Financial Risk Tolerance during a Major Negative Life Experience: The Case of the COVID-19 Pandemic

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Using Holt and Laury's (2002) risk tolerance measure, an online survey was conducted with 1643 participants at seven time points before the pandemic and during four restricted and two unrestricted periods. Results showed a significant reduction in financial risk-taking during the pandemic. Notably, the decrease was most evident in the first wave, despite no major differences across the restricted waves. Risk tolerance began to gradually return when restrictions were lifted but did not reach pre-pandemic levels. Subjective risk tolerance during the pandemic, which differed from the objective financial situation, influenced real-life investment decisions. These findings highlight the influence of contextual and emotional factors on risk tolerance. The results are discussed concerning risk-seeking behavior in commission-free online brokerages like Robinhood during the pandemic, with implications for policy guidelines. Figures Figure 1 Figure 2 Figure 3 Introduction Financial risk-taking refers to financial actions such as investments or business ventures that are associated with the likelihood that their actual result will differ from their expected return. Financial risk-tolerance is the maximum amount of financial risk an individual is willing to take when making a financial decision (Grable, 2000 ). In itself, financial risk is not inherently good or bad. Although the word “risk” has a negative connotation (and financial risk is no exception), taking financial risk at some level is important to achieve economic growth (Bucciol & Miniaci, 2018 ; Zhang et al., 2016 ), especially when its expected value exceeds the safer alternatives. Thus, identifying the factors influencing people’s risk tolerance is essential for effective financial market management in both corporate and personal settings (Cassar et al., 2017 ; Kim & Lee, 2014 ). Research suggests that about 25% of the variation in risk tolerance across individuals can be explained by genetic traits (Cesarini et al., 2010 ). Several demographic and situational factors are considered to explain the remaining variation. For example, males are more risk-tolerant than females (Charness & Gneezy, 2012 ; Sung & Hanna, 1996 ), and young people are usually more risk-tolerant than older people (Bakshi & Chen, 1994 ; Pålsson, 1996 ). Similarly, people with higher incomes are more risk-tolerant than those with lower incomes (Schooley & Worden, 1996 ), and education is positively correlated with risk-taking propensities (Grable & Joo, 1997 ). In addition, people appear to be risk-avoidant in the domain of gains but risk-seeking in the domain of losses. This classic framing effect has been demonstrated in numerous settings (e.g., Fishburn & Kochenberger, 1979 ; Igou & Bless, 2007 ; McElroy & Seta, 2003 ; Simon et al., 2004 ; Tversky & Kahneman, 1981 , 1988 , 1992 ; Tykocinski et al., 2017 ). In the last two decades, researchers have also explored the effects of major life experiences on financial risk tolerance (Bucciol & Zarri, 2015 ). This stream of research shows that major negative life experiences such as global crises (e.g., the Great Depression of 1929, the Korean War in the 1950s, and the financial crisis of 2008) profoundly affected individuals’ behavior (Hoffmann et al., 2013 ). Specifically, people’s willingness to take financial risks decreases significantly when they undergo negative events such as natural disasters, wars, and economic crises. For example, Cassar et al. ( 2017 ) showed that individuals who experienced the 2004 tsunami in Thailand presented lower risk tolerance. Similarly, Kim and Lee ( 2014 ) found that early childhood exposure to the Korean War decreased risk tolerance. This traumatic event was so profound that it made individuals less tolerant of risk as much as five decades later. One possible psychological mechanism underlying the effects of negative life experiences on risk tolerance was proposed by Loewenstein and colleagues (Loewenstein et al., 2001 ; Lerner et al., 2004 ). They suggested that negative life experiences invoke negative emotions such as fear and anxiety, thus increasing people’s sense of uncertainty and lack of control. To reduce stress, individuals become less tolerant of (financial) risk (Lerner & Keltner, 2001 ; Callen et al., 2014 ). In line with this notion, Levav and Argo ( 2010 ) found that greater financial risk-taking was associated with feelings of security. In their study, participants were instructed to engage in an investment task. The experimenter’s verbal instructions were either unaccompanied or accompanied by physical contact (a touch on the shoulder or a handshake). The authors found that a friendly pat on the shoulder increased risk-taking on the task, and a feeling of security moderated this behavioral tendency. Moreover, Grable and Roszkowski ( 2008 ) found that merely being in a good mood was positively correlated with a higher level of financial risk tolerance. The current study examined the ways in which the COVID-19 crisis affected risk tolerance. On March 11, 2020, the World Health Organization officially declared the COVID-19 outbreak a pandemic. It resulted in more than 4.5M deaths by November 2022 and had major health, social, and economic impacts (Couto et al., 2020 ). Thus, the COVID-19 pandemic presents a unique opportunity to investigate the effect of unprecedented, widespread, life-threatening experiences on risk-taking behavior. Although several studies have examined risk tolerance during the pandemic (e.g., Bordalo et al., 2020 ; Cori et al., 2020 ; Barrios & Hochberg, 2020; Bundorf et al., 2021; Fan et al., 2020 ; Plohl & Musil, 2021 ; Wise et al., 2020 ), most have focused on health risk perceptions and beliefs specific to the COVID-19 crisis. One exception is the work by Galil et al. ( 2022 ), who found that from March to April 2020, individuals in lower SES strata tended to withdraw money from their risky funds and switch to less risky investment tracks. The current study extends this literature by examining how this prolonged negative life experience impacted financial risk tolerance in Israeli citizens. Fortunately, we collected data on risk tolerance a few months before the COVID-19 outbreak. Thus, we were able to test whether the level of risk tolerance of people drawn from the same sampling population changed in direct response to the crisis and whether this tendency varied over the course of the different waves of the crisis. Prior to COVID-19, Israel’s economy was at a peak, with an unemployment rate of 3.4% and a consistent increase in real wages of 3%. The COVID-19 crisis began in Israel towards the end of February 2020. A complete lockdown was imposed two weeks later, marking the beginning of the first 1st restricted wave that lasted until the end of May. While the lockdown dramatically reduced the infection rate, it had massive negative financial consequences, with 1.3 million new people unemployed and an increase of 1.3% in the unemployment rate. Four months after the 1st restricted wave, the infection rate in Israel again soared and became one of the highest of all developed countries. This 2nd restricted wave led to the second lockdown in September 2020, which deepened the economic crisis and increased unemployment by 0.3%. After two months of stability, Israel then entered its 3rd restricted wave that included a third lockdown, which started on December 27 and ended on February 7, 2021. During this third wave, the unemployment rate remained steady at around 4.8% (Central Bureau of Statistics, 2021 ). Since the economic situation in Israel deteriorated as the crisis advanced, we were able to test if changes to risk-tolerance are rational and correspond to the objective financial situation. The fourth major wave started on November 26, 2021, when 4 cases of the Omicron variant were identified in Israel. This wave lasted until the end of January 2022, but due to changes in the composition of the Israeli government, very minimal restrictions were imposed. During this time, the unemployment rate decreased slightly to 4.2% in December and to 3.6% in January 2022 (Central Bureau of Statistics, 2022 ). Over the course of 2022, there were further cases resulting in an increase in infection rate, but again no government restrictions were imposed. During this period, we collected data twice: in May 2022 (5th wave) and November 2022 (6th wave). Results Overall, the average risk tolerance (based on a range of 1–10) across all respondents was 6.59 (SD = 1.99). Since higher levels of risk tolerance are indicative of higher risk aversion, this overall average shows that the sample generally exhibited low levels of risk tolerance. A one-way ANOVA indicated that the effect of the COVID-19 pandemic on risk tolerance was significant (F (6, 1636) = 24.63, p < 0.0001). Post-hoc analyses with Tukey corrections for multiple comparisons confirmed that before COVID, the average risk tolerance (M = 4.41, SD = 2.76) was significantly lower than for the 1st (M = 6.83, SD = 1.65), 2nd (M = 7.01, SD = 1.66), and 3rd (M = 7.27, SD = 1.35) restricted waves, and the 4th (M = 6.88, SD = 1.82), 5th (M = 6.22, SD = 2.26) and 6th (M = 6.24, SD = 2.05) unrestricted waves (all p-values < 5%). No significant difference was found between the 1st, 2nd, and 3rd restricted and 4th unrestricted waves. In addition, no significant differences were found between the 5th and 6th unrestricted waves. These results are summarized in Table 1 and Fig. 1 . As shown in the figure, there was a significant increase in risk tolerance during the COVID-19 pandemic across all waves relative to the pre-pandemic. This pattern of results suggests that people exhibited less risk tolerance as the pandemic unfolded, and preferred safer gambles. While the risk tolerance measure began to decline during the 5th -6th unrestricted waves towards its baseline (pre-pandemic) level, the scores remained relatively high even almost three years after the onset of the pandemic. This pattern of results is consistent with previous research on threatening events (e.g., Kim & Lee, 2014 ). Table 1 ANOVA for differences in risk tolerance across waves. Letters indicate mean values that did not reach statistical significance (waves with the same letter are not statistically different). Level Number Mean Std Error Lower 95% Upper 95% Pre- Covid 63 4.41 C 0.24 3.94 4.89 1st restricted Wave 256 6.83 A 0.12 6.59 7.06 2nd restricted Wave 146 7.01 A 0.16 6.70 7.32 3rd restricted Wave 193 7.27 A 0.14 7.00 7.54 4th unrestricted Wave 364 6.88 A 0.10 6.69 7.08 5th unrestricted Wave 323 6.22 B 0.11 6.01 6.43 6th unrestricted Wave 298 6.24 B 0.11 6.02 6.46 Interestingly, we also inspected the density of risk tolerance across the different time points. Pre-COVID-19, the respondents exhibited the most uniform distribution, with a slight peak at 4.4 and a density of about 0.15. During the COVID-19 crisis, the distribution tended to produce a clear peak that moved towards higher values (i.e., lower risk tolerance). The 1st restricted wave was centered around 6.83 with a density of 0.23. For the 2nd restricted wave, the peak moved further upwards to 7.01 with a similar density. For the 3rd restricted wave, the distribution exhibited the narrowest spread around its peak at 7.27 with a density of 0.35. After the restrictions were lifted in the 4th unrestricted wave, the peak of the distribution was at 6.88 with a density of 0.175. During the 5th and 6th unrestricted waves, there was a slow and gradual return to the pre-pandemic results, where the peak of the distributions was around 6.22–6.24, with densities of 0.15 and 0.21, respectively. These results are presented in Fig. 2 A. There were significant differences between waves for the variance of risk tolerance on the O’Brian test (F 6,1636 =18.64). The largest variance (SD) was before COVID-19 and was at its lowest level during the 3rd restricted wave (see Fig. 2 B for the standard deviations of risk tolerance by wave). Finally, to validate our results, we also analyzed real-time investment data provided by the Capital Market, Insurance, and Savings Authority in Israel. This Authority oversees financial services in the insurance, retirement, and provident funds markets and ensures stability and competitiveness in these markets. The Authority guarantees the stability of the institutions under its supervision to maintain proper management, and ensures that the institutions meet their obligations to the public. The Authority works to preserve fairness and professionalism for services provided to customers by verifying that the various products offered to the public are appropriate. The Authority achieves these objectives by regulating the activities of its supervised entities, auditing their business activities, and analyzing relationships between supervised entities and their customers. The data we were able to secure included information about the daily money transfers between investment tracks at different risk levels (high risk, medium risk, and low risk) for all financial products during the 1st restricted wave (March 1, 2020, to May 10, 2020) of the pandemic. As shown in Fig. 3 , after the onset of COVID-19, there was a shift in individuals’ investment behavior. Consistent with our results during the pandemic, people preferred to invest in safer options, and there was a major shift towards transferring money from risky and medium-risky investments towards safer ones. This pattern supports our findings and demonstrates the reduction in risk-tolerance in the wake of a major life-threatening event. Discussion The data we collected at 7 different time points demonstrates that a major life-threatening experience significantly reduces financial risk-taking. These results are consistent with previous research showing that facing negative life experiences on the micro (e.g., Bucciol & Zarri, 2015 ; Kim & Lee, 2014 ) and macro (e.g., Malmendier & Nagel, 2011 ) levels affects financial risk tolerance. The results are consistent with the literature on the effects of COVID-19 on risk behavior (Marotta et al., 2020 ). For example, Bernstein et al. ( 2020 ) showed that during the COVID-19 downturn, applicants searched for safer jobs as compared to the time before the pandemic. Similarly, Yue et al. ( 2020 ) found that during COVID-19, households in China decreased investments by 9.15% as people became more risk-averse. While the objective financial situation appeared to deteriorate linearly due to the unfolding crisis (Chetty et al., 2020 ; Martin et al., 2020 ), the decrease in risk tolerance was the most apparent in the 1st restricted wave, with no major differences between the first four waves of the pandemic. There were no restrictions during the 4th wave, but the resumption of regular risk tolerance levels was slower than the sharp decrease after the onset of the pandemic. Thus, we did not observe any difference between risk tolerance during the first 3 restricted waves and the 4th unrestricted wave. These results may provide important insights into why major life crises affect risk tolerance. The disconnect between the objective measure of financial risk (e.g., the unemployment rate ) and subjective risk tolerance suggests that changes in risk tolerance are more affected by psychological than rational considerations. The initial (restricted) waves of the pandemic led to complete lockdowns characterized by social distancing regulations and isolation. The stress associated with the lack of human contact may have undermined people’s sense of security (Nowicki et al., 2020 ), a psychological factor essential for financial risk-taking (Levav & Argo, 2010 ). Thus, our results support previous research that emphasizes the emotional component of risk-taking (Cohn et al., 2014 ; Loewenstein, 2000 ; Loewenstein et al., 2001 ; Weber et al., 2013 ), and highlight the importance of secure social contacts in decision-making. Future research should examine the role of both psychological and emotional factors in driving the underlying mechanism. The findings also indicate that while the overall propensity to engage in risky financial behaviors was slow to return to its pre-pandemic levels, the variance was reestablished more rapidly. To the best of our knowledge, this is the first demonstration of such an effect, which suggests that as the immediate effects of the traumatic event faded, individual differences in risk tolerance started to play a role even before full return to prior levels of risk-taking. This divergence in recovery rates might reflect underlying psychological mechanisms such as individual differences in resilience and adaptability. Given works suggesting that the variance is important in the ecology of communities (e.g., Ein-Dor, 2014 , 2015 ; Violle et al., 2012 ), this finding may be reassuring. It could also point toward the varying speeds at which economic and social environments recover and differentially influence personal decision-making frameworks across the population. It may suggest that policies and interventions should focus on individuals who did not return to their baseline, or encourage the few who recover faster and may be financial market leaders. These findings underscore the complexity of recovering financial risk behavior in the wake of major societal disruptions, and point to the need for targeted interventions. Understanding these patterns can help policymakers and financial analysts anticipate economic activity shifts and design strategies that cater to evolving consumer behaviors and market conditions. For example, the reduced variance during the pandemic may point to a convergence towards more homogeneous risk preferences. This might be attributed to the shared experiences and heightened uncertainty typical of a crisis, leading to a collectively cautious approach. This reduction in behavioral variance may have curtailed speculative trading and potentially lowered market volatility. Entrepreneurial activities, which are typically driven by varying risk appetites, are likely to have declined, which could account for the slowdown in economic innovation and diversification. Interestingly, the decline in risk-taking tendencies found in previous works as well as in the current study runs counter to the behavior of investors in commission-free online brokerages such as Robinhood where investors had a high appetite for risk during COVID-19 (Welch, 2020). However, these investors may not have exhibited risk-seeking behavior during the pandemic due to an increase in risk propensity. Rather, their behavior may have resulted from negative changes in everyday life, including people’s concerns about their financial future (Håkansson et al., 2021 ). Similar to online gambling (Håkansson et al., 2020 ) and gaming (King et al., 2020 ), online investment services provide quick turnarounds and may have alleviated, even if just for the short term, some of the psychological and financial effects of COVID-19 (Marotta et al., 2020 ). Moreover, as the fintech brokerage Robinhood was the first to offer zero-price trading on a simple mobile app, it might have given housebound individuals a type of gaming experience during the lockdowns (Pagano et al., 2021 ). One limitation of this study is the small sample size of participants before COVID-19 (which is natural because we could not have planned this project before the outbreak of COVID-19). Nevertheless, the observed risk tolerance level in the pre-pandemic group fits with what is usually reported in the literature. For example, in Holt and Laury’s ( 2002 ) original paper, the risk propensity ranged from 4 to 6. Similarly, a recent meta-analysis of 11 datasets with almost 50,000 observations reported an average risk tolerance score of 5.82 (Alm & Malézieux, 2021 ). While the options in our experiment were not incentivized, a recent three-country study (Honduras, Nigeria, and Spain) indicated that not paying, paying a fixed fee, or paying based on one’s choices made no difference at all (Brañas-Garza et al., 2021 ). Finally, although we intended to keep measuring risk tolerance in Israel post-pandemic this has been unfeasible since Israeli society has been at war since October 7th, 2023. This conflict has also involved the full evacuation of the population in two major areas in Israel and a sharp rise in the cost of living. Since risk tolerance has profound implications for economic decision-making (Cardenas & Carpenter, 2008 ; Cassar et al., 2017 ), our findings have important policy implications, including incentivizing or subsidizing risky ventures during and after major negative life events and creating financial products that appeal to people with low-risk tolerance. More generally, individuals should be provided with opportunities to maintain their social connections and personal support when making financial decisions even under strict social distancing regulations. Method To assess risk tolerance during the COVID-19 pandemic, we administered an online questionnaire to a wide-ranging sample of subjects. The questionnaire incorporated an array of questions related to risk tolerance and sociodemographic characteristics. Participant. The dataset was composed of 1,643 observations over 7 sampling rounds: 63 before the COVID-19 pandemic, 256 in the 1st restricted wave, and 146, 193, 364, 323, and 298 in the 2nd, 3rd, 4th, 5th, and 6th experimental rounds, in accordance. The sample size was not determined a-priori. Rather, we posted ads on social media and recruited as many individuals as possible during the two weeks after the start of each round. Of the sample, 748 (46%) were men, 948 (60%) married, 453 (29%) single, 202(13%) had no children, 187 (12%) had one child, 298 (19%) had 2 children, 352 (23%) had three children, and 525(33%) had more than 3 children. The average age was 40 years (SD = 15.0). In terms of the level of education, most of the sample had a BA, with 542 respondents (35%), followed by 526 (34%) high school graduates and 298 (19%) with an MA degree. All data is provided at https://osf.io/7u852/?view_only= . For full details on the sample’s demographics, see https://osf.io/ytzm8/ . Participants were recruited via Facebook and WhatsApp. Participation was voluntary and informed consent was obtained from all the participants. The Institutional Review Board of the School of Psychology at Reichman University approved this study, and the experiment was performed in accordance with all guidelines and regulations for studies with human subjects. Design. Participants were asked to fill out a web-based questionnaire (Qualtrics) based on Holt and Laury’s ( 2002 ) risk tolerance scale, which is widely used to examine risk tolerance in a variety of contexts (Hoffman et al., 2020 ). Participants were required to make 10 choices between paired lotteries that differed in risk level and expected value. One lottery (option B) was risky (the potential payoff between the two lotteries differed widely), while the other (option A) was safe (the potential payoffs only differed slightly). For instance: Alternative A: 0.1 chance of getting $ 20.00 0.9 chance of getting $ 16.00 Alternative B: 0.1 chance of getting $ 38.50 0.9 chance of getting $ 1.00 As seen in the example, the probability of a high payoff for both options was 1/10. The expected payoff incentive to choose option A was $ 16.40, whereas the expected payoff incentive to choose option B was $ 4.75. Thus, only an extreme risk seeker would choose option B. While the payoffs for each gamble were fixed, the probability of the high payoff in each gamble increased by 10%. When the probability of the high payoff increases enough, a rational decision would involve switching to option B. The complete payoff scheme for the 10 choice options can be found at https://osf.io/dbcrh/ . Risk tolerance was defined as the number of times the participants preferred the low-risk lottery (Holt & Laury, 2002 ). A score of 0–3 reflects high-risk tolerance, 4 indicates risk neutrality, and 5 and above reflects low-risk tolerance. The 10 paired choices were randomly presented to the participants. After making their 10 choices, the participants were asked to provide demographic questions covering age, gender, income, profession, marital status, etc. Participants who did not respond to the full set of questions were screened out of the sample. 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Does the degree of relative risk aversion vary with household characteristics?. Journal of Economic Psychology, 17(6), 771-787. Plohl, N., & Musil, B. (2021). Modeling compliance with COVID-19 prevention guidelines: the critical role of trust in science. Psychology, Health & Medicine, 26, 1-12. Schooley, D. K., & Worden, D. D. (1996). Risk aversion measures: Comparing attitudes and asset allocation. Financial Services Review, 5(2), 87-99. Simon, A. F., Fagley, N. S., & Halleran, J. G. (2004). Decision framing: Moderating effects of individual differences and cognitive processing. Journal of Behavioral Decision Making, 17, 77–93. Sung, J., & Hanna, S. D. (1996). Factors related to risk tolerance. Financial counseling and planning, 7. Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211, 453–458. Tversky, A., & Kahneman, D. (1988). Rational choice and the framing of decisions. In D. E. Bell, H. Raiffa, & A. Tversky (Eds.), Decision making: Descriptive, normative, and perspective interactions (pp. 197–192). New York: Cambridge University Press Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Insurance, 59, 297–323. Tykocinski, O. E., Amir, I., & Ayal, S. (2017). Embracing chance tactically: A different perspective on risk taking. Journal of Behavioral Decision Making, 30, 683-692. ‏ Violle, C., Enquist, B. J., McGill, B. J., Jiang, L. I. N., Albert, C. H., Hulshof, C., ... & Messier, J. (2012). The return of the variance: intraspecific variability in community ecology. Trends in Ecology & Evolution, 27(4), 244-252. Weber, M., Weber, E. U., & Nosic, A. (2013). Who takes risks when and why: Determinants of changes in investor risk taking. Review of Finance, 17, 847–883. Welch, I. (2022). The wisdom of the Robinhood crowd. The Journal of Finance, 77(3), 1489-1527. Wise, T., Zbozinek, T.D., Michelini, G., Hagan, C.C., & Mobbs, D. (2020). Changes in Risk Perception and Self-Reported Protective Behaviour During the First Week of the COVID-19 Pandemic in the United States. Royal Society Open Science, 7, 200742. Yue, P., Korkmaz, A.G., & Zhou, H. (2020). Household financial decision making amidst the COVID-19 pandemic. Emerging Markets Finance & Trade, 56, 2363-2377. Zhang, L., Zhang, S., & Tao, N. (2016). Financial System Risk Tolerance Capacity and Economic Growth: Evidence from a Cross-country Analysis. Global Economic Review, 45, 97-115. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4742565","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":336217734,"identity":"987997fc-5d31-4c81-8c1f-08afa09a1b23","order_by":0,"name":"Guy Hochman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYDACCQYGZhDNDyISQAQzcwNxWiQb4FoYidRicAAuRECLfHTzwc+FOXZ5xrfPmD14wGAnz8BOQIvhnWPJ0jO3JRebncsxN0hgSDZsIOQwwxk5Zsy825gTt53hMZNIYGBOIOgXqJb6xM09YC31hLXIS4C1HE7cwAPWcpiwFgOJtGRp3m3HE2ecYSuTSDA4bthG0JYZyQc/826rTuzvYd4m+aOiWp6f//AB/LagShswMLDhVQ+yBb8jRsEoGAWjYBQAAQD78zrZoOZf4wAAAABJRU5ErkJggg==","orcid":"","institution":"Reichman University","correspondingAuthor":true,"prefix":"","firstName":"Guy","middleName":"","lastName":"Hochman","suffix":""},{"id":336217735,"identity":"d132748a-aa0e-4293-8178-1475378a3371","order_by":1,"name":"Moran Ofir","email":"","orcid":"","institution":"Reichman University","correspondingAuthor":false,"prefix":"","firstName":"Moran","middleName":"","lastName":"Ofir","suffix":""},{"id":336217736,"identity":"f9070a37-118d-4f7b-a2f6-2723a9fcf8a8","order_by":2,"name":"Shahar Ayal","email":"","orcid":"","institution":"Reichman University","correspondingAuthor":false,"prefix":"","firstName":"Shahar","middleName":"","lastName":"Ayal","suffix":""}],"badges":[],"createdAt":"2024-07-15 11:44:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4742565/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4742565/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62631540,"identity":"46911f73-1998-4f1e-aa6b-7cf90c7d347f","added_by":"auto","created_at":"2024-08-16 16:05:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":336260,"visible":true,"origin":"","legend":"\u003cp\u003eRisk Tolerance by Wave. Each error bar was constructed using 1 standard deviation from the mean.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4742565/v1/38703c0e4f93c3e8feaa2936.png"},{"id":62631539,"identity":"f1f1d202-8eca-4e5b-9ca0-740a1f941efc","added_by":"auto","created_at":"2024-08-16 16:05:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":669852,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Risk Tolerance Density by Wave; (B) Standard Deviations of Risk Tolerance by Wave.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4742565/v1/c23554987fcd1585a29396a1.png"},{"id":62631541,"identity":"b9f8ea33-475d-4b4d-ad65-c0e051b578f7","added_by":"auto","created_at":"2024-08-16 16:05:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":260021,"visible":true,"origin":"","legend":"\u003cp\u003eDaily money transfers between investment tracks at different risk levels (green=high risk, orange=medium risk, blue=low risk) in all financial products during the first wave (March 1, 2020, to May 10, 2020) in millions of ILS.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4742565/v1/6e28eb775881338fe4317518.png"},{"id":66736724,"identity":"4cbb3d9e-f512-4cbb-9af7-ea1b39950bfb","added_by":"auto","created_at":"2024-10-16 05:01:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1776904,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4742565/v1/fe2f4b23-6466-4b56-96da-f2d126121c81.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Financial Risk Tolerance during a Major Negative Life Experience: The Case of the COVID-19 Pandemic","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFinancial risk-taking refers to financial actions such as investments or business ventures that are associated with the likelihood that their actual result will differ from their expected return. Financial risk-tolerance is the maximum amount of financial risk an individual is willing to take when making a financial decision (Grable, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). In itself, financial risk is not inherently good or bad. Although the word \u0026ldquo;risk\u0026rdquo; has a negative connotation (and financial risk is no exception), taking financial risk at some level is important to achieve economic growth (Bucciol \u0026amp; Miniaci, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), especially when its expected value exceeds the safer alternatives. Thus, identifying the factors influencing people\u0026rsquo;s risk tolerance is essential for effective financial market management in both corporate and personal settings (Cassar et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kim \u0026amp; Lee, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch suggests that about 25% of the variation in risk tolerance across individuals can be explained by genetic traits (Cesarini et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Several demographic and situational factors are considered to explain the remaining variation. For example, males are more risk-tolerant than females (Charness \u0026amp; Gneezy, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sung \u0026amp; Hanna, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), and young people are usually more risk-tolerant than older people (Bakshi \u0026amp; Chen, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; P\u0026aring;lsson, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Similarly, people with higher incomes are more risk-tolerant than those with lower incomes (Schooley \u0026amp; Worden, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), and education is positively correlated with risk-taking propensities (Grable \u0026amp; Joo, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). In addition, people appear to be risk-avoidant in the domain of gains but risk-seeking in the domain of losses. This classic framing effect has been demonstrated in numerous settings (e.g., Fishburn \u0026amp; Kochenberger, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Igou \u0026amp; Bless, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; McElroy \u0026amp; Seta, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Simon et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Tversky \u0026amp; Kahneman, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1981\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1988\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Tykocinski et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the last two decades, researchers have also explored the effects of major life experiences on financial risk tolerance (Bucciol \u0026amp; Zarri, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This stream of research shows that major negative life experiences such as global crises (e.g., the Great Depression of 1929, the Korean War in the 1950s, and the financial crisis of 2008) profoundly affected individuals\u0026rsquo; behavior (Hoffmann et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Specifically, people\u0026rsquo;s willingness to take financial risks decreases significantly when they undergo negative events such as natural disasters, wars, and economic crises. For example, Cassar et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) showed that individuals who experienced the 2004 tsunami in Thailand presented lower risk tolerance. Similarly, Kim and Lee (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that early childhood exposure to the Korean War decreased risk tolerance. This traumatic event was so profound that it made individuals less tolerant of risk as much as five decades later.\u003c/p\u003e \u003cp\u003eOne possible psychological mechanism underlying the effects of negative life experiences on risk tolerance was proposed by Loewenstein and colleagues (Loewenstein et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Lerner et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). They suggested that negative life experiences invoke negative emotions such as fear and anxiety, thus increasing people\u0026rsquo;s sense of uncertainty and lack of control. To reduce stress, individuals become less tolerant of (financial) risk (Lerner \u0026amp; Keltner, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Callen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In line with this notion, Levav and Argo (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) found that greater financial risk-taking was associated with feelings of security. In their study, participants were instructed to engage in an investment task. The experimenter\u0026rsquo;s verbal instructions were either unaccompanied or accompanied by physical contact (a touch on the shoulder or a handshake). The authors found that a friendly pat on the shoulder increased risk-taking on the task, and a feeling of security moderated this behavioral tendency. Moreover, Grable and Roszkowski (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) found that merely being in a good mood was positively correlated with a higher level of financial risk tolerance.\u003c/p\u003e \u003cp\u003eThe current study examined the ways in which the COVID-19 crisis affected risk tolerance. On March 11, 2020, the World Health Organization officially declared the COVID-19 outbreak a pandemic. It resulted in more than 4.5M deaths by November 2022 and had major health, social, and economic impacts (Couto et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, the COVID-19 pandemic presents a unique opportunity to investigate the effect of unprecedented, widespread, life-threatening experiences on risk-taking behavior.\u003c/p\u003e \u003cp\u003eAlthough several studies have examined risk tolerance during the pandemic (e.g., Bordalo et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cori et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Barrios \u0026amp; Hochberg, 2020; Bundorf et al., 2021; Fan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Plohl \u0026amp; Musil, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wise et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), most have focused on health risk perceptions and beliefs specific to the COVID-19 crisis. One exception is the work by Galil et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who found that from March to April 2020, individuals in lower SES strata tended to withdraw money from their risky funds and switch to less risky investment tracks. The current study extends this literature by examining how this prolonged negative life experience impacted financial risk tolerance in Israeli citizens. Fortunately, we collected data on risk tolerance a few months before the COVID-19 outbreak. Thus, we were able to test whether the level of risk tolerance of people drawn from the same sampling population changed in direct response to the crisis and whether this tendency varied over the course of the different waves of the crisis.\u003c/p\u003e \u003cp\u003ePrior to COVID-19, Israel\u0026rsquo;s economy was at a peak, with an unemployment rate of 3.4% and a consistent increase in real wages of 3%. The COVID-19 crisis began in Israel towards the end of February 2020. A complete lockdown was imposed two weeks later, marking the beginning of the first 1st restricted wave that lasted until the end of May. While the lockdown dramatically reduced the infection rate, it had massive negative financial consequences, with 1.3\u0026nbsp;million new people unemployed and an increase of 1.3% in the unemployment rate. Four months after the 1st restricted wave, the infection rate in Israel again soared and became one of the highest of all developed countries. This 2nd restricted wave led to the second lockdown in September 2020, which deepened the economic crisis and increased unemployment by 0.3%. After two months of stability, Israel then entered its 3rd restricted wave that included a third lockdown, which started on December 27 and ended on February 7, 2021. During this third wave, the unemployment rate remained steady at around 4.8% (Central Bureau of Statistics, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Since the economic situation in Israel deteriorated as the crisis advanced, we were able to test if changes to risk-tolerance are rational and correspond to the objective financial situation. The fourth major wave started on November 26, 2021, when 4 cases of the Omicron variant were identified in Israel. This wave lasted until the end of January 2022, but due to changes in the composition of the Israeli government, very minimal restrictions were imposed. During this time, the unemployment rate decreased slightly to 4.2% in December and to 3.6% in January 2022 (Central Bureau of Statistics, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Over the course of 2022, there were further cases resulting in an increase in infection rate, but again no government restrictions were imposed. During this period, we collected data twice: in May 2022 (5th wave) and November 2022 (6th wave).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOverall, the average risk tolerance (based on a range of 1\u0026ndash;10) across all respondents was 6.59 (SD\u0026thinsp;=\u0026thinsp;1.99). Since higher levels of risk tolerance are indicative of higher risk aversion, this overall average shows that the sample generally exhibited low levels of risk tolerance. A one-way ANOVA indicated that the effect of the COVID-19 pandemic on risk tolerance was significant (F\u003csub\u003e(6, 1636)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;24.63, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Post-hoc analyses with Tukey corrections for multiple comparisons confirmed that before COVID, the average risk tolerance (M\u0026thinsp;=\u0026thinsp;4.41, SD\u0026thinsp;=\u0026thinsp;2.76) was significantly lower than for the 1st (M\u0026thinsp;=\u0026thinsp;6.83, SD\u0026thinsp;=\u0026thinsp;1.65), 2nd (M\u0026thinsp;=\u0026thinsp;7.01, SD\u0026thinsp;=\u0026thinsp;1.66), and 3rd (M\u0026thinsp;=\u0026thinsp;7.27, SD\u0026thinsp;=\u0026thinsp;1.35) restricted waves, and the 4th (M\u0026thinsp;=\u0026thinsp;6.88, SD\u0026thinsp;=\u0026thinsp;1.82), 5th (M\u0026thinsp;=\u0026thinsp;6.22, SD\u0026thinsp;=\u0026thinsp;2.26) and 6th (M\u0026thinsp;=\u0026thinsp;6.24, SD\u0026thinsp;=\u0026thinsp;2.05) unrestricted waves (all p-values\u0026thinsp;\u0026lt;\u0026thinsp;5%). No significant difference was found between the 1st, 2nd, and 3rd restricted and 4th unrestricted waves. In addition, no significant differences were found between the 5th and 6th unrestricted waves. These results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As shown in the figure, there was a significant increase in risk tolerance during the COVID-19 pandemic across all waves relative to the pre-pandemic. This pattern of results suggests that people exhibited less risk tolerance as the pandemic unfolded, and preferred safer gambles. While the risk tolerance measure began to decline during the 5th -6th unrestricted waves towards its baseline (pre-pandemic) level, the scores remained relatively high even almost three years after the onset of the pandemic. This pattern of results is consistent with previous research on threatening events (e.g., Kim \u0026amp; Lee, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA for differences in risk tolerance across waves. Letters indicate mean values that did not reach statistical significance (waves with the same letter are not statistically different).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower 95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUpper 95%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre- Covid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.41\u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st restricted Wave\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.83\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd restricted Wave\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.01\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd restricted Wave\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.27\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4th unrestricted Wave\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.88\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5th unrestricted Wave\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.22\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6th unrestricted Wave\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.24\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInterestingly, we also inspected the density of risk tolerance across the different time points. Pre-COVID-19, the respondents exhibited the most uniform distribution, with a slight peak at 4.4 and a density of about 0.15. During the COVID-19 crisis, the distribution tended to produce a clear peak that moved towards higher values (i.e., lower risk tolerance). The 1st restricted wave was centered around 6.83 with a density of 0.23. For the 2nd restricted wave, the peak moved further upwards to 7.01 with a similar density. For the 3rd restricted wave, the distribution exhibited the narrowest spread around its peak at 7.27 with a density of 0.35. After the restrictions were lifted in the 4th unrestricted wave, the peak of the distribution was at 6.88 with a density of 0.175. During the 5th and 6th unrestricted waves, there was a slow and gradual return to the pre-pandemic results, where the peak of the distributions was around 6.22\u0026ndash;6.24, with densities of 0.15 and 0.21, respectively. These results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThere were significant differences between waves for the variance of risk tolerance on the O\u0026rsquo;Brian test (F\u003csub\u003e6,1636\u003c/sub\u003e=18.64). The largest variance (SD) was before COVID-19 and was at its lowest level during the 3rd restricted wave (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB for the standard deviations of risk tolerance by wave).\u003c/p\u003e \u003cp\u003e Finally, to validate our results, we also analyzed real-time investment data provided by the Capital Market, Insurance, and Savings Authority in Israel. This Authority oversees financial services in the insurance, retirement, and provident funds markets and ensures stability and competitiveness in these markets. The Authority guarantees the stability of the institutions under its supervision to maintain proper management, and ensures that the institutions meet their obligations to the public. The Authority works to preserve fairness and professionalism for services provided to customers by verifying that the various products offered to the public are appropriate. The Authority achieves these objectives by regulating the activities of its supervised entities, auditing their business activities, and analyzing relationships between supervised entities and their customers.\u003c/p\u003e \u003cp\u003eThe data we were able to secure included information about the daily money transfers between investment tracks at different risk levels (high risk, medium risk, and low risk) for all financial products during the 1st restricted wave (March 1, 2020, to May 10, 2020) of the pandemic. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, after the onset of COVID-19, there was a shift in individuals\u0026rsquo; investment behavior. Consistent with our results during the pandemic, people preferred to invest in safer options, and there was a major shift towards transferring money from risky and medium-risky investments towards safer ones. This pattern supports our findings and demonstrates the reduction in risk-tolerance in the wake of a major life-threatening event.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe data we collected at 7 different time points demonstrates that a major life-threatening experience significantly reduces financial risk-taking. These results are consistent with previous research showing that facing negative life experiences on the micro (e.g., Bucciol \u0026amp; Zarri, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kim \u0026amp; Lee, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and macro (e.g., Malmendier \u0026amp; Nagel, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) levels affects financial risk tolerance. The results are consistent with the literature on the effects of COVID-19 on risk behavior (Marotta et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For example, Bernstein et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) showed that during the COVID-19 downturn, applicants searched for safer jobs as compared to the time before the pandemic. Similarly, Yue et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that during COVID-19, households in China decreased investments by 9.15% as people became more risk-averse.\u003c/p\u003e \u003cp\u003eWhile the objective financial situation appeared to deteriorate linearly due to the unfolding crisis (Chetty et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Martin et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the decrease in risk tolerance was the most apparent in the 1st restricted wave, with no major differences between the first four waves of the pandemic. There were no restrictions during the 4th wave, but the resumption of regular risk tolerance levels was slower than the sharp decrease after the onset of the pandemic. Thus, we did not observe any difference between risk tolerance during the first 3 restricted waves and the 4th unrestricted wave.\u003c/p\u003e \u003cp\u003eThese results may provide important insights into why major life crises affect risk tolerance. The disconnect between the objective measure of financial risk (e.g., the unemployment rate ) and subjective risk tolerance suggests that changes in risk tolerance are more affected by psychological than rational considerations. The initial (restricted) waves of the pandemic led to complete lockdowns characterized by social distancing regulations and isolation. The stress associated with the lack of human contact may have undermined people\u0026rsquo;s sense of security (Nowicki et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), a psychological factor essential for financial risk-taking (Levav \u0026amp; Argo, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Thus, our results support previous research that emphasizes the emotional component of risk-taking (Cohn et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Loewenstein, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Loewenstein et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Weber et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and highlight the importance of secure social contacts in decision-making. Future research should examine the role of both psychological and emotional factors in driving the underlying mechanism.\u003c/p\u003e \u003cp\u003eThe findings also indicate that while the overall propensity to engage in risky financial behaviors was slow to return to its pre-pandemic levels, the variance was reestablished more rapidly. To the best of our knowledge, this is the first demonstration of such an effect, which suggests that as the immediate effects of the traumatic event faded, individual differences in risk tolerance started to play a role even before full return to prior levels of risk-taking. This divergence in recovery rates might reflect underlying psychological mechanisms such as individual differences in resilience and adaptability. Given works suggesting that the variance is important in the ecology of communities (e.g., Ein-Dor, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Violle et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), this finding may be reassuring. It could also point toward the varying speeds at which economic and social environments recover and differentially influence personal decision-making frameworks across the population. It may suggest that policies and interventions should focus on individuals who did not return to their baseline, or encourage the few who recover faster and may be financial market leaders.\u003c/p\u003e \u003cp\u003eThese findings underscore the complexity of recovering financial risk behavior in the wake of major societal disruptions, and point to the need for targeted interventions. Understanding these patterns can help policymakers and financial analysts anticipate economic activity shifts and design strategies that cater to evolving consumer behaviors and market conditions. For example, the reduced variance during the pandemic may point to a convergence towards more homogeneous risk preferences. This might be attributed to the shared experiences and heightened uncertainty typical of a crisis, leading to a collectively cautious approach. This reduction in behavioral variance may have curtailed speculative trading and potentially lowered market volatility. Entrepreneurial activities, which are typically driven by varying risk appetites, are likely to have declined, which could account for the slowdown in economic innovation and diversification.\u003c/p\u003e \u003cp\u003eInterestingly, the decline in risk-taking tendencies found in previous works as well as in the current study runs counter to the behavior of investors in commission-free online brokerages such as Robinhood where investors had a high appetite for risk during COVID-19 (Welch, 2020). However, these investors may not have exhibited risk-seeking behavior during the pandemic due to an increase in risk propensity. Rather, their behavior may have resulted from negative changes in everyday life, including people\u0026rsquo;s concerns about their financial future (H\u0026aring;kansson et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similar to online gambling (H\u0026aring;kansson et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and gaming (King et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), online investment services provide quick turnarounds and may have alleviated, even if just for the short term, some of the psychological and financial effects of COVID-19 (Marotta et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, as the fintech brokerage Robinhood was the first to offer zero-price trading on a simple mobile app, it might have given housebound individuals a type of gaming experience during the lockdowns (Pagano et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne limitation of this study is the small sample size of participants before COVID-19 (which is natural because we could not have planned this project before the outbreak of COVID-19). Nevertheless, the observed risk tolerance level in the pre-pandemic group fits with what is usually reported in the literature. For example, in Holt and Laury\u0026rsquo;s (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) original paper, the risk propensity ranged from 4 to 6. Similarly, a recent meta-analysis of 11 datasets with almost 50,000 observations reported an average risk tolerance score of 5.82 (Alm \u0026amp; Mal\u0026eacute;zieux, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While the options in our experiment were not incentivized, a recent three-country study (Honduras, Nigeria, and Spain) indicated that not paying, paying a fixed fee, or paying based on one\u0026rsquo;s choices made no difference at all (Bra\u0026ntilde;as-Garza et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Finally, although we intended to keep measuring risk tolerance in Israel post-pandemic this has been unfeasible since Israeli society has been at war since October 7th, 2023. This conflict has also involved the full evacuation of the population in two major areas in Israel and a sharp rise in the cost of living.\u003c/p\u003e \u003cp\u003eSince risk tolerance has profound implications for economic decision-making (Cardenas \u0026amp; Carpenter, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Cassar et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), our findings have important policy implications, including incentivizing or subsidizing risky ventures during and after major negative life events and creating financial products that appeal to people with low-risk tolerance. More generally, individuals should be provided with opportunities to maintain their social connections and personal support when making financial decisions even under strict social distancing regulations.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eTo assess risk tolerance during the COVID-19 pandemic, we administered an online questionnaire to a wide-ranging sample of subjects. The questionnaire incorporated an array of questions related to risk tolerance and sociodemographic characteristics.\u003c/p\u003e \u003cp\u003e\u003cem\u003eParticipant.\u003c/em\u003e The dataset was composed of 1,643 observations over 7 sampling rounds: 63 before the COVID-19 pandemic, 256 in the 1st restricted wave, and 146, 193, 364, 323, and 298 in the 2nd, 3rd, 4th, 5th, and 6th experimental rounds, in accordance. The sample size was not determined a-priori. Rather, we posted ads on social media and recruited as many individuals as possible during the two weeks after the start of each round. Of the sample, 748 (46%) were men, 948 (60%) married, 453 (29%) single, 202(13%) had no children, 187 (12%) had one child, 298 (19%) had 2 children, 352 (23%) had three children, and 525(33%) had more than 3 children. The average age was 40 years (SD\u0026thinsp;=\u0026thinsp;15.0). In terms of the level of education, most of the sample had a BA, with 542 respondents (35%), followed by 526 (34%) high school graduates and 298 (19%) with an MA degree. All data is provided at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/7u852/?view_only=\u003c/span\u003e\u003cspan address=\"https://osf.io/7u852/?view_only=\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. For full details on the sample\u0026rsquo;s demographics, see \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/ytzm8/\u003c/span\u003e\u003cspan address=\"https://osf.io/ytzm8/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Participants were recruited via Facebook and WhatsApp. Participation was voluntary and informed consent was obtained from all the participants. The Institutional Review Board of the School of Psychology at Reichman University approved this study, and the experiment was performed in accordance with all guidelines and regulations for studies with human subjects.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDesign.\u003c/em\u003e Participants were asked to fill out a web-based questionnaire (Qualtrics) based on Holt and Laury\u0026rsquo;s (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) risk tolerance scale, which is widely used to examine risk tolerance in a variety of contexts (Hoffman et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Participants were required to make 10 choices between paired lotteries that differed in risk level and expected value. One lottery (option B) was risky (the potential payoff between the two lotteries differed widely), while the other (option A) was safe (the potential payoffs only differed slightly). For instance:\u003c/p\u003e \u003cp\u003eAlternative A:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e0.1 chance of getting \u003cspan\u003e$\u003c/span\u003e20.00\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e0.9 chance of getting \u003cspan\u003e$\u003c/span\u003e16.00\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eAlternative B:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e0.1 chance of getting \u003cspan\u003e$\u003c/span\u003e38.50\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e0.9 chance of getting \u003cspan\u003e$\u003c/span\u003e1.00\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eAs seen in the example, the probability of a high payoff for both options was 1/10. The expected payoff incentive to choose option A was \u003cspan\u003e$\u003c/span\u003e16.40, whereas the expected payoff incentive to choose option B was \u003cspan\u003e$\u003c/span\u003e4.75. Thus, only an extreme risk seeker would choose option B. While the payoffs for each gamble were fixed, the probability of the high payoff in each gamble increased by 10%. When the probability of the high payoff increases enough, a rational decision would involve switching to option B. The complete payoff scheme for the 10 choice options can be found at \u003cspan class=\"ExternalRef\"\u003e \u003cspan class=\"RefSource\"\u003ehttps://osf.io/dbcrh/\u003c/span\u003e \u003cspan address=\"https://osf.io/dbcrh/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e \u003c/span\u003e.\u003c/p\u003e \u003cp\u003eRisk tolerance was defined as the number of times the participants preferred the low-risk lottery (Holt \u0026amp; Laury, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). A score of 0\u0026ndash;3 reflects high-risk tolerance, 4 indicates risk neutrality, and 5 and above reflects low-risk tolerance. The 10 paired choices were randomly presented to the participants. After making their 10 choices, the participants were asked to provide demographic questions covering age, gender, income, profession, marital status, etc. Participants who did not respond to the full set of questions were screened out of the sample.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor contributions: Conceptualization: GH, MO, SA Methodology: GH, MO, SA Writing\u0026mdash;original draft: GH Writing\u0026mdash;review \u0026amp; editing: GH, MO, SAAll authors reviewed the manuscript\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data is provided at https://osf.io/7u852/?view_only=\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlm, J., \u0026amp; Mal\u0026eacute;zieux, A. (2021). 40 years of tax evasion games: a meta‑analysis. Experimental Economics, 24, 699-750.\u003c/li\u003e\n\u003cli\u003eBakshi, G. S., \u0026amp; Chen, Z. (1994). 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Using Holt and Laury's (2002) risk tolerance measure, an online survey was conducted with 1643 participants at seven time points before the pandemic and during four restricted and two unrestricted periods. Results showed a significant reduction in financial risk-taking during the pandemic. Notably, the decrease was most evident in the first wave, despite no major differences across the restricted waves. Risk tolerance began to gradually return when restrictions were lifted but did not reach pre-pandemic levels. Subjective risk tolerance during the pandemic, which differed from the objective financial situation, influenced real-life investment decisions. These findings highlight the influence of contextual and emotional factors on risk tolerance. The results are discussed concerning risk-seeking behavior in commission-free online brokerages like Robinhood during the pandemic, with implications for policy guidelines.\u003c/p\u003e","manuscriptTitle":"Financial Risk Tolerance during a Major Negative Life Experience: The Case of the COVID-19 Pandemic","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-16 16:05:02","doi":"10.21203/rs.3.rs-4742565/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"11715497-1762-492c-93fd-40fd94c17da2","owner":[],"postedDate":"August 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-16T04:53:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-16 16:05:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4742565","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4742565","identity":"rs-4742565","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}