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 allocations or static life cycles are shown. The dynamic investment strategies have been constructed by using two different approaches. The first approach is rule-based and defines intermediate wealth targets for every year in the investment horizon. Investment decisions are taken based on performance compared to these targets. The second approach involves a dynamic programming algorithm. The asset allocation over time is not always stable when using dynamic programming. Methods to smooth the asset allocation over time and improve stability are discussed. Last, both approaches are combined in one strategy.

In this work, we consider rule-based investment strategies for managing a defined contribution pension savings scheme, under the Dutch pension fund testing model. We find that dynamic, rule-based investment strategies can outperform traditional static strategies, by which we mean that the investor may achieve the target retirement income with a higher probability or limit the shortfall when the target is not met. In comparison with dynamic programming-based strategies, the rule-based strategies have more stable asset allocations throughout time and avoid excessive transactions that may be hard to explain to an investor. We also study a combined strategy of a rule-based target with dynamic programming. A key feature of our setting is that there is no risk-free asset, instead, a matching portfolio is introduced for the investor to avoid unnecessary risk.@en