Analysing Trading Strategies and Uses of Machine Algorithm to Predict Stock Prices
Bibliographic record
Abstract
The investment banking and financial sectors have undergone significant transformation from the 19th century to the present, driven by technological advancements and evolving trading strategies. Traditional methods of buying and selling stocks have been replaced or supplemented by automated algorithms and machine learning techniques, enabling more precise stock market predictions. This review paper consolidates historical and contemporary strategies employed by traders and investors to maximize profits in the stock market. It emphasizes the application of machine learning in predicting stock market trends and decision-making for buying and selling securities. A systematic review of journal articles published between 2016 and 2022 was conducted to identify the primary markets, stock indices, and trading strategies utilized in stock market predictions. The paper contributes to the literature by providing (1) a detailed analysis of trading strategies and market indicators, and (2) a comprehensive review of machine learning techniques employed in stock market forecasting. Furthermore, a bibliometric analysis highlights the most influential studies in this domain. Technical analysis tools, such as moving averages and candlestick patterns, are explored alongside modern methodologies. The study also identifies existing gaps in trading strategy research, offering insights for future advancements in this field. This review serves as a valuable resource for understanding the intersection of traditional trading methods, machine learning algorithms, and stock market prediction.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".