Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Extensions on the Fractional Differencing Methodology for Portfolio Construction |
| Organization Unit | |
| Authors |
|
| Supervisors |
|
| Language |
|
| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 32 |
| Date | 2023 |
| Abstract Text | This paper explores an ARFIMA-based momentum trading strategy, extending the work of Chitsiripanich et al. (2022) and aiming to refine predictive accuracy and enhance profitability by incorporating long-memory attributes into stock returns modelling. Our focus revolved around the Sowell (1992) Maximum Likelihood Estimation methodology, targeting its benefits and limitations while suggesting enhancements. Notably, the ARFIMA(2, 0.4 + d2, 2) model outperformed other advanced strategies, showing promising risk-adjusted returns, less volatility, and minimal market dependence. However, the results should be considered with caution due to computational constraints and the scope of the data sample. Future research could leverage more substantial computing resources, extend the stock selection, or apply alternate estimation methodologies. |
| PDF File |
Download
|
| Export |
BibTeX
|