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Economic recession prediction using modified gradient boosting and principal component neural network algorithms
Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2025.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2025
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| _version_ | 1867613451882332160 |
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| access_status_str | Open Access |
| author2 | Nakhaeirad, Najmeh |
| author_browse | Nakhaeirad, Najmeh |
| author_facet | Nakhaeirad, Najmeh |
| collection | Thesis |
| dc_rights_str_mv | © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2025. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/100617 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:21.928Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/100617 Economic recession prediction using modified gradient boosting and principal component neural network algorithms Nakhaeirad, Najmeh u19098309@tuks.co.za Krishnannair, Anuroop UCTD Sustainable Development Goals (SDGs) Neural networks Economic recession Gradient boosting Non-Linear Principal Component Analysis (NLPCA) Ensemble models Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2025. In the ever-evolving landscape of global economics, predicting and understanding economic recessions remain paramount challenges for policymakers, researchers, and financial analysts. The outbreak of the COVID-19 pandemic in 2019 has introduced unprecedented complexities, reshaping the economic dynamics of nations worldwide. A limited number of economic recessions have been accurately predicted months in advance. To mitigate the growing impact of these downturns, it is essential to develop more effective predictive models that can assist businesses and governments in formulating policies to support millions of people before these periods occur, given the economy’s critical role in policy development. Traditionally, machine learning algorithms have been widely applied in pattern recognition,however, limited research has explored their use in finance, especially for predicting recessions. Given the novel application of machine learning for recession forecasting in finance, there are very few studies available in this area. This research gives the best performing models to assist businesses in predicting prior recession periods and identifies the most important variables to improve the overall performance of the models addressing the concern that previous studies have shown biases due to imbalanced class ratios. To achieve this, in addition to Artificial Neural Networks , machine learning techniques such as Random Forest and Support Vector Machines are used to provide an efficient prediction model to avoid greater government deficits, growing inequality, significantly decreased income, and higher unemployment. In this study, an ensemble approach of Logistic Regression and Non-Linear Principal Component Analysis Logistic Regression (NLPCA-LR) plus a Modified Gradient Boosting Neural Network (MGBNN) are proposed and compared to the latter models. A real dataset on historical recession periods in African countries is employed to demonstrate the performance of the proposed algorithms in practice. The performance analysis across the various models highlights the superior capabilities of the MGBNN and the NLPCA-LR models. This demonstrates the potential for machine learning models predictive power in the financial domain and thus alleviates the concern of these models as being black boxes. Statistics MSc (Advanced Data Analytics) Unrestricted Faculty of Natural and Agricultural Sciences SDG-08: Decent work and economic growth 2025-02-07T10:10:46Z 2025-02-07T10:10:46Z 2025-04 2025-02 Mini Dissertation * A2025 http://hdl.handle.net/2263/100617 https://doi.org/10.25403/UPresearchdata.28366661 en © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Sustainable Development Goals (SDGs) Neural networks Economic recession Gradient boosting Non-Linear Principal Component Analysis (NLPCA) Ensemble models Economic recession prediction using modified gradient boosting and principal component neural network algorithms |
| title | Economic recession prediction using modified gradient boosting and principal component neural network algorithms |
| title_full | Economic recession prediction using modified gradient boosting and principal component neural network algorithms |
| title_fullStr | Economic recession prediction using modified gradient boosting and principal component neural network algorithms |
| title_full_unstemmed | Economic recession prediction using modified gradient boosting and principal component neural network algorithms |
| title_short | Economic recession prediction using modified gradient boosting and principal component neural network algorithms |
| title_sort | economic recession prediction using modified gradient boosting and principal component neural network algorithms |
| topic | UCTD Sustainable Development Goals (SDGs) Neural networks Economic recession Gradient boosting Non-Linear Principal Component Analysis (NLPCA) Ensemble models |
| url | http://hdl.handle.net/2263/100617 https://doi.org/10.25403/UPresearchdata.28366661 |