Literature review on the application of ARMA model in stock price prediction
Bibliographic record
Abstract
Predicting stock prices is a perennial quest in finance, yet price series are famously noisy, volatile and nonlinear. Among the many tools on offer, the autoregressive-moving-average (ARMA) model remains a surprisingly resilient workhorse. This review first sketches the logic of ARMA and the Box-Jenkins recipe for transforming raw prices into stationary returns, selecting lags and checking residuals. A broad body of evidence especially for 1 to 5 day horizons, confirms that ARMA delivers reliable, low-cost forecasts and, when paired with GARCH, can track volatility bursts with notable precision. Head-to-head studies show that on small samples or thin markets, ARMA often rivals much heavier deep-learning engines, while recent hybrids such as ARMA-LSTM and ARMA-Transformer marry linear transparency with nonlinear flexibility and shine on high-frequency data. We synthesise domestic and global findings, chart three clear trends, model fusion, finer time grids and AutoML optimisation, and flag the model’s blind spots: fixed coefficients, linear assumptions and sparse use of unstructured signals. Looking ahead, regime-switching ARMA, online updating, sentiment-rich inputs and risk-band forecasts (e.g., VaR, CVaR) promise to keep this classic framework both relevant and insightful.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".