Generative Neural Network for Portfolio Optimization
Abstract: This thesis aims to overcome the drawbacks of traditional portfolio optimization by employing Generative Deep Neural Networks on real stock data. The proposed framework is capable of generating return data that have similar statistical characteristics as the original stock data. The result is acquired using Monte Carlo simulation method and presented in terms of individual risk. This method is tested on real Swedish stock market data. A practical example demonstrates how to optimize a portfolio based on the output of the proposed Generative Adversarial Networks.
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