Practical Machine Learning in Financial Market Trend Prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using the wavelet analysis for low-frequency time series extraction, the authors in this chapter conduct out-of-sample predictions of the S&P500 price index future trend (up and down) following two trading strategies. In particular, the goal is to separately predict an increase or decrease of stock market by 0.5%. Indeed, predicting market increases by 0.5% is suitable to active portfolio managers, whilst predicting its decreases by 0.5% is suitable to risk-averse portfolio managers to limit losses. The Support Vector Machine (SVM) with polynomial kernel is used as the baseline forecasting model. Its performance is respectively compared to that of the Probabilistic Neural Networks (PNN) and the well known k-Nearest Neighbour (k-NN) algorithm, which is a statistical classifier. The simulation results reveal that the predictive system based on the SVM with wavelet analysis coefficients as inputs outperforms all the other systems. The achieved accuracy is 98.13%. As a result, it is concluded that the wavelet transform and SVM as an integrated system are appropriate to capture the S&P500 price changes by more or less than 0.5%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it