dc.contributor.advisor | Van Vuuren, P. | |
dc.contributor.advisor | Hoffman, Alwyn | |
dc.contributor.author | Pooe, Kito | |
dc.date.accessioned | 2022-11-08T12:21:39Z | |
dc.date.available | 2022-11-08T12:21:39Z | |
dc.date.issued | 2021 | |
dc.identifier.uri | https://orcid.org/0000-0002-7169-9248 | |
dc.identifier.uri | http://hdl.handle.net/10394/40151 | |
dc.description | MEng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus | en_US |
dc.description.abstract | In portfolio management, asset allocation is one of the most crucial and difficult challenges
investors face. Asset allocation is defined as a decision making process of spreading available
funds into various financial assets. The most famous and widely used models for tackling asset
allocation problems are mean variance, mean valueatrisk
and Sharpe ratio. These models
are solvable by quadratic programming, and they all rely heavily on the mean and standard
deviation with the assumption that the data distribution is symmetrical. Unfortunately, a majority
of the realworld
problems exhibit asymmetric distributions; as a result, the modified Sharpe
ratio is introduced to include skewness and kurtosis as the third and fourth moments of return.
The results obtained in this study are based on the modified Sharpe ratio, and they apply
and compare genetic algorithm, particle swarm optimisation, and deep deterministic policy gradient
to solve the asset allocation problem. The former algorithms (genetic algorithms and particle
swarm optimisation) are widely employed to generate high quality solutions in optimisation
problems whilst the latter (deep deterministic policy gradient) has proved to be more effective
in solving complex problems that cannot be solved by conventional techniques. The algorithms
learn to evolve portfolio weights in maximising the modified Sharpe ratio. The dataset used is
extracted from the banking sector of the Johannesburg stock exchange and wellknown
stocks
in the United States stock exchange.
In measuring the performance of the three algorithms, a uniform allocation is used as a
baseline asset allocation strategy. Uniform allocation divides portfolio weights equally among
the assets in a portfolio. The results presented show that all three algorithms outclass the
uniform allocation on numerous occasions. In general, the genetic algorithm and particle swarm
optimisation provide relatively better results than deep deterministic policy gradient. The results
are then tested on buyandhold.
Even though the deep deterministic policy gradient did not
perform well in evolving portfolio weights and took too long to run in training, it is comparable
with the uniform allocation. The genetic algorithm outperforms the other algorithms with particle
swarm optimisation following. | en_US |
dc.language.iso | en | en_US |
dc.publisher | North-West University (South Africa). | en_US |
dc.subject | Asset allocation | en_US |
dc.subject | Modified Sharpe ratio | en_US |
dc.subject | Deep deterministic policy gradient | en_US |
dc.subject | Genetic algorithms | en_US |
dc.subject | Particles swarm optimisation | en_US |
dc.subject | Buyandhold strategy | en_US |
dc.subject | Uniform allocation | en_US |
dc.subject | Skewness | en_US |
dc.subject | Kurtosis | en_US |
dc.title | A comparison of evolutionary computation and deep reinforcement learning for portfolio optimization | en_US |
dc.type | Thesis | en_US |
dc.description.thesistype | Masters | en_US |
dc.contributor.researchID | 10732926 - Van Vuuren, Pieter Andries (Supervisor) | |
dc.contributor.researchID | 10196978 - Hoffman, Alwyn Jakobus (Supervisor) | |