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Is Platinum a Real Store of Wealth?

Author

Listed:
  • Marek Vochozka

    (Research Department of Economics and Natural Resources Management, Institute of Technology and Business in České Budějovice, 37001 České Budějovice, Czech Republic)

  • Andrea Bláhová

    (Research Department of Economics and Natural Resources Management, Institute of Technology and Business in České Budějovice, 37001 České Budějovice, Czech Republic)

  • Zuzana Rowland

    (Research Department of Economics and Natural Resources Management, Institute of Technology and Business in České Budějovice, 37001 České Budějovice, Czech Republic)

Abstract

The research goal is to determine whether platinum can be seen as a good investment. For this purpose, content analysis of documents and deep learning neural networks with recurrent neural network were used. The results show that it pays for a koruna investor (a person holding their wealth in Czech koruna) to preserve their wealth physically in the form of a precious metal—specifically, platinum. The research confirms that platinum is a store of value but also a koruna investor’s wealth multiplier. This can be due to its rare occurrence in nature, but also to its unique use in manufacturing. A research limitation is the period for which the data were used. The finding that platinum is a store of value, as well as a wealth multiplier, can thus be concretized when using the data for a five-year period. It shall also be added that no turbulent changes are anticipated (such as interruption of platinum supply, unexpected government regulation of trade, etc.).

Suggested Citation

  • Marek Vochozka & Andrea Bláhová & Zuzana Rowland, 2022. "Is Platinum a Real Store of Wealth?," IJFS, MDPI, vol. 10(3), pages 1-23, August.
    • Handle: RePEc:gam:jijfss:v:10:y:2022:i:3:p:70-:d:892080
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    References listed on IDEAS

    as
    1. Tomáš Rubín & Victor M. Panaretos, 2020. "Functional lagged regression with sparse noisy observations," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(6), pages 858-882, November.
    2. Marek Vochozka & Jakub Horák & Petr Šuleř, 2019. "Equalizing Seasonal Time Series Using Artificial Neural Networks in Predicting the Euro–Yuan Exchange Rate," JRFM, MDPI, vol. 12(2), pages 1-17, April.
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