  The portfolio optimisation problem, first raised by Harry Markowitz in 1952,
has been a fundamental and central topic to understanding the stock market and
making decisions. There has been plenty of works contributing to development of
the mean-variance optimisation (MVO) so far. In this paper, one kind of them,
namely, dynamic mean-variance optimisation (DMVO) is mainly discussed. One can
apply either precommitment or game-theoritical approach to address
time-inconsistency in DMVO. We use the second approach to seek for a
time-consistent strategy. After obtaining the optimal strategy, we extend the
result to a CEV-driven economy. In order to prove the usefulness of them,
strategies are fit into both real market data and simulated data. It turns out
that the strategy whose assumptions are close to market conditions generally
gives a better result. Lastly, a selected strategy is chosen to compare with
another strategy came up by deep learning technique.
