Relationship between Cryptocurrency Trading, Hopelessness, and Financial Well-Being: A Cross-Sectional Study Among Physicians

Introduction

Cryptocurrency markets operate 24 hours a day, 7 days a week, and are characterized by extremely high price volatility, which induces emotional, impulsive trading. Many experts now consider excessive cryptocurrency trading akin to compulsive gambling. Problematic cryptocurrency trading refers to excessive, repetitive, and uncontrolled participation in crypto trading in these volatile markets, which is often driven by the pursuit of quick gains or efforts to recoup losses [1]. Notably, the gambling comparison primarily applies to short-term, high-frequency speculative trading (eg, day trading) rather than long-term investment strategies. Short-term crypto trading can produce a psychological “rush” similar to that produced by gambling, and daily traders have been shown to exhibit significantly more pathological trading behaviors than infrequent traders [2]. Recent studies have linked cryptocurrency trading to gambling-like behaviors [1,2]. Physicians, a highly stressed professional group, may be particularly vulnerable. Chronic workload, burnout, and emotional strain are typical in healthcare workers and were exacerbated by the COVID-19 pandemic. For example, by late 2021, over 60% of doctors in the United States reported burnout [3]. Burnout in physicians is often accompanied by maladaptive coping behaviors, including addictive tendencies [3]. Indeed, some physicians turn to unhealthy outlets to manage stress, with a recent survey revealing over 20% of physicians consumed alcohol or controlled substances multiple times a day to cope with occupational pressures [4]. Financial stressors, such as rising living costs, also pressure some doctors. For example, many physicians have limited financial literacy [4], and financial stress has been identified as a contributor to physician burnout [5]. Under such stress, a physician who feels hopeless about the future might engage in high-risk investments as an escape or coping strategy. Hopelessness – a persistent sense of pessimism and futility – can fuel risky decision-making; in gambling, individuals often bet to escape negative emotions or despair [5], suggesting that persons with hopelessness may similarly turn to speculative trading for relief. Conversely, strong perceived financial security is generally linked to lower stress and might protect against rash investments. Perceived financial well-being, or one’s subjective feeling of financial security, could buffer against compulsive trading. Those who feel financially stable have less incentive to place risky bets, whereas individuals under subjective financial strain may resort to speculative trading as a quick fix for monetary problems. In gambling disorder, financial stress often leads to “chasing” losses [6] – a pattern that problematic cryptocurrency traders might also exhibit under economic pressure [1].

However, to date, no research has specifically examined cryptocurrency trading behavior in physicians. To address this gap, we aimed to investigate whether physician cryptocurrency trading is associated with hopelessness and financial well-being. Specifically, we sought to determine whether (1) demographic factors (age, sex, income) are linked to problematic crypto trading; (2) higher hopelessness is associated with more problematic trading; and (3) perceived financial well-being protects against or correlates with trading risk. Prior research has linked cryptocurrency trading to stress, sleep disturbances, and impaired quality of life in academics, as well as to addictive internet behaviors in university students [6,7]. Regionally, recent studies from Turkey underscore these issues. One cross-sectional study reported a problematic cryptocurrency trading prevalence of 26.3% among Turkish cryptocurrency investors, with problematic trading strongly associated with higher anxiety, depression, and gambling problems [7,8]. A national-level study by Özbek and Topal similarly found that Turkish traders with higher levels of anxiety and depression were significantly more likely to exhibit problematic cryptocurrency trading behaviors [9]. Another found that daily crypto traders exhibited significantly more impulsivity and pathological trading symptoms than did occasional traders, mirroring patterns seen in gambling addiction [2]. Furthermore, a study of Turkish university students noted that those who traded cryptocurrency had more attention-deficit/hyperactivity disorder (ADHD) symptoms and poorer sleep and quality of life than did non-traders [10,11]. Turan et al corroborated these findings, showing significant associations between crypto use and elevated ADHD traits, as well as diminished quality of life in Turkish student populations [12]. By focusing on physicians – a highly educated and uniquely stressed group – we aim to identify psychosocial drivers of speculative trading behavior in this population.

Materials and Methods

STUDY DESIGN, PARTICIPANTS, ETHICS APPROVAL:

We conducted an online cross-sectional survey of licensed physicians in Diyarbakır, Turkey. Diyarbakır’s Medical Chamber includes roughly 950 practicing physicians; therefore, our sample (n=300) represented approximately one-third of that population. A sample size of 300 was chosen a priori to achieve approximately 95% confidence with 5% margin of error. We received 303 responses; after excluding 3 inconsistent surveys, 300 complete responses were analyzed. All analyses were performed on these complete cases (no missing data imputation was required). Eligibility required being a licensed physician with 1 year or more of practice. Participants were recruited across public and private healthcare settings. Participation was voluntary and anonymous; informed consent was obtained electronically before participant inclusion in the study. The protocol was approved by the Dicle University Faculty of Medicine Ethics Committee (decision No. 472, date: November 24, 2021), and the study was conducted in accordance with the Declaration of Helsinki.

SAMPLE SIZE JUSTIFICATION:

A target sample size of 300 was chosen to achieve approximately 95% confidence with a 5% margin of error based on an estimated population of approximately 950 physicians in Diyarbakır. For the multivariable linear models (up to 6 predictors), this sample provided more than 0.80 power to detect small-to-moderate effect sizes at α=0.05 under conventional assumptions. This target was considered feasible given anticipated response rates and available recruitment channels.

HOPELESSNESS: Hopelessness was assessed with the Beck Hopelessness Scale (BHS), a 20-item true/false inventory of pessimism about the future (score range, 0–20). Higher scores indicate greater hopelessness. The BHS is a well-validated instrument [8].

FINANCIAL WELL-BEING: Financial well-being was measured via a validated self-report Financial Well-Being Scale (FWBS) that captures perceived financial security and satisfaction. Higher scores indicate better financial well-being (transformed to a 0–100 scale) [9].

CRYPTOCURRENCY TRADING: Cryptocurrency trading behavior was assessed using the Problematic Cryptocurrency Trading Scale (PCTS) developed by Menteş et al. This 16-item self-report scale (5-point Likert: 1=never to 5=always) probes behaviors such as preoccupation and loss-chasing in crypto trading. Total PCTS scores (16–80) reflect compulsive or risky crypto investment tendencies. Higher PCTS scores indicate more problematic trading. The PCTS has demonstrated good psychometric properties [10]. Participants also provided sociodemographic and occupational information (age, sex, years of practice, income level, marital status) and any history of psychiatric diagnoses or substance use.

STATISTICAL ANALYSIS:

Data were analyzed in SPSS version 22 (IBM Corp). Descriptive statistics (mean, SD, frequency) characterize the sample. We used chi-square or Fisher exact tests and t tests to compare groups (eg, by sex, income) on BHS, FWBS, and PCTS scores. We then conducted multivariable linear regressions to identify predictors of PCTS. The first model included demographic predictors of age, sex, income, and years of practice, and the second model included psychosocial predictors of BHS, FWBS, and years of practice. Years of practice was retained in both models as an indicator of career experience, to control for any effect of professional tenure on trading behavior, even if not statistically significant on its own. All predictor variables were continuous or dummy-coded. Regression assumptions were checked; significance was set at P<0.05. Internal consistency was excellent for all scales, with a Cronbach α>0.80. We used a hierarchical modeling approach, by entering demographics first, then adding psychological variables, to assess the incremental effect of hopelessness and financial well-being beyond baseline factors. Covariates (age, sex, income, years of practice) were selected based on prior literature and theoretical relevance to physician financial behaviors.

REGRESSION ASSUMPTION CHECKS:

Prior to modeling, assumptions for multiple linear regression were evaluated. Linearity and homoscedasticity were inspected via residual-vs-fitted plots; normality of residuals was assessed with Q-Q plots and Shapiro-Wilk tests. Independence of errors was examined using the Durbin-Watson statistic. Multicollinearity among predictors was assessed using variance inflation factors and tolerance; no problematic multicollinearity was observed: all variance inflation factors were below common thresholds, and tolerances were acceptable. Where mild departures from normality were observed, robust standard errors were additionally examined in sensitivity checks; results and inferences were unchanged.

Results

SAMPLE CHARACTERISTICS:

The mean age of participants was 39.8±7.2 years, and 70.0% of respondents were men. Physicians had 14.1±6.9 years of practice on average. Income distribution was 18% low, 42% medium, and 40% high. Ten percent reported a history of psychiatric disorder. Regular alcohol use was reported by 15%, illicit drug use by 8%, and gambling behavior by 5% (Table 1).

Chi-square tests showed that participant sex was significantly related to higher scores on the PCTS, whereas income was not. For clarity, we defined a high-risk trading group as scoring in the top quartile of the PCTS distribution (PCTS ≥36). Using this cutoff, a significantly greater proportion of male physicians fell into the high-risk category than did female physicians (χ2, P<0.01). No income-group differences were observed (P>0.05). Consistently, male physicians had higher mean PCTS scores than female physicians (33.0±6.8 vs 29.8±5.9; t=2.12, P=0.03), while BHS and FWBS scores did not differ by sex (both P>0.05). Thus, male physicians reported more problematic trading behaviors, whereas levels of hopelessness and financial satisfaction were similar across sexes. Notably, our use of the top-quartile threshold for high-risk aligns with prior studies that lacked established clinical cut-offs [11].

Bivariate correlations (Pearson r) among key variables showed that PCTS scores were positively correlated with hopelessness (r≈0.34, P<0.001) and age (r≈0.12, P<0.05), and negatively (trend-level) correlated with financial well-being (r≈−0.11, P=0.06). Hopelessness and financial well-being were moderately inversely correlated (r≈−0.29, P<0.001). Sex (male coded as 1) had a small positive point-biserial correlation with PCTS (r_pb≈0.12, P<0.05), consistent with the higher male PCTS scores noted above. This pattern indicates that physicians with greater pessimism and older age tended to report more problematic trading, whereas those with higher financial satisfaction reported slightly less.

REGRESSION ANALYSES:

Table 2 presents the multivariate predictors of PCTS. In the demographic model (model 1), older age (β=0.32, P=0.04) and male sex (β=1.45, P=0.02) were significant positive predictors of PCTS, while income level (medium or high vs low) and years of practice were not significant. Model 1 (demographics only) explained approximately 8% of the variance in PCTS (overall model P=0.02). In the psychosocial model (model 2), higher hopelessness (BHS) predicted higher PCTS (P=0.001), whereas financial well-being (FWBS) did not (P=0.067). As in model 1, years of practice was not significant in model 2. Model 2, which included psychosocial factors, had R2=0.35 (P<0.001). Beyond demographics, physicians with more pessimistic outlooks tended to engage in greater risky crypto trading (see Table 2).

Discussion

LIMITATIONS:

The cross-sectional design prevents us from inferring causality between hopelessness and problematic trading. First, the study was conducted in a single city (Diyarbakır), which limits the geographic generalizability of the findings. Second, the sample was predominantly male (70%), which may restrict the applicability of the results to female physicians. Third, data were collected in late 2021, a period marked by extraordinary fluctuations in cryptocurrency markets during the COVID-19 pandemic, which may have influenced participants’ trading behaviors and risk perceptions. All measures were self-reported, introducing potential recall and social desirability biases. Future research should use longitudinal or multi-site designs, incorporate objective trading metrics, and test mechanistic models, such as mood dysregulation leading to loss-chasing, to clarify the directionality and boundary conditions of the observed associations. It would also be valuable to examine whether targeted interventions, such as financial literacy training or wellness programs, can reduce problematic trading behaviors in physicians [17].

Conclusions

In physicians, male sex, older age, and higher levels of hopelessness are associated with more problematic cryptocurrency trading, whereas perceived financial well-being did not show a clear protective association. These findings highlight how pessimism influences speculative financial behavior in clinicians. We recommend developing screening tools to identify physicians prone to problematic cryptocurrency trading and providing psychoeducation about the risks of impulsive trading. Integrating financial literacy education, stress management training, and mental health support into physician wellness programs may help mitigate compulsive trading and promote healthier coping strategies. By implementing proactive interventions, healthcare institutions can help safeguard physician well-being in the face of emerging behavioral addictions.