  Traditional portfolio management methods can incorporate specific investor
preferences but rely on accurate forecasts of asset returns and covariances.
Reinforcement learning (RL) methods do not rely on these explicit forecasts and
are better suited for multi-stage decision processes. To address limitations of
the evaluated research, experiments were conducted on three markets in
different economies with different overall trends. By incorporating specific
investor preferences into our RL models' reward functions, a more comprehensive
comparison could be made to traditional methods in risk-return space.
Transaction costs were also modelled more realistically by including nonlinear
changes introduced by market volatility and trading volume. The results of this
study suggest that there can be an advantage to using RL methods compared to
traditional convex mean-variance optimisation methods under certain market
conditions. Our RL models could significantly outperform traditional
single-period optimisation (SPO) and multi-period optimisation (MPO) models in
upward trending markets, but only up to specific risk limits. In sideways
trending markets, the performance of SPO and MPO models can be closely matched
by our RL models for the majority of the excess risk range tested. The specific
market conditions under which these models could outperform each other
highlight the importance of a more comprehensive comparison of Pareto optimal
frontiers in risk-return space. These frontiers give investors a more granular
view of which models might provide better performance for their specific risk
tolerance or return targets.
