Stock Price Prediction Using Kalman Filter
Bibliographic record
Abstract
In this work we study the applicability of Kalman filters in stock prices prediction using the actual observations of stock prices.We investigate the behavior of two state space models where the acceleration or the velocity of the stock price is considered as a zero mean white noise sequence, due to the high fluctuation of the stock market.We propose time varying, time invariant, steady state and Finite Impulse Response form of steady state Kalman filters for each model.We deal with short term prediction, namely daily prediction.The proposed Kalman filters are implemented using historical data of stock price.It was found that the proposed Kalman filters produce reliable predictions.The percent mean absolute error may vary by model and filter; some filters give satisfactory results where the percent mean absolute error in stock price prediction is less than 2%.Furthermore, some filters present relative error less than 1% for 35%-50% of predictions.Finally, the average percent profit can reach 3.5% using the proposed Kalman filters.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".