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Stock linkage prediction based on optimized LSTM model

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Abstract

Stock linkage refers to the correlation or similar performance of two or more stocks in the stock market. The quantification of stock linkage relationship is the trend and difficulty of research in recent years. The study of stock linkage can dig out the potential relationship between stocks at a deeper level. At present, the existing research often only studies the linkage phenomenon from the perspective of the correlation or similarity of stock movement, and there is no unified and standard numerical index to effectively describe the degree of linkage phenomenon, which greatly hinders the progress of research. Aiming at the problem that it is difficult to quantify the phenomenon of stock linkage, we analyze the correlation and morphological similarity of time series, and propose the combination of correlation coefficient and time weighted distance as the numerical expression of stock linkage for the first time, so as to realize the quantification of stock linkage. In addition, the parallel network structure of LSTM model is designed, and the automatic noise reduction encoder and wavelet transform module are added as the noise reduction processing layer, which effectively improves the prediction performance of LSTM model for stock market linkage numerical time series. Three different types of comparative experiments based on 2.309 million stock market sequences show that the proposed optimized LSTM model has more accurate prediction effect, and its RMSE error is 18.68% lower than the compared DB-LSTM model and 46.38% lower than SDAE-LSTM model.

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References

  1. Ahlgren P, Jarneving B, Rousseau R (2014) Requirements for a cocitation similarity measure, with special reference to Pearson’s correlation coefficient[J]. J Assoc Inform Sci Technol 54(6):550–560

    Article  Google Scholar 

  2. Akita R, Yoshihara A, Matsubara T et al (2016) Deep learning for stock prediction using numerical and textual information[C]. IEEE/ACIS International Conference on Computer & Information Science. IEEE. https://doi.org/10.1109/ICIS.2016.7550882

  3. Al-Augby S, Majewski S, Nermend K et al (2016) Proposed investment decision support system for stock exchange using text mining method[C]. Al-Sadeq International Conference on Multidisciplinary in IT and Communication Science & Applications. https://doi.org/10.1109/AIC-MITCSA.2016.7759917

  4. Arshanapalli B (1993) International stock market linkages: Evidence from the pre- and post-October 1987 period[J]. J Bank Financ 17(1):193–208. https://doi.org/10.1016/0378-4266(93)90088-U

    Article  Google Scholar 

  5. Chen JF, Chen WL, Huang CP et al (2016) Financial time-series data analysis using deep convolutional neural networks[C]. 2016 7th International Conference on Cloud Computing and Big Data (CCBD). https://doi.org/10.1109/CCBD.2016.027

  6. Cheng SH (2014) Predicting stock returns by decision tree combining neural network[M]. Intelligent Information and Database Systems. https://doi.org/10.1007/978-3-319-05458-2_37

  7. Day MY, Lee C (2016) Deep learning for financial sentiment analysis on finance news providers[C]. 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ACM. https://doi.org/10.1109/ASONAM.2016.7752381

  8. Dixon M, Klabjan D, Bang JH (2016) Classiffication-based Financial markets prediction using deep neural networks[J]. Algorithmic Finance. https://ssrn.com/abstract=2756331

  9. Heaton JB, Polson NG, Witte JH (2017) Deep learning for finance: deep portfolios[J]. Applied Stochastic Models in Business and Industry 33(1):3–12

  10. Morel M, Achard C, Kulpa R et al (2018) Time-series averaging using constrained dynamic time warping with tolerance[J]. Pattern Recognit 74:77–89. https://doi.org/10.1016/j.patcog.2017.08.015

  11. Nieto B, Rodríguez R. Correlation between Individual Stock and Corporate Bond Returns[J]. Social Science Electronic Publishing. https://doi.org/10.2139/ssrn.2386043

  12. Okamoto T, Iida D, Toge K et al (2016) Optical correlation domain reflectometry based on coherence synchronization: theoretical analysis and proof-of-concept[J]. J Lightwave Technol 34(18):4259–4265. https://doi.org/10.1109/JLT.2016.2590507

  13. Pan MS, Fok CW, Liu YA (2007) Dynamic linkages between exchange rates and stock prices: Evidence from East Asian markets[J]. Int Rev Econ Finance 16(4):503–520. https://doi.org/10.1016/j.iref.2005.09.003

    Article  Google Scholar 

  14. Patton AJ (2012) A review of copula models for economic time series[J]. J Multivar Anal 110(none):4–18

    Article  MathSciNet  Google Scholar 

  15. Shao L, Cai Z, Liu L et al (2017) Performance evaluation of deep feature learning for RGB-D image/video classification[J]. Inf Sci 385-386:266–283. https://doi.org/10.1016/j.ins.2017.01.01

  16. Sohangir S, Wang D, Pomeranets A et al (2018) Big Data: Deep Learning for financial sentiment analysis[J]. J Big Data 5(1):3. https://doi.org/10.1186/s40537-017-0111-6

  17. Thomas S (2011) Dynamic linkages between fundamental drivers and stock market: an empirical analysis from India[J]. Social Science Electronic Publishing.https://ssrn.com/abstract=1853003

  18. Wei B, Jun Y, Yulei R et al (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory[J]. PLoS ONE 12(7):e0180944. https://doi.org/10.1371/journal.pone.0180944

    Article  Google Scholar 

  19. Zhao J, Itti L (2018) shapeDTW: Shape dynamic time warping[J]. Pattern Recognit 74:171–184

    Article  Google Scholar 

Download references

Funding

This paper is supported by the Foundation of Guangdong Educational Committee under the Grant No. 2018KTSCX218, No. 2021ZDJS082 and the Professorial and Doctoral Scientific Research Foundation of Huizhou University under the Grant No. 2018JB020.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Huizhou University, Huizhou, 516007, China

    Chi Ma, Shaofan Wang & Shengliang Lu

  2. School of Applied Technology, University of Science and Technology Liaoning, 114051, Anshan, China

    Yan Liang

  3. School of Computer Science and Software Engineering, University of Science and Technology Liaoning, 114051, Anshan, China

    Shaofan Wang & Shengliang Lu

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  1. Chi Ma
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  2. Yan Liang
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  3. Shaofan Wang
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  4. Shengliang Lu
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Correspondence to Yan Liang or Shaofan Wang.

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Ma, C., Liang, Y., Wang, S. et al. Stock linkage prediction based on optimized LSTM model. Multimed Tools Appl 81, 12599–12617 (2022). https://doi.org/10.1007/s11042-022-12381-6

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  • DOI: https://doi.org/10.1007/s11042-022-12381-6

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