{"title":"人工智能和大数据代币:认知联合,放牧模式起飞","authors":"","doi":"10.1016/j.ribaf.2024.102506","DOIUrl":null,"url":null,"abstract":"<div><p>Artificial intelligence (AI) and big data tokens have emerged as unique investment options, garnering interest due to their connectedness with other assets and financial markets. Utilizing Chang et al. (2000)'s cross-sectional absolute deviation (CSAD) model, we investigate static and time-varying herding in the AI and big data token markets. This research contributes to the growing discourse on AI and big data token investment through the lens of behavioral finance, with a particular focus on examining investor herding. The study's findings confirm market-wide herding of AI and big data tokens. The results suggest that investors exhibit herding in up markets, low volatility, and low volume days. Conversely, anti-herding is more prevalent in down markets, high volatility, and high volume days. Our analysis shows that herding is time-varying and emerges during a crisis period. The finding carries robust regulatory and policy implications to mitigate systemic risk and safeguard investor interests, ensuring market stability and resilience. The provided insights offer a valuable understanding of investors’ behavior across various market scenarios.</p></div>","PeriodicalId":51430,"journal":{"name":"Research in International Business and Finance","volume":null,"pages":null},"PeriodicalIF":6.3000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence and big data tokens: Where cognition unites, herding patterns take flight\",\"authors\":\"\",\"doi\":\"10.1016/j.ribaf.2024.102506\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Artificial intelligence (AI) and big data tokens have emerged as unique investment options, garnering interest due to their connectedness with other assets and financial markets. Utilizing Chang et al. (2000)'s cross-sectional absolute deviation (CSAD) model, we investigate static and time-varying herding in the AI and big data token markets. This research contributes to the growing discourse on AI and big data token investment through the lens of behavioral finance, with a particular focus on examining investor herding. The study's findings confirm market-wide herding of AI and big data tokens. The results suggest that investors exhibit herding in up markets, low volatility, and low volume days. Conversely, anti-herding is more prevalent in down markets, high volatility, and high volume days. Our analysis shows that herding is time-varying and emerges during a crisis period. The finding carries robust regulatory and policy implications to mitigate systemic risk and safeguard investor interests, ensuring market stability and resilience. The provided insights offer a valuable understanding of investors’ behavior across various market scenarios.</p></div>\",\"PeriodicalId\":51430,\"journal\":{\"name\":\"Research in International Business and Finance\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2024-08-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Research in International Business and Finance\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S027553192400299X\",\"RegionNum\":2,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research in International Business and Finance","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S027553192400299X","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
Artificial intelligence and big data tokens: Where cognition unites, herding patterns take flight
Artificial intelligence (AI) and big data tokens have emerged as unique investment options, garnering interest due to their connectedness with other assets and financial markets. Utilizing Chang et al. (2000)'s cross-sectional absolute deviation (CSAD) model, we investigate static and time-varying herding in the AI and big data token markets. This research contributes to the growing discourse on AI and big data token investment through the lens of behavioral finance, with a particular focus on examining investor herding. The study's findings confirm market-wide herding of AI and big data tokens. The results suggest that investors exhibit herding in up markets, low volatility, and low volume days. Conversely, anti-herding is more prevalent in down markets, high volatility, and high volume days. Our analysis shows that herding is time-varying and emerges during a crisis period. The finding carries robust regulatory and policy implications to mitigate systemic risk and safeguard investor interests, ensuring market stability and resilience. The provided insights offer a valuable understanding of investors’ behavior across various market scenarios.
期刊介绍:
Research in International Business and Finance (RIBAF) seeks to consolidate its position as a premier scholarly vehicle of academic finance. The Journal publishes high quality, insightful, well-written papers that explore current and new issues in international finance. Papers that foster dialogue, innovation, and intellectual risk-taking in financial studies; as well as shed light on the interaction between finance and broader societal concerns are particularly appreciated. The Journal welcomes submissions that seek to expand the boundaries of academic finance and otherwise challenge the discipline. Papers studying finance using a variety of methodologies; as well as interdisciplinary studies will be considered for publication. Papers that examine topical issues using extensive international data sets are welcome. Single-country studies can also be considered for publication provided that they develop novel methodological and theoretical approaches or fall within the Journal''s priority themes. It is especially important that single-country studies communicate to the reader why the particular chosen country is especially relevant to the issue being investigated. [...] The scope of topics that are most interesting to RIBAF readers include the following: -Financial markets and institutions -Financial practices and sustainability -The impact of national culture on finance -The impact of formal and informal institutions on finance -Privatizations, public financing, and nonprofit issues in finance -Interdisciplinary financial studies -Finance and international development -International financial crises and regulation -Financialization studies -International financial integration and architecture -Behavioral aspects in finance -Consumer finance -Methodologies and conceptualization issues related to finance