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Application of AI & News Sentiment in Finance [Research Presentations] Topic 1: Credit Risk Modeling by Dr Xiao Qiao Deep learning can be used to price and calibrate models of credit risk. Deep neural networks can learn structural and reduced-form models with high degrees of accuracy. For complex credit risk models, whose closed-form solutions are not available, deep learning offers a conceptually simple and more efficient alternative solution. We propose an approach that combines deep learning with the unscented Kalman filter to calibrate credit risk models on historical data, which attains an R-squared of 98.5 percent for the reduced-form model and 95 percent for the structural model. All this and more covered in the webinar recording. ----------------------------------------- This webinar was conducted on Tuesday, September 9, 2020 08:30 AM ET | 6:00 PM IST | 8:30 PM SGT ----------------------------------------- #10YEARSOFQUANTINSTI As they say, knowledge is the greatest gift in life. While celebrating our 10 years of existence, we’ve planned this series to thank our community. We are thankful to you from the bottom of our heart for showing the love & support in our journey over the years! ----------------------------------------- Speaker Profile: Dr Xiao Qiao (Co-Founder of Paraconic Technologies) Xiao is a Co-Founder of Paraconic Technologies. His research interest includes asset pricing, financial econometrics, investments, commodities and return predictability. His research has been featured by Forbes, the CFA Institute, and Institutional Investor. He is on the editorial board of the Journal of Portfolio Management and the Global Commodities Applied Research Digest. He received a Ph.D. in Finance from the University of Chicago, where he was Nobel laureate Eugene Fama’s teaching assistant. ----------------------------------------- Most useful links: Join EPAT – Executive Programme in Algorithmic Trading: https://goo.gl/3Oyf2B Visit us at: https://www.quantinsti.com/ ----------------------------------------- Like and Follow us on: Facebook: / quantinsti Twitter: / quantinsti LinkedIn: / quantinsti Instagram: / quantinstian -----------------------------------------