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Stock Option Valuations and Constraint Enforcement Using Neural Networks
Stock option valuations have long been studied, being inherently non-linear financial derivatives. These instruments have a ubiquitous presence in institutional investment practice, and present many favourable and unique benefits to an investment portfolio. Neural Networks on the other hand have bec...
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| Format: | Thesis |
| Language: | English |
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Centre for Actuarial Research (CARE)
2023
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| _version_ | 1867613961320398848 |
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| access_status_str | Open Access |
| author | Nutt, Frans Ignatius |
| author2 | Pienaar, Etienne |
| author_browse | Nutt, Frans Ignatius Pienaar, Etienne |
| author_facet | Pienaar, Etienne Nutt, Frans Ignatius |
| author_sort | Nutt, Frans Ignatius |
| collection | Thesis |
| description | Stock option valuations have long been studied, being inherently non-linear financial derivatives. These instruments have a ubiquitous presence in institutional investment practice, and present many favourable and unique benefits to an investment portfolio. Neural Networks on the other hand have become a more familiar concept in recent times. They are by design set to deal with complex, non-linear classification and prediction tasks. Using Neural Networks to predict stock option prices has been studied at length, by various authors in the last 30 years. These studies have considered their relative performance against closed-form pricing solutions like the infamous Black-Scholes-Merton model, as well as in real-world settings. The collective conclusion that is deduced from past literature presents a clear case for their use in finance, albeit that there are some notable pitfalls, like the lack of interpretability and the ability to explicitly enforce certain constraints. Constraints such as option price bounds (upper and lower) and the Put-Call parity, that a stock option's value should satisfy have not been considered in many prior studies. This dissertation sets out to study stock option valuations using Neural Networks with techniques to enforce constraints. First, a functional and appropriately performing Neural Network configuration is derived that outputs European call and put option prices under one model. Thereafter, enforcement of the lower, upper and relative bounds (Put-Call parity) is incorporated into the model. Finally, the Neural Network application is extended to the real-world setting. The performance of the Neural Network model is assessed by means of mean error, as well as percentiles. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/37695 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:44:27.873Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Centre for Actuarial Research (CARE) |
| publisherStr | Centre for Actuarial Research (CARE) |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/37695 Stock Option Valuations and Constraint Enforcement Using Neural Networks Nutt, Frans Ignatius Pienaar, Etienne Actuarial Science Stock option valuations have long been studied, being inherently non-linear financial derivatives. These instruments have a ubiquitous presence in institutional investment practice, and present many favourable and unique benefits to an investment portfolio. Neural Networks on the other hand have become a more familiar concept in recent times. They are by design set to deal with complex, non-linear classification and prediction tasks. Using Neural Networks to predict stock option prices has been studied at length, by various authors in the last 30 years. These studies have considered their relative performance against closed-form pricing solutions like the infamous Black-Scholes-Merton model, as well as in real-world settings. The collective conclusion that is deduced from past literature presents a clear case for their use in finance, albeit that there are some notable pitfalls, like the lack of interpretability and the ability to explicitly enforce certain constraints. Constraints such as option price bounds (upper and lower) and the Put-Call parity, that a stock option's value should satisfy have not been considered in many prior studies. This dissertation sets out to study stock option valuations using Neural Networks with techniques to enforce constraints. First, a functional and appropriately performing Neural Network configuration is derived that outputs European call and put option prices under one model. Thereafter, enforcement of the lower, upper and relative bounds (Put-Call parity) is incorporated into the model. Finally, the Neural Network application is extended to the real-world setting. The performance of the Neural Network model is assessed by means of mean error, as well as percentiles. 2023-04-13T08:33:56Z 2023-04-13T08:33:56Z 2022 2023-04-12T11:33:19Z Master Thesis Masters MCom http://hdl.handle.net/11427/37695 eng application/pdf Centre for Actuarial Research (CARE) Faculty of Commerce |
| spellingShingle | Actuarial Science Nutt, Frans Ignatius Stock Option Valuations and Constraint Enforcement Using Neural Networks |
| thesis_degree_str | Master's |
| title | Stock Option Valuations and Constraint Enforcement Using Neural Networks |
| title_full | Stock Option Valuations and Constraint Enforcement Using Neural Networks |
| title_fullStr | Stock Option Valuations and Constraint Enforcement Using Neural Networks |
| title_full_unstemmed | Stock Option Valuations and Constraint Enforcement Using Neural Networks |
| title_short | Stock Option Valuations and Constraint Enforcement Using Neural Networks |
| title_sort | stock option valuations and constraint enforcement using neural networks |
| topic | Actuarial Science |
| url | http://hdl.handle.net/11427/37695 |
| work_keys_str_mv | AT nuttfransignatius stockoptionvaluationsandconstraintenforcementusingneuralnetworks |