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Clustering of Latvian Pension Funds Using Convolutional Neural Network Extracted Features

Author

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  • Vitalija Serapinaitė

    (Department of Mathematical Modelling, Faculty of Mathematics and Natural Sciences, Kaunas University of Technology, 51368 Kaunas, Lithuania)

  • Audrius Kabašinskas

    (Department of Mathematical Modelling, Faculty of Mathematics and Natural Sciences, Kaunas University of Technology, 51368 Kaunas, Lithuania)

Abstract

Pension funds became a fundamental part of financial security in pensioners’ lives, guaranteeing stable income throughout the years and reducing the chance of living below the poverty level. However, participating in a pension accumulation scheme does not ensure financial safety at an older age. Various pension funds exist that result in different investment outcomes ranging from high return rates to underperformance. This paper aims to demonstrate alternative clustering of Latvian second pillar pension funds, which may help system participants make long-range decisions. Due to the demonstrated ability to extract meaningful features from raw time-series data, the convolutional neural network was chosen as a pension fund feature extractor that was used prior to the clustering process. In this paper, pension fund cluster analysis was performed using trained (on daily stock prices) convolutional neural network feature extractors. The extractors were combined with different clustering algorithms. The feature extractors operate using the black-box principle, meaning the features they learned to recognize have low explainability. In total, 32 models were trained, and eight different clustering methods were used to group 20 second-pillar pension funds from Latvia. During the analysis, the 12 best-performing models were selected, and various cluster combinations were analyzed. The results show that funds from the same manager or similar performance measures are frequently clustered together.

Suggested Citation

  • Vitalija Serapinaitė & Audrius Kabašinskas, 2021. "Clustering of Latvian Pension Funds Using Convolutional Neural Network Extracted Features," Mathematics, MDPI, vol. 9(17), pages 1-45, August.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:17:p:2086-:d:624472
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    References listed on IDEAS

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    1. Clark, Gordon L. & Munnell, Alicia H. & Orszag, J. Michael (ed.), 2006. "The Oxford Handbook of Pensions and Retirement Income," OUP Catalogue, Oxford University Press, number 9780199272464.
    2. Swetava Ganguli & Jared Dunnmon, 2017. "Machine Learning for Better Models for Predicting Bond Prices," Papers 1705.01142, arXiv.org.
    3. Flavia BARNA & Victoria SEULEAN & Maria Luiza MOS, 2011. "A Cluster Analysis of OECD Pension Funds," Timisoara Journal of Economics, West University of Timisoara, Romania, Faculty of Economics and Business Administration, vol. 4(3(15)), pages 143-148.
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