Pub Date : 2026-01-27DOI: 10.1016/j.jbef.2026.101149
Zefeng Bai , Pengcheng Wang , Botong Xue
This paper investigates how cryptocurrency investment is associated with three undesirable financial behaviors: late mortgage payment, credit card misuse, and retirement borrowing. Drawing on Behavioral Portfolio Theory (BPT), we propose that cryptocurrency’s unique features encourage a “mental account invasion”, where investors engage in behaviors that build the aspirational layer at the expense of the protection layer. Utilizing 2018 and 2021 NFCS data, we find that cryptocurrency investment is associated with an increased likelihood of all three undesirable financial behaviors. Findings are robust to propensity score matching (PSM) and an instrumental variable (IV) design. Furthermore, we find that the observed association is more pronounced among social media users. Overall, our paper documents the potential adverse financial outcomes of crypto investments at the consumer behavior level, providing a novel perspective that contrasts with the predominantly optimistic market narrative about digital assets.
{"title":"Where is the risk? How cryptocurrency investment relates to undesirable financial behaviors","authors":"Zefeng Bai , Pengcheng Wang , Botong Xue","doi":"10.1016/j.jbef.2026.101149","DOIUrl":"10.1016/j.jbef.2026.101149","url":null,"abstract":"<div><div>This paper investigates how cryptocurrency investment is associated with three undesirable financial behaviors: late mortgage payment, credit card misuse, and retirement borrowing. Drawing on Behavioral Portfolio Theory (BPT), we propose that cryptocurrency’s unique features encourage a “mental account invasion”, where investors engage in behaviors that build the aspirational layer at the expense of the protection layer. Utilizing 2018 and 2021 NFCS data, we find that cryptocurrency investment is associated with an increased likelihood of all three undesirable financial behaviors. Findings are robust to propensity score matching (PSM) and an instrumental variable (IV) design. Furthermore, we find that the observed association is more pronounced among social media users. Overall, our paper documents the potential adverse financial outcomes of crypto investments at the consumer behavior level, providing a novel perspective that contrasts with the predominantly optimistic market narrative about digital assets.</div></div>","PeriodicalId":47026,"journal":{"name":"Journal of Behavioral and Experimental Finance","volume":"49 ","pages":"Article 101149"},"PeriodicalIF":4.7,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-26DOI: 10.1016/j.jbef.2026.101147
Xiaohui Wang , Yukihiko Funaki
In this study, we examine how different tournament-style bonus structures influence asset bubbles in experimental markets. We compare two structures: one that rewards a broad group of traders (“Bonus for Most”) and another that rewards only a few top performers (“Bonus for Few”). The findings indicate that the Bonus for Most structure is more likely to exacerbate bubble formation when traders gain experience. Under this structure, traders are more inclined to buy overpriced assets, not to improve long-term performance, but to boost short-term portfolio values and increase their chances of earning a bonus. This behavior, referred to as strategic asset accumulation, is less common under Bonus for Few, which offers lower opportunities for such manipulation-driven rewards. These findings demonstrate how short-term tournament incentives can unintentionally amplify price distortions, underscoring the importance of thoughtful incentive design in supporting market efficiency and stability.
{"title":"Bonus structures and bubble formation in experimental asset markets","authors":"Xiaohui Wang , Yukihiko Funaki","doi":"10.1016/j.jbef.2026.101147","DOIUrl":"10.1016/j.jbef.2026.101147","url":null,"abstract":"<div><div>In this study, we examine how different tournament-style bonus structures influence asset bubbles in experimental markets. We compare two structures: one that rewards a broad group of traders (“Bonus for Most”) and another that rewards only a few top performers (“Bonus for Few”). The findings indicate that the Bonus for Most structure is more likely to exacerbate bubble formation when traders gain experience. Under this structure, traders are more inclined to buy overpriced assets, not to improve long-term performance, but to boost short-term portfolio values and increase their chances of earning a bonus. This behavior, referred to as strategic asset accumulation, is less common under Bonus for Few, which offers lower opportunities for such manipulation-driven rewards. These findings demonstrate how short-term tournament incentives can unintentionally amplify price distortions, underscoring the importance of thoughtful incentive design in supporting market efficiency and stability.</div></div>","PeriodicalId":47026,"journal":{"name":"Journal of Behavioral and Experimental Finance","volume":"49 ","pages":"Article 101147"},"PeriodicalIF":4.7,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1016/j.jbef.2026.101142
Matthew Flynn, Yifan Liu
Bitcoin options have rapidly emerged as a major derivatives market built on an underlying asset characterized by extreme volatility and high levels of speculation. This setting provides a natural environment to study how gambling related preferences influence investor behavior. Specifically, this study examines how state-level gambling propensity drives investor attention to Bitcoin options and affects market outcomes. Through the collection of Google Trends search volumes, we find that local gambling propensity significantly increases attention to Bitcoin options. Exploiting staggered sports betting legalization, we find low-friction gambling substitutes reduce Bitcoin option attention in high-gambling states, consistent with substitution among entertainment venues. Gambling propensity increases option volume, open interest, and preference for out-of-the-money contracts, while attention increases underlying Bitcoin volatility. These findings highlight distinct behavioral channels through which gambling motives and attention shocks influence Bitcoin options and spot markets.
{"title":"Gambling on Bitcoin options?","authors":"Matthew Flynn, Yifan Liu","doi":"10.1016/j.jbef.2026.101142","DOIUrl":"10.1016/j.jbef.2026.101142","url":null,"abstract":"<div><div>Bitcoin options have rapidly emerged as a major derivatives market built on an underlying asset characterized by extreme volatility and high levels of speculation. This setting provides a natural environment to study how gambling related preferences influence investor behavior. Specifically, this study examines how state-level gambling propensity drives investor attention to Bitcoin options and affects market outcomes. Through the collection of Google Trends search volumes, we find that local gambling propensity significantly increases attention to Bitcoin options. Exploiting staggered sports betting legalization, we find low-friction gambling substitutes reduce Bitcoin option attention in high-gambling states, consistent with substitution among entertainment venues. Gambling propensity increases option volume, open interest, and preference for out-of-the-money contracts, while attention increases underlying Bitcoin volatility. These findings highlight distinct behavioral channels through which gambling motives and attention shocks influence Bitcoin options and spot markets.</div></div>","PeriodicalId":47026,"journal":{"name":"Journal of Behavioral and Experimental Finance","volume":"49 ","pages":"Article 101142"},"PeriodicalIF":4.7,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1016/j.jbef.2026.101146
Donglai Ning, Yukihiro Yasuda
This study investigates whether biodiversity risk disclosures reduce stock price crash risk, leveraging the early adopter announcements of the Taskforce on Nature-related Financial Disclosures (TNFD) in January 2024. Using a difference-in-differences framework, we find that TNFD adoption significantly reduces crash risk. The effect is pronounced in firms with high biodiversity exposure, strong governance, and high retail investor presence, consistent with risk-materiality, agency problem, and behavioral channels, respectively. We further show that TNFD provides incremental crash-risk reduction for firms that had previously adopted the framework of the Taskforce on Climate-related Financial Disclosures (TCFD), with benefits strongest among early TCFD adopters. Overall, our findings highlight the value of nature-related disclosures in enhancing transparency and mitigating downside risk, offering timely insights for regulators and investors as biodiversity reporting frameworks continue to evolve.
{"title":"Biodiversity risk disclosures and stock price crash risk","authors":"Donglai Ning, Yukihiro Yasuda","doi":"10.1016/j.jbef.2026.101146","DOIUrl":"10.1016/j.jbef.2026.101146","url":null,"abstract":"<div><div>This study investigates whether biodiversity risk disclosures reduce stock price crash risk, leveraging the early adopter announcements of the Taskforce on Nature-related Financial Disclosures (TNFD) in January 2024. Using a difference-in-differences framework, we find that TNFD adoption significantly reduces crash risk. The effect is pronounced in firms with high biodiversity exposure, strong governance, and high retail investor presence, consistent with risk-materiality, agency problem, and behavioral channels, respectively. We further show that TNFD provides incremental crash-risk reduction for firms that had previously adopted the framework of the Taskforce on Climate-related Financial Disclosures (TCFD), with benefits strongest among early TCFD adopters. Overall, our findings highlight the value of nature-related disclosures in enhancing transparency and mitigating downside risk, offering timely insights for regulators and investors as biodiversity reporting frameworks continue to evolve.</div></div>","PeriodicalId":47026,"journal":{"name":"Journal of Behavioral and Experimental Finance","volume":"49 ","pages":"Article 101146"},"PeriodicalIF":4.7,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.jbef.2026.101145
Tae-Young Pak
This study provides a comprehensive analysis of how individuals use large language models (LLMs) like ChatGPT for everyday financial management. Using survey data from 2170 Korean adults aged 25–59, this study examines the breadth and depth of LLM use across ten domains of personal finance, including budgeting, savings, investment, tax filing, debt, insurance, housing, fraud detection, financial literacy, and psychological support. Results indicate that 67.8 % of respondents have used LLMs for at least one financial task, and 58.7 % have engaged with them across two or more domains. About 15 % reported using LLMs for all ten financial tasks, while 32.2 % indicated that they have never used LLM for financial purposes. The most common applications were stock investment (50.3 %), savings planning (48.2 %), budget management (47.6 %), and tax filing and planning (46.5 %). Usage was significantly higher among men, younger adults, those with higher education, and full-time workers, whereas differences by income, wealth, and home ownership were not significant. Individuals most often used LLM as an on-demand tutor - seeking explanations of terms, concepts, and processes - or as a search engine to retrieve targeted information and compare financial products, though some utilized it for personalized advice or even emotional support. Overall, this study shows that LLMs are already widely used in personal finance, though adoption varies across financial tasks and demographic groups.
{"title":"How individuals use generative AI for personal financial management","authors":"Tae-Young Pak","doi":"10.1016/j.jbef.2026.101145","DOIUrl":"10.1016/j.jbef.2026.101145","url":null,"abstract":"<div><div>This study provides a comprehensive analysis of how individuals use large language models (LLMs) like ChatGPT for everyday financial management. Using survey data from 2170 Korean adults aged 25–59, this study examines the breadth and depth of LLM use across ten domains of personal finance, including budgeting, savings, investment, tax filing, debt, insurance, housing, fraud detection, financial literacy, and psychological support. Results indicate that 67.8 % of respondents have used LLMs for at least one financial task, and 58.7 % have engaged with them across two or more domains. About 15 % reported using LLMs for all ten financial tasks, while 32.2 % indicated that they have never used LLM for financial purposes. The most common applications were stock investment (50.3 %), savings planning (48.2 %), budget management (47.6 %), and tax filing and planning (46.5 %). Usage was significantly higher among men, younger adults, those with higher education, and full-time workers, whereas differences by income, wealth, and home ownership were not significant. Individuals most often used LLM as an <em>on-demand tutor</em> - seeking explanations of terms, concepts, and processes - or as a <em>search engine</em> to retrieve targeted information and compare financial products, though some utilized it for <em>personalized advice</em> or even <em>emotional support</em>. Overall, this study shows that LLMs are already widely used in personal finance, though adoption varies across financial tasks and demographic groups.</div></div>","PeriodicalId":47026,"journal":{"name":"Journal of Behavioral and Experimental Finance","volume":"49 ","pages":"Article 101145"},"PeriodicalIF":4.7,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1016/j.jbef.2026.101144
Pattanaporn Chatjuthamard , Pornsit Jiraporn , Sang Mook Lee
We examine the effect of board size on the adoption of artificial intelligence (AI)-skilled employees, a critical determinant of firms’ ability to leverage AI technologies. Board size, a crucial dimension of board governance, plays a pivotal role in shaping strategic decision-making and a firm’s adaptability to technological change. Using a novel dataset by Babina et al. (2024) that identifies AI-related roles through advanced textual analysis of resumes, we utilize the share of AI workers in firms over time. Our findings reveal that smaller boards significantly enhance the integration of AI-skilled employees. Specifically, a reduction in board size by one standard deviation increases the share of AI workers by 8.4 %. Interaction analyses show that smaller boards are particularly advantageous in R&D-intensive firms and those with substantial cash reserves. Smaller boards also result in greater variability in AI workforce integration, reflecting their capacity to foster flexibility, and adaptive learning in dynamic environments.
{"title":"Artificial intelligence (AI) and corporate governance: Evidence from board size","authors":"Pattanaporn Chatjuthamard , Pornsit Jiraporn , Sang Mook Lee","doi":"10.1016/j.jbef.2026.101144","DOIUrl":"10.1016/j.jbef.2026.101144","url":null,"abstract":"<div><div>We examine the effect of board size on the adoption of artificial intelligence (AI)-skilled employees, a critical determinant of firms’ ability to leverage AI technologies. Board size, a crucial dimension of board governance, plays a pivotal role in shaping strategic decision-making and a firm’s adaptability to technological change. Using a novel dataset by Babina et al. (2024) that identifies AI-related roles through advanced textual analysis of resumes, we utilize the share of AI workers in firms over time. Our findings reveal that smaller boards significantly enhance the integration of AI-skilled employees. Specifically, a reduction in board size by one standard deviation increases the share of AI workers by 8.4 %. Interaction analyses show that smaller boards are particularly advantageous in R&<span>D</span>-intensive firms and those with substantial cash reserves. Smaller boards also result in greater variability in AI workforce integration, reflecting their capacity to foster flexibility, and adaptive learning in dynamic environments.</div></div>","PeriodicalId":47026,"journal":{"name":"Journal of Behavioral and Experimental Finance","volume":"49 ","pages":"Article 101144"},"PeriodicalIF":4.7,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-11DOI: 10.1016/j.jbef.2026.101143
Zhen Che , Wenke Yang , Changqi Wu , Qin Gao
This study examines the impact of emotional factors and knowledge spillover on the behavioral tendencies of firms, universities, and research institutions within dynamic collaborative innovation environments. Drawing on Rank-Dependent Expected Utility (RDEU) theory with integrated emotional functions, we develop a collaborative innovation model to investigate how knowledge spillovers and emotions shape cooperative dynamics. The results show that players’ emotional states exert a nonlinear influence on strategic decisions, with outcomes determined not by optimism or pessimism alone, but by the intensity of emotions and mutual expectations. Furthermore, knowledge spillovers condition these dynamics by weakening cooperative incentives among optimistic players, while strengthening the willingness of more cautious players to sustain collaboration, with cooperative stability evolving across different stages of interaction. These findings provide new insights into the strategic processes of collaborative innovation from both emotional and knowledge spillover perspectives, offering governance implications for enhancing cooperation among industry, universities, and research institutions.
{"title":"Emotional dynamics and knowledge spillover in collaborative innovation","authors":"Zhen Che , Wenke Yang , Changqi Wu , Qin Gao","doi":"10.1016/j.jbef.2026.101143","DOIUrl":"10.1016/j.jbef.2026.101143","url":null,"abstract":"<div><div>This study examines the impact of emotional factors and knowledge spillover on the behavioral tendencies of firms, universities, and research institutions within dynamic collaborative innovation environments. Drawing on Rank-Dependent Expected Utility (RDEU) theory with integrated emotional functions, we develop a collaborative innovation model to investigate how knowledge spillovers and emotions shape cooperative dynamics. The results show that players’ emotional states exert a nonlinear influence on strategic decisions, with outcomes determined not by optimism or pessimism alone, but by the intensity of emotions and mutual expectations. Furthermore, knowledge spillovers condition these dynamics by weakening cooperative incentives among optimistic players, while strengthening the willingness of more cautious players to sustain collaboration, with cooperative stability evolving across different stages of interaction. These findings provide new insights into the strategic processes of collaborative innovation from both emotional and knowledge spillover perspectives, offering governance implications for enhancing cooperation among industry, universities, and research institutions.</div></div>","PeriodicalId":47026,"journal":{"name":"Journal of Behavioral and Experimental Finance","volume":"49 ","pages":"Article 101143"},"PeriodicalIF":4.7,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.jbef.2026.101140
Khanh Hoang
This paper investigates the impact of local corruption on the career decisions of financial regulators, focusing on their choice to exit public service. We develop a novel utility-based model that demonstrates how corruption in local government reduces the utility of remaining in regulatory roles, thereby increasing the likelihood of regulators’ departure. Our empirical analysis, based on a comprehensive dataset of 1914 SEC financial regulators, robustly confirms this relationship. Our findings underscore the importance of institutional quality in retaining skilled regulators essential for effective oversight and public policy implementation.
{"title":"Local corruption and financial regulators’ exit option: A utility approach","authors":"Khanh Hoang","doi":"10.1016/j.jbef.2026.101140","DOIUrl":"10.1016/j.jbef.2026.101140","url":null,"abstract":"<div><div>This paper investigates the impact of local corruption on the career decisions of financial regulators, focusing on their choice to exit public service. We develop a novel utility-based model that demonstrates how corruption in local government reduces the utility of remaining in regulatory roles, thereby increasing the likelihood of regulators’ departure. Our empirical analysis, based on a comprehensive dataset of 1914 SEC financial regulators, robustly confirms this relationship. Our findings underscore the importance of institutional quality in retaining skilled regulators essential for effective oversight and public policy implementation.</div></div>","PeriodicalId":47026,"journal":{"name":"Journal of Behavioral and Experimental Finance","volume":"49 ","pages":"Article 101140"},"PeriodicalIF":4.7,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-05DOI: 10.1016/j.jbef.2025.101135
Vijaya B. Marisetty , Wouter van Heeswijk , Archana Narayanan
Rumors in financial markets impact investors’ decisions, driving asset prices away from their fundamental valuations. From a regulatory perspective, it is challenging to contain such rumors. We develop an agent-based model to understand the price discovery process in a simulated stock market that allows heterogeneous agents, who differ in financial literacy and cognitive ability to interact for price formation. We show that both financial literacy and cognitive ability are important determinants of rumor spread in stock markets: Higher (lower) cognitive ability and higher (lower) financial literacy reduce (increase) rumor spread. Our results suggest that both the prevalence and intensity of financial literacy play a significant role in reducing rumor induced volatility and promoting market stability.
{"title":"An agent-based model of rumor-induced volatility in financial markets","authors":"Vijaya B. Marisetty , Wouter van Heeswijk , Archana Narayanan","doi":"10.1016/j.jbef.2025.101135","DOIUrl":"10.1016/j.jbef.2025.101135","url":null,"abstract":"<div><div>Rumors in financial markets impact investors’ decisions, driving asset prices away from their fundamental valuations. From a regulatory perspective, it is challenging to contain such rumors. We develop an agent-based model to understand the price discovery process in a simulated stock market that allows heterogeneous agents, who differ in financial literacy and cognitive ability to interact for price formation. We show that both financial literacy and cognitive ability are important determinants of rumor spread in stock markets: Higher (lower) cognitive ability and higher (lower) financial literacy reduce (increase) rumor spread. Our results suggest that both the prevalence and intensity of financial literacy play a significant role in reducing rumor induced volatility and promoting market stability.</div></div>","PeriodicalId":47026,"journal":{"name":"Journal of Behavioral and Experimental Finance","volume":"49 ","pages":"Article 101135"},"PeriodicalIF":4.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-02DOI: 10.1016/j.jbef.2026.101139
John Hua Fan , Mingyi Li , Xinyu Wang
We document a surge in gambling demand surrounding the Lunar New Year (LNY) period in the Chinese A-share market. Lottery-like stocks significantly outperform non-lottery-like stocks by 4.1 % over the ten trading days following the LNY. These gains gradually diminish over the longer term, consistent with a short-term mispricing effect. This effect is specific to the LNY window and is not observed during other major Chinese holidays or ordinary trading periods. Furthermore, the phenomenon is not driven by state-owned enterprises or Special Treatment (ST) stocks. Overall, our findings suggest that the cultural significance of the Chinese Lunar New Year plays a distinct role in shaping investor gambling demand and asset pricing dynamics.
{"title":"Cultural celebrations and investor gambling behavior","authors":"John Hua Fan , Mingyi Li , Xinyu Wang","doi":"10.1016/j.jbef.2026.101139","DOIUrl":"10.1016/j.jbef.2026.101139","url":null,"abstract":"<div><div>We document a surge in gambling demand surrounding the Lunar New Year (LNY) period in the Chinese A-share market. Lottery-like stocks significantly outperform non-lottery-like stocks by 4.1 % over the ten trading days following the LNY. These gains gradually diminish over the longer term, consistent with a short-term mispricing effect. This effect is specific to the LNY window and is not observed during other major Chinese holidays or ordinary trading periods. Furthermore, the phenomenon is not driven by state-owned enterprises or Special Treatment (ST) stocks. Overall, our findings suggest that the cultural significance of the Chinese Lunar New Year plays a distinct role in shaping investor gambling demand and asset pricing dynamics.</div></div>","PeriodicalId":47026,"journal":{"name":"Journal of Behavioral and Experimental Finance","volume":"49 ","pages":"Article 101139"},"PeriodicalIF":4.7,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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