stock price trend during a market bubble-and-crash scenario, and we continuously monitored their skin conductance level. We found that participants earning the most were characterized by an adaptive pattern of risk-taking —they invested much in the asset in the initial phase of the bubble but sold their stocks before the crash. Their skin conductance level was closely associated with the price trend, peaking before the crash started. This suggests that skin conductance provided a bodily warning signal in this group. Moreover, in high earners, skin conductance level correlated negatively with the proportion of stocks, indicating that the high earners used this warning signal to sell stocks. These results underscore the adaptive role of bodily signals in decision-making and elucidate the neural basis of success in uncertain financial markets

Skin conductance predicts earnings in a market bubble-and-crash scenario

In financial markets, profit is usually associated with risk-taking, as those who take risks, use the opportunities that markets present. However, during market bubbles, risk-taking might lead to losses, whereas risk aversion can lead to more profit. Emotion-based warning signals might play a role here by helping to recognize when risk aversion is preferable. To study this, we used a trading simulator, where 27 male participants traded on a historical stock price trend during a market bubble-and-crash scenario, and we continuously monitored their skin conductance level. We found that participants earning the most were characterized by an adaptive pattern of risk-taking —they invested much in the asset in the initial phase of the bubble but sold their stocks before the crash. Their skin conductance level was closely associated with the price trend, peaking before the crash started. This suggests that skin conductance provided a bodily warning signal in this group. Moreover, in high earners, skin conductance level correlated negatively with the proportion of stocks, indicating that the high earners used this warning signal to sell stocks. These results underscore the adaptive role of bodily signals in decision-making and elucidate the neural basis of success in uncertain financial markets