Part I: Students will be introduced to research studies that examine unstructured data (e.g., textual) and/or use non-traditional research methods (e.g., machine learning). We will focus primarily on popular textual measures (e.g., sentiment, readability, and similarity) and machine learning methods (e.g., Naïve Bayes and Support Vector Machines). The focus will be both on learning the underlying techniques, as well as developing an understanding of the relevant economic contexts in which to apply them to accounting questions. Part II: This part of the course will focus on current empirical techniques used in archival accounting research. The focus will be on research design and identification issues in the context of recent and evolving research on disclosure, information processing costs and real effects of reporting and disclosure.