TITLE:
A Hybrid Wavelet-LSTM Model for Forecasting NSPO Stock Prices: Structure, Methodology, and Empirical Application
AUTHORS:
Nooruldeen A. Noori, Hiba H. Abdullah
KEYWORDS:
Wavelet Decomposition, LSTM, Stock Price Forecasting, Logarithmic Returns, Rolling-Origin Evaluation, Information Leakage, NSPO Stock Prices
JOURNAL NAME:
Journal of Applied Mathematics and Physics,
Vol.14 No.10,
October
14,
2026
ABSTRACT: Financial time series forecasting is sensitive to nonlinear dynamics, abrupt shocks, and preprocessing choices that can contaminate out-of-sample evaluation. This study evaluates a hybrid Wavelet-Long Short-Term Memory (Wavelet-LSTM) model for weekly stock prices of Northern Upper Egypt Development Company (NSPO) under a leakage-safe chronological protocol. The dataset contains 319 weekly prices from 5 January 2020 to 8 March 2026, producing 318 logarithmic returns. The final 10 returns and corresponding prices were reserved exclusively for rolling one-step-ahead testing, while the preceding 20 returns formed a chronological validation segment and the first 288 returns formed the base training segment. At every test origin, the db4 level-3 discrete wavelet decomposition and min-max normalization were recomputed using only information available before the target observation. Hyperparameters were selected without using the final test period. The selected Wavelet-LSTM used 12 lags, 40 hidden units per component, dropout 0.20, 150 epochs, and an initial learning rate of 0.005. Across the 10 rolling test origins, the Wavelet-LSTM achieved return RMSE = 0.1757, return MAE = 0.0946, and directional accuracy = 50%. On the reconstructed price scale, RMSE = 0.1163, MAE = 0.0714, and MAPE = 8.40%. A matched standard LSTM achieved price RMSE = 0.1086 and MAPE = 8.11%. Thus, the leakage-safe evaluation does not support a forecasting-accuracy advantage for wavelet decomposition on this short terminal holdout, although the framework remains useful for multi-scale representation. The revised protocol provides a reproducible assessment that prevents future observations from entering decomposition, scaling, model selection, or forecasting.