Portfolio Optimization with Random Matrix Theory and Artificial Neural Networks

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Master Thesis Presentation.



Financial market data from the German stock market is analyzed for optimal capital allocation. There are two fundamental concepts in Modern Portfolio Theory which serve as reference theories. In the framework of mean-variance analysis, historical data is used to estimate expected returns of individual assets and cross-correlations across different assets in the market. These estimations are required when attempting to optimize the risk and return of potential investment portfolios. The Efficient Market Hypothesis states that all available information about a security is already priced into its price at any point in time and that consequently, the best estimation of future prices are the current prices. In this thesis, a systematic approach to choose investment portfolios is analyzed. The resulting portfolios show lower combined risk than the ones obtained by mean-variance analysis. This is achieved by applying Random Matrix Theory to improve future volatility estimations in an in-depth analysis of correlations. Furthermore, a neural network architecture is trained to estimate expected returns of securities. The resulting predictions are compared to mean-variance and the Efficient Market Hypothesis approaches.



Advisor: Pere Colet



Jury: Tobias Galla, Miguel C. Soriano and Pere Colet



https://us02web.zoom.us/j/84858504414?pwd=QVloRUM2V2UyYWFuK0N0OWMraGltZz09



Meeting ID: 848 5850 4414

Passcode: 605952



 



Contact details:

Pere Colet
971 17 33 82
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