Hedge fund performance is modeled from publically available data using feed-forward neural networks trained using a resilient backpropagation algorithm. The neural network’s performance is then compared with linear regression models. Additionally, a stepwise factor regression approach is introduced to reduce the number of inputs supplied to the models in order to increase precision.
Three main conclusions are drawn: (1) neural networks effectively model hedge fund returns, illustrating the strong non-linear relationships between the economic risk factors and hedge fund performance, (2) while the group of 25risk factors we draw variables from are used to explain hedge fund performance, the best model performance is achieved using different subsets of the 25 risk factors, and, (3) out-of-sample model performance degrades across the time during the recent (and still on-going) financial crisis compared to less volatile time periods, indicating the models’ inability to predict severely volatile economic scenarios such as economic crises.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/32497 |
| Date | 23 July 2012 |
| Creators | Tryphonas, Marinos |
| Contributors | Paradi, Joseph C. |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0018 seconds