Machine learning in weekly movement prediction

ArXiv ID: 2407.09831 “View on arXiv”

Authors: Unknown

Abstract

To predict the future movements of stock markets, numerous studies concentrate on daily data and employ various machine learning (ML) models as benchmarks that often vary and lack standardization across different research works. This paper tries to solve the problem from a fresh standpoint by aiming to predict the weekly movements, and introducing a novel benchmark of random traders. This benchmark is independent of any ML model, thus making it more objective and potentially serving as a commonly recognized standard. During training process, apart from the basic features such as technical indicators, scaling laws and directional changes are introduced as additional features, furthermore, the training datasets are also adjusted by assigning varying weights to different samples, the weighting approach allows the models to emphasize specific samples. On back-testing, several trained models show good performance, with the multi-layer perception (MLP) demonstrating stability and robustness across extensive and comprehensive data that include upward, downward and cyclic trends. The unique perspective of this work that focuses on weekly movements, incorporates new features and creates an objective benchmark, contributes to the existing literature on stock market prediction.

Keywords: Stock Market Prediction, Machine Learning, Scaling Laws, Directional Changes, Feature Weighting, Equities

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 4.0/10
• Quadrant: Street Traders
• Why: The paper employs standard ML techniques (e.g., MLP) and basic financial metrics without advanced mathematical derivations, but its empirical focus on weekly predictions, specific features (scaling laws, directional changes), and a proposed ‘random trader’ benchmark indicates a practical, implementation-heavy approach, albeit likely limited by the absence of detailed code or backtest metrics in the excerpt.

  flowchart TD
    A["Research Goal<br>Predict Weekly Stock Movements"] --> B["Novel Benchmark<br>Random Trader"]
    A --> C["Methodology<br>Scaling Laws & Directional Changes"]
    B --> D["Data & Models<br>Weighted Datasets + MLP/LSTM/GBM"]
    C --> D
    D --> E["Back-Testing<br>Rolling Window Validation"]
    E --> F{"Outcomes"}
    F --> G["MLP: Robust & Stable<br>Across Up/Down/Cyclic Trends"]
    F --> H["New Objective Standard<br>Weekly Prediction Focus"]