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使用热点图可视化来评估 DJ30 和纳斯达克 100 指数在不同 VMA 交易规则下的表现。

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

机构信息

Department of Accounting, Chung Yuan Christian University, Taoyuan, Taiwan.

Department of International Business, Soochow University, Taipei, Taiwan.

出版信息

PLoS One. 2023 May 11;18(5):e0284918. doi: 10.1371/journal.pone.0284918. eCollection 2023.

DOI:10.1371/journal.pone.0284918
PMID:37167329
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10174563/
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.

摘要

我们研究了使用各种 VMA 交易规则是否会因 VMA 交易规则的灵活性和 Heatmap 可视化的辅助而提高投资业绩。此前,投资者经常根据有限的 VMA 交易规则选择表现最好的规则。然而,我们的新设计可以使用 Heatmap 可视化显示所有结果,表明纳斯达克 100 指数的年化回报率优于道琼斯 30 指数,周数据的表现优于日数据。这些发现可能对那些交易追踪道琼斯 30 指数和纳斯达克 100 指数的指数 ETF 的人、做出投资决策的投资者以及评估 VMA 交易规则的结果并使用热图矩阵为指数 ETF 投资者提供见解的文献有帮助,因为在相关文献中很少探索和呈现热图矩阵。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ee1/10174563/50a13af488fc/pone.0284918.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ee1/10174563/4f67f46e6ad2/pone.0284918.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ee1/10174563/3e7bda10aef1/pone.0284918.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ee1/10174563/50a13af488fc/pone.0284918.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ee1/10174563/4f67f46e6ad2/pone.0284918.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ee1/10174563/3e7bda10aef1/pone.0284918.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ee1/10174563/50a13af488fc/pone.0284918.g003.jpg

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本文引用的文献

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Herding and feedback trading in cryptocurrency markets.加密货币市场中的羊群行为与反馈交易。
Ann Oper Res. 2021;300(1):79-96. doi: 10.1007/s10479-020-03874-4. Epub 2021 Jan 13.
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Systematic Comparison of Heatmapping Techniques in Deep Learning in the Context of Diabetic Retinopathy Lesion Detection.糖尿病视网膜病变病变检测背景下深度学习中热图技术的系统比较
Transl Vis Sci Technol. 2020 Dec 29;9(2):64. doi: 10.1167/tvst.9.2.64. eCollection 2020 Dec.
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VisBicluster: A Matrix-Based Bicluster Visualization of Expression Data.VisBicluster:一种基于矩阵的表达数据关联聚类可视化方法。
J Comput Biol. 2020 Sep;27(9):1384-1396. doi: 10.1089/cmb.2019.0385. Epub 2020 Feb 7.
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Interaction of Theory and Practice to Assess External Validity.理论与实践的相互作用以评估外部效度。
Eval Rev. 2017 Oct;41(5):436-471. doi: 10.1177/0193841X15625289. Epub 2016 Jan 18.