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Tangled String for Multi-Scale Explanation of Contextual Shifts in Stock Market

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  • Yukio Ohsawa
  • Teruaki Hayashi
  • Takaaki Yoshino

Abstract

The original research question here is given by marketers in general, i.e., how to explain the changes in the desired timescale of the market. Tangled String, a sequence visualization tool based on the metaphor where contexts in a sequence are compared to tangled pills in a string, is here extended and diverted to detecting stocks that trigger changes in the market and to explaining the scenario of contextual shifts in the market. Here, the sequential data on the stocks of top 10 weekly increase rates in the First Section of the Tokyo Stock Exchange for 12 years are visualized by Tangled String. The changing in the prices of stocks is a mixture of various timescales and can be explained in the time-scale set as desired by using TS. Also, it is found that the change points found by TS coincided by high precision with the real changes in each stock price. As TS has been created from the data-driven innovation platform called Innovators Marketplace on Data Jackets and is extended to satisfy data users, this paper is as evidence of the contribution of the market of data to data-driven innovations.

Suggested Citation

  • Yukio Ohsawa & Teruaki Hayashi & Takaaki Yoshino, 2019. "Tangled String for Multi-Scale Explanation of Contextual Shifts in Stock Market," Papers 1901.09469, arXiv.org.
  • Handle: RePEc:arx:papers:1901.09469
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    References listed on IDEAS

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    1. Lux, Thomas, 1998. "The socio-economic dynamics of speculative markets: interacting agents, chaos, and the fat tails of return distributions," Journal of Economic Behavior & Organization, Elsevier, vol. 33(2), pages 143-165, January.
    2. Paul Fearnhead & Zhen Liu, 2007. "On‐line inference for multiple changepoint problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 589-605, September.
    3. Vivien Lespagnol & Juliette Rouchier, 2014. "Trading Volume and Market Efficiency: An Agent Based Model with Heterogenous Knowledge about Fundamentals," Working Papers halshs-00997573, HAL.
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