Life financial outcomes carry a significant heritable component, but the mechanisms by which genes influence financial choices remain unclear. Focusing on a polymorphism in the promoter region of the serotonin transporter gene (5-HTTLPR), we found that individuals possessing the short allele of this gene invested less in equities, were less engaged in actively making investment decisions, and had fewer credit lines. Short allele carriers also showed higher levels of the personality trait neuroticism, despite not differing from others with respect to cognitive skills, education, or wealth. Mediation analysis suggested that the presence of the 5-HTTLPR short allele decreased real life measures of financial risk taking through its influence on neuroticism. These findings show that 5-HTTLPR short allele carriers avoid risky and complex financial choices due to negative emotional reactions, and have implications for understanding and managing individual differences in financial choice.

Funding: This work received funding from the FINRA Investor Education Foundation, the National Institute on Aging (AG030778) and the Zell Center for Risk Research at the Kellogg School of Management. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Recent findings suggest that various aspects of economic behavior are heritable. Studies comparing choices of identical and fraternal twins find that inherited (and likely genetic) factors can account for 20%–30% of variation across individuals in terms of experimentally-elicited risk preferences [1] , allocation to risky assets in real life portfolios [2] , [3] , and the propensity to save [4] . Further, genetic variation related to the functioning of two broadly distributed and influential neurotransmitters, serotonin and dopamine, have been shown to correlate with economic behavior in healthy individuals [5] , [6] , [7] , and in individuals with diagnosed disorders including pathological gamblers and those with anxiety disorders [8] , [9] . While these findings convincingly suggest that genetic factors are related to economic choice, they do not address the equally important question of how genes influence behavior. For example, do genes influence cognitive abilities, do they shape the way people learn in financial markets, or do they determine risk attitudes? We sought to address this question by focusing on the role of a genetic polymorphism in the promoter region of the serotonin transporter gene (5-HTTLPR) which has recently been identified as important for financial risk taking. Prior research suggests that the short and the long variants of this gene may have different effects on risk taking, and therefore on economic behavior. The short allele has been associated with higher scores on neuroticism and harm avoidance [10] , [11] , a stronger attentional bias towards negative stimuli [12] , and lower life satisfaction [13] , as well as with less risky experimentally elicited portfolio allocation choices [6] . Nonetheless, it is still unknown whether differences in financial choice documented in the laboratory generalize to real life choices and outcomes among community members, and if so, which mechanism underlies the risk avoidant choices of the short allele carriers. The goal of this study was to test whether short versus long serotonin transporter allele status would influence financial choices in a community sample, and to explore potential psychological mediators.

Methods

The 60 subjects in this study (30 male, age range 20–85 years, mean age 54 years) were recruited by a survey research firm with the goal of being representative of the San Francisco Bay Area population. Data collection was conducted at Stanford University. Subjects gave written informed consent prior to participating. The study and consent procedure were approved by the Stanford IRB committee. For these individuals, we obtained demographic information, information regarding financial status (i.e., assets, debt and income), measures of cognitive ability and numeracy, measures of attitudes and beliefs concerning economic decisions and outcomes, and objective financial information from official credit reports. Subjects ability to learn from financial information was also measured using an investment task, described below. Summary statistics for these measures are presented in Table 1.

Salivary DNA was collected from all subjects (with a cheek swab), and genotyping of the 5-HTTLPR polymorphism was conducted according to standard protocols [10]. In this sample, 52% (32) subjects had the short/short (SS) genotype, 32% (19) had the short/long (SL) genotype, and 15% (9) had the long/long (LL) genotype. The distribution was consistent with that expected under Hardy Weinberg equilibrium ( , , ). Within the sample, chi-square tests indicated that genotype did not significantly vary as a function of gender or ethnicity.

Demographics and Life Financial Outcomes A questionnaire was administered to assess the age, marital and occupational status, level of income, number of years of education, and ethnicity of the subjects, as well as their assets and debt. Household income was measured using a scale from 1 to 12, where 1 represented “less than $15,000” and 12 represented “higher than $500,000”. Assets were assessed with the question “What are your approximate current assets? (i.e., home value, bank accounts, investments, belongings)” using a 16-category ordinal response scale ranging from <$500.00 in the lowest category to >$1,500,000.00 in the highest. Debt was assessed with the question “What are your approximate current debts? (i.e., outstanding home loans, outstanding car loans, outstanding student loans, credit card debt, medical debt)” using a 16 category ordinal response scale ranging from <$500.00 in the lowest category to >$1,500,000.00 in the highest.

Cognitive Ability and Numeracy Subjects were also administered standard tests of cognitive ability. The Trail Making Test [14] assessed cognitive flexibility. The test has two parts (A & B) which are both timed until completion. The first part (Trails A) requires that subjects sequentially connect 25 encircled numbers (1, 2, 3, etc) that are randomly arranged on a sheet of paper. The second part (Trails B) requires that subjects connect a series of numbers and letters in an alternating pattern (1, A, 2, B, 3, C, etc.) that are randomly arranged on a sheet of paper. The score on this test is calculated as the difference between the time taken to complete Trails B versus Trails A, and indicates how easily individuals can alternate or switch between different activities. Higher scores correspond to less cognitive flexibility. The Letter-Number Sequencing subtest from the Wechsler Adult Intelligence Scale [15] assessed memory capacity. An experimenter verbally listed a series of randomly ordered numbers and letters to the subject (e.g., C, 1, A, 6, 2) and asked the subject to repeat the series back in with the numbers listed first, in numerical order, followed by the letters in alphabetical order (e.g., 1, 2, 6, A, C). Performance on this test requires that subjects both maintain the series of randomly ordered numbers and letters in short term memory and manipulate the stored information by sorting the representation in memory before verbally repeating it. Researchers refer to this combination of short-term memory maintenance and manipulation as “working memory”. This measure of working memory correlates well with general intelligence. A numeracy inventory assessed quantitative skills with basic number problems [16]. This 11-item measure contains questions such as: “The chance of getting a viral infection is.0005. Out of 10,000 people, about how many of them are expected to get infected?”. All of the questions are focused on computing probabilities or proportions. The measure is considered to index an individual’s ability to accurately compute numerical information about risk.

Financial Choices To assess subjects’ willingness to take financial risk, we asked: “If you suddenly received $10,000, how much would you allocate to each of the following? (out of 100%): a. equities: – % (includes stocks, mutual funds, or other equity components); b. bonds: – % (includes government bonds, municipal or corporate bonds, bond mutual funds, or other fixed-income components), c. cash: –% (includes money market accounts).” To measure subjects’ involvement in their finances, we asked the question: “How much experience do you have with investing?”. The possible answers were:(1) “I have had a savings account, but no other investments”; (2) “I have had investments other than a savings account (e.g., stocks, bonds, or mutual funds), but I do not tend to make my own decisions about those investments”; (3) “I actively make decisions about investing my money (e.g., in stocks, bonds, and other types of investments).”

Beliefs To index subjects’ beliefs about the risk of investing in equities, we asked: “Rate how risky you perceive this activity to be: Investing 5% of your annual income in a very risky stock”. The answer was an integer from 1 (“not at all risky”) to 7 (“extremely risky”).

Socioeconomic Status A subjective assessment of subjects’ socioeconomic status (SES) indexed their perceived standing in society and associated perceived control over life outcomes, since prior research suggests that individuals with low subjective SES perceive the world as less controllable [17]. Subjects saw a picture of a ladder along with instructions that read: “Think of this ladder as representing where people stand in the United States. At the top of the ladder are the people who are best off - those who have the most money, the most education and the most respected jobs. At the bottom are the people who are the worst off who have the least money, least education, and the least respected job or no job. The higher up you are on this ladder, the closer you are to the people at the top; the lower you are, the closer you are to the people at the bottom. Where would you place yourself on this ladder? Place an X on the rung where you think you stand at this time of your life relative to other people in the United States.” For local status, the instructions read: “Think of this ladder as showing where people stand in their communities. People define community in different ways. Please define it in whatever way is most meaningful to you. At the top of the ladder are the people who have the highest standing in their community. At the bottom are the people who have the lowest standing in their community. Where would you place yourself on this ladder? Place an X on the rung where you think you stand at this time of your life relative to other people in your community.” The answers can range from 1 to 10, where 1 represent the lowest rung on the ladder, while 10 represents the highest.

Credit Report Data We obtained credit reports for a subsample of 31 subjects, from which we extracted subjects’ overall FICO credit scores, and calculated the available credit amount and the percent of credit used as objective proxies of assets and debt, respectively. The correlations between these objective measures (using log values) and the subjective self-reported assets and debt were significant and robust (r = 0.56 for assets, and r = 0.85 for debt, ), supporting the validity of the self-reported information.

Financial Learning Task This task indexed subjects’ ability to learn from financial gains or losses [18]. Gain and loss learning were separately assessed to account for the possibility that depending on subjects’ 5-HTTLPR genotype, they may differentially attend to, encode, or retrieve gain or loss information. Subjects made 24 choices between two risky assets in both gain and loss conditions. In each condition, the two assets were represented by a pair of abstract symbols. After choosing one of the assets from each pair, subjects saw the outcome associated with their choice. On average, one of the assets yielded a better outcome, while the other yielded a worse outcome. Specifically, in the gain condition the better asset had a 66% probability of yielding a $1 dividend and a 33% probability of yielding a $0 dividend. These probabilities were reversed for the worse asset (i.e., the $1 dividend had only a 33% chance of being obtained). In loss condition, the better asset had a 66% probability of yielding a $0 dividend and a 33% probability of yielding -$1.00, and outcome probabilities were reversed for the worse asset. Within each pair, assets appeared randomly and with equal frequency on the left or right side of the screen. Asset pairings with better or worse outcomes were randomly assigned by the computer at the start of the experiment and counterbalanced across subjects. Subjects were explicitly informed about the dividend distributions and instructed to try to maximize their earnings throughout the experiment by choosing the assets they believed to be the better ones. To quantify how well subjects learn from financial information, we calculated the fraction of trials in the gain condition and in the loss condition when subjects made the correct Bayesian choice, conditional on the information set available at the time. For this calculation, we excluded trials in which both options had equal chances of being optimal (including the first trial). For incentive compatibility, dividends accumulated during this task determined subjects’ payment.