On Non-Stationarity in Reinforced Deep Markov Models with Applications in Portfolio Optimization

In this thesis, we aim to improve the application of deep reinforcement learning in portfo- lio optimization. Reinforcement learning has in recent years been applied to a wide range of problems, from games to control systems in the physical world and also to finance. While reinforcement learning has shown success in simulated environments (e.g....

Multi-period Robust Mean-Risk Portfolio Optimization: Minimizing Risk and Enhancing Returns in Uncertain Market Environments

Portfolio optimization, a fundamental area of study in financial engineering,<br/>plays a crucial role in creating efficient portfolios. In this thesis, we consider<br/>a robust multi-period Mean-Variance portfolio optimization framework and<br/>apply it to real-world market data. The approach we look at incorporates a<br/>time-consistent...

Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing

In this work, we introduce a new workflow to solve portfolio optimization problems on annealing platforms. We combine a classical preprocessing step with a modified unconstrained binary optimization (QUBO) model and evaluate it using simulated annealing (classical computer), digital annealing (Fujitsu’s Digital Annealing Unit), and quantum...

An efficient metaheuristic to solve the project portfolio selection and scheduling problem for industrial projects

Industrial companies aim for optimizing profit from delivering project outcomes. Maximizing profit relies on optimization of using resources, production capacity and available time. To reach this goal, companies are typically reliant on planning and production schedules. This problem is known as the project portfolio selection and scheduling...

Dynamic Life Cycle Investing

This thesis studies the asset allocation of a DC pension investor over a long time horizon. Investors allocate their portfolio wealth between two assets: a return portfolio and a matching portfolio. Investors can adjust their allocation once a year. Several dynamic investment strategies that improve investment results compared to fixed...

Multi-objective project portfolio optimization: Application in railway infrastructure networks

Investment projects in railway infrastructure networks are needed to maintain a high condition of the network, maximize the capacity of the network, and to keep the risk low. Railway organizations are aiming to achieve these goals, but also want to minimize the costs of the investments. The desires of stakeholders in railway networks such as the...