Abstract
Background and Aims
There is growing evidence regarding the overlap between trading behaviors and gambling. However, problematic trading behaviors are often assessed using gambling-related instruments, which may not fully capture the nuances of trading. The present study developed and evaluated the psychometric properties of the Trading Disorder Scale (TDS), grounded in in the research criteria proposed by Guglielmo et al. (2016), based on DSM-5 criteria for gambling disorder and internet gaming disorder.
Methods
A cross-sectional survey was administered to 403 Spanish amateur traders. The TDS was tested for reliability, validity, and factorial structure. Latent class analysis (LCA) was used to identify patterns of disordered trading.
Results
EFA and CFA supported a one-factor solution for the TDS, which showed strong internal consistency (ωu-cat = 0.938, KR-20 = 0.877). The scale showed good concurrent validity with PGSI (r = 0.559) and good convergent validity with trading-related variables. LCA identified three classes: non-disordered trading (72.2%), at-risk trading (17.6%), and disordered trading (10.2%). Individuals in the disordered trading group scored higher on TDS, traded more frequently, monitored markets more intensively, and exhibited higher rates of problem gambling (PGSI≥5), impulsivity, and substance use. Guglielmo's cut-off point (≥5 criteria) effectively differentiated individuals with disordered trading behaviors from those at-risk and those without disordered trading.
Conclusions
The TDS is a reliable and valid instrument for assessing disordered trading among amateur investors. Further research is needed to explore the scale's predictive validity.
Introduction
The expansion of online trading platforms has significantly increased access to a wide range of financial activities, from traditional stock investments to highly speculative investments such as cryptocurrencies (Campino & Yang, 2024; Kamolsareeratana & Kouwenberg, 2023; Oksanen, Mantere, Vuorinen, & Savolainen, 2022; Senarathne, 2019). This marked increase in platform availability, coupled with extensive advertising campaigns, has resulted in a sharp rise in the number of individuals engaging in non-professional trading activities (Andrade & Newall, 2023; Chong, Ong, & Tan, 2021; Griffiths, 2018; Johnson, Co, et al., 2023; Lee, Lewis, & Mills, 2023; Oksanen, Mantere, et al., 2022; Torrance, Heath, Andrade, & Newall, 2023).
While trading offers opportunities for financial gain, it has also raised concerns due to its speculative nature, short-term profit-seeking, and rapid transactions, sometimes completed within the same day, which bear striking similarities to gambling behaviors (Arthur, Williams, & Delfabbro, 2016; Delfabbro, King, & Williams, 2021; Mosenhauer, Newall, & Walasek, 2021). The financial market as a space for risky gambling behavior is not a novel concept. The South Oaks Gambling Screen (SOGS; Lesieur & Blume, 1987), one of the most widely used tools for assessing problem gambling, includes an item specifically addressing gambling on the stock and commodities market. However, an increasing body of evidence now links trading, particularly in high-risk assets, to gambling disorder (Johnson, Co, et al., 2023; Lee et al., 2023).
Both trading and gambling share key structural characteristics, including high levels of risk, uncertainty, and the potential for significant financial losses (Arthur et al., 2016; Delfabbro, King, & Williams, 2021; Delfabbro, King, Williams, & Georgiou, 2021; Oksanen, Mantere, et al., 2022). Previous studies have suggested that some investors view trading as form of entertainment, either as a substitute for or an extension of traditional gambling (Guzmán, Pinto-Gutiérrez, & Trujillo, 2021; Huang et al., 2022; Mosenhauer et al., 2021; Prachayanant, Kraiwanit, & Chutipat, 2023; Weidner, 2022). This overlap has led to concerns about excessive involvement in trading, which has been found to be strongly correlated with disordered gambling, both in terms of behavioral similarities and the profiles of individuals engaging in these activities (Coloma-Carmona et al., 2024; Grall-Bronnec et al., 2017; Johnson, Co, et al., 2023; Oksanen, Hagfors, Vuorinen, & Savolainen, 2022; Steinmetz, 2023; Sudzina, Dobes, & Pavlicek, 2023). However, problematic trading behavior has largely been analyzed through the lens of its relationship with gambling disorder, using gambling-related tools or criteria that were not fully adapted or validated for the trading context (Arthur & Delfabbro, 2017; Cox, Kamolsareeratana, & Kouwenberg, 2020; Delfabbro, King, & Williams, 2021; Grall-Bronnec et al., 2017; Granero et al., 2012; Shin, Choi, Ha, Choi, & Kim, 2015).
To address this gap, Guglielmo, Ioime, and Janiri (2016) proposed 13 research criteria for disordered trading, primarily based on the DSM-5 criteria for gambling disorder (APA, 2013). These criteria capture maladaptive and recurrent trading behaviors that negatively impact personal, familiar, and professional life, including symptoms such as loss of control, chasing losses, tolerance, and withdrawal. Additionally, Guglielmo et al. included symptoms such as sleep disturbances and suicidal ideation, observed in both amateur and professional investors (Guglielmo et al., 2016; Johnson, Sun, Stjepanović, Vu, & Chan, 2023; Koh & Han, 2023). While these symptoms are not part of the standard DSM-5 gambling diagnosis, they align with the components model of addiction (Griffiths, 2005), which outlines six core components of addictive behavior: salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse (Table 1).
Components evaluated according to the criteria proposed by Guglielmo (2016) for disordered trading
| DSM-5 criteria for gambling disordera and internet gaming disorderb | Addiction components model (Griffiths, 2005) | |
| 1. Have you often found your mind occupied with investment and/or trading activities (e.g., reliving past investment experiences, analyzing, or planning the next investment, reading literature or online forums related to the financial world, making investments/trading the main activity of your daily life)? | Preoccupationa.b | Salience (cognitive) |
| 2. Have you felt the need to invest and/or trade increasingly larger amounts of money to achieve the desired excitement? | Tolerancea | Tolerance |
| 3. Have you felt the need to spend more and more time making investments/trading and/or looking for new financial instruments to invest in? | Toleranceb | Tolerance |
| 4. Have you been nervous or irritable when you tried to reduce or stop investing and/or trading? | Withdrawala.b | Withdrawal |
| 5. Has your sleep pattern been disrupted by investment and/or trading activities (e.g., staying up at night to be online at the opening of foreign financial markets)? | – | Salience (behavioral) |
| 6. Have you made repeated efforts to control, reduce, or stop investing and/or trading, always without succeeding? | Loss of controla.b | Relapse |
| 7. Except for investments and/or trading, have you lost interest in social and/or recreational activities that you previously enjoyed because of investments/trading? | Decrease in other activitiesb | Conflict (with other activities) |
| 8. Have you often invested and/or traded when you felt distressed (e.g., helplessness, guilt, anxiety, depression)? | Trading when feeling distresseda.b | Mood modification |
| 9. After losing money in investments and/or trading, have you immediately or another day invested again to try to recover the losses? | Chasing lossesa | Relapse |
| 10. Have you lied to conceal your level of involvement with investments and/or trading (e.g., lying about financial losses, only talking about investments where you made money)? | Lie/Deceptiona.b | Conflict (interpersonal) |
| 11. Have you jeopardized or lost an important relationship, your job, or opportunities in your studies or career because of investments and/or trading activities? | Jeopardized or lost significant mattersa.b | Conflict (with other activities) |
| 12. Do you rely on others to give you money to relieve your desperate financial situation caused by investments and/or trading? | Relying on others financiallya | Conflict (interpersonal) |
| 13. Due to investments and/or trading, have you had thoughts about taking your own life, with or without planning, or have you attempted to take your own life? | – | Conflict (intra-psychic) |
Note: Superscript letters indicate which DSM-5 criteria for aGambling Disorder and bInternet Gaming Disorder correspond to each item. The relationship between items and Griffiths' components model of behavioral addictions (2005) is also displayed in the table.
Although Guglielmo's criteria have been cited in multiple studies on disordered trading (Johnson, Co, et al., 2023; Roza, Tavares, Kessler, & Passos, 2024; Sonkurt & Altınöz, 2021; Turan, Kokaçya, Yılmaz, & Arı, 2024; Vismara, Caricasole, Varinelli, & Fineberg, 2022; Yığman, Bora Nazlı, & Yılmaz, 2023; Şentürk, Coşar, & Arikan, 2023), they have not yet been formalized into a validated assessment tool. Furthermore, the proposed diagnostic threshold (i.e., meeting five or more criteria) has not been empirically tested for its accuracy in identifying truly problematic trading behaviors.
In contrast, more recent tools such as the Stock Addiction Index (Youn, Choi, Kim, & Choi, 2016) and the Problematic Cryptocurrency Trading Scale (Mentes, Yolbas, & Bulut, 2021) have adapted existing measures such as the Problem Gambling Severity Index (PGSI; Ferris & Wynne, 2001), to assess problematic trading behaviors. However, these instruments were designed for specific financial assets, such as cryptocurrencies (Mentes et al., 2021), or specific investor groups, such as stock traders (Youn et al., 2016), without accounting for whether the sample comprised professional or amateur investors. Amateur investors have experienced the most significant growth in recent years and are more likely to use mobile trading apps, which increase accessibility and can lead to excessive involvement in trading activities (Davies & Ferris, 2022; Newall & Weiss-Cohen, 2022; Weiss-Cohen et al., 2024). Furthermore, those with limited experience in trading often suffer the most adverse consequences and financial losses as a result of their involvement in trading activities (Khan, 2022). Therefore, there remains a need for instruments applicable across various financial assets and investor profiles, particularly in the context of retail investors. Moreover, neither of the existing scales provide a clear cut-off point for identifying problematic trading behaviors.
Consequently, the present study developed and validated a scale based on Guglielmo's criteria for disordered trading among a sample of amateur investors involved in trading of different financial assets (e.g., cryptocurrencies, Forex, exchange-traded funds). The study examined the scale's factor structure, internal consistency, and validity (concurrent and convergent), as well as its ability to distinguish different levels of trading severity using a latent class analysis approach.
Methods
Participants and procedure
The sample comprised 403 amateur investors, the majority of whom were male (65.3%, n = 263) and highly educated, with 60.8% (n = 245) having completed university-level studies. The average age of participants was 38.44 years (SD = 12.9). The most frequently reported trading activities included cryptocurrency trading (47.9%, n = 193), stock market investments (44.7%, n = 180), and trading in exchange-traded funds (ETFs; 22.8%, n = 92). Engagement in higher-risk trading activities such as commodities (16.6%, n = 67), Forex (9.7%, n = 39), futures (8.7%, n = 35), contracts for difference (CFDs; 5.2%, n = 21), and options (5%, n = 20) was less frequent. Supplementary Table S1 provides more detailed information on the sample characteristics based on the study variables.
The sample was derived from a larger set of 1,429 Spanish individuals who completed an online cross-sectional survey conducted between March 24 and April 22, 2022. This broader sample was recruited through an online panel managed by an independent research agency. The sample's representativeness was ensured by setting quotas based on age, gender, geographic region, and population size of the participants' living area. To confirm feasibility and refine the survey, a pretest was conducted with 100 individuals, confirming that the average survey completion time was 25 minutes.
A total of 8,549 invitations were sent to adults aged 18–64 years residing in Spain. Of these, 3,749 individuals initiated the survey after reviewing the study's aims and providing informed consent. To ensure data validity and reliability, several quality control procedures were applied. Out of the 3,749 initial respondents, 2,320 were excluded due to incomplete responses, unusually short completion times, exceeding predefined quotas, data inconsistencies, or evidence of automated response patterns. Additionally, respondents scoring higher than 3 on the Oviedo Infrequency Scale (Fonseca-Pedrero, Paíno-Piñeiro, Lemos-Giráldez, Villazón-García, & Muñiz, 2009) were excluded to avoid random or inattentive responses. Following these exclusions, 1,429 valid responses remained, yielding a maximum margin of error of 3.2% with a confidence level of 95.5% (Z = 1.96). Of these, 409 participants reported engaging in financial trading within the past 12 months. Six professional traders were excluded from the analysis because the study focused solely on amateur investors, resulting in a final sample of 403 amateur traders. This sample size was sufficient for factor analysis and estimating IRT parameters (Edelen & Reeve, 2007; Guadagnoli & Velicer, 1988; Muñiz & Fonseca-Pedrero, 2019).
Measures
Sociodemographic data
Participants provided basic demographic information, including age, sex, marital status, education level, employment status, and monthly income.
Trading Disorder Scale (TDS)
The Trading Disorder Scale was developed based on the 13 criteria proposed by Guglielmo et al. (2016) for assessing disordered trading behaviors. Following the International Test Commission guidelines (Muñiz, Elosua, & Hambleton, 2013), the criteria were translated into Spanish, after which a back-translation into English was performed. No significant discrepancies were identified between the original and back-translated versions, confirming the accuracy of the translation. Each criterion was presented as a checklist, with participants providing ‘yes/no’ responses to indicate whether they had experienced each symptom over the past 12 months (see Appendix A and B). Participants endorsing five or more criteria were classified as exhibiting disordered trading behaviors (Guglielmo et al., 2016).
Other trading-related variables
Participants reported their engagement in financial trading over the past year for various assets: Forex, cryptocurrencies, commodities, exchange-traded funds (ETFs), contracts for difference (CFDs), futures, options, and stock market investments. Based on items proposed by Delfabbro King, Williams, et al. (2021), they indicated trading frequency on a six-point Likert scale (ranging from “less than monthly” to “daily”). The highest reported frequency was used to categorize participants as less than monthly, monthly, weekly, or daily traders.
Additional data collected included hours spent researching or analyzing markets daily, frequency of checking market data during trading days, and mean trade size in euros (i.e., the average amount of money invested per trade across all traded assets). The preferred time frame for trades (i.e., the length of time – measured in seconds, hours, days, or weeks/months-, that an investor retains ownership of a specific financial asset, also known as holding position) was also collected. Participants were categorized based on the financial asset they held for the shortest period. Moreover, they were asked to indicate whether they engaged in trading as professional or as retail investors.
Gambling-related variables and psychological correlates
Participants were also asked to provide information regarding their participation in gambling activities during the preceding 12 months. These activities included buying lottery tickets/scratch-cards, sports betting, horse race betting, slot machine gambling, card game gambling (e.g., poker), playing bingo, playing casino games, and contests involving monetary bets (Spanish Observatory on Drugs and Addictions, 2023). The Problem Gambling Severity Index (PGSI; Ferris & Wynne, 2001) was administered to evaluate the severity of gambling problems, using the nine-item Spanish version (Lopez-González, Estévez, & Griffiths, 2018). This version has demonstrated robust reliability (αordinal = 0.97) and convergent validity with DSM-IV scores (r = 0.77). A cut-off score of ≥5 was applied to identify individuals with problem gambling (Williams & Volberg, 2014) and those with a PGSI score of 0-4 were classed as non-problem gamblers. Impulsivity was assessed using the Spanish version of the UPPS-P scale (Billieux et al., 2012; Cándido, Orduña, Perales, Verdejo-García, & Billieux, 2012), which evaluates five dimensions of impulsivity across 20 items. A total score for general impulsivity was also computed, with higher scores indicating greater levels of impulsivity. Substance use in the past year, including alcohol, tobacco, cannabis, cocaine, and hallucinogens, was assessed through binary items (yes/no).
Statistical analysis
Statistical analysis was conducted using SPSS v.27, R packages misty and DescTools and Mplus v.8.8 (Muthén & Muthén, 2017).
Descriptive analysis
Descriptive statistics (frequencies, percentages, means, and standard deviations) were calculated to analyze participants' demographics and the endorsement rates of items on the TDS.
Structure validity
To assess the factor structure of the TDS, the dataset (N = 403) was randomly split into two groups. Exploratory factor analysis (EFA) was conducted on the first group (n = 191) using the Weighted Least Square Mean and Variance Adjusted (WLSMV) estimator with Geomin oblique rotation, assuming correlation among factors. Given that χ2 statistic is sensitive to sample size, multiple criteria were used to determine the number of factors to retain, including eigenvalues greater than one, Root Mean Square Error of Approximation (RMSEA) with its 90% Confidence Interval (CI), Comparative Fit Index (CFI), and Tucker–Lewis index (TLI). Standardized Root Mean Square Residuals (SRMR) performance has been found to be inconsistent with binary data, therefore it is not reported. Instead, RMSEA, CFI, and TLI were prioritized for model evaluation (Yu, 2002). The best factor solution was selected based on pattern of standardized factor loadings (λ), interpretability of the factor solution, and overall fit indices. Strong primary factor loadings were defined as λ ≥ 0.40, with cross-loadings above 0.30 being problematic (Brown, 2015).
Inferential model comparisons were also performed using the chi-square difference test (Δχ2). A p-value below 0.05 indicated a significant difference between models, suggesting that the more complex model provides a significantly better fit. Confirmatory factor analysis (CFA) on the second group's data (n = 212) evaluated the hypothesized model, using the following thresholds for good model fit: RMSEA <0.05, CFI >0.95, and TLI >0.95 (Hu & Bentler, 1999).
Reliability, concurrent validity, and convergent validity
Reliability of the TDS was assessed using both Classical Test Theory and Item Response Theory (IRT) frameworks. Internal consistency was assessed with Kuder Richardson Formula 20 (KR-20) and McDonalds' categorical omega (ωu-cat) coefficients, with values > 0.70 indicating acceptable reliability (Nunnally & Bernstein, 1994). Corrected item-total correlations were computed to evaluate item quality, adjusting for item overlap. Based on Ebel and Frisbie (1991), item discrimination was categorized as excellent (≥0.40), good (0.30–0.39), acceptable (0.20–0.29), or poor (<0.20). From the IRT perspective, item discrimination (a) and difficulty (b) were estimated. Discrimination values (ranging 0–3) reflected how well items differentiated between individuals with different levels of disordered trading behavior. Following Kim and Baker (2018), discrimination levels were classified as very low (<0.34), low (0.35–0.64), moderate (0.65–1.34), high (1.35–1.69), and very high (≥1.70). Difficulty parameters, typically ranging from −3 to 3, indicate the level of the trait required for a 50% chance of endorsing the item. Higher difficulty values indicate that a greater severity of disordered trading is necessary to endorse the item.
Concurrent and convergent validity of the TDS were assessed with Pearson's r for continuous variables and Spearman's ρ correlations for ordinal variables. Correlations were calculated between the TDS, the PGSI, and various trading-related variables, including past-year frequency of trading, number of assets traded, hours spent daily studying markets, frequency of daily market monitoring, preferred time frame for trading, and mean trade size values (i.e., average amount of money invested per trade). Correlation strengths were interpreted as small (r/ρ < 0.30), moderate (r/ρ = 0.30–0.49), or large (r/ρ ≥ 0.50), according to Cohen's (1988) guidelines.
Latent class analysis
Latent class analysis (LCA) was conducted to determine whether the scale effectively captured distinct patterns of disordered trading behaviors among amateur investors. Optimal number of latent classes was determined based on (i) log-likelihood values, with higher values indicating a better model fit; (ii) the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and Sample-size Adjusted BIC (SABIC), where lower values suggest a better model fit and parsimony; and (iii) the p-values of the Vuong-Lo-Mendell-Rubin LRT, and Adjusted Lo-Mendell-Rubin LRT that compare model with k classes and k-1 classes, where a p-value below 0.05 indicates that solution with the one fewer class should be rejected. Although the bootstrapped LRT (BLRT) p-values were reported, they were not considered because previous studies have demonstrated that BLRT may favor models with more classes (Sinha, Calfee, & Delucchi, 2021). Entropy values were used to assess class separation, with values closer to 1.0 indicating clearer distinctions. Conditional probabilities were categorized as low (<0.40), moderate (0.40–0.69), or high (≥0.70) (Lanza, Flaherty, & Collins, 2003).
Sociodemographic, trading-related, gambling-related, and psychological variables across latent classes were analyzed using chi-square tests (χ2) with Bonferroni-corrected pairwise comparisons for non-continuous variables, and Kruskal-Wallis (H) tests with post hoc Mann-Whitney U tests for continuous variables because variables did not meet the assumption of normality. Effect sizes were computed for each test, with φ and Cramér's V being used for categorical variables (with values of ≥0.10, ≥0.30, and ≥0.50 representing small, moderate, and large effect sizes respectively; or ≥0.06, ≥0.17, ≥0.29, when degrees of freedom were ≥3), and eta squared (η2) for continuous variables (small ≥0.01, moderate ≥0.06, large ≥0.14) (Cohen, 1988).
In the absence of a gold-standard measure for disordered trading, the latent classes were also compared to the prevalence of disordered trading, as defined by the ≥5 cut-off point proposed by Guglielmo (2016). To further assess the appropriateness of this cut-off, all assessed variables were analyzed based on whether individuals were categorized as experiencing disordered trading (TDS scores ≥5) or not (TDS<5). Chi-square tests with effect sizes (φ or Cramér's V) were used for categorical variables, and Mann-Whitney U tests were applied for continuous variables, with Rosenthal's r as the effect size (small ≥0.10, moderate ≥0.30, large ≥0.50).
Sensitivity analysis
The main analyses were conducted using the raw data. To assess the robustness of the findings, sensitivity analyses were performed using winsorized values for those continuous variables with identified outliers (z-scores ± 3.29). In this process, extreme outliers were replaced with the closest non-outlier value. Outliers were identified in the following variables: TDS total scores (n = 5), number of financial assets traded (n = 3), mean trade size (n = 8), hours per day studying markets (n = 9), PGSI total scores (n = 5), and UPPS-P lack of premeditation dimension (n = 1), with the overall percentage of outliers being less than 2.5%. As the findings remained consistent across both analyses, the results presented are based on the original, non-winsorized data. Supplementary Table S1 provides data distribution overview for all assessed variables.
Ethics
The study was conducted in line with the principles of the Declaration of Helsinki and approved by the Committee of Research and Ethics at the first author's university (Reference: DPP.ACC.01.21). Participation in the study was entirely voluntary, although panel members were compensated with redeemable points by the panel provider for completing the survey. All participants were informed about the study and provided their informed consent to participate.
Results
Exploratory and confirmatory factor analysis
Several EFA models with different number of factors were tested to determine the best representation of the data (Table S2). While all models showed acceptable fit, the three-factor solution exhibited the best fit indices (χ2 = 95.100, df = 65, CFI = 0.982, TLI = 0.978, RMSEA = 0.049). However, the chi-square difference test (Δχ2) between the two- and three-factor models was non-significant (p > 0.05), favoring the simpler two-factor model for its parsimony. Despite this, the correlation between the two factors was high (r = 0.618), suggesting a lack of distinctiveness between them (Table S3). Additionally, all items had strong standardized factor loadings on both factors (Factor 1: λ range = 0.577–0.898; Factor 2: λ range = 0.437–0.955), and cross-loadings above 0.30, which complicated the interpretation of the two-factor model. Therefore, the one-factor model was considered as the most parsimonious solution, with all items loading strongly onto a single factor (λ range = 0.706–0.955).
The results from the CFA supported the one-factor structure identified through the EFA. All items loaded strongly onto a single latent factor, with factor loadings ranging from λ = 0.701 (Item 1: Mind occupied with investment and/or trading activities) to λ = 0.972 (Item 12: Rely on others to provide money to relieve a desperate financial situation caused by investing/trading). The model also showed satisfactory fit indices (χ2 = 93.920, df = 65, CFI = 0.985, TLI = 0.982, RMSEA = 0.045), confirming that the scale adequately assesses a single dimension of disordered trading behavior (Table S3).
Reliability, concurrent validity, and convergent validity
Table 2 shows results of reliability statistics for the TDS, using both CTT and IRT approaches. The scale demonstrated very good to excellent internal consistency (ωu-cat = 0.938, and KR-20 = 0.877). According to values obtained using CTT, item discrimination was excellent, with corrected item-test correlations ranging from 0.476 (Item 1: Mind occupied with investment and/or trading activities) to 0.650 (Item 5: Sleep pattern disrupted by investment and/or trading activities). Item discrimination indices (a) obtained using IRT were also high or very high for almost all items of the scale (range of a values: 1.415–4.133), except for Item 1 (Mind occupied with investment and/or trading activities), Item 10 (Lie to conceal the extent of involvement in investing/trading), and Item 8 (Use of investing/trading to escape/relieve a negative emotion), which showed moderate discrimination power (a-values of 0.982, 0.990, and 1.118, respectively).
Descriptive and reliability statistics for the Trading Disorder Scale (N = 403)
| Items of the Trading Disorder Scale | Endorsement, % (n)a | CTT† | IRT† | KR-20 if deleted | ωu-cat if deleted | |
| Item discriminationb | Item discrimination a (SE) | Item difficulty b (SE) | ||||
| Item 1. Mind occupied with investment and/or trading activities | 23.1 (93) | 0.476 | 0.982 (0.191) | 1.187 (0.185) | 0.876 | 0.865 |
| Item 2. Need to invest/trade with increasing amounts of money | 7.4 (30) | 0.530 | 1.993 (0.570) | 1.571 (0.175) | 0.869 | 0.928 |
| Item 3. Need to invest/trade with increasing amounts of time | 10.4 (42) | 0.523 | 1.439 (0.309) | 1.415 (0.177) | 0.869 | 0.927 |
| Item 4. Restlessness when cutting down (withdrawal) | 15.4 (62) | 0.586 | 1.486 (0.332) | 1.269 (0.164) | 0.866 | 0.924 |
| Item 5. Disrupted sleep pattern | 10.9 (44) | 0.650 | 2.541 (0.744) | 1.413 (0.149) | 0.862 | 0.928 |
| Item 6. Unsuccessful efforts to control or stop trading/investing | 10.7 (43) | 0.627 | 2.025 (0.517) | 1.498 (0.167) | 0.864 | 0.927 |
| Item 7. Loss of interest in previous hobbies | 9.2 (37) | 0.580 | 2.206 (0.576) | 1.507 (0.160) | 0.866 | 0.933 |
| Item 8. Trading/investing to cope negative emotions | 8.7 (35) | 0.608 | 1.118 (0.293) | 2.021 (0.318) | 0.865 | 0.931 |
| Item 9. Chasing losses | 12.7 (51) | 0.587 | 1.415 (0.317) | 1.452 (0.183) | 0.866 | 0.932 |
| Item 10. Lie about trading/investing | 9.2 (37) | 0.544 | 0.990 (0.261) | 1.995 (0.340) | 0.868 | 0.935 |
| Item 11. Jeopardized or lost a significant relationship/job/career | 4.7 (19) | 0.516 | 2.284 (0.829) | 1.940 (0.232) | 0.871 | 0.925 |
| Item 12. Rely on others for money | 6.7 (27) | 0.586 | 4.133 (1.885) | 1.630 (0.156) | 0.867 | 0.909 |
| Item 13. Suicidal behavior (ideation, plans or acts) | 5.5 (22) | 0.531 | 2.090 (0.638) | 1.804 (0.204) | 0.870 | 0.923 |
†Item discrimination and difficulty was assessed using Classical Test Theory (CTT) and Item Response Theory (IRT).
aPercentage of the sample (403 amateur investors that reported trading financial assets at least once in the previous 12-month period) endorsing the item. bCorrected item-total correlation.
Reliability of the total scale: Categorical McDonalds' omega coefficient (ωu-cat) = 0.938 (95% CI: 0.929–0.947), Kuder Richardson Formula 20 (KR-20) = 0.877 (95% CI: 0.872–0.882).
The difficulty parameters (b) for all items on the scale were positive, ranging from 1.187 (Item 1) to 2.021 (Item 8). According to the b-values, a greater level of disordered trading severity was needed to endorse items assessing mood modification (trading when feeling distressed) and conflicts (lied/deceived, jeopardized, or lost significant matters, experienced suicidal ideation, and relied on others financially), which had the highest b-values. On the contrary, cognitive salience (i.e., mind occupied with trading activities) was the least difficult criterion compared to the other criteria, as well as the most frequently endorsed (23.1%, n = 93).
The results from Pearson's r and Spearman's ρ-correlations between TDS and measures related to both trading behaviors and disordered gambling provided evidence for concurrent validity and convergent validity. TDS score demonstrated a strong positive correlation with the PGSI score (r = 0.559, p < 0.001). Additionally, TDS score showed significant correlations with several trading-related variables, including past-year frequency of trading (ρ = 0.356, p < 0.001), number of assets traded (r = 0.439, p < 0.001), hours spent daily studying/researching markets (r = 0.233, p < 0.001), and frequency of daily market monitoring (ρ = 0.462, p < 0.001).
In contrast, the TDS score did not show significant correlations with financial aspects of trading, such as the shortest preferred time frame (i.e., the shortest holding period use for the financial assets traded, ρ = −0.010, p = 0.862) or the average amount of money invested per trade (r = 0.026, p = 0.603).
Latent class analysis
Table S4 provides model fit statistics for the one- to four-class solutions. Model fit improved substantially from the one to three-class models, with significant reductions in AIC, BIC and SABIC values, and significant LRT p-values (p < 0 .05), while maintaining good class separation (entropy = 0.864). The four-class model offered only marginal improvement, as indicated by non-significant Vuong-Lo-Mendell-Rubin and Adjusted Lo-Mendell-Rubin LRT p-values (p > 0.05), suggesting that the three-class solution was the most optimal. Figure 1 shows the estimated indicator probabilities and class proportions for this model.
Latent class analyses of the Trading Disorder Scale items. Plot show the estimated indicator probabilities and class proportions for the 3-class model
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2025.00019
Class 1, termed non-disordered trading, comprised the majority of the sample (72.2%, n = 291) and showed minimal endorsement of any disordered trading indicators, with probabilities near zero for nearly all items on the scale. In contrast, individuals in Class 2, labeled disordered trading (10.2%, n = 41), exhibited the highest severity of disordered trading, with moderate to high endorsement probabilities across all items. Preoccupation with trading, disrupted sleep patterns, and using trading to cope with negative emotions were the symptoms with the highest endorsement probabilities. Additionally, members of this class also commonly endorsed feeling restlessness when attempting to cut down or stop trading, chasing losses, and difficulty controlling their trading activities. Lastly, individuals in Class 3, termed at-risk trading (17.6%, n = 71), displayed symptoms such as preoccupation, withdrawal, and chasing losses, though with lower endorsement probabilities compared to those in the disordered trading class. However, items related to financial, social, or interpersonal problems were infrequently endorsed in this class.
Differences between latent classes and TDS categories
Cross-class comparisons of the three identified latent classes showed significant differences across trading-related variables and supported the appropriateness of Guglielmo's proposed cut-off for disordered trading (2016). Specifically, 97.6% (40 out 41) of individuals in the disordered trading class met the five or more criteria cut-off, compared to 8.5% (6 out 71) in the at-risk trading class and 0% in the non-disordered trading class, with a very large effect size (Cramér's V = 0.92, p < 0.001). Individuals in the disordered trading class also had the highest total TDS scores (mean = 7.7, SD = 2.1), traded more frequently, held positions for shorter periods of time, monitored markets more intensively, and spent more time studying markets compared to the at-risk trading and non-disordered trading groups (p < 0.001), with moderate to large effect sizes (Table 4). Interestingly, participants belonging to the disordered trading class had a significantly lower amount of money invested per trade (mean = €3,854.5, SD = 10,929.3) compared to those in the at-risk trading class (mean = €4,537.7, SD = 10,688), although the effect size was small (η2 < 0.01, p < 0.001).
In addition, comparisons across latent classes demonstrated significant differences in gambling-related variables and psychological correlates (Table 4). Participants in the disordered trading class exhibited significantly higher gambling severity (PGSI mean = 9.2, SD = 6.3) compared to those in the at-risk (mean = 1.9, SD = 3.4) and non-disordered trading classes (mean = 0.9, SD = 2.8; p < 0.001), with a large effect size (η2 = 0.35). Moreover, 58.5% of individuals in this class met the threshold for problem gambling (PGSI ≥5), compared to 9.9% in the at-risk and 3.4% in the non-disordered trading classes (Cramér's V = 0.54, p < 0.001). Impulsivity scores (total and per dimension) were found to be significantly higher in the disordered trading class compared to the other groups (p < 0.001, η2 = 0.03–0.09), although with small effect sizes. Similarly, a significantly higher prevalence of tobacco, cannabis, cocaine, and hallucinogen use was found in this group (p < 0.01), with moderate effect sizes (Cramér's V = 0.17–0.21).
No significant demographic differences were found among the classes, except for educational level, where a higher proportion of individuals in the disordered trading class had not completed basic educational studies (Cramér's V = 0.18, p = 0.044) (Table 3). The observed pattern of differences during cross-class comparisons were maintained when participants were classified based on the proposed TDS cut-off score (Tables 3 and 4).
Comparisons of demographic variables across latent classes of disordered trading symptoms and TDS categories (<5 vs. ≥5 symptoms)
| Variables | Latent classes | Statistic (p) | ES | TDS categories | Statistic (p) | ES | |||
| Non-disordered trading (n = 291) | At-risk trading (n = 71) | Disordered trading (n = 41) | Non-disordered trading (score<5) (n = 357) | Disordered trading (score ≥5) (n = 46) | |||||
| Mean (SD) age, in years | 38.1 (11.7)a | 39.5 (12.9)a | 38.7 (13.1)a | 0.467 (0.792) | <0.01 | 38.6 (11.9) | 37.6 (13.3) | −0.583 (0.560) | |
| % (n) Male sex | 65.6 (100)a | 60.6 (28)a | 70.7 (12)a | 1.250 (0.535) | 0.06 | 65.3 (233) | 65.2 (30) | <0.001 (0.999) | <0.01 |
| % (n) Marital status | |||||||||
| Single | 54.6 (159)a | 36.6 (26)b | 46.3 (19)ab | 11.108 (0.196) | 0.17 | 51.5 (184)a | 53.5 (20)a | 2.355 (0.671) | 0.08 |
| Married | 36.1 (105)a | 54.9 (39)b | 43.9 (18)ab | 39.8 (142)a | 43.5 (20)a | ||||
| Divorced | 4.5 (13)a | 2.8 (2)a | 4.9 (2)a | 3.9 (14)a | 6.5 (3)a | ||||
| Widowed | 1 (3)a | 0 (0)a | 0 (0)a | 0.8 (3)a | 0 (0)a | ||||
| Other | 3.8 (11)a | 5.6 (4)a | 4.9 (2)a | 3.9 (14)a | 6.5 (3)a | ||||
| % (n) Education level | |||||||||
| None | 0 (0)a | 0 (0)ab | 2.4 (1)b | 12.935 (0.044)* | 0.18 | 0 (0)a | 2.2 (1)b | 10.729 (0.013)* | 0.16 |
| Primary | 2.4 (7)a | 2.8 (2)a | 7.3 (3)a | 2.5 (9)a | 6.5 (3)a | ||||
| High School | 36.4 (106)a | 32.4 (23)a | 39 (16)a | 35.6 (127)a | 39.1 (18)a | ||||
| University studies | 61.2 (178)a | 64.8 (46)a | 51.2 (21)a | 61.9 (221)a | 52.2 (24)a | ||||
| % (n) Employment status | |||||||||
| Student | 9.6 (28)a | 7 (5)a | 12.2 (5)a | 5.972 (0.426) | 0.12 | 9 (32)a | 13 (6)a | 1.071 (0.784) | 0.05 |
| Employed | 78.4 (228)a | 70.4 (50)a | 73.2 (30)a | 77 (275)a | 71.7 (33)a | ||||
| Unemployed/domestic work | 9.3 (27)a | 16.9 (12)a | 12.2 (5)a | 10.9 (39)a | 10.9 (5)a | ||||
| Retired | 2.7 (8)a | 5.6 (4)a | 2.4 (1)a | 3.1 (11)a | 4.3 (2)a | ||||
| % (n) Income | |||||||||
| <1000€ | 13.4 (39)a | 8.5 (6)a | 24.4 (10)a | 6.353 (0.385) | 0.13 | 12.6 (45)a | 21.7 (10)a | 3.087 (0.378) | 0.09 |
| 1000–1999€ | 43.3 (126)a | 43.7 (31)a | 41.5 (17)a | 43.4 (155)a | 41.3 (19)a | ||||
| ≥2000€ | 23.4 (68)a | 28.2 (20)a | 19.5 (8)a | 24.1 (86)a | 21.7 (10)a | ||||
| Don't know/Prefer not to disclose | 19.9 (58)a | 19.7 (14)a | 14.6 (6)a | 19.9 (71)a | 15.2 (7)a | ||||
Note: TDS categories are based on the number of disordered trading symptoms met. Endorsing ≥5 symptoms was considered as disordered trading (Guglielmo et al., 2016). Effect sizes (ES) are reported for all tests: η2 for the Kruskal-Wallis H test (latent class comparisons) and Rosenthal's r for the Mann-Whitney U test (TDS categories comparisons) in continuous variables, and φ/Cramér's V for χ2 tests in categorical variables. In post-hoc comparisons, significant differences are indicated with different superscript letters. *p < 0.05, **p < 0.01.
Comparisons of trading-related, gambling-related and psychological correlates across latent classes of disordered trading symptoms and TDS categories (<5 vs. ≥5 symptoms)
| Variables | Latent classes | Statistic (p) | ES | TDS categories | Statistic (p) | ES | |||
| Non-disordered trading (n = 291) | At-risk trading (n = 71) | Disordered trading (n = 41) | Non-disordered trading (score<5) (n = 357) | Disordered trading (score ≥5) (n = 46) | |||||
| Trading involvement | |||||||||
| Disordered trading | |||||||||
| % (n) Met ≥5 criteria | 0 (0)a | 8.5 (6)b | 97.6 (40)c | 339.028 (<0.001)** | 0.92 | – | – | – | – |
| Mean (SD) Total severity score | 0.1 (0.4)a | 2.7 (1.1)b | 7.7 (2.1)c | 325.242 (<0.001)** | 0.88 | 0.6 (1) | 7.5 (2.1) | −12.746 (<0.001)** | 0.64 |
| Mean (SD) No. of financial assets traded | 1.4 (0.7)a | 1.9 (1.3)b | 2.7 (1.8)c | 43.022 (<0.001)** | 0.15 | 1.5 (0.9) | 2.7 (1.7) | −5.838 (<0.001)** | 0.29 |
| % (n) Past-year frequency of trading† | |||||||||
| Less than once a month | 61.9 (180)a | 33.8 (24)b | 24.4 (10)b | 53.052 (<0.001)** | 0.36 | 57.1 (204)a | 21.7 (10)b | 37.850 (<0.001) | 0.31 |
| Monthly | 27.8 (81)a | 47.9 (34)b | 31.7 (13)ab | 31.4 (112)a | 34.8 (16)a | ||||
| Weekly | 8.9 (26)a | 12.7 (9)a | 31.7 (13)b | 9.2 (33)a | 32.6 (15)b | ||||
| Daily | 1.4 (4)a | 5.6 (4)ab | 12.2 (5)b | 2.2 (8)a | 10.9 (5)b | ||||
| Mean (SD) Mean trade size, in euros† | 2,663.2 (6,749.9)a | 4,537.7 (10,688)b | 3,854.5 (10,929.3)a | 14.947 (<0.001)** | <0.01 | 3,129.2 (8,084.9) | 3,002.3 (8,085.6) | −0.162 (0.872) | 0.01 |
| % (n) Shortest preferred time frame for trading† | |||||||||
| Seconds | 31 (66)a | 12.5 (8)b | 17.5 (7)ab | 35.634 (<0.001)** | 0.34 | 26.7 (73)a | 18.2 (8)a | 19.811 (<0.001)** | 0.25 |
| Hours | 15.5 (33)a | 21.9 (14)a | 47.5 (19)b | 17.2 (47)a | 43.2 (19)b | ||||
| Days | 14.1 (30)a | 28.1 (18)b | 20 (8)ab | 16.8 (46)a | 22.7 (10)a | ||||
| Weeks-months | 39.4 (84)a | 37.5 (24)a | 15 (6)b | 39.2 (107)a | 15.9 (7)b | ||||
| % (n) Frequency of daily market monitoring | |||||||||
| Never or not daily | 66 (192)a | 29.6 (21)b | 12.2 (5)b | 98.244 (<0.001)** | 0.50 | 59.7 (213)a | 10.9 (5)b | 72.610 (<0.001)** | 0.42 |
| 1–3 times per day | 30.9 (90)a | 57.7 (41)b | 48.8 (20)ab | 35.6 (127)a | 52.2 (24)b | ||||
| Every hour | 2.4 (7)a | 9.9 (7)b | 29.3 (12)c | 3.9 (14)a | 26.1 (12)b | ||||
| Every few minutes | 0.7 (2)a | 2.8 (2)ab | 9.8 (4)b | 0.8 (3)a | 10.9 (5)b | ||||
| Mean (SD) Hours per day studying trading markets | 1.3 (2.5)a | 3 (4.6)b | 3.3 (3.1)c | 60.032 (<0.001)** | 0.07 | 1.6 (3.1) | 3.3 (3.3) | −5.435 (<0.001)** | 0.27 |
| Gambling behaviors and psychological correlates | |||||||||
| % (n) Past-year gambling participation | 71.8 (209)a | 80.3 (57)a | 80.5 (33)a | 3.078 (0.215) | 0.09 | 73.1 (261) | 82.6 (38) | 1.456 (0.227) | 0.07 |
| Mean (SD) Problem gambling (PGSI) severity | 0.9 (2.8)a | 1.9 (3.4)b | 9.2 (6.3)c | 72.252 (<0.001)** | 0.35 | 1.1 (2.9) | 8.4 (6.5) | −7.512 (<0.001)** | 0.37 |
| % (n) Problem gambling (PGSI ≥5) | 3.4 (10)a | 9.9 (7)a | 58.5 (17)b | 119.398 (<0.001)** | 0.54 | 4.5 (16) | 54.3 (25) | 110.879 (<0.001)** | 0.53 |
| Mean (SD) Impulsivity (UPPS-P) | |||||||||
| Total score | 41.3 (8.3)a | 43.8 (7.7)b | 49.3 (6.9)c | 35.183 (<0.001)** | 0.09 | 41.7 (8.16) | 49 (7.8) | −5.529 (<0.001)** | 0.28 |
| Negative urgency | 9.3 (2.9)a | 9.8 (2.6)a | 11.2 (2.1)b | 17.347 (<0.001)** | 0.04 | 9.4 (2.80) | 11.1 (2.2) | −3.801 (<0.001)** | 0.19 |
| Lack of premeditation | 6.7 (2.1)a | 7 (2.3)a | 8.3 (2.2)b | 20.497 (<0.001)** | 0.05 | 6.7 (2.08) | 8.3 (2.5) | −4.269 (<0.001)** | 0.21 |
| Lack of perseverance | 7 (2.2)a | 7.4 (2.3)a | 8.5 (2)b | 16.631 (<0.001)** | 0.04 | 7 (2.2) | 8.5 (2.1) | −4.306 (<0.001)** | 0.21 |
| Sensation seeking | 9.3 (2.7)a | 9.9 (2.8)ab | 10.8 (2)b | 12.135 (0.002)** | 0.03 | 9.5 (2.7) | 10.5 (2.5) | −2.679 (<0.001)** | 0.13 |
| Positive urgency | 8.9 (2.5)a | 9.7 (2.3)b | 10.5 (2.3)b | 18.058 (<0.001)** | 0.04 | 9.1 (2.5) | 10.5 (2.5) | −3.551 (<0.001)** | 0.18 |
| % (n) Substance use | |||||||||
| Alcohol | 87.6 (255)a | 90.1 (64)a | 80.5 (33)a | 2.267 (0.322)a | 0.08 | 88.2 (315) | 80.4 (37) | 1.593 (0.207) | 0.08 |
| Tobacco | 23 (67)a | 29.6 (21)a | 53.7 (22)b | 17.221 (<0.001)** | 0.21 | 24.1 (86) | 52.2 (24) | 14.811 (<0.001)** | 0.20 |
| Cannabis | 10.3 (30)a | 14.1 (10)ab | 29.3 (12)b | 11.601 (0.003)** | 0.17 | 10.9 (39) | 28.3 (13) | 9.410 (0.002)** | 0.16 |
| Cocaine | 2.1 (6)a | 9.9 (7)b | 12.2 (5)b | 14.522 (<0.001)** | 0.19 | 3.1 (11) | 15.2 (7) | 11.365 (<0.001)** | 0.18 |
| Hallucinogens | 2.1 (6)a | 4.2 (3)ab | 14.6 (6)b | 15.912 (<0.001)** | 0.20 | 2.2 (8) | 15.2 (7) | 15.698 (<0.001)** | 0.22 |
Note: TDS categories are based on the number of disordered trading symptoms met. Endorsing ≥5 symptoms was considered as disordered trading (Guglielmo et al., 2016). Effect sizes (ES) are reported for all tests: η2 for the Kruskal-Wallis H test (latent class comparisons) and Rosenthal's r for the Mann-Whitney U test (TDS categories comparisons) in continuous variables, and φ/Cramér's V for χ2 tests in categorical variables. In post-hoc comparisons, significant differences are indicated with different superscript letters. Large effect sizes are shown in bold.
†Trading frequency is based on the financial asset in which participants engaged most frequently. Mean trade size refers to the average amount of money invested per trade across all assets. The preferred time frame reflects the shortest duration participants held positions in financial assets.
*p < 0.05, **p < 0.01.
Sensitivity analysis
In order to ensure the robustness of the findings, sensitivity analyses were conducted by winsorizing outliers in continuous variables. These analyses yielded results that were consistent with the original dataset, with no substantial changes in statistical significance, observed differences, or the strength of correlations between TDS scores, PGSI scores, and trading-related variables (see Supplementary Tables S5 and S6 for the results using winsorized data).
Discussion
Problematic trading behaviors have mostly been assessed using tools designed for gambling disorder. For this reason, the present study developed and validated a scale for disordered trading behaviors based on the proposed criteria by Guglielmo et al. (2016). The exploratory and confirmatory factor analysis supported a unidimensional structure for the scale, consistent with the DSM-5's conceptualization of a continuum of disorder severity in addictive behaviors (Király et al., 2017; Petry et al., 2014; Sleczka, Braun, Piontek, Bühringer, & Kraus, 2015). The scale also demonstrated very good to excellent internal consistency (ωu-cat = 0.938, and KR-20 = 0.877), with most items showing high or very high discrimination indices.
The results obtained using the IRT framework showed that items related to using trading to cope with negative emotions, and those reflecting intra-psychic (i.e., suicidal ideation due to trading), interpersonal (i.e., lying about trading), and conflicts with others (i.e., jeopardizing or losing significant matters), had the highest difficulty values. This indicates that endorsing these items requires a higher level of trading disorder severity, which was further corroborated by the results from the LCA.
The LCA identified three distinct classes of trading behaviors: non-disordered trading, at-risk trading, and disordered trading. The majority of participants were classified as engaging in non-disordered trading (72.2%), displaying minimal or no endorsement of disordered trading symptoms. Participants in the at-risk trading (17.6%) and disordered trading (10.2%) groups exhibited shared symptoms, including preoccupation with trading, withdrawal, and chasing losses behaviors. However, individuals in the disordered trading class were more likely to endorse these and other symptoms, showing more severe trading patterns.
Notably, items such as trading as an emotional coping mechanism, loss of control, disrupted sleep patterns, conflicts (e.g., lying, risking significant matters, relying on others financially), and suicidal ideation were almost exclusively endorsed by participants in the disordered trading class. These findings are consistent with existing research in gambling and internet-related disorders, such as internet gaming disorder, where more severe cases are characterized by loss of control, negative consequences, and interpersonal conflicts (James, O’Malley, & Tunney, 2016; Király et al., 2017; Mide, Arvidson, & Gordh, 2023; Sleczka et al., 2015). On the other hand, individuals classed as at-risk trading aligned with the profile of “preoccupied chasers” frequently observed in studies examining patterns of symptoms among gamblers and internet gamers (Banerjee, Chen, Clark, & Noël, 2023; Chamberlain, Stochl, Redden, Odlaug, & Grant, 2017; McBride, Adamson, & Shevlin, 2010). “Preoccupied chasers” experience significant involvement in their activity, particularly with preoccupation and loss-chasing behaviors, yet have not progressed to the severe consequences typically associated with pathological levels of the behavior. As such, the at-risk trading group represents an intermediate severity level, exhibiting some problematic trading behaviors without the extensive negative outcomes observed in disordered trading.
One notable distinction between the at-risk trading and disordered trading classes was the lower probability of endorsing withdrawal symptoms among individuals classed as at-risk trading. This is consistent with previous research, where gamblers or gamers who experienced withdrawal symptoms less frequently or with milder intensity, were less likely to be classified as disordered, particularly when is not accompanied by loss of control and negative consequences (Pontes, Schivinski, Brzozowska-Woś, & Stavropoulos, 2019). The distinction between individuals with at-risk trading and disordered trading was further supported by the application of the disordered trading classification based on Guglielmo et al.’s (2016) proposed cut-off. Nearly all individuals classified in the disordered group (97.6%) met the cut-off of five or more criteria, compared to only 8.5% of those belonging to the at-risk trading group and none of the non-disordered trading class. These findings, together with the consistency of results in both latent class and cut-off-based comparisons, appear to demonstrate the effectiveness of this threshold in identifying severe cases of disordered trading.
In the cross-class comparisons, the disordered trading class exhibited higher overall severity scores, traded more frequently, and spent more time monitoring trading markets than both at-risk trading and non-disordered trading classes. Interestingly, despite their higher trading frequency and use of shorter holding periods (i.e., the length of time that a trader had ownership of a financial asset, with shorter periods indicating short-term investment strategies), individuals in this class reported lower mean trade size values (i.e., the average amount of money invested per trade) than participants in the at-risk trading class. This suggests a compulsive engagement in trading (Mosenhauer et al., 2021), which parallels patterns observed among frequent gamblers, who tend to place smaller bets compared to less frequent gamblers who make larger individual bets (Gainsbury, Sadeque, Mizerski, & Blaszczynski, 2012; Petry, 2016).
In addition, individuals in the disordered trading class exhibited higher levels of impulsivity and substance use compared to the other groups, factors that have been consistently associated with gambling problems and trading-related behaviors (Leslie, Shaw, & McGrath, 2024; Mallorquí-Bagué et al., 2019; Moreira, Azeredo, & Dias, 2023; Oksanen, Mantere, et al., 2022; Sonkurt & Altınöz, 2021). In fact, more than half of the participants in this class met the PGSI cut-off for problem gambling (58.5%). All these findings remained consistent when comparisons were performed using the ≥5 cut-off for the TDS, highlighting the clinical relevance of this group and supporting the classification of disordered trading as a construct with meaningful psychological and behavioral correlates.
The TDS's validity was reinforced by its strong correlations with related constructs. TDS score showed a strong positive correlation with PGSI score, which assesses problematic gambling behaviors, demonstrating good concurrent validity. Moreover, significant correlations were found with trading-engagement variables, such as past-year frequency of trading, the number of assets traded, and daily market monitoring (Delfabbro, King, & Williams, 2021), supporting the scale's convergent validity. Importantly, when disordered trading was treated as a continuous measure (i.e., overall TDS scores), no significant correlations were found with financial metrics such as mean trade size values or preferred holding periods. In contrast, when traders were grouped based on specific combinations of symptoms though latent class analysis, an association was detected between disordered trading and the adoption of riskier strategies, such as shorter holding periods coupled with lower amounts of money invested. This suggests that the relationship between disordered trading and financial metrics becomes clearer when considering distinct behavioral profiles rather than assuming a linear progression with increasing severity. The effect sizes for these financial variables were smaller compared to other trading-related variables, particularly for the mean trade size variable, which further indicates that the scale primarily captures the psychological and behavioral dimensions of trading rather than financial outcomes.
These findings are particularly important given that a recent criticism of this type of work is the potential for pathologizing typical aspects of trading behavior (Billieux, Schimmenti, Khazaal, Maurage, & Heeren, 2015). It can be posited that specific trading behaviors may be characterized by intense involvement, which could potentially explain why the preoccupation item exhibited slightly lower (albeit still satisfactory) discriminative power. However, the findings suggest that disordered trading, as assessed using the TDS, is not exclusively characterized by engagement metrics, but also by clinically relevant harm-related indicators, including financial, relational, emotional, and health-related consequences. Moreover, the cross-class comparisons showed that individuals in the disordered trading class displayed compulsive trading behaviors and significant negative outcomes that extended beyond mere engagement in the activity. This supports the notion that higher scores on the TDS do not merely reflect a strong commitment to trading but are indicative of a maladaptive and harmful pattern.
As has occurred with the expansion of gambling products into the online environment, new technologies have significantly altered how individuals engage in trading activities. Many trading apps and platforms incorporate design elements commonly used by the gambling industry, aiming to increase user engagement (Davies & Ferris, 2022; Newall & Weiss-Cohen, 2022). These gamblified features may encourage the practice of trading in a manner that is analogous to gambling (Lee, 2022; Macey, Hamari, & Adam, 2024; Newall & Weiss-Cohen, 2022) and contribute to the emergence of harmful patterns of engagement (Andrade & Newall, 2023; Håkansson, Fernández-Aranda, & Jiménez-Murcia, 2021; Newall & Weiss-Cohen, 2022). This is further reflected in the increasing number of gambling helpline consultations related to cryptocurrency trading issues (Marionneau, Kristiansen, & Wall, 2024), highlighting the growing intersection between trading and gambling-related harms.
However, in order to determine whether disordered trading should be considered a form of gambling, or a distinct phenomenon, it is essential to rely on assessment tools specifically designed to capture the unique characteristics of trading-related impairment. The development of reliable and valid measures is also crucial for advancing research in this area and improving clinical identification and intervention strategies for problematic trading behaviors. To date, existing psychometric instruments have been limited in their applicability across different financial assets and investor profiles. The present study is the first to validate a scale for disordered trading among a sample of amateur investors and to evaluate the psychometric properties of a diagnostic threshold for identifying such behaviors. Despite these strengths, several limitations should be noted. First, the sample was recruited from an online panel, which may affect the generalizability of the findings. Although quotas were used to match the demographic characteristics of the sample to the Spanish population, panel members might have differed from the general population in terms of internet use, education, and socioeconomic status (Hays, Liu, & Kapteyn, 2015). While online panels are generally considered reliable for examining similar behaviors (Lee et al., 2023; Wardle & Tipping, 2023), caution is warranted when extrapolating these results to the larger populations of retail investors.
Moreover, the cross-sectional design of the study did not allow the testing of predictive validity or temporal stability of the scale. The present study did not employ test-retest reliability, and no longitudinal analyses were conducted, which limited the possibility of assessing the ability of the TDS to identify individuals at risk of developing severe trading problems, or to monitor changes during the treatment of disordered trading. While a sample size five to ten times the number of items is typically deemed sufficient for factorial analysis, particularly when the questionnaire structure is unidimensional (Wolf, Harrington, Clark, & Miller, 2013; Wu, 2010), further studies with larger samples are recommended to confirm the factorial structure identified in this study. Finally, the accuracy of the diagnostic cut-off point used to define disordered trading was primarily tested through LCA, given the absence of an established gold standard.
While the results underscore the ability of the TDS cut-off in effectively distinguishing disordered trading, which was the primary focus of the study, the specific characteristics observed in the at-risk class identified in the LCA, provide a foundation for future studies to consider incorporating intermediate severity levels, similar to those used in gambling research (e.g., PGSI categories). Such an approach could assist in more accurately identifying individuals at moderate risk who may not yet meet the threshold for disordered trading but exhibit patterns of problematic behavior. Moreover, given that all measures comprised self-report data, which may have been influenced by social-desirability and recall biases (Heirene, Wang, & Gainsbury, 2022), additional research is needed to validate this threshold using external benchmarks or expert clinical assessments to confirm its reliability.
Funding sources
This publication is the result of the research project entitled Intención de cambio y búsqueda de tratamiento en jugadores e inversores patológicos: un estudio experimental en adultos jóvenes (Intention to change and treatment-seeking in disordered gamblers and investors: an experimental study in young adults), with reference number SUBV23/00004, funded by the Dirección General de Ordenación del Juego (Ministerio de Derechos Sociales, Consumo y Agenda 2030)
Authors' contribution
Study concept and design: ACC, JLC. Obtained funding: ACC, JLC, FML, JCA. Study supervision: ACC. Statistical analysis: ACC. Analysis and interpretation of the data: ACC. First draft: ACC, JLC, MDG. Subsequent drafts: All authors. All authors have read and agreed to the final version of the manuscript.
Conflicts of interest
The authors declare no conflict of interest except MDG. MDG has received research funding from Norsk Tipping (the gambling operator owned by the Norwegian government). MDG has received funding for a number of research projects in the area of gambling education for young people, social responsibility in gambling and gambling treatment from Gamble Aware (formerly the Responsibility in Gambling Trust), a charitable body which funds its research program based on donations from the gambling industry. MDG undertakes consultancy for various gambling companies in the areas of player protection, harm minimization, and social responsibility in gambling.
Acknowledgments
The data used in this study were collected within the framework of the research project Gamblification in Spain: the convergence of gambling, trading, and video games (Ref. No. LCF/PR/FS21/60000002), led by the corresponding author. We sincerely thank the Social Observatory of the “La Caixa” Foundation for providing access to these data.
Supplementary material
Supplementary data to this article can be found online at https://doi.org/10.1556/2006.2025.00019.
References
American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Publishing.
Andrade, M., & Newall, P. W. S. (2023). Cryptocurrencies as gamblified financial assets and cryptocasinos: Novel risks for a public health approach to gambling. Risks, 11(3), 3. https://doi.org/10.3390/risks11030049.
Arthur, J. N., & Delfabbro, P. (2017). Day traders in South Australia: Similarities and differences with traditional gamblers. Journal of Gambling Studies, 33(3), 855–866. https://doi.org/10.1007/s10899-016-9659-x.
Arthur, J. N., Williams, R. J., & Delfabbro, P. H. (2016). The conceptual and empirical relationship between gambling, investing, and speculation. Journal of Behavioral Addictions, 5(4), 580–591. https://doi.org/10.1556/2006.5.2016.084.
Banerjee, N., Chen, Z., Clark, L., & Noël, X. (2023). Behavioural expressions of loss-chasing in gambling: A systematic scoping review. Neuroscience and Biobehavioral Reviews, 153, 105377. https://doi.org/10.1016/j.neubiorev.2023.105377.
Billieux, J., Rochat, L., Ceschi, G., Carré, A., Offerlin-Meyer, I., Defeldre, A.-C., … Van der Linden, M. (2012). Validation of a short French version of the UPPS-P Impulsive Behavior Scale. Comprehensive Psychiatry, 53(5), 609–615. https://doi.org/10.1016/j.comppsych.2011.09.001.
Billieux, J., Schimmenti, A., Khazaal, Y., Maurage, P., & Heeren, A. (2015). Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research. Journal of Behavioral Addictions, 4(3), 119–123. https://doi.org/10.1556/2006.4.2015.009.
Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). The Guilford Press.
Campino, J., & Yang, S. (2024). Decoding the cryptocurrency user: An analysis of demographics and sentiments. Heliyon, 10(5), e26671. https://doi.org/10.1016/j.heliyon.2024.e26671.
Cándido, A., Orduña, E., Perales, J. C., Verdejo-García, A., & Billieux, J. (2012). Validation of a short Spanish version of the UPPS-P Impulsive Behaviour Scale. Trastornos Adictivos, 14(3), 73–78. https://doi.org/10.1016/S1575-0973(12)70048-X.
Chamberlain, S. R., Stochl, J., Redden, S. A., Odlaug, B. L., & Grant, J. E. (2017). Latent class analysis of gambling subtypes and impulsive/compulsive associations: Time to rethink diagnostic boundaries for gambling disorder? Addictive Behaviors, 72, 79–85. https://doi.org/10.1016/j.addbeh.2017.03.020.
Chong, L.-L., Ong, H.-B., & Tan, S.-H. (2021). Acceptability of mobile stock trading application: A study of young investors in Malaysia. Technology in Society, 64, 101497. https://doi.org/10.1016/j.techsoc.2020.101497.
Cohen, J. (1988). Statistical power analysis for the behavioural science (2nd ed.). Lawrence Erlbaum Associates.
Coloma-Carmona, A., Carballo, J. L., Sancho-Domingo, C., Rodríguez-Espinosa, S., Miró-Llinares, F., & C. Aguerri, J. (2024). Engagement in new gambling practices and its association with gambling disorder, impulsivity, and cognitive distortions: Findings from a nationally representative sample of Spanish gamblers. Journal of Gambling Studies. Advance online publication. https://doi.org/10.1007/s10899-024-10361-z.
Cox, R., Kamolsareeratana, A., & Kouwenberg, R. (2020). Compulsive gambling in the financial markets: Evidence from two investor surveys. Journal of Banking & Finance, 111, 105709. https://doi.org/10.1016/j.jbankfin.2019.105709.
Davies, N., & Ferris, S. (2022). Cryptocurrency and new financial instruments: Unquantified public health harms. The Lancet Public Health, 7(8), e655. https://doi.org/10.1016/S2468-2667(22)00173-6.
Delfabbro, P., King, D. L., & Williams, J. (2021). The psychology of cryptocurrency trading: Risk and protective factors. Journal of Behavioral Addictions, 10(2), 201–207. https://doi.org/10.1556/2006.2021.00037.
Delfabbro, P., King, D., Williams, J., & Georgiou, N. (2021). Cryptocurrency trading, gambling and problem gambling. Addictive Behaviors, 122, 107021. https://doi.org/10.1016/j.addbeh.2021.107021.
Ebel, R. L., & Frisbie, D. A. (1991). Essentials of educational measurement. Prentice-Hall.
Edelen, M. O., & Reeve, B. B. (2007). Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement. Quality of Life Research, 16(1), 5. https://doi.org/10.1007/s11136-007-9198-0.
Ferris, J., & Wynne, H. (2001). The Canadian Problem Gambling Index: Final report. Canadian Centre on Substance Abuse.
Fonseca-Pedrero, E., Paíno-Piñeiro, M., Lemos-Giráldez, S., Villazón-García, U., & Muñiz, J. (2009). Validation of the schizotypal personality questionnaire-brief form in adolescents. Schizophrenia Research, 111(1–3), 53–60. https://doi.org/10.1016/j.schres.2009.03.006.
Gainsbury, S., Sadeque, S., Mizerski, D., & Blaszczynski, A. (2012). Wagering in Australia: A retrospective behavioural analysis of betting patterns based on player account data. Journal of Gambling Business and Economics, 6(2), 50–68.
Grall-Bronnec, M., Sauvaget, A., Boutin, C., Bulteau, S., Jiménez-Murcia, S., Fernández-Aranda, F., … Caillon, J. (2017). Excessive trading, a gambling disorder in its own right? A case study on a French disordered gamblers cohort. Addictive Behaviors, 64, 340–348. https://doi.org/10.1016/j.addbeh.2015.12.006.
Granero, R., Tárrega, S., Fernández-Aranda, F., Aymamí, N., Gómez-Peña, M., Moragas, L., … Jiménez-Murcia, S. (2012). Gambling on the stock market: An unexplored issue. Comprehensive Psychiatry, 53(6), 666–673. https://doi.org/10.1016/j.comppsych.2011.12.004.
Griffiths, M. (2005). A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10(4), 191–197. https://doi.org/10.1080/14659890500114359.
Griffiths, M. D. (2018). Hot topics in gambling: Gambling blocking apps, loot boxes, and ‘crypto-trading addiction’. Online Gambling Lawyer, 17(7), 9–11.
Guadagnoli, E., & Velicer, W. F. (1988). Relation of sample size to the stability of component patterns. Psychological Bulletin, 103(2), 265–275. https://doi.org/10.1037/0033-2909.103.2.265.
Guglielmo, R., Ioime, L., & Janiri, L. (2016). Is pathological trading an overlooked form of addiction? Addiction & Health, 8(3), 207–209.
Guzmán, A., Pinto-Gutiérrez, C., & Trujillo, M.-A. (2021). Trading cryptocurrencies as a pandemic pastime: COVID-19 lockdowns and bitcoin volume. Mathematics, 9(15), 15. https://doi.org/10.3390/math9151771.
Håkansson, A., Fernández-Aranda, F., & Jiménez-Murcia, S. (2021). Gambling-Like day trading during the COVID-19 pandemic—need for research on a pandemic-related risk of indebtedness and mental health impact. Frontiers in Psychiatry, 12, 715946. https://doi.org/10.3389/fpsyt.2021.715946.
Hays, R. D., Liu, H., & Kapteyn, A. (2015). Use of internet panels to conduct surveys. Behavior Research Methods, 47(3), 685–690. https://doi.org/10.3758/s13428-015-0617-9.
Heirene, R. M., Wang, A., & Gainsbury, S. M. (2022). Accuracy of self-reported gambling frequency and outcomes: Comparisons with account data. Psychology of Addictive Behaviors, 36(4), 333–346. https://doi.org/10.1037/adb0000792.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118.
Huang, Z., Liu, Z., Chen, J., He, Q., Wu, S., Zhu, L., & Wang, M. (2022). Who is gambling? Finding cryptocurrency gamblers using multi-modal retrieval methods. International Journal of Multimedia Information Retrieval, 11(4), 539–551. https://doi.org/10.1007/s13735-022-00264-3.
James, R. J. E., O’Malley, C., & Tunney, R. J. (2016). Loss of control as a discriminating factor between different latent classes of disordered gambling severity. Journal of Gambling Studies, 32(4), 1155–1173. https://doi.org/10.1007/s10899-016-9592-z.
Johnson, B., Co, S., Sun, T., Lim, C. C. W., Stjepanović, D., Leung, J., … Chan, G. C. K. (2023). Cryptocurrency trading and its associations with gambling and mental health: A scoping review. Addictive Behaviors, 136, 107504. https://doi.org/10.1016/j.addbeh.2022.107504.
Johnson, B., Sun, T., Stjepanović, D., Vu, G., & Chan, G. C. K. (2023). “Buy high, sell low”: A qualitative study of cryptocurrency traders who experience harm. International Journal of Environmental Research and Public Health, 20(10), 5833. https://doi.org/10.3390/ijerph20105833.
Kamolsareeratana, A., & Kouwenberg, R. (2023). Compulsive gambling in the stock market: Evidence from an emerging market. Economies, 11(1), 28. https://doi.org/10.3390/economies11010028.
Khan, M. T. I. (2022). Prior perceived losses and investment objectives after stock market crisis: A moderated-mediation model of risk tolerance and loss aversion. SN Business & Economics, 2(7), 83. https://doi.org/10.1007/s43546-022-00259-6.
Kim, S.-H., & Baker, F. B. (2018). birtr: A package for “The basics of item response theory using R”. Applied Psychological Measurement, 42(5), 403–404. https://doi.org/10.1177/0146621617748327.
Király, O., Sleczka, P., Pontes, H. M., Urbán, R., Griffiths, M. D., & Demetrovics, Z. (2017). Validation of the Ten-Item Internet Gaming Disorder Test (IGDT-10) and evaluation of the nine DSM-5 internet gaming disorder criteria. Addictive Behaviors, 64, 253–260. https://doi.org/10.1016/j.addbeh.2015.11.005.
Koh, K., & Han, H. (2023). Stock market risk and suicide. Pacific-Basin Finance Journal, 78, 101979. https://doi.org/10.1016/j.pacfin.2023.101979.
Lanza, S. T., Flaherty, B. P., & Collins, L. M. (2003). Latent class and latent transition analysis. In J. A. Schinka, & W. F. Velicer (Eds.), Handbook of psychology: Research methods in psychology (Vol. 2, pp. 663–685). John Wiley & Sons, Inc. https://doi.org/10.1002/0471264385.wei0226.
Lee, S. C. (2022). Magical capitalism, gambler subjects: South Korea’s bitcoin investment frenzy. Cultural Studies, 36(1), 96–119. https://doi.org/10.1080/09502386.2020.1788620.
Lee, U., Lewis, L. E., & Mills, D. J. (2023). Association between gambling and financial trading: A systemic review. F1000Research, 12, 111. https://doi.org/10.12688/f1000research.129754.1.
Lesieur, H. R., & Blume, S. B. (1987). The South Oaks gambling screen (SOGS): A new instrument for the identification of pathological gamblers. The American Journal of Psychiatry, 144(9), 1184–1188. https://doi.org/10.1176/ajp.144.9.1184.
Leslie, R. D., Shaw, C. A., & McGrath, D. S. (2024). Correlates of gambling behaviours among day Traders: Evidence from a national study. Journal of Gambling Studies. Advance online publication. https://doi.org/10.1007/s10899-024-10314-6.
Lopez-González, H., Estévez, A., & Griffiths, M. D. (2018). Spanish validation of the Problem Gambling Severity Index: A confirmatory factor analysis with sports bettors. Journal of Behavioral Addictions, 7(3), 814–820. https://doi.org/10.1556/2006.7.2018.84.
Macey, J., Hamari, J., & Adam, M. (2024). A conceptual framework for understanding and identifying gamblified experiences. Computers in Human Behavior, 152, 108087. https://doi.org/10.1016/j.chb.2023.108087.
Mallorquí-Bagué, N., Vintró-Alcaraz, C., Verdejo-García, A., Granero, R., Fernández-Aranda, F., Magaña, P., … Jiménez-Murcia, S. (2019). Impulsivity and cognitive distortions in different clinical phenotypes of gambling disorder: Profiles and longitudinal prediction of treatment outcomes. European Psychiatry, 61, 9–16. https://doi.org/10.1016/j.eurpsy.2019.06.006.
Marionneau, V., Kristiansen, S., & Wall, H. (2024). Harmful types of gambling: Changes and emerging trends in longitudinal helpline data. European Journal of Public Health, ckae024. https://doi.org/10.1093/eurpub/ckae024.
McBride, O., Adamson, G., & Shevlin, M. (2010). A latent class analysis of DSM-IV pathological gambling criteria in a nationally representative British sample. Psychiatry Research, 178(2), 401–407. https://doi.org/10.1016/j.psychres.2009.11.010.
Mentes, N., Yolbas, I., & Bulut, M. (2021). Development and verification of Problematic Cryptocurrency Trading Scale. Psychiatry and Clinical Psychopharmacology, 31(3), 310–318. https://doi.org/10.5152/pcp.2021.21094.
Mide, M., Arvidson, E., & Gordh, A. S. (2023). Clinical differences of mild, moderate, and severe gambling disorder in a sample of treatment seeking pathological gamblers in Sweden. Journal of Gambling Studies, 39(3), 1129–1153. https://doi.org/10.1007/s10899-022-10183-x.
Moreira, D., Azeredo, A., & Dias, P. (2023). Risk factors for gambling disorder: A systematic review. Journal of Gambling Studies, 39(2), 483–511. https://doi.org/10.1007/s10899-023-10195-1.
Mosenhauer, M., Newall, P. W. S., & Walasek, L. (2021). The stock market as a casino: Associations between stock market trading frequency and problem gambling. Journal of Behavioral Addictions, 10(3), 683–689. https://doi.org/10.1556/2006.2021.00058.
Muñiz, J., Elosua, P., & Hambleton, R. K. (2013). Directrices para la traducción y adaptación de los tests: Segunda edición. Psicothema, 25(2), 151–157. https://doi.org/10.7334/psicothema2013.24.
Muñiz, J., & Fonseca-Pedrero, E. (2019). Diez pasos para la construcción de un test. Psicothema, 31(1), 7–16. https://doi.org/10.7334/psicothema2018.291.
Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (8th ed.). Muthén & Muthén.
Newall, P. W. S., & Weiss-Cohen, L. (2022). The gamblification of investing: How a new generation of investors is being born to lose. International Journal of Environmental Research and Public Health, 19(9), 5391. https://doi.org/10.3390/ijerph19095391.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill, Inc.
Oksanen, A., Hagfors, H., Vuorinen, I., & Savolainen, I. (2022). Longitudinal perspective on cryptocurrency trading and increased gambling problems: A 3 wave national survey study. Public Health, 213, 85–90. https://doi.org/10.1016/j.puhe.2022.10.002.
Oksanen, A., Mantere, E., Vuorinen, I., & Savolainen, I. (2022). Gambling and online trading: Emerging risks of real-time stock and cryptocurrency trading platforms. Public Health, 205, 72–78. https://doi.org/10.1016/j.puhe.2022.01.027.
Petry, N. M. (Ed.) (2016). Behavioral addictions: DSM-5 and beyond. Oxford University Press. http://public.eblib.com/EBLPublic/PublicView.do?ptiID=3564669.
Petry, N. M., Blanco, C., Auriacombe, M., Borges, G., Bucholz, K., Crowley, T. J., … O’Brien, C. (2014). An overview of and rationale for changes proposed for pathological gambling in DSM-5. Journal of Gambling Studies, 30(2), 493–502. https://doi.org/10.1007/s10899-013-9370-0.
Pontes, H. M., Schivinski, B., Brzozowska-Woś, M., & Stavropoulos, V. (2019). Laxer clinical criteria for gaming disorder may hinder future efforts to devise an efficient diagnostic approach: A tree-based model study. Journal of Clinical Medicine, 8(10), 1730. https://doi.org/10.3390/jcm8101730.
Prachayanant, P., Kraiwanit, T., & Chutipat, V. (2023). Cryptocurrency gamification: Having fun or making money. Journal of Governance and Regulation, 12(2), 184–193. Scopus. https://doi.org/10.22495/jgrv12i2art17.
Roza, T. H., Tavares, H., Kessler, F. H. P., & Passos, I. C. (2024). Problematic trading: Gambling-like behavior in day trading and cryptocurrency investing. Trends in Psychiatry and Psychotherapy, 46, e20230623. https://doi.org/10.47626/2237-6089-2023-0623.
Senarathne, C. W. (2019). Gambling behaviour in the cryptocurrency market. International Journal of Applied Behavioral Economics, 8(4), 1–16. https://doi.org/10.4018/IJABE.2019100101.
Şentürk, E., Coşar, B., & Arikan, Z. (2023). Relationship of cryptocurrencies with gambling and addiction. Psikiyatride Güncel Yaklaşımlar, 15(2), 348–355. https://doi.org/10.18863/pgy.1127924.
Shin, Y.-C., Choi, S.-W., Ha, J., Choi, J.-S., & Kim, D.-J. (2015). Gambling disorder in financial markets: Clinical and treatment-related features. Journal of Behavioral Addictions, 4(4), 244–249. https://doi.org/10.1556/2006.4.2015.032.
Sinha, P., Calfee, C. S., & Delucchi, K. L. (2021). Practitioner’s guide to latent class analysis: Methodological considerations and common pitfalls. Critical Care Medicine, 49(1), e63–e79. https://doi.org/10.1097/CCM.0000000000004710.
Sleczka, P., Braun, B., Piontek, D., Bühringer, G., & Kraus, L. (2015). DSM-5 criteria for gambling disorder: Underlying structure and applicability to specific groups of gamblers. Journal of Behavioral Addictions, 4(4), 226. https://doi.org/10.1556/2006.4.2015.035.
Sonkurt, H. O., & Altınöz, A. E. (2021). Cryptocurrency investment: A safe venture or a new type of gambling? Journal of Gambling Issues, 199–216. https://doi.org/10.4309/jgi.2021.47.8.
Spanish Observatory on Drugs and Addictions (2023). Report on behavioral addictions and other addictive disorders 2023. Ministerio de Sanidad: Delegación del Gobierno para el Plan Nacional sobre Drogas.
Steinmetz, F. (2023). The interrelations of cryptocurrency and gambling: Results from a representative survey. Computers in Human Behavior, 138, 107437. https://doi.org/10.1016/j.chb.2022.107437.
Sudzina, F., Dobes, M., & Pavlicek, A. (2023). Towards the psychological profile of cryptocurrency early adopters: Overconfidence and self-control as predictors of cryptocurrency use. Current Psychology, 42(11), 8713–8717. https://doi.org/10.1007/s12144-021-02225-1.
Torrance, J., Heath, C., Andrade, M., & Newall, P. (2023). Gambling, cryptocurrency, and financial trading app marketing in English premier league football: A frequency analysis of in-game logos. Journal of Behavioral Addictions, 12(4), 972–982. https://doi.org/10.1556/2006.2023.00066.
Turan, A., Kokaçya, M. H., Yılmaz, E., & Arı, M. (2024). Relationship of cryptocurrency use with attention deficit hyperactivity symptoms, quality of life and sleep quality in university students. Psychiatry and Behavioral Sciences, 14(2), 107–115. https://doi.org/10.5455/PBS.20240111031440.
Vismara, M., Caricasole, V., Varinelli, A., & Fineberg, N. A. (2022). Cyberchondria, cyberhoarding, and other compulsive online disorders. In D. J. Stein, N. A. Fineberg, & S. R. Chamberlain (Eds.), Mental health in a digital world (pp. 261–283). Academic Press. https://doi.org/10.1016/B978-0-12-822201-0.00001-0.
Wardle, H., & Tipping, S. (2023). The relationship between problematic gambling severity and engagement with gambling products: Longitudinal analysis of the Emerging Adults Gambling Survey. Addiction, 118(6), 1127–1139. https://doi.org/10.1111/add.16125.
Weidner, L. (2022). Gambling and financial markets a comparison from a regulatory perspective. Frontiers in Sociology, 7, 1023307. https://doi.org/10.3389/fsoc.2022.1023307.
Weiss-Cohen, L., Newall, P., Bart, Y., Zloteanu, M., Peacey, M., Ayton, P., & Clacher, I. (2024). When Vegas comes to Wall Street: Associations between stock price volatility and trading frequency amongst gamblers. International Journal of Mental Health and Addiction. Advance online publication. https://doi.org/10.1007/s11469-023-01229-1.
Williams, R. J., & Volberg, R. A. (2014). The classification accuracy of four problem gambling assessment instruments in population research. International Gambling Studies, 14(1), 15–28. https://doi.org/10.1080/14459795.2013.839731.
Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educational and Psychological Measurement, 76(6), 913–934. https://doi.org/10.1177/0013164413495237.
Wu, M. (2010). Practice of questionnaire statistical analysis SPSS operation and application. Chongqing University Press.
Yığman, F., Bora Nazlı, Ş., & Yılmaz, O. (2023). A new danger of behavioral addiction: Cryptocurrency with mobile investing. Psychiatria Danubina, 35(3), 386–394. https://doi.org/10.24869/psyd.2023.386.
Youn, H., Choi, J.-S., Kim, D.-J., & Choi, S.-W. (2016). Development and validation of a Stock Addiction Inventory (SAI). Annals of General Psychiatry, 15, 16. https://doi.org/10.1186/s12991-016-0105-3.
Yu, C.-Y. (2002). Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcomes. Berkeley, USA: University of California.
Appendices
Appendix A. Trading Disorder Scale – English version
| In relation to your participation in trading activities, please indicate if, in the PAST 12 MONTHS, you have experienced any of the following situations (yes/no). | Yes | No |
| 1. Have you often found your mind occupied with investment and/or trading activities (e.g., reliving past investment experiences, analyzing, or planning the next investment, reading literature or online forums related to the financial world, making investments/trading the main activity of your daily life)? | ||
| 2. Have you felt the need to invest and/or trade increasingly larger amounts of money to achieve the desired excitement? | ||
| 3. Have you felt the need to spend more and more time making investments/trading and/or looking for new financial instruments to invest in? | ||
| 4. Have you been nervous or irritable when you tried to reduce or stop investing and/or trading? | ||
| 5. Has your sleep pattern been disrupted by investment and/or trading activities (e.g., staying up at night to be online at the opening of foreign financial markets)? | ||
| 6. Have you made repeated efforts to control, reduce, or stop investing and/or trading, always without succeeding? | ||
| 7. Except for investments and/or trading, have you lost interest in social and/or recreational activities that you previously enjoyed because of investments/trading? | ||
| 8. Have you often invested and/or traded when you felt distressed (e.g., helplessness, guilt, anxiety, depression)? | ||
| 9. After losing money in investments and/or trading, have you immediately or another day invested again to try to recover the losses? | ||
| 10. Have you lied to conceal your level of involvement with investments and/or trading (e.g., lying about financial losses, only talking about investments where you made money)? | ||
| 11. Have you jeopardized or lost an important relationship, your job, or opportunities in your studies or career because of investments and/or trading activities? | ||
| 12. Do you rely on others to give you money to relieve your desperate financial situation caused by investments and/or trading? | ||
| 13. Due to investments and/or trading, have you had thoughts about taking your own life, with or without planning, or have you attempted to take your own life? |
Scoring: Yes=1, No=0. Total score*: __________
*A score of ≥5 indicates the possible presence of disordered trading.
Appendix B. Trading Disorder Scale – Spanish version
| En relación con tu participación en actividades de trading, por favor indica si, durante los ÚLTIMOS 12 MESES, te ha ocurrido (sí/no) alguna de las situaciones que se describen a continuación. | Sí | No |
| 1. ¿A menudo has tenido la mente ocupada en actividades de inversión y/o trading – compraventa - (p.ej. reviviendo experiencias de inversiones pasadas, analizando o planificando la próxima inversión, leyendo literatura o foros online relacionados con el mundo financiero, convirtiéndose las inversiones/trading en la actividad principal de tu vida cotidiana…)? | ||
| 2. ¿Has tenido la necesidad de invertir y/o realizar trading (compraventa) de cantidades de dinero cada vez mayores para conseguir la excitación deseada? | ||
| 3. ¿Has tenido la necesidad de pasar cada vez más tiempo realizando inversiones/trading y/o buscando nuevos instrumentos financieros en los que invertir? | ||
| 4. ¿Has estado nervioso/a o irritado/a cuando has intentado reducir o abandonar las inversiones y/o el trading? | ||
| 5. ¿Has visto alterado tu patrón de sueño por realizar actividades de inversión y/o trading (p.ej. quedarte despierto/a por la noche para estar conectado/a en la apertura de mercados financieros extranjeros)? | ||
| 6. ¿Has hecho esfuerzos repetidos por controlar, reducir o abandonar las inversiones y/o el trading, siempre sin éxito? | ||
| 7. A excepción de las inversiones y/o el trading, ¿has perdido interés en actividades sociales y/o recreativas que previamente realizabas, a causa de las inversiones/trading? | ||
| 8. ¿A menudo has invertido y/o realizado trading cuando sentías desasosiego (p.ej. desamparo, culpabilidad, ansiedad, depresión…)? | ||
| 9. Después de perder dinero en las inversiones y/o el trading, ¿has vuelto a invertir inmediatamente u otro día para intentar ganar y así recuperar las pérdidas? | ||
| 10. ¿Has mentido para ocultar tu grado de implicación con las inversiones y/o el trading (p.ej. mentir sobre pérdidas financieras, hablar solo de las inversiones en las que has ganado dinero,…)? | ||
| 11. ¿Has puesto en peligro o has perdido alguna relación importante, tu empleo u oportunidades en tus estudios o en tu carrera profesional a causa de las inversiones y/o actividades de trading? | ||
| 12. ¿Cuentas con los demás para que te den dinero para aliviar tu situación financiera desesperada provocada por las inversiones y/o el trading? | ||
| 13. Debido a las inversiones y/o el trading, ¿has tenido pensamientos sobre quitarte la vida, con o sin planificación, o has intentado quitarte la vida? |
Corrección: Sí=1, No=0 Puntuación total*: __________
*Una puntuación ≥5 es indicativa de un posible trastorno del trading.
