Information Services and Electronic Markets
Prof. Dr. Andreas Geyer-Schulz | Schroff-Stiftungslehrstuhl

Institute of Information Systems and Marketing
Department of Economics and Business Engineering

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Informationsdienste und elektronische Märkte
Impressum

Content

The volume, variety, and velocity of available data in finance have increased in recent years, and the available datasets present several new challenges in empirical applications and need new techniques for analysis. As a powerful technique for big data analysis, deep learning has recently been successfully utilized in several big data domains such as image processing, handwriting recognition, speech recognition, information extraction, management and control automation, and prediction. This seminar will cover some applications of big data and deep learning in finance.

Topics:
Genetic Programming
Deep Learning for Natural Language Processing
Deep Learning for Prediction Poverty
Deep Learning for FOREX Market Prediction
Deep Learning and Linear Factor Models
Deep Learning for Credit Risk Management
Social Network and Financial Market Prediction
Big Data Analytics in FinTech

Literature:
Goodfellow, I., Bengio, Y., & Courville, A. (2017). Deep Learning. MIT Press.
Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790-794.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Leskovec, J., Rajaraman, A., & Ullman, J. D. (2014). Mining of Massive Datasets. Cambridge University Press.
Lopez De Prado, M. (2018). Advances in Financial Machine Learning. John Wiley & Sons