In this work, we address the challenge of discovering financial signals in narrative financial reports. As these documents are often lengthy and tend to blend routine information with new information, it is challenging for professionals to discern critical financial signals. To this end, we leverage the inherent nature of the year-to-year structure of reports to define a novel signal-highlighting task; more importantly, we propose a compare-and-contrast multistage pipeline that recognizes different relationships between the reports and locates relevant rationales for these relationships. We also create and publicly release a human-annotated dataset for our task. Our experiments on the dataset validate the effectiveness of our pipeline, and we provide detailed analyses and ablation studies to support our findings.
Dr. Chuan-Ju Wang (王釧茹) received her Ph.D. degree in Computer Science and Information Engineering at National Taiwan University in 2011. She is now an Associate Research Fellow of the Research Center for IT Innovation, Academia Sinica in Taiwan. Her research interests include computational finance and data analytics. Dr. Wang was a recipient of the K. T. Li Young Researcher Award 2020, the 2020 Young Scholars' Creativity Award of the Foundation for the Advancement of Outstanding Scholarship, the 12th ACM Conference on Recommender Systems (RecSys’18) Runner-up Best Short Paper Award, the 10th S&F Best Paper Award, the 2015 Annual Meeting of the Financial Management Association (FMA) Best Paper Award, and the 2012 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Best Paper Award.