Literature and Hypotheses

Algorithm aversion is neither a new concept, nor limited to a particular do-main. Researchers as early as Meehl (1954) discuss the superior performance of algorithms in various prediction tasks. In comparing statistical and clinical prediction, this line of research pits a statistical algorithm against a human clinician. Dawes (1979) confirms the superiority of even improper specified

algorithms and already reports common objections against the use of algo-rithms. These include technical issues raised against the particular methodol-ogy applied, psychological misperceptions of performance, and ethical prob-lems with algorithms deciding in sensitive areas.

In meta studies, Grove and Meehl (1996) and Grove et al. (2000) corrob-orate the hypothesis that for many forecasting tasks, algorithms are better suited than humans. The tendency to discount algorithms has been docu-mented in a variety of settings as well. In medicine, recommendations ing from a physician are rated higher than recommendations from a com-puter system or from a physician aided by a computed system (Promberger and Baron, 2006; Shaffer et al., 2013). In matters of personal taste, Yeomans et al. (2017) provide evidence that although an algorithm outperforms hu-mans at recommending jokes that participants rate funny, they still prefer to receive joke recommendations from other humans.

A first hypothesis emerging from this literature is that algorithm aversion exists and that people shy away from using algorithms, most likely also in financial decisions:

Hypothesis 1: A larger fraction of participants will initially select to in-vest with the human fund manager than with the inin-vest- invest-ment algorithm.

Hypothesis 1a: Participants’ willingness-to-pay for the human fund manager (i.e., fees) will initially be higher than their willingness-to-pay for the algorithm.

Hypothesis 1a is added as a measure for the strength of preference for a finan-cial intermediary. By attaching a price to investing, we are able to determine at what price people are indifferent between investing with the human fund manager and the algorithm.

periment participants observe predictions of human judges and algorithms in domains such as MBA student performance or U.S. air traffic. In several conditions, the amount of information participants observe is varied. They can either tie their incentives to the performance of an algorithm or to a hu-man judge (which is in some conditions themselves and sometimes another participant). Dietvorst et al. (2015) find that algorithm aversion is most pro-nounced after seeing the algorithm perform, even if this performance is supe-rior to the human judge. They conclude that people are particularly troubled by seeing the algorithm err and abandon it in response.

We can thus specify the expected reaction to seeing the investment algo-rithm perform and to mistakes that is makes:

Hypothesis 2: Participants will disregard higher performance of the al-gorithm and continue to favor the human fund manager after outcomes are observed.

Hypothesis 3: After mistakes by the algorithm, participants will be more prone to switching from the algorithm to the human fund manager than vice versa.

In a follow-up article, Dietvorst et al. (2018) find that allowing partici-pants to modify the forecast of an algorithm makes them considerably more likely to use it. At the same time the modification option increases partic-ipants’ satisfaction with and belief in the algorithm. There exists further evidence for situations in which humans do rely on algorithms. In a task of evaluating statements and reducing them to a logical problem, partici-pants rely more on algorithms than on other people (Dijkstra et al., 1998), or even themselves (Dijkstra, 1999). As Logg (2017) elaborates, confound-ing factors in existconfound-ing studies make it difficult to establish a clear case for or against algorithm aversion. She shows that participants prefer advice from

algorithms over advice from other people, and that they particularly prefer advice from algorithms for objective decisions (e.g., estimating air traffic), whereas they prefer advice from humans for subjective decisions (e.g., rec-ommending jokes).

Financial decision making might be perceived as a domain of objective decision making, which would work against Hypotheses 1-3. Little attention has yet been paid to algorithm aversion in a financial context. To our knowl-edge, there is only a handful of studies on the role of algorithm aversion in finance. In an experiment, Önkal et al. (2009) show that stock price forecasts provided by a statistical forecasting method are more severely discounted than forecasts by a human expert. Based on fund flow data, Harvey et al.

(2017) report that algorithm-based (“systematic”) hedge funds receive less in-flows than actively managed (“discretionary”) hedge funds. However, they do not find a performance gap justifying this aversion towards algorithm-based hedge funds.

Most recently, Hodge et al. (2018) provide experimental evidence that in-vestors are more likely to follow the advice of a robo-advisor in an anony-mous setting, while they are more likely to follow the advice of a human ad-visor when adad-visors are humanized (e.g., by adding a name). Unlike in our study, however, their setting does not feature actual human advisors, nor do the human or the algorithm advisor act in the experiments. D’Acunto et al.

(2018) study the characteristics of investors who adopt robo-advising tool and find that they are demographically similar to non-adopters, but have larger portfolios, trade more, and achieve higher risk-adjusted performance.

Following their interpretation, more sophisticated investors are more likely to adopt the algorithm.

Our study contributes to this emerging literature on the presence (or ab-sence) of algorithm aversion in financial decision making in multiple ways.

To our knowledge, we are the first to use an experimental setting in which

served to act. Due to the straightforward design, we are able to exclude many of the confounding factors that make conclusions about algorithm aversion otherwise difficult (Logg, 2017). By presenting the decisions and investment outcomes to participants, we generate rich data on how they respond to per-formance and to mistakes, which has been described as one of the key ele-ments of algorithm aversion. Finally, we explore the underlying beliefs that shape people’s preferences for a human or algorithmic intermediary.