A Walk-Forward Multi-Factor Machine Learning Investment Process
| dc.contributor.advisor | Engelbrecht, Andries P. | en_ZA |
| dc.contributor.author | Wessels, Zander | en_ZA |
| dc.contributor.other | Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. | en_ZA |
| dc.date.accessioned | 2025-05-02T13:17:21Z | |
| dc.date.available | 2025-05-02T13:17:21Z | |
| dc.date.issued | 2024-12 | |
| dc.description | Thesis (PhD)--Stellenbosch University, 2024. | en_ZA |
| dc.description.abstract | It can be said that traditional asset management modelling lacks true empiricism due to the difficulty of accurate simulation of investment strategies through time. This is often demonstrated by positively biased academic results that do not reflect real-world outcomes. This thesis addresses this problem by developing a walk-forward platform that is inspired by the blackboard-expert architecture and that can simulate investment processes through time, providing reliable repeatability, which is the essence of good science. The proposed platform accounts for common biases, such as survivorship bias and forwardlooking bias, to create a true hypothesis-testing engine. When considering the investment process as it pertains to equities, this thesis argues that the process can be fully closed under the questions of “what should I buy?”, “when should I buy?”, and “how much should I buy?”. The thesis then aims to use the hypothesis engine to build an example investment pipeline that can test and automate a whole investment process. The proposed investment pipeline will answer the questions of “what”, “when”, and “how much”, respectively. Traditional factor models and machine learning models for stock selection will be explored to answer the “what” question. The thesis argues that training models using the proposed engine is the correct way to do so in a non-stationary time series setting. The “how much” question will be addressed using portfolio optimisation, with particular consideration given to particle swarm optimisation. The “when” question will be briefly discussed as a further research idea. | en_ZA |
| dc.description.version | Doctoral | en_ZA |
| dc.format.extent | 368 pages | en_ZA |
| dc.identifier.uri | https://scholar.sun.ac.za/handle/10019.1/131970 | |
| dc.publisher | Stellenbosch : Stellenbosch University | en_ZA |
| dc.rights.holder | Stellenbosch University | en_ZA |
| dc.title | A Walk-Forward Multi-Factor Machine Learning Investment Process | en_ZA |
| dc.type | Thesis | en_ZA |