7,377 research outputs found
Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods
We quantify the amount of information filtered by different hierarchical
clustering methods on correlations between stock returns comparing it with the
underlying industrial activity structure. Specifically, we apply, for the first
time to financial data, a novel hierarchical clustering approach, the Directed
Bubble Hierarchical Tree and we compare it with other methods including the
Linkage and k-medoids. In particular, by taking the industrial sector
classification of stocks as a benchmark partition, we evaluate how the
different methods retrieve this classification. The results show that the
Directed Bubble Hierarchical Tree can outperform other methods, being able to
retrieve more information with fewer clusters. Moreover, we show that the
economic information is hidden at different levels of the hierarchical
structures depending on the clustering method. The dynamical analysis on a
rolling window also reveals that the different methods show different degrees
of sensitivity to events affecting financial markets, like crises. These
results can be of interest for all the applications of clustering methods to
portfolio optimization and risk hedging.Comment: 31 pages, 17 figure
Real time clustering of time series using triangular potentials
Motivated by the problem of computing investment portfolio weightings we
investigate various methods of clustering as alternatives to traditional
mean-variance approaches. Such methods can have significant benefits from a
practical point of view since they remove the need to invert a sample
covariance matrix, which can suffer from estimation error and will almost
certainly be non-stationary. The general idea is to find groups of assets which
share similar return characteristics over time and treat each group as a single
composite asset. We then apply inverse volatility weightings to these new
composite assets. In the course of our investigation we devise a method of
clustering based on triangular potentials and we present associated theoretical
results as well as various examples based on synthetic data.Comment: AIFU1
Evaluation of an unsupervised learning approach for portfolio optimization
Throughout this directed research, we aim to identify opportunities for machine learning to
support portfolio optimization. Based on a thorough literature review we decide to pursue an
unsupervised learning approach and test its performance by conducting benchmarking against
classic portfolio optimization techniques. To ensure the validity of our findings we explore the
model’s robustness by conducting an array of experiments. In summary, we deem our version
of the clustering algorithm to provide a suitable investment framework for return-focused
investors with lower risk aversion. We suggest further research towards mitigating the
algorithm’s inconsistencies and exploring additional tuning methodologies
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