Journal of Behavioral and Experimental Finance最新文献

Individual investors’ decisions to switch investments very often lead to significantly lower investment returns so having an effective predictive model of these switches would be of value to clients, advisors and investment managers. A random forest algorithm was applied to a new dataset of over 20 million observations relating to 95,685 clients on Momentum Investments’ platform between 2018 and 2024. It identified a combination of investor characteristics (number of holdings, past switching behaviour, total assets) and external features (past returns, macroeconomic variables) as the key features of investor switch behaviour. This model exceeds commercially accepted standards in respect of the AUC and Gini metrics showcasing the model’s strength in its ranking capability. It can thus provide a useful basis for client segmentation and engagement by financial advisors.

This paper examines the effect of perceived trustworthiness on the turnover of Chief Executive Officers (CEOs) and its moderating role in the relationship between firm performance and CEO turnover. We employ a unique dataset comprising headshots of male CEOs in US-listed firms from 2010 to 2019 and a machine learning-based facial landmark detector to construct a composite facial trustworthiness index. Our results show that CEOs exhibiting high facial trustworthiness experience a lower turnover risk. We also find that facial trustworthiness strongly influences the link between firm performance and CEO turnover. Well-performing executives have a lower risk of being dismissed from their positions, and the dismissal risk is even lower among CEOs perceived as having a high facial trustworthiness. Conversely, underperforming CEOs are more likely to be dismissed, and the dismissal probability is greater among CEOs who have been perceived as being highly trustworthy. Our findings are consistent with the predictions of the expectancy violation theory, which posits that trustworthy-looking executives are more likely to be punished if they do not live up to the expectations held by the board of directors.

The objective of financial reporting is to provide decision-useful information for a wide range of existing and potential stakeholders. However, the lack of regulation regarding the use of visual imagery may enable companies to engage in impression management and subtly influence the reader’s judgments through careful selection of images and how they obfuscate or clarify information. This study utilizes eye-tracking technology to experimentally examine how non-explanatory photographs influence the performance judgments of non-professional investors comprising business students and auditors. Participants were exposed to an excerpt of the management summary of a fictitious company’s annual report. The findings suggest while a non-explanatory photograph attracts attention in a text-image combination, the information search process and performance-related judgments are free of influences from the photograph. This implies other photographic attributes, such as triggering emotional influences or providing explanatory information, may be more relevant for impression management in influencing judgments over attentional influences.

Cybercrime increased dramatically during the Covid-19 pandemic, when people’s online exposure rose significantly. In this paper, we analyze the relation between being a victim of online financial fraud and financial literacy, using representative Italian data collected in 2020. We find that basic financial knowledge is negatively associated with the probability of being defrauded online, while individuals who are overconfident about their level of financial literacy are more likely to fall victim to fraud. Moreover, those who were forced to work remotely because of the pandemic experienced a higher risk of fraud, that was reduced by financial literacy. Our findings suggest that financial literacy may provide protection against online fraud even in a context of high online exposure.

This paper analyzes how financial literacy and the perception of own eccentricity in pension preferences relates to citizens’ desire to make own choices or to delegate these to the government. It also considers how these factors relate to what regulation citizens want for their co-citizens, and to what extent the regulation they want for themselves relates to the regulation they want for others. We find that respondents with more financial knowledge want less government regulation. Furthermore, those that perceive themselves as having different preferences than the average population want less government regulation. The amount of regulation that respondents want for themselves is highly correlated with what they want for others. However, some respondents hold different preferences for themselves than for others. Specifically, those that want less government regulation for themselves and have more financial knowledge want, on average, more government regulation for others.

This study delves into the impact of gamification usage on the customer relationship management performance of microfinance platforms in China. Specifically, we systematically investigate the positive and negative perceptions influencing gamification adoption, which in turn affect satisfaction, customer engagement, and retention. Our results show that perceived benefit positively influences gamification usage intention, whereas perceived sacrifice exerts a negative impact. Furthermore, gamification usage intention correlates positively with satisfaction, customer engagement, and retention. Finally, the study highlights the positive relationships between satisfaction and customer engagement, as well as between customer engagement and retention. However, no significant relationship is observed between customer satisfaction and customer retention. These insights offer essential implications for enhancing user experiences and fostering customer loyalty.

This study builds on the seminal work of Tversky and Kahneman (1974), exploring the presence and extent of anchoring bias in forecasts generated by four Large Language Models (LLMs): GPT-4, Claude 2, Gemini Pro and GPT-3.5. In contrast to recent findings of advanced reasoning capabilities in LLMs, our randomised controlled trials reveal the presence of anchoring bias across all models: forecasts are significantly influenced by prior mention of high or low values. We examine two mitigation prompting strategies, ‘Chain of Thought’ and ‘ignore previous’, finding limited and varying degrees of effectiveness. Our results extend the anchoring bias research in finance beyond human decision-making to encompass LLMs, highlighting the importance of deliberate and informed prompting in AI forecasting in both ad hoc LLM use and in crafting few-shot examples.

This paper evaluates how Chinese stocks respond to the onboarding of China-focused ESG scores on the Bloomberg Professional Terminal in the short term. By utilizing the event study approach, we find that the top 10 % of ESG-rated stocks react significantly positively to the onboarding event, whereas the bottom 10 % of ESG-rated stocks experience significant and negative cumulative average abnormal returns. Moreover, this effect is asymmetric in that the negative returns have a greater and more prominent magnitude than the positive returns. By comparing the cross-sectional data results before and after the rating event, we propose several channels through which these effects may function. The findings of this study also have economic and policy implications for investors and policy-makers.