Emotional economy

Aim of the study: The aim of the article is to examine the impact of social moods and investor sentiment on the functioning of financial markets and to demonstrate that collective emotions represent a significant, measurable, and increasingly powerful determinant of asset valuation. Methodology: The article is based on an analysis of the literature in economics, behavioral finance, and narrative economics, as well as on case studies of selected market events. It considers both historical concepts and contemporary sentiment measurement methods – from traditional indicators to Big Data tools and alternative data analytics. Main findings: Investor sentiment influences valuation processes through mechanisms such as herding behavior, informational cascades, and feedback loops amplified by social media and algorithmic trading. The analysis of examples – from the dot-com bubble, through the 2008 financial crisis, to the COVID-19 crash and the GameStop saga – shows that collective emotions can destabilize markets but also create new investment opportunities. Application of the study: The findings are relevant for individual and institutional investors as well as regulators. They may serve to improve risk management, design tools for monitoring market sentiment, and develop more adequate regulations in the digital era. Originality/Novelty of the study: The article highlights the integration of behavioral finance, narrative economics, and modern data analysis methods, emphasizing the growing role of the “economy of emotions” in the functioning of contemporary financial markets

Behavioral economics · Big data · Efficient-market hypothesis · Financial market · Herding · Narrative · Sentiment analysis · Social media · Financial Markets and Investment Strategies · Impact of AI and Big Data on Business and Society · Stock Market Forecasting Methods