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A novel hybrid framework for forecasting stock indices based on the nonlinear time series models
16 references·1 citing·48 second hop
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Kothari, Owen Lamont, Mark Mitchell, Hersh Shefrin, Robert Shiller, Rex Sinquefield, Richard Thaler, Theo Vermaelen, Robert Vishny, Ivo Welch, and a referee have been helpful. Kenneth French and Jay Ritter get special thanks.1 (1998)Financial time series forecasting model based on CEEMDAN and LSTM (2018)Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models (2018)Deep LSTM and LSTM-Attention Q-learning based reinforcement learning in oil and gas sector prediction (2023)Development of a Robust Stock Market Prediction Mechanism based on Enhanced Comprehensive Learning Principles (2023)Deep learning for financial forecasting: A review of recent trends (2025)Pluralizing and problematizing carbon finance (2019)Green policies and financial development in G7 economies: An in‐depth analysis of environmental regulations and green economic growth (2024)Leveraging quantum‐inspired chimp optimization and deep neural networks for enhanced profit forecasting in financial accounting systems (2024)From resources to resilience: Understanding the impact of standard of living and energy consumption on natural resource rent in Asia (2025)Does Anything Beat 5-Minute RV? 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References

  • 2017Deep learning with long short-term memory networks for financial market predictions2,827 cited
  • 2002Comparing Predictive Accuracy1,745 cited
  • 2019A systematic review of fundamental and technical analysis of stock market predictions545 cited
  • 2022Machine learning techniques and data for stock market forecasting: A literature review542 cited
  • 2009Prediction of Stock Market Index Movement by Ten Data Mining Techniques165 cited
  • 2023A Deep Neural Network-Based Assistive Decision Method for Financial Risk Prediction in Carbon Trading Market135 cited
  • 2011A novel text mining approach to financial time series forecasting134 cited
  • 2023Prediction of Complex Stock Market Data Using an Improved Hybrid EMD-LSTM Model133 cited
  • 2016A data analytic approach to forecasting daily stock returns in an emerging market111 cited
  • 2020The role of investor attention in predicting stock prices: The long short-term memory networks perspective94 cited
  • 2023Forecasting Day-Ahead Brent Crude Oil Prices Using Hybrid Combinations of Time Series Models62 cited
  • 2018International stock market contagion: A CEEMDAN wavelet analysis54 cited
  • 2022AEI-DNET: A Novel DenseNet Model with an Autoencoder for the Stock Market Predictions Using Stock Technical Indicators48 cited
  • 2019Neural networks in financial trading

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41 cited
  • 2022Forecasting Stock Market Crashes via Machine Learning39 cited
  • 2024Near miss prediction in commercial aviation through a combined model of grey neural network37 cited
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    • 2025A Hybrid LMD–ARIMA–Machine Learning Framework for Enhanced Forecasting of Financial Time Series: Evidence from the NASDAQ Composite Index31 cited