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Time series forecasting of stock market indices based on DLWR-LSTM model

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  • Yao, Dingjun
  • Yan, Kai

Abstract

Stock index forecasting is a hot research topic in the financial field. The traditional forecasting methods mostly use ARMA, ARIMA and GARCH to forecast the stock index. In recent years, many scholars have introduced machine learning such as SVM and RNN into the stock index forecasting model, but the accuracy of these forecasting results still needs to be improved. In this paper, by constructing DLWR-LSTM model, the trend of three indexes in Shanghai Stock Exchange is separated and predicted by layers to improve the accuracy of stock market index prediction, and the final prediction MAPE (average absolute percentage error) is close to 1 %. In this paper, different samples with different volatility but similar overall trend are replaced for experiments. The results show that the prediction accuracy of DLWR-LSTM model is not affected by the fluctuation of sample time series, and its final prediction result is maintained at around 1 % regardless of the variance of time series.

Suggested Citation

  • Yao, Dingjun & Yan, Kai, 2024. "Time series forecasting of stock market indices based on DLWR-LSTM model," Finance Research Letters, Elsevier, vol. 68(C).
    • Handle: RePEc:eee:finlet:v:68:y:2024:i:c:s1544612324008511
    DOI: 10.1016/j.frl.2024.105821
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    1. Zhi Su & Xuanye Cai & You Wu, 2023. "Exchange rates forecasting and trend analysis after the COVID-19 outbreak: new evidence from interpretable machine learning," Applied Economics Letters, Taylor & Francis Journals, vol. 30(15), pages 2052-2059, September.
    2. Ozdemir, Ali Can & Buluş, Kurtuluş & Zor, Kasım, 2022. "Medium- to long-term nickel price forecasting using LSTM and GRU networks," Resources Policy, Elsevier, vol. 78(C).
    3. Haibin Xie & Yuying Sun & Pengying Fan, 2023. "Return direction forecasting: a conditional autoregressive shape model with beta density," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-16, December.
    4. Zhang, Junting & Liu, Haifei & Bai, Wei & Li, Xiaojing, 2024. "A hybrid approach of wavelet transform, ARIMA and LSTM model for the share price index futures forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 69(PB).
    5. Luo, Qin & Ma, Feng & Wang, Jiqian & Wu, You, 2024. "Changing determinant driver and oil volatility forecasting: A comprehensive analysis," Energy Economics, Elsevier, vol. 129(C).
    6. Feng, Ying & Wang, Hong & Sha, Yezhou, 2023. "Delamination of information disclosure and stock price synchronicity — Evidence from China’s NEEQ market," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 614-623.
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