Active Allocation in Private Debt Using Natural Language Processing with Alternative Data Sources
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| Publicat a: | PQDT - Global (2024) |
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ProQuest Dissertations & Theses
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| Accés en línia: | Citation/Abstract Full Text - PDF Full text outside of ProQuest |
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| 020 | |a 9798283121871 | ||
| 035 | |a 3224577075 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 189128 |2 nlm | ||
| 100 | 1 | |a Royden-Turner, Stuart | |
| 245 | 1 | |a Active Allocation in Private Debt Using Natural Language Processing with Alternative Data Sources | |
| 260 | |b ProQuest Dissertations & Theses |c 2024 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a Portfolio analysis is benefiting from the surge of alternative sources of data coupled with new modelling frameworks, introduced by machine-learning. I collected alternative data and applied new frameworks (machine-learning techniques and technology) to the domain of private debt. This is an interesting and complex asset class, which has a significant shortage of data from which to model. To counter this issue, I incorporated advanced macro-finance and asset-pricing models. Such will provide the correct context in which to model this asset class as part of a sophisticated multi-asset portfolio construction framework. To ensure that the credit-risk models are fully understood, I selected a modelling technique from a broad array of options in a mature environment of credit modelling largely performed in banking (whilst ensuring the technique is suitable for asset management). The modelling framework is geared to account for the dynamics of business cycles, this being an important results driver in unlisted credit and other asset classes alike. My thorough macro-finance research allows me to design non-trivial processes to incorporate alternative signals as part of an asset-pricing framework on which to generate information for use in portfolio construction via the data simulated. My economic scenario generator considers the relative changes to asset classes at various points in the business cycle as part of a long-term investment program suitable for a defined-benefit pension funds portfolio. The final portfolio models are put together using a reinforcement learning framework. This framework connects the macro- finance theory dealing with the business cycle dynamics, together with the credit-risk techniques, to portfolio modelling techniques which I believe compounds to a sophisticated modelling framework for strategic asset allocation in a data-sparse environment. | |
| 653 | |a Natural language processing | ||
| 653 | |a Python | ||
| 653 | |a Neural networks | ||
| 653 | |a Social networks | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Web studies | ||
| 773 | 0 | |t PQDT - Global |g (2024) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3224577075/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3224577075/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u https://uir.unisa.ac.za/handle/10500/31205 |