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Stock movement prediction: A multi‐input LSTM approach

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  • Pan Tang
  • Cheng Tang
  • Keren Wang

Abstract

Generally, the nonlinear and non‐stationary financial time series becomes an obstacle in the process of stock movement prediction. But the recent theories of machine learning and deep learning have provided with some new solutions. Based on LSTM (long short‐term memory), we propose a hybrid model of wavelet transform (WT) and multi‐input LSTM to predict the trend of SSE composite index. It can mine valid data in time series and support different types of data as input. The whole model is divided into two stages. In the first stage, we adopt the level 1 decomposition with db4 mother wavelet to eliminate noise. In the second stage, combinative and qualitative analysis was made base on the data from Chinese stock market, US stock market, and technical indicators as input. According to the result, the proposed model, with the accuracy of 72.19%, performs better than single‐input LSTM, decision tree, random forest, Support Vector Machine (SVM), and XGBoost.

Suggested Citation

  • Pan Tang & Cheng Tang & Keren Wang, 2024. "Stock movement prediction: A multi‐input LSTM approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1199-1211, August.
    • Handle: RePEc:wly:jforec:v:43:y:2024:i:5:p:1199-1211
    DOI: 10.1002/for.3071
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    References listed on IDEAS

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    1. Ye, George L., 2014. "The interactions between China and US stock markets: New perspectives," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 31(C), pages 331-342.
    2. Dutta, Anupam, 2018. "Implied volatility linkages between the U.S. and emerging equity markets: A note," Global Finance Journal, Elsevier, vol. 35(C), pages 138-146.
    3. repec:bla:jfinan:v:44:y:1989:i:4:p:849-69 is not listed on IDEAS
    4. Basak, Suryoday & Kar, Saibal & Saha, Snehanshu & Khaidem, Luckyson & Dey, Sudeepa Roy, 2019. "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 552-567.
    5. Agnolucci, Paolo, 2009. "Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models," Energy Economics, Elsevier, vol. 31(2), pages 316-321, March.
    6. Ramsey, J.B., 2002. "Wavelets in Economics and Finance: Past and Future," Working Papers 02-02, C.V. Starr Center for Applied Economics, New York University.
    7. Goh, Jeremy C. & Jiang, Fuwei & Tu, Jun & Wang, Yuchen, 2013. "Can US economic variables predict the Chinese stock market?," Pacific-Basin Finance Journal, Elsevier, vol. 22(C), pages 69-87.
    8. Torbat, Sheida & Khashei, Mehdi & Bijari, Mehdi, 2018. "A hybrid probabilistic fuzzy ARIMA model for consumption forecasting in commodity markets," Economic Analysis and Policy, Elsevier, vol. 58(C), pages 22-31.
    9. Anders Johansson, 2010. "China's financial market integration with the world," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 8(3), pages 293-314.
    10. Yousefi, Shahriar & Weinreich, Ilona & Reinarz, Dominik, 2005. "Wavelet-based prediction of oil prices," Chaos, Solitons & Fractals, Elsevier, vol. 25(2), pages 265-275.
    11. Ramsey James B., 2002. "Wavelets in Economics and Finance: Past and Future," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(3), pages 1-29, November.
    12. Lin, Yu & Yan, Yan & Xu, Jiali & Liao, Ying & Ma, Feng, 2021. "Forecasting stock index price using the CEEMDAN-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    13. Xiaojun Li & Pan Tang, 2020. "Stock index prediction based on wavelet transform and FCD‐MLGRU," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1229-1237, December.
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