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Application of AI & News Sentiment in Finance [Research Presentations]
This Webinar is over| Date | Sep 29, 2020 |
| Time | 07:30 AM EDT |
| Cost | Free |
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Online
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#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!
ABOUT 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.
Topic 2: Long Term Enterprise Valuation Prediction by Prof S Chandrasekhar
The talk will focus to predict the long-term Enterprise value (EV) of a company using advanced machine learning and natural language processing. Enterprise value provides a better valuation of the company compared to market capitalization. Market capitalizations main focus is towards shareholder value whereas enterprise value text long term debt as well as cash in hand. To get EV we will add to market capitalization long term debt and deduct cash in hand. Predicting the enterprise value for a long term up to 6 months ahead on a rolling basis will help Investors, rating companies to obtain a long term view of their investment growth and also help in managing the Risk.
Registration Link: https://www.quantinsti.com/research-presentations-29-september-2020?utm_source=tellonline&utm_medium=referral&utm_campaign=22sept2020web
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!
ABOUT 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.
Topic 2: Long Term Enterprise Valuation Prediction by Prof S Chandrasekhar
The talk will focus to predict the long-term Enterprise value (EV) of a company using advanced machine learning and natural language processing. Enterprise value provides a better valuation of the company compared to market capitalization. Market capitalizations main focus is towards shareholder value whereas enterprise value text long term debt as well as cash in hand. To get EV we will add to market capitalization long term debt and deduct cash in hand. Predicting the enterprise value for a long term up to 6 months ahead on a rolling basis will help Investors, rating companies to obtain a long term view of their investment growth and also help in managing the Risk.
Registration Link: https://www.quantinsti.com/research-presentations-29-september-2020?utm_source=tellonline&utm_medium=referral&utm_campaign=22sept2020web
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