Dependency evaluation of financial market returns for classifying and grouping stocks
Dependency evaluation of financial market returns for classifying and grouping stocks
Following the globalization of the economy and the increasing significance of international trade investments, linkages among economic variables of different countries are becoming strikingly evident. There is a strong interest among researchers to capture presence and extent of such negative or positive correlations. In this paper, we embark a novel methodology to identify the correlated market by a modified clustering procedure and finding the optimal number of countries within the clusters. The proposed methodology mainly works with the k-means clustering method in which its performance is improved by particle swarm optimization algorithm (PSO). The integration of these methods aims at finding the best number of clusters (k) within the dataset with a distance-based index in order to achieve the most appropriate stock market assigned to each cluster. As a case study, an experiment on daily and monthly stock market returns of 50 counties has been evaluated.
Clustering, Dependency, Particle swarm optimization, Stock Market
193-198
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
2017
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Barak, Sasan
(2017)
Dependency evaluation of financial market returns for classifying and grouping stocks.
In,
2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS).
IEEE, .
(doi:10.1109/ICSPIS.2017.8311615).
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Book Section
Abstract
Following the globalization of the economy and the increasing significance of international trade investments, linkages among economic variables of different countries are becoming strikingly evident. There is a strong interest among researchers to capture presence and extent of such negative or positive correlations. In this paper, we embark a novel methodology to identify the correlated market by a modified clustering procedure and finding the optimal number of countries within the clusters. The proposed methodology mainly works with the k-means clustering method in which its performance is improved by particle swarm optimization algorithm (PSO). The integration of these methods aims at finding the best number of clusters (k) within the dataset with a distance-based index in order to achieve the most appropriate stock market assigned to each cluster. As a case study, an experiment on daily and monthly stock market returns of 50 counties has been evaluated.
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Published date: 2017
Keywords:
Clustering, Dependency, Particle swarm optimization, Stock Market
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Local EPrints ID: 434850
URI: http://eprints.soton.ac.uk/id/eprint/434850
PURE UUID: 64c22f06-32bf-4b4d-9845-e56986aacb67
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Date deposited: 11 Oct 2019 16:30
Last modified: 16 Mar 2024 04:42
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