IMPROVING FORECASTING ACCURACY OF THE S&P500 INTRA-DAY PRICE DIRECTION USING BOTH WAVELET LOW AND HIGH FREQUENCY COEFFICIENTS
Bibliographic record
Abstract
Numerous research works have successfully applied the wavelet analysis for the decomposition and forecasting of financial data. Particularly, using the discrete wavelet transform (DWT) stock price time series were analyzed following a fixed sub-band coding scheme, which provides a low time resolution for low frequencies and a high time resolution for high frequencies. Following the standard approach found in the literature, only low frequency components were considered as main features to predict stock prices. However, this approach lacks of details about the generative process of the original data. In this paper, we rely on DWT high frequency sub-band to extract short interval hidden information for better classification of future Standard and Poors (S&P500) one minute ahead direction using artificial neural network trained with backpropagation algorithm. The simulation results show that our approach that uses both low (approximation) and high (detail) frequency coefficients provides better classification rates than the standard one. In addition, simulation results show that low frequency components are appropriate to detect future downshifts in S&P500, whilst our approach is suitable to predict future upwards. Thus, the standard approach provides valuable information for risk averse investors trading S&P500, and our approach that combines low and high frequency coefficients is strongly useful for aggressive investors seeking short-term profits when trading S&P500.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".