Although traditional behavioral theories suggest that agents seek out as much information as possible in order to make an informed decision, recent studies indicate that humans may also be motivated to avoid new information that might lead to a potentially unpleasant emotional response. Such human characteristics become even more relevant in financial markets where information in the form of financial news is intended to serve as an important basis for financial decision-making. This paper therefore aims to enhance the understanding of how humans perceive information embedded in financial news. For this purpose, we conduct a controlled NeuroIS laboratory experiment and utilize advanced methods from statistical learning to systematically analyze the information processing of financial news. Overall, this study aims to contribute to Information Systems theory by adapting the theoretical concepts of cognitive dissonance and information avoidance to financial markets in order to better understand how decision-makers process and act upon information.