Deep Generation of Financial Time Series
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Host institution: Cardo S.R.L, Italy (CAR)
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Starting month: M9
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Duration: 36 months
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Pillar 1: Foundation of data science (BabeÈ™-Bolyai University, 4 ECTs), Work Package 1
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Work Packages: WP1, WP6, WP7, WP8
Objectives
​Macroeconomics factors such as central banks’ interest rates, inflation, unemployment rate, house price indices, to name a few, are of foremost importance in Financial Markets. The aim of this project is to benchmark various methods from classical statistical learning and modern machine learning in order to predict their point value in the future. As a second step the student will be using the above predictions to forecast future market scenarios in a what-if fashion.
Expected Results
The project's outcomes will contribute to the expanding body of knowledge concerning the applications of cutting-edge machine learning and artificial intelligence techniques to traditional financial problems. We will apply recent findings from the ML literature on time series forecasting in the first step. In the second phase of the project, the ESR will be able to conduct research in the field of causal inference in finance, which also appears to be an extremely promising area of study. The anticipated outcome will be three research/conference papers describing the data analysis, modelling approaches, and experimental results.
Planned Secondments
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WU Vienna University of Economics and Business (WWU), Prof. Dr. Kurt Hornik, M27, 14 months, theoretical modelling and mathematics for deep learning
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Fraunhofer Institute (FRA), Prof. Dr. Ralf Korn, M41, 4 months, applied industry-research, exposure to world-leading research centre and infrastructure