Researchers have long recognized the importance of understanding corporate risk disclosures and conducted empirical studies to investigate various research questions. This dissertation particularly focuses on investigating the impact of these textual risk disclosures on post-disclosure risk perceptions of investors. The objective of this dissertation is two-fold. Firstly, we aim to design an automated analytical tool to simultaneously discover and quantify the variables of interest from large amount of unstructured text. Although this tool is developed and utilized for discovering and quantifying types of risk from textual risk disclosures, it could be easily applied to other textual data such as the user generated textual online reviews. Secondly, we seek to investigate the specific effects of textual risk disclosures on investors’ post-disclosure risk perceptions. This part of the study is expected to contribute to literature about the study of risk disclosures by providing an alternative explanation of the previous findings on how risk disclosures affect the risk perceptions of investors.