Resting Vagally Mediated Heart Rate Variability is Associated with Financial Risk Preferences under Stress
Variabilidad de la frecuencia cardíaca en reposo mediada por el nervio vago y su asociación con las preferencias de riesgo financiero bajo estrés
Variabilidade da frequência cardíaca mediada por via vagal em repouso associada a preferências de risco financeiro sob estresse
DOI:
https://doi.org/10.15446/innovar.v34n94.116813Palabras clave:
Autonomous nervous system, risk aversion, decision making, heart rate variability, stress (en)sistema nervioso autónomo, aversión al riesgo, toma de decisiones, variabilidad de la frecuencia cardíaca, estrés (es)
sistema nervoso autônomo, aversão ao risco, tomada de decisão, variabilidade da frequência cardíaca, estresse (pt)
Descargas
Business financial risk-taking usually occurs under conditions of stress. Biologically, the stress response has two components: one linked to the hypothalamic-pituitary-adrenal (HPA) axis and the other to the autonomic nervous system (ANS). The existing literature on financial risk-taking has primarily focused on the effects of cortisol, related to the HPA axis. This article, however, examines the influence of the ANS, as measured by vagally mediated heart rate variability at rest (VMHRV). A total of 121 participants (60 female) were divided based on whether their VMHRV was below the median. Participants were then randomly assigned to either a stress test group (tsst) or a control group. Financial risk preferences were assessed using an incentive-compatible 50-50% Eckel and Grossman task. Participants in the tsst with high VMHRV had a higher probability of choosing riskier lotteries compared to the other participants (P = 0,0189). This finding suggests that greater parasympathetic modulation enables individuals to make riskier financial decisions when under stress. Thus, the article contributes to the literature by demonstrating that individuals with a higher physiological capacity to cope with external stressors are less risk-averse in financial decisions under social stress conditions.
la toma de riesgos financieros en el ámbito empresarial generalmente ocurre bajo condiciones de estrés. Biológicamente, la respuesta al estrés tiene dos componentes: uno vinculado al eje hipotálamo-hipófisis-adrenal (HHA) y el otro al sistema nervioso autónomo (SNA). La literatura sobre la toma de riesgos financieros se ha centrado principalmente en los efectos del cortisol, relacionado con el eje HHA. Sin embargo, este artículo examina la influencia del SNA, medida a través de la variabilidad de la frecuencia cardíaca en reposo mediada por el nervio vago (VMHRV). Un total de 121 participantes (60 mujeres) fueron divididos en función de si su VMHRV estaba por debajo de la mediana. Paso seguido, los participantes fueron asignados al azar a un grupo de prueba de estrés (tsst) o a un grupo de control. Las preferencias de riesgo financiero se evaluaron utilizando una tarea de Eckel y Grossman con una probabilidad del 50-50% compatible con incentivos. Los participantes en el TSST con alta VMHRV tenían una mayor probabilidad de elegir loterías con nivel de riesgo más alto, en comparación con los otros participantes (P = 0,0189). Este hallazgo sugiere que una mayor modulación parasimpática permite a los individuos tomar decisiones financieras más arriesgadas bajo condiciones de estrés. Por lo tanto, el presente artículo contribuye a la literatura al demostrar que las personas con una mayor capacidad fisiológica para afrontar estresores externos son menos renuentes al riesgo en decisiones financieras en condiciones de estrés social.
Assumir riscos financeiros empresariais geralmente é algo que ocorre sob condições de estresse. Biologicamente, a resposta ao estresse tem dois componentes: um ligado ao eixo hipotálamo-pituitária-adrenal (hpa) e outro ao sistema nervoso autônomo (sna). A literatura existente sobre a assunção de riscos financeiros tem se concentrado principalmente nos efeitos do cortisol relacionados ao eixo hpa. Este artigo, no entanto, examina a influência do sna, medida pela variabilidade da frequência cardíaca mediada por via vagal em repouso (vmhrv). Um total de 121 participantes (60 mulheres) foram divididos com base no fato de sua vmhrv estar abaixo da mediana. Os participantes foram então designados aleatoriamente para um grupo de teste de estresse (tsst) ou um grupo de controle. As preferências de risco financeiro foram avaliadas usando uma tarefa de Eckel e Grossman compatível com incentivos de 50%-50%. Os participantes do tsst com alta vmhrv tiveram maior probabilidade de escolher loterias mais arriscadas em comparação com os demais participantes (P = 0,0189). Essa descoberta sugere que uma maior modulação parassimpática permite que os indivíduos tomem decisões financeiras mais arriscadas quando estão sob estresse. Assim, o artigo contribui para a literatura ao demonstrar que indivíduos com maior capacidade fisiológica para lidar com estressores externos são menos avessos ao risco em decisões financeiras sob condições de estresse social.
Referencias
Apicella, C. L., Dreber, A., Campbell, B., Gray, P. B., Hoffman, M., & Little, A. C. (2008). Testosterone and financial risk preferences. Evolution and Human Behavior, 29(6), 384-390. https://doi.org/10.1016/j.evolhumbehav.2008.07.001 DOI: https://doi.org/10.1016/j.evolhumbehav.2008.07.001
Arakaki, X., Arechavala, R. J., Choy, E. H., Bautista, J., Bliss, B., Molloy, C., Wu, D. A., Shimojo, S., Jian, Y., Kleinman, M. T., Kloner, R. A. (2023). The connection between heart rate variability (HRV), neurological health, and cognition: A literature review. Frontiers in Neuroscience, 17, 1-10. https://doi.org/10.3389/fnins.2023.1055445 DOI: https://doi.org/10.3389/fnins.2023.1055445
Attanasio, O., Barr, A., Cardenas, J. C., Genicot, G., & Meghir, C. (2012). Risk pooling, risk preferences, and social networks. American Economic Journal: Applied Economics, 4(2), 134-167. https://doi.org/10.1257/app.4.2.134 DOI: https://doi.org/10.1257/app.4.2.134
Beffara, B., Bret, A. G., Vermeulen, N., & Mermilod, M. (2016). Resting high frequency heart rate variability selectively predicts cooperative behavior. Physiology & Behavior, 164(Part A), 417-428. https://doi.org/10.1016/j.physbeh.2016.06.011 DOI: https://doi.org/10.1016/j.physbeh.2016.06.011
Binswanger, H. P. (1980). Attitudes toward risk: Experimental measurement in rural India. American Journal of Agricultural Economics, 62(3), 395-407. https://doi.org/10.2307/1240194 DOI: https://doi.org/10.2307/1240194
Birkett, M. A. (2011). The Trier Social Stress Test Protocol for Inducing Psychological Stress. Journal of Visualized Experiments, 56(3238). https://doi.org/10.3791/3238 DOI: https://doi.org/10.3791/3238
Brañas-Garza, P., Galizzi, M. M., & Nieboer, J. (2018). Experimental and self-reported Measures of risk Taking and Digit Ratio (2D:4D): Evidence from a large systematic study. International Economic Review, 59(3), 1121-1157. https://doi.org/10.1111/iere.12299 DOI: https://doi.org/10.1111/iere.12299
Brunborg, G. S., Johnsen, B. H., Pallesen, S., Molde, H., Mentzoni, R. A., & Myrseth, H. (2010). The relationship between aversive conditioning and risk-avoidance in gambling. Journal of Gambling Studies, 26(4), 545-559. https://doi.org/10.1007/s10899-010-9178-0 DOI: https://doi.org/10.1007/s10899-010-9178-0
Buckert, M., Schwieren, C., Kudielka, B. M., & Fiebach, C. J. (2014). Acute stress affects risk taking but not ambiguity aversion. Frontiers in Neuroscience, 8, 82. https://doi.org/10.3389/fnins.2014.00082 DOI: https://doi.org/10.3389/fnins.2014.00082
Cacioppo, J. T., Tassinary, L. G., & Berntson, G. (2007). Handbook of psychophysiology. Cambridge University Press.
Cahlikova, J., & Cingl, L. (2017). Risk preferences under acute stress. Experimental Economics, 20(1), 209-236. https://doi.org/10.1007/s10683-016-9482-3 DOI: https://doi.org/10.1007/s10683-016-9482-3
Cesarini, D., Johannesson, M., Lichtenstein, P., Sandewall, O., & Wallace, B. (2010). Genetic variation in financial decision-making. The Journal of Finance, 65(5), 1725-1754. http://doi.org/10.1111/j.1540-6261.2010.01592.x DOI: https://doi.org/10.1111/j.1540-6261.2010.01592.x
Charness, G., Gneezy U., & Imas, A. (2013). Experimental methods: eliciting risk preferences. Journal of Economic Behavior & Organization, 87, 43-51. http://dx.doi.org/10.1016/j.jebo.2012.12.023 DOI: https://doi.org/10.1016/j.jebo.2012.12.023
Chicaiza-Becerra, L., & Garcia-Molina, M. (2017). Prenatal testosterone predicts financial risk taking: Evidence from Latin America. Personality and Individual Differences, 116(1), 32-37. http://doi.org/10.1016/j.paid.2017.04.021 DOI: https://doi.org/10.1016/j.paid.2017.04.021
Coates, J., & Gurnell, J. (2017). Combining field work and laboratory work in the study of financial risk-taking. Hormones and Behavior, 92, 13-19. https://doi.org/10.1016/j.yhbeh.2017.01.008 DOI: https://doi.org/10.1016/j.yhbeh.2017.01.008
Coates, J., & Herbert, M. (2008). Endogenous steroids and financial risk taking on a London trading floor. Proceedings of the National Academy of Sciences, 105(16), 6167-6172. https://doi.org/10.1073/jpnas.0704025105 DOI: https://doi.org/10.1073/pnas.0704025105
Coates, J. M., Gurnell, M., & Sarnyai, Z. (2010). From molecule to market: Steroid hormones and financial risk-taking. Philosophical Transactions of the Royal Society of London. Series B Biological Sciences, 365(1538), 331-343. https://doi.org/10.1098/rstb.2009.0193 DOI: https://doi.org/10.1098/rstb.2009.0193
Cohen, H., Kotler, M., Matar, M. A., Kaplan, Z., Loewenthal, U., Miodownik, H., & Cassuto, Y. (1998). Analysis of heart rate variability in posttraumatic stress disorder patients in response to a trauma-related reminder. Biological Psychiatry, 44(10), 1054-1059. https://doi.org/10.1016/S0006-3223(97)00475-7 DOI: https://doi.org/10.1016/S0006-3223(97)00475-7
Cueva, C., Roberts, R. E., Spencer, T., Rani, N., Tempest, M., Tobler, P. N., Herbert, J., & Rustichini, A. (2015). Cortisol and testosterone increase financial risk taking and may destabilize markets. Scientific Reports, 5, 11206. https://doi.org/10.1038/srep11206 DOI: https://doi.org/10.1038/srep11206
da Estrela, C., MacNeil, S., & Gouin, J. P. (2021). Heart rate variability moderates the between- and within-person associations between daily stress and negative affect. International Journal of Psychophysiology, 162, 79-85. https://doi.org/10.1016/j.ijpsycho.2021.02.001 DOI: https://doi.org/10.1016/j.ijpsycho.2021.02.001
Dave, C., Eckel, C. C., Johnson, C. A., & Rojas C. (2010). Eliciting risk preferences: when is simple better? Journal of Risk and Uncertainty, 41(3), 219-243. https://doi.org/10.1007/s11166-010-9103-z DOI: https://doi.org/10.1007/s11166-010-9103-z
De Martino, B., Camerer, C. F., & Adolphs, R. (2010). Amygdala damage eliminates monetary loss aversion. Proceedings of the National Academy of Science of the United States of America, 107(8), 3788-3792. https://doi.org/10.1073/pnas.0910230107 DOI: https://doi.org/10.1073/pnas.0910230107
Eckel, C. C., & Grossman, P. J. (2002). Sex differences and statistical stereotyping in attitudes toward financial risk. Evolution and Human Behavior, 23(4), 281-295. https://doi.org/10.1016/S1090-5138(02)00097-1 DOI: https://doi.org/10.1016/S1090-5138(02)00097-1
Faris, A. A., Jwan, H. K., & Al-Bidari, K. H. A. (2024). From traditional finance to neurofinance: Literature review. Periodicals of Engineering and Natural Sciences, 12(1), 191-204. http://dx.doi.org/10.21533/pen.v12i1.4006 DOI: https://doi.org/10.21533/pen.v12.i1.30
Fenton-O’Creevy, M., Lins, J. T., Vohra, S., Richards, D. W., Davies, G., & Schaaff, K. (2012). Emotion regulation and trader expertise: Heart rate variability on the trading floor. Journal of Neuroscience, Psychology, and Economics, 5(4), 227-237. https://doi.org/10.1037/a0030364 DOI: https://doi.org/10.1037/a0030364
Gong, J., Wang, G. J., Xie, C., & Uddin, G. S. (2024). How do market volatility and risk aversion sentiment inter-influence over time? Evidence from Chinese SSE 50 ETF options. International Review of Financial Analysis, 95(B), 103440. https://doi.org/10.1016/j.irfa.2024.103440 DOI: https://doi.org/10.1016/j.irfa.2024.103440
Hammerstein, P., & Stevens, J. R. (2012). Evolution and the mechanisms of decision making. MIT Press. DOI: https://doi.org/10.7551/mitpress/9780262018081.001.0001
Herbert, J. (2018). Testosterone, cortisol and financial risk-taking. Frontiers in Behavioral Neuroscience, 12,101. https://doi.org/10.3389/fnbeh.2018.00101 DOI: https://doi.org/10.3389/fnbeh.2018.00101
Honjo, Y., Ikeuchi, K., & Nakamura, H. (2024). Does risk aversion affect individuals´ interests and actions in angel investing? Empirical evidence from Japan. Japan and the Word Economy, 70, 101253. https://doi.org/10.1016/j.japwor.2024.101253 DOI: https://doi.org/10.1016/j.japwor.2024.101253
Kandasamy, N., Hardy, B., Page, L., Schaffner, M., Graggaber, J., Powlson, A. S., Fletcher, P. C., Gurnell, M., & Coates, J. (2014). Cortisol shifts financial risk preferences. Proceedings of the National Academy of Sciences, 111, 3608-3613. https://doi.org/10.1073/pnas.1317908111 DOI: https://doi.org/10.1073/pnas.1317908111
Kirschbaum, C., Pirke, K. M., & Hellhammer, D. H. (1993). The ‘Trier Social Stress Test’ – A tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology, 28(1-2), 76-81. https://doi.org/10.1159/000119004 DOI: https://doi.org/10.1159/000119004
Kvadsheim, E., Sorensen, L., Fasmer, O. B., Osnes, B., Haavik, J., Williams, D. P., Thayer, J. F., & Koenig, J. (2022). Vagally mediated heart rate variability, stress, and perceived social support: A focus on sex differences. Stress, 25(1), 113-121. https://doi.org/10.1080/10253890.2022.2043271 DOI: https://doi.org/10.1080/10253890.2022.2043271
Lischke, A., Pahnke, R., Mau-Moeller, A., Behrens, M., Grabe, H. J., Freyberger, H. J., Hamm, A. O., & Weippert, M. (2018). Inter-individual differences in heart rate variability are associated with inter-individual differences in empathy and alesithymia. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.00229 DOI: https://doi.org/10.3389/fpsyg.2018.00229
Lischke, A., Mau-Moeller, A., Jacksteit, R., Pahnke, R., Hamm, A., & Weippert, M. (2018). Heart rate variability is associated with social value orientation in males but not females. Scientific Reports, 8, 7336. https://doi.org/10.1038/s41598-018-25739-4 DOI: https://doi.org/10.1038/s41598-018-25739-4
Lischke, A., Weippert, M., Mau-Moelller, A., Päschke, S., Jacksteit, R., Hamm, A. O., & Pahnke, R. (2019). Sex-specific associations between inter-individual differences in heart rate variability and inter-individual differences in emotion regulation. Frontiers in Neuroscience, 12, 1040. https://doi.org/10.3389/fnins.2018.01040 DOI: https://doi.org/10.3389/fnins.2018.01040
Liu, P., Chen, Y., & Mu, Y. (2024). The impact of climate risk aversion on agribusiness share price volatility. Finance Research Letters, 61, 104797. https://doi.org/10.1016/j.frl.2023.104797 DOI: https://doi.org/10.1016/j.frl.2023.104797
Lo, A. W., & Rapin, D. W. (2002). The psychobiology of real-time risk processing. Journal of Cognitive Neuroscience, 14(3), 323-339. https://doi.org/10.1162/089892902317361877 DOI: https://doi.org/10.1162/089892902317361877
Malik, M., & Camm, A. J. (1995). Heart rate variability. Future House Publishing.
Mann, H., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18(1), 50-60. https://doi.org/10.1214/aoms/1177730491 DOI: https://doi.org/10.1214/aoms/1177730491
Marini, M. M. (2023). Emotions and financial risk-taking in the lab: A meta-analysis. Journal of Behavioral Decision Making, 36(4), e2342. https://doi.org/10.1002/bdm.2342 DOI: https://doi.org/10.1002/bdm.2342
Mason, M. F., Norton, M. I., Van Horn, J. D., Wegner, D. M., Grafton, S. T., & Macrae, C. N. (2007). Wandering minds: The default network and stimulus-independent thought. Science, 315(5810), 393-395. https://doi.org/10.1126/science.1131295 DOI: https://doi.org/10.1126/science.1131295
Miendlarzewska, E. A., Kometer, M., & Preuschoff, K. (2017). Neurofinance. Organizational Research Methods, 22(1), 196-222. https://doi.org/10.1177/1094428117730891 DOI: https://doi.org/10.1177/1094428117730891
Moon, S. J. E., Schlenk, E. A., & Lee, H. (2023). Heart rate variability in adults with substance use disorder: A comprehensive narrative review, 30(2). 240-251. https://doi.org/10.1177/10783903221145142 DOI: https://doi.org/10.1177/10783903221145142
Nazaripour, M., Zakizadeh, B., Afshar, A., & Mohammadi, A. (2020). Investigating the behavior of investors in the Tehran Stoch Exchange based on the model of the five personality types. Quarterly Journal of New Psychological Ideas, 6(10), 1-14. https://jnip.ir/article-1-354-en.html
Nofsinger, J. R., Patterson, F. M., & Shank, C. A. (2018). Decision-making, financial risk aversion, and behavioral biases: The role of testosterone and stress. Economics & Human Biology, 29, 1-16. https://doi.org/10.1016/j.ehb.2018.01.003 DOI: https://doi.org/10.1016/j.ehb.2018.01.003
Oitzl, M. S., Champagne, D. L., van der Veen, R., & de Kloet, E. R. (2010). Brain development under stress: Hypotheses of glucocorticoid actions revisited. Neuroscience & Biobehavioral Review, 34, 853-866. https://doi.org/10.1016/j.neubiorev.2009.07.006 DOI: https://doi.org/10.1016/j.neubiorev.2009.07.006
Pabst, S., Brand, M., & Wolf, O. T. (2013). Stress effects on framed decisions: There are differences for gains and losses. Frontiers in Behavioral Neuroscience, 7, 142. https://doi.org/10.3389/fnbeh.2013.00142 DOI: https://doi.org/10.3389/fnbeh.2013.00142
Porges, S. W. (2007). The polyvagal perspective. Biological Psychology, 74, 116-143. DOI: 10.1016/j.biopsycho.2006.06.009 DOI: https://doi.org/10.1016/j.biopsycho.2006.06.009
Reynaud, A., & Couture, S. (2012). Stability of risk preference measures: results from a field experiment on French farmers. Theory and Decision, 73(2), 203-221. https://doi.org/10.1007/s11238-012-9296-5 DOI: https://doi.org/10.1007/s11238-012-9296-5
Rotenberg, S., & McGrath, J. J. (2016). Inter-relation between autonomic and HPA axis activity in children and adolescents. Biological Psychology, 117, 16-25. https://doi.org/10.1016/j.biopsycho.2016.01.015 DOI: https://doi.org/10.1016/j.biopsycho.2016.01.015
Sassenrath, C., Barthelmas, M., Saur, J., & Keller, J. (2020). Inducing empathy affects cardiovascular reactivity reflected in changes in high frequency heart rate variability. Cognition and Emotion, 35(2), 393-399. https://doi.org/10.1080/02699931.2020.1826910 DOI: https://doi.org/10.1080/02699931.2020.1826910
Smith, T. W., Deits-Lebehn, C., Williams, P. G; Baucom, B., & Uchino, B. (2020). Toward a social psychophysiology of vagally mediated heart rate variability: Concepts and methods in self-regulation, emotional, and interpersonal processes. Social and Personality Psychology Compass, 14(3), e12516. https://doi.org/10.1111/spc3.12516 DOI: https://doi.org/10.1111/spc3.12516
Starcke, K., Polzer, C., Wolf, O. T., & Brand, M. (2011). Does stress alter everyday moral decision-making? Psychoneuroendocrinology, 36(2), 210-219. https://doi.org/10.1016/j.psyneuen.2010.07.010 DOI: https://doi.org/10.1016/j.psyneuen.2010.07.010
Taylor, S. E., Klein, L. C., Lewis, B .P., Gruenewald, T. L., Gurung, R. A., & Updegraff, J. A. (2000). Biobehavioral responses to stress in females: tend-and-befriend, not fight-or-flight. Psychological Review, 107(3), 411-429. https://doi.org/10.1037/0033-295x.107.3.411 DOI: https://doi.org/10.1037//0033-295X.107.3.411
Thayer, J. F., Ahs, F., Fredrikson, M., Sollers III, J. J., & Wager, T. D. (2012). A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health. Neuroscience and Biobehavioral Reviews. 36(2), 747-756. https://doi.org/10.1016/j.neubiorev.2011.11.009 DOI: https://doi.org/10.1016/j.neubiorev.2011.11.009
van den Bos, R., Harteveld, M., & Stoop, H. (2009). Stress and decision-making in humans: Performance is related to cortisol reactivity, albeit differently in men and women. Psychoneuroendocrinology, 34(10), 1449-1458. https://doi.org/10.1016/j.psyneuen.2009.04.016 DOI: https://doi.org/10.1016/j.psyneuen.2009.04.016
Yaghoubian, M. M., Moghaddam, A. N., Kashani, F. H., & Nasrolahi, B. (2024). Modeling the behavior of individual investors in the stock market based on the neuro-finance approach. International Journal of Finance and Managerial Accounting, 9(32), 101-112.
Cómo citar
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Descargar cita
CrossRef Cited-by
1. Isabel Abínzano, Lucas Ayres Barreira de Campos Barros, Zuray Melgarejo, Paula Andrea Navarro Pérez, Mary Analí Vera-Colina. (2024). Nota editorial – Número especial “Decisiones financieras empresariales en países emergentes”. Innovar, 34(94), p.e116823. https://doi.org/10.15446/innovar.v34n94.116823.
Dimensions
PlumX
Visitas a la página del resumen del artículo
Descargas
Licencia
Derechos de autor 2024 Innovar

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
Todos los artículos publicados por Innovar se encuentran disponibles globalmente con acceso abierto y licenciados bajo los términos de Creative Commons Atribución-No_Comercial-Sin_Derivadas 4.0 Internacional (CC BY-NC-ND 4.0).
Una vez seleccionados los artículos para un número, y antes de iniciar la etapa de cuidado y producción editorial, los autores deben firmar una cesión de derechos patrimoniales de su obra. Innovar se ciñe a las normas colombianas en materia de derechos de autor.
El material de esta revista puede ser reproducido o citado con carácter académico, citando la fuente.
Esta obra está bajo una Licencia Creative Commons: