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Summary

This study introduces the ICE2DE-MDL model, a novel hybrid approach using entropy and decomposition to denoise financial data for accurate stock price prediction. The model outperforms existing methods in forecasting stock market indices and individual stocks.

Keywords:
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Area of Science:

  • * Computational Finance
  • * Data Science
  • * Financial Time Series Analysis

Background:

  • * Financial time series data often contains noise, hindering accurate price prediction.
  • * Existing stock forecasting models struggle with noise reduction and predictive accuracy.
  • * Novel methods are needed to improve the reliability of stock market predictions.

Purpose of the Study:

  • * To propose and evaluate a novel hybrid model, ICE2DE-MDL, for stock closing price prediction.
  • * To effectively eliminate noise in financial time series using entropy and the ICEEMDAN methodology.
  • * To compare the performance of ICE2DE-MDL against existing hybrid models in stock forecasting.

Main Methods:

  • * Developed the ICE2DE-MDL model integrating secondary decomposition, entropy, and machine/deep learning.
  • * Applied a denoising method using entropy and two-level Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN).
  • * Utilized Long-Short Term Memory (LSTM), LSTM-BN, Gated Recurrent Unit (GRU), and Support Vector Regression (SVR) on denoised intrinsic mode functions (IMFs).

Main Results:

  • * The ICE2DE-MDL model achieved high accuracy, with R-squared values ranging from 0.905 to 0.998.
  • * Performance metrics (RMSE, MAE, MAPE) demonstrated the model's effectiveness across eight stock market indices and three stock datasets.
  • * ICE2DE-MDL significantly outperformed existing hybrid models in predicting stock market indices and individual stocks.

Conclusions:

  • * The ICE2DE-MDL model offers a superior approach to stock price forecasting by effectively handling noisy financial data.
  • * This study presents the first known application of entropy and ICEEMDAN for noise elimination in stock data.
  • * The research contributes a novel, high-performing hybrid model to the field of financial time series prediction.