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Incremental reinforcement learning for portfolio optimisation
Thesis (MEng)--Stellenbosch University, 2023.
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| Format: | Thesis |
| Language: | en_ZA en_ZA |
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Stellenbosch : Stellenbosch University
2023
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| _version_ | 1867614027086036992 |
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| access_status_str | Open Access |
| author | Refiloe, Shabe |
| author2 | Engelbrecht, Andries |
| author_browse | Engelbrecht, Andries Refiloe, Shabe |
| author_facet | Engelbrecht, Andries Refiloe, Shabe |
| author_sort | Refiloe, Shabe |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MEng)--Stellenbosch University, 2023. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/127011 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA en_ZA |
| last_indexed | 2026-06-10T12:45:30.338Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/127011 Incremental reinforcement learning for portfolio optimisation Refiloe, Shabe Engelbrecht, Andries Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Portfolio management -- Mathematical models Reinforcement learning Mathematical optimization Thesis (MEng)--Stellenbosch University, 2023. ENGLISH ABSTRACT: Portfolio optimisation is a decision-making problem that involves allocation of a certain fund across different financial assets, with the objective of maximising profit and minimising risk, simultaneously. Portfolio optimisation is a difficult problem to analyse. There is a wide range of research on various portfolio optimisation approaches in finance and computational intelligence. The two fields overlap. Thus, the use of meta-heuristics to make intelligent investment decisions is a result of the intersection of finance and computational intelligence. Meta-heuristics formulated the portfolio optimisation problem as a static optimisation problem and successfully obtained optimal portfolios. However, in the real world, investment decision-making is a dynamic problem that involves daily trading. Therefore, it is more representative of real-world investments to formulate the portfolio optimisation problem as a dynamic optimisation problem. This thesis explores a reinforcement learning approach to formulate a dynamic investment strategy. The concept of reinforcement learning has improved the development of multistage stochastic optimisation; a primary component in sequential portfolio optimisation. A recurrent form of a reinforcement learning algorithm called proximal policy optimisation (PPO), that allocates portfolios based on historic asset prices is presented. The results provide a conclusive support for the ability of PPO to identify good-quality portfolios. The results also show that the strategy becomes outdated overtime as it fails to perform as well during the COVID-19 pandemic. Based on this finding, the recurrent PPO approach was improved in order to take into account the presence of concept drift caused by pandemics and potential financial contagions. The approach was adapted to incrementally learn the financial market as the portfolio optimisation process takes place. The incremental recurrent PPO algorithm is shown to be able to adapt to drastic changes in the market and obtain optimal portfolios. AFRIKAANS OPSOMMING: Portefeulje-optimering is ’n besluitnemingsprobleem wat die toewysing van ’n sekere fonds in verskillende finansi¨ele bates behels, terwyl die wins terselfdertyd maksimeer en risiko geminimaliseer word. Portefeulje-optimering is ’n moeilike probleem om te ontleed. Daar is ’n wye reeks navorsing oor verskeie portefeulje-optimaliseringsbenaderings in finansies en rekenaarintelligensie. Die twee velde oorvleuel. Dus, die gebruik van metaheuristiek om intelligente beleggingsbesluite te neem is ’n gevolg van die kruising van finansies en rekenaarintelligensie. Metaheuristiek het die portefeulje-optimeringsprobleem as ’n statiese optimeringsprobleem geformuleer en optimale portefeuljes suksesvol verkry. In die regte wˆereld is beleggingsbesluitneming egter ’n dinamiese probleem wat daaglikse handel behels. Daarom is dit meer verteenwoordigend van werklike beleggings om die portefeuljeoptimeringsprobleem as ’n dinamiese optimaliseringsprobleem te formuleer. Hierdie tesis ondersoek ’n versterkingsleerbenadering om ’n dinamiese beleggingstrategie te formuleer. Die konsep van versterkingsleer het die ontwikkeling van meerfase stogastiese optimering verbeter; ’n primˆere komponent in opeenvolgende portefeulje-optimering. ’n Herhalende vorm van ’n versterkende leeralgoritme genaamd proksimale beleidsoptimering (PPO), wat portefeuljes toewys op grond van historiese batepryse, word aangebied. Die resultate bied ’n afdoende ondersteuning vir die vermo¨e van herhalende PPO om goeie gehalte portefeuljes te identifiseer. Die resultate toon ook dat die strategie oortyd verouderd raak aangesien dit nie so goed presteer tydens die COVID-19-toediening nie. Op grond van hierdie bevinding is die herhalende PPO-benadering verbeter om die teenwoordigheid van konsepverskuiwing wat deur pandemies en potensi¨ele finansi¨ele besmettings veroorsaak word, in ag te neem. Die benadering is aangepas om die finansi¨ele mark inkrementeel te leer namate die portefeulje-optimeringsproses plaasvind. Daar word getoon dat die inkrementele herhalende PPO-algoritme in staat is om aan te pas by drastiese veranderinge in die mark en optimale portefeuljes te verkry. Masters 2023-01-30T19:26:30Z 2023-05-18T07:00:01Z 2023-01-30T19:26:30Z 2023-05-18T07:00:01Z 2023-03 Thesis http://hdl.handle.net/10019.1/127011 en_ZA en_ZA Stellenbosch University xi, 111 pages : illustrations. application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Portfolio management -- Mathematical models Reinforcement learning Mathematical optimization Refiloe, Shabe Incremental reinforcement learning for portfolio optimisation |
| title | Incremental reinforcement learning for portfolio optimisation |
| title_full | Incremental reinforcement learning for portfolio optimisation |
| title_fullStr | Incremental reinforcement learning for portfolio optimisation |
| title_full_unstemmed | Incremental reinforcement learning for portfolio optimisation |
| title_short | Incremental reinforcement learning for portfolio optimisation |
| title_sort | incremental reinforcement learning for portfolio optimisation |
| topic | Portfolio management -- Mathematical models Reinforcement learning Mathematical optimization |
| url | http://hdl.handle.net/10019.1/127011 |
| work_keys_str_mv | AT refiloeshabe incrementalreinforcementlearningforportfoliooptimisation |