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Stock recommendation methods for stability

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Abstract

In this study, we propose a method for recommending appropriate combinations of stocks based on the waveforms of stock price changes. Many Japanese prefer to maintain stability when managing their assets. Specialized knowledge is required to invest in stocks while reducing risk. Hence, stock recommendation methods with different characteristics are required. Stock price movements can be captured as waveforms. Dynamic time warping (DTW), cross–correlation functions, and fast Fourier transforms (FFTs) are used to compare the features of the waveforms. A combination of stocks with different waveforms avoids the risk of simultaneous crashes. In the current experiment, one–year stock waveforms are used to obtain the recommended stock. The combined stocks selected using the DTW, cross–correlation functions, and FFT are shown to be suitable.

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References

  1. iDeCo. https://www.ideco-koushiki.jp/english/

  2. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  3. Roondiwala M, Patel H, Varma S (2017) Predicting stock prices using Lstm. Int J Sci Res (IJSR) 6(4):1754

    Google Scholar 

  4. Chen BT, Chen MY, Fan MH, Chen CC(2012) Forecasting stock price based on fuzzy time–series with equal–frequency partitioning and fast Fourier transform algorithm. In Proceedings of the Computing, Communications and Applications Conference (ComComAp):238–243

  5. DTW suite. https://dynamictimewarping.github.io/

  6. Zebende GF (2011) DCCA cross-correlation coefficient: quantifying level of cross-correlation. Phys A Stat Mech Appl 390(4):614–618

    Article  Google Scholar 

  7. stooq. https://stooq.com/db/h/

Download references

Acknowledgements

This work was supported by the Organization for the Promotion of Gender Equality at Nara Women’s University. We would like to thank Editage (www.editage.jp) for English language editing.

Funding

the Organization for the Promotion of Gender Equality at Nara Women’s University.

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Authors and Affiliations

  1. Research Group of Information and Communication Technology for Life, Nara Women’s University, Kitauoya–Nishi–machi, Nara, 6308506, Japan

    Masami Takata

  2. Graduate School of Humanities and Sciences, Nara Women’s University, Kitauoya–Nishi–machi, Nara, 6308506, Japan

    Natsu Kidoguchi & Miho Chiyonobu

Authors
  1. Masami Takata
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  2. Natsu Kidoguchi
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  3. Miho Chiyonobu
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Correspondence to Masami Takata.

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Takata, M., Kidoguchi, N. & Chiyonobu, M. Stock recommendation methods for stability. J Supercomput 80, 12091–12101 (2024). https://doi.org/10.1007/s11227-024-05902-7

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  • DOI: https://doi.org/10.1007/s11227-024-05902-7

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