Behavioral Finance: An Exploration of Investor Psychology and Market Anomalies

This research explores the role of behavioral finance in interpreting the dynamics of financial markets, with particular reference to the tourism sector. Starting from the analysis of traditional financial theories, which postulate the efficiency of markets and the assumption of rational behavior by investors, the study highlights the limitations of these approaches in explaining market anomalies. Behavioral finance introduces a new perspective, integrating psychological principles to analyze the influence of emotions and cognitive biases in investment decisions. Through an empirical survey based on real-world data, the research used the Economic Sentiment Indicator (ESI) as a measure of market optimism and confidence, comparing this data with the performance of the iShares STOXX Europe 600 Travel & Leisure ETF index, which represents major companies in the travel and entertainment industry. The results show a significant correlation between ESI and index performance, highlighting the importance of collective economic perceptions in influencing industry returns. The analysis found that positive or negative shocks to economic sentiment produce immediate effects on returns, but these effects diminish in the short run. This phenomenon confirms the importance of gregarious behavior, with investors reacting quickly to news, often driven by emotion, and then gradually returning to a more rational approach. The study also delved into key cognitive biases, such as overconfidence, anchoring, gregarious behavior, and loss aversion. Overconfidence leads to overestimating one's ability to predict, while anchoring involves overreliance on initial information, even when new data indicate a different direction. Gregarious behavior occurs when investors follow the choices of the majority, contributing to the formation of speculative bubbles. Loss aversion, on the other hand, drives investors to avoid risks even in situations where they might be reasonable. To examine these dynamics, econometric models, including linear regression and Vector Autoregression (VAR), were used to analyze the dynamic relationships among the variables considered. The results indicate that incorporating sentiment-related variables improves predictive ability compared to models based solely on historical financial data. This shows how emotions and collective perceptions can amplify market fluctuations, especially during periods of high uncertainty