Literature review on the application of ARMA model in stock price prediction
Why this work is in the frame
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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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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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