A Comparative Analysis of the Application of Machine Learning Algorithms and Econometric Models in Stock Market 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
Forecasting the future price trend of a stock traded on a financial exchange is the aim of stock market prediction. In recent decades, stock market prediction has been a fascinating topic in the domain of Data Science and Finance. In reality, the stock movement is ambiguous and chaotic due to various influencing factors such as government policy, current events, interest rates Etc. At the same time, accurate enough forecasting of stock price movement leads to substantial benefits for investors. This paper provides a comprehensive review of the application and comparison of Machine Learning (ML) algorithms and Econometric Models in stock market prediction. The mentioned models are categorized into (i) ML algorithms, including Linear Regression (LR), K-nearest neighbors (KNN), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM). (ii) Econometric Models, including Autoregressive Integrated Moving Average (ARIMA) Model, Capital Asset Pricing Model (CAPM), and Fama-French (FF) Factor Model.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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