Abstract:

Quantitative finance is interdisciplinary among three fields: computer science, mathematics, and financial economics. Traditionally, quantitative analysts focus on developing and implementing complex mathematical models for risk management, investments, and pricing. Due to the explosion of data generation and the technological revolution, both academia and industry in finance are now putting the focus on the effective use of information, thus giving rise to the field of data science. In this talk, I will first review several approaches of the traditional quantitative finance. Then, I will discuss how to exploit the soft information in finance, which usually refers to text, including opinions, ideas, and market commentary, to study financial risk among companies and to discover new finance keywords. Finally, a brief demonstration on our newly developed web-based information system, Fin10K, will be given to show its ability to facilitate the analysis on textual information in finance.

Bio

Dr. Chuan-Ju Wang received her Ph.D. degree in Computer Science and Information Engineering at National Taiwan University in 2011. Before joining the Research Center of Information Technology Innovation (CITI), Academia Sinica as an assistant research fellow in 2016, she worked as an associate professor at the University of Taipei, Taiwan. Her research interests include computational finance and data analytics.

Dr. Wang was a recipient of 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.

All faculties and students are welcome to join.