Journal of Behavioral and Experimental Finance最新文献_第2页

Loss aversion in investment: A bibliometric and network visualization analysis 投资中的损失厌恶：文献计量和网络可视化分析

IF 4.7 2区经济学

Journal of Behavioral and Experimental Finance Pub Date : 2026-03-01 Epub Date: 2026-02-06 DOI: 10.1016/j.jbef.2026.101155

Siddhesh S. Soman , Abhijit Chirputkar , Krunal K. Punjani

{"title":"Loss aversion in investment: A bibliometric and network visualization analysis","authors":"Siddhesh S. Soman , Abhijit Chirputkar , Krunal K. Punjani","doi":"10.1016/j.jbef.2026.101155","DOIUrl":"10.1016/j.jbef.2026.101155","url":null,"abstract":"<div><div>Loss aversion is widely considered one of the most influential behavioral biases among investors and has been studied by several researchers over the years. However, a bibliometric study on the topic of ‘Loss Aversion in Investment’ does not exist. To address this gap, we present the first-ever bibliometric and network visualization analysis based on 427 research articles published from 1996 to October 2025. A comprehensive bibliometric protocol was followed, covering document selection, bibliometric analysis, network analysis, and content analysis. Data extracted from ‘Scopus’ database was analyzed using ‘Bibliometrix’ tool. Key indicators included annual research output, citation patterns, co-authorship, co-citation, bibliographic coupling, thematic trends, and keyword analysis. Network visualization was conducted using ‘VOSviewer’, while thematic analysis utilized Biblioshiny to identify emerging research themes. These analyses reveal key clusters in behavioral finance theories and emerging themes in asset allocation. The findings provide theoretical insights, practical implications, and directions for future research.</div></div>","PeriodicalId":47026,"journal":{"name":"Journal of Behavioral and Experimental Finance","volume":"49 ","pages":"Article 101155"},"PeriodicalIF":4.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188866","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}

引用次数: 0

Corrigendum to “Higher order risk attitudes of financial experts” [J. Behav. Exp. Finance 34 (2022) 100658] 金融专家的高阶风险态度[J]。Behav。财政34 (2022)100658]

IF 4.7 2区经济学

Journal of Behavioral and Experimental Finance Pub Date : 2026-03-01 Epub Date: 2025-12-06 DOI: 10.1016/j.jbef.2025.101124

Anna Bottasso , Sébastien Duchêne , Eric Guerci , Nobuyuki Hanaki , Charles N. Noussair

引用次数: 0

Emotional dynamics and knowledge spillover in collaborative innovation 协同创新中的情感动力与知识溢出

IF 4.7 2区经济学

Journal of Behavioral and Experimental Finance Pub Date : 2026-03-01 Epub Date: 2026-01-11 DOI: 10.1016/j.jbef.2026.101143

Zhen Che , Wenke Yang , Changqi Wu , Qin Gao

{"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":"2026-03-01","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}

引用次数: 0

Attention to detail: Learning about mergers 注意细节：了解合并

IF 4.7 2区经济学

Journal of Behavioral and Experimental Finance Pub Date : 2026-03-01 Epub Date: 2026-02-27 DOI: 10.1016/j.jbef.2026.101163

Adam L. Aiken , Choonsik Lee

引用次数: 0

The role of alternative data in micro-enterprises’ credit risk assessment in China — Empirical evidence based on machine learning 替代数据在中国微型企业信用风险评估中的作用——基于机器学习的经验证据

IF 4.7 2区经济学

Journal of Behavioral and Experimental Finance Pub Date : 2026-03-01 Epub Date: 2026-02-04 DOI: 10.1016/j.jbef.2026.101154

Min Jiang , Jichuan Shi , Yukai Zheng , Wei Zhou

{"title":"The role of alternative data in micro-enterprises’ credit risk assessment in China — Empirical evidence based on machine learning","authors":"Min Jiang , Jichuan Shi , Yukai Zheng , Wei Zhou","doi":"10.1016/j.jbef.2026.101154","DOIUrl":"10.1016/j.jbef.2026.101154","url":null,"abstract":"<div><div>Applying alternative data in credit reporting can enhance financial institutions' ability to assess the credit risk of micro-enterprises. This paper uses data from an internet bank serving micro-enterprises and categorizes alternative data into historical credit data (HCD) and behavioral data—the latter including economic transaction data (ETD) and social stability data (SSD). The random forest method is employed to compare these data types in credit risk assessment. The findings reveal that multi-dimensional alternative data holds significant credit value for micro-enterprise risk assessment and can improve the predictive performance and stability of the models. The behavioral data-based models demonstrate superior risk identification capability compared with the HCD-based one. ETD generally outperforms SSD in assessment, though SSD is more stable under external shocks. While external environmental shocks may reduce the model's precision in detecting potential defaults, the model's overall ranking stability remains intact. Notably, the integration of multi-dimensional alternative data can mitigate data volatility through feature complementarity, thereby enhancing model robustness.</div></div>","PeriodicalId":47026,"journal":{"name":"Journal of Behavioral and Experimental Finance","volume":"49 ","pages":"Article 101154"},"PeriodicalIF":4.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188867","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}

引用次数: 0

An agent-based model of rumor-induced volatility in financial markets 金融市场谣言引发波动的基于主体模型

IF 4.7 2区经济学

Journal of Behavioral and Experimental Finance Pub Date : 2026-03-01 Epub Date: 2026-01-05 DOI: 10.1016/j.jbef.2025.101135

Vijaya B. Marisetty , Wouter van Heeswijk , Archana Narayanan

引用次数: 0

Google Trends—Augmented XGBoost for market volatility prediction: A machine learning early warning system 趋势-增强XGBoost市场波动预测：机器学习预警系统

IF 4.7 2区经济学

Journal of Behavioral and Experimental Finance Pub Date : 2026-03-01 Epub Date: 2026-02-19 DOI: 10.1016/j.jbef.2026.101159

Gagan Deep , Akash Deep , Svetlozar T. Rachev , Frank J. Fabozzi

{"title":"Google Trends—Augmented XGBoost for market volatility prediction: A machine learning early warning system","authors":"Gagan Deep , Akash Deep , Svetlozar T. Rachev , Frank J. Fabozzi","doi":"10.1016/j.jbef.2026.101159","DOIUrl":"10.1016/j.jbef.2026.101159","url":null,"abstract":"<div><div>We develop GT-XGBoost, a machine learning early warning system that integrates Google search attention signals with traditional volatility indicators for market spike prediction. Using monthly search volume indices for recession, financial crisis, volatility, and stock market crash terms (2004–2024), we construct behavioral features through momentum, acceleration, and composite attention measures. Our optimized model achieves one-month-ahead VIX spike (<span><math><mrow><mo>≥</mo><mn>30</mn></mrow></math></span>) prediction with ROC AUC of 0.745, demonstrating competitive performance among thirteen academic benchmarks including GARCH family models, stochastic volatility approaches, and realized volatility specifications. The system demonstrates 70.6% precision with financial crisis searches providing a one-month leading indicator (correlation: 0.660). Validation through safe-haven gold price analysis confirms systematic 3.32% returns during volatility episodes. Our findings establish that behavioral attention signals, particularly recession-related search patterns, provide dominant predictive information for institutional risk management when integrated with traditional volatility indicators.</div></div>","PeriodicalId":47026,"journal":{"name":"Journal of Behavioral and Experimental Finance","volume":"49 ","pages":"Article 101159"},"PeriodicalIF":4.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396466","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}

引用次数: 0

Golden eye — How traders focus on and select information in experimental asset markets 黄金眼——交易者如何在实验性资产市场中关注和选择信息

IF 4.7 2区经济学

Journal of Behavioral and Experimental Finance Pub Date : 2026-03-01 Epub Date: 2025-12-23 DOI: 10.1016/j.jbef.2025.101138

Matthias Herrmann-Romero , Simon Liegl , Martin Angerer , Thomas Stöckl

引用次数: 0

Bonus structures and bubble formation in experimental asset markets 实验性资产市场中的奖金结构与泡沫形成

IF 4.7 2区经济学

Journal of Behavioral and Experimental Finance Pub Date : 2026-03-01 Epub Date: 2026-01-26 DOI: 10.1016/j.jbef.2026.101147

Xiaohui Wang , Yukihiko Funaki

引用次数: 0

Time-varying risk aversion and the equity term structure 时变风险厌恶与股权期限结构