Author | Sudheer Chava, Fred Hu, Nikhil Paradkar

Source | JFQA

Translation | Yan Zilin

1. Introduction

Since the emergence of Bitcoin in 2009, the cryptocurrency market has experienced explosive growth. During this period, thousands of cryptocurrency tokens—digital assets created on a blockchain (a decentralized distributed digital ledger)—have been launched. These tokens can represent various assets and utilities: well-known cases like Bitcoin and Ethereum primarily serve as mediums of exchange or stores of value, while other cryptocurrencies can be used to access specific products or services on blockchain platforms or represent ownership of physical and digital items. Along with the market expansion, consumer interest has surged, with over 20% of American adults having invested in, traded, or used cryptocurrencies (CNBC (2022)), and the estimated number of global cryptocurrency investors has reached 580 million (Crypto.com (2024)).

Despite the significant growth of the retail cryptocurrency investor base, there is limited direct evidence regarding the characteristics of these investors due to the anonymous nature of blockchain. Meanwhile, the surge in cryptocurrency investors has raised concerns among policymakers, especially considering the extreme volatility of the cryptocurrency market. For example, the total market capitalization of cryptocurrencies reached nearly $2.8 trillion in November 2021, then fell to $1.2 trillion in June 2022, and rose again to $2.6 trillion in May 2024 (Forbes (2024)). This dramatic volatility raises concerns that retail investors may not fully understand the associated risks. Specifically, the returns on cryptocurrencies exhibit a highly positive skew, indicating a small probability of achieving extremely high returns (Liu and Tsyvinski (2021), Liu, Tsyvinski, and Wu (2022)). This return pattern is similar to lottery products, making it highly attractive to investors with strong gambling preferences (Kumar (2009)). Therefore, this paper explores whether gambling preferences can predict retail interest in the cryptocurrency market. Understanding whether retail investors view cryptocurrencies as lottery-like products can help policymakers determine appropriate disclosure standards and regulatory frameworks (such as the legislative proposals put forth by Lummis and Gillibrand (2023)).

In the absence of direct data, this paper draws on the research of Da, Engelberg, and Gao (2011) and uses Google search interest as a proxy for retail interest, focusing on two significant types of cryptocurrency tokens: Initial Coin Offerings (ICOs) and Non-Fungible Token (NFT) series. Unlike tokens that serve as general currencies, ICOs focus on project investment, while NFTs emphasize digital ownership and collectibles. Consistent with the view that gambling preferences predict interest in cryptocurrencies, this paper finds that regions with higher per capita lottery sales show significantly greater interest in cryptocurrencies. This finding is robust to other gambling-related demographic characteristics identified by Kumar (2009) and Kumar, Page, and Spalt (2011). To alleviate concerns that "interest does not equal investment," this paper documents that interest in cryptocurrency wallets also surged before and after token issuance, and higher interest is associated with greater funding amounts and participant numbers. Additionally, this paper rules out other explanatory paths such as advertising, risk preferences, or distrust in institutions.

This paper further explores the token-level factors influencing gambling-driven interest. First, ICOs and NFT projects launched during the cryptocurrency market bubble attract more attention from regions with higher gambling tendencies. Second, in the ICO market, tokens with lower opening prices (i.e., more "lottery-like" characteristics) and those lacking "Know Your Customer" (KYC) protocols (Li, Shin, and Wang (2021)) also generate greater interest in these regions. Furthermore, this paper uses the gradual legalization of sports betting across U.S. states as a natural experiment and finds that after legal sports betting is permitted, regions with high gambling tendencies show a relative decline in interest in token issuance. This suggests that retail investors largely view cryptocurrencies as substitutes for traditional gambling products.

Finally, this paper examines the relationship between cryptocurrency interest and consumer credit outcomes. Using data from Equifax, this paper finds that in regions with high gambling tendencies, the default rate on consumer credit spikes following periods of high cryptocurrency interest, particularly among financially constrained subprime borrowers. Lagged analysis shows that the rise in interest precedes the increase in default rates.

This paper contributes to multiple literatures: first, it provides a new perspective on retail characteristics and motivations in the ICO market (Li and Mann (2025), Lee and Parlour (2021), Cong, Li, and Wang (2021, 2022), etc.); second, it enriches the NFT literature by revealing the relationship between retail interest and primary market performance (Kong and Lin (2021), Borri, Liu, and Tsyvinski (2022), Oh, Rosen, and Zhang (2023)); third, it expands the literature on how gambling preferences influence financial product prices and trading volumes (Barberis and Huang (2008), Bali, Cakici, and Whitelaw (2011), Kumar (2009), Green and Hwang (2012)); fourth, it connects research on retail investor behavior (Barber and Odean (2000, 2008), Welch (2022), Fedyk (2022), Barber et al. (2022)); finally, this paper adds to the emerging literature on cryptocurrency investor characteristics (Dhawan and Putniņš (2023), Hackethal et al. (2022), Kogan et al. (2024), Aiello et al. (2023), Divakaruni and Zimmerman (2024), Sun (2023)), demonstrating that gambling preferences are an important factor in predicting retail interest in the cryptocurrency market.

2. Data and Descriptive Statistics

This section introduces the data sources used in the study and the descriptive statistics of the variables in the regression analysis.

A. Data Sources

1. Retail Interest: This paper adopts the method of Da et al. (2011) and uses online interest obtained from Google Trends as a proxy for investment behavior. Its advantage lies in capturing investors' search intentions in private settings. The study employs the Search Volume Index (SVI), which ranges from 0 to 100. Data is collected at a more granular designated market area (DMA) level, covering 209 DMAs in the U.S. For each token project, the area with the highest interest (SVI=100) represents the highest relative popularity of that project in that region.

2. Initial Coin Offerings (ICOs): ICOs are a way for blockchain startups to raise funds. Unlike IPOs, these tokens do not represent equity but typically represent some utility within the project's ecosystem.

   (1) Sample Selection: Data is integrated from ICOBench.io, excluding projects that did not meet the fundraising soft cap and those that U.S. investors could not participate in.

   (2) Data Volume: A total of 937 completed ICOs from January 2016 to December 2018 were ultimately selected.

   (3) Contributor Identification: Wallet addresses are obtained from white papers, and the number of unique wallet addresses is tracked using Etherscan.io to infer the actual number of contributors.

3. Non-Fungible Tokens (NFTs): NFTs represent ownership of unique items (such as artwork) on the blockchain.

   (1) Sample Selection: Data comes from the largest trading platform, OpenSea. Since Google Trends often shows 0 for low-volume entries, this paper focuses on the top 100 NFT series by trading volume from 2017 to 2022.

   (2) Selection Criteria: Projects with a total count exceeding 10,000 or a minting average price of 0 are excluded. The final sample includes 46 NFT series.

4. Regional Demographic Characteristics: This paper uses per capita lottery sales as a proxy for regional gambling tendencies. Data is manually collected from state gaming commissions and aggregated to the DMA level. To avoid "look-ahead bias," all demographic data is set at a 2015 baseline to capture static cross-sectional differences.

5. Consumer Credit Characteristics: Default data (90 days overdue is considered a default) is obtained from Equifax. This paper calculates default rates at the DMA-year-month level and compares the subprime segment (subprime, 620) and non-subprime segment (≥ 620) based on credit scores.

B. Descriptive Statistics

1. Regional Characteristics: In 197 DMAs with lottery data, the average annual lottery expenditure per adult is $199, but there is significant regional variation (ranging from less than $1 to over $800).

2. ICO Characteristics: The average amount raised by ICOs is $26.3 million (about 40% of the hard cap). 36% of projects require KYC (identity verification), and 57% of projects have publicly available code on GitHub.

3. NFT Characteristics: The median issuance volume of NFT series in the sample is approximately 9,200. The vast majority (about 90%) are active on Twitter and Discord, and 85% of projects promote "rare items."

3. Regional Gambling Tendencies and Retail Cryptocurrency Interest

This study examines how differences in regional gambling tendencies affect the attention received by cryptocurrency tokens by estimating the following general regression model:

Where SVL represents the attention received by ICO or NFT series i in designated market area (DMA) d during the issuance period. The core coefficient measures the impact of gambling tendencies at the DMA level on cryptocurrency interest. This paper uses per capita lottery sales as a proxy for gambling tendencies and controls for regional demographic characteristics and project fixed effects.

Key conclusions:

1. ICO Attention Analysis: The study finds a significant positive correlation between per capita lottery sales and ICO attention. Specifically, for every one standard deviation increase in gambling tendencies, the attention received by ICOs increases by approximately 12.8% on average. This conclusion remains valid after including regional demographic variables or project fixed effects for robustness checks. This indicates that regions with higher gambling tendencies show greater retail interest in ICOs.

2. NFT Series Analysis: Research on NFTs shows a more significant association. For every one standard deviation increase in gambling tendencies, the attention received by NFT series increases by approximately 20%. Although the attention for NFTs is more concentrated geographically than for ICOs, the predictive power of gambling preferences for interest in NFTs remains very strong.

A. Robustness Checks: Alternative Gambling Preference Indicators

This paper references existing research (Kumar (2009)) and uses various socioeconomic characteristics as alternative indicators of gambling preferences. The findings show that in regions with a higher proportion of Catholics, severe income inequality, higher unemployment rates, and a higher percentage of minority populations, the attention to cryptocurrency tokens is significantly higher. Conversely, regions with higher education levels, higher marriage rates, or higher income levels show lower attention to cryptocurrency tokens. This further confirms the high consistency between interest in crypto assets and traditional gambling psychological characteristics.

B. External Validation: Does Attention Equate to Investment?

To verify whether "attention" effectively reflects "investment behavior," this paper conducted two tests:

1. Cryptocurrency Wallet Attention: The study found that during the token issuance period, regions with high gambling tendencies saw a simultaneous surge in search volume for cryptocurrency wallets like MetaMask and Coinbase Wallet. Since participation in ICOs/NFTs requires such wallets, this provides strong evidence for the conversion of attention into actual investment intentions.

2. Primary Market Performance: By introducing "anchor tokens" to compare the absolute search popularity of different projects, the study found that high-attention ICO projects raised more funds, had a higher proportion of reaching their fundraising caps, and saw a significant increase in the number of contributing participants on the first day; high-attention NFT series were able to raise more funds, had more minting wallets, and significantly reduced the time required to complete minting (an increase of one standard deviation in search popularity can shorten minting time by approximately 71 days).

C. Excluding Other Explanatory Paths

This paper examined other potential channels that could interfere with the conclusions, and the results found:

1. Anti-establishment Sentiment and Institutional Distrust: Using the vote share of the Libertarian Party and the complaint rate of the Consumer Financial Protection Bureau (CFPB) to measure regional distrust, it was found that these factors do not explain the association between gambling tendencies and cryptocurrency attention.

2. General Risk Preference: Introducing survey data to measure regional risk preferences revealed that it does not replace the explanatory power of gambling tendencies for interest in cryptocurrencies.

3. Regional Advertising Expenditure: For the NFT sample, the study controlled for regional advertising expenditures of cryptocurrency exchanges and found that even after considering the impact of advertising marketing, regional gambling tendencies remain the core variable predicting cryptocurrency attention.

Conclusion Summary: Empirical results consistently indicate that regional gambling preferences are the core driving force behind retail attention to cryptocurrency tokens, and this attention directly translates into primary market fundraising performance, rather than being driven solely by institutional distrust, general risk preferences, or marketing strategies.

4. Factors Driving Gambling-type Token Attention

In this section, this paper explores various factors that moderate retail investors' gambling-type attention to cryptocurrency tokens, including the characteristics of the tokens themselves and changes in the external gambling environment.

A. Token Characteristics Analysis

This paper examines specific token attributes that may trigger retail gambling psychology.

1. Low Price Characteristics (Lottery-like Attributes): According to existing literature (Kumar (2009)), low price is a core feature of lottery-type stocks. Empirical findings show that ICO projects with lower opening prices on the first day receive significantly more attention from regions with high gambling tendencies compared to high-priced projects. The interaction term coefficient indicates that low-priced tokens see an additional increase in attention of about 5% in these regions.

2. Verification Protocols (KYC) and Risk Preferences: Price manipulation behaviors such as "pump and dump" schemes are common in the cryptocurrency market, and such projects typically have weak KYC (Know Your Customer) checks. The study found that ICOs lacking KYC protocols attract extremely high attention from retail investors in high gambling tendency regions, indicating that these investors are more inclined to participate in high-risk, poorly regulated projects.

3. Market Bubble/Prosperity Period Effects: This paper defines the second half of 2017 to early 2018 as the "prosperity period" of the ICO market and the price surge phase of the NFT market from 2021 to 2022 as the "explosion period." Regression results show that token projects launched during these two phases received significantly more attention from high gambling tendency regions compared to non-bubble periods. For NFTs, the attention from high gambling tendency regions during the bubble period was approximately 23% higher than during non-bubble periods.

B. Impact of Sports Betting Legalization

To further confirm that cryptocurrency attention is driven by gambling preferences, this paper uses the phased legalization of sports betting in U.S. states as a natural experiment. If cryptocurrency tokens are viewed as substitutes for gambling, then when legal gambling channels emerge, the attention to tokens should decline. This paper estimates the following regression model:

Where PostSG is a dummy variable that takes the value of 1 when sports betting has been legalized in the state where DMA d is located and the ICO occurs after the legalization date.

Key Conclusions:

1. Significant Substitution Effect: Empirical results show that after the legalization of sports betting, attention to ICOs in the relevant regions significantly declines.

2. Stronger Response in High Gambling Tendency Regions: After introducing the interaction term of "gambling legalization" and "regional per capita lottery sales," a significant negative correlation was found. This indicates that in regions with already high gambling tendencies, the opening of sports betting has the most pronounced "crowding out effect" on cryptocurrency attention.

3. Conclusion Summary: This finding strongly demonstrates that retail investors view cryptocurrency tokens as substitutes for traditional gambling products. When residents have legal sports betting channels to satisfy their gambling appetites, their attention to the cryptocurrency market diminishes.

5. Retail Cryptocurrency Attention and Consumer Credit Outcomes

Existing research (Barber and Odean (2000); Barber et al. (2022)) indicates that retail investors often perform poorly in traditional stock markets. If their performance in the cryptocurrency market is similarly lackluster, they may fall into financial distress. Therefore, this section examines the relationship between retail cryptocurrency attention and subsequent consumer credit outcomes, and how this relationship varies with consumers' credit constraints. This paper measures credit constraints using credit scores and divides them into subprime borrowers (subprime, score 620) and non-subprime borrowers (≥ 620). Due to the comprehensiveness of the ICO sample compared to the NFT sample, this section focuses on the relationship between retail attention to ICOs and consumer default rates.

This paper estimates the following regression model at the designated market area (DMA) - credit segment - year - month level:

Where the dependent variable is the change in the default rate between the current month t and the next 6 months (t+6). HighSVI is a dummy variable indicating the top third of attention for the month.

Key Conclusions:

1. Association between Cryptocurrency Frenzy and Default Rates: The study found that the interaction term between per capita lottery sales (gambling tendency) and the ICO attention indicator is significantly positive. This indicates that in regions with high gambling tendencies and high ICO attention, subsequent consumer credit default rates significantly increase.

2. Vulnerability of Subprime Borrowers: Further analysis shows that the surge in default rates is entirely driven by subprime borrowers. In regions where high gambling tendencies and high attention coexist, the default rate of subprime borrowers increases by approximately 2.3% within 6 months. In contrast, the default situation of non-subprime borrowers (those in better financial condition) does not show significant changes.

3. Lead-Lag Relationship and Pre-Trend Testing: To rule out the possibility that default behavior itself leads to an increase in attention, this paper conducted a pre-trend analysis of changes in default rates. The conclusion shows that during the period before the attention surge (t-6 to t), there are no significant differences in default rates across regions (no pre-trend); in the period after the attention surge (t+1 to t+6), the default rate of subprime borrowers in high gambling tendency regions begins to rise significantly. This temporal lead-lag relationship indicates that it is the attention surge in the cryptocurrency market that signals subsequent credit deterioration, rather than the reverse.

Conclusion Summary:

This chapter's research demonstrates that gambling-driven investment tendencies in cryptocurrency assets can have negative economic consequences for socially vulnerable financial groups. For subprime borrowers, who already face financial constraints, participating in such high-risk, lottery-like cryptocurrency investments often comes with subsequent real financial default risks.

6. Conclusion

This paper delves into the fundamental driving forces behind retail investor participation in the cryptocurrency market, finding that gambling preferences are the core factor explaining this phenomenon. By analyzing Google Trends search data, this paper confirms that in regions with higher per capita lottery sales and a stronger speculative atmosphere, retail attention to Initial Coin Offerings (ICOs) and Non-Fungible Token (NFT) projects significantly outpaces that of other regions. This attention is not mere hype; it is highly synchronized with the download and use of cryptocurrency wallets and directly positively impacts the fundraising amounts and participant numbers of tokens in the primary market.

Further moderation effect analysis shows that this gambling-driven investment motivation is particularly strong during market "bubble periods" and when tokens exhibit "lottery-like characteristics" (such as extremely low unit prices, lack of verification protocols/KYC, and susceptibility to price manipulation). The study also finds through a natural experiment of sports betting legalization across U.S. states that when legal gambling channels emerge, the previously active attention to cryptocurrency tokens significantly declines, strongly proving that retail investors view cryptocurrency tokens as substitutes for traditional gambling products.

Most critically, this gambling preference-based speculative behavior poses a substantial threat to individual and societal financial health. Utilizing microdata from Equifax, the study finds that cryptocurrency attention surges in high gambling tendency regions often precede increases in consumer default rates in the following months, and this credit deterioration is entirely concentrated among the financially weakest subprime borrowers. This finding challenges the simplistic notion that "cryptocurrency assets are inclusive financial tools," revealing their potential predatory nature as speculative instruments on the wealth of the lower strata of society. In summary, this paper provides important academic evidence for global regulatory agencies: cryptocurrencies are largely viewed by retail investors as a new type of gambling tool, and regulation of such assets should not be limited to financial risks but should also consider public health and consumer protection perspectives, establishing stricter disclosure standards and entry thresholds.

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