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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Financial Time Series Clustering for Portfolio Optimization |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 49 |
| Date | 2020 |
| Abstract Text | Optimization of financial portfolios has been rigorously studied in the literature, with Harry Markowitz being the first to consider the risk-return trade-off of a portfolio as a whole in his Nobel Prize-winning paper [11]. He proposed to solve a quadratic optimization problem that outputs a set of efficient portfolios using as inputs the vector of expected returns and the matrix of covariances. Inversion of the covariance matrix is, however, needed for the solution. If a covariance matrix is estimated on too few data points compared to its dimension, inversion may amplify estimation errors and lead to undiversified portfolios. The stability and usefulness of the expected return estimates are also doubted in the literature. This thesis tries to answer the question of whether one can construct diversified portfolios using only the historical return time series of a universe of assets while avoiding expected return estimation and covariance matrix inversion. A hierarchical clustering approach is chosen and assets are clustered using newly-defined distance functions based on semicorrelation and momentum. A comparison is made with the correlation distance clustering, which is the default method used in the cited literature. The results show that correlation and semicorrelation clustering is able to uncover information relevant to portfolio construction. In combination with investing in low variance or semivariance assets, one can construct clustering portfolios that perform similarly to the Markowitz minimum-variance portfolio. It is also shown that pairing clustering with the selection of high-momentum assets leads to a high-yielding portfolio with an impressive Sharpe ratio. |
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