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Using Heatmap Visualization to assess the performance of the DJ30 and NASDAQ100 Indices under diverse VMA trading rules

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  • Yuhsin Chen
  • Paoyu Huang
  • Min-Yuh Day
  • Yensen Ni
  • Mei-Chu Liang

Abstract

We investigate whether using various VMA trading rules would improve investment performance due to the flexibility of VMA trading rules and the aid of Heatmap Visualization. Previously, investors frequently chose the best performance derived from limited VMA trading rules. However, our new design, which can display all results using Heatmap Visualization, shows that the NASDAQ100 index outperforms the DJ30 index and that weekly data outperforms daily data when measured by annualized return. These findings may be useful to those who trade index ETFs tracking the DJ30 and NASDAQ100 indices, as well as investors making investment decisions, and may contribute to the existing literature by evaluating the outcomes of VMA trading rules and providing insights for index ETF investors using a heatmap matrix, which is rarely explored and presented in the relevant literature.

Suggested Citation

  • Yuhsin Chen & Paoyu Huang & Min-Yuh Day & Yensen Ni & Mei-Chu Liang, 2023. "Using Heatmap Visualization to assess the performance of the DJ30 and NASDAQ100 Indices under diverse VMA trading rules," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-16, May.
    • Handle: RePEc:plo:pone00:0284918
    DOI: 10.1371/journal.pone.0284918
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    1. Cysne, Rubens Penha & Turchick, David, 2010. "Welfare costs of inflation when interest-bearing deposits are disregarded: A calculation of the bias," Journal of Economic Dynamics and Control, Elsevier, vol. 34(6), pages 1015-1030, June.
    2. Liyun Zhou & Jialiang Huang, 2019. "Investor trading behaviour and stock price crash risk," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(1), pages 227-240, January.
    3. Maria Rosa Borges, 2010. "Efficient market hypothesis in European stock markets," The European Journal of Finance, Taylor & Francis Journals, vol. 16(7), pages 711-726.
    4. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    5. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    6. Noman Arshed & Rukhsana Kalim, 2021. "Modelling demand and supply of Islamic banking deposits," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2813-2831, April.
    7. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
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