Association between gambling and financial trading: A systemic review.

Is investing gambling?

Since the primary purpose of trading within financial markets is to build wealth through investing, a central question is whether or not investing is dissimilar from gambling. This is a long-debated topic and will not be fully discussed here. Nonetheless, we provide a brief overview for context. First, a survey of nearly 13,000 North American adults by Williams, Stevens, and Nixon (2011; as cited by Williams et al., 2017) collected data on individuals’ personal perspective of what counts as gambling. Activities ranged from buying lottery tickets, horse betting, purchasing insurance, and starting a business. Expectedly, traditional gambling activities (e.g., scratch tickets, slot machines, casino table games) were identified as gambling-related activities by at least 75% of the sample. However, their findings also show that 36.5% view “buying ‘blue chip’ stocks” as a form of gambling, suggesting that even when buying shares of high-value, high-quality companies, a meaningful part of the population views this as a form of gambling. Additional data from this survey revealed 57.2% view “buying high-risk stocks” to be a form of gambling. For clarification, it is expected that most individuals define high-risk stock as shares of low-value, low-quality companies that often trade for less than $5 a share qualifying them as “penny stocks.” It is also possible that this includes recent start-ups of high-quality but still high-risk due to the amount of debt these companies take-on during their initial phases. It is perhaps a bit surprising that a greater proportion of individuals perceived high-risk stock trading to be gambling than “betting on sports,” which was viewed as a gambling activity by 52.5% of North American adults. Findings from this survey suggest that whether or not stock trading is viewed as gambling appears to depend on the quality of the underlying company and individual perspectives. Moreover, every activity was rated to be a gambling activity by some participants suggesting that the public view more activities to be gambling-related than many scholars’ definitions allow for. But, when it comes to the contrast between investing versus gambling, a more conceptual review is required.

Arthur et al. (2016) provide the most extensive review of the conceptual similarities and differences between investing and gambling to date. They argue that investing is a low-risk activity with positive expected values due to buying and holding assets across a long time period with no definitive event. On the other hand, gambling is a high-risk activity with negative expected values due to staking positions (versus purchasing assets) over a short (often instant) time period that includes a definitive event. Interestingly, Arthur and colleagues do not dismiss the role of chance for both investing and gambling, and rate its influence as high for both activities. Thus, investing and gambling are chance-based activities based on their review.

In addition to investing and gambling, Arthur et al. (2016) define speculating as an activity that rests somewhere between investing and gambling. The definition of speculating is a bit more ambiguous, perhaps purposely so. It is usually a high-risk activity with mixed expected values due to purchasing assets as well as staking positions over both short and long time periods that usually include a definitive event. Despite the ambiguity in the definition of speculating, it appears at face value to be accurate. For instance, day-trading is where individuals purchase assets but hold them for less than a day, which constitutes a short time period. Because of the short duration it is not investing but it is also not gambling. Another example is the purchase of long-dated options contracts (for an overview of options contracts, see Hargrave, n.d.). Again, due to the long duration, it is not gambling but it also not investing since no assets are purchased, only the “option” to buy or sell an underlying asset. Therefore, the takeaway from Arthur and colleagues is the complexity of delineating between investing and gambling, and that speculating may be a better term to highlight the increased exposure to risk.

Psychological characteristics of traders versus gamblers

Beyond the complexity of distinguishing investing from gambling, researchers have considered the psychological characteristics that either distinguish individuals who engage in financial trading versus those who engage in gambling or explore the similarity between the two activities. Moreover, financial traders and gamblers share similar psychological characteristics including personality traits, cognitions-related to gambling, and gambling motivations.

Personality traits such as sensation-seeking and risk-taking were found to be common in both gamblers and financial traders (Powell et al., 1999; Wong & Carducci, 1991). For instance, in a simulation among college students, the frequency of financial investment was positively associated with risk-taking propensity (Markiewicz & Weber, 2013). Additionally, competitiveness is predictive of both trading and gambling frequency, while emotional instability and impulsivity were only positively related with trading frequency, not gambling frequency (Jadlow & Mowen, 2010). Therefore, collectively traders appear to share some psychological similarities with gamblers.

Research has also found evidence of similar erroneous cognitions between traders and gamblers. For example, traders and gamblers are often over confident in their abilities, and possess both illusion of control (Barber & Odean, 2001), and confirmation bias (Barber & Odean, 2001; Rabin & Schrag, 1999). Also, individuals who engage in financial trading and gambling show similar motives (i.e., fun, excitement) for engagement (Binde, 2013; Dorn & Sengmueller, 2009; Neighbors et al., 2002). These similarities in psychological characteristics can place individuals at an increased risk for problem gambling.

Consequently, it would be expected that a relation would exist between financial trading and problem gambling since trading is similar to gambling, and the individuals participating in the activity share similar traits and cognitions. However, there are some questions that need to be answered due to the neutral attribute of both financial trading and gambling. To what extent is it dangerous or problematic behavior? How can we explore the potential relationship between the two similar constructs? How can we find the tangible evidence for intervention, prevention and decision making for policy? Similar debates have been discussed in previous research, resulting in a collective effort to develop diagnostic standards or specific assessment tools such as the Problem Gambling Severity Index (Ferris & Wynne, 2001) and South Oaks Gambling Screen (Lesieur & Blume, 1987). Thus, the present review focused on the studies which used clear assessment for measuring the degree of gambling problems or harm.

Existing research on problem gambling among financial traders

A growing body of literature has shown financial trading to be associated with problem gambling, however, no review has yet attempted to synthesize these findings. The two similar behaviors have only recently been explored together through preliminary studies that measure the co-occurring behavior or prevalence. An empirical study with a Canadian sample investigated the relationship between engaging in financial trading and gambling and showed that high-risk stock traders were more likely to engage in gambling, which was also found in an Australian sample of day traders (Arthur et al., 2015; Arthur & Delfabbro, 2017). Cox et al. (2020) applied the gambling disorder criteria on trading behavior and described speculative features of financial market investors who met the diagnostic criteria. Håkansson et al. (2021) insisted that stock trading, typically in the case of excessive day trading, may cause gambling disorder and related problems. Therefore, the authors emphasize the need for more in-depth research given the current pandemic period when volatile financial trading behavior surges. Moreover, cryptocurrency trading has rapidly become accepted as a trading instrument worldwide over the past few years. Since this novel form of trading has much higher volatility, there were concerns that cryptocurrency trading behavior could be linked to gambling behavior (Delfabbro et al., 2021). One of the preliminary studies, Mills and Nower (2019) suggested that cryptocurrency trading can have more risk to gamblers with high gambling severity.

While many view investing and gambling as separate, there is increasing evidence of gambling within financial markets as well as greater problem gambling severity among financial traders. However, there is a lack of studies that review the research on both entities together. Thus, the present study aims to review the literature on financial trading and gambling, by examining the prevalence of participation in both, and the relationship between problem gambling and trading behavior.

Data sources and search strategy

In this review of the literature, an electronic search was conducted using the Scopus, PsycINFO, and Web of Science databases. The search strategy and protocol were developed in consultation with a specialist in behavioral addiction research and registered with PROSPERO (CRD42022374861). We chose search terms for financial trading and gambling (see Table 1) with the filters of year of publication (2013 to 2022), document type (Article), and language used (English). The initial search identified 2,220 related articles from the three databases. The criteria for study selection that was used to evaluate each article, listings of the initial articles, as well as the final selection of articles, are provided in the Data Availability statement.

Study selection

The study selection process followed PRISMA guidelines, and was completed by two independent researchers, with a review and consultation stage for each step in the process. Consistent with the study objectives, eligibility criteria included studies that assessed the prevalence of problem gambling among financial traders and/or the association between financial trading and problem gambling. As such, studies must either assess problem gambling either in conjunction with an assessment of financial trading frequency or among financial traders to be included in the review. Following the exclusion of duplicates across the three databases, 1,444 articles were identified. Titles and abstracts were independently reviewed by two trained graduate students to assess each article’s eligibility for a full-text review. Any discrepancies in the ratings were resolved through consultation, and the 21 articles were deemed to be eligible for a full-text review. Again, the full-text of each article was independently reviewed by two trained graduate students to assess each article’s eligibility for inclusion. Any discrepancies in the ratings were resolved, and the 12 articles were deemed to be eligible for inclusion into the present systematic review (see Figure 1). The process and criteria used for study selection can be found under Extended data (Lee, 2023).

Figure 1. PRISMA flow chart.

Data extraction and synthesis

This systematic review used the synthesis without meta-analysis (SWiM) guidelines and checklist (Campbell et al., 2020) (See supplemental material in Reporting guidelines). Researchers extracted characteristics of the studies (i.e., sampling method, sample size and characteristics, and the prevalence of gambling among samples) and the relationship between gambling and financial trading (i.e., effect size) using Microsoft Excel. The data extraction protocol was pilot tested with two randomly selected studies and refined to include prevalence of problem gambling accordingly. The first and second author read all selected studies using the data extraction protocol independently. Disagreements regarding extracted data were few and resolved via mutual discussion. The third author then revisited all the studies to confirm the accuracy of extracted data. For data synthesis, the selected articles were grouped by the type of financial trading. Since the identified associations between gambling and financial trading were investigated with various method and populations, the standardized metric or transformation method were not used. The prevalence of engagement to gambling activities and problem gambling was used to investigate the possible heterogeneity across the groups. Also, the results of group comparison, bivariate or multivariate analysis were used. The certainty of evidence is assessed by the p-value if applicable. The synthesized results are described in Table 3.

Study characteristics

The characteristics of the articles are presented in Table 2. Many of the articles were published in 2021, with a significant increase after 2019, demonstrating a steady interest in this topic. All of the included articles were designed for cross-sectional study using quantitative data from population-based surveys. While the sampling method varied, most of the articles used a convenience or purposive sampling method, except three of the articles which used probability samples of large population data collected by national level surveys. Three articles used samples of individuals who reported having trading experience, or financial trading products, two of the articles used gambling experience solely as inclusion criteria for participants, and two articles employed samples of individuals who engaged in both financial trading and gambling. The remaining two articles used general populations for the study sample. Six of the twelve articles recruited participants via online crowdsourcing platforms (e.g., Mturk, Prolific, Embrain) or panels (e.g., Norstat, KMPMORF), while the other half used traditional online methods (e.g., social media, email).

Most of the study samples have more male participants than female, and the ratio ranges from 44.3% to 96.7% male. The sample sizes ranged from 203 participants to 23,952, with a median sample size of 876. Specifically, five of the studies had a sample size under 1,000, three of the studies had a sample size of 500–999, and the remaining four articles had a sample size of 200–400. The samples were from various nations including Europe (e.g., United Kingdom, Turkey, Finland), Asia (e.g., South Korea), North America (e.g., United States, Canada, Mexico), South Africa, and Australia. The mean age ranges from 32.2 to 48.5 years, and the median age was 40.1 years from nine articles.

Problem gambling measures

The identified articles primarily used two gambling measures, the Problem Gambling Severity Index (PGSI; Ferris & Wynne, 2001) and South Oaks Gambling Screen (SOGS; Lesieur & Blume, 1987), with one article having used the Diagnostic and Statistical Manual 5 (DSM-5) criteria of gambling disorder. These measures were used to assess gambling participation, as well as the presence and severity of problem gambling. One article also assessed family history of gambling through a single item. The scores indicating problem gambling are reported in Table 2, and overall prevalence ranged from 0.4% - 47.2% total, while specific investor group problem gambling ranged from 3.5% - 30.7%. Since the prevalence rates for problem gambling were highly dependent on the type of trading participants engaged in, the results section below will first discuss stock traders, followed by day traders, and then cryptocurrency traders.

Gambling participation and problem gambling among financial traders

Stock trading

Five of the 12 articles focused on individuals who participate in stock trading (Arthur et al., 2015; Mosenhauer et al., 2021; Whiting et al., 2019; Williams et al., 2022; Youn et al., 2016). Frequency of trading was the most used indicator, but other dimensions such as trade size, portfolio value, and turnover (volume of trades relative to portfolio value) were also measured. One article used a specific stock trading measures, the Stock Addiction Inventory (Youn et al., 2016). The results for gambling engagement revealed a wider range of gambling experience in those who participate in high-risk stock trading than those who do not. Williams et al. (2022) reported 78.3% of those who engaged in any form of trading within the last year also participated in gambling, compared to 66.1% of those who did not. Further, Williams et al., found stock traders also participated in sports betting and casino table games at a significantly higher rate than non-traders. Additionally, a significantly higher gambling frequency for high-risk traders reported by Arthur et al. (2015) (M_HR = 9.4, M_NHR = 6.7). Problem gambling prevalence ranged from 2.1% to 41.5%, with a median of 6.7% among 8 stock trading samples of these 5 articles.

Youn et al. (2016) reported that stock addiction scores were higher in those with a high level of problem gambling (SOGS ≥ 5), and were positively correlated with pathological gambling scores. Whiting et al. (2019) stated that there was no significant difference in stock participation for recreational gamblers compared to problem gamblers when reporting t-test results, but Arthur et al. (2015), who used two different samples, did find a difference. In both samples presented in the article, the high-risk trading group had more participants who scored five or more on the CPGI, than non-high-risk traders (M_HR = 5.7, 7.6, M_NHR = 2.1, 3.3), and when predicting gamblers who engaged in high-risk trading, problem gambling had a positively significant relationship (B = 1.43).

Williams et al. (2022) reported the result of a logistic regression predicting past year speculation and found that higher gambling frequency was the strongest predictor (OR = 1.65), and higher PGSI was also a significant predictor (OR = 1.07). In an article using a Prolific sample of individuals who had experience with gambling and current investments (Mosenhauer et al., 2021), risk for problem gambling predicted high trading frequency (i.e. turn over) over and above the other predictors (i.e. age, sex, overconfidence, and financial literacy).

Day-trading

While day trading is often aggregated with other trading activity, one article by Arthur and Delfabbro (2017) investigated day trading behavior as a distinctive type of financial trading. Day trading is described as trading activity that occurs within the same market day, or before the market closes. While there is no specific measure of day trading, they assessed day trading through three questions. Participants were asked if they bought and sold financial instruments within one market day, what type of financial products they traded in, and the amount of money they accessed in a day to conduct such trading. Prevalence of day trading in the sample was found to be 0.66%, which was lower than forms of other gambling-related activities such as lottery (55.5%), EGM (26.5%), and sports betting (6.1%). Similarly, of the 158 high-risk traders surveyed in Arthur’s 2015 paper (N = 8,498), only 16 participated in day trading alone, and 15 reported participating in day trading along with stocks, option, and futures. Most of the day traders (84.4%) traded in stocks, and 90.8% of them participated in any form of gambling. The rate of gambling compared to the general adult population in that area was found to be significantly different (z = 3.7), and the problem gambling rates among those day traders were significantly higher than individuals who did not participate in day trading, 7.6% compared to 1.4% (z = 4.1). The final finding by Arthur and Delfabbro (2017) was that problem gambling significantly predicted the differences between day traders and non-day traders (B = .40), day traders and gamblers (B = .30), and day traders and skill-based gamblers (B = .23) in a binary logistic regression. A limitation discussed in the current literature was the lack of disaggregation for day traders, because while the prevalence may be low, the risk for gambling problems are high.

Cryptocurrency trading

Finally, six articles (Aslan & Kilincel, 2021; Delfabbro et al., 2021; Kim et al., 2020; Mills & Nower, 2019; Oksanen et al., 2022; Sonkurt & Altınöz, 2021) investigated cryptocurrency traders. In most of the articles, cryptocurrency trading was measured by trading frequency, but one article used the Pathological Trading Scale (Guglielmo et al., 2016) to capture pathological trading behaviors (Sonkurt & Altınöz, 2021). The proportion of crypto traders vary depending on inclusion criteria within the recruiting process (ranging from 3.6 to 100%) and are typically higher in younger males. Gambling engagement, and cryptocurrency trading frequency were positively correlated with greater frequency of gambling activities in multiple papers. Casino games are the most commonly used among cryptocurrency traders (53.6%) according to findings by Delfabbro et al. (2021), but Mills and Nower (2019) reported that casino gambling had a negative relationship with cryptocurrency trading frequency, while sports betting and daily fantasy sports predicted higher engagement in cryptocurrency trading. In addition, those who engaged in both cryptocurrency trading and sports betting gambled at higher rates in casino games, race betting, and slots than those who engaged in either sport betting or cryptocurrency alone (Delfabbro et al., 2021). Problem gambling prevalence ranged from 2% to 47.2% with a median of 9.5% among 5 samples of the 4 articles available.

In line with gambling engagement, risk for problem gambling was significantly higher in people with experience in cryptocurrency trading. Sonkurt and Altınöz (2021) reported the bivariate relation that pathological trading scores were positively correlated with pathological gambling scores (r = .20). Also, PGSI scores predicted cryptocurrency investment, trading frequency, and related behaviors (i.e., daily monitoring, daily trading hours), but it was not a significant predictor of stock investing (Kim et al., 2020). Delfabbro et al. (2021) reported 30.7% of individuals who engage in sports betting and cryptocurrency investment together are at high risk for problem gambling, and it was significantly higher than those who engaged in either sport betting or cryptocurrency alone. Another finding by Kim et al. (2020) confirms problem gambling risk, reporting a significant difference in K-CPGI scores for bitcoin investors compared to stock investors (t = 7.2). This same article also found problem gambling scores predictive of bitcoin investment (B = .80). Aslan and Kilincel (2021) compared SOGS scores of individuals who purchased bitcoin and those who did not and found a significant difference. Within the sample of frequent gamblers in Mills and Nower’s article (2019), 47.2% scored in the high-risk category of the PGSI, and the PGSI was predictive of crypto trading frequency (β = .11). In another paper, problem gambling scores were higher for crypto traders than regular investors and investors who use real-time platforms for regular investments (IRR = 5.98; Oksanen et al., 2022). All of these findings highlight the importance of understanding the connection between cryptocurrency use and problem gambling.

Gambling participation and problem gambling among financial traders

An association between financial trading and gambling was observed throughout the three types of financial market traders identified in this study (i.e., Stock traders, day traders, cryptocurrency traders). An overlap between gambling and stock trading was found in the present review, and the closer the stock trading is to speculative investments, the more apparent the overlap is. Both gambling participation and risk of problem gambling are significant predictors of high-risk or speculative stock trading. Specifically, one of the typical examples of speculation, day trading, shows significantly higher gambling frequency and problem gambling prevalence. The studies propose evidence of a behavioral relationship between gambling and speculative stock trading, induced by similarity in psychological characteristics.

In terms of cryptocurrency trading, there is also a relationship with gambling participation and problem gambling. Cryptocurrency traders have a relatively higher risk of problem gambling than traders engaging in other financial products. Furthermore, when crypto trading is combined with sports betting, the risk for problematic gambling is higher, presenting evidence that more speculative behavior increases risk for problem gambling. This can be explained by the high accessibility and volatility embedded in the nature of the cryptocurrency market. The high prevalence of pathological trading among cryptocurrency traders might stem from those risky characteristics of cryptocurrency trading, resulting in expanded gambling activities and severe problem gambling. Also, given that sports betting, a certain type of gambling, turned out to have potential harm when combined with cryptocurrency trading, future research needs to more fully explore the profile of cryptocurrency traders to identify the highest risk profiles among individuals who engage in cryptocurrency trading more accurately. In conclusion, the present review reveals empirical evidence for the conceptual similarities of financial trading, speculative trading, and gambling, which have been discussed previously in the literature.

The supported argument of the relationship between financial trading and problem gambling was also explored in articles excluded from the present review. Håkansson et al. (2021) considered stock trading as a form of gambling and highlighted the risk of day trading due to the association between day trading and problematic gambling. A qualitative study of excessive financial traders reported a link between excessive trading and gambling disorder, with common individual characteristics such as incapacity to stop and chasing losses within both groups (Dixon et al., 2018). Also, Grall-Bronnec et al. (2017) conducted a case study for outpatients with excessive trading, seeking treatment in a problem gambling unit. The results pointed out high event frequency and short event duration in financial trading, which are characteristics frequently found in gambling disorder. Moreover, the researchers proposed that profit from financial trading increased the participants’ belief (e.g., I have special skills necessary to understand the stock markets; I was so close to succeed though I lost after all) that is similar to the concepts of illusion of control and near miss in gambling literature.

Demographic characteristics and potential at-risk population

All the articles included adult populations and the participants across the articles were more likely to be male. We assume that it reflects the results of previous literature regarding financial trading or gambling (see review by Calado & Griffiths, 2016). Among cryptocurrency traders, there is the dominant tendency of young males being the majority participants, which mirrors the findings of other literature (Johnson et al., 2023) and highlights the need to study this population in future studies. Individuals entering the early adult stage have been considered the primary users of the cryptocurrency market given their engagement with novel technological innovation (Ross, 2017). Due to the high prevalence of cryptocurrency content on social media, Johnson et al. (2023) emphasize the importance of social media in cryptocurrency trading research. Specifically, many of Generation Z (those born after 1996) are increasingly seeking financial advice on social media and those young people are likely to invest in cryptocurrency as a result of being overexposed to cryptocurrency contents on social media (Glenski et al., 2019; King et al., 2014). Adolescents are experiencing gambling behaviors all over the world, and gambling recently became one of the most frequently observed addictions among adolescents (Calado et al., 2017).

Most articles revealed that people have experience in trading financial products and/or gambling. Studies using random sampling show relatively low ratios of problem gambling prevalence within participants. These differences based on study samples offer the idea that when it comes to intervention or prevention for harmful outcomes of financial products or gambling, it will be more efficient to focus on at-risk samples to find observable tendencies. In addition, arguments about the prevalence and relations of gambling among traders of financial products are largely supported by panel samples of online platforms. In the present review, we found that half of all existing articles used data from panel samples collected by crowdsourcing. Therefore, we argue that using panel data is a stable method for research of gambling and financial trading and future research should continue to do so.

Practical implication

Although certain standards for capturing specific features of financial trading activities provided a more subdivided understanding of safe or risky investments, more research is needed to make appropriate treatment applications in the field. We propose further research captures the precise profiles of problematic and non-problematic behaviors in financial trading and gambling. For example, the development of sensitive measures is required to understand what cryptocurrency trading looks like among individuals who engage in gambling without problems (i.e., recreational gamblers). Expanding the argument, we need to have a more thorough understanding of trading across all types of financial products to answer questions such as what proportion of a problematic gambler’s portfolio is dedicated to more speculative assets or trading behaviors. Therefore, further investigations are required to develop these arguments and create practical implications to be applied to effective prevention interventions and building regulation policies.

Emerging issues of digital technologies

In addition, in the context of ‘digital convergence’, the rapid evolution of gambling brought new challenges for regulators and policymakers, communities, and treatment providers, requiring new approaches to understanding and addressing gambling harms that arise from these new technologies (Lawn et al., 2020). Traders have also experienced rapid changes from traditional stock market to cryptocurrency trading and online investment. The changes in financial trading are embedded in digitization. Global shifting induced by development of networks and mobile technologies, offer a new ecology of financial trading. Using online apps and platforms, financial market traders have access to diverse fast paced opportunities and risks. These new methods enable traders to overcome existing limitations by providing free trading without charging commission (e.g., Robinhood) or offering broader options in terms of access time and choices (e.g., 24/7 real-time trading, cryptocurrency trading). In response to this new phenomenon, there has been concern over gamification in the financial trade market, leading to the argument by Warren Buffett who said the stock market is becoming more like a casino through the introduction of trading applications (Fitzgerald, 2021).

Another issue induced by the development of digital technologies is play-to-earn gaming (PTE). PTE allows those who play the game to receive monetary rewards for their gameplay. This new form of gaming has been focused on with increasing interest in cryptocurrency, however, there have been concerns regarding harmful effects embedded in the monetized structure. Similar to the evidence found in studies of gambling-like aspects of monetized games, such as buying loot box in game (Carey et al., 2022), the monetary reward may be more appealing to individuals who are at risk of problem gambling or have strong financial need. Delic and Delfabbro (2022) argued that engaging in PTE may pose the risk of being motivated by extrinsic forces leading to temptation to grind out rewards, thus a monitoring system is required, especially for vulnerable players. The changes created by the development of new technologies are ongoing, therefore, it is essential that researchers keep up with changes in financial products and gambling phenomenon to provide appropriate policies and regulations in response to new technologies in this area.

Limitations of the present study

The present review has several limitations that should be outlined for consideration while interpreting the findings. First, it only includes articles based on specific criteria built into this review. Second, the review includes articles published within 10 years of the search, therefore the time span of research includes the COVID-19 pandemic period. As Håkansson et al. (2021) argued, this global situation brought about various impacts on people including online investments. Considering the events during the pandemic period that have potential effects on the result of trading research, such as lockdown and gambling restrictions, the results may be different depending on the time or country. Therefore, it is required to have a time-sensitive perspective when trying to apply the review of this study.