A Hybrid Model Combining Discrete Wavelet Transform and Nonlinear Autoregressive Neural Network for Stock Price Prediction: An Application in the Egyptian Exchange
Bibliographic record
Abstract
Forecasting stock prices is crucial for successful investment in financial markets. However, it is challenging due to the nonlinearity and high volatility caused by various factors influencing price movements. This paper proposes a hybrid model that integrates the discrete wavelet transform (DWT) with the nonlinear autoregressive neural network (NARNN) to predict stock prices. Following the division of stock prices into training and testing sets, the DWT decomposes the training set into low- and high-frequency components reducing the noise and lessening the data's nonlinearity. Then, the obtained components are used to train the NARNNs. To predict the future components, the model decomposes the preceding available prices at each time step and utilizes the latest eight points as input to the NARNNs. Eventually, NARNNs' outputs are combined to provide the final predicted prices. In previous works, the entire dataset is first decomposed and then partitioned into training and testing sets. This unrealistic approach causes the testing set to inherit information regarding stocks' future performance, leading to optimistic deceptive results. Twenty-four stocks from the Egyptian Exchange (EGX-30) are utilized to validate the proposed model's performance. The DWT-NARNN model is compared against other methods, and the empirical findings show that it performs the best.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".