While the classical Merton type framework is among the most comprehensively studied and has spurred an extensive theoretical literature, its empirical implementation has been disappointing with a large literature documenting its shortcomings. In particular, its numerical implementation via stochastic dynamic programming has been neglected in the community largely due to the curse of dimensionality. The objective of this project is to explore the recent advances in the statistical community for dimension reduction in such a way that the dimension reduction of the objective functions is aligned with portfolio optimisation in the context of stochastic optimal control setting and ultimately results in a better portfolio performance in a high dimensional investment space.