1 Introduction
To make profitable investment decisions, investors must know and understand the risks they face. Investors’ comprehension of risk can differ considerably depending on how that risk is learned or communicated to them. Finance professionals typically communicate risks using descriptions—for instance, in the form of financial reports, investment brochures, insurance brochures, investor education programs, and market research reports. These documents often describe risks using a summary of historical returns and their respective chances. Investors could also acquire knowledge about financial risks in other ways, namely, by observing the development of stock prices or through their own investment experience. For instance, day traders decide to purchase a stock by simply observing prior stock movements, or individual investors increase their subscription to initial public offering auctions subsequent to previous successful experience (e.g., Kaustia and Knüpfer, Reference Kaustia and Knüpfer2008). Indeed, the way in which knowledge about risks is acquired has a dramatic influence on investors’ understanding of risk and their willingness to accept it (Malmendier and Nagel, Reference Malmendier and Nagel2011). For example, our previous work shows experimentally that people who learn about a stock market crash from experience are more likely to stay out of the market than people who learn about the same crash from descriptions (i.e., the ‘depression babies’ effect), even when wealth effects are kept constant (Lejarraga et al., Reference Lejarraga, Woike and Hertwig2016).
In general, people seem not only to be persuaded more by their experience than by described sources of information, but to have more accurate subjective assessments of risks, even if experience is simulated (Hertwig and Wulff, Reference Hertwig and Wulff2022). Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013) and Bradbury et al. (Reference Bradbury, Hens and Zeisberger2015) show that simulations of the stock market, which are closer to the notion of witnessing rather than experiencing through action, help investors understand risk accurately, and lead them to invest more in the stock market than investors who learn from described sources. On the other hand, Lejarraga and Gonzalez (Reference Lejarraga and Gonzalez2011) show that exhaustive descriptive information is often neglected when the participants have the chance to also experience the information by sampling; that is, people who have experience and descriptions often make choices as if descriptions had been omitted. Consistent with this observation, Barron et al. (Reference Barron, Leider and Stack2008) show that people are more likely to ignore a described warning if they have already experienced a series of safe outcomes, but they are more likely to be persuaded by the warning if they have no previous experience. These studies converge in that people tend to overweight the information they gain from experience over that which is provided in a descriptive form; this can lead to more accurate risk assessments, as long as the experiences are representative of the environment (for reviews, see Hertwig and Wulff, Reference Hertwig and Wulff2022; Wulff et al., Reference Wulff, Mergenthaler-Canseco and Hertwig2018).
We build on previous research in 2 ways. First, we conduct a conceptual replication of the study undertaken by Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013). This involves exposing experimental investors to four different risk communication interventions and observing how these interventions affect participants’ risk taking, factual knowledge about the encountered decision environment, and subjective assessments of confidence and satisfaction. Second, we extend the study conducted by Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013) by manipulating whether or not investors had prior experience of the decision environment before they were exposed to the risk communication intervention. Specifically, and akin to Barron et al. (Reference Barron, Leider and Stack2008), one group of people build up experience with the decision environment by making a series of decisions with feedback prior to being exposed to the risk communication intervention, whereas the other group, akin to Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013), are given the risk communication intervention straight away.
Using this setup, we will be able to provide a better estimate of how effective the four risk interventions are in informing people about financial risks in situations involving no prior experience and to explore how and whether prior experience impacts the effectiveness of the four interventions.
2 Overview of studies
To study the relative effectiveness of the risk communication interventions and how prior experience interacts with them, we conducted 2 preregistered experimental studies.Footnote 1 Both studies had the same between-subjects design with 9 conditions comprising 1 control condition and 8 treatment conditions that result from crossing 2 factors: (a) prior experience with 2 levels (with and without) and (b) the intervention used to learn about the options, with four levels (description, experience, distribution, and the ‘risk tool’) (Figure 1). Participants played an investment game in which their task was to allocate funds between a risky and a safe investment option for a number of periods. The risky option was a stock offering a variable rate of return and the safe option was a deposit offering a fixed rate of return.
Experimental design and procedure.
Note: Yellow boxes indicate manipulated factors.
In Study 1, the stock had a higher expectation value than the deposit. In Study 2, the stock involved the possibility of a large but rare loss. Thus, in terms of expectation, the stock was less attractive than the deposit. Because rare events are unlikely to be encountered in a small sample of experiences, the setting in Study 2 can be conceptualized as a ‘wicked’ investment environment, and Study 1 as a ‘kind’ environment (Hogarth, Reference Hogarth2001; Hogarth et al., Reference Hogarth, Lejarraga and Soyer2015).
Studies 3 and 4 follow the same design of Study 1, with small variations. In Study 3, we revised the instructions to improve clarity and labeled the y-axis of the histogram in the risk tool. In Study 4, we eliminated the starting position of the response scale to avoid suggesting a 50–50 allocation, and we also increased the incentives. In Studies 3 and 4, we only conducted the risk tool and the description conditions. The experimental implementations of all conditions in all studies are available at https://harnessing-demo.exp.arc.mpib.org.
2.1 Prior experience
Participants who were assigned to a prior-experience condition were endowed with a portfolio of £100 and were asked to make one investment decision in each of 20 periods (20 decisions in total). Initially, participants knew that one option was a stock and the other a deposit, but were not informed about the return distributions of these options. Immediately after each investment, the obtained return was automatically added to their running portfolio, providing some feedback to participants about the options. After the initial 20 investment periods, participants in the 8 treatment conditions (but not participants in the control condition) were presented with 1 of the 4 communication tools and were allowed to use the tool to explore the investment options for as long as they wanted. After a required minimum exploration, participants returned to the investment game to continue investing for another 20 periods.
Participants who were assigned to a no-prior-experience condition entered the experiment without any prior experience; namely, they started the investment game by using one of the communication tools directly. After exploring the options with one tool, participants began the investment game for 20 investment periods. To keep wealth constant between the prior-experience and the no-prior-experience conditions, participants in the latter were yoked to participants in the former: We recorded all the portfolio amounts of participants in the prior-experience conditions after period 20 and used them as starting portfolios for participants in the no-prior-experience condition. Therefore, portfolios in the prior and no-prior-experience conditions were constant at the start of period 21.
After participants finished playing the investment game, they completed a task survey including the following questions (Kaufmann et al., Reference Kaufmann, Weber and Haisley2013):
-
• How risky do you perceive the stock (the risky asset) to be? (1 = not risky at all, 7 = very risky)
-
• How confident do you feel about investing in the risky asset? (1 = completely unconfident, 7 = completely confident)
-
• If we put £100 in the risky asset, what is the expected return of the £100 after 5 years? (Give your best estimate.)
-
• If we put £100 in the risky asset, in how many out of 100 cases will the return fall below £100 after 5 years?
-
• If we put £100 in the risky asset, in how many out of 100 cases will the return be above £150 after 5 years?
-
• How informed do you feel about the 2 assets (the deposit and the stock)? (1 = completely uninformed, 7 = completely informed)
After the survey, participants were shown their final account balance and were asked: How satisfied are you with your return? (1 = completely unsatisfied, 7 = completely satisfied).
Participants then completed the Berlin Numeracy Test (Cokely et al., Reference Cokely, Galesic, Schulz, Ghazal and Garcia-Retamero2012), a survey of financial behavior (Kaufmann et al., Reference Kaufmann, Weber and Haisley2013), an investment quiz (Cohn et al., Reference Cohn, Engelmann, Fehr and Maréchal2015), and a question about general propensity to take risks (Goebel et al., Reference Goebel, Grabka, Liebig, Kroh, Richter, Schröder and Schupp2019; SOEP). Finally, participants completed a demographic survey, including questions about their income and wealth.
2.2 Risk communication tools
The description tool describes the options in full, consistent with Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013). For example, in Study 1, participants read ‘The deposit is a safe asset. It has a guaranteed return of 0.83% for sure. If for 20 periods you invest the full £100 in the deposit, you will have a return of £117.91. The stock is a risky asset. It has an expected return of 2.16% with a standard deviation of 7.42%. If you invest the full £100 in the stock, you will have an expected final outcome of £153.30’. In Study 2, the description of the deposit did not change, but the description of the stock was ‘… It has an expected return of 0.72% with a standard deviation of 12.53%. If for 20 periods you invest the full £100 in the stock, you will have an expected final outcome of £115.43’. The description tool also allows participants to see the expected return of a specific investment allocation and its corresponding 70% and 95% confidence intervals in relative frequencies. For example, in Study 2, if participants distributed £100 equally between options, they received the following message: ‘In 70 out of 100 cases your return will be between £97.35 and £105.29 and in 95 out of 100 cases between £91.96 and £108.74’. Participants could sample different allocations using a slider that determined the proportion of funds allocated to each option. Participants were forced to sample initially an investment where all funds were allocated to the safe deposit, then an allocation where all funds were allocated to the risky stock, and finally an allocation involving any mix of stock and deposit. After these 3 forced samples, participants were allowed to use the tool for as long as they wished, sampling as many different allocations as they wanted.
The risk tool was programmed following Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013). Participants could choose an investment allocation and see the outcome of their decision plotted on a histogram. They could simulate as many outcomes as they wanted using different simulation modes. They could simulate one outcome at a time, or they could simulate outcomes automatically, in either slow or fast motion mode. As outcomes accumulate in the histogram, the graph becomes increasingly representative of the underlying distribution that generates the outcomes. As in the description tool, participants were initially forced to sample—using the 3 modes—a fully safe investment, a fully risky investment, and finally any mix of safe and risky investments of their choice. Only after these 3 forced samples, did the risk tool allow them to sample as many different investment allocations as they wanted before they could return to the investment game.
The distribution tool showed the distribution of potential returns of participants’ investments by plotting the density function in a graph. Participants could change their investment allocation and observe how the distribution of potential returns changed. As in the other tools, participants were initially forced to sample a fully safe, a fully risky, and a mixed allocation. Only then did the distribution tool allow them to sample as many different investment allocations as they wanted before they were allowed to return to the investment game.
The experience tool allowed participants to sample outcomes from their investment allocations. In contrast to the risk tool, in which outcomes were plotted in a graph, the experience tool showed a single numerical outcome for each allocation. Participants could sample as many outcomes as they wished for a particular allocation, and they could also explore outcomes for a different allocation. As in the other tools, participants were initially forced to sample a fully safe, a fully risky, and a mixed allocation before they were allowed to return to the investment game.
The 4 tools were designed to be consistent with the tools used in Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013), and they were sent to the first author of that article for feedback. Our implementations are available in harnessing-demo.exp.arc.mpib.org.
2.3 Investment options
In both studies, the risky option was a stock offering a variable rate of return, and the safe option was a deposit offering a fixed rate of return. The deposit was constant in Studies 1 and 2. To keep our study consistent with Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013), we transformed their annual rate of 3.35% into quarterly rates of 0.83%.
The stock differed in Studies 1 and 2. Specifically, in Study 1, the stock offered 2.16% (
$SD$
of 7.42%) and was computed as the quarterly rate of return that was equivalent to the annual rate used by Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013) (8.95%)—which, in turn, was the average annual return from the Morgan Stanley Capital International USA Index between 1973 and 2008. Thus, in Study 1, the stock was more attractive in expectation than the deposit.
In Study 2, we introduced the possibility of a large but rare loss. Thus, the stock offered a draw from the same distribution of Study 1 (2.16%,
$SD = 7.42\%$
) with a probability of 0.98, and with 0.02 probability, a draw from a rare negative event distribution resulting in a loss of 70% (
$SD = 7.42\%$
). Thus, the overall expected return of the stock was 0.72% (
$SD = 12.53\%$
), making the deposit more attractive than the stock.
2.4 Participants
We followed the ‘2.5 rule’ to plan our study samples (Simonsohn, Reference Simonsohn2015). Specifically, our planned samples were 2.5 times the average sample size per condition in Studies 2 and 3 of Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013), which was 69. The resulting sample for each of our 9 conditions is 172.5, which we rounded to 173.
Data were collected online using Prolific (https://www.prolific.co). Eligible participants were UK residents, had learned English as their first language, and were aged 18 or older at the time of data collection. Participants were compensated with a participation fee (£1.25 in Study 1 and £1.40 in Study 2) and a bonus that depended on their investment decisions in the experiment and was on average £0.42 and £0.27 in Studies 1 and 2, respectively. On average, participants received £1.62 in Study 1 and £1.7 in Study 2 for approximately 15 minutes of participation.
Overall, there were 1,560 participants in Study 1 and 1,546 in Study 2. We used 6 attention checks to filter out inattentive participants. Those who failed 2 checks were excluded from the sample, but the results are robust to more stringent exclusion criteria (see Additional Analyses). The proportion of rejected participants ranged from 22% to 28% across the different studies. The raw data of all studies are available at https://osf.io/8vjna.
3 Results
Figure 2 shows the proportion of funds invested in the stock across conditions and studies. To compare investments across conditions, we compute the mean proportion of funds allocated to the stock across periods for each individual. We then report means and 95% confidence intervals across individuals. Figure 3 shows the distribution of responses to all knowledge questions. The average allocation across periods (and the average in the first allocation) to the risky option are reported in Table 1.
Proportion of funds invested in the risky option across periods, by condition and study.
Note: Gray plots indicate the conditions with prior experience. Blue plots indicate the conditions without prior experience. Gold plots show the control conditions. The thick line in each plot denotes the mean proportion of funds invested in the risky stock. The dashed lines indicate the 95% confidence intervals of the mean. Darker regions in the plot indicate higher density than lighter regions.
Subjective risk assessments by condition and study.
Note: The results are displayed as raincloud plots that show the original data (points), the density distribution (polygon), and the average value across participants (large circle).
Average/first allocation to the risky option after tool exposure
Note: Cells in columns 3–6 indicate, in the left, the average allocation to the risky option across periods and participants, and in the right, the average allocation to the risky option across people in the first investment after the intervention.
3.1 Impact of risk communication tools on investments
3.1.1 Investments in a kind environment
Investments in Study 1 did not differ according to the tool used. The mean proportion of funds invested in the stock was 0.53 (95% CI: 0.50, 0.57) for those who used the risk tool, 0.55 (0.52, 0.58) for those who used the distribution tool, 0.5 (0.47, 0.54) for those who used the sampling tool, and 0.51 (0.47, 0.54) for those who used the description tool. None of these conditions differed from the control condition, in which participants used no tool and invested on average 0.52 (0.49, 0.55) in the stock. Compared with previous results (Kaufmann et al., Reference Kaufmann, Weber and Haisley2013), participants who used the risk tool (
$\bar {X}= 0.53$
; 95% CI: 0.50, 0.57) did not take more risks than participants who used other tools (0.52; 0.5, 0.54)
The investments were also unchanged by previous experience. Participants who had no previous experience invested a mean of 0.52 (0.51, 0.54) in the stock, the same as the 0.52 (0.5, 0.53) by participants who had already invested for 20 periods beforehand. Specifically, after prior experience, participants who used the risk tool invested 0.52 (0.49, 0.55) in the stock. Similar levels of risk were taken by participants in the other conditions. Those who used the distribution tool invested 0.51 (0.48, 0.55), those who used the sampling tool invested 0.53 (0.5, 0.56), and those who used the description tool invested 0.5 (0.47, 0.54) in the stock. Similarly, having experienced 20 investment periods did not make one tool better or worse than another.
We also examined the influence of tool use on the very first investment allocation after the intervention. This analysis is comparable to that by Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013), and provides the key metric to judge the extent of our replication and extension. The analysis shows that the tools did not have a differential impact on the first investment.
3.1.2 Investments in a wicked environment
A similar pattern of results emerged in the wicked investment environment of Study 2. Again, investments did not differ across different tools. The mean proportion of funds invested in the stock was 0.56 (0.52, 0.6) for those who used the risk tool, 0.53 (0.49, 0.56) for those who used the distribution tool, 0.52 (0.48, 0.55) for those who used the sampling tool, and 0.47 (0.43, 0.51) for those who used the description tool. Again, none of these conditions differed markedly from the control condition, in which participants invested on average 0.53 (0.5, 0.57) in the stock. Furthermore, participants who used the risk tool (0.56; 0.52, 0.6) did not take more risks than participants who used other tools (0.5; 0.48, 0.53). Here again, we do not replicate results reported by Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013)
After prior experience, participants who used the risk tool invested 0.48 (0.45, 0.52) in the stock. Those who used the distribution tool invested 0.49 (0.46, 0.53), those who used the sampling tool invested 0.54 (0.5, 0.58), and those who used the description tool invested 0.4 (0.36, 0.45) in the stock.
In contrast to the kind environment of Study 1, participants who had prior experience took less risk (0.48; 0.46, 0.5) than those who had no previous experience (0.52; 0.51, 0.54). In this wicked environment, more experience increases the chances that participants learn about the possibility of a large loss. Results suggest that participants with prior experience became more cautious. However, having prior experience did not systematically impact the effect of tools. One possible exception is the description tool. Participants who had prior experience and used the description tool took the least financial risk (0.4; 0.36, 0.45) compared to the rest of the tools (0.5; 0.48, 0.53). Overall, the description tool led to the most cautious investments by participants who both had prior experience and did not have prior experience.
We again examined the influence of tools for the first investment allocation after the intervention but found no differences.
In sum, compared to previous findings, we observe no systematic effect of risk communication tools across Studies 1 and 2, except for the description tool being more effective at revealing the risk of a large but rare loss. Similarly, experience did not influence risk taking except in revealing the possibility of a rare event.
3.2 Impact of risk communication tools on subjective assessments of risk
3.2.1 Assessments in a kind environment
In previous work, participants who used the risk tool perceived less risk than those who used other tools. In our study, we did not observe an effect of the tools on risk perception (Figure 3). Participants who used the risk tool scored 4.5 (4.29, 4.71) in risk perception, not different from participants who used the description tool (4.55; 4.33, 4.78), the distribution tool (4.65; 4.44, 4.86), and the sampling tool (4.51; 4.30, 4.73).
Similarly, previous findings indicated that the risk tool led to the highest feeling of being informed; however, this observation was not replicated in our studies. Participants who used the risk tool (3.33; 3.12, 3.55) did not feel more or less informed than participants who used other tools. Participants who used the description tool scored 3.60 (3.37, 3.82), those who used the distribution tool scored 3.21 (2.98, 3.44), and those who used the sampling tool scored 3.40 (3.17, 3.62).
In terms of confidence, the tools had no differential impact. The risk tool led to a mean confidence score of 3.98 (3.75, 4.20), the description tool to 4.01 (3.76, 4.26), the distribution tool to 3.92 (3.70, 4.15), and the sampling tool to 4.10 (3.87, 4.32). Tools also had no differential impact on satisfaction. The risk tool led to a mean satisfaction score of 5.24 (5.03, 5.46), the description tool to 5.19 (4.99, 5.39), the distribution tool to 4.80 (4.56, 5.04), and the sampling tool to 5.18 (4.97, 5.39).
With respect to the perceived chances of ending with a positive return, the tools again had no different impact. The risk tool led to a mean chance score of 39.3 (35.2, 43.3), the description tool to 40.5 (36.1, 44.8), the distribution tool to 37.8 (34.0, 41.7), and the sampling tool to 35.1 (30.9, 39.3). The same pattern emerges for the perceived chances of ending with a loss. The risk tool led to a mean chance score of 38.9 (35.7, 42.1), the description tool to 37.9 (33.9, 41.9), the distribution tool to 40.1 (36.7, 43.4), and the sampling tool to 37.8 (34.1, 41.5). Again, the tools had no effect on perceived return. Excluding one outlier, the risk tool led to a mean perceived return of 272 (198, 346), the description tool to 228 (187, 270), the distribution tool to 322 (225, 418), and the sampling tool to 226 (181, 272).
In general, the tools had no impact on subjective assessments of the investment problem faced by participants in the kind environment.
3.2.2 Assessments in a wicked environment
Participants across tools judged the wicked environment to be more risky than participants in the kind environment; however, no clear pattern emerged across tools and subjective assessments.
In the wicked environment (Figure 3), participants who used the risk tool scored 4.66 (4.43, 4.89) on risk perception, no different from participants who used the description tool (4.85; 4.62, 5.08) or the distribution tool (4.81; 4.59, 5.03), but less than those who used the sampling tool (4.99, 4.78, 5.20).
Compared to previous findings, participants who used the risk tool felt less informed (3.49; 3.27, 3.72) than participants who used the description tool (3.94; 3.70, 4.17), but not differently informed than participants who used the distribution (3.36; 3.14, 3.58) or sampling tools (3.36; 3.12, 3.61).
The risk tool led to higher confidence (3.92; 3.68, 4.16) than the distribution tool (3.53; 3.30, 3.76), but no more confidence than the description (3.74; 3.50, 3.98) or sampling tools (3.70; 3.48, 3.92).
In terms of satisfaction, the description tool led to the highest scores. Participants who used the description tool not only felt most informed but were also more satisfied (4.72; 4.43, 5.02) than participants who used the risk tool (4.41; 4.13, 4.69), the distribution tool (4.34; 4.08, 4.61), or the sampling tool (4.40; 4.12, 4.69).
With respect to the perceived chances of ending with a positive return, the description tool led to the highest chances (40.8; 35.8, 45.9) compared to the distribution tool (34.5; 30.5, 38.6), the sampling tool (35.2; 30.9, 39.5), and the risk tool (33.9; 29.6, 38.3). The perception of the chance of ending with a loss did not differ across tools. The risk tool led to a mean chance score of 37.1 (33.4, 40.7), the description tool to 41.0 (36.8, 45.2), the distribution tool to 37.7 (34.3, 41.2), and the sampling tool to 39.1 (35.4, 42.9). Again, there was no effect on perceived return. Excluding one outlier, the risk tool led to a mean perceived return of 240 (158, 323), the description tool to 197 (138, 257), the distribution tool to 252 (186, 317), and the sampling tool to 160 (132, 188).
3.3 Interim discussion
In previous work, the risk tool led—relative to other tools—to a lower perception of risk and higher levels of feeling informed and confidence, which translated into more financial risk taking. Our results do not replicate these observations, either in a kind or in a wicked environment. Both investments and subjective assessments of the investment decision did not differ markedly or systematically across tools and environments. Although the risk tool led to the perception of lowest risk in the wicked environment, this observation was not accompanied by a higher feeling of being informed or by being more confident. Across results, the risk tool did not emerge as the tool that promotes investments in either kind or wicked environments. In contrast to previous work, there was an indication that the description tool led participants to feel informed, both in the kind and wicked environments.
3.4 Validity analyses
Our results indicate that there is no systematic effect of the different risk communication tools on the amount of risk that people take or on their subjective assessments of financial risk. Here, we examine the possibility that these null results might be caused by the possibility of participants giving inconsistent responses and that they may have been unresponsive to the structure of the investment game. The results are illustrated in Figure 4.
Moderator analysis of the effect of risk intervention tools.
Note: Each square shows for each combination of moderator (x-axis) and outcome variable (color; see legend) the change in adjusted
$R^2$
that results from adding the interaction between the moderator and a variable coding the risk communication tool to a regression with only main effects. Prior experience contrasts participants with and without access to the investment task before entering the risk communication tool. Kind vs. wicked environment contrasts participants in Studies 1 and 2. Attention contrasts participants who have failed 1 or 0 attention checks. Tool engagement is a composite of the time spent exploring the risk communication tool and the number of allocations explored. Financial expertise is a composite of the number of correct answers in the investment quiz, self-reported investment frequency, and the number of correct answers in the Berlin Numeracy Test. Studies 3 and 4 contrast the data of Studies 1 and 2 with the new Studies 3 and 4, respectively (see text).
3.4.1 Consistency
We examined whether participants who see themselves as greater risk takers do indeed take more financial risks, as well as those who judge losses as being less likely, those who judge the stock as being less risky, and those who are more confident in the stock. Results indicate a high consistency in responses. Participants who see themselves as greater risk takers took more risks in the investment task, as measured by the positive correlation between the score in the SOEP’s general risk item and the proportion of funds invested in stocks by each participant (
$r = .27$
; 0.24, 0.31). Indeed, higher financial risk was also taken by those who judged the stock as less risky (
$r = -.183$
;
$-$
0.22,
$-$
0.15), those who perceived losses as being less likely (
$r = -.18$
,
$-0.21$
,
$-0.15$
), and those who were more confident in the stock (
$r = .38$
; 0.35, 0.42). Moreover, we observed no correlation between risk perception and expected returns (
$r = .00$
;
$-0.03$
,
$0.03$
), but a strong correlation between risk perception and expected losses (
$r = .29$
; 0.26, 0.32), which is in line with findings suggesting that laypeople’s understanding of risk is driven by losses (Wulff and Mata, Reference Wulff and Mata2022; Zeisberger, Reference Zeisberger2022).
3.4.2 Responsiveness
The participants were also responsive to the outcomes of their investments. After a gain, participants were 1.7 times more likely to increase their investment in the stock as compared to decreasing it, whereas, after a loss, participants were 1.2 times more likely to decrease their investment in the stock as compared to increasing it. Furthermore, in Study 2, participants who invested at least 50% of their portfolio in the stock were 2.4 times more likely to decrease their investment in the stock relative to increasing it after experiencing the rare extreme loss.
Finally, participants were sensitive to the structure of the investment decision. Overall, risk taking in Study 1, where the stock was a relatively attractive prospect, was significantly higher than in Study 2, where the deposit was better. The difference in the mean proportion of risk taken was
$\delta = -1.38$
(95% CI:
$-1.52$
,
$-1.25$
). This pattern is also present in participants’ risk perception—with higher risk perceived in Study 2 (
$\delta = .433$
; .425, .441), confidence (
$\delta = -.417$
;
$-.425$
,
$-.408$
), and feelings of being informed (
$\delta = .046$
; .038, .055).
3.5 Robustness of null effects
Our results show that the 4 risk communication tools used in our experiments have no differential impact on the way people take financial risk and form subjective representations of that risk. This pattern of null results was robust to whether or not participants had prior experience (as in Studies 1 and 2) and to whether or not the risky stock entailed the possibility of a rare and large loss (Study 2). First, we examine the robustness of these null results in Studies 1 and 2 across a variety of potential moderators, namely, the amount of attention that participants paid to the task (based on the whether participants passed 5 or 6 attention checks), the degree of engagement with the tools (according to a composite measure of the number of allocations sampled and time spent with the tool), and participants’ financial expertise (based on a composite measure of the Berlin Numeracy Test, the survey of investment frequency, and the investment quiz). The results are illustrated in Figure 4.
To examine the moderating effects of these factors, we estimated linear models for each response variable (risk taking, risk perception, confidence, and feeling informed) and each of several different moderators. Figure 3 shows, on the y-axis, the additional explained variance (change in adjusted
$R^2$
) of a model that includes the interaction term relative to a model with only the main effects of tool and moderator. The x-axis shows the moderators that were examined and response variables that are each represented by a color-coded square. Results show that the null effects of the tools are robust across many moderators and response variables. For example, tools have no different impact independently of whether participants are attentive, engaged or expert, or whether the risk tool offers more detailed instructions. Of the 24 moderating interactions, 22 are not significant, which means that null effects are robust. Only prior experience and the type of environment interact with the tools to influence risk taking; namely, participants using the sampling tool took more financial risk when they had prior experience than when they did not. For participants using other tools, no such increased risk taking was observed under the prior experience condition. Furthermore, participants in wicked environments who used the sampling and the risk tools took more risk than those who used other tools; however, neither prior experience nor the type of environment had any effects on how the tools influence risk perception, confidence, and feeling informed.
3.5.1 Study 3: Improved instructions and labeling of the y-axis
To rule out the possibility that the null effects observed in Studies 1 and 2 were caused by participants failing to understand the instructions of the task, we conducted a third experiment in which we provided more detailed instructions about the risk tool and compared responses with those of participants in the description tool with unchanged instructions. The implementations of Study 3 (and the other studies) can be examined in https://harnessing-demo.exp.arc.mpib.org. Additionally, we labeled the y-axis of the risk tool with the word ‘Frequency’ to improve understanding. If the instructions and the y-axis were ambiguous in Studies 1 and 2 and are clearer in the current version of the risk tool—and the risk tool promotes risk taking—then responses should differ across the 2 conditions. If responses do not differ, the null effect should not be attributed to the ambiguity in the instructions. We pre-registered our predictions at https://osf.io/yc89g.
This study replicates 2 conditions from Study 1, namely, the risk tool and the description tools without previous experience. The investment options were those of Study 1. The deposit offered a constant return of 0.83%, and the stock offered 2.16% (
$SD$
of 7.42%) on average. Data were collected online using Prolific. As in Studies 1 and 2, eligible participants were UK residents, had learned English as their first language, and were aged 18 or older at the time of data collection. We collected responses from 349 participants. They were paid a participation fee of £1.25 and a performance-dependent bonus of £0.43 on average for approximately 12 minutes of participation.
Responses in this third study (in terms of risk taking and subjective assessments of risk) did not differ across participants who used the risk tool (0.55; 0.52, 0.58) with improved instructions (and labeling) and the description tool (0.53; 0.49, 0.57) with the older instructions (Figure 4, ‘Study 3’). In short, the results of this third study suggest that the null results in Studies 1 and 2 were not driven by ambiguous instructions.
3.5.2 Study 4: No default and higher incentives
Participants in Studies 1–3 expressed their desired investments by moving a scale that determined the proportion of their portfolio to be allocated across the deposit and stock. This scale was centered at the midpoint by default. It is possible that this default setting of the scale influenced investment decisions toward an equal allocation across options. To examine this possibility, we conducted a fourth study that was identical to Study 3 in all aspects except that we used a scale with no default position. In this implementation, participants chose an initial allocation by clicking any point along the response scale. After a click, a small circle appeared on the scale, indicating the desired allocation. This circle could then be dragged along the scale to change the allocation. This implementation eliminates the possibility that the default position of the scale may drive the null effect.
We collected responses from 349 participants. In this case, we increased the performance-dependent bonus significantly compared to Studies 1 and 2. More precisely, participants were paid a participation fee of £1.25 and a performance-dependent bonus of £0.61 on average, which was 45% higher than in Study 1 and 125% higher than in Study 2.
Although incentives were higher and the response scale had no default setting, risk taking and subjective assessments of risk did not differ across participants who used the risk tool (0.54; 0.51, 0.57) and the description tool (0.5; 0.46, 0.54; Figure 4). The results of this fourth study suggest that the null results in Studies 1–3 were not driven by the potential influence of the starting position of the response scale, and also do not appear to be caused by insufficient incentives. This pattern of results suggests that an equal allocation of funds into the available options is a deliberate investment strategy, one that has been identified as the ‘1/N heuristic’ or ‘naive diversification’ in the context of savings plans (Benartzi and Thaler, Reference Benartzi and Thaler2001).
3.6 Comparison with Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013)
The results of our studies speak unambiguously. They contrast those of Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013). Whereas Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013) showed that the risk tool promotes more informed and confident investors who are willing to take more risk, our results show no traces of such an effect. Why do the results differ so markedly? One possible explanation is that the incentives for participants were different. In Kaufmann et al.’s (Reference Kaufmann, Weber and Haisley2013) Experiment 1, 10 of the 133 University of Mannheim students who participated in the study were compensated with Amazon.com gift cards whose worth was proportional to their investment performance. The gift cards were worth between €10 and €18. Thus, participants were paid, on average, €1.05. In their Experiment 2, 190 participants from the pool of the Yale School of Management earned a $5 gift card and had a 5% chance to earn an unspecified additional performance-based compensation. In Experiment 3, 362 participants—also from the Yale School of Management—had a 50% chance to earn a $5 Amazon.com gift card and a 2.5% chance to earn an unspecified additional pay. On average, these participants received a minimum of $2.5. In Experiment 4, 212 participants from Amazon MTurk earned $1.30 and a ‘20% chance to earn additional performance-based pay’. And, in Experiment 5, 5 out of 39 students from the University of Mannheim, Germany, who acted as participants, earned an ‘Amazon.com gift card for the amount of the financial market simulation divided by 100’, which translates into €1.42, on average. Therefore, with the exception of participants from the pool of the Yale School of Management, who earned between $2.5 and $5, participants in Europe earned between €1.05 and €1.42 for the experiment, and those in MTurk $1.30. These amounts are slightly below the compensation that we paid our participants in Prolific, namely, £1.62 in Study 1 and £1.7 in Study 2. Moreover, because our experiments were conducted in Prolific, our compensation schemes had to meet Prolific’s principle of ‘ethical rewards’, which required fair minimum pay for participation. In short, the compensation of participants across the original experiments and our studies does not seem to differ sufficiently to be the cause of the distinct results.
Another possible explanation might lie in the composition of the sample of participants. In their Experiment 1, Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013) used a sample of participants from the University of Mannheim (61% male), with an average age of 22 (18–50), of whom approximately 30% reported owning stocks. Experiments 2 and 3 used a similar sample of participants (41% male) recruited from the pool of the Yale School of Management, aged on average 34 (18–70), with a median income of $40,000 ($0–$199,000), of whom 50% were college educated and 45% owned stocks. Experiment 4 used an MTurk sample of US participants (49% male), aged on average 36 (20–68), with a median income of $39,000 ($0–$200,000), of whom 51% were college educated and 31% owned stocks. Experiment 5 used University of Mannheim students (59% male), aged on average 24 (18–43), with approximately 36% who owned stocks. In other words, the samples used by Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013) are characterized by being diverse in terms of age, gender (47% male across experiments), education, and income, and in which more than a third of the participants owned stocks. Our experiments used 2 Prolific samples from the same participant pool. In both of our studies, 3,106 participants were, on average, 37.8 years old (18–86), 35.7% were male, and 43% reported dealing with investment instruments at least once a year. The mean income was approximately £23,000 (£0–£120,000). Overall, these numbers reflect only moderate differences between the samples used by Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013) and in our studies, implying that differences in sample composition do not offer a convincing explanation of the differences in results.
There is another difference that may have caused the different pattern of risk taking. The risky option in Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013) offered an annual rate of return of 3.35%. To keep our study consistent, we transformed this rate into quarterly rates of 0.83%. It is possible that, although equivalent on an yearly basis, the apparently lower rates of return in our study could have led participants to choose less risk.
Our results also contrast the work of Bradbury et al. (Reference Bradbury, Hens and Zeisberger2015). The authors conducted an experiment with 535 participants who were shown 5 financial products and were asked to choose 1. Then, participants were forced to simulate the investment options and were asked to again choose 1 of the 5 products. Similar to Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013), the authors found that simulated experience leads people to take more financial risk without regretting it. Participants (from diverse backgrounds and with a mean age of 22.7) and incentives (on average, participants earned 26 CHF for 65 minutes) were similar to ours and do not suggest that the difference in results lies in these aspects. However, the key difference might lie in that, also as Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013), this study involves a one-shot decision without feedback (reported before and after the simulation) and does not explore repeated decisions with feedback as we do here.
4 General discussion
The power of simulations to communicate risk effectively has become evident in recent years (Hertwig and Wulff, Reference Hertwig and Wulff2022). Simulations help people make probability judgements more accurately (Hogarth and Soyer, Reference Hogarth and Soyer2011), judge the risk of medications such as opioids more precisely (Wegwarth et al., Reference Wegwarth, Ludwig, Spies, Schulte and Hertwig2022), and engage in financial risk with more knowledge and confidence (Bradbury et al., Reference Bradbury, Hens and Zeisberger2015; Kaufmann et al., Reference Kaufmann, Weber and Haisley2013). As much as simulations seem to promote effective risk communication, it is important to understand under which conditions they work well. Here, we conducted a conceptual replication of the work by Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013) with minor variations that, a priori, should not have caused different results from the original study. Namely, we created our own implementations of the risk communication tools—including the main simulator, the risk tool—according to the original design and feedback from the original author. Furthermore, instead of testing the effectiveness of the risk tool (and of other tools) in a one-shot investment decision, we examined the tools in a set of 20 investment periods (as we have done in other decisions from experience, Wulff et al., Reference Wulff, Hills and Hertwig2015). Even though the differences were arguably minimal, across 4 studies amounting to 3,804 participants, the risk tool did not lead to more risk taking or to more accurate subjective representations of risk as observed in previous studies and as we hypothesized and pre-registered. Moreover, this pattern of results is also present when only considering only the first allocation in the conditions with no prior experience, a comparison that most closely matches the original analysis by Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013). Also, having some experience with the investment problem did not have any influence on the effectiveness of the risk tool, nor on the influence of other tools on behavior.
If our implementation of the risk communication tools had no effect on investment behavior or on subjective representations of risk, one might suspect that our samples included a large share of inattentive participants. Indeed, online data collection has been questioned on the basis of participants’ lack of attention (e.g., McCrea et al., Reference McCrea, Penningroth and Radakovich2015). For this reason, we employed 6 different attention checks and filtered the data accordingly. Several signs indicate that the data are reliable: Across both studies, participants who perceived more risk also took less risk, more confident participants took more risks, and overall participants showed systematic reactions to the outcomes of their investments. Moreover, the general pattern of null results does not change when we restrict all the analysis to the most attentive, most engaged, and most expert participants.
Throughout the analysis, we have reported that the aggregate tendency of risk-taking tends toward a roughly even allocation of funds across investment options. This aggregate pattern hides substantial individual heterogeneity (Figure 4). Indeed, a 50–50 allocation was relatively rare across studies. In Study 1, participants used the 50–50 allocation in 3.5% of all investment decisions, and this proportion was 4.3% and 3% in Studies 2 and 3. In Study 4, in which there was no default scale positioned at the 50–50 allocation point, participants used the 50–50 allocation in 5% of the decisions. In short, the results suggest that the aggregate pattern of even allocation across investment options does not reflect the modal individual behavior.
The consistent pattern of null results poses new questions worth exploring in the future. Are minor implementation decisions critical to how well the risk tool and other simulators communicate risk? Is the risk tool effective in a one-shot decision situation but not when people know they will engage in several investment decisions? Finally, is the risk tool effective only for a specific profile of potential investors but not for the general public, as we explored in these studies? New studies should continue to seek answers to these open questions and help us understand when, how, and for whom financial simulators work well.
Appendix: Preliminary study
We conducted a preliminary study before Study 1 in which we observed that the tools were not adequate representations of the tools used by Kaufmann et al. (Reference Kaufmann, Weber and Haisley2013), for example, participants were not required to explore a minimum number of samples before they could continue investing. Also, this experiment did not include effective attention checks. We therefore improved the implementation of the tools for both of our studies in line with the original implementations and added attention checks. Results from this experiment were largely consistent with the results reported in the main text. Although we do not report the details of this experiment here, the data are available at the project’s Open Science Foundation repository (https://osf.io/8vjna).
Data availability statement
The raw data of all studies are available at https://osf.io/8vjna.
Acknowledgments
We thank Christine Laudenbach for her helpful comments and for feedback in the implementation of the risk communication tools in our studies. We also thank Ralph Hertwig for his comments and Laura Wiles for editing the manuscript.
Funding statement
This work was supported by a grant from the Biäsch Foundation for the Advancement of Applied Psychology to Dirk U. Wulff (2019–23).
Open access
