Invited Talk
Dr. Alaa Alhamzeh
Dr. Alaa Alhamzeh
Bio: Alaa Alhamzeh currently serves as a scientific researcher at the university of Passau, Germany. She earned her Ph.D. in computer science from the same university in collaboration with INSA Lyon, France. Her research covers natural language processing and computational argumentation, with a main focus on financial technology. Therefore, she co-organizes the pioneering series of Financial Argumentation Shared Tasks (FinArg).
Bio: Alaa Alhamzeh currently serves as a scientific researcher at the university of Passau, Germany. She earned her Ph.D. in computer science from the same university in collaboration with INSA Lyon, France. Her research covers natural language processing and computational argumentation, with a main focus on financial technology. Therefore, she co-organizes the pioneering series of Financial Argumentation Shared Tasks (FinArg).
Title: Argumentation in Financial Language: The Case of Earnings Conference Calls
Title: Argumentation in Financial Language: The Case of Earnings Conference Calls
Abstract: The recent advancement of NLP has greatly empowered financial text analysis. In this talk, we aim at exploring the given argumentation in financial speech with the goal of persuading market participants. In particular, we analyze the responses of company representatives’, during quarterly Earnings Conference Calls, using both argument mining and argument quality perspectives. We finally delve into how this analysis aids investor decision-making and its correlation with professional analysts' recommendations.
Abstract: The recent advancement of NLP has greatly empowered financial text analysis. In this talk, we aim at exploring the given argumentation in financial speech with the goal of persuading market participants. In particular, we analyze the responses of company representatives’, during quarterly Earnings Conference Calls, using both argument mining and argument quality perspectives. We finally delve into how this analysis aids investor decision-making and its correlation with professional analysts' recommendations.